From 2c858368c8c4b7e82c8d134786026a62a72d2676 Mon Sep 17 00:00:00 2001 From: Randy West Date: Mon, 18 Dec 2017 18:22:03 -0500 Subject: [PATCH 001/519] Compute test accuracy in batches to avoid OOM on GPUs. Reported here: https://github.com/tensorflow/tensorflow/issues/136 Alternative to this for mnist_deep.py: https://github.com/tensorflow/tensorflow/pull/157 --- tensorflow/examples/tutorials/mnist/mnist_deep.py | 11 +++++++++-- 1 file changed, 9 insertions(+), 2 deletions(-) diff --git a/tensorflow/examples/tutorials/mnist/mnist_deep.py b/tensorflow/examples/tutorials/mnist/mnist_deep.py index 1e0294db27..2699738735 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_deep.py +++ b/tensorflow/examples/tutorials/mnist/mnist_deep.py @@ -34,6 +34,8 @@ from tensorflow.examples.tutorials.mnist import input_data import tensorflow as tf +import numpy + FLAGS = None @@ -164,8 +166,13 @@ def main(_): print('step %d, training accuracy %g' % (i, train_accuracy)) train_step.run(feed_dict={x: batch[0], y_: batch[1], keep_prob: 0.5}) - print('test accuracy %g' % accuracy.eval(feed_dict={ - x: mnist.test.images, y_: mnist.test.labels, keep_prob: 1.0})) + # compute in batches to avoid OOM on GPUs + accuracy_l = [] + for i in range(50): + batch = mnist.test.next_batch(500, shuffle=False) + accuracy_l.append(accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})) + print('test accuracy %g' % numpy.mean(accuracy_l)) + if __name__ == '__main__': parser = argparse.ArgumentParser() -- GitLab From 3f18817317940253e6ec0e6b412492c5add5927b Mon Sep 17 00:00:00 2001 From: Randy West Date: Mon, 18 Dec 2017 23:18:30 -0500 Subject: [PATCH 002/519] Fix basic arithmetic fail + make loop pythonic --- tensorflow/examples/tutorials/mnist/mnist_deep.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/examples/tutorials/mnist/mnist_deep.py b/tensorflow/examples/tutorials/mnist/mnist_deep.py index 2699738735..47d2777813 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_deep.py +++ b/tensorflow/examples/tutorials/mnist/mnist_deep.py @@ -168,7 +168,7 @@ def main(_): # compute in batches to avoid OOM on GPUs accuracy_l = [] - for i in range(50): + for _ in range(20): batch = mnist.test.next_batch(500, shuffle=False) accuracy_l.append(accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})) print('test accuracy %g' % numpy.mean(accuracy_l)) -- GitLab From 5eb246cb79e37b6a7006b6dead99219ffd25de31 Mon Sep 17 00:00:00 2001 From: DavidNorman Date: Wed, 16 May 2018 17:05:24 +0100 Subject: [PATCH 003/519] Don't do int64 tests for devices which do not support int64 --- tensorflow/compiler/tests/binary_ops_test.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 1e4dd32916..64eeed8312 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -686,11 +686,12 @@ class BinaryOpsTest(XLATestCase): np.array([[10], [7], [2]], dtype=np.float32), np.float32(7), expected=np.array([[False], [False], [True]], dtype=np.bool)) - self._testBinary( - less_op, - np.array([[10], [7], [2], [-1]], dtype=np.int64), - np.int64(7), - expected=np.array([[False], [False], [True], [True]], dtype=np.bool)) + if np.int64 in self.numeric_types: + self._testBinary( + less_op, + np.array([[10], [7], [2], [-1]], dtype=np.int64), + np.int64(7), + expected=np.array([[False], [False], [True], [True]], dtype=np.bool)) for less_equal_op in [math_ops.less_equal, (lambda x, y: x <= y)]: self._testBinary( -- GitLab From 8f3635d15438af7c8f6f047a5999352d14901db7 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 17 May 2018 17:50:20 +0000 Subject: [PATCH 004/519] Improve fast_tensor_util for bfloat16 In 19180, improvement has been done to speed up the fast_tensor_util for `float16`. As both `float16` and `bfloat16` uses the same size, `bfloat16` could be improved as well. This fix speeds up `bfloat16` in a similiar fashion as `float16`. This fix is related to 19180. Signed-off-by: Yong Tang --- tensorflow/python/framework/fast_tensor_util.pyx | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/tensorflow/python/framework/fast_tensor_util.pyx b/tensorflow/python/framework/fast_tensor_util.pyx index 17d112a1ec..2e3e15f53a 100644 --- a/tensorflow/python/framework/fast_tensor_util.pyx +++ b/tensorflow/python/framework/fast_tensor_util.pyx @@ -6,6 +6,13 @@ cimport numpy as np from tensorflow.python.util import compat +def AppendBFloat16ArrayToTensorProto( + tensor_proto, np.ndarray[np.uint16_t, ndim=1] nparray): + cdef long i, n + n = nparray.size + for i in range(n): + tensor_proto.half_val.append(nparray[i]) + def AppendFloat16ArrayToTensorProto( # For numpy, npy_half is a typedef for npy_uint16, -- GitLab From 6d5f5efadc6047828e79e0bdb4af133e0269faa7 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 17 May 2018 17:53:07 +0000 Subject: [PATCH 005/519] Update tensorflow/python/framework/tensor_util.py Signed-off-by: Yong Tang --- tensorflow/python/framework/tensor_util.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py index ca63efbc84..ebb1db534c 100644 --- a/tensorflow/python/framework/tensor_util.py +++ b/tensorflow/python/framework/tensor_util.py @@ -67,10 +67,14 @@ def SlowAppendBFloat16ArrayToTensorProto(tensor_proto, proto_values): [ExtractBitsFromBFloat16(x) for x in proto_values]) +def FastAppendBFloat16ArrayToTensorProto(tensor_proto, proto_values): + fast_tensor_util.AppendBFloat16ArrayToTensorProto(tensor_proto, np.asarray(proto_values, dtype=dtypes.bfloat16.as_numpy_dtype).view(np.uint16)) + + if _FAST_TENSOR_UTIL_AVAILABLE: _NP_TO_APPEND_FN = { dtypes.bfloat16.as_numpy_dtype: - SlowAppendBFloat16ArrayToTensorProto, + FastAppendBFloat16ArrayToTensorProto, np.float16: _MediumAppendFloat16ArrayToTensorProto, np.float32: -- GitLab From 535fa4919c9e247e2df673d8af874c3a39a38976 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Thu, 17 May 2018 17:55:10 +0000 Subject: [PATCH 006/519] Pylint fix. Signed-off-by: Yong Tang --- tensorflow/python/framework/tensor_util.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py index ebb1db534c..e229d6105e 100644 --- a/tensorflow/python/framework/tensor_util.py +++ b/tensorflow/python/framework/tensor_util.py @@ -68,7 +68,9 @@ def SlowAppendBFloat16ArrayToTensorProto(tensor_proto, proto_values): def FastAppendBFloat16ArrayToTensorProto(tensor_proto, proto_values): - fast_tensor_util.AppendBFloat16ArrayToTensorProto(tensor_proto, np.asarray(proto_values, dtype=dtypes.bfloat16.as_numpy_dtype).view(np.uint16)) + fast_tensor_util.AppendBFloat16ArrayToTensorProto( + tensor_proto, np.asarray( + proto_values, dtype=dtypes.bfloat16.as_numpy_dtype).view(np.uint16)) if _FAST_TENSOR_UTIL_AVAILABLE: -- GitLab From f2e22502fd58e8d81c9e080b9242375fbf2bc772 Mon Sep 17 00:00:00 2001 From: Jesse Date: Tue, 5 Jun 2018 14:35:38 +0200 Subject: [PATCH 007/519] Updated line for creating global step + grammar tf.train.get_global_step() returns None if there is no global step, preventing the pruning from working. Therefore, tf.train.get_or_create_global_step() is a safer option. --- tensorflow/contrib/model_pruning/README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/model_pruning/README.md b/tensorflow/contrib/model_pruning/README.md index 86f4fd6adf..50e7e5d7cd 100644 --- a/tensorflow/contrib/model_pruning/README.md +++ b/tensorflow/contrib/model_pruning/README.md @@ -66,10 +66,10 @@ is the sparsity_function_begin_step. In this equation, the sparsity_function_exponent is set to 3. ### Adding pruning ops to the training graph -The final step involves adding ops to the training graph that monitors the -distribution of the layer's weight magnitudes and determines the layer threshold -such masking all the weights below this threshold achieves the sparsity level -desired for the current training step. This can be achieved as follows: +The final step involves adding ops to the training graph that monitor the +distribution of the layer's weight magnitudes and determine the layer threshold, +such that masking all the weights below this threshold achieves the sparsity +level desired for the current training step. This can be achieved as follows: ```python tf.app.flags.DEFINE_string( @@ -79,7 +79,7 @@ tf.app.flags.DEFINE_string( with tf.graph.as_default(): # Create global step variable - global_step = tf.train.get_global_step() + global_step = tf.train.get_or_create_global_step() # Parse pruning hyperparameters pruning_hparams = pruning.get_pruning_hparams().parse(FLAGS.pruning_hparams) -- GitLab From f9c7fe82cb930ee26d281e4bf21211ed352d176e Mon Sep 17 00:00:00 2001 From: Jesse Date: Tue, 5 Jun 2018 14:49:04 +0200 Subject: [PATCH 008/519] Put some emphasis on incrementing global step Pruning will not work if the global step is not incremented --- tensorflow/contrib/model_pruning/README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/model_pruning/README.md b/tensorflow/contrib/model_pruning/README.md index 50e7e5d7cd..9143d082bf 100644 --- a/tensorflow/contrib/model_pruning/README.md +++ b/tensorflow/contrib/model_pruning/README.md @@ -103,6 +103,7 @@ with tf.graph.as_default(): mon_sess.run(mask_update_op) ``` +Ensure that `global_step` is being [incremented](https://www.tensorflow.org/api_docs/python/tf/train/Optimizer#minimize), otherwise pruning will not work! ## Example: Pruning and training deep CNNs on the cifar10 dataset -- GitLab From e106a458dd26db58c7d5abbd4afef60f8ce33252 Mon Sep 17 00:00:00 2001 From: Jesse Date: Tue, 5 Jun 2018 15:22:07 +0200 Subject: [PATCH 009/519] Prevent redundant ":0" in summary names Take identical approach as is done with thresholds: using tf.Variable.op.name instead of tf.Variable.name, to prevent TensorFlow saying summary names are illegal (due to ":") --- tensorflow/contrib/model_pruning/python/pruning.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/model_pruning/python/pruning.py b/tensorflow/contrib/model_pruning/python/pruning.py index 4b7af18b33..e6f9acc139 100644 --- a/tensorflow/contrib/model_pruning/python/pruning.py +++ b/tensorflow/contrib/model_pruning/python/pruning.py @@ -520,7 +520,7 @@ class Pruning(object): thresholds = get_thresholds() for index, mask in enumerate(masks): if not self._exists_in_do_not_prune_list(mask.name): - summary.scalar(mask.name + '/sparsity', nn_impl.zero_fraction(mask)) + summary.scalar(mask.op.name + '/sparsity', nn_impl.zero_fraction(mask)) summary.scalar(thresholds[index].op.name + '/threshold', thresholds[index]) -- GitLab From 90b28b7316edb644b71b01edaaa8553d5913fc19 Mon Sep 17 00:00:00 2001 From: Jesse Date: Wed, 6 Jun 2018 16:07:20 +0200 Subject: [PATCH 010/519] Removed redundant use of enumeration Since every mask has an accompanying threshold, zip(masks, thresholds) can be used instead of enumerate(masks) and calling thresholds by index. --- tensorflow/contrib/model_pruning/python/pruning.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/model_pruning/python/pruning.py b/tensorflow/contrib/model_pruning/python/pruning.py index e6f9acc139..d843fa26d5 100644 --- a/tensorflow/contrib/model_pruning/python/pruning.py +++ b/tensorflow/contrib/model_pruning/python/pruning.py @@ -518,11 +518,10 @@ class Pruning(object): summary.scalar('last_mask_update_step', self._last_update_step) masks = get_masks() thresholds = get_thresholds() - for index, mask in enumerate(masks): + for mask, threshold in zip(masks, thresholds): if not self._exists_in_do_not_prune_list(mask.name): summary.scalar(mask.op.name + '/sparsity', nn_impl.zero_fraction(mask)) - summary.scalar(thresholds[index].op.name + '/threshold', - thresholds[index]) + summary.scalar(threshold.op.name + '/threshold', threshold) def print_hparams(self): logging.info(self._spec.to_json()) -- GitLab From 02b7fa3dfe3e82ca61581bf3365788c8acaa2b19 Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Wed, 6 Jun 2018 14:04:40 -0700 Subject: [PATCH 011/519] Adding a constraint for the setuptools version. --- tensorflow/tools/pip_package/setup.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 78d955c637..97f625e7e9 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -54,6 +54,7 @@ REQUIRED_PACKAGES = [ 'numpy >= 1.13.3', 'six >= 1.10.0', 'protobuf >= 3.4.0', + 'setuptools <= 39.1.0', 'tensorboard >= 1.8.0, < 1.9.0', 'termcolor >= 1.1.0', ] -- GitLab From da3f4f86267a42f1a7780222143d79b167a75eb1 Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Wed, 6 Jun 2018 14:27:59 -0700 Subject: [PATCH 012/519] Removing the force downgrade install. --- tensorflow/tools/ci_build/builds/pip.sh | 4 ---- 1 file changed, 4 deletions(-) diff --git a/tensorflow/tools/ci_build/builds/pip.sh b/tensorflow/tools/ci_build/builds/pip.sh index 883bb93647..5fa75e1d61 100755 --- a/tensorflow/tools/ci_build/builds/pip.sh +++ b/tensorflow/tools/ci_build/builds/pip.sh @@ -322,10 +322,6 @@ create_activate_virtualenv_and_install_tensorflow() { pip install -v ${PIP_FLAGS} ${WHL_PATH} || \ die "pip install (forcing to reinstall tensorflow) FAILED" echo "Successfully installed pip package ${TF_WHEEL_PATH}" - - # Force downgrade setuptools. - pip install --upgrade setuptools==39.1.0 - } ################################################################################ -- GitLab From 60cb7f88afda606df2b700ce0bb662f22e1a7709 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Thu, 7 Jun 2018 12:53:11 -0700 Subject: [PATCH 013/519] Consolidate `tf.data` release notes. --- RELEASE.md | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index c1ed69bd45..8f76e7efb4 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -14,8 +14,13 @@ ## Bug Fixes and Other Changes * `tf.data`: - * The `DatasetBase::DebugString()` method is now `const`. - * Added the `tf.contrib.data.sample_from_datasets()` API for randomly sampling from multiple datasets. + * `Dataset.from_generator()` now accepts an `args` list, in order to create nested generators. + * `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed. + * `tf.contrib.data.sample_from_datasets()` and `tf.contrib.data.choose_from_datasets()` make it easier to sample or deterministically choose elements from multiple datasets. + * `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings, and two infrequently used arguments removed. + * (C++) `DatasetBase::DebugString()` is now `const`. + * (C++) `DatasetBase::MakeIterator()` has been renamed to `DatasetBase::MakeIteratorInternal()`. + * (C++) `IteratorBase::Initialize()` method was added to support raising errors during iterator construction. * Eager Execution: * `tf.keras`: * Move Keras code out of _impl folder and remove API files. @@ -24,8 +29,6 @@ * Accelerated Linear Algebra (XLA): * TensorFlow Debugger (tfdbg) CLI: * `tf.contrib`: - * Add `tf.contrib.data.choose_from_datasets()`. - * `tf.contrib.data.make_csv_dataset()` now supports line breaks in quoted strings. Two arguments were removed from `make_csv_dataset`. * `tf.contrib.framework.zero_initializer` supports ResourceVariable. * Adding "constrained_optimization" to tensorflow/contrib. * Other: @@ -35,7 +38,6 @@ * More consistent GcsFileSystem behavior for certain reads past EOF. * Update benchmark for tf.scan to match ranges across eager and graph modes. * Fixed bug in `tf.reduce_prod gradient` for complex dtypes. - * Add optional `args` argument to `Dataset.from_generator()`. * Allow the use of '.' in variables (e.g. "hparams.parse('a.b=1.0')"), which would previously raise an error. This will correspond to an attribute name with an embedded '.' symbol (e.g. 'a.b'), which can only be accessed indirectly (e.g. through getattr and setattr). To set this up the user will first need to explicitly add the variable to the hparam object (e.g. "hparams.add_hparam(name='a.b', value=0.0)"). * Benchmark for tf.scan in graph and eager modes. * Added complex128 support to FFT, FFT2D, FFT3D, IFFT, IFFT2D, and IFFT3D. @@ -45,7 +47,6 @@ * LinearOperator[1D,2D,3D]Circulant added to `tensorflow.linalg`. * Conv3D, Conv3DBackpropInput, Conv3DBackpropFilter now supports arbitrary. * Added `tf.train.Checkpoint` for reading/writing object-based checkpoints. - * `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed. * Added LinearOperatorKronecker, a dense-free implementation of the Kronecker Product. * Allow LinearOperator to broadcast. * SavedModelBuilder will now deduplicate asset names that point to files with the same basename and the same contents. Note that this may result in new asset files included in SavedModels in cases where assets with the same name but different contents were previously overwriting each other. -- GitLab From d3b482dadfa1b59ec04ee668ebd899e6bcb4b7b8 Mon Sep 17 00:00:00 2001 From: Shanqing Cai Date: Fri, 8 Jun 2018 14:55:26 -0400 Subject: [PATCH 014/519] Update RELEASE.md (r1.9) for tfdbg and XLA --- RELEASE.md | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 8f76e7efb4..879ce6e440 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -26,8 +26,7 @@ * Move Keras code out of _impl folder and remove API files. * `tf.keras.Model.save_weights` now saves in TensorFlow format by default. * Enable dataset iterators to be passed to `tf.keras.Model` training/eval methods. -* Accelerated Linear Algebra (XLA): -* TensorFlow Debugger (tfdbg) CLI: +* TensorFlow Debugger (tfdbg) CLI: fix an issue in which the TensorBoard Debugger Plugin could not handle total source file size exceeding gRPC message size limit (4 MB). * `tf.contrib`: * `tf.contrib.framework.zero_initializer` supports ResourceVariable. * Adding "constrained_optimization" to tensorflow/contrib. -- GitLab From a08c8a79f3d0ea5a7fac74d8f5e9da5def89170b Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Mon, 4 Jun 2018 11:11:06 -0700 Subject: [PATCH 015/519] Fix visibility for tf.keras.__version__ PiperOrigin-RevId: 199161696 --- tensorflow/python/keras/__init__.py | 4 ++++ tensorflow/python/keras/integration_test.py | 3 +++ 2 files changed, 7 insertions(+) diff --git a/tensorflow/python/keras/__init__.py b/tensorflow/python/keras/__init__.py index 197f306097..3493069a5b 100644 --- a/tensorflow/python/keras/__init__.py +++ b/tensorflow/python/keras/__init__.py @@ -41,8 +41,12 @@ from tensorflow.python.keras.layers import Input from tensorflow.python.keras.models import Model from tensorflow.python.keras.models import Sequential +from tensorflow.python.util.tf_export import tf_export + __version__ = '2.1.6-tf' +tf_export('keras.__version__').export_constant(__name__, '__version__') + del absolute_import del division del print_function diff --git a/tensorflow/python/keras/integration_test.py b/tensorflow/python/keras/integration_test.py index 2e83544d97..2a05699407 100644 --- a/tensorflow/python/keras/integration_test.py +++ b/tensorflow/python/keras/integration_test.py @@ -29,6 +29,9 @@ from tensorflow.python.platform import test class KerasIntegrationTest(test.TestCase): + def test_version(self): + self.assertTrue(keras.__version__.endswith('-tf')) + def test_vector_classification_sequential(self): with self.test_session(): np.random.seed(1337) -- GitLab From 0eac1ebafc1e16e6440658d6b431998f3e682bbc Mon Sep 17 00:00:00 2001 From: Francois Chollet Date: Mon, 4 Jun 2018 14:46:38 -0700 Subject: [PATCH 016/519] Add various missing aliases for symbols in tf.keras submodules. PiperOrigin-RevId: 199198086 --- tensorflow/python/keras/losses.py | 35 ++++++++++++--- tensorflow/python/ops/init_ops.py | 21 +++++---- ...nsorflow.keras.initializers.constant.pbtxt | 18 ++++++++ ...nsorflow.keras.initializers.identity.pbtxt | 18 ++++++++ ...tensorflow.keras.initializers.normal.pbtxt | 18 ++++++++ .../tensorflow.keras.initializers.ones.pbtxt | 18 ++++++++ ...orflow.keras.initializers.orthogonal.pbtxt | 18 ++++++++ .../tensorflow.keras.initializers.pbtxt | 40 +++++++++++++++++ ...low.keras.initializers.random_normal.pbtxt | 18 ++++++++ ...ow.keras.initializers.random_uniform.pbtxt | 18 ++++++++ ....keras.initializers.truncated_normal.pbtxt | 18 ++++++++ ...ensorflow.keras.initializers.uniform.pbtxt | 18 ++++++++ .../tensorflow.keras.initializers.zeros.pbtxt | 18 ++++++++ .../api/golden/tensorflow.keras.losses.pbtxt | 44 +++++++++++++++++++ .../api/golden/tensorflow.keras.metrics.pbtxt | 44 +++++++++++++++++++ 15 files changed, 350 insertions(+), 14 deletions(-) create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.initializers.constant.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.initializers.identity.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.initializers.normal.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.initializers.ones.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.initializers.orthogonal.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.initializers.random_normal.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.initializers.random_uniform.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.initializers.truncated_normal.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.initializers.uniform.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.keras.initializers.zeros.pbtxt diff --git a/tensorflow/python/keras/losses.py b/tensorflow/python/keras/losses.py index d82ebd9c31..9f548bfe04 100644 --- a/tensorflow/python/keras/losses.py +++ b/tensorflow/python/keras/losses.py @@ -30,19 +30,31 @@ from tensorflow.python.util.tf_export import tf_export @tf_export('keras.metrics.mean_squared_error', - 'keras.losses.mean_squared_error') + 'keras.metrics.mse', + 'keras.metrics.MSE', + 'keras.losses.mean_squared_error', + 'keras.losses.mse', + 'keras.losses.MSE') def mean_squared_error(y_true, y_pred): return K.mean(math_ops.square(y_pred - y_true), axis=-1) @tf_export('keras.metrics.mean_absolute_error', - 'keras.losses.mean_absolute_error') + 'keras.metrics.mae', + 'keras.metrics.MAE', + 'keras.losses.mean_absolute_error', + 'keras.losses.mae', + 'keras.losses.MAE') def mean_absolute_error(y_true, y_pred): return K.mean(math_ops.abs(y_pred - y_true), axis=-1) @tf_export('keras.metrics.mean_absolute_percentage_error', - 'keras.losses.mean_absolute_percentage_error') + 'keras.metrics.mape', + 'keras.metrics.MAPE', + 'keras.losses.mean_absolute_percentage_error', + 'keras.losses.mape', + 'keras.losses.MAPE') def mean_absolute_percentage_error(y_true, y_pred): diff = math_ops.abs( (y_true - y_pred) / K.clip(math_ops.abs(y_true), K.epsilon(), None)) @@ -50,7 +62,11 @@ def mean_absolute_percentage_error(y_true, y_pred): @tf_export('keras.metrics.mean_squared_logarithmic_error', - 'keras.losses.mean_squared_logarithmic_error') + 'keras.metrics.msle', + 'keras.metrics.MSLE', + 'keras.losses.mean_squared_logarithmic_error', + 'keras.losses.msle', + 'keras.losses.MSLE') def mean_squared_logarithmic_error(y_true, y_pred): first_log = math_ops.log(K.clip(y_pred, K.epsilon(), None) + 1.) second_log = math_ops.log(K.clip(y_true, K.epsilon(), None) + 1.) @@ -117,7 +133,11 @@ def binary_crossentropy(y_true, y_pred): @tf_export('keras.metrics.kullback_leibler_divergence', - 'keras.losses.kullback_leibler_divergence') + 'keras.metrics.kld', + 'keras.metrics.KLD', + 'keras.losses.kullback_leibler_divergence', + 'keras.losses.kld', + 'keras.losses.KLD') def kullback_leibler_divergence(y_true, y_pred): y_true = K.clip(y_true, K.epsilon(), 1) y_pred = K.clip(y_pred, K.epsilon(), 1) @@ -129,7 +149,10 @@ def poisson(y_true, y_pred): return K.mean(y_pred - y_true * math_ops.log(y_pred + K.epsilon()), axis=-1) -@tf_export('keras.metrics.cosine_proximity', 'keras.losses.cosine_proximity') +@tf_export('keras.metrics.cosine_proximity', + 'keras.metrics.cosine', + 'keras.losses.cosine_proximity', + 'keras.losses.cosine') def cosine_proximity(y_true, y_pred): y_true = nn.l2_normalize(y_true, axis=-1) y_pred = nn.l2_normalize(y_pred, axis=-1) diff --git a/tensorflow/python/ops/init_ops.py b/tensorflow/python/ops/init_ops.py index 1f8d8dc4f3..2df230d470 100644 --- a/tensorflow/python/ops/init_ops.py +++ b/tensorflow/python/ops/init_ops.py @@ -86,7 +86,7 @@ class Initializer(object): @tf_export("keras.initializers.Zeros", "initializers.zeros", - "zeros_initializer") + "zeros_initializer", "keras.initializers.zeros") class Zeros(Initializer): """Initializer that generates tensors initialized to 0.""" @@ -102,7 +102,8 @@ class Zeros(Initializer): return {"dtype": self.dtype.name} -@tf_export("keras.initializers.Ones", "initializers.ones", "ones_initializer") +@tf_export("keras.initializers.Ones", "initializers.ones", "ones_initializer", + "keras.initializers.ones") class Ones(Initializer): """Initializer that generates tensors initialized to 1.""" @@ -119,7 +120,7 @@ class Ones(Initializer): @tf_export("keras.initializers.Constant", "initializers.constant", - "constant_initializer") + "constant_initializer", "keras.initializers.constant") class Constant(Initializer): """Initializer that generates tensors with constant values. @@ -225,7 +226,8 @@ class Constant(Initializer): @tf_export("keras.initializers.RandomUniform", "initializers.random_uniform", - "random_uniform_initializer") + "random_uniform_initializer", "keras.initializers.uniform", + "keras.initializers.random_uniform") class RandomUniform(Initializer): """Initializer that generates tensors with a uniform distribution. @@ -262,7 +264,8 @@ class RandomUniform(Initializer): @tf_export("keras.initializers.RandomNormal", "initializers.random_normal", - "random_normal_initializer") + "random_normal_initializer", "keras.initializers.normal", + "keras.initializers.random_normal") class RandomNormal(Initializer): """Initializer that generates tensors with a normal distribution. @@ -299,7 +302,8 @@ class RandomNormal(Initializer): @tf_export("keras.initializers.TruncatedNormal", - "initializers.truncated_normal", "truncated_normal_initializer") + "initializers.truncated_normal", "truncated_normal_initializer", + "keras.initializers.truncated_normal") class TruncatedNormal(Initializer): """Initializer that generates a truncated normal distribution. @@ -482,7 +486,7 @@ class VarianceScaling(Initializer): @tf_export("keras.initializers.Orthogonal", "initializers.orthogonal", - "orthogonal_initializer") + "orthogonal_initializer", "keras.initializers.orthogonal") class Orthogonal(Initializer): """Initializer that generates an orthogonal matrix. @@ -1062,7 +1066,8 @@ class ConvolutionOrthogonal3D(ConvolutionOrthogonal): return self._dict_to_tensor(p, ksize, ksize, ksize) -@tf_export("keras.initializers.Identity", "initializers.identity") +@tf_export("keras.initializers.Identity", "initializers.identity", + "keras.initializers.identity") class Identity(Initializer): """Initializer that generates the identity matrix. diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.constant.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.constant.pbtxt new file mode 100644 index 0000000000..bddc37b907 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.constant.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.constant" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'value\', \'dtype\', \'verify_shape\'], varargs=None, keywords=None, defaults=[\'0\', \"\", \'False\'], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.identity.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.identity.pbtxt new file mode 100644 index 0000000000..a4c5a61490 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.identity.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.identity" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'gain\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.normal.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.normal.pbtxt new file mode 100644 index 0000000000..7485772784 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.normal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.normal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.ones.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.ones.pbtxt new file mode 100644 index 0000000000..a89f78d1e1 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.ones.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.ones" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.orthogonal.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.orthogonal.pbtxt new file mode 100644 index 0000000000..ee1e9bbae2 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.orthogonal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.orthogonal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'gain\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.pbtxt index 093c56595b..14a667870d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.initializers.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.pbtxt @@ -40,6 +40,46 @@ tf_module { name: "Zeros" mtype: "" } + member { + name: "constant" + mtype: "" + } + member { + name: "identity" + mtype: "" + } + member { + name: "normal" + mtype: "" + } + member { + name: "ones" + mtype: "" + } + member { + name: "orthogonal" + mtype: "" + } + member { + name: "random_normal" + mtype: "" + } + member { + name: "random_uniform" + mtype: "" + } + member { + name: "truncated_normal" + mtype: "" + } + member { + name: "uniform" + mtype: "" + } + member { + name: "zeros" + mtype: "" + } member_method { name: "deserialize" argspec: "args=[\'config\', \'custom_objects\'], varargs=None, keywords=None, defaults=[\'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.random_normal.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.random_normal.pbtxt new file mode 100644 index 0000000000..a6df1e87a3 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.random_normal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.random_normal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.random_uniform.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.random_uniform.pbtxt new file mode 100644 index 0000000000..37a0fa0d55 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.random_uniform.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.random_uniform" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'minval\', \'maxval\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.truncated_normal.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.truncated_normal.pbtxt new file mode 100644 index 0000000000..f97e93f0b7 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.truncated_normal.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.truncated_normal" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'mean\', \'stddev\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0.0\', \'1.0\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.uniform.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.uniform.pbtxt new file mode 100644 index 0000000000..58186b1383 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.uniform.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.uniform" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'minval\', \'maxval\', \'seed\', \'dtype\'], varargs=None, keywords=None, defaults=[\'0\', \'None\', \'None\', \"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.initializers.zeros.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.initializers.zeros.pbtxt new file mode 100644 index 0000000000..a262390687 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.keras.initializers.zeros.pbtxt @@ -0,0 +1,18 @@ +path: "tensorflow.keras.initializers.zeros" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member_method { + name: "__init__" + argspec: "args=[\'self\', \'dtype\'], varargs=None, keywords=None, defaults=[\"\"], " + } + member_method { + name: "from_config" + argspec: "args=[\'cls\', \'config\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "get_config" + argspec: "args=[\'self\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.keras.losses.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.losses.pbtxt index ae5f6305b7..eca6b91538 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.losses.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.losses.pbtxt @@ -1,5 +1,25 @@ path: "tensorflow.keras.losses" tf_module { + member_method { + name: "KLD" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MAE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MAPE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MSE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MSLE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "binary_crossentropy" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" @@ -12,6 +32,10 @@ tf_module { name: "categorical_hinge" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "cosine" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "cosine_proximity" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" @@ -28,6 +52,10 @@ tf_module { name: "hinge" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "kld" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "kullback_leibler_divergence" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" @@ -36,6 +64,14 @@ tf_module { name: "logcosh" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "mae" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "mape" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "mean_absolute_error" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" @@ -52,6 +88,14 @@ tf_module { name: "mean_squared_logarithmic_error" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "mse" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "msle" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "poisson" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt index 42729e4237..a97a9b5758 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt @@ -1,5 +1,25 @@ path: "tensorflow.keras.metrics" tf_module { + member_method { + name: "KLD" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MAE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MAPE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MSE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "MSLE" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "binary_accuracy" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" @@ -16,6 +36,10 @@ tf_module { name: "categorical_crossentropy" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "cosine" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "cosine_proximity" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" @@ -32,10 +56,22 @@ tf_module { name: "hinge" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "kld" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "kullback_leibler_divergence" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "mae" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "mape" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "mean_absolute_error" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" @@ -52,6 +88,14 @@ tf_module { name: "mean_squared_logarithmic_error" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" } + member_method { + name: "mse" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } + member_method { + name: "msle" + argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "poisson" argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" -- GitLab From 7c33a7751d77cfd70a5c441da369440f4f6b633a Mon Sep 17 00:00:00 2001 From: Pavithra Vijay Date: Thu, 7 Jun 2018 09:20:57 -0700 Subject: [PATCH 017/519] Fix bug due to incorrect nesting of return statement in eager iterator evaluation. PiperOrigin-RevId: 199645638 --- .../python/keras/engine/training_eager.py | 10 ++-- .../keras/engine/training_eager_test.py | 54 +++++++++++++++++++ 2 files changed, 59 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py index 081e46aa66..a70b488f25 100644 --- a/tensorflow/python/keras/engine/training_eager.py +++ b/tensorflow/python/keras/engine/training_eager.py @@ -501,11 +501,11 @@ def iterator_test_loop(model, inputs, steps, verbose=0): if verbose == 1: progbar.update(step_index + 1) - for i in range(len(outs)): - outs[i] /= num_samples - if len(outs) == 1: - return outs[0] - return outs + for i in range(len(outs)): + outs[i] /= num_samples + if len(outs) == 1: + return outs[0] + return outs def batch_test_loop(model, diff --git a/tensorflow/python/keras/engine/training_eager_test.py b/tensorflow/python/keras/engine/training_eager_test.py index d9446fd437..7906d208eb 100644 --- a/tensorflow/python/keras/engine/training_eager_test.py +++ b/tensorflow/python/keras/engine/training_eager_test.py @@ -20,6 +20,7 @@ from __future__ import print_function import numpy as np +from tensorflow.python.data.ops import dataset_ops from tensorflow.python import keras from tensorflow.python.framework import ops from tensorflow.python.framework import test_util as tf_test_util @@ -670,6 +671,59 @@ class CorrectnessTest(test.TestCase): outs = model.evaluate(x, y) self.assertEqual(outs[1], 0.) + @tf_test_util.run_in_graph_and_eager_modes() + def test_loss_correctness_with_iterator(self): + # Test that training loss is the same in eager and graph + # (by comparing it to a reference value in a deterministic case) + model = keras.Sequential() + model.add( + keras.layers.Dense( + 3, activation='relu', input_dim=4, kernel_initializer='ones')) + model.add( + keras.layers.Dense(2, activation='softmax', kernel_initializer='ones')) + model.compile( + loss='sparse_categorical_crossentropy', + optimizer=RMSPropOptimizer(learning_rate=0.001)) + x = np.ones((100, 4), dtype=np.float32) + np.random.seed(123) + y = np.random.randint(0, 1, size=(100, 1)) + dataset = dataset_ops.Dataset.from_tensor_slices((x, y)) + dataset = dataset.repeat(100) + dataset = dataset.batch(10) + iterator = dataset.make_one_shot_iterator() + history = model.fit(iterator, epochs=1, steps_per_epoch=10) + self.assertEqual(np.around(history.history['loss'][-1], decimals=4), 0.6173) + + @tf_test_util.run_in_graph_and_eager_modes() + def test_metrics_correctness_with_iterator(self): + model = keras.Sequential() + model.add( + keras.layers.Dense( + 8, activation='relu', input_dim=4, kernel_initializer='ones')) + model.add( + keras.layers.Dense(1, activation='sigmoid', kernel_initializer='ones')) + model.compile( + loss='binary_crossentropy', + metrics=['accuracy'], + optimizer=RMSPropOptimizer(learning_rate=0.001)) + np.random.seed(123) + x = np.random.randint(10, size=(100, 4)).astype(np.float32) + y = np.random.randint(2, size=(100, 1)).astype(np.float32) + dataset = dataset_ops.Dataset.from_tensor_slices((x, y)) + dataset = dataset.batch(10) + iterator = dataset.make_one_shot_iterator() + outs = model.evaluate(iterator, steps=10) + self.assertEqual(np.around(outs[1], decimals=1), 0.5) + + y = np.zeros((100, 1), dtype=np.float32) + dataset = dataset_ops.Dataset.from_tensor_slices((x, y)) + dataset = dataset.repeat(100) + dataset = dataset.batch(10) + iterator = dataset.make_one_shot_iterator() + outs = model.evaluate(iterator, steps=10) + self.assertEqual(outs[1], 0.) + + if __name__ == '__main__': ops.enable_eager_execution() test.main() -- GitLab From 5177fd2f9acb9b46b9182ad782bb8b7b9386baeb Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 5 Jun 2018 15:59:21 -0700 Subject: [PATCH 018/519] Only calls compare function if values were read from event file PiperOrigin-RevId: 199373169 --- tensorflow/python/estimator/exporter.py | 7 ++-- tensorflow/python/estimator/exporter_test.py | 34 ++++++++++++++++++++ 2 files changed, 38 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/estimator/exporter.py b/tensorflow/python/estimator/exporter.py index a7212bb83e..766ea23f2a 100644 --- a/tensorflow/python/estimator/exporter.py +++ b/tensorflow/python/estimator/exporter.py @@ -360,9 +360,10 @@ class BestExporter(Exporter): for value in event.summary.value: if value.HasField('simple_value'): event_eval_result[value.tag] = value.simple_value - if best_eval_result is None or self._compare_fn( - best_eval_result, event_eval_result): - best_eval_result = event_eval_result + if event_eval_result: + if best_eval_result is None or self._compare_fn( + best_eval_result, event_eval_result): + best_eval_result = event_eval_result return best_eval_result diff --git a/tensorflow/python/estimator/exporter_test.py b/tensorflow/python/estimator/exporter_test.py index 4cb4bffc8d..c4b006955c 100644 --- a/tensorflow/python/estimator/exporter_test.py +++ b/tensorflow/python/estimator/exporter_test.py @@ -148,6 +148,40 @@ class BestExporterTest(test.TestCase): "checkpoint_path", {"loss": 20}, False) self.assertEqual(None, export_result) + def test_best_exporter_with_empty_event(self): + + def _serving_input_receiver_fn(): + pass + + export_dir_base = tempfile.mkdtemp() + gfile.MkDir(export_dir_base) + gfile.MkDir(export_dir_base + "/export") + gfile.MkDir(export_dir_base + "/eval") + + eval_dir_base = os.path.join(export_dir_base, "eval_continuous") + estimator_lib._write_dict_to_summary(eval_dir_base, {}, 1) + estimator_lib._write_dict_to_summary(eval_dir_base, {"loss": 60}, 2) + + exporter = exporter_lib.BestExporter( + name="best_exporter", + serving_input_receiver_fn=_serving_input_receiver_fn, + event_file_pattern="eval_continuous/*.tfevents.*", + assets_extra={"from/path": "to/path"}, + as_text=False, + exports_to_keep=1) + + estimator = test.mock.Mock(spec=estimator_lib.Estimator) + estimator.model_dir = export_dir_base + estimator.export_savedmodel.return_value = "export_result_path" + + export_result = exporter.export(estimator, export_dir_base, + "checkpoint_path", {"loss": 100}, False) + self.assertEqual(None, export_result) + + export_result = exporter.export(estimator, export_dir_base, + "checkpoint_path", {"loss": 10}, False) + self.assertEqual("export_result_path", export_result) + def test_garbage_collect_exports(self): export_dir_base = tempfile.mkdtemp() gfile.MkDir(export_dir_base) -- GitLab From 4fe8d4a14936dc38558a858283574993909c9895 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sun, 27 May 2018 10:49:12 -0700 Subject: [PATCH 019/519] TPUEstimator.export_savedmodel() saves a SavedModel with both TPU and CPU graphs. PiperOrigin-RevId: 198229550 --- tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 4465833f88..c8c08a5a63 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -1807,7 +1807,7 @@ class TPUEstimator(estimator_lib.Estimator): export_outputs['classes'] = export_output_lib.ClassificationOutput(classes=classes) - tpu.outside_compilation(host_call, logits) + tpu.outside_compilation(host_call, [logits]) ... ``` @@ -1969,7 +1969,7 @@ class TPUEstimator(estimator_lib.Estimator): input_receiver_fn_map[mode]} export_tags = [tag_constants.SERVING, tag_constants.TPU] mode = _REWRITE_FOR_INFERENCE_MODE - try: + if self._export_to_tpu: (super(TPUEstimator, self). _add_meta_graph_for_mode(builder, input_receiver_fn_map, @@ -1978,9 +1978,6 @@ class TPUEstimator(estimator_lib.Estimator): save_variables=False, mode=mode, export_tags=export_tags)) - except Exception as error: # pylint: disable=broad-except - logging.warning('Saving meta graph for TPU failed: {}.' - .format(str(error))) def _call_model_fn(self, features, labels, mode, config): if mode == _REWRITE_FOR_INFERENCE_MODE: -- GitLab From 51ad43efe5d918e7c57bd6c612fc1d0efd0b0664 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 29 May 2018 14:28:59 -0700 Subject: [PATCH 020/519] In TPUEstimator.export_savedmodel(), if saving TPU metegraph fails, issue a warning instead so that user can still use the CPU metagraph. PiperOrigin-RevId: 198458571 -- GitLab From 982f3e3038f8d07964b2c58843a51bd9745a8990 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 1 Jun 2018 16:32:20 -0700 Subject: [PATCH 021/519] Allow user to opt out of saving metagraph for TPU with TPUEstimator.export_output(). PiperOrigin-RevId: 198944144 --- tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 13 +++++++++---- 1 file changed, 9 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index c8c08a5a63..7c770912b4 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -1830,6 +1830,7 @@ class TPUEstimator(estimator_lib.Estimator): predict_batch_size=None, batch_axis=None, eval_on_tpu=True, + export_to_tpu=True, warm_start_from=None): """Constructs an `TPUEstimator` instance. @@ -1872,6 +1873,8 @@ class TPUEstimator(estimator_lib.Estimator): False or `PER_HOST_V2`, batch_axis is ignored. eval_on_tpu: If False, evaluation runs on CPU or GPU. In this case, the model_fn must return `EstimatorSpec` when called with `mode` as `EVAL`. + export_to_tpu: If True, `export_savedmodel()` exports a metagraph for + serving on TPU besides the one on CPU. warm_start_from: Optional string filepath to a checkpoint or SavedModel to warm-start from, or a `tf.estimator.WarmStartSettings` object to fully configure warm-starting. If the string @@ -1943,6 +1946,8 @@ class TPUEstimator(estimator_lib.Estimator): use_tpu, eval_on_tpu) + self._export_to_tpu = export_to_tpu + self._is_input_fn_invoked = None def _add_meta_graph_for_mode(self, @@ -1965,11 +1970,11 @@ class TPUEstimator(estimator_lib.Estimator): save_variables, mode=mode) - input_receiver_fn_map = {_REWRITE_FOR_INFERENCE_MODE: - input_receiver_fn_map[mode]} - export_tags = [tag_constants.SERVING, tag_constants.TPU] - mode = _REWRITE_FOR_INFERENCE_MODE if self._export_to_tpu: + input_receiver_fn_map = {_REWRITE_FOR_INFERENCE_MODE: + input_receiver_fn_map[mode]} + export_tags = [tag_constants.SERVING, tag_constants.TPU] + mode = _REWRITE_FOR_INFERENCE_MODE (super(TPUEstimator, self). _add_meta_graph_for_mode(builder, input_receiver_fn_map, -- GitLab From 6cc2741eb1c9b19742b32b8edda39090afbf5abf Mon Sep 17 00:00:00 2001 From: DavidNorman Date: Tue, 12 Jun 2018 09:32:53 +0100 Subject: [PATCH 022/519] Fix python lint errors --- tensorflow/compiler/tests/binary_ops_test.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tensorflow/compiler/tests/binary_ops_test.py b/tensorflow/compiler/tests/binary_ops_test.py index 64eeed8312..823afbbbdc 100644 --- a/tensorflow/compiler/tests/binary_ops_test.py +++ b/tensorflow/compiler/tests/binary_ops_test.py @@ -687,11 +687,12 @@ class BinaryOpsTest(XLATestCase): np.float32(7), expected=np.array([[False], [False], [True]], dtype=np.bool)) if np.int64 in self.numeric_types: - self._testBinary( - less_op, - np.array([[10], [7], [2], [-1]], dtype=np.int64), - np.int64(7), - expected=np.array([[False], [False], [True], [True]], dtype=np.bool)) + self._testBinary( + less_op, + np.array([[10], [7], [2], [-1]], dtype=np.int64), + np.int64(7), + expected=np.array( + [[False], [False], [True], [True]], dtype=np.bool)) for less_equal_op in [math_ops.less_equal, (lambda x, y: x <= y)]: self._testBinary( -- GitLab From fd44596bc4b3ea8c67838b728b450a44e35c1b89 Mon Sep 17 00:00:00 2001 From: Anna R Date: Mon, 11 Jun 2018 17:21:06 -0700 Subject: [PATCH 023/519] Merging --- tensorflow/tools/api/generator/BUILD | 24 ++++++ .../tools/api/generator/create_python_api.py | 54 +++++++++++-- tensorflow/tools/api/generator/doc_srcs.py | 65 +++++++++++++++ .../tools/api/generator/doc_srcs_test.py | 80 +++++++++++++++++++ 4 files changed, 217 insertions(+), 6 deletions(-) create mode 100644 tensorflow/tools/api/generator/doc_srcs.py create mode 100644 tensorflow/tools/api/generator/doc_srcs_test.py diff --git a/tensorflow/tools/api/generator/BUILD b/tensorflow/tools/api/generator/BUILD index f0c5877a90..3a28153e52 100644 --- a/tensorflow/tools/api/generator/BUILD +++ b/tensorflow/tools/api/generator/BUILD @@ -5,12 +5,21 @@ licenses(["notice"]) # Apache 2.0 exports_files(["LICENSE"]) +load("//tensorflow/tools/api/generator:api_gen.bzl", "TENSORFLOW_API_INIT_FILES") + +py_library( + name = "doc_srcs", + srcs = ["doc_srcs.py"], + srcs_version = "PY2AND3", +) + py_binary( name = "create_python_api", srcs = ["create_python_api.py"], srcs_version = "PY2AND3", visibility = ["//visibility:public"], deps = [ + ":doc_srcs", "//tensorflow/python:no_contrib", ], ) @@ -24,3 +33,18 @@ py_test( "//tensorflow/python:client_testlib", ], ) + +py_test( + name = "tensorflow_doc_srcs_test", + srcs = ["doc_srcs_test.py"], + args = [ + "--package=tensorflow.python", + ] + TENSORFLOW_API_INIT_FILES, + main = "doc_srcs_test.py", + srcs_version = "PY2AND3", + deps = [ + ":doc_srcs", + "//tensorflow/python:client_testlib", + "//tensorflow/python:no_contrib", + ], +) diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py index 9f210ad42b..31f287b7fe 100644 --- a/tensorflow/tools/api/generator/create_python_api.py +++ b/tensorflow/tools/api/generator/create_python_api.py @@ -25,6 +25,8 @@ import os import sys from tensorflow.python.util import tf_decorator +from tensorflow.python.util import tf_export +from tensorflow.tools.api.generator import doc_srcs _API_CONSTANTS_ATTR = '_tf_api_constants' @@ -36,10 +38,9 @@ _SYMBOLS_TO_SKIP_EXPLICITLY = { # would have side effects. 'tensorflow.python.platform.flags.FLAGS' } -_GENERATED_FILE_HEADER = """\"\"\"Imports for Python API. - -This file is MACHINE GENERATED! Do not edit. -Generated by: tensorflow/tools/api/generator/create_python_api.py script. +_GENERATED_FILE_HEADER = """# This file is MACHINE GENERATED! Do not edit. +# Generated by: tensorflow/tools/api/generator/create_python_api.py script. +\"\"\"%s \"\"\" from __future__ import print_function @@ -254,6 +255,44 @@ def get_module(dir_path, relative_to_dir): return dir_path.replace('/', '.').strip('.') +def get_module_docstring(module_name, package): + """Get docstring for the given module. + + This method looks for docstring in the following order: + 1. Checks if module has a docstring specified in doc_srcs. + 2. Checks if module has a docstring source module specified + in doc_srcs. If it does, gets docstring from that module. + 3. Checks if module with module_name exists under base package. + If it does, gets docstring from that module. + 4. Returns a default docstring. + + Args: + module_name: module name relative to tensorflow + (excluding 'tensorflow.' prefix) to get a docstring for. + package: Base python package containing python with target tf_export + decorators. + + Returns: + One-line docstring to describe the module. + """ + # Module under base package to get a docstring from. + docstring_module_name = module_name + + if module_name in doc_srcs.TENSORFLOW_DOC_SOURCES: + docsrc = doc_srcs.TENSORFLOW_DOC_SOURCES[module_name] + if docsrc.docstring: + return docsrc.docstring + if docsrc.docstring_module_name: + docstring_module_name = docsrc.docstring_module_name + + docstring_module_name = package + '.' + docstring_module_name + if (docstring_module_name in sys.modules and + sys.modules[docstring_module_name].__doc__): + return sys.modules[docstring_module_name].__doc__ + + return 'Public API for tf.%s namespace.' % module_name + + def create_api_files( output_files, package, root_init_template, output_dir): """Creates __init__.py files for the Python API. @@ -296,7 +335,10 @@ def create_api_files( continue contents = '' if module or not root_init_template: - contents = _GENERATED_FILE_HEADER + text + _GENERATED_FILE_FOOTER + contents = ( + _GENERATED_FILE_HEADER % + get_module_docstring(module, package) + text + + _GENERATED_FILE_FOOTER) else: # Read base init file with open(root_init_template, 'r') as root_init_template_file: @@ -309,7 +351,7 @@ def create_api_files( raise ValueError( 'Missing outputs for python_api_gen genrule:\n%s.' 'Make sure all required outputs are in the ' - 'tensorflow/tools/api/generator/BUILD file.' % + 'tensorflow/tools/api/generator/api_gen.bzl file.' % ',\n'.join(sorted(missing_output_files))) diff --git a/tensorflow/tools/api/generator/doc_srcs.py b/tensorflow/tools/api/generator/doc_srcs.py new file mode 100644 index 0000000000..74f6db98fd --- /dev/null +++ b/tensorflow/tools/api/generator/doc_srcs.py @@ -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. +# ============================================================================== +"""Specifies sources of doc strings for API modules.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import collections + + +# Specifies docstring source for a module. +# Only one of docstring or docstring_module_name should be set. +# * If docstring is set, then we will use this docstring when +# for the module. +# * If docstring_module_name is set, then we will copy the docstring +# from docstring source module. +DocSource = collections.namedtuple( + 'DocSource', ['docstring', 'docstring_module_name']) +# Each attribute of DocSource is optional. +DocSource.__new__.__defaults__ = (None,) * len(DocSource._fields) + +TENSORFLOW_DOC_SOURCES = { + 'app': DocSource(docstring_module_name='platform.app'), + 'compat': DocSource(docstring_module_name='util.compat'), + 'distributions': DocSource( + docstring_module_name='ops.distributions.distributions'), + 'bitwise': DocSource(docstring_module_name='ops.bitwise_ops'), + 'errors': DocSource(docstring_module_name='framework.errors'), + 'gfile': DocSource(docstring_module_name='platform.gfile'), + 'graph_util': DocSource(docstring_module_name='framework.graph_util'), + 'image': DocSource(docstring_module_name='ops.image_ops'), + 'keras.estimator': DocSource(docstring_module_name='estimator.keras'), + 'linalg': DocSource(docstring_module_name='ops.linalg_ops'), + 'logging': DocSource(docstring_module_name='ops.logging_ops'), + 'losses': DocSource(docstring_module_name='ops.losses.losses'), + 'manip': DocSource(docstring_module_name='ops.manip_ops'), + 'math': DocSource(docstring_module_name='ops.math_ops'), + 'metrics': DocSource(docstring_module_name='ops.metrics'), + 'nn': DocSource(docstring_module_name='ops.nn_ops'), + 'nn.rnn_cell': DocSource(docstring_module_name='ops.rnn_cell'), + 'python_io': DocSource(docstring_module_name='lib.io.python_io'), + 'resource_loader': DocSource( + docstring_module_name='platform.resource_loader'), + 'sets': DocSource(docstring_module_name='ops.sets'), + 'sparse': DocSource(docstring_module_name='ops.sparse_ops'), + 'spectral': DocSource(docstring_module_name='ops.spectral_ops'), + 'strings': DocSource(docstring_module_name='ops.string_ops'), + 'sysconfig': DocSource(docstring_module_name='platform.sysconfig'), + 'test': DocSource(docstring_module_name='platform.test'), + 'train': DocSource(docstring_module_name='training.training'), + 'train.queue_runner': DocSource( + docstring_module_name='training.queue_runner'), +} diff --git a/tensorflow/tools/api/generator/doc_srcs_test.py b/tensorflow/tools/api/generator/doc_srcs_test.py new file mode 100644 index 0000000000..9ba95a3439 --- /dev/null +++ b/tensorflow/tools/api/generator/doc_srcs_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 tensorflow.tools.api.generator.doc_srcs.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse +import importlib +import sys + +from tensorflow.python.platform import test +from tensorflow.tools.api.generator import doc_srcs + + +FLAGS = None + + +class DocSrcsTest(test.TestCase): + + def testModulesAreValidAPIModules(self): + for module_name in doc_srcs.TENSORFLOW_DOC_SOURCES: + # Convert module_name to corresponding __init__.py file path. + file_path = module_name.replace('.', '/') + if file_path: + file_path += '/' + file_path += '__init__.py' + + if file_path not in FLAGS.outputs: + self.assertFalse('%s is not a valid API module' % module_name) + + def testHaveDocstringOrDocstringModule(self): + for module_name, docsrc in doc_srcs.TENSORFLOW_DOC_SOURCES.items(): + if docsrc.docstring and docsrc.docstring_module_name: + self.assertFalse( + '%s contains DocSource has both a docstring and a ' + 'docstring_module_name. ' + 'Only one of "docstring" or "docstring_module_name" should be set.' + % (module_name)) + + def testDocstringModulesAreValidModules(self): + for _, docsrc in doc_srcs.TENSORFLOW_DOC_SOURCES.items(): + if docsrc.docstring_module_name: + doc_module_name = '.'.join([ + FLAGS.package, docsrc.docstring_module_name]) + if doc_module_name not in sys.modules: + sys.assertFalse( + 'docsources_module %s is not a valid module under %s.' % + (docsrc.docstring_module_name, FLAGS.package)) + + +if __name__ == '__main__': + parser = argparse.ArgumentParser() + parser.add_argument( + 'outputs', metavar='O', type=str, nargs='+', + help='create_python_api output files.') + parser.add_argument( + '--package', type=str, + help='Base package that imports modules containing the target tf_export ' + 'decorators.') + FLAGS, unparsed = parser.parse_known_args() + + importlib.import_module(FLAGS.package) + + # Now update argv, so that unittest library does not get confused. + sys.argv = [sys.argv[0]] + unparsed + test.main() -- GitLab From e042e3e051d3bd6bfb63dfd4ad407a82f7d1dacc Mon Sep 17 00:00:00 2001 From: Anna R Date: Tue, 12 Jun 2018 17:47:58 -0700 Subject: [PATCH 024/519] Remove unused tf_export import --- tensorflow/tools/api/generator/create_python_api.py | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/tools/api/generator/create_python_api.py b/tensorflow/tools/api/generator/create_python_api.py index 31f287b7fe..e3ab056efc 100644 --- a/tensorflow/tools/api/generator/create_python_api.py +++ b/tensorflow/tools/api/generator/create_python_api.py @@ -25,7 +25,6 @@ import os import sys from tensorflow.python.util import tf_decorator -from tensorflow.python.util import tf_export from tensorflow.tools.api.generator import doc_srcs -- GitLab From f055a9f2f21154140785b9da7c3b2eae88e65623 Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Tue, 12 Jun 2018 18:09:35 -0700 Subject: [PATCH 025/519] Check to ensure the Cloud TPU is ready before resolving. Cherry picking this into the TF 1.9 release. PiperOrigin-RevId: 200095692 Previous commit: 32c8013f0ab3feb139648ae759e2d0168fb5dc95 --- .../python/training/tpu_cluster_resolver.py | 3 ++ .../training/tpu_cluster_resolver_test.py | 44 +++++++++++++++++++ 2 files changed, 47 insertions(+) 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 880fca4ea6..935ad5ff37 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py +++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py @@ -255,6 +255,9 @@ class TPUClusterResolver(ClusterResolver): request = self._service.projects().locations().nodes().get(name=full_name) response = request.execute() + if 'state' in response and response['state'] != 'READY': + raise RuntimeError('TPU "%s" is not yet ready; state: "%s"' % + (self._tpu, response['state'])) if 'health' in response and response['health'] != 'HEALTHY': raise RuntimeError('TPU "%s" is unhealthy: "%s"' % (self._tpu, response['health'])) 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 5fac55fd02..7e002cc72f 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 @@ -157,6 +157,50 @@ class TPUClusterResolverTest(test.TestCase): job { name: 'worker' tasks { key: 0 value: '10.1.2.3:8470' } } """ self._verifyClusterSpecEquality(actual_cluster_spec, expected_proto) + + @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + mock_request_compute_metadata) + def testUnhealthyCloudTpu(self): + tpu_map = { + 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { + 'ipAddress': '10.1.2.3', + 'port': '8470', + 'health': 'UNHEALTHY' + } + } + + tpu_cluster_resolver = TPUClusterResolver( + project=None, + zone=None, + tpu='test-tpu-1', + coordinator_name=None, + credentials=None, + service=self.mock_service_client(tpu_map=tpu_map)) + + with self.assertRaises(RuntimeError): + tpu_cluster_resolver.cluster_spec() + + @mock.patch.object(TPUClusterResolver, '_requestComputeMetadata', + mock_request_compute_metadata) + def testNotReadyCloudTpu(self): + tpu_map = { + 'projects/test-project/locations/us-central1-c/nodes/test-tpu-1': { + 'ipAddress': '10.1.2.3', + 'port': '8470', + 'state': 'CREATING' + } + } + + tpu_cluster_resolver = TPUClusterResolver( + project=None, + zone=None, + tpu='test-tpu-1', + coordinator_name=None, + credentials=None, + service=self.mock_service_client(tpu_map=tpu_map)) + + with self.assertRaises(RuntimeError): + tpu_cluster_resolver.cluster_spec() def testSimpleSuccessfulRetrieval(self): tpu_map = { -- GitLab From 9a087a42293be8342570039d2c6d329a0589b773 Mon Sep 17 00:00:00 2001 From: Nick Felt Date: Wed, 13 Jun 2018 00:30:09 -0700 Subject: [PATCH 026/519] Update tensorboard dependency to 1.9.x --- tensorflow/tools/pip_package/setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 97f625e7e9..92a1465cea 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -55,7 +55,7 @@ REQUIRED_PACKAGES = [ 'six >= 1.10.0', 'protobuf >= 3.4.0', 'setuptools <= 39.1.0', - 'tensorboard >= 1.8.0, < 1.9.0', + 'tensorboard >= 1.9.0, < 1.10.0', 'termcolor >= 1.1.0', ] -- GitLab From b1d0048f2be83d6c6f7e1be996ef9c8358922aa6 Mon Sep 17 00:00:00 2001 From: Pete Warden Date: Wed, 13 Jun 2018 01:06:50 -0700 Subject: [PATCH 027/519] Documentation for Raspberry Pi installation --- tensorflow/docs_src/install/index.md | 2 + .../docs_src/install/install_raspbian.md | 317 ++++++++++++++++++ 2 files changed, 319 insertions(+) create mode 100644 tensorflow/docs_src/install/install_raspbian.md diff --git a/tensorflow/docs_src/install/index.md b/tensorflow/docs_src/install/index.md index 4f85383925..c2e5a991d4 100644 --- a/tensorflow/docs_src/install/index.md +++ b/tensorflow/docs_src/install/index.md @@ -6,6 +6,7 @@ operating systems: * macOS 10.12.6 (Sierra) or later. * Ubuntu 16.04 or later * Windows 7 or later. + * Raspbian 9.0 or later. Although you might be able to install TensorFlow on other laptop or desktop systems, we only support (and only fix issues in) the preceding configurations. @@ -16,6 +17,7 @@ that enables you to write applications in Python: * @{$install_linux$Installing TensorFlow on Ubuntu} * @{$install_mac$Installing TensorFlow on macOS} * @{$install_windows$Installing TensorFlow on Windows} + * @{$install_raspbian$Installing TensorFlow on a Raspberry Pi} * @{$install_sources$Installing TensorFlow from Sources} Many aspects of the Python TensorFlow API changed from version 0.n to 1.0. diff --git a/tensorflow/docs_src/install/install_raspbian.md b/tensorflow/docs_src/install/install_raspbian.md new file mode 100644 index 0000000000..2f425162a1 --- /dev/null +++ b/tensorflow/docs_src/install/install_raspbian.md @@ -0,0 +1,317 @@ +# Installing TensorFlow on Raspbian + +This guide explains how to install TensorFlow on a Raspberry Pi running +Raspbian. Although these instructions might also work on other Pi variants, we +have only tested (and we only support) these instructions on machines meeting +the following requirements: + +* Raspberry Pi devices running Raspbian 9.0 or higher + +## Determine how to install TensorFlow + +You must pick the mechanism by which you install TensorFlow. The supported +choices are as follows: + +* "Native" pip. +* Cross-compiling from sources. + +**We recommend pip installation.** + +## Installing with native pip + +We have uploaded the TensorFlow binaries to piwheels.org. Therefore, you can +install TensorFlow through pip. + +The [REQUIRED_PACKAGES section of +setup.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py) +lists the packages that pip will install or upgrade. + +### Prerequisite: Python + +In order to install TensorFlow, your system must contain one of the following +Python versions: + +* Python 2.7 +* Python 3.4+ + +If your system does not already have one of the preceding Python versions, +[install](https://wiki.python.org/moin/BeginnersGuide/Download) it now. It +should already be included when Raspbian was installed though, so no extra steps +should be needed. + +### Prerequisite: pip + +[Pip](https://en.wikipedia.org/wiki/Pip_\(package_manager\)) installs and +manages software packages written in Python. If you intend to install with +native pip, then one of the following flavors of pip must be installed on your +system: + +* `pip3`, for Python 3.n (preferred). +* `pip`, for Python 2.7. + +`pip` or `pip3` was probably installed on your system when you installed Python. +To determine whether pip or pip3 is actually installed on your system, issue one +of the following commands: + +
$ pip3 -V # for Python 3.n
+$ pip -V  # for Python 2.7
+ +If it gives the error "Command not found", then the package has not been +installed yet. To install if for the first time, run: + +
$ sudo apt-get install python3-pip # for Python 3.n
+sudo apt-get install python-pip # for Python 2.7
+ +You can find more help on installing and upgrading pip in +[the Raspberry Pi documentation](https://www.raspberrypi.org/documentation/linux/software/python.md). + +### Prerequisite: Atlas + +[Atlas](http://math-atlas.sourceforge.net/) is a linear algebra library that +numpy depends on, and so needs to be installed before TensorFlow. To add it to +your system, run the following command: + +
$ sudo apt install libatlas-base-dev
+ +### Install TensorFlow + +Assuming the prerequisite software is installed on your Pi, install TensorFlow +by invoking **one** of the following commands: + + 
 $ pip3 install tensorflow     # Python 3.n
+     $ pip install tensorflow      # Python 2.7
+ +This can take some time on certain platforms like the Pi Zero, where some Python +packages like scipy that TensorFlow depends on need to be compiled before the +installation can complete. The Python 3 version will typically be faster to +install because piwheels.org has pre-built versions of the dependencies +available, so this is our recommended option. + +### Next Steps + +After installing TensorFlow, [validate your +installation](#ValidateYourInstallation) to confirm that the installation worked +properly. + +### Uninstalling TensorFlow + +To uninstall TensorFlow, issue one of following commands: + +
$ pip uninstall tensorflow
+$ pip3 uninstall tensorflow
+ +## Cross-compiling from sources + +Cross-compilation means building on a different machine than than you'll be +deploying on. Since Raspberry Pi's only have limited RAM and comparatively slow +processors, and TensorFlow has a large amount of source code to compile, it's +easier to use a MacOS or Linux desktop or laptop to handle the build process. +Because it can take over 24 hours to build on a Pi, and requires external swap +space to cope with the memory shortage, we recommend using cross-compilation if +you do need to compile TensorFlow from source. To make the dependency management +process easier, we also recommend using Docker to help simplify building. + +Note that we provide well-tested, pre-built TensorFlow binaries for Raspbian +systems. So, don't build a TensorFlow binary yourself unless you are very +comfortable building complex packages from source and dealing with the +inevitable aftermath should things not go exactly as documented + +### Prerequisite: Docker + +Install Docker on your machine as described in the [Docker +documentation](https://docs.docker.com/engine/installation/#/on-macos-and-windows). + +### Clone the TensorFlow repository + +Start the process of building TensorFlow by cloning a TensorFlow repository. + +To clone **the latest** TensorFlow repository, issue the following command: + +
$ git clone https://github.com/tensorflow/tensorflow
+ +The preceding git clone command creates a subdirectory named +`tensorflow`. After cloning, you may optionally build a **specific branch** +(such as a release branch) by invoking the following commands: + +
+$ cd tensorflow
+$ git checkout Branch # where Branch is the desired branch
+
+ +For example, to work with the `r1.0` release instead of the master release, +issue the following command: + +
$ git checkout r1.0
+ +### Build from source + +To compile TensorFlow and produce a binary pip can install, do the following: + +1. Start a terminal. +2. Navigate to the directory containing the tensorflow source code. +3. Run a command to cross-compile the library, for example: + +
$ CI_DOCKER_EXTRA_PARAMS="-e CI_BUILD_PYTHON=python3 -e CROSSTOOL_PYTHON_INCLUDE_PATH=/usr/include/python3.4" \
+tensorflow/tools/ci_build/ci_build.sh PI-PYTHON3 tensorflow/tools/ci_build/pi/build_raspberry_pi.sh
+
+ +This will build a pip .whl file for Python 3.4, with Arm v7 instructions that +will only work on the Pi models 2 or 3. These NEON instructions are required for +the fastest operation on those devices, but you can build a library that will +run across all Pi devices by passing `PI_ONE` at the end of the command line. +You can also target Python 2.7 by omitting the initial docker parameters. Here's +an example of building for Python 2.7 and Raspberry Pi model Zero or One +devices: + +
$ tensorflow/tools/ci_build/ci_build.sh PI tensorflow/tools/ci_build/pi/build_raspberry_pi.sh PI_ONE
+ +This will take some time to complete, typically twenty or thirty minutes, and +should produce a .whl file in an output-artifacts sub-folder inside your source +tree at the end. This wheel file can be installed through pip or pip3 (depending +on your Python version) by copying it to a Raspberry Pi and running a terminal +command like this (with the name of your actual file substituted): + +
$ pip3 install tensorflow-1.9.0-cp34-none-linux_armv7l.whl
+ +### Troubleshooting the build + +The build script uses Docker internally to create a Linux virtual machine to +handle the compilation. If you do have problems running the script, first check +that you're able to run Docker tests like `docker run hello-world` on your +system. + +If you're building from the latest development branch, try syncing to an older +version that's known to work, for example release 1.9, with a command like this: + +
$ git checkout r1.0
+ + + +## Validate your installation + +To validate your TensorFlow installation, do the following: + +1. Ensure that your environment is prepared to run TensorFlow programs. +2. Run a short TensorFlow program. + +### Prepare your environment + +If you installed on native pip, Virtualenv, or Anaconda, then do the following: + +1. Start a terminal. +2. If you installed TensorFlow source code, navigate to any directory *except* + one containing TensorFlow source code. + +### Run a short TensorFlow program + +Invoke python from your shell as follows: + +
$ python
+ +Enter the following short program inside the python interactive shell: + +```python +# Python +import tensorflow as tf +hello = tf.constant('Hello, TensorFlow!') +sess = tf.Session() +print(sess.run(hello)) +``` + +If the system outputs the following, then you are ready to begin writing +TensorFlow programs: + +
Hello, TensorFlow!
+ +If you're running with Python 3.5, you may see a warning when you first import +TensorFlow. This is not an error, and TensorFlow should continue to run with no +problems, despite the log message. + +If the system outputs an error message instead of a greeting, see [Common +installation problems](#common_installation_problems). + +If you are new to machine learning, we recommend the [Machine Learning Crash +Course](https://developers.google.com/machine-learning/crash-course). + +If you are experienced with machine learning but new to TensorFlow, see +@{$get_started/eager}. + +## Common installation problems + +We are relying on Stack Overflow to document TensorFlow installation problems +and their remedies. The following table contains links to Stack Overflow answers +for some common installation problems. If you encounter an error message or +other installation problem not listed in the following table, search for it on +Stack Overflow. If Stack Overflow doesn't show the error message, ask a new +question about it on Stack Overflow and specify the `tensorflow` tag. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Stack Overflow Link Error Message
42006320
ImportError: Traceback (most recent call last):
+File ".../tensorflow/core/framework/graph_pb2.py", line 6, in 
+from google.protobuf import descriptor as _descriptor
+ImportError: cannot import name 'descriptor'
+
33623453
IOError: [Errno 2] No such file or directory:
+  '/tmp/pip-o6Tpui-build/setup.py'
+
35190574 
SSLError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify
+  failed
42009190
+  Installing collected packages: setuptools, protobuf, wheel, numpy, tensorflow
+  Found existing installation: setuptools 1.1.6
+  Uninstalling setuptools-1.1.6:
+  Exception:
+  ...
+  [Errno 1] Operation not permitted:
+  '/tmp/pip-a1DXRT-uninstall/.../lib/python/_markerlib'
33622019
ImportError: No module named copyreg
37810228During a pip install operation, the system returns: + 
OSError: [Errno 1] Operation not permitted
+
33622842An import tensorflow statement triggers an error such as the + following:
Traceback (most recent call last):
+  File "", line 1, in 
+  File "/usr/local/lib/python2.7/site-packages/tensorflow/__init__.py",
+    line 4, in 
+    from tensorflow.python import *
+    ...
+  File "/usr/local/lib/python2.7/site-packages/tensorflow/core/framework/tensor_shape_pb2.py",
+    line 22, in 
+    serialized_pb=_b('\n,tensorflow/core/framework/tensor_shape.proto\x12\ntensorflow\"d\n\x10TensorShapeProto\x12-\n\x03\x64im\x18\x02
+      \x03(\x0b\x32
+      .tensorflow.TensorShapeProto.Dim\x1a!\n\x03\x44im\x12\x0c\n\x04size\x18\x01
+      \x01(\x03\x12\x0c\n\x04name\x18\x02 \x01(\tb\x06proto3')
+  TypeError: __init__() got an unexpected keyword argument 'syntax'
+
-- GitLab From 76b8b01740233ff289d70a0d516c6e0ac0e6b042 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Mon, 11 Jun 2018 11:55:34 -0700 Subject: [PATCH 028/519] Use the Keras session for saving/loading in TensorFlow format Fixes issues when there's no default session PiperOrigin-RevId: 200088574 --- tensorflow/python/keras/engine/network.py | 10 +++- tensorflow/python/keras/engine/saving_test.py | 52 +++++++++++++------ 2 files changed, 44 insertions(+), 18 deletions(-) diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index 9dbf94a276..3d567b8378 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -20,6 +20,7 @@ from __future__ import division from __future__ import print_function import copy +import functools import json import os import weakref @@ -1264,7 +1265,11 @@ class Network(base_layer.Layer): with h5py.File(filepath, 'w') as f: saving.save_weights_to_hdf5_group(f, self.layers) else: - self._checkpointable_saver.save(filepath) + if context.executing_eagerly(): + session = None + else: + session = backend.get_session() + self._checkpointable_saver.save(filepath, session=session) def load_weights(self, filepath, by_name=False): """Loads all layer weights, either from a TensorFlow or an HDF5 weight file. @@ -1324,7 +1329,8 @@ class Network(base_layer.Layer): 'loading TensorFlow-formatted weights (got by_name=True to ' 'load_weights).') if not context.executing_eagerly(): - finalizer = status.run_restore_ops + session = backend.get_session() + finalizer = functools.partial(status.run_restore_ops, session=session) if self.built: finalizer() else: diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 30bcd3d185..b5448a9be1 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -404,26 +404,27 @@ class TestWholeModelSaving(test.TestCase): os.remove(fname) def test_saving_lambda_numpy_array_arguments(self): - if h5py is None: - self.skipTest('h5py required to run this test') + with self.test_session(): + if h5py is None: + self.skipTest('h5py required to run this test') - 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']) + 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) + fd, fname = tempfile.mkstemp('.h5') + keras.models.save_model(model, fname) - model = keras.models.load_model(fname) - os.close(fd) - os.remove(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']) + self.assertAllClose(mean, model.layers[1].arguments['mu']) + self.assertAllClose(std, model.layers[1].arguments['std']) def test_saving_model_with_long_layer_names(self): if h5py is None: @@ -580,6 +581,25 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): # Indirectly tests that the user is prompted model.save_weights(prefix, save_format='tensorflow', overwrite=False) + def test_no_default_session(self): + with ops.Graph().as_default(): + self.assertFalse(ops.get_default_session()) + data = np.random.random((1000, 32)).astype(np.float32) + labels = np.random.random((1000, 10)).astype(np.float32) + + model = keras.models.Sequential([ + keras.layers.Dense(10, activation='softmax'), + keras.layers.Dense(10, activation='softmax')]) + + model.compile(optimizer=training_module.RMSPropOptimizer(0.001), + loss='categorical_crossentropy', + metrics=['accuracy']) + + model.fit(data, labels) + fname = os.path.join(self.get_temp_dir(), 'weights', 'ckpt') + model.save_weights(fname) + model.load_weights(fname) + def test_no_graph_pollution(self): with context.graph_mode(): graph = ops.Graph() -- GitLab From 50ba6dd3a182c9578bc10cb2a21d7914a1e7bac1 Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Mon, 11 Jun 2018 10:42:15 -0700 Subject: [PATCH 029/519] Don't call back into python during insert (which will leave the set in a broken condition if the runtime decides to let another thread run). Thank you for finding the bug. The watched_variables_ set should not really require a lock since all our functions hold the GIL (verified by looking at the generated SWIG). The reason that there was a concurrent access to the set is that the insert was calling back into python (which might release the GIL and let another thread run, which will also attempt to insert a variable and break the set). I included the lock to be safe though, since its non-trivial to verify without looking at the generated swig wrappers that the GIL is held. PiperOrigin-RevId: 200074843 --- tensorflow/python/eager/pywrap_tfe_src.cc | 82 ++++++++++++----------- 1 file changed, 43 insertions(+), 39 deletions(-) diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index e3ce0ef9d0..52b3268903 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -873,22 +873,6 @@ static tensorflow::DataType FastTensorDtype(PyObject* tensor) { return static_cast(id); } -static tensorflow::int64 FastHandleId(PyObject* variable) { - PyObject* handle = PyObject_GetAttrString(variable, "handle"); - if (handle == nullptr) { - return -1; - } - tensorflow::int64 id = FastTensorId(handle); - Py_DECREF(handle); - return id; -} - -struct CompareByHandleId { - bool operator()(PyObject* lhs, PyObject* rhs) { - return FastHandleId(lhs) < FastHandleId(rhs); - } -}; - class GradientTape : public tensorflow::eager::GradientTape { public: @@ -897,35 +881,63 @@ class GradientTape persistent) {} virtual ~GradientTape() { - for (PyObject* v : watched_variables_) { - Py_DECREF(v); + for (const IdAndVariable& v : watched_variables_) { + Py_DECREF(v.variable); } } void WatchVariable(PyObject* v) { - auto insert_result = watched_variables_.insert(v); - if (insert_result.second) { - // Only increment the reference count if we aren't already watching this - // variable. - Py_INCREF(v); - } - PyObject* handle = PyObject_GetAttrString(v, "handle"); + tensorflow::Safe_PyObjectPtr handle(PyObject_GetAttrString(v, "handle")); if (handle == nullptr) { return; } - tensorflow::int64 id = FastTensorId(handle); - Py_DECREF(handle); + tensorflow::int64 id = FastTensorId(handle.get()); + if (!PyErr_Occurred()) { this->Watch(id); } + + tensorflow::mutex_lock l(watched_variables_mu_); + auto insert_result = watched_variables_.emplace(id, v); + + if (insert_result.second) { + // Only increment the reference count if we aren't already watching this + // variable. + Py_INCREF(v); + } } - const std::set WatchedVariables() { - return watched_variables_; + PyObject* GetVariablesAsPyTuple() { + tensorflow::mutex_lock l(watched_variables_mu_); + PyObject* result = PyTuple_New(watched_variables_.size()); + Py_ssize_t pos = 0; + for (const IdAndVariable& id_and_variable : watched_variables_) { + PyTuple_SET_ITEM(result, pos++, id_and_variable.variable); + Py_INCREF(id_and_variable.variable); + } + return result; } private: - std::set watched_variables_; + // We store an IdAndVariable in the map since the map needs to be locked + // during insert, but should not call back into python during insert to avoid + // deadlocking with the GIL. + struct IdAndVariable { + tensorflow::int64 id; + PyObject* variable; + + IdAndVariable(tensorflow::int64 id, PyObject* variable) + : id(id), variable(variable) {} + }; + struct CompareById { + bool operator()(const IdAndVariable& lhs, const IdAndVariable& rhs) { + return lhs.id < rhs.id; + } + }; + + tensorflow::mutex watched_variables_mu_; + std::set watched_variables_ + GUARDED_BY(watched_variables_mu_); }; typedef struct { @@ -1217,15 +1229,7 @@ void TFE_Py_TapeSetWatchVariable(PyObject* variable) { } PyObject* TFE_Py_TapeWatchedVariables(PyObject* tape) { - const auto& watched_variables = - reinterpret_cast(tape)->tape->WatchedVariables(); - PyObject* result = PyTuple_New(watched_variables.size()); - Py_ssize_t pos = 0; - for (PyObject* variable : watched_variables) { - PyTuple_SET_ITEM(result, pos++, variable); - Py_INCREF(variable); - } - return result; + return reinterpret_cast(tape)->tape->GetVariablesAsPyTuple(); } namespace { -- GitLab From ec769c7ec368adf90aaa0b6d2a97525da14e1a37 Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Mon, 11 Jun 2018 16:27:12 -0700 Subject: [PATCH 030/519] Remove memory leak in read variable call, and record gradient call. Fix #19385 PiperOrigin-RevId: 200132949 --- tensorflow/python/eager/pywrap_tfe_src.cc | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index 52b3268903..6c9481c3af 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -1873,6 +1873,8 @@ PyObject* RecordGradient(PyObject* op_name, PyObject* inputs, PyObject* attrs, delete backward_function; }); + Py_DECREF(num_inputs); + Py_RETURN_NONE; } @@ -1931,8 +1933,10 @@ bool ReadVariableOp(const FastPathOpExecInfo& parent_op_exec_info, 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)) { + tensorflow::Safe_PyObjectPtr op_string( + GetPythonObjectFromString("ReadVariableOp")); + if (!RecordGradient(op_string.get(), inputs.get(), Py_None, outputs.get(), + Py_None)) { return false; } } -- GitLab From c77fead531bc3756d765ba90e2e549abd7adf320 Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Wed, 13 Jun 2018 15:46:12 -0700 Subject: [PATCH 031/519] Make GCS ops work in open source --- tensorflow/contrib/cloud/__init__.py | 5 +++-- tensorflow/contrib/cloud/kernels/BUILD | 1 + tensorflow/core/platform/cloud/gcs_file_system.cc | 4 +++- tensorflow/core/platform/default/build_config.bzl | 2 ++ 4 files changed, 9 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/cloud/__init__.py b/tensorflow/contrib/cloud/__init__.py index a6e13ea3ae..ef7aa7624c 100644 --- a/tensorflow/contrib/cloud/__init__.py +++ b/tensorflow/contrib/cloud/__init__.py @@ -27,8 +27,9 @@ from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ 'BigQueryReader', - 'ConfigureColabSession', - 'ConfigureGcs', + 'BlockCacheParams', + 'configure_colab_session', + 'configure_gcs', 'ConfigureGcsHook', ] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/cloud/kernels/BUILD b/tensorflow/contrib/cloud/kernels/BUILD index 40160706f7..1311063ec0 100644 --- a/tensorflow/contrib/cloud/kernels/BUILD +++ b/tensorflow/contrib/cloud/kernels/BUILD @@ -79,6 +79,7 @@ tf_kernel_library( srcs = ["gcs_config_ops.cc"], visibility = ["//tensorflow:internal"], deps = [ + "//tensorflow/contrib/cloud:gcs_config_ops_op_lib", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core/platform/cloud:curl_http_request", diff --git a/tensorflow/core/platform/cloud/gcs_file_system.cc b/tensorflow/core/platform/cloud/gcs_file_system.cc index 22ae6121e0..803b08f1a3 100644 --- a/tensorflow/core/platform/cloud/gcs_file_system.cc +++ b/tensorflow/core/platform/cloud/gcs_file_system.cc @@ -804,7 +804,9 @@ void GcsFileSystem::ResetFileBlockCache(size_t block_size_bytes, mutex_lock l(block_cache_lock_); file_block_cache_ = MakeFileBlockCache(block_size_bytes, max_bytes, max_staleness_secs); - stats_->Configure(this, &throttle_, file_block_cache_.get()); + if (stats_) { + stats_->Configure(this, &throttle_, file_block_cache_.get()); + } } // A helper function to build a FileBlockCache for GcsFileSystem. diff --git a/tensorflow/core/platform/default/build_config.bzl b/tensorflow/core/platform/default/build_config.bzl index 9e52ba344a..f12732b434 100644 --- a/tensorflow/core/platform/default/build_config.bzl +++ b/tensorflow/core/platform/default/build_config.bzl @@ -633,6 +633,7 @@ def tf_additional_cloud_op_deps(): "//tensorflow:with_gcp_support_ios_override": [], "//tensorflow:with_gcp_support": [ "//tensorflow/contrib/cloud:bigquery_reader_ops_op_lib", + "//tensorflow/contrib/cloud:gcs_config_ops_op_lib", ], "//conditions:default": [], }) @@ -645,6 +646,7 @@ def tf_additional_cloud_kernel_deps(): "//tensorflow:with_gcp_support_ios_override": [], "//tensorflow:with_gcp_support": [ "//tensorflow/contrib/cloud/kernels:bigquery_reader_ops", + "//tensorflow/contrib/cloud/kernels:gcs_config_ops", ], "//conditions:default": [], }) -- GitLab From f9a44a69c35dcf7f1c0f42e1ae9971bae0148099 Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Wed, 13 Jun 2018 18:05:39 -0700 Subject: [PATCH 032/519] Update the docs and api_def. --- .../contrib/cloud/ops/gcs_config_ops.cc | 42 +------------------ .../api_def_GcsConfigureBlockCache.pbtxt | 9 ++++ .../api_def_GcsConfigureCredentials.pbtxt | 33 +++++++++++++++ 3 files changed, 44 insertions(+), 40 deletions(-) create mode 100644 tensorflow/core/api_def/base_api/api_def_GcsConfigureBlockCache.pbtxt create mode 100644 tensorflow/core/api_def/base_api/api_def_GcsConfigureCredentials.pbtxt diff --git a/tensorflow/contrib/cloud/ops/gcs_config_ops.cc b/tensorflow/contrib/cloud/ops/gcs_config_ops.cc index 9cf85f5f18..5e31a15498 100644 --- a/tensorflow/contrib/cloud/ops/gcs_config_ops.cc +++ b/tensorflow/contrib/cloud/ops/gcs_config_ops.cc @@ -21,50 +21,12 @@ namespace tensorflow { REGISTER_OP("GcsConfigureCredentials") .Input("json: string") - .SetShapeFn(shape_inference::NoOutputs) - .Doc(R"doc( -Configures the credentials used by the GCS client of the local TF runtime. - -The json input can be of the format: - -1. Refresh Token: -{ - "client_id": "", - "client_secret": "", - "refresh_token: "", - "type": "authorized_user", -} - -2. Service Account: -{ - "type": "service_account", - "project_id": "", - "private_key_id": "", - "private_key": "------BEGIN PRIVATE KEY-----\n\n-----END PRIVATE KEY------\n", - "client_email": "@.iam.gserviceaccount.com", - "client_id": "", - # Some additional fields elided -} - -Note the credentials established through this method are shared across all -sessions run on this runtime. - -Note be sure to feed the inputs to this op to ensure the credentials are not -stored in a constant op within the graph that might accidentally be checkpointed -or in other ways be persisted or exfiltrated. -)doc"); + .SetShapeFn(shape_inference::NoOutputs); REGISTER_OP("GcsConfigureBlockCache") .Input("max_cache_size: uint64") .Input("block_size: uint64") .Input("max_staleness: uint64") - .SetShapeFn(shape_inference::NoOutputs) - .Doc(R"doc( -Re-configures the GCS block cache with the new configuration values. - -If the values are the same as already configured values, this op is a no-op. If -they are different, the current contents of the block cache is dropped, and a -new block cache is created fresh. -)doc"); + .SetShapeFn(shape_inference::NoOutputs); } // namespace tensorflow diff --git a/tensorflow/core/api_def/base_api/api_def_GcsConfigureBlockCache.pbtxt b/tensorflow/core/api_def/base_api/api_def_GcsConfigureBlockCache.pbtxt new file mode 100644 index 0000000000..9d32940c64 --- /dev/null +++ b/tensorflow/core/api_def/base_api/api_def_GcsConfigureBlockCache.pbtxt @@ -0,0 +1,9 @@ +op { + graph_op_name: "GcsConfigureBlockCache" + summary: "Re-configures the GCS block cache with the new configuration values." + description: <", + "client_secret": "", + "refresh_token: "", + "type": "authorized_user", +} + +2. Service Account: +{ + "type": "service_account", + "project_id": "", + "private_key_id": "", + "private_key": "------BEGIN PRIVATE KEY-----\n\n-----END PRIVATE KEY------\n", + "client_email": "@.iam.gserviceaccount.com", + "client_id": "", + # Some additional fields elided +} + +Note the credentials established through this method are shared across all +sessions run on this runtime. + +Note be sure to feed the inputs to this op to ensure the credentials are not +stored in a constant op within the graph that might accidentally be checkpointed +or in other ways be persisted or exfiltrated. +END0 +} -- GitLab From ea3bdbc7ea72e488566326aeb446681a557f4334 Mon Sep 17 00:00:00 2001 From: Michael Case Date: Thu, 14 Jun 2018 06:17:00 -0700 Subject: [PATCH 033/519] Update version strings for 1.9.0-rc1. --- tensorflow/core/public/version.h | 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 | 18 +++++++-------- tensorflow/docs_src/install/install_mac.md | 10 ++++----- .../docs_src/install/install_sources.md | 4 ++-- tensorflow/tools/pip_package/setup.py | 2 +- 8 files changed, 31 insertions(+), 31 deletions(-) diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index cb1fd09dbb..9e5e747557 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/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 2901848745..2f81ae0c40 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}-${OS}-x86_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0-rc1.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 55bc0f64e7..1c03dd223e 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc1.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index b3b739212e..c73e2f4281 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.9.0-rc0 + 1.9.0-rc1 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.9.0-rc0 + 1.9.0-rc1 @@ -124,12 +124,12 @@ instead: org.tensorflow libtensorflow - 1.9.0-rc0 + 1.9.0-rc1 org.tensorflow libtensorflow_jni_gpu - 1.9.0-rc0 + 1.9.0-rc1 ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc1.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc1.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,10 +175,10 @@ 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-1.9.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-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_64-1.9.0-rc0.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0-rc1.zip). 3. Extract this .zip file. @@ -227,7 +227,7 @@ 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: -
javac -cp libtensorflow-1.9.0-rc0.jar HelloTF.java
+
javac -cp libtensorflow-1.9.0-rc1.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.9.0-rc0.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.9.0-rc1.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.9.0-rc0.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.9.0-rc1.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, 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 2ecab808c4..9baf6870be 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -438,7 +438,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: 
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc1-cp34-cp34m-linux_x86_64.whl ## Validate your installation @@ -684,14 +684,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc1-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc1-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -703,14 +703,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc1-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc1-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -722,14 +722,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc1-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc1-cp35-cp35m-linux_x86_64.whl
@@ -741,14 +741,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc1-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc1-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 9d01271c5a..693254f876 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: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc1-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc1-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc1-py2-none-any.whl @@ -522,7 +522,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc1-py2-none-any.whl
@@ -530,5 +530,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-a 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc1-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index d25e641cee..70e97cf556 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -328,10 +328,10 @@ 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 1.9.0rc0 on Linux: +for TensorFlow 1.9.0rc1 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc1-py2-none-any.whl
## Validate your installation diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 92a1465cea..eb2e359ee5 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -45,7 +45,7 @@ DOCLINES = __doc__.split('\n') # 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 = '1.9.0-rc0' +_VERSION = '1.9.0-rc1' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', -- GitLab From f5ee4df50af4041dc0063d0adc31c7a6eebdbcd3 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Fri, 8 Jun 2018 15:47:19 -0700 Subject: [PATCH 034/519] Copy edits to Keras guide, formatting, moving some things around. Make the right TOC nav more useful. PiperOrigin-RevId: 199863216 --- .../docs_src/programmers_guide/keras.md | 870 ++++++++---------- 1 file changed, 389 insertions(+), 481 deletions(-) diff --git a/tensorflow/docs_src/programmers_guide/keras.md b/tensorflow/docs_src/programmers_guide/keras.md index 6a9df12a25..c6aca7ebf4 100644 --- a/tensorflow/docs_src/programmers_guide/keras.md +++ b/tensorflow/docs_src/programmers_guide/keras.md @@ -1,334 +1,304 @@ # Keras -## What's Keras? - -Keras is a high-level API specification for building and training deep learning -models, suitable for fast prototyping, advanced research, and production. -It offers three key advantages: - -- **User friendliness.** Keras follows best practices for reducing - cognitive load: it offers consistent & simple interfaces, - it minimizes the number of user actions required for common use cases, - and it provides clear and actionable feedback upon user error. -- **Modularity and composability.** A Keras model is composed of - fully-configurable building blocks that can be plugged together - with as few restrictions as possible -- like Lego bricks. -- **Easy extensibility.** You can easily write your own building blocks - (such as new layers, new loss functions, new models where you write - the forward pass from scratch). This allows for total expressiveness, - making Keras suitable for advanced research. - - -## What's tf.keras? - -`tf.keras` is TensorFlow's implementation of the Keras API specification, that -serves as the TensorFlow high-level API: it's how you build models in TensorFlow. -`tf.keras` seamlessly integrates with the rest of the TensorFlow API -(such as `tf.data` input pipelines), bringing you the full power and flexibility -of TensorFlow through an easy-to-use interface. - -You can import `tf.keras` via: +Keras is a high-level API to build and train deep learning models. It's used for +fast prototyping, advanced research, and production, with three key advantages: + +- *User friendly*
+ Keras has a simple, consistent interface optimized for common use cases. It + provides clear and actionable feedback for user errors. +- *Modular and composable*
+ Keras models are made by connecting configurable building blocks together, + with few restrictions. +- *Easy to extend*
Write custom building blocks to express new ideas for + research. Create new layers, loss functions, and develop state-of-the-art + models. + +## Import tf.keras + +`tf.keras` is TensorFlow's implementation of the +[Keras API specification](https://keras.io){:.external}. This is a high-level +API to build and train models that includes first-class support for +TensorFlow-specific functionality, such as [eager execution](#eager_execution), +`tf.data` pipelines, and [Estimators](/programmers_guide/estimators). +`tf.keras` makes TensorFlow easier to use without sacrificing flexibility and +performance. + +To get started, import `tf.keras` as part of your TensorFlow program setup: ```python +import tensorflow as tf from tensorflow import keras ``` -What follows is a quick introduction to the basics of `tf.keras`. +`tf.keras` can run any Keras-compatible code, but keep in mind: +* The `tf.keras` version in the latest TensorFlow release might not be the same + as the latest `keras` version from PyPI. Check `tf.keras.__version__`. +* When [saving a model's weights](#weights_only), `tf.keras` defaults to the + [checkpoint format](/get_started/checkpoints). Pass `save_format='h5'` to use + HDF5. -## Table of contents +## Build a simple model -- [Getting started: the Sequential model](#getting-started-the-sequential-model) -- [Configuring layers](#configuring-layers) -- [Configuring training](#configuring-training) -- [Training and evaluation](#training-and-evaluation) -- [Building advanced models: the functional API](#building-advanced-models-the-functional-api) -- [Building fully-customizable research models: the Model subclassing API](#building-fully-customizable-research-models-the-model-subclassing-api) -- [Callbacks](#callbacks) -- [Saving and serialization](#saving-and-serialization) -- [Developing custom layers](#developing-custom-layers) -- [Eager execution](#eager-execution) -- [Further reading](#further-reading) -- [FAQ](#faq) +### Sequential model +In Keras, you assemble *layers* to build *models*. A model is (usually) a graph +of layers. The most common type of model is a stack of layers: the +`tf.keras.Sequential` model. ---- - -## Getting started: the Sequential model - -In `tf.keras`, you're assembling together **layers** to build **models**. -A model is generally a graph of layers. -The most common type of model is just a stack of layers: the `Sequential` class. - -Here's how to build a simple fully-connected network (multi-layer perceptron): +To build a simple, fully-connected network (i.e. multi-layer perceptron): ```python -from tensorflow import keras -from tensorflow.keras import layers - model = keras.Sequential() -# This adds to the model a densely-connected layer with 64 units: -model.add(Dense(64, activation='relu')) -# Another one: -model.add(Dense(64, activation='relu')) -# This adds a softmax layer with 10 output units: -model.add(Dense(10, activation='softmax')) +# Adds a densely-connected layer with 64 units to the model: +model.add(keras.layers.Dense(64, activation='relu')) +# Add another: +model.add(keras.layers.Dense(64, activation='relu')) +# Add a softmax layer with 10 output units: +model.add(keras.layers.Dense(10, activation='softmax')) ``` ---- - -## Configuring layers - -Each layer may have unique constructor arguments, but some common arguments include: +### Configure the layers -- `activation`: the activation function to be used. - It could be specified by name, as a string (for built-in functions) - or as a callable object. By default, no activation is applied. -- `kernel_initializer` and `bias_initializer`: the initialization schemes to use - to create the layer's weights (kernel and bias). - Likewise, they may be passed either by name or by specifying a callable. - By default, the "Glorot uniform" initializer is used. -- `kernel_regularizer` and `bias_regularizer`: the regularization schemes to - apply to the layer's weights (kernel and bias), such as L1 - or L2 regularization. By default, no regularization is applied. +There are many `tf.keras.layers` available with some common constructor +parameters: +* `activation`: Set the activation function for the layer. This parameter is + specified by the name of a built-in function or as a callable object. By + default, no activation is applied. +* `kernel_initializer` and `bias_initializer`: The initialization schemes + that create the layer's weights (kernel and bias). This parameter is a name or + a callable object. This defaults to the `"Glorot uniform"` initializer. +* `kernel_regularizer` and `bias_regularizer`: The regularization schemes + that apply the layer's weights (kernel and bias), such as L1 or L2 + regularization. By default, no regularization is applied. -### Examples +The following instantiates `tf.keras.layers.Dense` layers using constructor +arguments: ```python -import tensorflow as tf -from tensorflow.keras.layers import Dense -from tensorflow.keras import regularizers -from tensorflow.keras import initializers - -# A sigmoid layer: -Dense(64, activation='sigmoid') -# Another way to define the same sigmoid layer: -Dense(64, activation=tf.sigmoid) - -# A linear layer with L1 regularization of factor 0.01 -# applied to the kernel matrix: -Dense(64, kernel_regularizer=regularizers.l1(0.01)) -# A linear layer with L2 regularization of factor 0.01 -# applied to the bias vector: -Dense(64, bias_regularizer=regularizers.l2(0.01)) +# Create a sigmoid layer: +layers.Dense(64, activation='sigmoid') +# Or: +layers.Dense(64, activation=tf.sigmoid) + +# A linear layer with L1 regularization of factor 0.01 applied to the kernel matrix: +layers.Dense(64, kernel_regularizer=keras.regularizers.l1(0.01)) +# A linear layer with L2 regularization of factor 0.01 applied to the bias vector: +layers.Dense(64, bias_regularizer=keras.regularizers.l2(0.01)) # A linear layer with a kernel initialized to a random orthogonal matrix: -Dense(64, kernel_initializer='orthogonal') +layers.Dense(64, kernel_initializer='orthogonal') # A linear layer with a bias vector initialized to 2.0s: -Dense(64, bias_initializer=initializers.constant(2.0)) +layers.Dense(64, bias_initializer=keras.initializers.constant(2.0)) ``` ---- +## Train and evaluate -## Configuring training +### Set up training -Once your model looks good, configure its learning process by calling `compile`: +After the model is constructed, configure its learning process by calling the +`compile` method: ```python -import tensorflow as tf - model.compile(optimizer=tf.train.AdamOptimizer(0.001), loss='categorical_crossentropy', metrics=['accuracy']) ``` -There are three key arguments that you need to specify: +`tf.keras.Model.compile` takes three important arguments: -- An `optimizer`: this object specifies the training procedure. - We recommend that you pass instances of optimizers from the `tf.train` module - (such as [`AdamOptimizer`](https://www.tensorflow.org/api_docs/python/tf/train/AdamOptimizer), - [`RMSPropOptimizer`](https://www.tensorflow.org/api_docs/python/tf/train/RMSPropOptimizer), - or [`GradientDescentOptimizer`](https://www.tensorflow.org/api_docs/python/tf/train/GradientDescentOptimizer)). -- A `loss` function to minimize: this specifies the optimization objective. - Common choices include mean square error (`mse`), `categorical_crossentropy` - and `binary_crossentropy`. Loss functions may be specified by name - or by passing a callable (e.g. from the `tf.keras.losses` module). -- Some `metrics` to monitor during training: again, you can pass these as either - string names or callables (e.g. from the `tf.keras.metrics` module). +* `optimizer`: This object specifies the training procedure. Pass it optimizer + instances from the `tf.train` module, such as + [`AdamOptimizer`](/api_docs/python/tf/train/AdamOptimizer), + [`RMSPropOptimizer`](/api_docs/python/tf/train/RMSPropOptimizer), or + [`GradientDescentOptimizer`](/api_docs/python/tf/train/GradientDescentOptimizer). +* `loss`: The function to minimize during optimization. Common choices include + mean square error (`mse`), `categorical_crossentropy`, and + `binary_crossentropy`. Loss functions are specified by name or by + passing a callable object from the `tf.keras.losses` module. +* `metrics`: Used to monitor training. These are string names or callables from + the `tf.keras.metrics` module. - -### Examples +The following shows a few examples of configuring a model for training: ```python -# Configures a model to do mean-squared error regression. +# Configure a model for mean-squared error regression. model.compile(optimizer=tf.train.AdamOptimizer(0.01), - loss='mse', # mean squared error + loss='mse', # mean squared error metrics=['mae']) # mean absolute error -``` -```python -# Configures a model to do categorical classification. + +# Configure a model for categorical classification. model.compile(optimizer=tf.train.RMSPropOptimizer(0.01), - loss=tf.keras.losses.categorical_crossentropy, - metrics=[tf.keras.metrics.categorical_accuracy]) + loss=keras.losses.categorical_crossentropy, + metrics=[keras.metrics.categorical_accuracy]) ``` ---- - -## Training and evaluation +### Input NumPy data -### From Numpy data - -When running locally on small datasets, the easiest way to do training and -evaluation is to pass data to your model as Numpy arrays of inputs and targets. -You can "fit" your model to some training data using the `model.fit()` method: +For small datasets, use in-memory [NumPy](https://www.numpy.org/){:.external} +arrays to train and evaluate a model. The model is "fit" to the training data +using the `fit` method: ```python import numpy as np -data = np.random.random(shape=(1000, 32)) -targets = np.random.random(shape=(1000, 10)) +data = np.random.random((1000, 32)) +labels = np.random.random((1000, 10)) -model.fit(data, targets, epochs=10, batch_size=32) +model.fit(data, labels, epochs=10, batch_size=32) ``` -Here are some key arguments you can pass to the `fit` method: - -- `epochs`: Training is structured into **epochs**. An epoch is one iteration - over the entire input data (which is done in smaller batches). -- `batch_size`: when passing Numpy data, the model will slice the data into - smaller batches and iterate over these batches during training. - This integer specifies the size of each batch - (the last batch may be smaller if the total number of samples is not - divisible by the batch size). -- `validation_data`: when prototyping a model, you want to be able to quickly - monitor its performance on some validation data. - When you pass this argument (it expects a tuple of inputs and targets), - the model will display the loss and metrics in inference mode on the data - you passed, at the end of each epoch. +`tf.keras.Model.fit` takes three important arguments: + +* `epochs`: Training is structured into *epochs*. An epoch is one iteration over + the entire input data (this is done in smaller batches). +* `batch_size`: When passed NumPy data, the model slices the data into smaller + batches and iterates over these batches during training. This integer + specifies the size of each batch. Be aware that the last batch may be smaller + if the total number of samples is not divisible by the batch size. +* `validation_data`: When prototyping a model, you want to easily monitor its + performance on some validation data. Passing this argument—a tuple of inputs + and labels—allows the model to display the loss and metrics in inference mode + for the passed data, at the end of each epoch. Here's an example using `validation_data`: ```python import numpy as np -data = np.random.random(shape=(1000, 32)) -targets = np.random.random(shape=(1000, 10)) +data = np.random.random((1000, 32)) +labels = np.random.random((1000, 10)) -val_data = np.random.random(shape=(100, 32)) -val_targets = np.random.random(shape=(100, 10)) +val_data = np.random.random((100, 32)) +val_labels = np.random.random((100, 10)) -model.fit(data, targets, epochs=10, batch_size=32, - validation_data=(val_data, val_targets)) +model.fit(data, labels, epochs=10, batch_size=32, + validation_data=(val_data, val_labels)) ``` -### From tf.data datasets +### Input tf.data datasets -When you need to scale to large datasets or multi-device training, -training from Numpy arrays in memory will not be ideal. -In such cases, you should use [the `tf.data` API](https://www.tensorflow.org/programmers_guide/datasets). -You can pass a `tf.data.Dataset` instance to the `fit` method: +Use the [Datasets API](/programmers_guide/datasets) to scale to large datasets +or multi-device training. Pass a `tf.data.Dataset` instance to the `fit` +method: ```python -import tensorflow as tf - # Instantiates a toy dataset instance: -dataset = tf.data.Dataset.from_tensor_slices((data, targets)).batch(32) +dataset = tf.data.Dataset.from_tensor_slices((data, labels)) +dataset = dataset.batch(32) +dataset = dataset.repeat() # Don't forget to specify `steps_per_epoch` when calling `fit` on a dataset. model.fit(dataset, epochs=10, steps_per_epoch=30) ``` -When doing so, the dataset itself will yield batches of data, -so the model does not need to be passed `batch_size` information. -Instead, the model needs to know for how many steps (or batches of data) -it should run at each epoch. -You specify this with the `steps_per_epoch` argument: it's the number of -training steps the model will run before moving on the next epoch. +Here, the `fit` method uses the `steps_per_epoch` argument—this is the number of +training steps the model runs before it moves to the next epoch. Since the +`Dataset` yields batches of data, this snippet does not require a `batch_size`. -You can also pass datasets for validation: +Datasets can also be used for validation: ```python -dataset = tf.data.Dataset.from_tensor_slices((data, targets)).batch(32) -val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_targets)).batch(32) +dataset = tf.data.Dataset.from_tensor_slices((data, labels)) +dataset = dataset.batch(32).repeat() -model.fit(dataset, epochs=10, steps_per_epoch=30, validation_data=val_dataset, validation_steps=3) +val_dataset = tf.data.Dataset.from_tensor_slices((val_data, val_labels)) +val_dataset = val_dataset.batch(32).repeat() + +model.fit(dataset, epochs=10, steps_per_epoch=30, + validation_data=val_dataset, + validation_steps=3) ``` ### Evaluate and predict -In addition, you get access to the following methods -(both with Numpy data and dataset instances): +The `tf.keras.Model.evaluate` and `tf.keras.Model.predict` methods can use NumPy +data and a `tf.data.Dataset`. -- `model.evaluate(x, y, batch_size=32)` or `model.evaluate(dataset, steps=30)` - will return the inference-mode loss and metrics for the data provided. -- `model.predict(x, y, batch_size=32)` or `model.predict(dataset, steps=30)` - will return the output(s) of the last layer(s) in inference on the data - provided, as Numpy array(s). +To *evaluate* the inference-mode loss and metrics for the data provided: ---- +```python +model.evaluate(x, y, batch_size=32) -## Building advanced models: the functional API +model.evaluate(dataset, steps=30 +``` -The `Sequential` model cannot represent arbitrary models -- only simple stacks -of layers. If you need to use more complex model topologies, -such as multi-input models, multi-output models, -models with a same layer called several times (shared layers), -or models with non-sequential data flows (e.g. residual connections), -you can use the 'functional API'. +And to *predict* the output of the last layer in inference for the data provided, +as a NumPy array: -Here's how it works: +``` +model.predict(x, batch_size=32) -- A layer instance is callable (on a tensor), and it returns a tensor. -- Input tensor(s) and output tensor(s) can then be used to define a `Model` instance. -- Such a model can be trained just like the `Sequential` model. +model.predict(dataset, steps=30) +``` -Here's a basic example showing the same model we previously defined, -built using the functional API: +## Build advanced models -```python -from tensorflow import keras -from tensorflow.keras import layers +### Functional API -# This returns a placeholder tensor: -inputs = keras.Input(shape=(784,)) +The `tf.keras.Sequential` model is a simple stack of layers that cannot +represent arbitrary models. Use the +[Keras functional API](https://keras.io/getting-started/functional-api-guide/){:.external} +to build complex model topologies such as: + +* Multi-input models, +* Multi-output models, +* Models with shared layers (the same layer called several times), +* Models with non-sequential data flows (e.g. residual connections). + +Building a model with the functional API works like this: + +1. A layer instance is callable and returns a tensor. +2. Input tensors and output tensors are used to define a `tf.keras.Model` + instance. +3. This model is trained just like the `Sequential` model. + +The following example uses the functional API to build a simple, fully-connected +network: + +```python +inputs = keras.Input(shape=(32,)) # Returns a placeholder tensor # A layer instance is callable on a tensor, and returns a tensor. -x = layers.Dense(64, activation='relu')(inputs) -x = layers.Dense(64, activation='relu')(x) -predictions = layers.Dense(10, activation='softmax')(x) +x = keras.layers.Dense(64, activation='relu')(inputs) +x = keras.layers.Dense(64, activation='relu')(x) +predictions = keras.layers.Dense(10, activation='softmax')(x) -# Instantiates the model given inputs and outputs. +# Instantiate the model given inputs and outputs. model = keras.Model(inputs=inputs, outputs=predictions) -# The "compile" step specifies the training configuration. -model.compile(optimizer='rmsprop', +# The compile step specifies the training configuration. +model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), loss='categorical_crossentropy', metrics=['accuracy']) -# Trains for 5 epochs. +# Trains for 5 epochs model.fit(data, labels, batch_size=32, epochs=5) ``` -This API enables you to create models with multiple inputs and outputs, -and to "share" layers across different inputs -(i.e. to reuse a same instance multiple times). -For examples of these use cases, -please see [this guide to the functional API in Keras](https://keras.io/getting-started/functional-api-guide/). +### Model subclassing ---- +Build a fully-customizable model by subclassing `tf.keras.Model` and defining +your own forward pass. Create layers in the `__init__` method and set them as +attributes of the class instance. Define the forward pass in the `call` method. -## Building fully-customizable research models: the Model subclassing API +Model subclassing is particularly useful when +[eager execution](/programmers_guide/eager) is enabled since the forward pass +can be written imperatively. -Besides `Sequential` and the functional API, one last, more flexible way to -define models is to directly subclass the `Model` class and define your own -forward pass manually. +Key Point: Use the right API for the job. While model subclassing offers +flexibility, it comes at a cost of greater complexity and more opportunities for +user errors. If possible, prefer the functional API. -In this API, you instante layers in `__init__` and set them as attribute of the -class instance. Then you specify the forward pass in `call`. -This API is particularly valuable when using TensorFlow with [eager execution](https://www.tensorflow.org/programmers_guide/eager), -since eager execution allows you to write your forward pass in an -imperative fashion (as if you were writing Numpy code, for instance). +The following example shows a subclassed `tf.keras.Model` using a custom forward +pass: ```python -import tensorflow as tf -from tensorflow import keras - - class MyModel(keras.Model): - def __init__(self, num_classes=2): + def __init__(self, num_classes=10): super(MyModel, self).__init__(name='my_model') self.num_classes = num_classes # Define your layers here. @@ -351,10 +321,10 @@ class MyModel(keras.Model): # Instantiates the subclassed model. -model = MyModel(num_classes=2) +model = MyModel(num_classes=10) -# The "compile" step specifies the training configuration. -model.compile(optimizer='rmsprop', +# The compile step specifies the training configuration. +model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), loss='categorical_crossentropy', metrics=['accuracy']) @@ -362,353 +332,291 @@ model.compile(optimizer='rmsprop', model.fit(data, labels, batch_size=32, epochs=5) ``` -**Remember:** use the right API for the right job. -Using the `Model` subclassing API offers more flexibility, -but at the cost of greater complexity and a larger potential user error surface. -Prefer using the functional API when possible. ---- +### Custom layers -## Callbacks +Create a custom layer by subclassing `tf.keras.layers.Layer` and implementing +the following methods: -Callbacks are objects that you can pass to your model that customize and extend -its behavior during training. -There are callbacks for saving checkpoints of your model at regular intervals -(`tf.keras.callbacks.ModelCheckpoint`), -to dynamically change the learning rate (`tf.keras.callbacks.LearningRateScheduler`) -or to interrupt training when validation performance has stopped improving -(`tf.keras.callbacks.EarlyStopping`). -You can also use a callback to monitor your model's behavior using -[TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard) -(`tf.keras.callbacks.TensorBoard`). -You can also write your own custom callbacks. - -Different built-in callback are found in `tf.keras.callbacks`. -You use them by passing a `Callback` instance to `fit`: +* `build`: Create the weights of the layer. Add weights with the `add_weight` + method. +* `call`: Define the forward pass. +* `compute_output_shape`: Specify how to compute the output shape of the layer + given the input shape. +* Optionally, a layer can be serialized by implementing the `get_config` method + and the `from_config` class method. + +Here's an example of a custom layer that implements a `matmul` of an input with +a kernel matrix: ```python -from tensorflow import keras +class MyLayer(keras.layers.Layer): + + def __init__(self, output_dim, **kwargs): + self.output_dim = output_dim + super(MyLayer, self).__init__(**kwargs) + + def build(self, input_shape): + shape = tf.TensorShape((input_shape[1], self.output_dim)) + # Create a trainable weight variable for this layer. + self.kernel = self.add_weight(name='kernel', + shape=shape, + initializer='uniform', + trainable=True) + # Be sure to call this at the end + super(MyLayer, self).build(input_shape) -callbacks = [ - # Interrupt training if `val_loss` stops improving for over 2 epochs - keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'), - # Write TensorBoard logs to `./logs` directory - keras.callbacks.TensorBoard(log_dir='./logs') -] -model.fit(data, labels, batch_size=32, epochs=5, callbacks=callbacks) -``` + def call(self, inputs): + return tf.matmul(inputs, self.kernel) ---- + def compute_output_shape(self, input_shape): + shape = tf.TensorShape(input_shape).as_list() + shape[-1] = self.output_dim + return tf.TensorShape(shape) -## Saving and serialization + def get_config(self): + base_config = super(MyLayer, self).get_config() + base_config['output_dim'] = self.output_dim -### Weights-only saving + @classmethod + def from_config(cls, config): + return cls(**config) -You can save the weight values of a model via `model.save_weights(filepath)`: -```python -# Saves weights to a SavedModel file. -model.save_weights('my_model') +# Create a model using the custom layer +model = keras.Sequential([MyLayer(10), + keras.layers.Activation('softmax')]) -# Restores the model's state -# (this requires a model that has the same architecture). -model.load_weights('my_model') +# The compile step specifies the training configuration +model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), + loss='categorical_crossentropy', + metrics=['accuracy']) + +# Trains for 5 epochs. +model.fit(data, targets, batch_size=32, epochs=5) ``` -By default, this saves the weight in the TensorFlow -[`SavedModel`](https://www.tensorflow.org/programmers_guide/saved_model) format. -You could also save them in the Keras HDF5 format -(which is the default in the multi-backend implementation of Keras): -```python -# Saves weights to a HDF5 file. -model.save_weights('my_model.h5', format='h5') +## Callbacks -# Restores the model's state. -model.load_weights('my_model.h5') -``` +A callback is an object passed to a model to customize and extend its behavior +during training. You can write your own custom callback, or use the built-in +`tf.keras.callbacks` that include: -### Configuration-only saving (serialization) +* `tf.keras.callbacks.ModelCheckpoint`: Save checkpoints of your model at + regular intervals. +* `tf.keras.callbacks.LearningRateScheduler`: Dynamically change the learning + rate. +* `tf.keras.callbacks.EarlyStopping`: Interrupt training when validation + performance has stopped improving. +* `tf.keras.callbacks.TensorBoard`: Monitor the model's behavior using + [TensorBoard](/programmers_guide/summaries_and_tensorboard). -You can also save the model's configuration -(its architecture, without any weight values), -which allows you to recreate the same model later (freshly initialized) even if -you don't have the code that defined it anymore. -Two possible serialization formats are JSON and YAML: +To use a `tf.keras.callbacks.Callback`, pass it to the model's `fit` method: ```python -from tensorflow.keras import models - -# Serializes a model to JSON. -json_string = model.to_json() -# Recreates the model (freshly initialized). -fresh_model = models.from_json(json_string) - -# Serializes a model to YAML. -yaml_string = model.to_yaml() -# Recreates the model. -fresh_model = models.from_yaml(yaml_string) +callbacks = [ + # Interrupt training if `val_loss` stops improving for over 2 epochs + keras.callbacks.EarlyStopping(patience=2, monitor='val_loss'), + # Write TensorBoard logs to `./logs` directory + keras.callbacks.TensorBoard(log_dir='./logs') +] +model.fit(data, labels, batch_size=32, epochs=5, callbacks=callbacks, + validation_data=(val_data, val_targets)) ``` -Note that this feature is not available with subclassed models, -because they are simply not serializable: -their architecture is defined as Python code -(the body of the `call` method of the model). -### Whole-model saving +## Save and restore -Finally, you can also save a model wholesale, to a file that will contain both -the weight values, the model's configuration, -and even the optimizer's configuration. -The allows you to checkpoint a model and resume training later -- -from the exact same state -- even if you don't have access to the original code. +### Weights only -```python -from tensorflow.keras import models +Save and load the weights of a model using `tf.keras.Model.save_weights`: -model.save('my_model.h5') +```python +# Save weights to a TensorFlow Checkpoint file +model.save_weights('./my_model') -# Recreates the exact same model, complete with weights and optimizer. -model = models.load_model('my_model.h5') +# Restore the model's state, +# this requires a model with the same architecture. +model.load_weights('my_model') ``` ---- - -## Developing custom layers - -You can write your own custom layers by subclassing the class -`tf.keras.layers.Layer`. You will need to implement the following three methods: - -- `build`: Creates the weights of the layer. - Weights should be added via the `add_weight` method. -- `call`: Specifies the forward pass. -- `compute_output_shape`: Specifies how to compute the output shape of the layer - given the input shape. - -Optionally, you may also implement the method `get_config()` and the -class method `from_config()` if you want your layer to be serializable. - -Here's a simple example of a custom layer that implements a `matmul` -of an input with a kernel matrix: +By default, this saves the model's weights in the +[TensorFlow checkpoint](/get_started/checkpoints) file format. Weights can also +be saved to the Keras HDF5 format (the default for the multi-backend +implementation of Keras): ```python -import tensorflow as tf -from tensorflow.keras import layers - -class MyLayer(layers.Layer): - - def __init__(self, output_dim, **kwargs): - self.output_dim = output_dim - super(MyLayer, self).__init__(**kwargs) - - def build(self, input_shape): - # Create a trainable weight variable for this layer. - self.kernel = self.add_weight(name='kernel', - shape=(input_shape[1], self.output_dim), - initializer='uniform', - trainable=True) - # Be sure to call this at the end - super(MyLayer, self).build(input_shape) - - def call(self, inputs): - return tf.matmul(inputs, self.kernel) - - def compute_output_shape(self, input_shape): - shape = tf.TensorShape(input_shape).as_list() - shape[-1] = self.output_dim - return tf.TensorShape(shape) - - def get_config(self): - base_config = super(MyLayer, self).get_config() - base_config['output_dim'] = self.output_dim - - @classmethod - def from_config(cls, config): - return cls(**config) -``` +# Save weights to a HDF5 file +model.save_weights('my_model.h5', save_format='h5') ---- - -## Eager execution +# Restore the model's state +model.load_weights('my_model.h5') +``` -[Eager execution](https://www.tensorflow.org/programmers_guide/eager) -is a way to write TensorFlow code imperatively. -All three `tf.keras` model-building APIs -(`Sequential`, the functional API `Model(inputs, outputs)`, -and the subclassing API `MyModel(Model)`) are compatible with eager execution. -When using `Sequential` or the functional API, it makes no difference to the -user experience whether the model is executing eagerly or not. -Eager execution is most beneficial when used with the `Model` subclassing API, -or when prototyping a custom layer -- that is to say, in APIs that require you -to *write a forward pass as code*, rather than in APIs that allow you to create -models by assembling together existing layers. +### Configuration only -While the same training and evaluating APIs presented in this guide work -as usual with eager execution, you can in addition -write custom training loops using the eager `GradientTape` -and define-by-run autodifferentiation: +A model's configuration can be saved—this serializes the model architecture +without any weights. A saved configuration can recreate and initialize the same +model, even without the code that defined the original model. Keras supports +JSON and YAML serialization formats: ```python -import tensorflow as tf -from tensorflow.contrib import eager as tfe - -# This call begins the eager execution session. -tf.enable_eager_execution() - -model = ... # Defines a Keras model (we recommend Model subclassing in this case). -dataset = ... # Defines a `tf.data` dataset. +# Serialize a model to JSON format +json_string = model.to_json() -optimizer = tf.train.AdamOptimizer(0.01) +# Recreate the model (freshly initialized) +fresh_model = keras.models.from_json(json_string) -for data, labels in dataset: - # Runs the forward pass and loss computation under a `GradientTape` scope, - # which will record all operations in order to prepare for the backward pass. - with tfe.GradientTape() as tape: - predictions = model(data) - loss = loss_function(labels, predictions) +# Serializes a model to YAML format +yaml_string = model.to_yaml() - # Runs the backward pass manually using the operations recorded - # by the gradient tape. - grads = tape.gradient(loss, model.trainable_weights) - optimizer.apply_gradients(zip(grads, model.trainable_weights), - global_step=tf.train.get_or_create_global_step()) +# Recreate the model +fresh_model = keras.models.from_yaml(yaml_string) ``` ---- +Caution: Subclassed models are not serializable because their architecture is +defined by the Python code in the body of the `call` method. -## Further reading -### Documentation +### Entire model -- [tf.keras documentation](https://www.tensorflow.org/api_docs/python/tf/keras) -- [keras.io](https://keras.io/) +The entire model can be saved to a file that contains the weight values, the +model's configuration, and even the optimizer's configuration. This allows you +to checkpoint a model and resume training later—from the exact same +state—without access to the original code. -### tf.keras tutorials and examples - -- [Fashion-MNIST with tf.Keras](https://medium.com/tensorflow/hello-deep-learning-fashion-mnist-with-keras-50fcff8cd74a) -- [Predicting the price of wine with the Keras Functional API and TensorFlow]( - https://medium.com/tensorflow/predicting-the-price-of-wine-with-the-keras-functional-api-and-tensorflow-a95d1c2c1b03) +```python +# Create a trivial model +model = keras.Sequential([ + keras.layers.Dense(10, activation='softmax', input_shape=(32,)), + keras.layers.Dense(10, activation='softmax') +]) +model.compile(optimizer='rmsprop', + loss='categorical_crossentropy', + metrics=['accuracy']) +model.fit(data, targets, batch_size=32, epochs=5) ---- +# Save entire model to a HDF5 file +model.save('my_model.h5') -## FAQ +# Recreate the exact same model, including weights and optimizer. +model = keras.models.load_model('my_model.h5') +``` -### What are the differences between tf.keras and the multi-backend Keras implementation? -`tf.keras` includes first-class support for important TensorFlow-specific -functionality not found in other Keras implementations, in particular: +## Eager execution -- Support for eager execution. -- Support for the `tf.data` API. -- Integration with the - [`tf.estimator` API](https://www.tensorflow.org/programmers_guide/estimators), - via `tf.keras.estimator.model_to_estimator`. +[Eager execution](/programmers_guide/eager) is an imperative programming +environment that evaluates operations immediately. This is not required for +Keras, but is supported by `tf.keras` and useful for inspecting your program and +debugging. -In terms of API differences: `tf.keras` is a full implementation of the -Keras API, so any code targeting the Keras API will run on `tf.keras`. -However, keep in mind that: +All of the `tf.keras` model-building APIs are compatible with eager execution. +And while the `Sequential` and functional APIs can be used, eager execution +especially benefits *model subclassing* and building *custom layers*—the APIs +that require you to write the forward pass as code (instead of the APIs that +create models by assembling existing layers). -- The `tf.keras` API version in the latest TensorFlow release might not be the - same as the latest `keras` version from PyPI. - Check out `tf.keras.__version__` if in doubt. -- In `tf.keras`, the default file format saved by `model.save_weights` is the - TensorFlow `SavedModel` format. - To use HDF5, you can pass the `format='h5'` argument. +See the [eager execution guide](/programmers_guide/eager#build_a_model) for +examples of using Keras models with custom training loops and `tf.GradientTape`. -### What is the relationship between tf.keras and tf.estimator? +## Distribution -The [`tf.estimator` API](https://www.tensorflow.org/programmers_guide/estimators) -is a high-level TensorFlow API for training "estimator" models, -in particular in distributed settings. -This API targets industry use cases, such as distributed training -on large datasets with a focus on eventually exporting a production model. +### Estimators -If you have a `tf.keras` model that would like to train with the `tf.estimator` -API, you can convert your model to an `Estimator` object via the -`model_to_estimator` utility](https://www.tensorflow.org/programmers_guide/estimators#creating_estimators_from_keras_models): +The [Estimators](/programmers_guide/estimators) API is used for training models +for distributed environments. This targets industry use cases such as +distributed training on large datasets that can export a model for production. +A `tf.keras.Model` can be trained with the `tf.estimator` API by converting the +model to an `tf.estimator.Estimator` object with +`tf.keras.estimator.model_to_estimator`. See +[Creating Estimators from Keras models](/programmers_guide/estimators#creating_estimators_from_keras_models). ```python -estimator = tf.keras.estimator.model_to_estimator(model) -``` +model = keras.Sequential([layers.Dense(10,activation='softmax'), + layers.Dense(10,activation='softmax')]) -When using `model_to_estimator`, enabling eager execution is helpful for -developing and debugging your `input_fn` -(as it allows you to easily print your data). +model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), + loss='categorical_crossentropy', + metrics=['accuracy']) + +estimator = keras.estimator.model_to_estimator(model) +``` +Note: Enable [eager execution](/programmers_guide/eager) for debugging +[Estimator input functions](/programmers_guide/premade_estimators#create_input_functions) +and inspecting data. -### How can I run tf.keras models on multiple GPUs? +### Multiple GPUs -You can run tf.keras models on multiple GPUs using the -[`DistributionStrategy API`](https://www.tensorflow.org/versions/master/api_docs/python/tf/contrib/distribute/DistributionStrategy). -The `DistributionStrategy` API allow you to distribute training on multiple GPUs -with almost no changes to your existing code. +`tf.keras` models can run on multiple GPUs using +`tf.contrib.distribute.DistributionStrategy`. This API provides distributed +training on multiple GPUs with almost no changes to existing code. -Currently [`MirroredStrategy`](https://www.tensorflow.org/versions/master/api_docs/python/tf/contrib/distribute/MirroredStrategy) -is the only supported strategy. -`MirroredStrategy` allows you to do in-graph replication with synchronous -training using all-reduce on a single machine. -To use `DistributionStrategy` with a `tf.keras` model, -you can use the `model_to_estimator` utility to convert a `tf.keras` model to -an `Estimator` and then train the estimator. +Currently, `tf.contrib.distribute.MirroredStrategy` is the only supported +distribution strategy. `MirroredStrategy` does in-graph replication with +synchronous training using all-reduce on a single machine. To use +`DistributionStrategy` with Keras, convert the `tf.keras.Model` to a +`tf.estimator.Estimator` with `tf.keras.estimator.model_to_estimator`, then +train the estimator -Here is a simple example of distributing a `tf.keras` model across multiple GPUs -on a single machine. +The following example distributes a `tf.keras.Model` across multiple GPUs on a +single machine. -Let's first define a simple model: +First, define a simple model: ```python -model = tf.keras.Sequential() -model.add(tf.keras.layers.Dense(16, activation='relu', input_shape=(10,))) -model.add(tf.keras.layers.Dense(1, activation='sigmoid')) +model = keras.Sequential() +model.add(keras.layers.Dense(16, activation='relu', input_shape=(10,))) +model.add(keras.layers.Dense(1, activation='sigmoid')) + optimizer = tf.train.GradientDescentOptimizer(0.2) + model.compile(loss='binary_crossentropy', optimizer=optimizer) model.summary() ``` -Let's use `model_to_estimator` to create an `Estimator` instance from the -`tf.keras` model defined above. +Convert the Keras model to a `tf.estimator.Estimator` instance: ```python -keras_estimator = tf.keras.estimator.model_to_estimator( - keras_model=model, - config=config, - model_dir='/tmp/model_dir') +keras_estimator = keras.estimator.model_to_estimator( + keras_model=model, + config=config, + model_dir='/tmp/model_dir') ``` -We'll use `tf.data.Datasets` to define our input pipeline. -Our `input_fn` returns a `tf.data.Dataset` object that we then use to distribute -the data across multiple devices with each device processing +Define an *input pipeline*. The `input_fn` returns a `tf.data.Dataset` object +used to distribute the data across multiple devices—with each device processing a slice of the input batch. ```python def input_fn(): - x = np.random.random((1024, 10)) - y = np.random.randint(2, size=(1024, 1)) - x = tf.cast(x, tf.float32) - dataset = tf.data.Dataset.from_tensor_slices((x, y)) - dataset = dataset.repeat(10) - dataset = dataset.batch(32) - return dataset + x = np.random.random((1024, 10)) + y = np.random.randint(2, size=(1024, 1)) + x = tf.cast(x, tf.float32) + dataset = tf.data.Dataset.from_tensor_slices((x, y)) + dataset = dataset.repeat(10) + dataset = dataset.batch(32) + return dataset ``` -The next step is to create a `RunConfig` and set the train_distribute argument -to the new `MirroredStrategy` instance. -You can specify a list of devices or the `num_gpus` argument when creating -a `MirroredStrategy` instance. -Not specifying any arguments defaults to using all the available GPUs like we do -in this example. +Next, create a `tf.estimator.RunConfig` and set the `train_distribute` argument +to the `tf.contrib.distribute.MirroredStrategy` instance. When creating +`MirroredStrategy`, you can specify a list of devices or set the `num_gpus` +argument. The default uses all available GPUs, like the following: ```python strategy = tf.contrib.distribute.MirroredStrategy() config = tf.estimator.RunConfig(train_distribute=strategy) ``` -Call train on the `Estimator` instance providing the `input_fn` and `steps` -arguments as input: +Finally, train the `Estimator` instance by providing the `input_fn` and `steps` +arguments: ```python keras_estimator.train(input_fn=input_fn, steps=10) -- GitLab From 7e859ebc65bf7d77ed89f736c7fd6fede0a93c92 Mon Sep 17 00:00:00 2001 From: Michael Case Date: Mon, 18 Jun 2018 11:07:48 -0700 Subject: [PATCH 035/519] Add missing Eager relnotes for TensorFlow 1.9. (#20101) --- RELEASE.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/RELEASE.md b/RELEASE.md index 879ce6e440..510eca5467 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -22,6 +22,8 @@ * (C++) `DatasetBase::MakeIterator()` has been renamed to `DatasetBase::MakeIteratorInternal()`. * (C++) `IteratorBase::Initialize()` method was added to support raising errors during iterator construction. * Eager Execution: + * Added the ability to pause recording operations for gradient computation via `tf.GradientTape.stop_recording`. + * Updated documentation, introductory notebooks. * `tf.keras`: * Move Keras code out of _impl folder and remove API files. * `tf.keras.Model.save_weights` now saves in TensorFlow format by default. -- GitLab From 86a6b0d7efbe5a3fa1f511237b85c926a6aef3a5 Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Tue, 19 Jun 2018 17:47:37 -0700 Subject: [PATCH 036/519] [GCS] Typo in ConfigureGcsHook. This commit fixes a typo on ConfigureGcsHook that prevented its correct operation. --- tensorflow/contrib/cloud/python/ops/gcs_config_ops.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py b/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py index 8c8c5acb31..4f7300fd1f 100644 --- a/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py +++ b/tensorflow/contrib/cloud/python/ops/gcs_config_ops.py @@ -120,13 +120,17 @@ class ConfigureGcsHook(training.SessionRunHook): def begin(self): if self._credentials: self._credentials_placeholder = array_ops.placeholder(dtypes.string) - self._credentials_ops = gen_gcs_config_ops.gcs_configure_credentials( + self._credentials_op = gen_gcs_config_ops.gcs_configure_credentials( self._credentials_placeholder) + else: + self._credentials_op = None if self._block_cache: self._block_cache_op = gen_gcs_config_ops.gcs_configure_block_cache( max_cache_size=self._block_cache.max_bytes, block_size=self._block_cache.block_size, max_staleness=self._block_cache.max_staleness) + else: + self._block_cache_op = None def after_create_session(self, session, coord): del coord -- GitLab From fdbb80f217d3a153b4eda66c766df921b3f73ab4 Mon Sep 17 00:00:00 2001 From: Michael Case Date: Wed, 20 Jun 2018 14:08:57 -0700 Subject: [PATCH 037/519] Move external/ directory in pip package. Moving external/ directory in the pip packages (which is currently installed directly into site-packages directory). Moving the directory to tensorflow/include/external/. Also, removing all python files from external (since it should really only contain headers and license files.) --- .../tools/pip_package/build_pip_package.sh | 21 ++++++++++++------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index f7e42ce536..9e41514cfa 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -24,9 +24,15 @@ function real_path() { function cp_external() { local src_dir=$1 local dest_dir=$2 - for f in `find "$src_dir" -maxdepth 1 -mindepth 1 ! -name '*local_config_cuda*' ! -name '*local_config_tensorrt*' ! -name '*org_tensorflow*'`; do - cp -R "$f" "$dest_dir" + + pushd . + cd "$src_dir" + for f in `find . ! -type d ! -name '*.py' ! -name '*local_config_cuda*' ! -name '*local_config_tensorrt*' ! -name '*org_tensorflow*'`; do + mkdir -p "${dest_dir}/$(dirname ${f})" + cp "${f}" "${dest_dir}/$(dirname ${f})/" done + popd + mkdir -p "${dest_dir}/local_config_cuda/cuda/cuda/" cp "${src_dir}/local_config_cuda/cuda/cuda/cuda_config.h" "${dest_dir}/local_config_cuda/cuda/cuda/" } @@ -49,6 +55,8 @@ function prepare_src() { TMPDIR="$1" mkdir -p "$TMPDIR" + EXTERNAL_INCLUDES="${TMPDIR}/tensorflow/include/external" + echo $(date) : "=== Preparing sources in dir: ${TMPDIR}" if [ ! -d bazel-bin/tensorflow ]; then @@ -66,10 +74,9 @@ function prepare_src() { cp -R \ bazel-bin/tensorflow/tools/pip_package/simple_console_for_window_unzip/runfiles/org_tensorflow/tensorflow \ "${TMPDIR}" - mkdir "${TMPDIR}/external" cp_external \ bazel-bin/tensorflow/tools/pip_package/simple_console_for_window_unzip/runfiles \ - "${TMPDIR}/external" + "${EXTERNAL_INCLUDES}/" RUNFILES=bazel-bin/tensorflow/tools/pip_package/simple_console_for_window_unzip/runfiles/org_tensorflow else RUNFILES=bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow @@ -78,10 +85,9 @@ function prepare_src() { cp -R \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow \ "${TMPDIR}" - mkdir "${TMPDIR}/external" cp_external \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/external \ - "${TMPDIR}/external" + "${EXTERNAL_INCLUDES}" # Copy MKL libs over so they can be loaded at runtime so_lib_dir=$(ls $RUNFILES | grep solib) || true if [ -n "${so_lib_dir}" ]; then @@ -96,10 +102,9 @@ function prepare_src() { cp -R \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles/org_tensorflow/tensorflow \ "${TMPDIR}" - mkdir "${TMPDIR}/external" cp_external \ bazel-bin/tensorflow/tools/pip_package/build_pip_package.runfiles \ - "${TMPDIR}/external" + "${EXTERNAL_INCLUDES}" # Copy MKL libs over so they can be loaded at runtime so_lib_dir=$(ls $RUNFILES | grep solib) || true if [ -n "${so_lib_dir}" ]; then -- GitLab From 1adbc5aa6927d1a5d7151c31aea1da6e73a1b53c Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Thu, 31 May 2018 19:03:21 -0700 Subject: [PATCH 038/519] Add a single positional argument mode for shape inference in subclassed Models. Allows fit() when call's signature looks something like call(x, training=True). Calling conventions are "inputs", single positional, and multiple positional. Right now the distinction between "inputs" and single positional calling conventions is the text of one error message. Both support shape inference (which just hasn't been implemented for multiple positional input arguments yet). PiperOrigin-RevId: 198815483 --- tensorflow/python/keras/engine/base_layer.py | 45 ++++++++++++++--- tensorflow/python/keras/engine/network.py | 50 ++++++++++++++++--- tensorflow/python/keras/engine/training.py | 27 ++++++---- .../python/keras/model_subclassing_test.py | 4 +- 4 files changed, 98 insertions(+), 28 deletions(-) diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py index 24716cfbe4..4814275fd5 100644 --- a/tensorflow/python/keras/engine/base_layer.py +++ b/tensorflow/python/keras/engine/base_layer.py @@ -19,6 +19,7 @@ from __future__ import division from __future__ import print_function import collections +import enum # pylint: disable=g-bad-import-order import inspect # Necessary supplement to tf_inspect to deal with variadic args. import numpy as np @@ -50,6 +51,20 @@ from tensorflow.python.util import tf_inspect from tensorflow.python.util.tf_export import tf_export +class CallConvention(enum.Enum): + """Calling conventions for passing `Layer` inputs to `Layer.call`.""" + # The Layer takes inputs as its first argument, named "inputs" for + # compatibility with the signature of Layer.__call__. This is the mode assumed + # for Layers which are not subclassed Models. + EXPLICIT_INPUTS_ARGUMENT = 1 + # The Layer takes a single positional argument, not named "inputs". It's + # treated like an "inputs" argument. + SINGLE_POSITIONAL_ARGUMENT = 2 + # The Layer has multiple positional arguments to which its inputs should be + # bound. + POSITIONAL_ARGUMENTS_ARE_INPUTS = 3 + + @tf_export('keras.layers.Layer') class Layer(checkpointable.CheckpointableBase): """Base layer class. @@ -149,7 +164,7 @@ class Layer(checkpointable.CheckpointableBase): self._call_fn_args = function_utils.fn_args(self.call) self._compute_previous_mask = ('mask' in self._call_fn_args or hasattr(self, 'compute_mask')) - self._uses_inputs_arg = True + self._call_convention = CallConvention.EXPLICIT_INPUTS_ARGUMENT # These lists will be filled via successive calls # to self._add_inbound_node(). @@ -793,12 +808,22 @@ class Layer(checkpointable.CheckpointableBase): pass # C type such as dict. Masking not supported in this case. def _set_connectivity_metadata_(self, inputs, outputs, args, kwargs): - if args and getattr(self, '_uses_inputs_arg', True): - raise TypeError( - 'This Layer takes an `inputs` argument to call(), and only the ' - '`inputs` argument may be specified as a positional argument. ' - 'Pass everything else as a keyword argument (those arguments will' - ' not be tracked as inputs to the Layer).') + call_convention = getattr(self, '_call_convention', + CallConvention.EXPLICIT_INPUTS_ARGUMENT) + if args: + if call_convention == CallConvention.EXPLICIT_INPUTS_ARGUMENT: + raise TypeError( + 'This Layer takes an `inputs` argument to call(), and only the ' + '`inputs` argument may be specified as a positional argument. ' + 'Pass everything else as a keyword argument (those arguments will' + ' not be tracked as inputs to the Layer).') + elif call_convention == CallConvention.SINGLE_POSITIONAL_ARGUMENT: + raise TypeError( + 'This Layer takes a single positional argument to call(), which is ' + 'by convention the inputs argument, and only this argument may be ' + 'specified as a positional argument. Pass everything else as a ' + 'keyword argument (those arguments will not be tracked as inputs ' + 'to the Layer).') # If the layer returns tensors from its inputs, unmodified, # we copy them to avoid loss of tensor metadata. @@ -834,7 +859,11 @@ class Layer(checkpointable.CheckpointableBase): A tuple of (inputs, non_input_kwargs). These may be the same objects as were passed in (call_args and call_kwargs). """ - if getattr(self, '_uses_inputs_arg', True): + call_convention = getattr(self, '_call_convention', + CallConvention.EXPLICIT_INPUTS_ARGUMENT) + if (call_convention in ( + CallConvention.EXPLICIT_INPUTS_ARGUMENT, + CallConvention.SINGLE_POSITIONAL_ARGUMENT)): assert len(call_args) == 1 # TypeError raised earlier in __call__. return call_args[0], call_kwargs else: diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index 3d567b8378..6f27eea1e7 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -135,7 +135,7 @@ class Network(base_layer.Layer): self._in_progress_restore_finalizer = None def _init_graph_network(self, inputs, outputs, name=None): - self._uses_inputs_arg = True + self._call_convention = base_layer.CallConvention.EXPLICIT_INPUTS_ARGUMENT # Normalize and set self.inputs, self.outputs. if isinstance(inputs, (list, tuple)): self.inputs = list(inputs) # Tensor or list of tensors. @@ -295,19 +295,55 @@ class Network(base_layer.Layer): def _init_subclassed_network(self, name=None): self._base_init(name=name) self._is_graph_network = False - call_args = tf_inspect.getargspec(self.call).args - if 'training' in call_args: + call_argspec = tf_inspect.getargspec(self.call) + if 'training' in call_argspec.args: self._expects_training_arg = True else: self._expects_training_arg = False - if 'inputs' in call_args: - self._uses_inputs_arg = True - else: - self._uses_inputs_arg = False + self._call_convention = self._determine_call_convention(call_argspec) self.outputs = None self.inputs = None self.built = False + def _determine_call_convention(self, call_argspec): + """Decides how `self.call()` is invoked. See base_layer.CallConvention.""" + if call_argspec.varargs: + may_take_single_argument = False + else: + try: + # Note: tf_inspect doesn't raise a TypeError when regular inspect would, + # so we need to keep in mind that "getcallargs" may have returned + # something even though we under-specified positional arguments. + all_args = tf_inspect.getcallargs(self.call, None) + self_args = set() + for arg_name, obj in all_args.items(): + if obj is self: + self_args.add(arg_name) + may_take_single_argument = True + except TypeError: + may_take_single_argument = False + if may_take_single_argument: + # A single positional argument (plus "self") is considered equivalent to + # an "inputs" argument. + all_positional_args = len(call_argspec.args) + if call_argspec.defaults is not None: + all_positional_args -= len(call_argspec.defaults) + non_self_positional_args = all_positional_args + for positional_arg_name in call_argspec.args[:all_positional_args]: + if positional_arg_name in self_args: + non_self_positional_args -= 1 + if non_self_positional_args == 1: + if 'inputs' in call_argspec.args[all_positional_args:]: + raise TypeError( + "Model.call() takes a single positional argument (to which " + "inputs are passed by convention) and a separate 'inputs' " + "argument. Unable to determine which arguments are inputs.") + return base_layer.CallConvention.SINGLE_POSITIONAL_ARGUMENT + if 'inputs' in call_argspec.args: + return base_layer.CallConvention.EXPLICIT_INPUTS_ARGUMENT + else: + return base_layer.CallConvention.POSITIONAL_ARGUMENTS_ARE_INPUTS + def _track_layers(self, layers): """Add Checkpointable dependencies on a list of Layers.""" weight_layer_index = 0 diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 6d625f16c2..04a2aa7664 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -31,12 +31,11 @@ from tensorflow.python.keras import backend as K from tensorflow.python.keras import losses from tensorflow.python.keras import metrics as metrics_module from tensorflow.python.keras import optimizers +from tensorflow.python.keras.engine import base_layer from tensorflow.python.keras.engine import training_arrays from tensorflow.python.keras.engine import training_eager from tensorflow.python.keras.engine import training_generator from tensorflow.python.keras.engine import training_utils -from tensorflow.python.keras.engine.base_layer import DeferredTensor -from tensorflow.python.keras.engine.base_layer import Layer from tensorflow.python.keras.engine.network import Network from tensorflow.python.keras.utils.generic_utils import slice_arrays from tensorflow.python.ops import array_ops @@ -523,7 +522,7 @@ class Model(Network): # Keep track of state updates created by # stateful metrics (i.e. metrics layers). - if isinstance(metric_fn, Layer) and metric_fn.stateful: + if isinstance(metric_fn, base_layer.Layer) and metric_fn.stateful: self.stateful_metric_names.append(metric_name) self.stateful_metric_functions.append(metric_fn) self.metrics_updates += metric_fn.updates @@ -959,11 +958,17 @@ class Model(Network): whether to build the model's graph in inference mode (False), training mode (True), or using the Keras learning phase (None). """ - if not getattr(self, '_uses_inputs_arg', True): + call_convention = getattr( + self, + '_call_convention', + base_layer.CallConvention.EXPLICIT_INPUTS_ARGUMENT) + if call_convention not in ( + base_layer.CallConvention.EXPLICIT_INPUTS_ARGUMENT, + base_layer.CallConvention.SINGLE_POSITIONAL_ARGUMENT): raise NotImplementedError( - 'Subclassed Models without "inputs" in their call() signatures do ' - 'not yet support shape inference. File a feature request if this ' - 'limitation bothers you.') + 'Subclassed Models without "inputs" (or single positional arguments) ' + 'in their call() signatures do not yet support shape inference. File ' + 'a feature request if this limitation bothers you.') if self.__class__.__name__ == 'Sequential': # Note: we can't test whether the model is `Sequential` via `isinstance` # since `Sequential` depends on `Model`. @@ -1020,11 +1025,11 @@ class Model(Network): else: dummy_output_values = [dummy_output_values] self.outputs = [ - DeferredTensor(shape=(None for _ in v.shape), - dtype=v.dtype) for v in dummy_output_values] + base_layer.DeferredTensor(shape=(None for _ in v.shape), + dtype=v.dtype) for v in dummy_output_values] self.inputs = [ - DeferredTensor(shape=(None for _ in v.shape), - dtype=v.dtype) for v in dummy_input_values] + base_layer.DeferredTensor(shape=(None for _ in v.shape), + dtype=v.dtype) for v in dummy_input_values] self.input_names = [ 'input_%d' % (i + 1) for i in range(len(dummy_input_values))] self.output_names = [ diff --git a/tensorflow/python/keras/model_subclassing_test.py b/tensorflow/python/keras/model_subclassing_test.py index 86f7e20bec..8fb957da43 100644 --- a/tensorflow/python/keras/model_subclassing_test.py +++ b/tensorflow/python/keras/model_subclassing_test.py @@ -56,8 +56,8 @@ class SimpleTestModel(keras.Model): if self.use_bn: self.bn = keras.layers.BatchNormalization(axis=-1) - def call(self, inputs): - x = self.dense1(inputs) + def call(self, x): + x = self.dense1(x) if self.use_dp: x = self.dp(x) if self.use_bn: -- GitLab From 6fb75293ec2cb5cd8d815cf98ec33aa953442b34 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Wed, 20 Jun 2018 10:10:55 -0700 Subject: [PATCH 039/519] [tf.data] Properly export `tf.contrib.data.choose_from_datasets()` PiperOrigin-RevId: 201371642 --- tensorflow/contrib/data/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py index 1af1ed08b5..9c6a13333e 100644 --- a/tensorflow/contrib/data/__init__.py +++ b/tensorflow/contrib/data/__init__.py @@ -72,6 +72,7 @@ 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 choose_from_datasets from tensorflow.contrib.data.python.ops.interleave_ops import parallel_interleave from tensorflow.contrib.data.python.ops.interleave_ops import sample_from_datasets from tensorflow.contrib.data.python.ops.interleave_ops import sloppy_interleave -- GitLab From 579b598862b14b7a8e242cb1b094221f7e08b499 Mon Sep 17 00:00:00 2001 From: Yu Yi Date: Fri, 22 Jun 2018 11:46:33 -0400 Subject: [PATCH 040/519] update the py/py3 toolchain config - the python3 are installed in /opt/python3.6 in the base toolchain container: https://console.cloud.google.com/launcher/details/google/rbe-ubuntu16-04 --- third_party/toolchains/cpus/py/BUILD | 242 ++++++++++++-------------- third_party/toolchains/cpus/py3/BUILD | 234 +++++++++++++------------ 2 files changed, 231 insertions(+), 245 deletions(-) diff --git a/third_party/toolchains/cpus/py/BUILD b/third_party/toolchains/cpus/py/BUILD index c175742cbf..10184e215b 100644 --- a/third_party/toolchains/cpus/py/BUILD +++ b/third_party/toolchains/cpus/py/BUILD @@ -6,20 +6,26 @@ licenses(["restricted"]) package(default_visibility = ["//visibility:public"]) +# To build Python C/C++ extension on Windows, we need to link to python import library pythonXY.lib +# See https://docs.python.org/3/extending/windows.html +cc_import( + name = "python_lib", + interface_library = select({ + ":windows": ":python_import_lib", + # A placeholder for Unix platforms which makes --no_build happy. + "//conditions:default": "not-existing.lib", + }), + system_provided = 1, +) + cc_library( name = "python_headers", hdrs = [":python_include"], - data = select({ - ":windows": [":python_import_lib"], + deps = select({ + ":windows": [":python_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( @@ -37,161 +43,135 @@ config_setting( 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/tupleobject.h", - "python_include/object.h", - "python_include/ast.h", - "python_include/pymacconfig.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/pgenheaders.h", - "python_include/cellobject.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/pythread.h", - "python_include/cStringIO.h", - "python_include/boolobject.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/import.h", - "python_include/pymath.h", + "python_include/moduleobject.h", "python_include/node.h", - "python_include/funcobject.h", - "python_include/eval.h", - "python_include/longintrepr.h", - "python_include/floatobject.h", - "python_include/rangeobject.h", - "python_include/pyfpe.h", - "python_include/pystrcmp.h", - "python_include/dictobject.h", - "python_include/pyarena.h", + "python_include/object.h", "python_include/objimpl.h", - "python_include/bitset.h", - "python_include/memoryobject.h", - "python_include/bytearrayobject.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/weakrefobject.h", - "python_include/grammar.h", - "python_include/symtable.h", - "python_include/longobject.h", - "python_include/structmember.h", - "python_include/enumobject.h", - "python_include/classobject.h", - "python_include/unicodeobject.h", - "python_include/sliceobject.h", - "python_include/pystrtod.h", - "python_include/genobject.h", - "python_include/pymactoolbox.h", - "python_include/compile.h", "python_include/pyexpat.h", - "python_include/asdl.h", - "python_include/codecs.h", - "python_include/pyctype.h", - "python_include/sysmodule.h", - "python_include/methodobject.h", - "python_include/graminit.h", - "python_include/cobject.h", - "python_include/intrcheck.h", - "python_include/pyport.h", - "python_include/warnings.h", - "python_include/osdefs.h", - "python_include/fileobject.h", - "python_include/stringobject.h", - "python_include/timefuncs.h", - "python_include/traceback.h", - "python_include/ceval.h", - "python_include/bytes_methods.h", - "python_include/pyconfig.h", - "python_include/Python.h", - "python_include/moduleobject.h", - "python_include/pystate.h", - "python_include/descrobject.h", - "python_include/ucnhash.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/complexobject.h", - "python_include/structseq.h", - "python_include/datetime.h", + "python_include/pyport.h", + "python_include/pystate.h", + "python_include/pystrcmp.h", + "python_include/pystrtod.h", "python_include/pythonrun.h", - "python_include/numpy/oldnumeric.h", - "python_include/numpy/npy_1_7_deprecated_api.h", - "python_include/numpy/ufunc_api.txt", - "python_include/numpy/multiarray_api.txt", - "python_include/numpy/halffloat.h", - "python_include/numpy/npy_common.h", - "python_include/numpy/utils.h", - "python_include/numpy/npy_interrupt.h", - "python_include/numpy/npy_endian.h", - "python_include/numpy/__ufunc_api.h", - "python_include/numpy/_neighborhood_iterator_imp.h", - "python_include/numpy/ufuncobject.h", - "python_include/numpy/ndarraytypes.h", - "python_include/numpy/npy_math.h", - "python_include/numpy/noprefix.h", - "python_include/numpy/npy_3kcompat.h", - "python_include/numpy/arrayscalars.h", - "python_include/numpy/npy_os.h", - "python_include/numpy/ndarrayobject.h", - "python_include/numpy/npy_no_deprecated_api.h", - "python_include/numpy/arrayobject.h", - "python_include/numpy/_numpyconfig.h", - "python_include/numpy/__multiarray_api.h", - "python_include/numpy/npy_cpu.h", - "python_include/numpy/old_defines.h", - "python_include/numpy/numpyconfig.h", - "python_include/pycapsule.h", + "python_include/pythread.h", + "python_include/rangeobject.h", "python_include/setobject.h", - "python_include/listobject.h", - "python_include/bytesobject.h", - "python_include/pgen.h", - "python_include/patchlevel.h", - "python_include/opcode.h", - "python_include/parsetok.h", - "python_include/marshal.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/iterobject.h", - "python_include/abstract.h", - "python_include/py_curses.h", - "python_include/metagrammar.h", - "python_include/bufferobject.h", - "python_include/Python-ast.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/code.h" "$(@D)/python_include/code.h" && cp "/usr/include/python2.7/dtoa.h" "$(@D)/python_include/dtoa.h" && cp "/usr/include/python2.7/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp "/usr/include/python2.7/object.h" "$(@D)/python_include/object.h" && cp "/usr/include/python2.7/ast.h" "$(@D)/python_include/ast.h" && cp "/usr/include/python2.7/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp "/usr/include/python2.7/errcode.h" "$(@D)/python_include/errcode.h" && cp "/usr/include/python2.7/frameobject.h" "$(@D)/python_include/frameobject.h" && cp "/usr/include/python2.7/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp "/usr/include/python2.7/cellobject.h" "$(@D)/python_include/cellobject.h" && cp "/usr/include/python2.7/intobject.h" "$(@D)/python_include/intobject.h" && cp "/usr/include/python2.7/pythread.h" "$(@D)/python_include/pythread.h" && cp "/usr/include/python2.7/cStringIO.h" "$(@D)/python_include/cStringIO.h" && cp "/usr/include/python2.7/boolobject.h" "$(@D)/python_include/boolobject.h" && cp "/usr/include/python2.7/modsupport.h" "$(@D)/python_include/modsupport.h" && cp "/usr/include/python2.7/import.h" "$(@D)/python_include/import.h" && cp "/usr/include/python2.7/pymath.h" "$(@D)/python_include/pymath.h" && cp "/usr/include/python2.7/node.h" "$(@D)/python_include/node.h" && cp "/usr/include/python2.7/funcobject.h" "$(@D)/python_include/funcobject.h" && cp "/usr/include/python2.7/eval.h" "$(@D)/python_include/eval.h" && cp "/usr/include/python2.7/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp "/usr/include/python2.7/floatobject.h" "$(@D)/python_include/floatobject.h" && cp "/usr/include/python2.7/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp "/usr/include/python2.7/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp "/usr/include/python2.7/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp "/usr/include/python2.7/dictobject.h" "$(@D)/python_include/dictobject.h" && cp "/usr/include/python2.7/pyarena.h" "$(@D)/python_include/pyarena.h" && cp "/usr/include/python2.7/objimpl.h" "$(@D)/python_include/objimpl.h" && cp "/usr/include/python2.7/bitset.h" "$(@D)/python_include/bitset.h" && cp "/usr/include/python2.7/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp "/usr/include/python2.7/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.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/weakrefobject.h" "$(@D)/python_include/weakrefobject.h" && cp "/usr/include/python2.7/grammar.h" "$(@D)/python_include/grammar.h" && cp "/usr/include/python2.7/symtable.h" "$(@D)/python_include/symtable.h" && cp "/usr/include/python2.7/longobject.h" "$(@D)/python_include/longobject.h" && cp "/usr/include/python2.7/structmember.h" "$(@D)/python_include/structmember.h" && cp "/usr/include/python2.7/enumobject.h" "$(@D)/python_include/enumobject.h" && cp "/usr/include/python2.7/classobject.h" "$(@D)/python_include/classobject.h" && cp "/usr/include/python2.7/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp "/usr/include/python2.7/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp "/usr/include/python2.7/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp "/usr/include/python2.7/genobject.h" "$(@D)/python_include/genobject.h" && cp "/usr/include/python2.7/pymactoolbox.h" "$(@D)/python_include/pymactoolbox.h" && cp "/usr/include/python2.7/compile.h" "$(@D)/python_include/compile.h" && cp "/usr/include/python2.7/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp "/usr/include/python2.7/asdl.h" "$(@D)/python_include/asdl.h" && cp "/usr/include/python2.7/codecs.h" "$(@D)/python_include/codecs.h" && cp "/usr/include/python2.7/pyctype.h" "$(@D)/python_include/pyctype.h" && cp "/usr/include/python2.7/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp "/usr/include/python2.7/methodobject.h" "$(@D)/python_include/methodobject.h" && cp "/usr/include/python2.7/graminit.h" "$(@D)/python_include/graminit.h" && cp "/usr/include/python2.7/cobject.h" "$(@D)/python_include/cobject.h" && cp "/usr/include/python2.7/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp "/usr/include/python2.7/pyport.h" "$(@D)/python_include/pyport.h" && cp "/usr/include/python2.7/warnings.h" "$(@D)/python_include/warnings.h" && cp "/usr/include/python2.7/osdefs.h" "$(@D)/python_include/osdefs.h" && cp "/usr/include/python2.7/fileobject.h" "$(@D)/python_include/fileobject.h" && cp "/usr/include/python2.7/stringobject.h" "$(@D)/python_include/stringobject.h" && cp "/usr/include/python2.7/timefuncs.h" "$(@D)/python_include/timefuncs.h" && cp "/usr/include/python2.7/traceback.h" "$(@D)/python_include/traceback.h" && cp "/usr/include/python2.7/ceval.h" "$(@D)/python_include/ceval.h" && cp "/usr/include/python2.7/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp "/usr/include/python2.7/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp "/usr/include/python2.7/Python.h" "$(@D)/python_include/Python.h" && cp "/usr/include/python2.7/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp "/usr/include/python2.7/pystate.h" "$(@D)/python_include/pystate.h" && cp "/usr/include/python2.7/descrobject.h" "$(@D)/python_include/descrobject.h" && cp "/usr/include/python2.7/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp "/usr/include/python2.7/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp "/usr/include/python2.7/pymem.h" "$(@D)/python_include/pymem.h" && cp "/usr/include/python2.7/complexobject.h" "$(@D)/python_include/complexobject.h" && cp "/usr/include/python2.7/structseq.h" "$(@D)/python_include/structseq.h" && cp "/usr/include/python2.7/datetime.h" "$(@D)/python_include/datetime.h" && cp "/usr/include/python2.7/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp "/usr/include/python2.7/numpy/oldnumeric.h" "$(@D)/python_include/numpy/oldnumeric.h" && cp "/usr/include/python2.7/numpy/npy_1_7_deprecated_api.h" "$(@D)/python_include/numpy/npy_1_7_deprecated_api.h" && cp "/usr/include/python2.7/numpy/ufunc_api.txt" "$(@D)/python_include/numpy/ufunc_api.txt" && cp "/usr/include/python2.7/numpy/multiarray_api.txt" "$(@D)/python_include/numpy/multiarray_api.txt" && cp "/usr/include/python2.7/numpy/halffloat.h" "$(@D)/python_include/numpy/halffloat.h" && cp "/usr/include/python2.7/numpy/npy_common.h" "$(@D)/python_include/numpy/npy_common.h" && cp "/usr/include/python2.7/numpy/utils.h" "$(@D)/python_include/numpy/utils.h" && cp "/usr/include/python2.7/numpy/npy_interrupt.h" "$(@D)/python_include/numpy/npy_interrupt.h" && cp "/usr/include/python2.7/numpy/npy_endian.h" "$(@D)/python_include/numpy/npy_endian.h" && cp "/usr/include/python2.7/numpy/__ufunc_api.h" "$(@D)/python_include/numpy/__ufunc_api.h" && cp "/usr/include/python2.7/numpy/_neighborhood_iterator_imp.h" "$(@D)/python_include/numpy/_neighborhood_iterator_imp.h" && cp "/usr/include/python2.7/numpy/ufuncobject.h" "$(@D)/python_include/numpy/ufuncobject.h" && cp "/usr/include/python2.7/numpy/ndarraytypes.h" "$(@D)/python_include/numpy/ndarraytypes.h" && cp "/usr/include/python2.7/numpy/npy_math.h" "$(@D)/python_include/numpy/npy_math.h" && cp "/usr/include/python2.7/numpy/noprefix.h" "$(@D)/python_include/numpy/noprefix.h" && cp "/usr/include/python2.7/numpy/npy_3kcompat.h" "$(@D)/python_include/numpy/npy_3kcompat.h" && cp "/usr/include/python2.7/numpy/arrayscalars.h" "$(@D)/python_include/numpy/arrayscalars.h" && cp "/usr/include/python2.7/numpy/npy_os.h" "$(@D)/python_include/numpy/npy_os.h" && cp "/usr/include/python2.7/numpy/ndarrayobject.h" "$(@D)/python_include/numpy/ndarrayobject.h" && cp "/usr/include/python2.7/numpy/npy_no_deprecated_api.h" "$(@D)/python_include/numpy/npy_no_deprecated_api.h" && cp "/usr/include/python2.7/numpy/arrayobject.h" "$(@D)/python_include/numpy/arrayobject.h" && cp "/usr/include/python2.7/numpy/_numpyconfig.h" "$(@D)/python_include/numpy/_numpyconfig.h" && cp "/usr/include/python2.7/numpy/__multiarray_api.h" "$(@D)/python_include/numpy/__multiarray_api.h" && cp "/usr/include/python2.7/numpy/npy_cpu.h" "$(@D)/python_include/numpy/npy_cpu.h" && cp "/usr/include/python2.7/numpy/old_defines.h" "$(@D)/python_include/numpy/old_defines.h" && cp "/usr/include/python2.7/numpy/numpyconfig.h" "$(@D)/python_include/numpy/numpyconfig.h" && cp "/usr/include/python2.7/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp "/usr/include/python2.7/setobject.h" "$(@D)/python_include/setobject.h" && cp "/usr/include/python2.7/listobject.h" "$(@D)/python_include/listobject.h" && cp "/usr/include/python2.7/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp "/usr/include/python2.7/pgen.h" "$(@D)/python_include/pgen.h" && cp "/usr/include/python2.7/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp "/usr/include/python2.7/opcode.h" "$(@D)/python_include/opcode.h" && cp "/usr/include/python2.7/parsetok.h" "$(@D)/python_include/parsetok.h" && cp "/usr/include/python2.7/marshal.h" "$(@D)/python_include/marshal.h" && cp "/usr/include/python2.7/token.h" "$(@D)/python_include/token.h" && cp "/usr/include/python2.7/iterobject.h" "$(@D)/python_include/iterobject.h" && cp "/usr/include/python2.7/abstract.h" "$(@D)/python_include/abstract.h" && cp "/usr/include/python2.7/py_curses.h" "$(@D)/python_include/py_curses.h" && cp "/usr/include/python2.7/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp "/usr/include/python2.7/bufferobject.h" "$(@D)/python_include/bufferobject.h" && cp "/usr/include/python2.7/Python-ast.h" "$(@D)/python_include/Python-ast.h" +cp "/usr/include/python2.7/Python-ast.h" "$(@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" "$(@D)/python_include/weakrefobject.h" """, ) genrule( name = "numpy_include", outs = [ - "numpy_include/numpy/oldnumeric.h", - "numpy_include/numpy/npy_1_7_deprecated_api.h", - "numpy_include/numpy/ufunc_api.txt", - "numpy_include/numpy/multiarray_api.txt", - "numpy_include/numpy/halffloat.h", - "numpy_include/numpy/npy_common.h", - "numpy_include/numpy/utils.h", - "numpy_include/numpy/npy_interrupt.h", - "numpy_include/numpy/npy_endian.h", + "numpy_include/numpy/__multiarray_api.h", "numpy_include/numpy/__ufunc_api.h", "numpy_include/numpy/_neighborhood_iterator_imp.h", - "numpy_include/numpy/ufuncobject.h", + "numpy_include/numpy/_numpyconfig.h", + "numpy_include/numpy/arrayobject.h", + "numpy_include/numpy/arrayscalars.h", + "numpy_include/numpy/halffloat.h", + "numpy_include/numpy/multiarray_api.txt", + "numpy_include/numpy/ndarrayobject.h", "numpy_include/numpy/ndarraytypes.h", - "numpy_include/numpy/npy_math.h", "numpy_include/numpy/noprefix.h", + "numpy_include/numpy/npy_1_7_deprecated_api.h", "numpy_include/numpy/npy_3kcompat.h", - "numpy_include/numpy/arrayscalars.h", - "numpy_include/numpy/npy_os.h", - "numpy_include/numpy/ndarrayobject.h", - "numpy_include/numpy/npy_no_deprecated_api.h", - "numpy_include/numpy/arrayobject.h", - "numpy_include/numpy/_numpyconfig.h", - "numpy_include/numpy/__multiarray_api.h", + "numpy_include/numpy/npy_common.h", "numpy_include/numpy/npy_cpu.h", - "numpy_include/numpy/old_defines.h", + "numpy_include/numpy/npy_endian.h", + "numpy_include/numpy/npy_interrupt.h", + "numpy_include/numpy/npy_math.h", + "numpy_include/numpy/npy_no_deprecated_api.h", + "numpy_include/numpy/npy_os.h", "numpy_include/numpy/numpyconfig.h", + "numpy_include/numpy/old_defines.h", + "numpy_include/numpy/oldnumeric.h", + "numpy_include/numpy/ufunc_api.txt", + "numpy_include/numpy/ufuncobject.h", + "numpy_include/numpy/utils.h", ], cmd = """ -cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h" +cp "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@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" "$(@D)/numpy_include/numpy/utils.h" """, ) diff --git a/third_party/toolchains/cpus/py3/BUILD b/third_party/toolchains/cpus/py3/BUILD index 932a25239f..28712a7cb1 100644 --- a/third_party/toolchains/cpus/py3/BUILD +++ b/third_party/toolchains/cpus/py3/BUILD @@ -6,20 +6,26 @@ licenses(["restricted"]) package(default_visibility = ["//visibility:public"]) +# To build Python C/C++ extension on Windows, we need to link to python import library pythonXY.lib +# See https://docs.python.org/3/extending/windows.html +cc_import( + name = "python_lib", + interface_library = select({ + ":windows": ":python_import_lib", + # A placeholder for Unix platforms which makes --no_build happy. + "//conditions:default": "not-existing.lib", + }), + system_provided = 1, +) + cc_library( name = "python_headers", hdrs = [":python_include"], - data = select({ - ":windows": [":python_import_lib"], + deps = select({ + ":windows": [":python_lib"], "//conditions:default": [], }), includes = ["python_include"], - linkopts = select({ - # TODO(pcloudy): Ideally, this should just go into deps after resolving - # https://github.com/bazelbuild/bazel/issues/3237, - ":windows": ["$(locations :python_import_lib)"], - "//conditions:default": [], - }), ) cc_library( @@ -37,143 +43,143 @@ config_setting( genrule( name = "python_include", outs = [ - "python_include/code.h", - "python_include/dtoa.h", - "python_include/tupleobject.h", - "python_include/object.h", - "python_include/ast.h", - "python_include/pymacconfig.h", - "python_include/errcode.h", - "python_include/frameobject.h", - "python_include/typeslots.h", - "python_include/pgenheaders.h", - "python_include/cellobject.h", - "python_include/pythread.h", - "python_include/boolobject.h", + "python_include/Python-ast.h", + "python_include/Python.h", + "python_include/abstract.h", "python_include/accu.h", - "python_include/modsupport.h", - "python_include/import.h", - "python_include/pymath.h", - "python_include/node.h", - "python_include/funcobject.h", - "python_include/eval.h", - "python_include/pyatomic.h", - "python_include/longintrepr.h", - "python_include/floatobject.h", - "python_include/rangeobject.h", - "python_include/pyfpe.h", - "python_include/pystrcmp.h", - "python_include/fileutils.h", - "python_include/dictobject.h", - "python_include/pyarena.h", - "python_include/osmodule.h", - "python_include/objimpl.h", + "python_include/asdl.h", + "python_include/ast.h", "python_include/bitset.h", - "python_include/memoryobject.h", + "python_include/bltinmodule.h", + "python_include/boolobject.h", "python_include/bytearrayobject.h", - "python_include/pydebug.h", - "python_include/pyerrors.h", - "python_include/weakrefobject.h", - "python_include/grammar.h", - "python_include/symtable.h", - "python_include/longobject.h", - "python_include/structmember.h", - "python_include/enumobject.h", - "python_include/pymacro.h", + "python_include/bytes_methods.h", + "python_include/bytesobject.h", + "python_include/cellobject.h", + "python_include/ceval.h", "python_include/classobject.h", - "python_include/unicodeobject.h", - "python_include/sliceobject.h", - "python_include/pystrtod.h", - "python_include/genobject.h", - "python_include/compile.h", - "python_include/pyexpat.h", - "python_include/asdl.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/dynamic_annotations.h", - "python_include/pyctype.h", - "python_include/sysmodule.h", - "python_include/methodobject.h", + "python_include/enumobject.h", + "python_include/errcode.h", + "python_include/eval.h", + "python_include/fileobject.h", + "python_include/fileutils.h", + "python_include/floatobject.h", + "python_include/frameobject.h", + "python_include/funcobject.h", + "python_include/genobject.h", "python_include/graminit.h", - "python_include/bltinmodule.h", + "python_include/grammar.h", + "python_include/import.h", "python_include/intrcheck.h", - "python_include/pyport.h", - "python_include/warnings.h", - "python_include/osdefs.h", - "python_include/pydtrace.h", - "python_include/pylifecycle.h", - "python_include/fileobject.h", - "python_include/pytime.h", - "python_include/traceback.h", - "python_include/ceval.h", - "python_include/bytes_methods.h", - "python_include/namespaceobject.h", - "python_include/pyconfig.h", - "python_include/Python.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/pystate.h", - "python_include/descrobject.h", + "python_include/namespaceobject.h", + "python_include/node.h", + "python_include/object.h", + "python_include/objimpl.h", "python_include/odictobject.h", - "python_include/ucnhash.h", + "python_include/opcode.h", + "python_include/osdefs.h", + "python_include/osmodule.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/pyatomic.h", + "python_include/pycapsule.h", + "python_include/pyconfig.h", + "python_include/pyctype.h", + "python_include/pydebug.h", + "python_include/pydtrace.h", + "python_include/pyerrors.h", + "python_include/pyexpat.h", + "python_include/pyfpe.h", "python_include/pygetopt.h", + "python_include/pyhash.h", + "python_include/pylifecycle.h", + "python_include/pymacconfig.h", + "python_include/pymacro.h", + "python_include/pymath.h", "python_include/pymem.h", - "python_include/complexobject.h", - "python_include/structseq.h", - "python_include/datetime.h", + "python_include/pyport.h", + "python_include/pystate.h", + "python_include/pystrcmp.h", + "python_include/pystrhex.h", + "python_include/pystrtod.h", "python_include/pythonrun.h", - "python_include/pyhash.h", - "python_include/pycapsule.h", + "python_include/pythread.h", + "python_include/pytime.h", + "python_include/rangeobject.h", "python_include/setobject.h", - "python_include/listobject.h", - "python_include/bytesobject.h", - "python_include/pgen.h", - "python_include/patchlevel.h", - "python_include/opcode.h", - "python_include/parsetok.h", - "python_include/pystrhex.h", - "python_include/marshal.h", + "python_include/sliceobject.h", + "python_include/structmember.h", + "python_include/structseq.h", + "python_include/symtable.h", + "python_include/sysmodule.h", "python_include/token.h", - "python_include/iterobject.h", - "python_include/abstract.h", - "python_include/py_curses.h", - "python_include/metagrammar.h", - "python_include/Python-ast.h", + "python_include/traceback.h", + "python_include/tupleobject.h", + "python_include/typeslots.h", + "python_include/ucnhash.h", + "python_include/unicodeobject.h", + "python_include/warnings.h", + "python_include/weakrefobject.h", ], cmd = """ -cp "/opt/python3.6/include/python3.6m/code.h" "$(@D)/python_include/code.h" && cp "/opt/python3.6/include/python3.6m/dtoa.h" "$(@D)/python_include/dtoa.h" && cp "/opt/python3.6/include/python3.6m/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp "/opt/python3.6/include/python3.6m/object.h" "$(@D)/python_include/object.h" && cp "/opt/python3.6/include/python3.6m/ast.h" "$(@D)/python_include/ast.h" && cp "/opt/python3.6/include/python3.6m/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp "/opt/python3.6/include/python3.6m/errcode.h" "$(@D)/python_include/errcode.h" && cp "/opt/python3.6/include/python3.6m/frameobject.h" "$(@D)/python_include/frameobject.h" && cp "/opt/python3.6/include/python3.6m/typeslots.h" "$(@D)/python_include/typeslots.h" && cp "/opt/python3.6/include/python3.6m/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp "/opt/python3.6/include/python3.6m/cellobject.h" "$(@D)/python_include/cellobject.h" && cp "/opt/python3.6/include/python3.6m/pythread.h" "$(@D)/python_include/pythread.h" && cp "/opt/python3.6/include/python3.6m/boolobject.h" "$(@D)/python_include/boolobject.h" && cp "/opt/python3.6/include/python3.6m/accu.h" "$(@D)/python_include/accu.h" && cp "/opt/python3.6/include/python3.6m/modsupport.h" "$(@D)/python_include/modsupport.h" && cp "/opt/python3.6/include/python3.6m/import.h" "$(@D)/python_include/import.h" && cp "/opt/python3.6/include/python3.6m/pymath.h" "$(@D)/python_include/pymath.h" && cp "/opt/python3.6/include/python3.6m/node.h" "$(@D)/python_include/node.h" && cp "/opt/python3.6/include/python3.6m/funcobject.h" "$(@D)/python_include/funcobject.h" && cp "/opt/python3.6/include/python3.6m/eval.h" "$(@D)/python_include/eval.h" && cp "/opt/python3.6/include/python3.6m/pyatomic.h" "$(@D)/python_include/pyatomic.h" && cp "/opt/python3.6/include/python3.6m/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp "/opt/python3.6/include/python3.6m/floatobject.h" "$(@D)/python_include/floatobject.h" && cp "/opt/python3.6/include/python3.6m/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp "/opt/python3.6/include/python3.6m/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp "/opt/python3.6/include/python3.6m/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp "/opt/python3.6/include/python3.6m/fileutils.h" "$(@D)/python_include/fileutils.h" && cp "/opt/python3.6/include/python3.6m/dictobject.h" "$(@D)/python_include/dictobject.h" && cp "/opt/python3.6/include/python3.6m/pyarena.h" "$(@D)/python_include/pyarena.h" && cp "/opt/python3.6/include/python3.6m/osmodule.h" "$(@D)/python_include/osmodule.h" && cp "/opt/python3.6/include/python3.6m/objimpl.h" "$(@D)/python_include/objimpl.h" && cp "/opt/python3.6/include/python3.6m/bitset.h" "$(@D)/python_include/bitset.h" && cp "/opt/python3.6/include/python3.6m/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp "/opt/python3.6/include/python3.6m/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp "/opt/python3.6/include/python3.6m/pydebug.h" "$(@D)/python_include/pydebug.h" && cp "/opt/python3.6/include/python3.6m/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp "/opt/python3.6/include/python3.6m/weakrefobject.h" "$(@D)/python_include/weakrefobject.h" && cp "/opt/python3.6/include/python3.6m/grammar.h" "$(@D)/python_include/grammar.h" && cp "/opt/python3.6/include/python3.6m/symtable.h" "$(@D)/python_include/symtable.h" && cp "/opt/python3.6/include/python3.6m/longobject.h" "$(@D)/python_include/longobject.h" && cp "/opt/python3.6/include/python3.6m/structmember.h" "$(@D)/python_include/structmember.h" && cp "/opt/python3.6/include/python3.6m/enumobject.h" "$(@D)/python_include/enumobject.h" && cp "/opt/python3.6/include/python3.6m/pymacro.h" "$(@D)/python_include/pymacro.h" && cp "/opt/python3.6/include/python3.6m/classobject.h" "$(@D)/python_include/classobject.h" && cp "/opt/python3.6/include/python3.6m/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp "/opt/python3.6/include/python3.6m/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp "/opt/python3.6/include/python3.6m/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp "/opt/python3.6/include/python3.6m/genobject.h" "$(@D)/python_include/genobject.h" && cp "/opt/python3.6/include/python3.6m/compile.h" "$(@D)/python_include/compile.h" && cp "/opt/python3.6/include/python3.6m/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp "/opt/python3.6/include/python3.6m/asdl.h" "$(@D)/python_include/asdl.h" && cp "/opt/python3.6/include/python3.6m/codecs.h" "$(@D)/python_include/codecs.h" && cp "/opt/python3.6/include/python3.6m/dynamic_annotations.h" "$(@D)/python_include/dynamic_annotations.h" && cp "/opt/python3.6/include/python3.6m/pyctype.h" "$(@D)/python_include/pyctype.h" && cp "/opt/python3.6/include/python3.6m/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp "/opt/python3.6/include/python3.6m/methodobject.h" "$(@D)/python_include/methodobject.h" && cp "/opt/python3.6/include/python3.6m/graminit.h" "$(@D)/python_include/graminit.h" && cp "/opt/python3.6/include/python3.6m/bltinmodule.h" "$(@D)/python_include/bltinmodule.h" && cp "/opt/python3.6/include/python3.6m/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp "/opt/python3.6/include/python3.6m/pyport.h" "$(@D)/python_include/pyport.h" && cp "/opt/python3.6/include/python3.6m/warnings.h" "$(@D)/python_include/warnings.h" && cp "/opt/python3.6/include/python3.6m/osdefs.h" "$(@D)/python_include/osdefs.h" && cp "/opt/python3.6/include/python3.6m/pydtrace.h" "$(@D)/python_include/pydtrace.h" && cp "/opt/python3.6/include/python3.6m/pylifecycle.h" "$(@D)/python_include/pylifecycle.h" && cp "/opt/python3.6/include/python3.6m/fileobject.h" "$(@D)/python_include/fileobject.h" && cp "/opt/python3.6/include/python3.6m/pytime.h" "$(@D)/python_include/pytime.h" && cp "/opt/python3.6/include/python3.6m/traceback.h" "$(@D)/python_include/traceback.h" && cp "/opt/python3.6/include/python3.6m/ceval.h" "$(@D)/python_include/ceval.h" && cp "/opt/python3.6/include/python3.6m/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp "/opt/python3.6/include/python3.6m/namespaceobject.h" "$(@D)/python_include/namespaceobject.h" && cp "/opt/python3.6/include/python3.6m/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp "/opt/python3.6/include/python3.6m/Python.h" "$(@D)/python_include/Python.h" && cp "/opt/python3.6/include/python3.6m/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp "/opt/python3.6/include/python3.6m/pystate.h" "$(@D)/python_include/pystate.h" && cp "/opt/python3.6/include/python3.6m/descrobject.h" "$(@D)/python_include/descrobject.h" && cp "/opt/python3.6/include/python3.6m/odictobject.h" "$(@D)/python_include/odictobject.h" && cp "/opt/python3.6/include/python3.6m/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp "/opt/python3.6/include/python3.6m/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp "/opt/python3.6/include/python3.6m/pymem.h" "$(@D)/python_include/pymem.h" && cp "/opt/python3.6/include/python3.6m/complexobject.h" "$(@D)/python_include/complexobject.h" && cp "/opt/python3.6/include/python3.6m/structseq.h" "$(@D)/python_include/structseq.h" && cp "/opt/python3.6/include/python3.6m/datetime.h" "$(@D)/python_include/datetime.h" && cp "/opt/python3.6/include/python3.6m/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp "/opt/python3.6/include/python3.6m/pyhash.h" "$(@D)/python_include/pyhash.h" && cp "/opt/python3.6/include/python3.6m/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp "/opt/python3.6/include/python3.6m/setobject.h" "$(@D)/python_include/setobject.h" && cp "/opt/python3.6/include/python3.6m/listobject.h" "$(@D)/python_include/listobject.h" && cp "/opt/python3.6/include/python3.6m/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp "/opt/python3.6/include/python3.6m/pgen.h" "$(@D)/python_include/pgen.h" && cp "/opt/python3.6/include/python3.6m/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp "/opt/python3.6/include/python3.6m/opcode.h" "$(@D)/python_include/opcode.h" && cp "/opt/python3.6/include/python3.6m/parsetok.h" "$(@D)/python_include/parsetok.h" && cp "/opt/python3.6/include/python3.6m/pystrhex.h" "$(@D)/python_include/pystrhex.h" && cp "/opt/python3.6/include/python3.6m/marshal.h" "$(@D)/python_include/marshal.h" && cp "/opt/python3.6/include/python3.6m/token.h" "$(@D)/python_include/token.h" && cp "/opt/python3.6/include/python3.6m/iterobject.h" "$(@D)/python_include/iterobject.h" && cp "/opt/python3.6/include/python3.6m/abstract.h" "$(@D)/python_include/abstract.h" && cp "/opt/python3.6/include/python3.6m/py_curses.h" "$(@D)/python_include/py_curses.h" && cp "/opt/python3.6/include/python3.6m/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp "/opt/python3.6/include/python3.6m/Python-ast.h" "$(@D)/python_include/Python-ast.h" +cp "/opt/python3.6/include/python3.6m/Python-ast.h" "$(@D)/python_include/Python-ast.h" && cp "/opt/python3.6/include/python3.6m/Python.h" "$(@D)/python_include/Python.h" && cp "/opt/python3.6/include/python3.6m/abstract.h" "$(@D)/python_include/abstract.h" && cp "/opt/python3.6/include/python3.6m/accu.h" "$(@D)/python_include/accu.h" && cp "/opt/python3.6/include/python3.6m/asdl.h" "$(@D)/python_include/asdl.h" && cp "/opt/python3.6/include/python3.6m/ast.h" "$(@D)/python_include/ast.h" && cp "/opt/python3.6/include/python3.6m/bitset.h" "$(@D)/python_include/bitset.h" && cp "/opt/python3.6/include/python3.6m/bltinmodule.h" "$(@D)/python_include/bltinmodule.h" && cp "/opt/python3.6/include/python3.6m/boolobject.h" "$(@D)/python_include/boolobject.h" && cp "/opt/python3.6/include/python3.6m/bytearrayobject.h" "$(@D)/python_include/bytearrayobject.h" && cp "/opt/python3.6/include/python3.6m/bytes_methods.h" "$(@D)/python_include/bytes_methods.h" && cp "/opt/python3.6/include/python3.6m/bytesobject.h" "$(@D)/python_include/bytesobject.h" && cp "/opt/python3.6/include/python3.6m/cellobject.h" "$(@D)/python_include/cellobject.h" && cp "/opt/python3.6/include/python3.6m/ceval.h" "$(@D)/python_include/ceval.h" && cp "/opt/python3.6/include/python3.6m/classobject.h" "$(@D)/python_include/classobject.h" && cp "/opt/python3.6/include/python3.6m/code.h" "$(@D)/python_include/code.h" && cp "/opt/python3.6/include/python3.6m/codecs.h" "$(@D)/python_include/codecs.h" && cp "/opt/python3.6/include/python3.6m/compile.h" "$(@D)/python_include/compile.h" && cp "/opt/python3.6/include/python3.6m/complexobject.h" "$(@D)/python_include/complexobject.h" && cp "/opt/python3.6/include/python3.6m/datetime.h" "$(@D)/python_include/datetime.h" && cp "/opt/python3.6/include/python3.6m/descrobject.h" "$(@D)/python_include/descrobject.h" && cp "/opt/python3.6/include/python3.6m/dictobject.h" "$(@D)/python_include/dictobject.h" && cp "/opt/python3.6/include/python3.6m/dtoa.h" "$(@D)/python_include/dtoa.h" && cp "/opt/python3.6/include/python3.6m/dynamic_annotations.h" "$(@D)/python_include/dynamic_annotations.h" && cp "/opt/python3.6/include/python3.6m/enumobject.h" "$(@D)/python_include/enumobject.h" && cp "/opt/python3.6/include/python3.6m/errcode.h" "$(@D)/python_include/errcode.h" && cp "/opt/python3.6/include/python3.6m/eval.h" "$(@D)/python_include/eval.h" && cp "/opt/python3.6/include/python3.6m/fileobject.h" "$(@D)/python_include/fileobject.h" && cp "/opt/python3.6/include/python3.6m/fileutils.h" "$(@D)/python_include/fileutils.h" && cp "/opt/python3.6/include/python3.6m/floatobject.h" "$(@D)/python_include/floatobject.h" && cp "/opt/python3.6/include/python3.6m/frameobject.h" "$(@D)/python_include/frameobject.h" && cp "/opt/python3.6/include/python3.6m/funcobject.h" "$(@D)/python_include/funcobject.h" && cp "/opt/python3.6/include/python3.6m/genobject.h" "$(@D)/python_include/genobject.h" && cp "/opt/python3.6/include/python3.6m/graminit.h" "$(@D)/python_include/graminit.h" && cp "/opt/python3.6/include/python3.6m/grammar.h" "$(@D)/python_include/grammar.h" && cp "/opt/python3.6/include/python3.6m/import.h" "$(@D)/python_include/import.h" && cp "/opt/python3.6/include/python3.6m/intrcheck.h" "$(@D)/python_include/intrcheck.h" && cp "/opt/python3.6/include/python3.6m/iterobject.h" "$(@D)/python_include/iterobject.h" && cp "/opt/python3.6/include/python3.6m/listobject.h" "$(@D)/python_include/listobject.h" && cp "/opt/python3.6/include/python3.6m/longintrepr.h" "$(@D)/python_include/longintrepr.h" && cp "/opt/python3.6/include/python3.6m/longobject.h" "$(@D)/python_include/longobject.h" && cp "/opt/python3.6/include/python3.6m/marshal.h" "$(@D)/python_include/marshal.h" && cp "/opt/python3.6/include/python3.6m/memoryobject.h" "$(@D)/python_include/memoryobject.h" && cp "/opt/python3.6/include/python3.6m/metagrammar.h" "$(@D)/python_include/metagrammar.h" && cp "/opt/python3.6/include/python3.6m/methodobject.h" "$(@D)/python_include/methodobject.h" && cp "/opt/python3.6/include/python3.6m/modsupport.h" "$(@D)/python_include/modsupport.h" && cp "/opt/python3.6/include/python3.6m/moduleobject.h" "$(@D)/python_include/moduleobject.h" && cp "/opt/python3.6/include/python3.6m/namespaceobject.h" "$(@D)/python_include/namespaceobject.h" && cp "/opt/python3.6/include/python3.6m/node.h" "$(@D)/python_include/node.h" && cp "/opt/python3.6/include/python3.6m/object.h" "$(@D)/python_include/object.h" && cp "/opt/python3.6/include/python3.6m/objimpl.h" "$(@D)/python_include/objimpl.h" && cp "/opt/python3.6/include/python3.6m/odictobject.h" "$(@D)/python_include/odictobject.h" && cp "/opt/python3.6/include/python3.6m/opcode.h" "$(@D)/python_include/opcode.h" && cp "/opt/python3.6/include/python3.6m/osdefs.h" "$(@D)/python_include/osdefs.h" && cp "/opt/python3.6/include/python3.6m/osmodule.h" "$(@D)/python_include/osmodule.h" && cp "/opt/python3.6/include/python3.6m/parsetok.h" "$(@D)/python_include/parsetok.h" && cp "/opt/python3.6/include/python3.6m/patchlevel.h" "$(@D)/python_include/patchlevel.h" && cp "/opt/python3.6/include/python3.6m/pgen.h" "$(@D)/python_include/pgen.h" && cp "/opt/python3.6/include/python3.6m/pgenheaders.h" "$(@D)/python_include/pgenheaders.h" && cp "/opt/python3.6/include/python3.6m/py_curses.h" "$(@D)/python_include/py_curses.h" && cp "/opt/python3.6/include/python3.6m/pyarena.h" "$(@D)/python_include/pyarena.h" && cp "/opt/python3.6/include/python3.6m/pyatomic.h" "$(@D)/python_include/pyatomic.h" && cp "/opt/python3.6/include/python3.6m/pycapsule.h" "$(@D)/python_include/pycapsule.h" && cp "/opt/python3.6/include/python3.6m/pyconfig.h" "$(@D)/python_include/pyconfig.h" && cp "/opt/python3.6/include/python3.6m/pyctype.h" "$(@D)/python_include/pyctype.h" && cp "/opt/python3.6/include/python3.6m/pydebug.h" "$(@D)/python_include/pydebug.h" && cp "/opt/python3.6/include/python3.6m/pydtrace.h" "$(@D)/python_include/pydtrace.h" && cp "/opt/python3.6/include/python3.6m/pyerrors.h" "$(@D)/python_include/pyerrors.h" && cp "/opt/python3.6/include/python3.6m/pyexpat.h" "$(@D)/python_include/pyexpat.h" && cp "/opt/python3.6/include/python3.6m/pyfpe.h" "$(@D)/python_include/pyfpe.h" && cp "/opt/python3.6/include/python3.6m/pygetopt.h" "$(@D)/python_include/pygetopt.h" && cp "/opt/python3.6/include/python3.6m/pyhash.h" "$(@D)/python_include/pyhash.h" && cp "/opt/python3.6/include/python3.6m/pylifecycle.h" "$(@D)/python_include/pylifecycle.h" && cp "/opt/python3.6/include/python3.6m/pymacconfig.h" "$(@D)/python_include/pymacconfig.h" && cp "/opt/python3.6/include/python3.6m/pymacro.h" "$(@D)/python_include/pymacro.h" && cp "/opt/python3.6/include/python3.6m/pymath.h" "$(@D)/python_include/pymath.h" && cp "/opt/python3.6/include/python3.6m/pymem.h" "$(@D)/python_include/pymem.h" && cp "/opt/python3.6/include/python3.6m/pyport.h" "$(@D)/python_include/pyport.h" && cp "/opt/python3.6/include/python3.6m/pystate.h" "$(@D)/python_include/pystate.h" && cp "/opt/python3.6/include/python3.6m/pystrcmp.h" "$(@D)/python_include/pystrcmp.h" && cp "/opt/python3.6/include/python3.6m/pystrhex.h" "$(@D)/python_include/pystrhex.h" && cp "/opt/python3.6/include/python3.6m/pystrtod.h" "$(@D)/python_include/pystrtod.h" && cp "/opt/python3.6/include/python3.6m/pythonrun.h" "$(@D)/python_include/pythonrun.h" && cp "/opt/python3.6/include/python3.6m/pythread.h" "$(@D)/python_include/pythread.h" && cp "/opt/python3.6/include/python3.6m/pytime.h" "$(@D)/python_include/pytime.h" && cp "/opt/python3.6/include/python3.6m/rangeobject.h" "$(@D)/python_include/rangeobject.h" && cp "/opt/python3.6/include/python3.6m/setobject.h" "$(@D)/python_include/setobject.h" && cp "/opt/python3.6/include/python3.6m/sliceobject.h" "$(@D)/python_include/sliceobject.h" && cp "/opt/python3.6/include/python3.6m/structmember.h" "$(@D)/python_include/structmember.h" && cp "/opt/python3.6/include/python3.6m/structseq.h" "$(@D)/python_include/structseq.h" && cp "/opt/python3.6/include/python3.6m/symtable.h" "$(@D)/python_include/symtable.h" && cp "/opt/python3.6/include/python3.6m/sysmodule.h" "$(@D)/python_include/sysmodule.h" && cp "/opt/python3.6/include/python3.6m/token.h" "$(@D)/python_include/token.h" && cp "/opt/python3.6/include/python3.6m/traceback.h" "$(@D)/python_include/traceback.h" && cp "/opt/python3.6/include/python3.6m/tupleobject.h" "$(@D)/python_include/tupleobject.h" && cp "/opt/python3.6/include/python3.6m/typeslots.h" "$(@D)/python_include/typeslots.h" && cp "/opt/python3.6/include/python3.6m/ucnhash.h" "$(@D)/python_include/ucnhash.h" && cp "/opt/python3.6/include/python3.6m/unicodeobject.h" "$(@D)/python_include/unicodeobject.h" && cp "/opt/python3.6/include/python3.6m/warnings.h" "$(@D)/python_include/warnings.h" && cp "/opt/python3.6/include/python3.6m/weakrefobject.h" "$(@D)/python_include/weakrefobject.h" """, ) genrule( name = "numpy_include", outs = [ - "numpy_include/numpy/oldnumeric.h", - "numpy_include/numpy/npy_1_7_deprecated_api.h", - "numpy_include/numpy/ufunc_api.txt", - "numpy_include/numpy/multiarray_api.txt", - "numpy_include/numpy/halffloat.h", - "numpy_include/numpy/npy_common.h", - "numpy_include/numpy/utils.h", - "numpy_include/numpy/npy_interrupt.h", - "numpy_include/numpy/npy_endian.h", + "numpy_include/numpy/__multiarray_api.h", "numpy_include/numpy/__ufunc_api.h", "numpy_include/numpy/_neighborhood_iterator_imp.h", - "numpy_include/numpy/ufuncobject.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/npy_math.h", "numpy_include/numpy/noprefix.h", + "numpy_include/numpy/npy_1_7_deprecated_api.h", "numpy_include/numpy/npy_3kcompat.h", - "numpy_include/numpy/arrayscalars.h", - "numpy_include/numpy/npy_os.h", - "numpy_include/numpy/ndarrayobject.h", - "numpy_include/numpy/npy_no_deprecated_api.h", - "numpy_include/numpy/arrayobject.h", - "numpy_include/numpy/_numpyconfig.h", - "numpy_include/numpy/__multiarray_api.h", + "numpy_include/numpy/npy_common.h", "numpy_include/numpy/npy_cpu.h", - "numpy_include/numpy/old_defines.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 "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h" +cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/__multiarray_api.h" "$(@D)/numpy_include/numpy/__multiarray_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/__ufunc_api.h" "$(@D)/numpy_include/numpy/__ufunc_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/_neighborhood_iterator_imp.h" "$(@D)/numpy_include/numpy/_neighborhood_iterator_imp.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/_numpyconfig.h" "$(@D)/numpy_include/numpy/_numpyconfig.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/arrayobject.h" "$(@D)/numpy_include/numpy/arrayobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/arrayscalars.h" "$(@D)/numpy_include/numpy/arrayscalars.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/halffloat.h" "$(@D)/numpy_include/numpy/halffloat.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/multiarray_api.txt" "$(@D)/numpy_include/numpy/multiarray_api.txt" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ndarrayobject.h" "$(@D)/numpy_include/numpy/ndarrayobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ndarraytypes.h" "$(@D)/numpy_include/numpy/ndarraytypes.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/noprefix.h" "$(@D)/numpy_include/numpy/noprefix.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_1_7_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_1_7_deprecated_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_3kcompat.h" "$(@D)/numpy_include/numpy/npy_3kcompat.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_common.h" "$(@D)/numpy_include/numpy/npy_common.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_cpu.h" "$(@D)/numpy_include/numpy/npy_cpu.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_endian.h" "$(@D)/numpy_include/numpy/npy_endian.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_interrupt.h" "$(@D)/numpy_include/numpy/npy_interrupt.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_math.h" "$(@D)/numpy_include/numpy/npy_math.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_no_deprecated_api.h" "$(@D)/numpy_include/numpy/npy_no_deprecated_api.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/npy_os.h" "$(@D)/numpy_include/numpy/npy_os.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/numpyconfig.h" "$(@D)/numpy_include/numpy/numpyconfig.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/old_defines.h" "$(@D)/numpy_include/numpy/old_defines.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/oldnumeric.h" "$(@D)/numpy_include/numpy/oldnumeric.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ufunc_api.txt" "$(@D)/numpy_include/numpy/ufunc_api.txt" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/ufuncobject.h" "$(@D)/numpy_include/numpy/ufuncobject.h" && cp "/opt/python3.6/lib/python3.6/site-packages/numpy/core/include/numpy/utils.h" "$(@D)/numpy_include/numpy/utils.h" """, ) -- GitLab From f283e65a1bdb797070be9b84a69ef323268f7c3c Mon Sep 17 00:00:00 2001 From: Tom Hennigan Date: Tue, 5 Jun 2018 03:56:47 -0700 Subject: [PATCH 041/519] Handle scalar input to assert_equal in eager. PiperOrigin-RevId: 199274329 --- tensorflow/python/kernel_tests/check_ops_test.py | 7 +++++++ tensorflow/python/ops/check_ops.py | 4 ++-- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/kernel_tests/check_ops_test.py b/tensorflow/python/kernel_tests/check_ops_test.py index 5a83ec8d30..7ef841c96b 100644 --- a/tensorflow/python/kernel_tests/check_ops_test.py +++ b/tensorflow/python/kernel_tests/check_ops_test.py @@ -88,6 +88,13 @@ class AssertEqualTest(test.TestCase): out = array_ops.identity(small) self.evaluate(out) + @test_util.run_in_graph_and_eager_modes() + def test_scalar_comparison(self): + const_true = constant_op.constant(True, name="true") + const_false = constant_op.constant(False, name="false") + with self.assertRaisesRegexp(errors.InvalidArgumentError, "fail"): + check_ops.assert_equal(const_true, const_false, message="fail") + def test_returns_none_with_eager(self): with context.eager_mode(): small = constant_op.constant([1, 2], name="small") diff --git a/tensorflow/python/ops/check_ops.py b/tensorflow/python/ops/check_ops.py index cabc1e724c..375a5ec2c3 100644 --- a/tensorflow/python/ops/check_ops.py +++ b/tensorflow/python/ops/check_ops.py @@ -341,8 +341,8 @@ def assert_equal(x, y, data=None, summarize=None, message=None, name=None): y_sum, y_np[:y_sum])) index_and_values_str = '' - if x.shape == y.shape: - # If the shapes of x and y are the same, + if x.shape == y.shape and x.shape.as_list(): + # If the shapes of x and y are the same (and not scalars), # Get the values that actually differed and their indices. # If shapes are different this information is more confusing # than useful. -- GitLab From e71f9b863097086c91b2a3f5aea1e081f275ceca Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 21 Jun 2018 18:53:05 -0700 Subject: [PATCH 042/519] Update Eigen version to commit e5e305a158a029f5b5f837bf821411a51439a970. PiperOrigin-RevId: 201624024 --- .../distributions/dirichlet_multinomial_test.py | 6 +++--- tensorflow/workspace.bzl | 8 ++++---- 2 files changed, 7 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py b/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py index 7922fb0606..daea699514 100644 --- a/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py +++ b/tensorflow/python/kernel_tests/distributions/dirichlet_multinomial_test.py @@ -250,9 +250,9 @@ class DirichletMultinomialTest(test.TestCase): dist.variance(), dist.stddev(), ]) - self.assertAllClose(sample_mean_, analytic_mean, atol=0., rtol=0.04) - self.assertAllClose(sample_cov_, analytic_cov, atol=0., rtol=0.05) - self.assertAllClose(sample_var_, analytic_var, atol=0., rtol=0.05) + self.assertAllClose(sample_mean_, analytic_mean, atol=0., rtol=0.06) + self.assertAllClose(sample_cov_, analytic_cov, atol=0., rtol=0.07) + self.assertAllClose(sample_var_, analytic_var, atol=0., rtol=0.07) self.assertAllClose(sample_stddev_, analytic_stddev, atol=0., rtol=0.02) def testCovariance(self): diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 50a69598a1..43152c88cf 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -107,11 +107,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "eigen_archive", urls = [ - "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/6913f0cf7d06.tar.gz", - "https://bitbucket.org/eigen/eigen/get/6913f0cf7d06.tar.gz", + "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/e5e305a158a0.tar.gz", + "https://bitbucket.org/eigen/eigen/get/e5e305a158a0.tar.gz", ], - sha256 = "791b836cacd03e20bae5bdd25f1c4a5505a0a9975ba94a61eb4e2631fbd1d53a", - strip_prefix = "eigen-eigen-6913f0cf7d06", + sha256 = "8bbe676d69e7f59070c83a949454b8b6344034e0ebbf686b337528e5dc04c7de", + strip_prefix = "eigen-eigen-e5e305a158a0", build_file = clean_dep("//third_party:eigen.BUILD"), patch_file = clean_dep("//third_party:eigen_fix_cuda_compilation.patch") ) -- GitLab From 5c450d2e1d0d3a1abae4997df0da1b8d73684e01 Mon Sep 17 00:00:00 2001 From: Rasmus Munk Larsen Date: Fri, 22 Jun 2018 13:36:57 -0700 Subject: [PATCH 043/519] Update workspace.bzl --- tensorflow/workspace.bzl | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 43152c88cf..3c657c4a5b 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -113,7 +113,6 @@ def tf_workspace(path_prefix="", tf_repo_name=""): sha256 = "8bbe676d69e7f59070c83a949454b8b6344034e0ebbf686b337528e5dc04c7de", strip_prefix = "eigen-eigen-e5e305a158a0", build_file = clean_dep("//third_party:eigen.BUILD"), - patch_file = clean_dep("//third_party:eigen_fix_cuda_compilation.patch") ) tf_http_archive( -- GitLab From df2c8315211895afab0d7ba1ff64e831d9d3ce3b Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 19 Jun 2018 23:11:00 -0700 Subject: [PATCH 044/519] Get started landing page. Move "Datasets Quickstart" to "Datasets for Estimators" under guide. PiperOrigin-RevId: 201301717 --- tensorflow/docs_src/get_started/_index.yaml | 255 ++++++++++++++++++ .../get_started/basic_classification.md | 3 + .../docs_src/get_started/basic_regression.md | 3 + .../get_started/basic_text_classification.md | 3 + tensorflow/docs_src/get_started/eager.md | 2 +- tensorflow/docs_src/get_started/index.md | 29 -- tensorflow/docs_src/get_started/leftnav_files | 12 +- tensorflow/docs_src/get_started/next_steps.md | 36 +++ .../get_started/overfit_and_underfit.md | 3 + .../get_started/save_and_restore_models.md | 3 + tensorflow/docs_src/install/install_linux.md | 8 +- tensorflow/docs_src/install/install_mac.md | 6 +- .../docs_src/install/install_raspbian.md | 6 +- .../docs_src/install/install_sources.md | 2 +- .../docs_src/install/install_windows.md | 7 +- .../datasets_for_estimators.md} | 2 +- .../docs_src/programmers_guide/index.md | 1 + .../docs_src/programmers_guide/leftnav_files | 1 + .../programmers_guide/premade_estimators.md | 8 +- tensorflow/docs_src/tutorials/index.md | 5 +- 20 files changed, 329 insertions(+), 66 deletions(-) create mode 100644 tensorflow/docs_src/get_started/_index.yaml create mode 100644 tensorflow/docs_src/get_started/basic_classification.md create mode 100644 tensorflow/docs_src/get_started/basic_regression.md create mode 100644 tensorflow/docs_src/get_started/basic_text_classification.md delete mode 100644 tensorflow/docs_src/get_started/index.md create mode 100644 tensorflow/docs_src/get_started/next_steps.md create mode 100644 tensorflow/docs_src/get_started/overfit_and_underfit.md create mode 100644 tensorflow/docs_src/get_started/save_and_restore_models.md rename tensorflow/docs_src/{get_started/datasets_quickstart.md => programmers_guide/datasets_for_estimators.md} (99%) diff --git a/tensorflow/docs_src/get_started/_index.yaml b/tensorflow/docs_src/get_started/_index.yaml new file mode 100644 index 0000000000..af255a482d --- /dev/null +++ b/tensorflow/docs_src/get_started/_index.yaml @@ -0,0 +1,255 @@ +project_path: /_project.yaml +book_path: /_book.yaml +description: +landing_page: + show_side_navs: True + rows: + - description: > +

Get Started with TensorFlow

+

+ TensorFlow is an open-source machine learning library for research and + production. TensorFlow offers APIs for beginners and experts to develop + for desktop, mobile, web, and cloud. See the sections below to get + started. +

+ items: + - custom_html: > + +
+ +

Learn and use ML

+ +
+

+ The high-level Keras API provides building blocks to create and + train deep learning models. Start with these beginner-friendly + notebook examples, then read the + TensorFlow Keras guide. +

+
    +
  1. Basic classification
    2. +
  2. Text classification
    3. +
  3. Regression
    4. +
  4. Overfitting and underfitting
    5. +
  5. Save and load
    6. +
+
+
+ Read the Keras guide +
+
+ - classname: tfo-landing-row-item-code-block + code_block: | + 
+        import tensorflow as tf
+        mnist = tf.keras.datasets.mnist
+
+        (x_train, y_train),(x_test, y_test) = mnist.load_data()
+        x_train, x_test = x_train / 255.0, x_test / 255.0
+
+        model = tf.keras.models.Sequential([
+          tf.keras.layers.Flatten(),
+          tf.keras.layers.Dense(512, activation=tf.nn.relu),
+          tf.keras.layers.Dropout(0.2),
+          tf.keras.layers.Dense(10, activation=tf.nn.softmax)
+        ])
+        model.compile(optimizer='adam',
+                      loss='sparse_categorical_crossentropy',
+                      metrics=['accuracy'])
+
+        model.fit(x_train, y_train, epochs=5)
+        model.evaluate(x_test, y_test)
+
+ {% dynamic if request.tld != 'cn' %} + Run in a Notebook + {% dynamic endif %} + + - items: + - custom_html: > +
+ +

Research and experimentation

+ +
+

+ Eager execution provides an imperative, define-by-run interface for advanced operations. Write custom layers, forward passes, and training loops with auto‑differentiation. Start with + these notebooks, then read the eager execution guide. +

+
    +
  1. + {% dynamic if request.tld == 'cn' %} + Eager execution basics + {% dynamic else %} + Eager execution basics + {% dynamic endif %} +
    2. +
  2. + {% dynamic if request.tld == 'cn' %} + Automatic differentiation and gradient tapes + {% dynamic else %} + Automatic differentiation and gradient tapes + {% dynamic endif %} +
    3. +
  3. + {% dynamic if request.tld == 'cn' %} + Variables, models, and training + {% dynamic else %} + Variables, models, and training + {% dynamic endif %} +
    4. +
  4. + {% dynamic if request.tld == 'cn' %} + Custom layers + {% dynamic else %} + Custom layers + {% dynamic endif %} +
    5. +
  5. Custom training walkthrough
    6. +
  6. + {% dynamic if request.tld == 'cn' %} + Example: Neural machine translation w/ attention + {% dynamic else %} + Example: Neural machine translation w/ attention + {% dynamic endif %} +
    7. +
+
+
+ Read the eager execution guide +
+
+ - custom_html: > +
+ +

ML at production scale

+ +
+

+ Estimators can train large models on multiple machines in a + production environment. Try the examples below and read the + Estimators guide. +

+
    +
  1. How to build a simple text classifier with TF-Hub
    2. +
  2. Classifying Higgs boson processes
    3. +
  3. Wide and deep learning using estimators
    4. +
+
+
+ Read the Estimators guide +
+
+ + - description: > +

Google Colab: An easy way to learn and use TensorFlow

+

+ Colaboratory + is a Google research project created to help disseminate machine learning + education and research. It's a Jupyter notebook environment that requires + no setup to use and runs entirely in the cloud. + Read the blog post. +

+ + - description: > +

Build your first ML app

+

Create and deploy TensorFlow models on web and mobile.

+ background: grey + items: + - custom_html: > +
+ +

Web developers

+ +
+ TensorFlow.js is a WebGL accelerated, JavaScript library to train and + deploy ML models in the browser and for Node.js. +
+
+ - custom_html: > +
+ +

Mobile developers

+ +
+ TensorFlow Lite is lightweight solution for mobile and embedded devices. +
+
+ + - description: > +

Videos and updates

+

+ Subscribe to the TensorFlow + YouTube channel + and blog for + the latest videos and updates. +

+ items: + - description: > +

Get started with TensorFlow's High-Level APIs

+ youtube_id: tjsHSIG8I08 + buttons: + - label: Watch the video + path: https://www.youtube.com/watch?v=tjsHSIG8I08 + - description: > +

Eager execution

+ youtube_id: T8AW0fKP0Hs + background: grey + buttons: + - label: Watch the video + path: https://www.youtube.com/watch?v=T8AW0fKP0Hs + - description: > +

tf.data: Fast, flexible, and easy-to-use input pipelines

+ youtube_id: uIcqeP7MFH0 + buttons: + - label: Watch the video + path: https://www.youtube.com/watch?v=uIcqeP7MFH0 diff --git a/tensorflow/docs_src/get_started/basic_classification.md b/tensorflow/docs_src/get_started/basic_classification.md new file mode 100644 index 0000000000..91bbd85b24 --- /dev/null +++ b/tensorflow/docs_src/get_started/basic_classification.md @@ -0,0 +1,3 @@ +# Basic Classification + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_classification.ipynb) diff --git a/tensorflow/docs_src/get_started/basic_regression.md b/tensorflow/docs_src/get_started/basic_regression.md new file mode 100644 index 0000000000..a535f22f5a --- /dev/null +++ b/tensorflow/docs_src/get_started/basic_regression.md @@ -0,0 +1,3 @@ +# Basic Regression + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_regression.ipynb) diff --git a/tensorflow/docs_src/get_started/basic_text_classification.md b/tensorflow/docs_src/get_started/basic_text_classification.md new file mode 100644 index 0000000000..7c5d4f7896 --- /dev/null +++ b/tensorflow/docs_src/get_started/basic_text_classification.md @@ -0,0 +1,3 @@ +# Basic Text Classification + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/basic_text_classification.ipynb) diff --git a/tensorflow/docs_src/get_started/eager.md b/tensorflow/docs_src/get_started/eager.md index bbb25e20c6..ddf239485a 100644 --- a/tensorflow/docs_src/get_started/eager.md +++ b/tensorflow/docs_src/get_started/eager.md @@ -1,3 +1,3 @@ -# Get Started with Eager Execution +# Custom Training Walkthrough [Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.9.0/samples/core/get_started/eager.ipynb) diff --git a/tensorflow/docs_src/get_started/index.md b/tensorflow/docs_src/get_started/index.md deleted file mode 100644 index 232d2f1547..0000000000 --- a/tensorflow/docs_src/get_started/index.md +++ /dev/null @@ -1,29 +0,0 @@ -# Get Started - -If you are new to machine learning, we recommend taking the following online -course prior to diving into TensorFlow documentation: - - * [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/), - which introduces machine learning concepts and encourages experimentation - with existing TensorFlow code. - -TensorFlow is a tool for machine learning. While it contains a wide range of -functionality, TensorFlow is mainly designed for deep neural network models. - -The easiest way to get started with TensorFlow is by using Eager Execution. - - * @{$get_started/eager}, is for anyone new to machine learning or TensorFlow. - -TensorFlow provides many APIs. The remainder of this section focuses on the -Estimator API which provide scalable, high-performance models. See the -@{$estimators} guide. - -For more advanced users: - - * The @{$low_level_intro$Low Level Introduction} demonstrates how to use - TensorFlow outside of the Estimator framework, for debugging and - experimentation. - * The @{$programmers_guide$Programmer's Guide} details major - TensorFlow components. - * The @{$tutorials$Tutorials} provide walkthroughs of a variety of - TensorFlow models. diff --git a/tensorflow/docs_src/get_started/leftnav_files b/tensorflow/docs_src/get_started/leftnav_files index e6cc8d5658..9a60496cb5 100644 --- a/tensorflow/docs_src/get_started/leftnav_files +++ b/tensorflow/docs_src/get_started/leftnav_files @@ -1,4 +1,10 @@ -index.md +### Learn and use ML +basic_classification.md +basic_text_classification.md +basic_regression.md +overfit_and_underfit.md +save_and_restore_models.md +next_steps.md -eager.md -datasets_quickstart.md +### Research and experimentation +custom_training_walkthrough.md diff --git a/tensorflow/docs_src/get_started/next_steps.md b/tensorflow/docs_src/get_started/next_steps.md new file mode 100644 index 0000000000..79c0ef3346 --- /dev/null +++ b/tensorflow/docs_src/get_started/next_steps.md @@ -0,0 +1,36 @@ +# Next Steps + +## Learn more about TensorFlow + +* The [TensorFlow Guide](/programmers_guide) includes usage guides for the + high-level APIs, as well as advanced TensorFlow operations. +* [Premade Estimators](/programmers_guide/premade_estimators) are designed to + get results out of the box. Use TensorFlow without building your own models. +* [TensorFlow.js](https://js.tensorflow.org/) allows web developers to train and + deploy ML models in the browser and using Node.js. +* [TFLite](/mobile/tflite) allows mobile developers to do inference efficiently + on mobile devices. +* [TensorFlow Serving](/serving) is an open-source project that can put + TensorFlow models in production quickly. +* The [ecosystem](/ecosystem) contains more projects, including + [Magenta](https://magenta.tensorflow.org/), [TFX](/tfx), + [Swift for TensorFlow](https://github.com/tensorflow/swift), and more. + +## Learn more about machine learning + +Recommended resources include: + +* [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/), + a course from Google that introduces machine learning concepts. +* [CS 20: Tensorflow for Deep Learning Research](http://web.stanford.edu/class/cs20si/), + notes from an intro course from Stanford. +* [CS231n: Convolutional Neural Networks for Visual Recognition](http://cs231n.stanford.edu/), + a course that teaches how convolutional networks work. +* [Machine Learning Recipes](https://www.youtube.com/watch?v=cKxRvEZd3Mw&list=PLOU2XLYxmsIIuiBfYad6rFYQU_jL2ryal), + a video series that introduces basic machine learning concepts with few prerequisites. +* [Deep Learning with Python](https://www.manning.com/books/deep-learning-with-python), + a book by Francois Chollet about the Keras API, as well as an excellent hands on intro to Deep Learning. +* [Hands-on Machine Learning with Scikit-Learn and TensorFlow](https://github.com/ageron/handson-ml), + a book by Aurélien Geron's that is a clear getting-started guide to data science and deep learning. +* [Deep Learning](https://www.deeplearningbook.org/), a book by Ian Goodfellow et al. + that provides a technical dive into learning machine learning. diff --git a/tensorflow/docs_src/get_started/overfit_and_underfit.md b/tensorflow/docs_src/get_started/overfit_and_underfit.md new file mode 100644 index 0000000000..e5b5ae7b5a --- /dev/null +++ b/tensorflow/docs_src/get_started/overfit_and_underfit.md @@ -0,0 +1,3 @@ +# Overfitting and Underfitting + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/overfit_and_underfit.ipynb) diff --git a/tensorflow/docs_src/get_started/save_and_restore_models.md b/tensorflow/docs_src/get_started/save_and_restore_models.md new file mode 100644 index 0000000000..44b3772945 --- /dev/null +++ b/tensorflow/docs_src/get_started/save_and_restore_models.md @@ -0,0 +1,3 @@ +# Save and restore Models + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/save_and_restore_models.ipynb) diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 9baf6870be..41619ca230 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -491,13 +491,7 @@ TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -If you are new to machine learning, we recommend the following: - -* [Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course) -* @{$get_started/eager} - -If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/eager}. +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). ## TensorFlow GPU support diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 693254f876..eeca389617 100644 --- a/tensorflow/docs_src/install/install_mac.md +++ b/tensorflow/docs_src/install/install_mac.md @@ -403,11 +403,7 @@ writing TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -If you are new to machine learning, we recommend the -[Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course). - -If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/eager}. +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). ## Common installation problems diff --git a/tensorflow/docs_src/install/install_raspbian.md b/tensorflow/docs_src/install/install_raspbian.md index 2f425162a1..0caab6d335 100644 --- a/tensorflow/docs_src/install/install_raspbian.md +++ b/tensorflow/docs_src/install/install_raspbian.md @@ -230,11 +230,7 @@ problems, despite the log message. If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -If you are new to machine learning, we recommend the [Machine Learning Crash -Course](https://developers.google.com/machine-learning/crash-course). - -If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/eager}. +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). ## Common installation problems diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 70e97cf556..7afcd340aa 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -362,7 +362,7 @@ TensorFlow programs: 
Hello, TensorFlow!
-If you are new to TensorFlow, see @{$get_started/eager}. +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md index 6c4f5b85ab..7fe94f0bc3 100644 --- a/tensorflow/docs_src/install/install_windows.md +++ b/tensorflow/docs_src/install/install_windows.md @@ -157,12 +157,7 @@ TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -If you are new to machine learning, we recommend the -[Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course). - -If you are experienced with machine learning but new to TensorFlow, see -@{$get_started/eager}. - +To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). ## Common installation problems diff --git a/tensorflow/docs_src/get_started/datasets_quickstart.md b/tensorflow/docs_src/programmers_guide/datasets_for_estimators.md similarity index 99% rename from tensorflow/docs_src/get_started/datasets_quickstart.md rename to tensorflow/docs_src/programmers_guide/datasets_for_estimators.md index 020e40dd3b..345a31b985 100644 --- a/tensorflow/docs_src/get_started/datasets_quickstart.md +++ b/tensorflow/docs_src/programmers_guide/datasets_for_estimators.md @@ -1,4 +1,4 @@ -# Datasets Quick Start +# Datasets for Estimators The @{tf.data} module contains a collection of classes that allows you to easily load data, manipulate it, and pipe it into your model. This document diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/programmers_guide/index.md index 0c2d4afb11..9c58a3b45e 100644 --- a/tensorflow/docs_src/programmers_guide/index.md +++ b/tensorflow/docs_src/programmers_guide/index.md @@ -22,6 +22,7 @@ works. The units are as follows: design yourself. * @{$feature_columns}, which shows how an Estimator can handle a variety of input data types without changes to the model. +* @{$datasets_for_estimators} describes using tf.data with estimators. * @{$checkpoints}, which explains how to save training progress and resume where you left off. diff --git a/tensorflow/docs_src/programmers_guide/leftnav_files b/tensorflow/docs_src/programmers_guide/leftnav_files index 3bcf864e13..357a2a1cb9 100644 --- a/tensorflow/docs_src/programmers_guide/leftnav_files +++ b/tensorflow/docs_src/programmers_guide/leftnav_files @@ -10,6 +10,7 @@ estimators.md: Introduction to Estimators premade_estimators.md custom_estimators.md feature_columns.md +datasets_for_estimators.md checkpoints.md ### Accelerators diff --git a/tensorflow/docs_src/programmers_guide/premade_estimators.md b/tensorflow/docs_src/programmers_guide/premade_estimators.md index f6dd75eaca..02e2caf64b 100644 --- a/tensorflow/docs_src/programmers_guide/premade_estimators.md +++ b/tensorflow/docs_src/programmers_guide/premade_estimators.md @@ -81,7 +81,7 @@ We strongly recommend writing TensorFlow programs with the following APIs: * @{$programmers_guide/estimators$Estimators}, which represent a complete model. The Estimator API provides methods to train the model, to judge the model's accuracy, and to generate predictions. -* @{$get_started/datasets_quickstart$Datasets}, which build a data input +* @{$programmers_guide/datasets_for_estimators}, which build a data input pipeline. The Dataset API has methods to load and manipulate data, and feed it into your model. The Dataset API meshes well with the Estimators API. @@ -424,9 +424,7 @@ Now that you've gotten started writing TensorFlow programs, consider the following material: * @{$checkpoints$Checkpoints} to learn how to save and restore models. -* @{$get_started/datasets_quickstart$Datasets} to learn more about importing - data into your - model. +* @{$programmers_guide/datasets_for_estimators} to learn more about importing + data into your model. * @{$custom_estimators$Creating Custom Estimators} to learn how to write your own Estimator, customized for a particular problem. - diff --git a/tensorflow/docs_src/tutorials/index.md b/tensorflow/docs_src/tutorials/index.md index af01d3eaa1..6bd3a3a897 100644 --- a/tensorflow/docs_src/tutorials/index.md +++ b/tensorflow/docs_src/tutorials/index.md @@ -2,9 +2,8 @@ This section contains tutorials demonstrating how to do specific tasks -in TensorFlow. If you are new to TensorFlow, we recommend reading the -documents in the "@{$get_started$Get Started}" section before reading -these tutorials. +in TensorFlow. If you are new to TensorFlow, we recommend reading +[Get Started with TensorFlow](/get_started/). ## Images -- GitLab From d9e006e80990e54913c25de70a1f8e7db2f22bc8 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Wed, 20 Jun 2018 10:02:25 -0700 Subject: [PATCH 045/519] Fix eager path in get_started leftnav PiperOrigin-RevId: 201370156 --- tensorflow/docs_src/get_started/leftnav_files | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/get_started/leftnav_files b/tensorflow/docs_src/get_started/leftnav_files index 9a60496cb5..5c400a67f0 100644 --- a/tensorflow/docs_src/get_started/leftnav_files +++ b/tensorflow/docs_src/get_started/leftnav_files @@ -7,4 +7,4 @@ save_and_restore_models.md next_steps.md ### Research and experimentation -custom_training_walkthrough.md +eager.md -- GitLab From 4e0b1612e0a71b0e14da2bc37c49e3d65744342c Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Fri, 22 Jun 2018 15:37:58 -0700 Subject: [PATCH 046/519] Add Install Raspbian to leftnav. PiperOrigin-RevId: 201752380 --- tensorflow/docs_src/install/leftnav_files | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/docs_src/install/leftnav_files b/tensorflow/docs_src/install/leftnav_files index e523e06f67..ace275c0e8 100644 --- a/tensorflow/docs_src/install/leftnav_files +++ b/tensorflow/docs_src/install/leftnav_files @@ -4,6 +4,7 @@ index.md install_linux.md: Ubuntu install_mac.md: MacOS install_windows.md: Windows +install_raspbian.md: Raspbian install_sources.md: From source >>> migration.md -- GitLab From 2897538b938dcd6d9c63a97f0870232ac9e4819e Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Mon, 25 Jun 2018 12:48:40 -0700 Subject: [PATCH 047/519] Update r1.9 release notes. - link to new get_started. - Add keras CuDNN layers. - Links for gradient boosted estimators. - Added new contrib-estimators and string-processing. - Bumped some minor sounding things down from "Major" to "Bugfix+Other" --- RELEASE.md | 35 +++++++++++++++++++++++++++-------- 1 file changed, 27 insertions(+), 8 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 510eca5467..bfe0da8739 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,18 +1,37 @@ # Release 1.9.0 ## Major Features And Improvements -* Update tf.keras to the Keras 2.1.6 API. +* New `tf.keras` based [get_started](http://tensorflow.org/versions/r1.9/get_started) +* Update `tf.keras` to the Keras 2.1.6 API. +* Added [`tf.keras.layers.CuDNNGRU`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNGRU) and [`tf.keras.layers.CuDNNLSTM`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNLSTM) layers. [Try it](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb?linkId=53292082). +* Adding support of core [feature columns](https://www.tensorflow.org/get_started/feature_columns) and [losses](https://www.tensorflow.org/api_docs/python/tf/losses) to [gradient boosted trees estimators](https://github.com/tensorflow/models/tree/master/official/boosted_trees). +* The [python interface](https://tensorflow-dot-devsite.googleplex.com/versions/r1.9/api_docs/python/tf/contrib/lite) + for the [TFLite Optimizing Converter](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/toco/README.md) + has been expanded, and the command line interface (AKA: `toco`, `tflite_convert`) is once again + included in the standard `pip` installation. +* Improved data-loading and text processing with: + * [`tf.decode_compressed`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/decode_compressed) + * [`tf.string_strip`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/string_strip) + * [`tf.strings.regex_full_match`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/strings/regex_full_match) +* Added experimental support for new pre-made Estimators: + * [`tf.contrib.estimator.BaselineEstimator`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/BaselineEstimator) + * [`tf.contrib.estimator.RNNClassifier`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/RNNEstimator) + * [`tf.contrib.estimator.RNNEstimator`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/estimator/RNNClassifier) +* The [distributions.Bijector](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/distributions/bijectors/Bijector) + API supports broadcasting for Bijectors with new API changes. + +## Breaking Chances + * If you're opening empty variable scopes; replace `variable_scope('', ...)` by + `variable_scope(tf.get_variable_scope(), ...)`. + +## Bug Fixes and Other Changes + * `tfe.Network` is deprecated. Please inherit from `tf.keras.Model`. -* Adding support of core feature columns and losses to gradient boosted trees estimators. -* The distributions.Bijector API supports broadcasting for Bijectors with new API changes. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/distributions/bijectors/Bijector) for more details. * Layered variable names have changed in the following conditions: * Using `tf.keras.layers` with custom variable scopes. - * Using `tf.layers` in a subclassed `tf.keras.Model` class. See [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details + * Using `tf.layers` in a subclassed `tf.keras.Model` class. See + [here](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/layers) for more details -## Breaking Chances - * If you're opening empty variable scopes; replace `variable_scope`('', ...) by `variable_scope`(`tf.get_variable_scope()`, ...). - -## Bug Fixes and Other Changes * `tf.data`: * `Dataset.from_generator()` now accepts an `args` list, in order to create nested generators. * `Dataset.list_files()` now produces determinstic results when `shuffle=False` or a `seed` is passed. -- GitLab From 56fba15b868145f87109bd5cb155527b0c0640d1 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Mon, 25 Jun 2018 13:16:52 -0700 Subject: [PATCH 048/519] Update RELEASE.md --- RELEASE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/RELEASE.md b/RELEASE.md index bfe0da8739..f6a52a2951 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,7 +1,7 @@ # Release 1.9.0 ## Major Features And Improvements -* New `tf.keras` based [get_started](http://tensorflow.org/versions/r1.9/get_started) +* New `tf.keras` based [get_started](http://tensorflow.org/versions/r1.9/get_started), and [programmers_guide](http://tensorflow.org/versions/r1.9/programmers_guide/keras). * Update `tf.keras` to the Keras 2.1.6 API. * Added [`tf.keras.layers.CuDNNGRU`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNGRU) and [`tf.keras.layers.CuDNNLSTM`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNLSTM) layers. [Try it](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb?linkId=53292082). * Adding support of core [feature columns](https://www.tensorflow.org/get_started/feature_columns) and [losses](https://www.tensorflow.org/api_docs/python/tf/losses) to [gradient boosted trees estimators](https://github.com/tensorflow/models/tree/master/official/boosted_trees). -- GitLab From ce03a10d70884d2b6d8134b30ad3c5d181877403 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Mon, 25 Jun 2018 13:22:14 -0700 Subject: [PATCH 049/519] Update RELEASE.md --- RELEASE.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/RELEASE.md b/RELEASE.md index f6a52a2951..5c79ebec34 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -1,7 +1,8 @@ # Release 1.9.0 ## Major Features And Improvements -* New `tf.keras` based [get_started](http://tensorflow.org/versions/r1.9/get_started), and [programmers_guide](http://tensorflow.org/versions/r1.9/programmers_guide/keras). +* Updated docs for `tf.keras`: New Keras-based [get started](http://tensorflow.org/versions/r1.9/get_started), + and [programmers guide page](http://tensorflow.org/versions/r1.9/programmers_guide/keras). * Update `tf.keras` to the Keras 2.1.6 API. * Added [`tf.keras.layers.CuDNNGRU`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNGRU) and [`tf.keras.layers.CuDNNLSTM`](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/keras/layers/CuDNNLSTM) layers. [Try it](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb?linkId=53292082). * Adding support of core [feature columns](https://www.tensorflow.org/get_started/feature_columns) and [losses](https://www.tensorflow.org/api_docs/python/tf/losses) to [gradient boosted trees estimators](https://github.com/tensorflow/models/tree/master/official/boosted_trees). -- GitLab From b0f2eee339a041de4e7837b68a9ff4fc77ca7c4a Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Fri, 22 Jun 2018 17:40:27 -0700 Subject: [PATCH 050/519] Rename programmers_guide/ directory to guide/. Update references in source files and docs in tensorflow and related projects. PiperOrigin-RevId: 201766994 --- README.md | 2 +- RELEASE.md | 6 ++-- tensorflow/contrib/autograph/README.md | 2 +- tensorflow/contrib/data/__init__.py | 2 +- tensorflow/contrib/eager/README.md | 2 +- .../examples/notebooks/3_datasets.ipynb | 6 ++-- .../contrib/eager/python/g3doc/guide.md | 4 +-- tensorflow/contrib/lite/toco/README.md | 2 +- .../contrib/tpu/python/tpu/tpu_estimator.py | 2 +- tensorflow/core/protobuf/config.proto | 6 ++-- .../docs_src/api_guides/python/client.md | 2 +- .../api_guides/python/input_dataset.md | 3 +- .../api_guides/python/reading_data.md | 8 ++--- tensorflow/docs_src/deploy/distributed.md | 2 +- tensorflow/docs_src/extend/architecture.md | 5 ++- tensorflow/docs_src/get_started/_index.yaml | 12 +++---- tensorflow/docs_src/get_started/next_steps.md | 4 +-- .../checkpoints.md | 8 ++--- .../custom_estimators.md | 0 .../{programmers_guide => guide}/datasets.md | 0 .../datasets_for_estimators.md | 8 ++--- .../{programmers_guide => guide}/debugger.md | 0 .../{programmers_guide => guide}/eager.md | 0 .../{programmers_guide => guide}/embedding.md | 0 .../estimators.md | 2 +- .../{programmers_guide => guide}/faq.md | 0 .../feature_columns.md | 2 +- .../{programmers_guide => guide}/graph_viz.md | 0 .../{programmers_guide => guide}/graphs.md | 2 +- .../{programmers_guide => guide}/index.md | 34 +++++++++---------- .../{programmers_guide => guide}/keras.md | 28 +++++++-------- .../leftnav_files | 0 .../low_level_intro.md | 2 +- .../premade_estimators.md | 8 ++--- .../saved_model.md | 4 +-- .../summaries_and_tensorboard.md | 0 .../tensorboard_histograms.md | 0 .../{programmers_guide => guide}/tensors.md | 4 +-- .../{programmers_guide => guide}/using_gpu.md | 0 .../{programmers_guide => guide}/using_tpu.md | 4 +-- .../{programmers_guide => guide}/variables.md | 0 .../version_compat.md | 0 tensorflow/docs_src/install/install_go.md | 2 +- tensorflow/docs_src/install/install_java.md | 2 +- tensorflow/docs_src/tutorials/deep_cnn.md | 2 +- tensorflow/docs_src/tutorials/layers.md | 2 +- .../reading_data/fully_connected_reader.py | 2 +- tensorflow/java/README.md | 5 ++- .../java/org/tensorflow/package-info.java | 2 +- tensorflow/python/data/__init__.py | 2 +- tensorflow/python/data/ops/dataset_ops.py | 14 ++++++++ tensorflow/python/debug/BUILD | 2 +- tensorflow/python/debug/README.md | 4 +-- tensorflow/python/debug/examples/README.md | 4 +-- tensorflow/python/estimator/keras.py | 2 +- tensorflow/python/ops/script_ops.py | 2 +- tensorflow/python/tools/saved_model_cli.py | 4 +-- third_party/examples/eager/spinn/README.md | 2 +- 58 files changed, 119 insertions(+), 110 deletions(-) rename tensorflow/docs_src/{programmers_guide => guide}/checkpoints.md (96%) rename tensorflow/docs_src/{programmers_guide => guide}/custom_estimators.md (100%) rename tensorflow/docs_src/{programmers_guide => guide}/datasets.md (100%) rename tensorflow/docs_src/{programmers_guide => guide}/datasets_for_estimators.md (97%) rename tensorflow/docs_src/{programmers_guide => guide}/debugger.md (100%) rename tensorflow/docs_src/{programmers_guide => guide}/eager.md (100%) rename tensorflow/docs_src/{programmers_guide => guide}/embedding.md (100%) rename tensorflow/docs_src/{programmers_guide => guide}/estimators.md (99%) rename tensorflow/docs_src/{programmers_guide => guide}/faq.md (100%) rename tensorflow/docs_src/{programmers_guide => guide}/feature_columns.md (99%) rename tensorflow/docs_src/{programmers_guide => guide}/graph_viz.md (100%) rename tensorflow/docs_src/{programmers_guide => guide}/graphs.md (99%) rename tensorflow/docs_src/{programmers_guide => guide}/index.md (72%) rename tensorflow/docs_src/{programmers_guide => guide}/keras.md (95%) rename tensorflow/docs_src/{programmers_guide => guide}/leftnav_files (100%) rename tensorflow/docs_src/{programmers_guide => guide}/low_level_intro.md (99%) rename tensorflow/docs_src/{programmers_guide => guide}/premade_estimators.md (98%) rename tensorflow/docs_src/{programmers_guide => guide}/saved_model.md (99%) rename tensorflow/docs_src/{programmers_guide => guide}/summaries_and_tensorboard.md (100%) rename tensorflow/docs_src/{programmers_guide => guide}/tensorboard_histograms.md (100%) rename tensorflow/docs_src/{programmers_guide => guide}/tensors.md (98%) rename tensorflow/docs_src/{programmers_guide => guide}/using_gpu.md (100%) rename tensorflow/docs_src/{programmers_guide => guide}/using_tpu.md (98%) rename tensorflow/docs_src/{programmers_guide => guide}/variables.md (100%) rename tensorflow/docs_src/{programmers_guide => guide}/version_compat.md (100%) diff --git a/README.md b/README.md index 6fb4486d0d..4e4d139bd1 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture enables you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting -code. TensorFlow also includes [TensorBoard](https://www.tensorflow.org/programmers_guide/summaries_and_tensorboard), a data visualization toolkit. +code. TensorFlow also includes [TensorBoard](https://www.tensorflow.org/guide/summaries_and_tensorboard), a data visualization toolkit. TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research diff --git a/RELEASE.md b/RELEASE.md index 510eca5467..5fec61af7e 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -467,7 +467,7 @@ answered questions, and were part of inspiring discussions. ## Major Features And Improvements * `tf.keras` is now part of the core TensorFlow API. -* [`tf.data`](http://tensorflow.org/programmers_guide/datasets) is now part of +* [`tf.data`](http://tensorflow.org/guide/datasets) is now part of the core TensorFlow API. * The API is now subject to backwards compatibility guarantees. @@ -495,7 +495,7 @@ answered questions, and were part of inspiring discussions. * TensorFlow Debugger (tfdbg): * Add `eval` command to allow evaluation of arbitrary Python/numpy expressions in tfdbg command-line interface. See - [Debugging TensorFlow Programs](https://www.tensorflow.org/programmers_guide/debugger) + [Debugging TensorFlow Programs](https://www.tensorflow.org/guide/debugger) for more details. * Usability improvement: The frequently used tensor filter `has_inf_or_nan` is now added to `Session` wrappers and hooks by default. So there is no need @@ -782,7 +782,7 @@ answered questions, and were part of inspiring discussions. * Support client-provided ClusterSpec's and propagate them to all workers to enable the creation of dynamic TensorFlow clusters. * TensorFlow C library now available for Windows. * We released a new open-source version of TensorBoard. -* [`SavedModel CLI`](https://www.tensorflow.org/versions/master/programmers_guide/saved_model_cli) tool available to inspect and execute MetaGraph in SavedModel +* [`SavedModel CLI`](https://www.tensorflow.org/versions/master/guide/saved_model_cli) tool available to inspect and execute MetaGraph in SavedModel * Android releases of TensorFlow are now pushed to jcenter for easier integration into apps. See https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/android/README.md diff --git a/tensorflow/contrib/autograph/README.md b/tensorflow/contrib/autograph/README.md index 674859bed4..47b1d4a99a 100644 --- a/tensorflow/contrib/autograph/README.md +++ b/tensorflow/contrib/autograph/README.md @@ -4,7 +4,7 @@ IMPORTANT: AutoGraph is alpha software, and under active development. Expect rou AutoGraph is a Python to TensorFlow compiler. -With AutoGraph, you can write [Eager style](https://www.tensorflow.org/programmers_guide/eager) code in a concise manner, and run it as a TensorFlow graph. AutoGraph uses source code transformation and partial evaluation to generate Python code that builds an equivalent TensorFlow subgraph. The result is code that behaves like ops and can be freely combined with other TensorFlow ops. +With AutoGraph, you can write [Eager style](https://www.tensorflow.org/guide/eager) code in a concise manner, and run it as a TensorFlow graph. AutoGraph uses source code transformation and partial evaluation to generate Python code that builds an equivalent TensorFlow subgraph. The result is code that behaves like ops and can be freely combined with other TensorFlow ops. For example, this Python function: diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py index 9c6a13333e..3510e7b1ad 100644 --- a/tensorflow/contrib/data/__init__.py +++ b/tensorflow/contrib/data/__init__.py @@ -20,7 +20,7 @@ be used in conjunction with the @{tf.data.Dataset} API. Note that the guarantees as `tf.data`, but we will provide deprecation advice in advance of removing existing functionality. -See the @{$datasets$Importing Data} Programmer's Guide for an overview. +See @{$guide/datasets$Importing Data} for an overview. @@Counter @@CheckpointInputPipelineHook diff --git a/tensorflow/contrib/eager/README.md b/tensorflow/contrib/eager/README.md index 4384431e7b..86d203452e 100644 --- a/tensorflow/contrib/eager/README.md +++ b/tensorflow/contrib/eager/README.md @@ -44,7 +44,7 @@ Installation instructions at https://www.tensorflow.org/install/ For an introduction to eager execution in TensorFlow, see: -- [User Guide](https://www.tensorflow.org/programmers_guide/eager) ([source](../../docs_src/programmers_guide/eager.md)) +- [User Guide](https://www.tensorflow.org/guide/eager) ([source](../../docs_src/guide/eager.md)) - Notebook: [Basic Usage](python/examples/notebooks/1_basics.ipynb) - Notebook: [Gradients](python/examples/notebooks/2_gradients.ipynb) - Notebook: [Importing Data](python/examples/notebooks/3_datasets.ipynb) diff --git a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb index bfcc7feb07..d268cbcd91 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb @@ -9,7 +9,7 @@ "source": [ "# Eager Execution Tutorial: Importing Data\n", "\n", - "This notebook demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/programmers_guide/datasets) to build pipelines to feed data to your program. It covers:\n", + "This notebook demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/guide/datasets) to build pipelines to feed data to your program. It covers:\n", "\n", "* Creating a `Dataset`.\n", "* Iteration over a `Dataset` with eager execution enabled.\n", @@ -18,7 +18,7 @@ "\n", "If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly simpler.\n", "You can use Python iteration over the `tf.data.Dataset` object and do not need to explicitly create an `tf.data.Iterator` object.\n", - "As a result, the discussion on iterators in the [Programmer's Guide](https://www.tensorflow.org/programmers_guide/datasets) is not relevant when eager execution is enabled." + "As a result, the discussion on iterators in the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets) is not relevant when eager execution is enabled." ] }, { @@ -63,7 +63,7 @@ "source": [ "# Step 1: Create a source `Dataset`\n", "\n", - "Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [Programmer's Guide](https://www.google.com/url?sa=D\u0026q=https%3A%2F%2Fwww.tensorflow.org%2Fprogrammers_guide%2Fdatasets%23reading_input_data) for more information." + "Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets#reading_input_data) for more information." ] }, { diff --git a/tensorflow/contrib/eager/python/g3doc/guide.md b/tensorflow/contrib/eager/python/g3doc/guide.md index 2d2aba6908..23f33d0230 100644 --- a/tensorflow/contrib/eager/python/g3doc/guide.md +++ b/tensorflow/contrib/eager/python/g3doc/guide.md @@ -4,8 +4,8 @@ Eager execution is a feature that makes TensorFlow execute operations immediately: concrete values are returned, instead of creating a computational graph that is executed later. -A user guide is available: https://www.tensorflow.org/programmers_guide/eager -([source file](../../../../docs_src/programmers_guide/eager.md)) +A user guide is available: https://www.tensorflow.org/guide/eager +([source file](../../../../docs_src/guide/eager.md)) We welcome feedback through [GitHub issues](https://github.com/tensorflow/tensorflow/labels/comp:eager). diff --git a/tensorflow/contrib/lite/toco/README.md b/tensorflow/contrib/lite/toco/README.md index 522e260ad2..ee83c7a6e3 100644 --- a/tensorflow/contrib/lite/toco/README.md +++ b/tensorflow/contrib/lite/toco/README.md @@ -17,7 +17,7 @@ Usage information is given in these documents: Once an application developer has a trained TensorFlow model, TOCO will accept that model and generate a TensorFlow Lite [FlatBuffer](https://google.github.io/flatbuffers/) file. TOCO currently supports -[SavedModels](https://www.tensorflow.org/programmers_guide/saved_model#using_savedmodel_with_estimators) +[SavedModels](https://www.tensorflow.org/guide/saved_model#using_savedmodel_with_estimators) and frozen graphs (models generated via [freeze_graph.py](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/tools/freeze_graph.py)). The TensorFlow Lite FlatBuffer file can be shipped to client devices, generally diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 7c770912b4..c57acd0a2d 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -1103,7 +1103,7 @@ class _InputPipeline(object): err_msg = ('Input pipeline contains one or more QueueRunners. ' 'It could be slow and not scalable. Please consider ' 'converting your input pipeline to use `tf.data` instead (see ' - 'https://www.tensorflow.org/programmers_guide/datasets for ' + 'https://www.tensorflow.org/guide/datasets for ' 'instructions.') if _WRAP_INPUT_FN_INTO_WHILE_LOOP: raise RuntimeError(err_msg) diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto index 9a48f43a63..d83215d5c2 100644 --- a/tensorflow/core/protobuf/config.proto +++ b/tensorflow/core/protobuf/config.proto @@ -147,7 +147,7 @@ message GPUOptions { // Everything inside experimental is subject to change and is not subject // to API stability guarantees in - // https://www.tensorflow.org/programmers_guide/version_compat. + // https://www.tensorflow.org/guide/version_compat. Experimental experimental = 9; }; @@ -381,7 +381,7 @@ message ConfigProto { // Everything inside Experimental is subject to change and is not subject // to API stability guarantees in - // https://www.tensorflow.org/programmers_guide/version_compat. + // https://www.tensorflow.org/guide/version_compat. message Experimental { // Task name for group resolution. string collective_group_leader = 1; @@ -426,7 +426,7 @@ message RunOptions { // Everything inside Experimental is subject to change and is not subject // to API stability guarantees in - // https://www.tensorflow.org/programmers_guide/version_compat. + // https://www.tensorflow.org/guide/version_compat. message Experimental { // If non-zero, declares that this graph is going to use collective // ops and must synchronize step_ids with any other graph with this diff --git a/tensorflow/docs_src/api_guides/python/client.md b/tensorflow/docs_src/api_guides/python/client.md index eef23696db..27fc8610bf 100644 --- a/tensorflow/docs_src/api_guides/python/client.md +++ b/tensorflow/docs_src/api_guides/python/client.md @@ -3,7 +3,7 @@ This library contains classes for launching graphs and executing operations. -@{$programmers_guide/low_level_intro$This guide} has examples of how a graph +@{$guide/low_level_intro$This guide} has examples of how a graph is launched in a @{tf.Session}. ## Session management diff --git a/tensorflow/docs_src/api_guides/python/input_dataset.md b/tensorflow/docs_src/api_guides/python/input_dataset.md index a6e2fc48e0..a6612d1bf7 100644 --- a/tensorflow/docs_src/api_guides/python/input_dataset.md +++ b/tensorflow/docs_src/api_guides/python/input_dataset.md @@ -2,8 +2,7 @@ [TOC] @{tf.data.Dataset} allows you to build complex input pipelines. See the -@{$datasets$programmer's guide} for an in-depth explanation of how to use this -API. +@{$guide/datasets} for an in-depth explanation of how to use this API. ## Reader classes diff --git a/tensorflow/docs_src/api_guides/python/reading_data.md b/tensorflow/docs_src/api_guides/python/reading_data.md index 5bbbfd3216..d7d0904ae2 100644 --- a/tensorflow/docs_src/api_guides/python/reading_data.md +++ b/tensorflow/docs_src/api_guides/python/reading_data.md @@ -16,8 +16,8 @@ There are four methods of getting data into a TensorFlow program: ## `tf.data` API -See the @{$datasets$programmer's guide} for an in-depth explanation of -@{tf.data.Dataset}. The `tf.data` API enables you to extract and preprocess data +See the @{$guide/datasets} for an in-depth explanation of @{tf.data.Dataset}. +The `tf.data` API enables you to extract and preprocess data from different input/file formats, and apply transformations such as batching, shuffling, and mapping functions over the dataset. This is an improved version of the old input methods---feeding and `QueueRunner`---which are described @@ -511,8 +511,8 @@ You can have the train and eval in the same graph in the same process, and share their trained variables or layers. See @{$variables$the shared variables tutorial}. To support the single-graph approach -@{$programmers_guide/datasets$`tf.data`} also supplies -@{$programmers_guide/datasets#creating_an_iterator$advanced iterator types} that +@{$guide/datasets$`tf.data`} also supplies +@{$guide/datasets#creating_an_iterator$advanced iterator types} that that allow the user to change the input pipeline without rebuilding the graph or session. diff --git a/tensorflow/docs_src/deploy/distributed.md b/tensorflow/docs_src/deploy/distributed.md index d7ed6b1deb..8e2c818e39 100644 --- a/tensorflow/docs_src/deploy/distributed.md +++ b/tensorflow/docs_src/deploy/distributed.md @@ -2,7 +2,7 @@ This document shows how to create a cluster of TensorFlow servers, and how to distribute a computation graph across that cluster. We assume that you are -familiar with the @{$programmers_guide/low_level_intro$basic concepts} of +familiar with the @{$guide/low_level_intro$basic concepts} of writing low level TensorFlow programs. ## Hello distributed TensorFlow! diff --git a/tensorflow/docs_src/extend/architecture.md b/tensorflow/docs_src/extend/architecture.md index c8f522a03a..84435a57f2 100644 --- a/tensorflow/docs_src/extend/architecture.md +++ b/tensorflow/docs_src/extend/architecture.md @@ -7,9 +7,8 @@ learning models and system-level optimizations. This document describes the system architecture that makes this combination of scale and flexibility possible. It assumes that you have basic familiarity with TensorFlow programming concepts such as the computation graph, operations, -and sessions. See @{$programmers_guide/low_level_intro$this document} -for an introduction to these topics. Some familiarity -with @{$distributed$distributed TensorFlow} +and sessions. See @{$guide/low_level_intro$this document} for an introduction to +these topics. Some familiarity with @{$distributed$distributed TensorFlow} will also be helpful. This document is for developers who want to extend TensorFlow in some way not diff --git a/tensorflow/docs_src/get_started/_index.yaml b/tensorflow/docs_src/get_started/_index.yaml index af255a482d..277fc852fb 100644 --- a/tensorflow/docs_src/get_started/_index.yaml +++ b/tensorflow/docs_src/get_started/_index.yaml @@ -74,7 +74,7 @@ landing_page: The high-level Keras API provides building blocks to create and train deep learning models. Start with these beginner-friendly notebook examples, then read the - TensorFlow Keras guide. + TensorFlow Keras guide.

  1. Basic classification
    2. @@ -85,7 +85,7 @@ landing_page:
- Read the Keras guide + Read the Keras guide
- classname: tfo-landing-row-item-code-block @@ -123,7 +123,7 @@ landing_page:

Eager execution provides an imperative, define-by-run interface for advanced operations. Write custom layers, forward passes, and training loops with auto‑differentiation. Start with - these notebooks, then read the eager execution guide. + these notebooks, then read the eager execution guide.

  1. @@ -165,7 +165,7 @@ landing_page:
- Read the eager execution guide + Read the eager execution guide
- custom_html: > @@ -177,7 +177,7 @@ landing_page:

Estimators can train large models on multiple machines in a production environment. Try the examples below and read the - Estimators guide. + Estimators guide.

  1. How to build a simple text classifier with TF-Hub
    2. @@ -186,7 +186,7 @@ landing_page:
- Read the Estimators guide + Read the Estimators guide
diff --git a/tensorflow/docs_src/get_started/next_steps.md b/tensorflow/docs_src/get_started/next_steps.md index 79c0ef3346..6318a39c6c 100644 --- a/tensorflow/docs_src/get_started/next_steps.md +++ b/tensorflow/docs_src/get_started/next_steps.md @@ -2,9 +2,9 @@ ## Learn more about TensorFlow -* The [TensorFlow Guide](/programmers_guide) includes usage guides for the +* The [TensorFlow Guide](/guide) includes usage guides for the high-level APIs, as well as advanced TensorFlow operations. -* [Premade Estimators](/programmers_guide/premade_estimators) are designed to +* [Premade Estimators](/guide/premade_estimators) are designed to get results out of the box. Use TensorFlow without building your own models. * [TensorFlow.js](https://js.tensorflow.org/) allows web developers to train and deploy ML models in the browser and using Node.js. diff --git a/tensorflow/docs_src/programmers_guide/checkpoints.md b/tensorflow/docs_src/guide/checkpoints.md similarity index 96% rename from tensorflow/docs_src/programmers_guide/checkpoints.md rename to tensorflow/docs_src/guide/checkpoints.md index 8dfd91e3c8..dfb2626b86 100644 --- a/tensorflow/docs_src/programmers_guide/checkpoints.md +++ b/tensorflow/docs_src/guide/checkpoints.md @@ -8,9 +8,8 @@ Estimators. TensorFlow provides two model formats: * SavedModel, which is a format independent of the code that created the model. -This document focuses on checkpoints. For details on SavedModel, see the -@{$saved_model$Saving and Restoring} chapter of the -*TensorFlow Programmer's Guide*. +This document focuses on checkpoints. For details on `SavedModel`, see the +@{$saved_model$Saving and Restoring} guide. ## Sample code @@ -232,8 +231,7 @@ This separation will keep your checkpoints recoverable. Checkpoints provide an easy automatic mechanism for saving and restoring models created by Estimators. -See the @{$saved_model$Saving and Restoring} -chapter of the *TensorFlow Programmer's Guide* for details on: +See the @{$saved_model$Saving and Restoring} guide for details about: * Saving and restoring models using low-level TensorFlow APIs. * Exporting and importing models in the SavedModel format, which is a diff --git a/tensorflow/docs_src/programmers_guide/custom_estimators.md b/tensorflow/docs_src/guide/custom_estimators.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/custom_estimators.md rename to tensorflow/docs_src/guide/custom_estimators.md diff --git a/tensorflow/docs_src/programmers_guide/datasets.md b/tensorflow/docs_src/guide/datasets.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/datasets.md rename to tensorflow/docs_src/guide/datasets.md diff --git a/tensorflow/docs_src/programmers_guide/datasets_for_estimators.md b/tensorflow/docs_src/guide/datasets_for_estimators.md similarity index 97% rename from tensorflow/docs_src/programmers_guide/datasets_for_estimators.md rename to tensorflow/docs_src/guide/datasets_for_estimators.md index 345a31b985..b04af78cd8 100644 --- a/tensorflow/docs_src/programmers_guide/datasets_for_estimators.md +++ b/tensorflow/docs_src/guide/datasets_for_estimators.md @@ -91,8 +91,8 @@ print(mnist_ds) ``` This will print the following line, showing the -@{$programmers_guide/tensors#shapes$shapes} and -@{$programmers_guide/tensors#data_types$types} of the items in +@{$guide/tensors#shapes$shapes} and +@{$guide/tensors#data_types$types} of the items in the dataset. Note that a `Dataset` does not know how many items it contains. ``` None @@ -128,7 +128,7 @@ print(dataset) Here we see that when a `Dataset` contains structured elements, the `shapes` and `types` of the `Dataset` take on the same structure. This dataset contains -dictionaries of @{$programmers_guide/tensors#rank$scalars}, all of type +dictionaries of @{$guide/tensors#rank$scalars}, all of type `tf.float64`. The first line of the iris `train_input_fn` uses the same functionality, but @@ -382,6 +382,6 @@ Estimator. Consider the following documents next: * The @{$low_level_intro#datasets$Low Level Introduction}, which demonstrates how to experiment directly with `tf.data.Datasets` using TensorFlow's low level APIs. -* @{$programmers_guide/datasets} which goes into great detail about additional +* @{$guide/datasets} which goes into great detail about additional functionality of `Datasets`. diff --git a/tensorflow/docs_src/programmers_guide/debugger.md b/tensorflow/docs_src/guide/debugger.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/debugger.md rename to tensorflow/docs_src/guide/debugger.md diff --git a/tensorflow/docs_src/programmers_guide/eager.md b/tensorflow/docs_src/guide/eager.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/eager.md rename to tensorflow/docs_src/guide/eager.md diff --git a/tensorflow/docs_src/programmers_guide/embedding.md b/tensorflow/docs_src/guide/embedding.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/embedding.md rename to tensorflow/docs_src/guide/embedding.md diff --git a/tensorflow/docs_src/programmers_guide/estimators.md b/tensorflow/docs_src/guide/estimators.md similarity index 99% rename from tensorflow/docs_src/programmers_guide/estimators.md rename to tensorflow/docs_src/guide/estimators.md index b13b47184d..78b30c3040 100644 --- a/tensorflow/docs_src/programmers_guide/estimators.md +++ b/tensorflow/docs_src/guide/estimators.md @@ -81,7 +81,7 @@ of the following four steps: ... # manipulate dataset, extracting the feature dict and the label return feature_dict, label - (See @{$programmers_guide/datasets} for full details.) + (See @{$guide/datasets} for full details.) 2. **Define the feature columns.** Each @{tf.feature_column} identifies a feature name, its type, and any input pre-processing. diff --git a/tensorflow/docs_src/programmers_guide/faq.md b/tensorflow/docs_src/guide/faq.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/faq.md rename to tensorflow/docs_src/guide/faq.md diff --git a/tensorflow/docs_src/programmers_guide/feature_columns.md b/tensorflow/docs_src/guide/feature_columns.md similarity index 99% rename from tensorflow/docs_src/programmers_guide/feature_columns.md rename to tensorflow/docs_src/guide/feature_columns.md index 90f5c53a17..1013ec910c 100644 --- a/tensorflow/docs_src/programmers_guide/feature_columns.md +++ b/tensorflow/docs_src/guide/feature_columns.md @@ -534,7 +534,7 @@ embedding_column = tf.feature_column.embedding_column( dimension=embedding_dimensions) ``` -@{$programmers_guide/embedding$Embeddings} is a significant topic within machine +@{$guide/embedding$Embeddings} is a significant topic within machine learning. This information was just to get you started using them as feature columns. diff --git a/tensorflow/docs_src/programmers_guide/graph_viz.md b/tensorflow/docs_src/guide/graph_viz.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/graph_viz.md rename to tensorflow/docs_src/guide/graph_viz.md diff --git a/tensorflow/docs_src/programmers_guide/graphs.md b/tensorflow/docs_src/guide/graphs.md similarity index 99% rename from tensorflow/docs_src/programmers_guide/graphs.md rename to tensorflow/docs_src/guide/graphs.md index f0dd8def17..e6246ef148 100644 --- a/tensorflow/docs_src/programmers_guide/graphs.md +++ b/tensorflow/docs_src/guide/graphs.md @@ -93,7 +93,7 @@ to all API functions in the same context. For example: stored value. The @{tf.Variable} object also has methods such as @{tf.Variable.assign$`assign`} and @{tf.Variable.assign_add$`assign_add`} that create @{tf.Operation} objects that, when executed, update the stored value. - (See @{$programmers_guide/variables} for more information about variables.) + (See @{$guide/variables} for more information about variables.) * Calling @{tf.train.Optimizer.minimize} will add operations and tensors to the default graph that calculates gradients, and return a @{tf.Operation} that, diff --git a/tensorflow/docs_src/programmers_guide/index.md b/tensorflow/docs_src/guide/index.md similarity index 72% rename from tensorflow/docs_src/programmers_guide/index.md rename to tensorflow/docs_src/guide/index.md index 9c58a3b45e..eefdb9ceae 100644 --- a/tensorflow/docs_src/programmers_guide/index.md +++ b/tensorflow/docs_src/guide/index.md @@ -1,17 +1,17 @@ -# Programmer's Guide +# TensorFlow Guide The documents in this unit dive into the details of how TensorFlow works. The units are as follows: ## High Level APIs - * @{$programmers_guide/keras}, TensorFlow's high-level API for building and + * @{$guide/keras}, TensorFlow's high-level API for building and training deep learning models. - * @{$programmers_guide/eager}, an API for writing TensorFlow code + * @{$guide/eager}, an API for writing TensorFlow code imperatively, like you would use Numpy. - * @{$programmers_guide/estimators}, a high-level API that provides + * @{$guide/estimators}, a high-level API that provides fully-packaged models ready for large-scale training and production. - * @{$programmers_guide/datasets}, easy input pipelines to bring your data into + * @{$guide/datasets}, easy input pipelines to bring your data into your TensorFlow program. ## Estimators @@ -34,13 +34,13 @@ works. The units are as follows: ## Low Level APIs - * @{$programmers_guide/low_level_intro}, which introduces the + * @{$guide/low_level_intro}, which introduces the basics of how you can use TensorFlow outside of the high Level APIs. - * @{$programmers_guide/tensors}, which explains how to create, + * @{$guide/tensors}, which explains how to create, manipulate, and access Tensors--the fundamental object in TensorFlow. - * @{$programmers_guide/variables}, which details how + * @{$guide/variables}, which details how to represent shared, persistent state in your program. - * @{$programmers_guide/graphs}, which explains: + * @{$guide/graphs}, which explains: * dataflow graphs, which are TensorFlow's representation of computations as dependencies between operations. * sessions, which are TensorFlow's mechanism for running dataflow graphs @@ -50,19 +50,19 @@ works. The units are as follows: such as Estimators or Keras, the high-level API creates and manages graphs and sessions for you, but understanding graphs and sessions can still be helpful. - * @{$programmers_guide/saved_model}, which + * @{$guide/saved_model}, which explains how to save and restore variables and models. ## ML Concepts - * @{$programmers_guide/embedding}, which introduces the concept + * @{$guide/embedding}, which introduces the concept of embeddings, provides a simple example of training an embedding in TensorFlow, and explains how to view embeddings with the TensorBoard Embedding Projector. ## Debugging - * @{$programmers_guide/debugger}, which + * @{$guide/debugger}, which explains how to use the TensorFlow debugger (tfdbg). ## TensorBoard @@ -70,17 +70,17 @@ works. The units are as follows: TensorBoard is a utility to visualize different aspects of machine learning. The following guides explain how to use TensorBoard: - * @{$programmers_guide/summaries_and_tensorboard}, + * @{$guide/summaries_and_tensorboard}, which introduces TensorBoard. - * @{$programmers_guide/graph_viz}, which + * @{$guide/graph_viz}, which explains how to visualize the computational graph. - * @{$programmers_guide/tensorboard_histograms} which demonstrates the how to + * @{$guide/tensorboard_histograms} which demonstrates the how to use TensorBoard's histogram dashboard. ## Misc - * @{$programmers_guide/version_compat}, + * @{$guide/version_compat}, which explains backward compatibility guarantees and non-guarantees. - * @{$programmers_guide/faq}, which contains frequently asked + * @{$guide/faq}, which contains frequently asked questions about TensorFlow. diff --git a/tensorflow/docs_src/programmers_guide/keras.md b/tensorflow/docs_src/guide/keras.md similarity index 95% rename from tensorflow/docs_src/programmers_guide/keras.md rename to tensorflow/docs_src/guide/keras.md index c6aca7ebf4..83172dab7f 100644 --- a/tensorflow/docs_src/programmers_guide/keras.md +++ b/tensorflow/docs_src/guide/keras.md @@ -19,7 +19,7 @@ fast prototyping, advanced research, and production, with three key advantages: [Keras API specification](https://keras.io){:.external}. This is a high-level API to build and train models that includes first-class support for TensorFlow-specific functionality, such as [eager execution](#eager_execution), -`tf.data` pipelines, and [Estimators](/programmers_guide/estimators). +`tf.data` pipelines, and [Estimators](./estimators.md). `tf.keras` makes TensorFlow easier to use without sacrificing flexibility and performance. @@ -35,8 +35,8 @@ from tensorflow import keras * The `tf.keras` version in the latest TensorFlow release might not be the same as the latest `keras` version from PyPI. Check `tf.keras.__version__`. * When [saving a model's weights](#weights_only), `tf.keras` defaults to the - [checkpoint format](/get_started/checkpoints). Pass `save_format='h5'` to use - HDF5. + [checkpoint format](../get_started/checkpoints.md). Pass `save_format='h5'` to + use HDF5. ## Build a simple model @@ -179,7 +179,7 @@ model.fit(data, labels, epochs=10, batch_size=32, ### Input tf.data datasets -Use the [Datasets API](/programmers_guide/datasets) to scale to large datasets +Use the [Datasets API](./datasets.md) to scale to large datasets or multi-device training. Pass a `tf.data.Dataset` instance to the `fit` method: @@ -285,7 +285,7 @@ your own forward pass. Create layers in the `__init__` method and set them as attributes of the class instance. Define the forward pass in the `call` method. Model subclassing is particularly useful when -[eager execution](/programmers_guide/eager) is enabled since the forward pass +[eager execution](./eager.md) is enabled since the forward pass can be written imperatively. Key Point: Use the right API for the job. While model subclassing offers @@ -410,7 +410,7 @@ during training. You can write your own custom callback, or use the built-in * `tf.keras.callbacks.EarlyStopping`: Interrupt training when validation performance has stopped improving. * `tf.keras.callbacks.TensorBoard`: Monitor the model's behavior using - [TensorBoard](/programmers_guide/summaries_and_tensorboard). + [TensorBoard](./summaries_and_tensorboard.md). To use a `tf.keras.callbacks.Callback`, pass it to the model's `fit` method: @@ -442,8 +442,8 @@ model.load_weights('my_model') ``` By default, this saves the model's weights in the -[TensorFlow checkpoint](/get_started/checkpoints) file format. Weights can also -be saved to the Keras HDF5 format (the default for the multi-backend +[TensorFlow checkpoint](../get_started/checkpoints.md) file format. Weights can +also be saved to the Keras HDF5 format (the default for the multi-backend implementation of Keras): ```python @@ -509,7 +509,7 @@ model = keras.models.load_model('my_model.h5') ## Eager execution -[Eager execution](/programmers_guide/eager) is an imperative programming +[Eager execution](./eager.md) is an imperative programming environment that evaluates operations immediately. This is not required for Keras, but is supported by `tf.keras` and useful for inspecting your program and debugging. @@ -520,7 +520,7 @@ especially benefits *model subclassing* and building *custom layers*—the APIs that require you to write the forward pass as code (instead of the APIs that create models by assembling existing layers). -See the [eager execution guide](/programmers_guide/eager#build_a_model) for +See the [eager execution guide](./eager.md#build_a_model) for examples of using Keras models with custom training loops and `tf.GradientTape`. @@ -528,14 +528,14 @@ examples of using Keras models with custom training loops and `tf.GradientTape`. ### Estimators -The [Estimators](/programmers_guide/estimators) API is used for training models +The [Estimators](./estimators.md) API is used for training models for distributed environments. This targets industry use cases such as distributed training on large datasets that can export a model for production. A `tf.keras.Model` can be trained with the `tf.estimator` API by converting the model to an `tf.estimator.Estimator` object with `tf.keras.estimator.model_to_estimator`. See -[Creating Estimators from Keras models](/programmers_guide/estimators#creating_estimators_from_keras_models). +[Creating Estimators from Keras models](./estimators.md#creating_estimators_from_keras_models). ```python model = keras.Sequential([layers.Dense(10,activation='softmax'), @@ -548,8 +548,8 @@ model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), estimator = keras.estimator.model_to_estimator(model) ``` -Note: Enable [eager execution](/programmers_guide/eager) for debugging -[Estimator input functions](/programmers_guide/premade_estimators#create_input_functions) +Note: Enable [eager execution](./eager.md) for debugging +[Estimator input functions](./premade_estimators.md#create_input_functions) and inspecting data. ### Multiple GPUs diff --git a/tensorflow/docs_src/programmers_guide/leftnav_files b/tensorflow/docs_src/guide/leftnav_files similarity index 100% rename from tensorflow/docs_src/programmers_guide/leftnav_files rename to tensorflow/docs_src/guide/leftnav_files diff --git a/tensorflow/docs_src/programmers_guide/low_level_intro.md b/tensorflow/docs_src/guide/low_level_intro.md similarity index 99% rename from tensorflow/docs_src/programmers_guide/low_level_intro.md rename to tensorflow/docs_src/guide/low_level_intro.md index 478e2bb70b..665a5568b4 100644 --- a/tensorflow/docs_src/programmers_guide/low_level_intro.md +++ b/tensorflow/docs_src/guide/low_level_intro.md @@ -303,7 +303,7 @@ while True: break ``` -For more details on Datasets and Iterators see: @{$programmers_guide/datasets}. +For more details on Datasets and Iterators see: @{$guide/datasets}. ## Layers diff --git a/tensorflow/docs_src/programmers_guide/premade_estimators.md b/tensorflow/docs_src/guide/premade_estimators.md similarity index 98% rename from tensorflow/docs_src/programmers_guide/premade_estimators.md rename to tensorflow/docs_src/guide/premade_estimators.md index 02e2caf64b..3e910c1fe2 100644 --- a/tensorflow/docs_src/programmers_guide/premade_estimators.md +++ b/tensorflow/docs_src/guide/premade_estimators.md @@ -78,10 +78,10 @@ provides a programming stack consisting of multiple API layers: We strongly recommend writing TensorFlow programs with the following APIs: -* @{$programmers_guide/estimators$Estimators}, which represent a complete model. +* @{$guide/estimators$Estimators}, which represent a complete model. The Estimator API provides methods to train the model, to judge the model's accuracy, and to generate predictions. -* @{$programmers_guide/datasets_for_estimators}, which build a data input +* @{$guide/datasets_for_estimators}, which build a data input pipeline. The Dataset API has methods to load and manipulate data, and feed it into your model. The Dataset API meshes well with the Estimators API. @@ -173,7 +173,7 @@ example is an Iris Versicolor. An Estimator is TensorFlow's high-level representation of a complete model. It handles the details of initialization, logging, saving and restoring, and many other features so you can concentrate on your model. For more details see -@{$programmers_guide/estimators}. +@{$guide/estimators}. An Estimator is any class derived from @{tf.estimator.Estimator}. TensorFlow provides a collection of @@ -424,7 +424,7 @@ Now that you've gotten started writing TensorFlow programs, consider the following material: * @{$checkpoints$Checkpoints} to learn how to save and restore models. -* @{$programmers_guide/datasets_for_estimators} to learn more about importing +* @{$guide/datasets_for_estimators} to learn more about importing data into your model. * @{$custom_estimators$Creating Custom Estimators} to learn how to write your own Estimator, customized for a particular problem. diff --git a/tensorflow/docs_src/programmers_guide/saved_model.md b/tensorflow/docs_src/guide/saved_model.md similarity index 99% rename from tensorflow/docs_src/programmers_guide/saved_model.md rename to tensorflow/docs_src/guide/saved_model.md index c6ef87c54a..27ef7bb0da 100644 --- a/tensorflow/docs_src/programmers_guide/saved_model.md +++ b/tensorflow/docs_src/guide/saved_model.md @@ -3,7 +3,7 @@ The @{tf.train.Saver} class provides methods to save and restore models. The @{tf.saved_model.simple_save} function is an easy way to build a @{tf.saved_model$saved model} suitable for serving. -[Estimators](@{$programmers_guide/estimators}) automatically save and restore +[Estimators](@{$guide/estimators}) automatically save and restore variables in the `model_dir`. ## Save and restore variables @@ -299,7 +299,7 @@ following: added attributes with defaults don't cause older model consumers to fail loading models regenerated with newer training binaries. -See [compatibility guidance](https://www.tensorflow.org/programmers_guide/version_compat) +See [compatibility guidance](./version_compat.md) for more information. ### Loading a SavedModel in Python diff --git a/tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md b/tensorflow/docs_src/guide/summaries_and_tensorboard.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/summaries_and_tensorboard.md rename to tensorflow/docs_src/guide/summaries_and_tensorboard.md diff --git a/tensorflow/docs_src/programmers_guide/tensorboard_histograms.md b/tensorflow/docs_src/guide/tensorboard_histograms.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/tensorboard_histograms.md rename to tensorflow/docs_src/guide/tensorboard_histograms.md diff --git a/tensorflow/docs_src/programmers_guide/tensors.md b/tensorflow/docs_src/guide/tensors.md similarity index 98% rename from tensorflow/docs_src/programmers_guide/tensors.md rename to tensorflow/docs_src/guide/tensors.md index 1248c3cabe..7227260f1a 100644 --- a/tensorflow/docs_src/programmers_guide/tensors.md +++ b/tensorflow/docs_src/guide/tensors.md @@ -26,7 +26,7 @@ some cases it's only possible to find the shape of a tensor at graph execution time. Some types of tensors are special, and these will be covered in other -units of the Programmer's guide. The main ones are: +units of the TensorFlow guide. The main ones are: * `tf.Variable` * `tf.constant` @@ -230,7 +230,7 @@ yet_another = tf.reshape(matrixAlt, [13, 2, -1]) # ERROR! ## Data types In addition to dimensionality, Tensors have a data type. Refer to the -`tf.DataType` page in the programmer's guide for a full list of the data types. +`tf.DType` page for a complete list of the data types. It is not possible to have a `tf.Tensor` with more than one data type. It is possible, however, to serialize arbitrary data structures as `string`s and store diff --git a/tensorflow/docs_src/programmers_guide/using_gpu.md b/tensorflow/docs_src/guide/using_gpu.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/using_gpu.md rename to tensorflow/docs_src/guide/using_gpu.md diff --git a/tensorflow/docs_src/programmers_guide/using_tpu.md b/tensorflow/docs_src/guide/using_tpu.md similarity index 98% rename from tensorflow/docs_src/programmers_guide/using_tpu.md rename to tensorflow/docs_src/guide/using_tpu.md index 44aabf0557..41d80d9d60 100644 --- a/tensorflow/docs_src/programmers_guide/using_tpu.md +++ b/tensorflow/docs_src/guide/using_tpu.md @@ -171,7 +171,7 @@ This section details the changes you must make to the model function During regular usage TensorFlow attempts to determine the shapes of each `tf.Tensor` during graph construction. During execution any unknown shape dimensions are determined dynamically, -see @{$programmers_guide/tensors#shape$Tensor Shapes} for more details. +see @{$guide/tensors#shape$Tensor Shapes} for more details. To run on Cloud TPUs TensorFlow models are compiled using @{$xla$XLA}. XLA uses a similar system for determining shapes at compile time. XLA requires @@ -195,7 +195,7 @@ TPU. Build your evaluation metrics dictionary in a stand-alone `metric_fn`. - + Evaluation metrics are an essential part of training a model. These are fully supported on Cloud TPUs, but with a slightly different syntax. diff --git a/tensorflow/docs_src/programmers_guide/variables.md b/tensorflow/docs_src/guide/variables.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/variables.md rename to tensorflow/docs_src/guide/variables.md diff --git a/tensorflow/docs_src/programmers_guide/version_compat.md b/tensorflow/docs_src/guide/version_compat.md similarity index 100% rename from tensorflow/docs_src/programmers_guide/version_compat.md rename to tensorflow/docs_src/guide/version_compat.md diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 1c03dd223e..5451e1b319 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -6,7 +6,7 @@ a Go application. This guide explains how to install and set up the [TensorFlow Go package](https://godoc.org/github.com/tensorflow/tensorflow/tensorflow/go). Warning: The TensorFlow Go API is *not* covered by the TensorFlow -[API stability guarantees](https://www.tensorflow.org/programmers_guide/version_semantics). +[API stability guarantees](../guide/version_semantics.md). ## Supported Platforms diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index c73e2f4281..ad3544b595 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -7,7 +7,7 @@ Java application. This guide explains how to install and use it in a Java application. Warning: The TensorFlow Java API is *not* covered by the TensorFlow -[API stability guarantees](https://www.tensorflow.org/programmers_guide/version_semantics). +[API stability guarantees](../guide/version_semantics.md). ## Supported Platforms diff --git a/tensorflow/docs_src/tutorials/deep_cnn.md b/tensorflow/docs_src/tutorials/deep_cnn.md index 6a4c9a9b07..44a32d9d1d 100644 --- a/tensorflow/docs_src/tutorials/deep_cnn.md +++ b/tensorflow/docs_src/tutorials/deep_cnn.md @@ -268,7 +268,7 @@ in `cifar10_input.py`. `cifar10_train.py` periodically @{tf.train.Saver$saves} all model parameters in -@{$programmers_guide/saved_model$checkpoint files} +@{$guide/saved_model$checkpoint files} but it does *not* evaluate the model. The checkpoint file will be used by `cifar10_eval.py` to measure the predictive performance (see [Evaluating a Model](#evaluating-a-model) below). diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/layers.md index 0f17899dae..212e337637 100644 --- a/tensorflow/docs_src/tutorials/layers.md +++ b/tensorflow/docs_src/tutorials/layers.md @@ -627,7 +627,7 @@ operation earlier when we generated the probabilities in `cnn_model_fn`. > argument, TensorFlow will assign a default name. A couple easy ways to > discover the names applied to operations are to visualize your graph on > @{$graph_viz$TensorBoard}) or to enable the -> @{$programmers_guide/debugger$TensorFlow Debugger (tfdbg)}. +> @{$guide/debugger$TensorFlow Debugger (tfdbg)}. Next, we create the `LoggingTensorHook`, passing `tensors_to_log` to the `tensors` argument. We set `every_n_iter=50`, which specifies that probabilities 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 307eede5c0..7402247448 100644 --- a/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py +++ b/tensorflow/examples/how_tos/reading_data/fully_connected_reader.py @@ -17,7 +17,7 @@ This version is like fully_connected_feed.py but uses data converted to a TFRecords file containing tf.train.Example protocol buffers. See: -https://www.tensorflow.org/programmers_guide/reading_data#reading_from_files +https://www.tensorflow.org/guide/reading_data#reading_from_files for context. YOU MUST run convert_to_records before running this (but you only need to diff --git a/tensorflow/java/README.md b/tensorflow/java/README.md index 2f1ce253b2..c7382ff231 100644 --- a/tensorflow/java/README.md +++ b/tensorflow/java/README.md @@ -1,7 +1,7 @@ # TensorFlow for Java > *WARNING*: The TensorFlow Java API is not currently covered by the TensorFlow -> [API stability guarantees](https://www.tensorflow.org/programmers_guide/version_semantics). +> [API stability guarantees](https://www.tensorflow.org/guide/version_semantics). > > For using TensorFlow on Android refer instead to > [contrib/android](https://www.tensorflow.org/code/tensorflow/contrib/android), @@ -23,8 +23,7 @@ native libraries will need to be built from source. 2. Setup the environment to build TensorFlow from source code ([Linux](https://www.tensorflow.org/install/install_sources#PrepareLinux) - or [Mac OS - X](https://www.tensorflow.org/install/install_sources#PrepareMac)). + or [macOS](https://www.tensorflow.org/install/install_sources#PrepareMac)). If you'd like to skip reading those details and do not care about GPU support, try the following: diff --git a/tensorflow/java/src/main/java/org/tensorflow/package-info.java b/tensorflow/java/src/main/java/org/tensorflow/package-info.java index 521c5c610c..f353ee3145 100644 --- a/tensorflow/java/src/main/java/org/tensorflow/package-info.java +++ b/tensorflow/java/src/main/java/org/tensorflow/package-info.java @@ -17,7 +17,7 @@ limitations under the License. * Defines classes to build, save, load and execute TensorFlow models. * *

WARNING: The API is currently experimental and is not covered by TensorFlow API stability + * href="https://www.tensorflow.org/guide/version_semantics">API stability * guarantees. See README.md for installation * instructions. diff --git a/tensorflow/python/data/__init__.py b/tensorflow/python/data/__init__.py index 7efe0948e7..3b9bf2469e 100644 --- a/tensorflow/python/data/__init__.py +++ b/tensorflow/python/data/__init__.py @@ -14,7 +14,7 @@ # ============================================================================== """`tf.data.Dataset` API for input pipelines. -See the @{$datasets$Importing Data} Programmer's Guide for an overview. +See @{$guide/datasets$Importing Data} for an overview. """ from __future__ import absolute_import diff --git a/tensorflow/python/data/ops/dataset_ops.py b/tensorflow/python/data/ops/dataset_ops.py index 6f9b12b123..0e020d86d0 100644 --- a/tensorflow/python/data/ops/dataset_ops.py +++ b/tensorflow/python/data/ops/dataset_ops.py @@ -212,6 +212,13 @@ class Dataset(object): def from_tensors(tensors): """Creates a `Dataset` with a single element, comprising the given tensors. + Note that if `tensors` contains a NumPy array, and eager execution is not + enabled, the values will be embedded in the graph as one or more + @{tf.constant} operations. For large datasets (> 1 GB), this can waste + memory and run into byte limits of graph serialization. If tensors contains + one or more large NumPy arrays, consider the alternative described in + @{$guide/datasets#consuming_numpy_arrays$this guide}. + Args: tensors: A nested structure of tensors. @@ -224,6 +231,13 @@ class Dataset(object): def from_tensor_slices(tensors): """Creates a `Dataset` whose elements are slices of the given tensors. + Note that if `tensors` contains a NumPy array, and eager execution is not + enabled, the values will be embedded in the graph as one or more + @{tf.constant} operations. For large datasets (> 1 GB), this can waste + memory and run into byte limits of graph serialization. If tensors contains + one or more large NumPy arrays, consider the alternative described in + @{$guide/datasets#consuming_numpy_arrays$this guide}. + Args: tensors: A nested structure of tensors, each having the same size in the 0th dimension. diff --git a/tensorflow/python/debug/BUILD b/tensorflow/python/debug/BUILD index 09062abd74..2d261f9be7 100644 --- a/tensorflow/python/debug/BUILD +++ b/tensorflow/python/debug/BUILD @@ -5,7 +5,7 @@ # # ":debug_py": Public Python methods and classes of tfdbg. # For API documentation, see https://www.tensorflow.org/api_docs/python/tfdbg -# For a user interface walkthrough, see https://www.tensorflow.org/programmers_guide/debugger +# For a user interface walkthrough, see https://www.tensorflow.org/guide/debugger # ":grpc_debug_server": Server interface for grpc:// debug URLs. package( diff --git a/tensorflow/python/debug/README.md b/tensorflow/python/debug/README.md index 269bbb19bd..9c16af4d79 100644 --- a/tensorflow/python/debug/README.md +++ b/tensorflow/python/debug/README.md @@ -28,7 +28,7 @@ models: * Easy access through session wrappers * Easy integration with common high-level APIs, such as - [TensorFlow Estimators](https://www.tensorflow.org/programmers_guide/estimators) and + [TensorFlow Estimators](https://www.tensorflow.org/guide/estimators) and [Keras](https://keras.io/) * Inspection of runtime tensor values and node connections * Conditional breaking after runs that generate tensors satisfying given @@ -43,7 +43,7 @@ models: ## How to use TFDBG? -* For a walkthrough of TFDBG command-line interface, see https://www.tensorflow.org/programmers_guide/debugger. +* For a walkthrough of TFDBG command-line interface, see https://www.tensorflow.org/guide/debugger. * For information on the web GUI of TFDBG (TensorBoard Debugger Plugin), see [this README](https://github.com/tensorflow/tensorboard/blob/master/tensorboard/plugins/debugger/README.md). * For programmatic use of the API of TFDBG, see https://www.tensorflow.org/api_docs/python/tfdbg. diff --git a/tensorflow/python/debug/examples/README.md b/tensorflow/python/debug/examples/README.md index cb4d484092..3b431e04dc 100644 --- a/tensorflow/python/debug/examples/README.md +++ b/tensorflow/python/debug/examples/README.md @@ -3,7 +3,7 @@ Hi, there! The documentation of **TensorFlow Debugger (tfdbg)** has moved. See the source version at -[this new location](../../../docs_src/programmers_guide/debugger.md). +[this new location](../../../docs_src/guide/debugger.md). See the public website version at -[https://www.tensorflow.org/programmers_guide/debugger](https://www.tensorflow.org/programmers_guide/debugger). +[https://www.tensorflow.org/guide/debugger](https://www.tensorflow.org/guide/debugger). diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py index 2f439f765e..6856b8b5a9 100644 --- a/tensorflow/python/estimator/keras.py +++ b/tensorflow/python/estimator/keras.py @@ -455,7 +455,7 @@ def model_to_estimator(keras_model=None, """Constructs an `Estimator` instance from given keras model. For usage example, please see - @{$programmers_guide/estimators$creating_estimators_from_keras_models}. + @{$guide/estimators$creating_estimators_from_keras_models}. Args: keras_model: A compiled Keras model object. This argument is mutually diff --git a/tensorflow/python/ops/script_ops.py b/tensorflow/python/ops/script_ops.py index 16c73213d5..f8df9b2c78 100644 --- a/tensorflow/python/ops/script_ops.py +++ b/tensorflow/python/ops/script_ops.py @@ -267,7 +267,7 @@ def eager_py_func(func, inp, Tout, name=None): or print statements as desired, and wrap those functions in `tf.contrib.eager.py_func`. - For more information on eager execution, see @{$programmers_guide/eager}. + For more information on eager execution, see @{$guide/eager}. `tf.contrib.eager.py_func` is similar in spirit to @{tf.py_func}, but unlike the latter, the former lets you use TensorFlow operations in the wrapped diff --git a/tensorflow/python/tools/saved_model_cli.py b/tensorflow/python/tools/saved_model_cli.py index 5b9d25d449..38fed5335e 100644 --- a/tensorflow/python/tools/saved_model_cli.py +++ b/tensorflow/python/tools/saved_model_cli.py @@ -15,7 +15,7 @@ """Command-line interface to inspect and execute a graph in a SavedModel. For detailed usages and examples, please refer to: -https://www.tensorflow.org/programmers_guide/saved_model_cli +https://www.tensorflow.org/guide/saved_model_cli """ @@ -720,7 +720,7 @@ def create_parser(): '\'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') + 'https://www.tensorflow.org/guide/saved_model_cli\n') parser_run = subparsers.add_parser( 'run', description=run_msg, formatter_class=argparse.RawTextHelpFormatter) parser_run.add_argument( diff --git a/third_party/examples/eager/spinn/README.md b/third_party/examples/eager/spinn/README.md index fbb1fde837..e2fd8009a0 100644 --- a/third_party/examples/eager/spinn/README.md +++ b/third_party/examples/eager/spinn/README.md @@ -22,7 +22,7 @@ Other eager execution examples can be found under [tensorflow/contrib/eager/pyth - [`data.py`](../../../../tensorflow/contrib/eager/python/examples/spinn/data.py): Pipeline for loading and preprocessing the [SNLI](https://nlp.stanford.edu/projects/snli/) data and [GloVe](https://nlp.stanford.edu/projects/glove/) word embedding, written - using the [`tf.data`](https://www.tensorflow.org/programmers_guide/datasets) + using the [`tf.data`](https://www.tensorflow.org/guide/datasets) API. - [`spinn.py`](./spinn.py): Model definition and training routines. This example illustrates how one might perform the following actions with -- GitLab From ab60fbc1fcfc600b800ad12c9f76cfccc4fb7087 Mon Sep 17 00:00:00 2001 From: Pete Warden Date: Tue, 26 Jun 2018 09:32:12 -0700 Subject: [PATCH 051/519] Fix for RPi OpenBLAS compile issues, by pinning to known good version --- .../ci_build/install/install_pi_python3_toolchain.sh | 8 ++++++++ tensorflow/tools/ci_build/pi/build_raspberry_pi.sh | 4 ++++ 2 files changed, 12 insertions(+) diff --git a/tensorflow/tools/ci_build/install/install_pi_python3_toolchain.sh b/tensorflow/tools/ci_build/install/install_pi_python3_toolchain.sh index 9d8e3df3b5..4afb2f1534 100755 --- a/tensorflow/tools/ci_build/install/install_pi_python3_toolchain.sh +++ b/tensorflow/tools/ci_build/install/install_pi_python3_toolchain.sh @@ -27,3 +27,11 @@ curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add - apt-get update rm -rf /usr/local/bin/bazel apt-get install -y bazel python3 python3-numpy python3-dev python3-pip + +# We're using Ubuntu 14.04 as our base image because that's needed by the Pi +# cross-compilation chain, but that doesn't have built-in Python 3.5 support, so +# install from a separate repository. +apt-get install -y software-properties-common +add-apt-repository ppa:fkrull/deadsnakes +apt-get update +apt-get install -y python3.5 python3.5-dev diff --git a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh index 4d1a30601e..5eff3e415d 100755 --- a/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh +++ b/tensorflow/tools/ci_build/pi/build_raspberry_pi.sh @@ -65,6 +65,10 @@ OPENBLAS_SRC_PATH=/tmp/openblas_src/ sudo rm -rf ${OPENBLAS_SRC_PATH} git clone https://github.com/xianyi/OpenBLAS ${OPENBLAS_SRC_PATH} cd ${OPENBLAS_SRC_PATH} +# The commit after this introduced Fortran compile issues. In theory they should +# be solvable using NOFORTRAN=1 on the make command, but my initial tries didn't +# work, so pinning to the last know good version. +git checkout 5a6a2bed9aff0ba8a18651d5514d029c8cae336a # If this path is changed, you'll also need to update # cxx_builtin_include_directory in third_party/toolchains/cpus/arm/CROSSTOOL.tpl OPENBLAS_INSTALL_PATH=/tmp/openblas_install/ -- GitLab From fe374d31f38ba7fa84284b58d28c55dc0087f2b3 Mon Sep 17 00:00:00 2001 From: Pete Warden Date: Tue, 26 Jun 2018 09:36:22 -0700 Subject: [PATCH 052/519] Removed Python 3.5 updates for RPi --- .../ci_build/install/install_pi_python3_toolchain.sh | 8 -------- 1 file changed, 8 deletions(-) diff --git a/tensorflow/tools/ci_build/install/install_pi_python3_toolchain.sh b/tensorflow/tools/ci_build/install/install_pi_python3_toolchain.sh index 4afb2f1534..9d8e3df3b5 100755 --- a/tensorflow/tools/ci_build/install/install_pi_python3_toolchain.sh +++ b/tensorflow/tools/ci_build/install/install_pi_python3_toolchain.sh @@ -27,11 +27,3 @@ curl https://bazel.build/bazel-release.pub.gpg | sudo apt-key add - apt-get update rm -rf /usr/local/bin/bazel apt-get install -y bazel python3 python3-numpy python3-dev python3-pip - -# We're using Ubuntu 14.04 as our base image because that's needed by the Pi -# cross-compilation chain, but that doesn't have built-in Python 3.5 support, so -# install from a separate repository. -apt-get install -y software-properties-common -add-apt-repository ppa:fkrull/deadsnakes -apt-get update -apt-get install -y python3.5 python3.5-dev -- GitLab From 8025ac34099ed1b38c3cf0c0f84244496b42fedb Mon Sep 17 00:00:00 2001 From: Michael Case Date: Tue, 26 Jun 2018 13:05:25 -0700 Subject: [PATCH 053/519] Moving StatusOr from XLA to stream_executor. PiperOrigin-RevId: 202179928 --- tensorflow/compiler/xla/BUILD | 17 +- tensorflow/compiler/xla/service/gpu/BUILD | 1 + .../xla/service/gpu/stream_executor_util.h | 1 + tensorflow/compiler/xla/statusor.h | 286 +---------------- tensorflow/stream_executor/BUILD | 2 - .../xla => stream_executor/lib}/statusor.cc | 8 +- tensorflow/stream_executor/lib/statusor.h | 290 +++++++++++++++++- .../lib}/statusor_internals.h | 15 +- .../lib}/statusor_test.cc | 11 +- 9 files changed, 310 insertions(+), 321 deletions(-) rename tensorflow/{compiler/xla => stream_executor/lib}/statusor.cc (89%) rename tensorflow/{compiler/xla => stream_executor/lib}/statusor_internals.h (94%) rename tensorflow/{compiler/xla => stream_executor/lib}/statusor_test.cc (99%) diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index c6deb959a5..afa8ce730b 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -143,30 +143,15 @@ cc_library( cc_library( name = "statusor", - srcs = ["statusor.cc"], hdrs = [ "statusor.h", - "statusor_internals.h", ], visibility = ["//visibility:public"], deps = [ ":status", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", - ], -) - -tf_cc_test( - name = "statusor_test", - size = "small", - srcs = ["statusor_test.cc"], - deps = [ - ":statusor", - ":test", - ":types", - "//tensorflow/core:lib", - "//tensorflow/core:test", - "//tensorflow/core:test_main", + "//tensorflow/stream_executor", ], ) diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 68297ad4ae..fe597bfb45 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -727,6 +727,7 @@ cc_library( hdrs = ["stream_executor_util.h"], deps = [ "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/core:stream_executor_no_cuda", ], diff --git a/tensorflow/compiler/xla/service/gpu/stream_executor_util.h b/tensorflow/compiler/xla/service/gpu/stream_executor_util.h index 8218f4fd11..39a6a38d00 100644 --- a/tensorflow/compiler/xla/service/gpu/stream_executor_util.h +++ b/tensorflow/compiler/xla/service/gpu/stream_executor_util.h @@ -16,6 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_GPU_STREAM_EXECUTOR_UTIL_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_STREAM_EXECUTOR_UTIL_H_ +#include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" diff --git a/tensorflow/compiler/xla/statusor.h b/tensorflow/compiler/xla/statusor.h index 0e1387c939..a32e2ad985 100644 --- a/tensorflow/compiler/xla/statusor.h +++ b/tensorflow/compiler/xla/statusor.h @@ -12,297 +12,17 @@ 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. ==============================================================================*/ - -// StatusOr is the union of a Status object and a T object. StatusOr models -// the concept of an object that is either a value, or an error Status -// explaining why such a value is not present. To this end, StatusOr does not -// allow its Status value to be Status::OK. -// -// The primary use-case for StatusOr is as the return value of a -// function which may fail. -// -// Example client usage for a StatusOr, where T is not a pointer: -// -// StatusOr result = DoBigCalculationThatCouldFail(); -// if (result.ok()) { -// float answer = result.ValueOrDie(); -// printf("Big calculation yielded: %f", answer); -// } else { -// LOG(ERROR) << result.status(); -// } -// -// Example client usage for a StatusOr: -// -// StatusOr result = FooFactory::MakeNewFoo(arg); -// if (result.ok()) { -// std::unique_ptr foo(result.ValueOrDie()); -// foo->DoSomethingCool(); -// } else { -// LOG(ERROR) << result.status(); -// } -// -// Example client usage for a StatusOr>: -// -// StatusOr> result = FooFactory::MakeNewFoo(arg); -// if (result.ok()) { -// std::unique_ptr foo = std::move(result.ValueOrDie()); -// foo->DoSomethingCool(); -// } else { -// LOG(ERROR) << result.status(); -// } -// -// Example factory implementation returning StatusOr: -// -// StatusOr FooFactory::MakeNewFoo(int arg) { -// if (arg <= 0) { -// return tensorflow::InvalidArgument("Arg must be positive"); -// } else { -// return new Foo(arg); -// } -// } -// -// Note that the assignment operators require that destroying the currently -// stored value cannot invalidate the argument; in other words, the argument -// cannot be an alias for the current value, or anything owned by the current -// value. #ifndef TENSORFLOW_COMPILER_XLA_STATUSOR_H_ #define TENSORFLOW_COMPILER_XLA_STATUSOR_H_ #include "tensorflow/compiler/xla/status.h" -#include "tensorflow/compiler/xla/statusor_internals.h" -#include "tensorflow/core/platform/macros.h" +#include "tensorflow/stream_executor/lib/statusor.h" namespace xla { -#if defined(__clang__) -// Only clang supports warn_unused_result as a type annotation. -template -class TF_MUST_USE_RESULT StatusOr; -#endif - -template -class StatusOr : private internal_statusor::StatusOrData, - private internal_statusor::TraitsBase< - std::is_copy_constructible::value, - std::is_move_constructible::value> { - template - friend class StatusOr; - - typedef internal_statusor::StatusOrData Base; - - public: - typedef T element_type; - - // Constructs a new StatusOr with Status::UNKNOWN status. This is marked - // 'explicit' to try to catch cases like 'return {};', where people think - // StatusOr> will be initialized with an empty vector, - // instead of a Status::UNKNOWN status. - explicit StatusOr(); - - // StatusOr will be copy constructible/assignable if T is copy - // constructible. - StatusOr(const StatusOr&) = default; - StatusOr& operator=(const StatusOr&) = default; - - // StatusOr will be move constructible/assignable if T is move - // constructible. - StatusOr(StatusOr&&) = default; - StatusOr& operator=(StatusOr&&) = default; - - // Conversion copy/move constructor, T must be convertible from U. - template ::value>::type* = nullptr> - StatusOr(const StatusOr& other); - template ::value>::type* = nullptr> - StatusOr(StatusOr&& other); - - // Conversion copy/move assignment operator, T must be convertible from U. - template ::value>::type* = nullptr> - StatusOr& operator=(const StatusOr& other); - template ::value>::type* = nullptr> - StatusOr& operator=(StatusOr&& other); - - // Constructs a new StatusOr with the given value. After calling this - // constructor, calls to ValueOrDie() will succeed, and calls to status() will - // return OK. - // - // NOTE: Not explicit - we want to use StatusOr as a return type - // so it is convenient and sensible to be able to do 'return T()' - // when the return type is StatusOr. - // - // REQUIRES: T is copy constructible. - StatusOr(const T& value); - - // Constructs a new StatusOr with the given non-ok status. After calling - // this constructor, calls to ValueOrDie() will CHECK-fail. - // - // NOTE: Not explicit - we want to use StatusOr as a return - // value, so it is convenient and sensible to be able to do 'return - // Status()' when the return type is StatusOr. - // - // REQUIRES: !status.ok(). This requirement is DCHECKed. - // In optimized builds, passing Status::OK() here will have the effect - // of passing tensorflow::error::INTERNAL as a fallback. - StatusOr(const Status& status); - StatusOr& operator=(const Status& status); - - // TODO(b/62186997): Add operator=(T) overloads. - - // Similar to the `const T&` overload. - // - // REQUIRES: T is move constructible. - StatusOr(T&& value); - - // RValue versions of the operations declared above. - StatusOr(Status&& status); - StatusOr& operator=(Status&& status); - - // Returns this->status().ok() - bool ok() const { return this->status_.ok(); } - - // Returns a reference to our status. If this contains a T, then - // returns Status::OK(). - const Status& status() const &; - Status status() &&; - - // Returns a reference to our current value, or CHECK-fails if !this->ok(). - // - // Note: for value types that are cheap to copy, prefer simple code: - // - // T value = statusor.ValueOrDie(); - // - // Otherwise, if the value type is expensive to copy, but can be left - // in the StatusOr, simply assign to a reference: - // - // T& value = statusor.ValueOrDie(); // or `const T&` - // - // Otherwise, if the value type supports an efficient move, it can be - // used as follows: - // - // T value = std::move(statusor).ValueOrDie(); - // - // The std::move on statusor instead of on the whole expression enables - // warnings about possible uses of the statusor object after the move. - // C++ style guide waiver for ref-qualified overloads granted in cl/143176389 - // See go/ref-qualifiers for more details on such overloads. - const T& ValueOrDie() const &; - T& ValueOrDie() &; - const T&& ValueOrDie() const &&; - T&& ValueOrDie() &&; - - T ConsumeValueOrDie() { return std::move(ValueOrDie()); } - - // Ignores any errors. This method does nothing except potentially suppress - // complaints from any tools that are checking that errors are not dropped on - // the floor. - void IgnoreError() const; -}; - -//////////////////////////////////////////////////////////////////////////////// -// Implementation details for StatusOr - -template -StatusOr::StatusOr() : Base(Status(tensorflow::error::UNKNOWN, "")) {} - -template -StatusOr::StatusOr(const T& value) : Base(value) {} - -template -StatusOr::StatusOr(const Status& status) : Base(status) {} - -template -StatusOr& StatusOr::operator=(const Status& status) { - this->Assign(status); - return *this; -} - -template -StatusOr::StatusOr(T&& value) : Base(std::move(value)) {} - -template -StatusOr::StatusOr(Status&& status) : Base(std::move(status)) {} - -template -StatusOr& StatusOr::operator=(Status&& status) { - this->Assign(std::move(status)); - return *this; -} - -template -template ::value>::type*> -inline StatusOr::StatusOr(const StatusOr& other) - : Base(static_cast::Base&>(other)) {} - -template -template ::value>::type*> -inline StatusOr& StatusOr::operator=(const StatusOr& other) { - if (other.ok()) - this->Assign(other.ValueOrDie()); - else - this->Assign(other.status()); - return *this; -} - -template -template ::value>::type*> -inline StatusOr::StatusOr(StatusOr&& other) - : Base(static_cast::Base&&>(other)) {} - -template -template ::value>::type*> -inline StatusOr& StatusOr::operator=(StatusOr&& other) { - if (other.ok()) { - this->Assign(std::move(other).ValueOrDie()); - } else { - this->Assign(std::move(other).status()); - } - return *this; -} - -template -const Status& StatusOr::status() const & { - return this->status_; -} -template -Status StatusOr::status() && { - return ok() ? Status::OK() : std::move(this->status_); -} - -template -const T& StatusOr::ValueOrDie() const & { - this->EnsureOk(); - return this->data_; -} - -template -T& StatusOr::ValueOrDie() & { - this->EnsureOk(); - return this->data_; -} - -template -const T&& StatusOr::ValueOrDie() const && { - this->EnsureOk(); - return std::move(this->data_); -} - -template -T&& StatusOr::ValueOrDie() && { - this->EnsureOk(); - return std::move(this->data_); -} - +// Use steam_executor's StatusOr so we don't duplicate code. template -void StatusOr::IgnoreError() const { - // no-op -} +using StatusOr = ::stream_executor::port::StatusOr; } // namespace xla diff --git a/tensorflow/stream_executor/BUILD b/tensorflow/stream_executor/BUILD index c68cda0100..21295abed1 100644 --- a/tensorflow/stream_executor/BUILD +++ b/tensorflow/stream_executor/BUILD @@ -33,7 +33,6 @@ cc_library( }), visibility = ["//visibility:public"], deps = [ - "//tensorflow/compiler/xla:statusor", "//tensorflow/core:lib", "//tensorflow/core:ptr_util", "@local_config_cuda//cuda:cuda_headers", @@ -48,7 +47,6 @@ cc_library( deps = [ "//tensorflow/core:lib", "//tensorflow/core:ptr_util", - "//tensorflow/compiler/xla:statusor", "@local_config_cuda//cuda:cuda_headers", ] + if_static([":stream_executor_impl"]), ) diff --git a/tensorflow/compiler/xla/statusor.cc b/tensorflow/stream_executor/lib/statusor.cc similarity index 89% rename from tensorflow/compiler/xla/statusor.cc rename to tensorflow/stream_executor/lib/statusor.cc index 72ab67ff81..e0e851f96e 100644 --- a/tensorflow/compiler/xla/statusor.cc +++ b/tensorflow/stream_executor/lib/statusor.cc @@ -13,12 +13,13 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/stream_executor/lib/statusor.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/platform/logging.h" -namespace xla { +namespace stream_executor { +namespace port { namespace internal_statusor { void Helper::HandleInvalidStatusCtorArg(Status* status) { @@ -35,4 +36,5 @@ void Helper::Crash(const Status& status) { } } // namespace internal_statusor -} // namespace xla +} // namespace port +} // namespace stream_executor diff --git a/tensorflow/stream_executor/lib/statusor.h b/tensorflow/stream_executor/lib/statusor.h index dab5909674..3c716acb46 100644 --- a/tensorflow/stream_executor/lib/statusor.h +++ b/tensorflow/stream_executor/lib/statusor.h @@ -1,4 +1,4 @@ -/* Copyright 2015 The TensorFlow Authors. All Rights Reserved. +/* 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. @@ -13,19 +13,297 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -// IWYU pragma: private, include "third_party/tensorflow/stream_executor/stream_executor.h" - +// StatusOr is the union of a Status object and a T object. StatusOr models +// the concept of an object that is either a value, or an error Status +// explaining why such a value is not present. To this end, StatusOr does not +// allow its Status value to be Status::OK. +// +// The primary use-case for StatusOr is as the return value of a +// function which may fail. +// +// Example client usage for a StatusOr, where T is not a pointer: +// +// StatusOr result = DoBigCalculationThatCouldFail(); +// if (result.ok()) { +// float answer = result.ValueOrDie(); +// printf("Big calculation yielded: %f", answer); +// } else { +// LOG(ERROR) << result.status(); +// } +// +// Example client usage for a StatusOr: +// +// StatusOr result = FooFactory::MakeNewFoo(arg); +// if (result.ok()) { +// std::unique_ptr foo(result.ValueOrDie()); +// foo->DoSomethingCool(); +// } else { +// LOG(ERROR) << result.status(); +// } +// +// Example client usage for a StatusOr>: +// +// StatusOr> result = FooFactory::MakeNewFoo(arg); +// if (result.ok()) { +// std::unique_ptr foo = std::move(result.ValueOrDie()); +// foo->DoSomethingCool(); +// } else { +// LOG(ERROR) << result.status(); +// } +// +// Example factory implementation returning StatusOr: +// +// StatusOr FooFactory::MakeNewFoo(int arg) { +// if (arg <= 0) { +// return tensorflow::InvalidArgument("Arg must be positive"); +// } else { +// return new Foo(arg); +// } +// } +// +// Note that the assignment operators require that destroying the currently +// stored value cannot invalidate the argument; in other words, the argument +// cannot be an alias for the current value, or anything owned by the current +// value. #ifndef TENSORFLOW_STREAM_EXECUTOR_LIB_STATUSOR_H_ #define TENSORFLOW_STREAM_EXECUTOR_LIB_STATUSOR_H_ -#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/core/platform/macros.h" +#include "tensorflow/stream_executor/lib/status.h" +#include "tensorflow/stream_executor/lib/statusor_internals.h" namespace stream_executor { namespace port { -// Use XLA's StatusOr so we don't duplicate code. +#if defined(__clang__) +// Only clang supports warn_unused_result as a type annotation. +template +class TF_MUST_USE_RESULT StatusOr; +#endif + +template +class StatusOr : private internal_statusor::StatusOrData, + private internal_statusor::TraitsBase< + std::is_copy_constructible::value, + std::is_move_constructible::value> { + template + friend class StatusOr; + + typedef internal_statusor::StatusOrData Base; + + public: + typedef T element_type; + + // Constructs a new StatusOr with Status::UNKNOWN status. This is marked + // 'explicit' to try to catch cases like 'return {};', where people think + // StatusOr> will be initialized with an empty vector, + // instead of a Status::UNKNOWN status. + explicit StatusOr(); + + // StatusOr will be copy constructible/assignable if T is copy + // constructible. + StatusOr(const StatusOr&) = default; + StatusOr& operator=(const StatusOr&) = default; + + // StatusOr will be move constructible/assignable if T is move + // constructible. + StatusOr(StatusOr&&) = default; + StatusOr& operator=(StatusOr&&) = default; + + // Conversion copy/move constructor, T must be convertible from U. + template ::value>::type* = nullptr> + StatusOr(const StatusOr& other); + template ::value>::type* = nullptr> + StatusOr(StatusOr&& other); + + // Conversion copy/move assignment operator, T must be convertible from U. + template ::value>::type* = nullptr> + StatusOr& operator=(const StatusOr& other); + template ::value>::type* = nullptr> + StatusOr& operator=(StatusOr&& other); + + // Constructs a new StatusOr with the given value. After calling this + // constructor, calls to ValueOrDie() will succeed, and calls to status() will + // return OK. + // + // NOTE: Not explicit - we want to use StatusOr as a return type + // so it is convenient and sensible to be able to do 'return T()' + // when the return type is StatusOr. + // + // REQUIRES: T is copy constructible. + StatusOr(const T& value); + + // Constructs a new StatusOr with the given non-ok status. After calling + // this constructor, calls to ValueOrDie() will CHECK-fail. + // + // NOTE: Not explicit - we want to use StatusOr as a return + // value, so it is convenient and sensible to be able to do 'return + // Status()' when the return type is StatusOr. + // + // REQUIRES: !status.ok(). This requirement is DCHECKed. + // In optimized builds, passing Status::OK() here will have the effect + // of passing tensorflow::error::INTERNAL as a fallback. + StatusOr(const Status& status); + StatusOr& operator=(const Status& status); + + // TODO(b/62186997): Add operator=(T) overloads. + + // Similar to the `const T&` overload. + // + // REQUIRES: T is move constructible. + StatusOr(T&& value); + + // RValue versions of the operations declared above. + StatusOr(Status&& status); + StatusOr& operator=(Status&& status); + + // Returns this->status().ok() + bool ok() const { return this->status_.ok(); } + + // Returns a reference to our status. If this contains a T, then + // returns Status::OK(). + const Status& status() const &; + Status status() &&; + + // Returns a reference to our current value, or CHECK-fails if !this->ok(). + // + // Note: for value types that are cheap to copy, prefer simple code: + // + // T value = statusor.ValueOrDie(); + // + // Otherwise, if the value type is expensive to copy, but can be left + // in the StatusOr, simply assign to a reference: + // + // T& value = statusor.ValueOrDie(); // or `const T&` + // + // Otherwise, if the value type supports an efficient move, it can be + // used as follows: + // + // T value = std::move(statusor).ValueOrDie(); + // + // The std::move on statusor instead of on the whole expression enables + // warnings about possible uses of the statusor object after the move. + // C++ style guide waiver for ref-qualified overloads granted in cl/143176389 + // See go/ref-qualifiers for more details on such overloads. + const T& ValueOrDie() const &; + T& ValueOrDie() &; + const T&& ValueOrDie() const &&; + T&& ValueOrDie() &&; + + T ConsumeValueOrDie() { return std::move(ValueOrDie()); } + + // Ignores any errors. This method does nothing except potentially suppress + // complaints from any tools that are checking that errors are not dropped on + // the floor. + void IgnoreError() const; +}; + +//////////////////////////////////////////////////////////////////////////////// +// Implementation details for StatusOr + +template +StatusOr::StatusOr() : Base(Status(tensorflow::error::UNKNOWN, "")) {} + +template +StatusOr::StatusOr(const T& value) : Base(value) {} + +template +StatusOr::StatusOr(const Status& status) : Base(status) {} + +template +StatusOr& StatusOr::operator=(const Status& status) { + this->Assign(status); + return *this; +} + +template +StatusOr::StatusOr(T&& value) : Base(std::move(value)) {} + +template +StatusOr::StatusOr(Status&& status) : Base(std::move(status)) {} + +template +StatusOr& StatusOr::operator=(Status&& status) { + this->Assign(std::move(status)); + return *this; +} + +template +template ::value>::type*> +inline StatusOr::StatusOr(const StatusOr& other) + : Base(static_cast::Base&>(other)) {} + +template +template ::value>::type*> +inline StatusOr& StatusOr::operator=(const StatusOr& other) { + if (other.ok()) + this->Assign(other.ValueOrDie()); + else + this->Assign(other.status()); + return *this; +} + +template +template ::value>::type*> +inline StatusOr::StatusOr(StatusOr&& other) + : Base(static_cast::Base&&>(other)) {} + +template +template ::value>::type*> +inline StatusOr& StatusOr::operator=(StatusOr&& other) { + if (other.ok()) { + this->Assign(std::move(other).ValueOrDie()); + } else { + this->Assign(std::move(other).status()); + } + return *this; +} + +template +const Status& StatusOr::status() const & { + return this->status_; +} +template +Status StatusOr::status() && { + return ok() ? Status::OK() : std::move(this->status_); +} + +template +const T& StatusOr::ValueOrDie() const & { + this->EnsureOk(); + return this->data_; +} + +template +T& StatusOr::ValueOrDie() & { + this->EnsureOk(); + return this->data_; +} + +template +const T&& StatusOr::ValueOrDie() const && { + this->EnsureOk(); + return std::move(this->data_); +} + +template +T&& StatusOr::ValueOrDie() && { + this->EnsureOk(); + return std::move(this->data_); +} + template -using StatusOr = ::xla::StatusOr; +void StatusOr::IgnoreError() const { + // no-op +} } // namespace port } // namespace stream_executor diff --git a/tensorflow/compiler/xla/statusor_internals.h b/tensorflow/stream_executor/lib/statusor_internals.h similarity index 94% rename from tensorflow/compiler/xla/statusor_internals.h rename to tensorflow/stream_executor/lib/statusor_internals.h index 14636bd144..09f88f5825 100644 --- a/tensorflow/compiler/xla/statusor_internals.h +++ b/tensorflow/stream_executor/lib/statusor_internals.h @@ -13,13 +13,15 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_ -#define TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_ +#ifndef TENSORFLOW_STREAM_EXECUTOR_LIB_STATUSOR_INTERNALS_H_ +#define TENSORFLOW_STREAM_EXECUTOR_LIB_STATUSOR_INTERNALS_H_ + -#include "tensorflow/compiler/xla/status.h" #include "tensorflow/core/platform/macros.h" +#include "tensorflow/stream_executor/lib/status.h" -namespace xla { +namespace stream_executor { +namespace port { namespace internal_statusor { class Helper { @@ -240,6 +242,7 @@ struct TraitsBase { }; } // namespace internal_statusor -} // namespace xla +} // namespace port +} // namespace stream_executor -#endif // TENSORFLOW_COMPILER_XLA_STATUSOR_INTERNALS_H_ +#endif // TENSORFLOW_STREAM_EXECUTOR_LIB_STATUSOR_INTERNALS_H_ diff --git a/tensorflow/compiler/xla/statusor_test.cc b/tensorflow/stream_executor/lib/statusor_test.cc similarity index 99% rename from tensorflow/compiler/xla/statusor_test.cc rename to tensorflow/stream_executor/lib/statusor_test.cc index 377a618ffb..56584e1892 100644 --- a/tensorflow/compiler/xla/statusor_test.cc +++ b/tensorflow/stream_executor/lib/statusor_test.cc @@ -15,18 +15,18 @@ limitations under the License. // Unit tests for StatusOr -#include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/stream_executor/lib/statusor.h" #include #include -#include "tensorflow/compiler/xla/test.h" -#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/core/platform/test.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/test_benchmark.h" -namespace xla { +namespace stream_executor { +namespace port { namespace { class Base1 { @@ -672,4 +672,5 @@ void BM_StatusOrFactoryFailLongMsg(int iters) { BENCHMARK(BM_StatusOrFactoryFailLongMsg); } // namespace -} // namespace xla +} // namespace port +} // namespace stream_executor -- GitLab From b8c1732664f41d5af2587e2f093880a3a7d83f43 Mon Sep 17 00:00:00 2001 From: Michael Case Date: Tue, 26 Jun 2018 15:07:23 -0700 Subject: [PATCH 054/519] Fix small typo in RELEASE.md --- RELEASE.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/RELEASE.md b/RELEASE.md index 879b995a5a..52cd9ef72b 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -21,7 +21,7 @@ * The [distributions.Bijector](https://www.tensorflow.org/versions/r1.9/api_docs/python/tf/contrib/distributions/bijectors/Bijector) API supports broadcasting for Bijectors with new API changes. -## Breaking Chances +## Breaking Changes * If you're opening empty variable scopes; replace `variable_scope('', ...)` by `variable_scope(tf.get_variable_scope(), ...)`. -- GitLab From 74ca837950536aaef358abf3e05b31b4d62248f7 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Tue, 26 Jun 2018 15:29:48 -0700 Subject: [PATCH 055/519] Update eigen version to a fixed version for ppc64. --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 3c657c4a5b..79274d66ad 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -107,11 +107,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "eigen_archive", urls = [ - "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/e5e305a158a0.tar.gz", - "https://bitbucket.org/eigen/eigen/get/e5e305a158a0.tar.gz", + "https://mirror.bazel.build/bitbucket.org/eigen/eigen/get/fd6845384b86.tar.gz", + "https://bitbucket.org/eigen/eigen/get/fd6845384b86.tar.gz", ], - sha256 = "8bbe676d69e7f59070c83a949454b8b6344034e0ebbf686b337528e5dc04c7de", - strip_prefix = "eigen-eigen-e5e305a158a0", + sha256 = "d956415d784fa4e42b6a2a45c32556d6aec9d0a3d8ef48baee2522ab762556a9", + strip_prefix = "eigen-eigen-fd6845384b86", build_file = clean_dep("//third_party:eigen.BUILD"), ) -- GitLab From 388a267b1191adf2df4006bf205a19b8a24813db Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 26 Jun 2018 11:24:37 -0700 Subject: [PATCH 056/519] Remove section links that don't go anywhere. --- tensorflow/docs_src/get_started/_index.yaml | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/tensorflow/docs_src/get_started/_index.yaml b/tensorflow/docs_src/get_started/_index.yaml index 277fc852fb..4060804892 100644 --- a/tensorflow/docs_src/get_started/_index.yaml +++ b/tensorflow/docs_src/get_started/_index.yaml @@ -66,9 +66,7 @@ landing_page: }

- -

Learn and use ML

- +

Learn and use ML

The high-level Keras API provides building blocks to create and @@ -117,9 +115,7 @@ landing_page: - items: - custom_html: >

- -

Research and experimentation

- +

Research and experimentation

Eager execution provides an imperative, define-by-run interface for advanced operations. Write custom layers, forward passes, and training loops with auto‑differentiation. Start with @@ -170,9 +166,7 @@ landing_page:

- custom_html: >
- -

ML at production scale

- +

ML at production scale

Estimators can train large models on multiple machines in a -- GitLab From 9f4fbdb05e35b512dd4a3da5ae80558021a291e5 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 26 Jun 2018 14:56:57 -0700 Subject: [PATCH 057/519] Fix leftnav for get_started --- tensorflow/docs_src/get_started/leftnav_files | 6 +++--- tensorflow/docs_src/get_started/next_steps.md | 2 +- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/docs_src/get_started/leftnav_files b/tensorflow/docs_src/get_started/leftnav_files index 5c400a67f0..99d2b2c3e1 100644 --- a/tensorflow/docs_src/get_started/leftnav_files +++ b/tensorflow/docs_src/get_started/leftnav_files @@ -1,7 +1,7 @@ ### Learn and use ML -basic_classification.md -basic_text_classification.md -basic_regression.md +basic_classification.md: Basic classification +basic_text_classification.md: Text classification +basic_regression.md: Regression overfit_and_underfit.md save_and_restore_models.md next_steps.md diff --git a/tensorflow/docs_src/get_started/next_steps.md b/tensorflow/docs_src/get_started/next_steps.md index 6318a39c6c..01c9f7204a 100644 --- a/tensorflow/docs_src/get_started/next_steps.md +++ b/tensorflow/docs_src/get_started/next_steps.md @@ -1,4 +1,4 @@ -# Next Steps +# Next steps ## Learn more about TensorFlow -- GitLab From 9809978ea09845d5429925b64c20cda461c20a66 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 26 Jun 2018 21:34:07 -0700 Subject: [PATCH 058/519] Fix checkpoints link in keras guide --- tensorflow/docs_src/guide/keras.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/docs_src/guide/keras.md b/tensorflow/docs_src/guide/keras.md index 83172dab7f..c799e9b12c 100644 --- a/tensorflow/docs_src/guide/keras.md +++ b/tensorflow/docs_src/guide/keras.md @@ -35,7 +35,7 @@ from tensorflow import keras * The `tf.keras` version in the latest TensorFlow release might not be the same as the latest `keras` version from PyPI. Check `tf.keras.__version__`. * When [saving a model's weights](#weights_only), `tf.keras` defaults to the - [checkpoint format](../get_started/checkpoints.md). Pass `save_format='h5'` to + [checkpoint format](./checkpoints.md). Pass `save_format='h5'` to use HDF5. ## Build a simple model @@ -442,7 +442,7 @@ model.load_weights('my_model') ``` By default, this saves the model's weights in the -[TensorFlow checkpoint](../get_started/checkpoints.md) file format. Weights can +[TensorFlow checkpoint](./checkpoints.md) file format. Weights can also be saved to the Keras HDF5 format (the default for the multi-backend implementation of Keras): -- GitLab From 32d4e6fd74fbeb91c8b2fd06c5ab0d4247d1784d Mon Sep 17 00:00:00 2001 From: Michael Case Date: Wed, 27 Jun 2018 09:46:45 -0700 Subject: [PATCH 059/519] Update version strings for TF 1.9.0-rc2. --- tensorflow/core/public/version.h | 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 | 18 +++++++-------- tensorflow/docs_src/install/install_mac.md | 10 ++++----- .../docs_src/install/install_sources.md | 4 ++-- tensorflow/tools/pip_package/setup.py | 2 +- 8 files changed, 31 insertions(+), 31 deletions(-) diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 9e5e747557..0e4a61ac1f 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 "-rc1" +#define TF_VERSION_SUFFIX "-rc2" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 2f81ae0c40..9aebf2bfa4 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_64-1.9.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0-rc2.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 5451e1b319..1907355341 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc2.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index ad3544b595..b9c9912816 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.9.0-rc1 + 1.9.0-rc2 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.9.0-rc1 + 1.9.0-rc2 @@ -124,12 +124,12 @@ instead: org.tensorflow libtensorflow - 1.9.0-rc1 + 1.9.0-rc2 org.tensorflow libtensorflow_jni_gpu - 1.9.0-rc1 + 1.9.0-rc2 ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc1.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc2.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc1.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc2.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,10 +175,10 @@ 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-1.9.0-rc1.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc2.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_64-1.9.0-rc1.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0-rc2.zip). 3. Extract this .zip file. @@ -227,7 +227,7 @@ 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: -

javac -cp libtensorflow-1.9.0-rc1.jar HelloTF.java
+
javac -cp libtensorflow-1.9.0-rc2.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.9.0-rc1.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.9.0-rc2.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.9.0-rc1.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.9.0-rc2.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, 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 41619ca230..ae3d50ff39 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -438,7 +438,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: 
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc1-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp34-cp34m-linux_x86_64.whl ## Validate your installation @@ -678,14 +678,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc1-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -697,14 +697,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc1-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -716,14 +716,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc1-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp35-cp35m-linux_x86_64.whl
@@ -735,14 +735,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc1-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index eeca389617..3de6da1342 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: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc1-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc1-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc1-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py2-none-any.whl @@ -518,7 +518,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc1-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py2-none-any.whl
@@ -526,5 +526,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc1-py2-none-a 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc1-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 7afcd340aa..3520f97c9a 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -328,10 +328,10 @@ 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 1.9.0rc1 on Linux: +for TensorFlow 1.9.0rc2 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc1-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc2-py2-none-any.whl
## Validate your installation diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index eb2e359ee5..ed7ce01b6b 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -45,7 +45,7 @@ DOCLINES = __doc__.split('\n') # 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 = '1.9.0-rc1' +_VERSION = '1.9.0-rc2' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', -- GitLab From f93e1b07282216d77e9d7d704f6722a893e9ef73 Mon Sep 17 00:00:00 2001 From: Michael Case Date: Thu, 28 Jun 2018 11:24:37 -0700 Subject: [PATCH 060/519] Potential fix for how pip installs headers used for custom ops. These headers were recently moved from site-packages/external into site-packages/tensorflow/include/external. Need to update setup.py to reflect that. --- RELEASE.md | 1 + tensorflow/tools/pip_package/setup.py | 10 +++++----- 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/RELEASE.md b/RELEASE.md index 52cd9ef72b..21207a7efa 100644 --- a/RELEASE.md +++ b/RELEASE.md @@ -24,6 +24,7 @@ ## Breaking Changes * If you're opening empty variable scopes; replace `variable_scope('', ...)` by `variable_scope(tf.get_variable_scope(), ...)`. + * Headers used for building custom ops have been moved from site-packages/external into site-packages/tensorflow/include/external. ## Bug Fixes and Other Changes diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index ed7ce01b6b..8c077580aa 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -170,8 +170,9 @@ class InstallHeaders(Command): # symlink within the directory hierarchy. # NOTE(keveman): Figure out how to customize bdist_wheel package so # we can do the symlink. - if 'external/eigen_archive/' in install_dir: - extra_dir = install_dir.replace('external/eigen_archive', '') + if 'tensorflow/include/external/eigen_archive/' in install_dir: + extra_dir = install_dir.replace( + 'tensorflow/include/external/eigen_archive', '') if not os.path.exists(extra_dir): self.mkpath(extra_dir) self.copy_file(header, extra_dir) @@ -204,13 +205,12 @@ def find_files(pattern, root): yield os.path.join(dirpath, filename) -matches = ['../' + x for x in find_files('*', 'external') if '.py' not in x] - so_lib_paths = [ i for i in os.listdir('.') if os.path.isdir(i) and fnmatch.fnmatch(i, '_solib_*') ] +matches = [] for path in so_lib_paths: matches.extend( ['../' + x for x in find_files('*', path) if '.py' not in x] @@ -225,7 +225,7 @@ headers = (list(find_files('*.h', 'tensorflow/core')) + list(find_files('*.h', 'tensorflow/stream_executor')) + list(find_files('*.h', 'google/protobuf_archive/src')) + list(find_files('*', 'third_party/eigen3')) + - list(find_files('*', 'external/eigen_archive'))) + list(find_files('*', 'tensorflow/include/external/eigen_archive'))) setup( name=project_name, -- GitLab From f09aaf0dd33869253020b095d7c44840d1b430fe Mon Sep 17 00:00:00 2001 From: Michael Case Date: Fri, 29 Jun 2018 10:19:06 -0700 Subject: [PATCH 061/519] Exclude test sources from stream executor builds. (#20423) PiperOrigin-RevId: 202423156 --- tensorflow/contrib/cmake/tf_stream_executor.cmake | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/cmake/tf_stream_executor.cmake b/tensorflow/contrib/cmake/tf_stream_executor.cmake index 9a37b68119..2f70e59d54 100644 --- a/tensorflow/contrib/cmake/tf_stream_executor.cmake +++ b/tensorflow/contrib/cmake/tf_stream_executor.cmake @@ -76,11 +76,11 @@ if (tensorflow_ENABLE_GPU) list(APPEND tf_stream_executor_srcs ${tf_stream_executor_gpu_srcs}) endif() -#file(GLOB_RECURSE tf_stream_executor_test_srcs -# "${tensorflow_source_dir}/tensorflow/stream_executor/*_test.cc" -# "${tensorflow_source_dir}/tensorflow/stream_executor/*_test.h" -#) -#list(REMOVE_ITEM tf_stream_executor_srcs ${tf_stream_executor_test_srcs}) +file(GLOB_RECURSE tf_stream_executor_test_srcs + "${tensorflow_source_dir}/tensorflow/stream_executor/*test.cc" + "${tensorflow_source_dir}/tensorflow/stream_executor/lib/*test.h" +) +list(REMOVE_ITEM tf_stream_executor_srcs ${tf_stream_executor_test_srcs}) if (NOT WIN32) set (CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -lgomp") -- GitLab From 648ef712f2c4fc996551373765aff30a0e48bc4c Mon Sep 17 00:00:00 2001 From: bhack Date: Sat, 30 Jun 2018 14:54:43 +0200 Subject: [PATCH 062/519] Advise batch_normalization with model_to_estimator --- tensorflow/docs_src/guide/keras.md | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/docs_src/guide/keras.md b/tensorflow/docs_src/guide/keras.md index c799e9b12c..d584ebe945 100644 --- a/tensorflow/docs_src/guide/keras.md +++ b/tensorflow/docs_src/guide/keras.md @@ -548,9 +548,11 @@ model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), estimator = keras.estimator.model_to_estimator(model) ``` -Note: Enable [eager execution](./eager.md) for debugging +Note: +* Enable [eager execution](./eager.md) for debugging [Estimator input functions](./premade_estimators.md#create_input_functions) and inspecting data. +* Don't use batch normalization or try to finetune batch normalization models with estimators created from `tf.keras.estimator.model_to_estimator`. More details at [#17950](https://github.com/tensorflow/tensorflow/issues/17950) ### Multiple GPUs -- GitLab From 8fab75ff2e551414d093b8fc3b3e4a78bf91754d Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sat, 30 Jun 2018 16:12:27 +0000 Subject: [PATCH 063/519] Update .gitignore for cmake generated file While running cmake in Linux: ``` tensorflow/tools/ci_build/ci_build.sh CMAKE tensorflow/tools/ci_build/builds/cmake.sh ``` the following file is generated and left out: ``` ubuntu@ubuntu:~/tensorflow$ git status On branch master Your branch is up-to-date with 'origin/master'. Untracked files: (use "git add ..." to include in what will be committed) estimator_api_init_files_list.txt nothing added to commit but untracked files present (use "git add" to track) ``` This fix add `/estimator_api_init_files_list.txt` in gitignore so that it will not be picked by `git add -A`. Signed-off-by: Yong Tang --- .gitignore | 1 + 1 file changed, 1 insertion(+) diff --git a/.gitignore b/.gitignore index b5306b8b79..5afe375f46 100644 --- a/.gitignore +++ b/.gitignore @@ -29,6 +29,7 @@ Podfile.lock /tensorflow/contrib/lite/examples/ios/simple/data/*.tflite xcuserdata/** /api_init_files_list.txt +/estimator_api_init_files_list.txt # Android .gradle -- GitLab From 9281603eeb149942952e3d8b35c2a121bbaa045e Mon Sep 17 00:00:00 2001 From: Paul Woitaschek Date: Sun, 1 Jul 2018 15:08:07 +0200 Subject: [PATCH 064/519] Added minSdkVersion to the manifest Fixes #20453 --- tensorflow/contrib/lite/java/AndroidManifest.xml | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/contrib/lite/java/AndroidManifest.xml b/tensorflow/contrib/lite/java/AndroidManifest.xml index f705feacbe..d9e10900bf 100644 --- a/tensorflow/contrib/lite/java/AndroidManifest.xml +++ b/tensorflow/contrib/lite/java/AndroidManifest.xml @@ -1,6 +1,9 @@ + + + -- GitLab From 1d7fcde539fcff854e261c375c8ec2fbff258c34 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E5=A4=A9=E5=90=AF?= Date: Sun, 1 Jul 2018 22:05:51 +0800 Subject: [PATCH 065/519] fix bug in maxout function The line "shape[axis] = -1" will make the shape wrong when dealing with batches with arbitrary sizes. --- tensorflow/contrib/layers/python/layers/layers.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index b7194ae333..a55d42c151 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -3117,7 +3117,7 @@ def maxout(inputs, num_units, axis=-1, scope=None): raise ValueError('number of features({}) is not ' 'a multiple of num_units({})'.format( num_channels, num_units)) - shape[axis] = -1 + shape[axis] = num_units shape += [num_channels // num_units] # Dealing with batches with arbitrary sizes -- GitLab From 11cd70438e7d7104904bf8f3b24fcaf6fd88eab5 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Mon, 2 Jul 2018 13:37:38 -0700 Subject: [PATCH 066/519] Fix lint error. --- tensorflow/contrib/model_pruning/python/pruning.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/model_pruning/python/pruning.py b/tensorflow/contrib/model_pruning/python/pruning.py index d843fa26d5..da9d398cbc 100644 --- a/tensorflow/contrib/model_pruning/python/pruning.py +++ b/tensorflow/contrib/model_pruning/python/pruning.py @@ -520,7 +520,8 @@ class Pruning(object): thresholds = get_thresholds() for mask, threshold in zip(masks, thresholds): if not self._exists_in_do_not_prune_list(mask.name): - summary.scalar(mask.op.name + '/sparsity', nn_impl.zero_fraction(mask)) + summary.scalar(mask.op.name + '/sparsity', + nn_impl.zero_fraction(mask)) summary.scalar(threshold.op.name + '/threshold', threshold) def print_hparams(self): -- GitLab From f394207e840c6ea72c153fb2bb4e781f63f5e119 Mon Sep 17 00:00:00 2001 From: Paul Woitaschek Date: Mon, 2 Jul 2018 23:12:26 +0200 Subject: [PATCH 067/519] Added target-sdk as well. --- tensorflow/contrib/lite/java/AndroidManifest.xml | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/lite/java/AndroidManifest.xml b/tensorflow/contrib/lite/java/AndroidManifest.xml index d9e10900bf..f954bba739 100644 --- a/tensorflow/contrib/lite/java/AndroidManifest.xml +++ b/tensorflow/contrib/lite/java/AndroidManifest.xml @@ -1,10 +1,11 @@ + package="org.tensorflow.lite"> - + - - + + - -- GitLab From 4664191b73959387c190f969d7f1fe3480a585f4 Mon Sep 17 00:00:00 2001 From: Yifei Feng <1192265+yifeif@users.noreply.github.com> Date: Mon, 2 Jul 2018 17:10:20 -0700 Subject: [PATCH 068/519] Match for path instead of name --- tensorflow/tools/pip_package/build_pip_package.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index 9e41514cfa..b0089d3360 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -27,7 +27,7 @@ function cp_external() { pushd . cd "$src_dir" - for f in `find . ! -type d ! -name '*.py' ! -name '*local_config_cuda*' ! -name '*local_config_tensorrt*' ! -name '*org_tensorflow*'`; do + for f in `find . ! -type d ! -name '*.py' ! -path '*local_config_cuda*' ! -path '*local_config_tensorrt*' ! -path '*org_tensorflow*'`; do mkdir -p "${dest_dir}/$(dirname ${f})" cp "${f}" "${dest_dir}/$(dirname ${f})/" done -- GitLab From 2496c49fab2893c9bfb154c4c2b6ae26d410fc3a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E6=99=93=E9=A3=9E?= <32763586+henry860916@users.noreply.github.com> Date: Tue, 3 Jul 2018 17:04:19 +0800 Subject: [PATCH 069/519] Update debugger.md Fix issue of "function object is not subscriptable" in sample code. --- tensorflow/docs_src/guide/debugger.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/guide/debugger.md b/tensorflow/docs_src/guide/debugger.md index dc4db58857..dad11e6226 100644 --- a/tensorflow/docs_src/guide/debugger.md +++ b/tensorflow/docs_src/guide/debugger.md @@ -781,7 +781,7 @@ sess.run(b) ``` python import numpy as np -a = tf.Variable(np.ones[10], name="a") +a = tf.Variable(np.ones(10), name="a") b = tf.add(a, a, name="b") sess = tf.Session() sess.run(tf.global_variables_initializer()) -- GitLab From 69e37cef0ca721f76d12a3808521d73299aab7ea Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Tue, 3 Jul 2018 12:29:18 +0000 Subject: [PATCH 070/519] Update calling of expand_dims with axis This fix updates calling of `expand_dims` with `dim -> axis` as the `dim=` in `tf.expand_dims` has been deprecated and was generating unnecessary warnings. Signed-off-by: Yong Tang --- tensorflow/contrib/learn/python/learn/estimators/head.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py index 339c4e0e36..dee0755204 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/head.py +++ b/tensorflow/contrib/learn/python/learn/estimators/head.py @@ -563,10 +563,10 @@ def _mean_squared_loss(labels, logits, weights=None): labels = ops.convert_to_tensor(labels) # To prevent broadcasting inside "-". if len(labels.get_shape()) == 1: - labels = array_ops.expand_dims(labels, dim=(1,)) + labels = array_ops.expand_dims(labels, axis=(1,)) # TODO(zakaria): make sure it does not recreate the broadcast bug. if len(logits.get_shape()) == 1: - logits = array_ops.expand_dims(logits, dim=(1,)) + logits = array_ops.expand_dims(logits, axis=(1,)) logits.get_shape().assert_is_compatible_with(labels.get_shape()) loss = math_ops.square(logits - math_ops.to_float(labels), name=name) return _compute_weighted_loss(loss, weights) -- GitLab From 00071753077dcd9f1486c1335f05eed80e68efcb Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Tue, 3 Jul 2018 12:32:35 +0000 Subject: [PATCH 071/519] Additional fix Signed-off-by: Yong Tang --- tensorflow/contrib/learn/python/learn/estimators/head.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py index dee0755204..e9c79f88b0 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/head.py +++ b/tensorflow/contrib/learn/python/learn/estimators/head.py @@ -579,10 +579,10 @@ def _poisson_loss(labels, logits, weights=None): labels = ops.convert_to_tensor(labels) # To prevent broadcasting inside "-". if len(labels.get_shape()) == 1: - labels = array_ops.expand_dims(labels, dim=(1,)) + labels = array_ops.expand_dims(labels, axis=(1,)) # TODO(zakaria): make sure it does not recreate the broadcast bug. if len(logits.get_shape()) == 1: - logits = array_ops.expand_dims(logits, dim=(1,)) + logits = array_ops.expand_dims(logits, axis=(1,)) logits.get_shape().assert_is_compatible_with(labels.get_shape()) loss = nn.log_poisson_loss(labels, logits, compute_full_loss=True, name=name) -- GitLab From a77a9689198675f62ced41eb5c737eec429b8fae Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Tue, 3 Jul 2018 12:33:58 +0000 Subject: [PATCH 072/519] Fix warning in _log_loss_with_two_classes as well Signed-off-by: Yong Tang --- tensorflow/contrib/learn/python/learn/estimators/head.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/learn/python/learn/estimators/head.py b/tensorflow/contrib/learn/python/learn/estimators/head.py index e9c79f88b0..ded93d4a7f 100644 --- a/tensorflow/contrib/learn/python/learn/estimators/head.py +++ b/tensorflow/contrib/learn/python/learn/estimators/head.py @@ -797,7 +797,7 @@ def _log_loss_with_two_classes(labels, logits, weights=None): # TODO(ptucker): This will break for dynamic shapes. # sigmoid_cross_entropy_with_logits requires [batch_size, 1] labels. if len(labels.get_shape()) == 1: - labels = array_ops.expand_dims(labels, dim=(1,)) + labels = array_ops.expand_dims(labels, axis=(1,)) loss = nn.sigmoid_cross_entropy_with_logits(labels=labels, logits=logits, name=name) return _compute_weighted_loss(loss, weights) -- GitLab From 486b96a51d6b0b394edf77d182f7283a8ec03e0d Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 3 Jul 2018 16:17:58 -0700 Subject: [PATCH 073/519] Update eager notebooks in 1.9 to match master --- .../nmt_with_attention.ipynb | 909 ++++++++++++++++++ .../examples/notebooks/2_gradients.ipynb | 323 ------- .../examples/notebooks/3_datasets.ipynb | 209 ---- .../notebooks/3_training_models.ipynb | 485 ---------- .../examples/notebooks/4_high_level.ipynb | 551 ----------- .../eager/python/examples/notebooks/README.md | 11 + .../notebooks/automatic_differentiation.ipynb | 364 +++++++ .../examples/notebooks/custom_layers.ipynb | 399 ++++++++ .../examples/notebooks/custom_training.ipynb | 478 +++++++++ .../{1_basics.ipynb => eager_basics.ipynb} | 502 +++++----- 10 files changed, 2443 insertions(+), 1788 deletions(-) create mode 100644 tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb delete mode 100644 tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb delete mode 100644 tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb delete mode 100644 tensorflow/contrib/eager/python/examples/notebooks/3_training_models.ipynb delete mode 100644 tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb create mode 100644 tensorflow/contrib/eager/python/examples/notebooks/README.md create mode 100644 tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb create mode 100644 tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb create mode 100644 tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb rename tensorflow/contrib/eager/python/examples/notebooks/{1_basics.ipynb => eager_basics.ipynb} (50%) diff --git a/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb new file mode 100644 index 0000000000..34ce5e0cc3 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb @@ -0,0 +1,909 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "nmt_with_attention.ipynb", + "version": "0.3.2", + "views": {}, + "default_view": {}, + "provenance": [ + { + "file_id": "1C4fpM7_7IL8ZzF7Gc5abywqQjeQNS2-U", + "timestamp": 1527858391290 + }, + { + "file_id": "1pExo6aUuw0S6MISFWoinfJv0Ftm9V4qv", + "timestamp": 1527776041613 + } + ], + "private_outputs": true, + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "metadata": { + "id": "AOpGoE2T-YXS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "##### Copyright 2018 The TensorFlow Authors.\n", + "\n", + "Licensed under the Apache License, Version 2.0 (the \"License\").\n", + "\n", + "# Neural Machine Translation with Attention\n", + "\n", + "
\n", + "\n", + " Run in Google Colab \n", + "\n", + "View source on GitHub
" + ] + }, + { + "metadata": { + "id": "CiwtNgENbx2g", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "This notebook trains a sequence to sequence (seq2seq) model for Spanish to English translation using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). This is an advanced example that assumes some knowledge of sequence to sequence models.\n", + "\n", + "After training the model in this notebook, you will be able to input a Spanish sentence, such as *\"¿todavia estan en casa?\"*, and return the English translation: *\"are you still at home?\"*\n", + "\n", + "The translation quality is reasonable for a toy example, but the generated attention plot is perhaps more interesting. This shows which parts of the input sentence has the model's attention while translating:\n", + "\n", + "\"spanish-english\n", + "\n", + "Note: This example takes approximately 10 mintues to run on a single P100 GPU." + ] + }, + { + "metadata": { + "id": "tnxXKDjq3jEL", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", + "# Import TensorFlow >= 1.9 and enable eager execution\n", + "import tensorflow as tf\n", + "\n", + "tf.enable_eager_execution()\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from sklearn.model_selection import train_test_split\n", + "\n", + "import unicodedata\n", + "import re\n", + "import numpy as np\n", + "import os\n", + "import time\n", + "\n", + "print(tf.__version__)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wfodePkj3jEa", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Download and prepare the dataset\n", + "\n", + "We'll use a language dataset provided by http://www.manythings.org/anki/. This dataset contains language translation pairs in the format:\n", + "\n", + "```\n", + "May I borrow this book?\t¿Puedo tomar prestado este libro?\n", + "```\n", + "\n", + "There are a variety of languages available, but we'll use the English-Spanish dataset. For convenience, we've hosted a copy of this dataset on Google Cloud, but you can also download your own copy. After downloading the dataset, here are the steps we'll take to prepare the data:\n", + "\n", + "1. Add a *start* and *end* token to each sentence.\n", + "2. Clean the sentences by removing special characters.\n", + "3. Create a word index and reverse word index (dictionaries mapping from word → id and id → word).\n", + "4. Pad each sentence to a maximum length." + ] + }, + { + "metadata": { + "id": "kRVATYOgJs1b", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Download the file\n", + "path_to_zip = tf.keras.utils.get_file(\n", + " 'spa-eng.zip', origin='http://download.tensorflow.org/data/spa-eng.zip', \n", + " extract=True)\n", + "\n", + "path_to_file = os.path.dirname(path_to_zip)+\"/spa-eng/spa.txt\"" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "rd0jw-eC3jEh", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Converts the unicode file to ascii\n", + "def unicode_to_ascii(s):\n", + " return ''.join(c for c in unicodedata.normalize('NFD', s)\n", + " if unicodedata.category(c) != 'Mn')\n", + "\n", + "\n", + "def preprocess_sentence(w):\n", + " w = unicode_to_ascii(w.lower().strip())\n", + " \n", + " # creating a space between a word and the punctuation following it\n", + " # eg: \"he is a boy.\" => \"he is a boy .\" \n", + " # Reference:- https://stackoverflow.com/questions/3645931/python-padding-punctuation-with-white-spaces-keeping-punctuation\n", + " w = re.sub(r\"([?.!,¿])\", r\" \\1 \", w)\n", + " w = re.sub(r'[\" \"]+', \" \", w)\n", + " \n", + " # replacing everything with space except (a-z, A-Z, \".\", \"?\", \"!\", \",\")\n", + " w = re.sub(r\"[^a-zA-Z?.!,¿]+\", \" \", w)\n", + " \n", + " w = w.rstrip().strip()\n", + " \n", + " # adding a start and an end token to the sentence\n", + " # so that the model know when to start and stop predicting.\n", + " w = ' ' + w + ' '\n", + " return w" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "OHn4Dct23jEm", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# 1. Remove the accents\n", + "# 2. Clean the sentences\n", + "# 3. Return word pairs in the format: [ENGLISH, SPANISH]\n", + "def create_dataset(path, num_examples):\n", + " lines = open(path, encoding='UTF-8').read().strip().split('\\n')\n", + " \n", + " word_pairs = [[preprocess_sentence(w) for w in l.split('\\t')] for l in lines[:num_examples]]\n", + " \n", + " return word_pairs" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "9xbqO7Iie9bb", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# This class creates a word -> index mapping (e.g,. \"dad\" -> 5) and vice-versa \n", + "# (e.g., 5 -> \"dad\") for each language,\n", + "class LanguageIndex():\n", + " def __init__(self, lang):\n", + " self.lang = lang\n", + " self.word2idx = {}\n", + " self.idx2word = {}\n", + " self.vocab = set()\n", + " \n", + " self.create_index()\n", + " \n", + " def create_index(self):\n", + " for phrase in self.lang:\n", + " self.vocab.update(phrase.split(' '))\n", + " \n", + " self.vocab = sorted(self.vocab)\n", + " \n", + " self.word2idx[''] = 0\n", + " for index, word in enumerate(self.vocab):\n", + " self.word2idx[word] = index + 1\n", + " \n", + " for word, index in self.word2idx.items():\n", + " self.idx2word[index] = word" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "eAY9k49G3jE_", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def max_length(tensor):\n", + " return max(len(t) for t in tensor)\n", + "\n", + "\n", + "def load_dataset(path, num_examples):\n", + " # creating cleaned input, output pairs\n", + " pairs = create_dataset(path, num_examples)\n", + "\n", + " # index language using the class defined above \n", + " inp_lang = LanguageIndex(sp for en, sp in pairs)\n", + " targ_lang = LanguageIndex(en for en, sp in pairs)\n", + " \n", + " # Vectorize the input and target languages\n", + " \n", + " # Spanish sentences\n", + " input_tensor = [[inp_lang.word2idx[s] for s in sp.split(' ')] for en, sp in pairs]\n", + " \n", + " # English sentences\n", + " target_tensor = [[targ_lang.word2idx[s] for s in en.split(' ')] for en, sp in pairs]\n", + " \n", + " # Calculate max_length of input and output tensor\n", + " # Here, we'll set those to the longest sentence in the dataset\n", + " max_length_inp, max_length_tar = max_length(input_tensor), max_length(target_tensor)\n", + " \n", + " # Padding the input and output tensor to the maximum length\n", + " input_tensor = tf.keras.preprocessing.sequence.pad_sequences(input_tensor, \n", + " maxlen=max_length_inp,\n", + " padding='post')\n", + " \n", + " target_tensor = tf.keras.preprocessing.sequence.pad_sequences(target_tensor, \n", + " maxlen=max_length_tar, \n", + " padding='post')\n", + " \n", + " return input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_tar" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "GOi42V79Ydlr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Limit the size of the dataset to experiment faster (optional)\n", + "\n", + "Training on the complete dataset of >100,000 sentences will take a long time. To train faster, we can limit the size of the dataset to 30,000 sentences (of course, translation quality degrades with less data):" + ] + }, + { + "metadata": { + "id": "cnxC7q-j3jFD", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Try experimenting with the size of that dataset\n", + "num_examples = 30000\n", + "input_tensor, target_tensor, inp_lang, targ_lang, max_length_inp, max_length_targ = load_dataset(path_to_file, num_examples)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4QILQkOs3jFG", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Creating training and validation sets using an 80-20 split\n", + "input_tensor_train, input_tensor_val, target_tensor_train, target_tensor_val = train_test_split(input_tensor, target_tensor, test_size=0.2)\n", + "\n", + "# Show length\n", + "len(input_tensor_train), len(target_tensor_train), len(input_tensor_val), len(target_tensor_val)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "rgCLkfv5uO3d", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Create a tf.data dataset" + ] + }, + { + "metadata": { + "id": "TqHsArVZ3jFS", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "BUFFER_SIZE = len(input_tensor_train)\n", + "BATCH_SIZE = 64\n", + "embedding_dim = 256\n", + "units = 1024\n", + "vocab_inp_size = len(inp_lang.word2idx)\n", + "vocab_tar_size = len(targ_lang.word2idx)\n", + "\n", + "dataset = tf.data.Dataset.from_tensor_slices((input_tensor_train, target_tensor_train)).shuffle(BUFFER_SIZE)\n", + "dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(BATCH_SIZE))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "TNfHIF71ulLu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Write the encoder and decoder model\n", + "\n", + "Here, we'll implement an encoder-decoder model with attention which you can read about in the TensorFlow [Neural Machine Translation (seq2seq) tutorial](https://www.tensorflow.org/tutorials/seq2seq). This example uses a more recent set of APIs. This notebook implements the [attention equations](https://www.tensorflow.org/tutorials/seq2seq#background_on_the_attention_mechanism) from the seq2seq tutorial. The following diagram shows that each input words is assigned a weight by the attention mechanism which is then used by the decoder to predict the next word in the sentence.\n", + "\n", + "\"attention\n", + "\n", + "The input is put through an encoder model which gives us the encoder output of shape *(batch_size, max_length, hidden_size)* and the encoder hidden state of shape *(batch_size, hidden_size)*. \n", + "\n", + "Here are the equations that are implemented:\n", + "\n", + "\"attention\n", + "\"attention\n", + "\n", + "We're using *Bahdanau attention*. Lets decide on notation before writing the simplified form:\n", + "\n", + "* FC = Fully connected (dense) layer\n", + "* EO = Encoder output\n", + "* H = hidden state\n", + "* X = input to the decoder\n", + "\n", + "And the pseudo-code:\n", + "\n", + "* `score = FC(tanh(FC(EO) + FC(H)))`\n", + "* `attention weights = softmax(score, axis = 1)`. Softmax by default is applied on the last axis but here we want to apply it on the *1st axis*, since the shape of score is *(batch_size, max_length, hidden_size)*. `Max_length` is the length of our input. Since we are trying to assign a weight to each input, softmax should be applied on that axis.\n", + "* `context vector = sum(attention weights * EO, axis = 1)`. Same reason as above for choosing axis as 1.\n", + "* `embedding output` = The input to the decoder X is passed through an embedding layer.\n", + "* `merged vector = concat(embedding output, context vector)`\n", + "* This merged vector is then given to the GRU\n", + " \n", + "The shapes of all the vectors at each step have been specified in the comments in the code:" + ] + }, + { + "metadata": { + "id": "avyJ_4VIUoHb", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def gru(units):\n", + " # If you have a GPU, we recommend using CuDNNGRU(provides a 3x speedup than GRU)\n", + " # the code automatically does that.\n", + " if tf.test.is_gpu_available():\n", + " return tf.keras.layers.CuDNNGRU(units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_initializer='glorot_uniform')\n", + " else:\n", + " return tf.keras.layers.GRU(units, \n", + " return_sequences=True, \n", + " return_state=True, \n", + " recurrent_activation='sigmoid', \n", + " recurrent_initializer='glorot_uniform')" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "nZ2rI24i3jFg", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class Encoder(tf.keras.Model):\n", + " def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):\n", + " super(Encoder, self).__init__()\n", + " self.batch_sz = batch_sz\n", + " self.enc_units = enc_units\n", + " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + " self.gru = gru(self.enc_units)\n", + " \n", + " def call(self, x, hidden):\n", + " x = self.embedding(x)\n", + " output, state = self.gru(x, initial_state = hidden) \n", + " return output, state\n", + " \n", + " def initialize_hidden_state(self):\n", + " return tf.zeros((self.batch_sz, self.enc_units))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "yJ_B3mhW3jFk", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class Decoder(tf.keras.Model):\n", + " def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):\n", + " super(Decoder, self).__init__()\n", + " self.batch_sz = batch_sz\n", + " self.dec_units = dec_units\n", + " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", + " self.gru = gru(self.dec_units)\n", + " self.fc = tf.keras.layers.Dense(vocab_size)\n", + " \n", + " # used for attention\n", + " self.W1 = tf.keras.layers.Dense(self.dec_units)\n", + " self.W2 = tf.keras.layers.Dense(self.dec_units)\n", + " self.V = tf.keras.layers.Dense(1)\n", + " \n", + " def call(self, x, hidden, enc_output):\n", + " # enc_output shape == (batch_size, max_length, hidden_size)\n", + " \n", + " # hidden shape == (batch_size, hidden size)\n", + " # hidden_with_time_axis shape == (batch_size, 1, hidden size)\n", + " # we are doing this to perform addition to calculate the score\n", + " hidden_with_time_axis = tf.expand_dims(hidden, 1)\n", + " \n", + " # score shape == (batch_size, max_length, hidden_size)\n", + " score = tf.nn.tanh(self.W1(enc_output) + self.W2(hidden_with_time_axis))\n", + " \n", + " # attention_weights shape == (batch_size, max_length, 1)\n", + " # we get 1 at the last axis because we are applying score to self.V\n", + " attention_weights = tf.nn.softmax(self.V(score), axis=1)\n", + " \n", + " # context_vector shape after sum == (batch_size, hidden_size)\n", + " context_vector = attention_weights * enc_output\n", + " context_vector = tf.reduce_sum(context_vector, axis=1)\n", + " \n", + " # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n", + " x = self.embedding(x)\n", + " \n", + " # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n", + " x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n", + " \n", + " # passing the concatenated vector to the GRU\n", + " output, state = self.gru(x)\n", + " \n", + " # output shape == (batch_size * max_length, hidden_size)\n", + " output = tf.reshape(output, (-1, output.shape[2]))\n", + " \n", + " # output shape == (batch_size * max_length, vocab)\n", + " x = self.fc(output)\n", + " \n", + " return x, state, attention_weights\n", + " \n", + " def initialize_hidden_state(self):\n", + " return tf.zeros((self.batch_sz, self.dec_units))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "P5UY8wko3jFp", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "encoder = Encoder(vocab_inp_size, embedding_dim, units, BATCH_SIZE)\n", + "decoder = Decoder(vocab_tar_size, embedding_dim, units, BATCH_SIZE)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "_ch_71VbIRfK", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Define the optimizer and the loss function" + ] + }, + { + "metadata": { + "id": "WmTHr5iV3jFr", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "optimizer = tf.train.AdamOptimizer()\n", + "\n", + "\n", + "def loss_function(real, pred):\n", + " mask = 1 - np.equal(real, 0)\n", + " loss_ = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=real, logits=pred) * mask\n", + " return tf.reduce_mean(loss_)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hpObfY22IddU", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Training\n", + "\n", + "1. Pass the *input* through the *encoder* which return *encoder output* and the *encoder hidden state*.\n", + "2. The encoder output, encoder hidden state and the decoder input (which is the *start token*) is passed to the decoder.\n", + "3. The decoder returns the *predictions* and the *decoder hidden state*.\n", + "4. The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss.\n", + "5. Use *teacher forcing* to decide the next input to the decoder.\n", + "6. *Teacher forcing* is the technique where the *target word* is passed as the *next input* to the decoder.\n", + "7. The final step is to calculate the gradients and apply it to the optimizer and backpropagate." + ] + }, + { + "metadata": { + "id": "ddefjBMa3jF0", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "EPOCHS = 10\n", + "\n", + "for epoch in range(EPOCHS):\n", + " start = time.time()\n", + " \n", + " hidden = encoder.initialize_hidden_state()\n", + " total_loss = 0\n", + " \n", + " for (batch, (inp, targ)) in enumerate(dataset):\n", + " loss = 0\n", + " \n", + " with tf.GradientTape() as tape:\n", + " enc_output, enc_hidden = encoder(inp, hidden)\n", + " \n", + " dec_hidden = enc_hidden\n", + " \n", + " dec_input = tf.expand_dims([targ_lang.word2idx['']] * BATCH_SIZE, 1) \n", + " \n", + " # Teacher forcing - feeding the target as the next input\n", + " for t in range(1, targ.shape[1]):\n", + " # passing enc_output to the decoder\n", + " predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)\n", + " \n", + " loss += loss_function(targ[:, t], predictions)\n", + " \n", + " # using teacher forcing\n", + " dec_input = tf.expand_dims(targ[:, t], 1)\n", + " \n", + " total_loss += (loss / int(targ.shape[1]))\n", + " \n", + " variables = encoder.variables + decoder.variables\n", + " \n", + " gradients = tape.gradient(loss, variables)\n", + " \n", + " optimizer.apply_gradients(zip(gradients, variables), tf.train.get_or_create_global_step())\n", + "\n", + " if batch % 100 == 0:\n", + " print('Epoch {} Batch {} Loss {:.4f}'.format(epoch + 1,\n", + " batch,\n", + " loss.numpy() / int(targ.shape[1])))\n", + " \n", + " print('Epoch {} Loss {:.4f}'.format(epoch + 1,\n", + " total_loss/len(input_tensor)))\n", + " print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "mU3Ce8M6I3rz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Translate\n", + "\n", + "* The evaluate function is similar to the training loop, except we don't use *teacher forcing* here. The input to the decoder at each time step is its previous predictions along with the hidden state and the encoder output.\n", + "* Stop predicting when the model predicts the *end token*.\n", + "* And store the *attention weights for every time step*.\n", + "\n", + "Note: The encoder output is calculated only once for one input." + ] + }, + { + "metadata": { + "id": "EbQpyYs13jF_", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):\n", + " attention_plot = np.zeros((max_length_targ, max_length_inp))\n", + " \n", + " sentence = preprocess_sentence(sentence)\n", + "\n", + " inputs = [inp_lang.word2idx[i] for i in sentence.split(' ')]\n", + " inputs = tf.keras.preprocessing.sequence.pad_sequences([inputs], maxlen=max_length_inp, padding='post')\n", + " inputs = tf.convert_to_tensor(inputs)\n", + " \n", + " result = ''\n", + "\n", + " hidden = [tf.zeros((1, units))]\n", + " enc_out, enc_hidden = encoder(inputs, hidden)\n", + "\n", + " dec_hidden = enc_hidden\n", + " dec_input = tf.expand_dims([targ_lang.word2idx['']], 0)\n", + "\n", + " for t in range(max_length_targ):\n", + " predictions, dec_hidden, attention_weights = decoder(dec_input, dec_hidden, enc_out)\n", + " \n", + " # storing the attention weigths to plot later on\n", + " attention_weights = tf.reshape(attention_weights, (-1, ))\n", + " attention_plot[t] = attention_weights.numpy()\n", + "\n", + " predicted_id = tf.multinomial(tf.exp(predictions), num_samples=1)[0][0].numpy()\n", + "\n", + " result += targ_lang.idx2word[predicted_id] + ' '\n", + "\n", + " if targ_lang.idx2word[predicted_id] == '':\n", + " return result, sentence, attention_plot\n", + " \n", + " # the predicted ID is fed back into the model\n", + " dec_input = tf.expand_dims([predicted_id], 0)\n", + "\n", + " return result, sentence, attention_plot" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "s5hQWlbN3jGF", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# function for plotting the attention weights\n", + "def plot_attention(attention, sentence, predicted_sentence):\n", + " fig = plt.figure(figsize=(10,10))\n", + " ax = fig.add_subplot(1, 1, 1)\n", + " ax.matshow(attention, cmap='viridis')\n", + " \n", + " fontdict = {'fontsize': 14}\n", + " \n", + " ax.set_xticklabels([''] + sentence, fontdict=fontdict, rotation=90)\n", + " ax.set_yticklabels([''] + predicted_sentence, fontdict=fontdict)\n", + "\n", + " plt.show()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "sl9zUHzg3jGI", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def translate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ):\n", + " result, sentence, attention_plot = evaluate(sentence, encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)\n", + " \n", + " print('Input: {}'.format(sentence))\n", + " print('Predicted translation: {}'.format(result))\n", + " \n", + " attention_plot = attention_plot[:len(result.split(' ')), :len(sentence.split(' '))]\n", + " plot_attention(attention_plot, sentence.split(' '), result.split(' '))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "WrAM0FDomq3E", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "translate('hace mucho frio aqui.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zSx2iM36EZQZ", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "translate('esta es mi vida.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "A3LLCx3ZE0Ls", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "translate('¿todavia estan en casa?', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "DUQVLVqUE1YW", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# wrong translation\n", + "translate('trata de averiguarlo.', encoder, decoder, inp_lang, targ_lang, max_length_inp, max_length_targ)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RTe5P5ioMJwN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Next steps\n", + "\n", + "* [Download a different dataset](http://www.manythings.org/anki/) to experiment with translations, for example, English to German, or English to French.\n", + "* Experiment with training on a larger dataset, or using more epochs\n" + ] + } + ] +} \ No newline at end of file diff --git a/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb deleted file mode 100644 index 9c1af9c208..0000000000 --- a/tensorflow/contrib/eager/python/examples/notebooks/2_gradients.ipynb +++ /dev/null @@ -1,323 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "vDJ4XzMqodTy" - }, - "source": [ - "# Automatic Differentiation\n", - "\n", - "In the previous tutorial we introduced `Tensor`s and operations on them. In this tutorial we will cover [automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation), a key technique for optimizing machine learning models." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "GQJysDM__Qb0" - }, - "source": [ - "## Setup\n" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "OiMPZStlibBv" - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "tf.enable_eager_execution()\n", - "\n", - "tfe = tf.contrib.eager # Shorthand for some symbols" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "1CLWJl0QliB0" - }, - "source": [ - "## Derivatives of a function\n", - "\n", - "TensorFlow provides APIs for automatic differentiation - computing the derivative of a function. The way that more closely mimics the math is to encapsulate the computation in a Python function, say `f`, and use `tfe.gradients_function` to create a function that computes the derivatives of `f` with respect to its arguments. If you're familiar with [autograd](https://github.com/HIPS/autograd) for differentiating numpy functions, this will be familiar. For example: " - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "9FViq92UX7P8" - }, - "outputs": [], - "source": [ - "from math import pi\n", - "\n", - "def f(x):\n", - " return tf.square(tf.sin(x))\n", - "\n", - "assert f(pi/2).numpy() == 1.0\n", - "\n", - "\n", - "# grad_f will return a list of derivatives of f\n", - "# with respect to its arguments. Since f() has a single argument,\n", - "# grad_f will return a list with a single element.\n", - "grad_f = tfe.gradients_function(f)\n", - "assert tf.abs(grad_f(pi/2)[0]).numpy() \u003c 1e-7" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "v9fPs8RyopCf" - }, - "source": [ - "### Higher-order gradients\n", - "\n", - "The same API can be used to differentiate as many times as you like:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "height": 276 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 730, - "status": "ok", - "timestamp": 1527005655565, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "3D0ZvnGYo0rW", - "outputId": "e23f8cc6-6813-4944-f20f-825b8a03c2ff" - }, - "outputs": [ - { - "data": { - "image/png": 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5nzUnrbzzj51MmZnKmUvGhbCnbQn1eH79RR77d5Zx+Q2zSEju+1F0u7YUsXVj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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7f385e198650\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "def f(x):\n", - " return tf.square(tf.sin(x))\n", - "\n", - "def grad(f):\n", - " return lambda x: tfe.gradients_function(f)(x)[0]\n", - "\n", - "x = tf.lin_space(-2*pi, 2*pi, 100) # 100 points between -2π and +2π\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "plt.plot(x, f(x), label=\"f\")\n", - "plt.plot(x, grad(f)(x), label=\"first derivative\")\n", - "plt.plot(x, grad(grad(f))(x), label=\"second derivative\")\n", - "plt.plot(x, grad(grad(grad(f)))(x), label=\"third derivative\")\n", - "plt.legend()\n", - "plt.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "-39gouo7mtgu" - }, - "source": [ - "## Gradient tapes\n", - "\n", - "Every differentiable TensorFlow operation has an associated gradient function. For example, the gradient function of `tf.square(x)` would be a function that returns `2.0 * x`. To compute the gradient of a user-defined function (like `f(x)` in the example above), TensorFlow first \"records\" all the operations applied to compute the output of the function. We call this record a \"tape\". It then uses that tape and the gradients functions associated with each primitive operation to compute the gradients of the user-defined function using [reverse mode differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation).\n", - "\n", - "Since operations are recorded as they are executed, Python control flow (using `if`s and `while`s for example) is naturally handled:\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "MH0UfjympWf7" - }, - "outputs": [], - "source": [ - "def f(x, y):\n", - " output = 1\n", - " for i in range(y):\n", - " output = tf.multiply(output, x)\n", - " return output\n", - "\n", - "def g(x, y):\n", - " # Return the gradient of `f` with respect to it's first parameter\n", - " return tfe.gradients_function(f)(x, y)[0]\n", - "\n", - "assert f(3.0, 2).numpy() == 9.0 # f(x, 2) is essentially x * x\n", - "assert g(3.0, 2).numpy() == 6.0 # And its gradient will be 2 * x\n", - "assert f(4.0, 3).numpy() == 64.0 # f(x, 3) is essentially x * x * x\n", - "assert g(4.0, 3).numpy() == 48.0 # And its gradient will be 3 * x * x" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "aNmR5-jhpX2t" - }, - "source": [ - "At times it may be inconvenient to encapsulate computation of interest into a function. For example, if you want the gradient of the output with respect to intermediate values computed in the function. In such cases, the slightly more verbose but explicit [tf.GradientTape](https://www.tensorflow.org/api_docs/python/tf/GradientTape) context is useful. All computation inside the context of a `tf.GradientTape` is \"recorded\".\n", - "\n", - "For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "bAFeIE8EuVIq" - }, - "outputs": [], - "source": [ - "x = tf.ones((2, 2))\n", - " \n", - "# TODO(b/78880779): Remove the 'persistent=True' argument and use\n", - "# a single t.gradient() call when the bug is resolved.\n", - "with tf.GradientTape(persistent=True) as t:\n", - " # TODO(ashankar): Explain with \"watch\" argument better?\n", - " t.watch(x)\n", - " y = tf.reduce_sum(x)\n", - " z = tf.multiply(y, y)\n", - "\n", - "# Use the same tape to compute the derivative of z with respect to the\n", - "# intermediate value y.\n", - "dz_dy = t.gradient(z, y)\n", - "assert dz_dy.numpy() == 8.0\n", - "\n", - "# Derivative of z with respect to the original input tensor x\n", - "dz_dx = t.gradient(z, x)\n", - "for i in [0, 1]:\n", - " for j in [0, 1]:\n", - " assert dz_dx[i][j].numpy() == 8.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "DK05KXrAAld3" - }, - "source": [ - "### Higher-order gradients\n", - "\n", - "Operations inside of the `GradientTape` context manager are recorded for automatic differentiation. If gradients are computed in that context, then the gradient computation is recorded as well. As a result, the exact same API works for higher-order gradients as well. For example:" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "cPQgthZ7ugRJ" - }, - "outputs": [], - "source": [ - "# TODO(ashankar): Should we use the persistent tape here instead? Follow up on Tom and Alex's discussion\n", - "\n", - "x = tf.constant(1.0) # Convert the Python 1.0 to a Tensor object\n", - "\n", - "with tf.GradientTape() as t:\n", - " with tf.GradientTape() as t2:\n", - " t2.watch(x)\n", - " y = x * x * x\n", - " # Compute the gradient inside the 't' context manager\n", - " # which means the gradient computation is differentiable as well.\n", - " dy_dx = t2.gradient(y, x)\n", - "d2y_dx2 = t.gradient(dy_dx, x)\n", - "\n", - "assert dy_dx.numpy() == 3.0\n", - "assert d2y_dx2.numpy() == 6.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "4U1KKzUpNl58" - }, - "source": [ - "## Next Steps\n", - "\n", - "In this tutorial we covered gradient computation in TensorFlow. With that we have enough of the primitives required to build an train neural networks, which we will cover in the [next tutorial](https://github.com/tensorflow/models/tree/master/official/contrib/eager/python/examples/notebooks/3_neural_networks.ipynb)." - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "default_view": {}, - "name": "Automatic Differentiation", - "provenance": [], - "version": "0.3.2", - "views": {} - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb deleted file mode 100644 index d268cbcd91..0000000000 --- a/tensorflow/contrib/eager/python/examples/notebooks/3_datasets.ipynb +++ /dev/null @@ -1,209 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "U9i2Dsh-ziXr" - }, - "source": [ - "# Eager Execution Tutorial: Importing Data\n", - "\n", - "This notebook demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/guide/datasets) to build pipelines to feed data to your program. It covers:\n", - "\n", - "* Creating a `Dataset`.\n", - "* Iteration over a `Dataset` with eager execution enabled.\n", - "\n", - "We recommend using the `Dataset`s API for building performant, complex input pipelines from simple, re-usable pieces that will feed your model's training or evaluation loops.\n", - "\n", - "If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly simpler.\n", - "You can use Python iteration over the `tf.data.Dataset` object and do not need to explicitly create an `tf.data.Iterator` object.\n", - "As a result, the discussion on iterators in the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets) is not relevant when eager execution is enabled." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "z1JcS5iBXMRO" - }, - "source": [ - "# Setup: Enable eager execution\n" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "cellView": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "RlIWhyeLoYnG" - }, - "outputs": [], - "source": [ - "# Import TensorFlow.\n", - "import tensorflow as tf\n", - "\n", - "# Enable eager execution\n", - "tf.enable_eager_execution()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "H9UySOPLXdaw" - }, - "source": [ - "# Step 1: Create a source `Dataset`\n", - "\n", - "Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets#reading_input_data) for more information." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "cellView": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "WPTUfGq6kJ5w" - }, - "outputs": [], - "source": [ - "ds_tensors = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6])\n", - "\n", - "# Create a CSV file\n", - "import tempfile\n", - "_, filename = tempfile.mkstemp()\n", - "with open(filename, 'w') as f:\n", - " f.write(\"\"\"Line 1\n", - "Line 2\n", - "Line 3\n", - " \"\"\")\n", - "ds_file = tf.data.TextLineDataset(filename)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "twBfWd5xyu_d" - }, - "source": [ - "# Step 2: Apply transformations\n", - "\n", - "Use the transformations functions like [`map`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map), [`batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#batch), [`shuffle`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) etc. to apply transformations to the records of the dataset. See the [API documentation for `tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) for details." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "cellView": "code", - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "ngUe237Wt48W" - }, - "outputs": [], - "source": [ - "ds_tensors = ds_tensors.map(tf.square).shuffle(2).batch(2)\n", - "ds_file = ds_file.batch(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "IDY4WsYRhP81" - }, - "source": [ - "# Step 3: Iterate\n", - "\n", - "When eager execution is enabled `Dataset` objects support iteration.\n", - "If you're familiar with the use of `Dataset`s in TensorFlow graphs, note that there is no need for calls to `Dataset.make_one_shot_iterator()` or `get_next()` calls." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "base_uri": "https://localhost:8080/", - "height": 153 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 388, - "status": "ok", - "timestamp": 1525154629129, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "lCUWzso6mbqR", - "outputId": "8e4b0298-d27d-4ac7-e26a-ef94af0594ec" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Elements of ds_tensors:\n", - "tf.Tensor([1 9], shape=(2,), dtype=int32)\n", - "tf.Tensor([16 25], shape=(2,), dtype=int32)\n", - "tf.Tensor([ 4 36], shape=(2,), dtype=int32)\n", - "\n", - "Elements in ds_file:\n", - "tf.Tensor(['Line 1' 'Line 2'], shape=(2,), dtype=string)\n", - "tf.Tensor(['Line 3' ' '], shape=(2,), dtype=string)\n" - ] - } - ], - "source": [ - "print('Elements of ds_tensors:')\n", - "for x in ds_tensors:\n", - " print(x)\n", - "\n", - "print('\\nElements in ds_file:')\n", - "for x in ds_file:\n", - " print(x)" - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "default_view": {}, - "name": "Eager Execution Tutorial: Importing Data", - "provenance": [], - "version": "0.3.2", - "views": {} - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/tensorflow/contrib/eager/python/examples/notebooks/3_training_models.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/3_training_models.ipynb deleted file mode 100644 index 84f1d031d4..0000000000 --- a/tensorflow/contrib/eager/python/examples/notebooks/3_training_models.ipynb +++ /dev/null @@ -1,485 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "k2o3TTG4TFpt" - }, - "source": [ - "# Training Models\n", - "\n", - "In the previous tutorial we covered the TensorFlow APIs for automatic differentiation, a basic building block for machine learning.\n", - "In this tutorial we will use the TensorFlow primitives introduced in the prior tutorials to do some simple machine learning.\n", - "\n", - "TensorFlow also includes a higher-level neural networks API (`tf.keras`) which provides useful abstractions to reduce boilerplate. We strongly recommend those higher level APIs for people working with neural networks. However, in this short tutorial we cover neural network training from first principles to establish a strong foundation." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "3LXMVuV0VhDr" - }, - "source": [ - "## Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "PJ64L90aVir3" - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "tf.enable_eager_execution()\n", - "tfe = tf.contrib.eager # Shorthand for some symbols" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "eMAWbDJFVmMk" - }, - "source": [ - "## Variables\n", - "\n", - "Tensors in TensorFlow are immutable stateless objects. Machine learning models, however, need to have changing state: as your model trains, the same code to compute predictions should behave differently over time (hopefully with a lower loss!). To represent this state which needs to change over the course of your computation, you can choose to rely on the fact that Python is a stateful programming language:\n" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "VkJwtLS_Jbn8" - }, - "outputs": [], - "source": [ - "# Using python state\n", - "x = tf.zeros([10, 10])\n", - "x += 2 # This is equivalent to x = x + 2, which does not mutate the original\n", - " # value of x\n", - "print(x)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "wfneTXy7JcUz" - }, - "source": [ - "TensorFlow, however, has stateful operations built in, and these are often more pleasant to use than low-level Python representations of your state. To represent weights in a model, for example, it's often convenient and efficient to use TensorFlow variables.\n", - "\n", - "A Variable is an object which stores a value and, when used in a TensorFlow computation, will implicitly read from this stored value. There are operations (`tf.assign_sub`, `tf.scatter_update`, etc) which manipulate the value stored in a TensorFlow variable." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "itxmrMil6DQi" - }, - "outputs": [], - "source": [ - "v = tfe.Variable(1.0)\n", - "assert v.numpy() == 1.0\n", - "\n", - "# Re-assign the value\n", - "v.assign(3.0)\n", - "assert v.numpy() == 3.0\n", - "\n", - "# Use `v` in a TensorFlow operation like tf.square() and reassign\n", - "v.assign(tf.square(v))\n", - "assert v.numpy() == 9.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "-paSaeq1JzwC" - }, - "source": [ - "Computations using Variables are automatically traced when computing gradients. For Variables representing embeddings TensorFlow will do sparse updates by default, which are more computation and memory efficient.\n", - "\n", - "Using Variables is also a way to quickly let a reader of your code know that this piece of state is mutable." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "BMiFcDzE7Qu3" - }, - "source": [ - "## Example: Fitting a linear model\n", - "\n", - "Let's now put the few concepts we have so far ---`Tensor`, `GradientTape`, `Variable` --- to build and train a simple model. This typically involves a few steps:\n", - "\n", - "1. Define the model.\n", - "2. Define a loss function.\n", - "3. Obtain training data.\n", - "4. Run through the training data and use an \"optimizer\" to adjust the variables to fit the data.\n", - "\n", - "In this tutorial, we'll walk through a trivial example of a simple linear model: `f(x) = x * W + b`, which has two variables - `W` and `b`. Furthermore, we'll synthesize data such that a well trained model would have `W = 3.0` and `b = 2.0`." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "gFzH64Jn9PIm" - }, - "source": [ - "### Define the model\n", - "\n", - "Let's define a simple class to encapsulate the variables and the computation." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "_WRu7Pze7wk8" - }, - "outputs": [], - "source": [ - "class Model(object):\n", - " def __init__(self):\n", - " # Initialize variable to (5.0, 0.0)\n", - " # In practice, these should be initialized to random values.\n", - " self.W = tfe.Variable(5.0)\n", - " self.b = tfe.Variable(0.0)\n", - " \n", - " def __call__(self, x):\n", - " return self.W * x + self.b\n", - " \n", - "model = Model()\n", - "\n", - "assert model(3.0).numpy() == 15.0" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "xa6j_yXa-j79" - }, - "source": [ - "### Define a loss function\n", - "\n", - "A loss function measures how well the output of a model for a given input matches the desired output. Let's use the standard L2 loss." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "Y0ysUFGY924U" - }, - "outputs": [], - "source": [ - "def loss(predicted_y, desired_y):\n", - " return tf.reduce_mean(tf.square(predicted_y - desired_y))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "qutT_fkl_CBc" - }, - "source": [ - "### Obtain training data\n", - "\n", - "Let's synthesize the training data with some noise." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "gxPTb-kt_N5m" - }, - "outputs": [], - "source": [ - "TRUE_W = 3.0\n", - "TRUE_b = 2.0\n", - "NUM_EXAMPLES = 1000\n", - "\n", - "inputs = tf.random_normal(shape=[NUM_EXAMPLES])\n", - "noise = tf.random_normal(shape=[NUM_EXAMPLES])\n", - "outputs = inputs * TRUE_W + TRUE_b + noise" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "-50nq-wPBsAW" - }, - "source": [ - "Before we train the model let's visualize where the model stands right now. We'll plot the model's predictions in red and the training data in blue." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "height": 293 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 1210, - "status": "ok", - "timestamp": 1527005898290, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "_eb83LtrB4nt", - "outputId": "3873f508-72fb-41e7-a7f5-3f513deefe38" - }, - "outputs": [ - { - "data": { - "image/png": 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sGElVVcPfq0zMNj9E8AWv5+y8Zj1710V4p817v0cJcg9U1h2E6lLZBiW90ZZj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\n2wtBEQbDU5bM/iQwGVXeeSc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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7f5be3c99f50\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Current loss: 9.48636\n" - ] - } - ], - "source": [ - "import matplotlib.pyplot as plt\n", - "\n", - "plt.scatter(inputs, outputs, c='b')\n", - "plt.scatter(inputs, model(inputs), c='r')\n", - "plt.show()\n", - "\n", - "print('Current loss: '),\n", - "print(loss(model(inputs), outputs).numpy())" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "sSDP-yeq_4jE" - }, - "source": [ - "### Define a training loop\n", - "\n", - "We now have our network and our training data. Let's train it, i.e., use the training data to update the model's variables (`W` and `b`) so that the loss goes down using [gradient descent](https://en.wikipedia.org/wiki/Gradient_descent). There are many variants of the gradient descent scheme that are captured in `tf.train.Optimizer` implementations. We'd highly recommend using those implementations, but in the spirit of building from first principles, in this particular example we will implement the basic math ourselves." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "MBIACgdnA55X" - }, - "outputs": [], - "source": [ - "def train(model, inputs, outputs, learning_rate):\n", - " with tf.GradientTape() as t:\n", - " current_loss = loss(model(inputs), outputs)\n", - " dW, db = t.gradient(current_loss, [model.W, model.b])\n", - " model.W.assign_sub(learning_rate * dW)\n", - " model.b.assign_sub(learning_rate * db)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "RwWPaJryD2aN" - }, - "source": [ - "Finally, let's repeatedly run through the training data and see how `W` and `b` evolve." - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "height": 446 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 569, - "status": "ok", - "timestamp": 1527005915434, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "XdfkR223D9dW", - "outputId": "c43591ae-d5ac-4f2b-a8e7-bfce607e0919" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Epoch 0: W=5.00 b=0.00, loss=9.48636\n", - "Epoch 1: W=4.58 b=0.42, loss=6.28101\n", - "Epoch 2: W=4.24 b=0.76, loss=4.29357\n", - "Epoch 3: W=3.98 b=1.02, loss=3.06128\n", - "Epoch 4: W=3.78 b=1.23, loss=2.29721\n", - "Epoch 5: W=3.61 b=1.39, loss=1.82345\n", - "Epoch 6: W=3.49 b=1.52, loss=1.52970\n", - "Epoch 7: W=3.38 b=1.62, loss=1.34756\n", - "Epoch 8: W=3.30 b=1.70, loss=1.23463\n", - "Epoch 9: W=3.24 b=1.76, loss=1.16460\n" - ] - }, - { - "data": { - "image/png": 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- "text/plain": [ - "\u003cmatplotlib.figure.Figure at 0x7f5be4b8ec50\u003e" - ] - }, - "metadata": { - "tags": [] - }, - "output_type": "display_data" - } - ], - "source": [ - "model = Model()\n", - "\n", - "# Collect the history of W-values and b-values to plot later\n", - "Ws, bs = [], []\n", - "epochs = range(10)\n", - "for epoch in epochs:\n", - " Ws.append(model.W.numpy())\n", - " bs.append(model.b.numpy())\n", - " current_loss = loss(model(inputs), outputs)\n", - "\n", - " train(model, inputs, outputs, learning_rate=0.1)\n", - " print('Epoch %2d: W=%1.2f b=%1.2f, loss=%2.5f' %\n", - " (epoch, Ws[-1], bs[-1], current_loss))\n", - "\n", - "# Let's plot it all\n", - "plt.plot(epochs, Ws, 'r',\n", - " epochs, bs, 'b')\n", - "plt.plot([TRUE_W] * len(epochs), 'r--',\n", - " [TRUE_b] * len(epochs), 'b--')\n", - "plt.legend(['W', 'b', 'true W', 'true_b'])\n", - "plt.show()\n", - " " - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "vPnIVuaSJwWz" - }, - "source": [ - "## Next Steps\n", - "\n", - "In this tutorial we covered `Variable`s and built and trained a simple linear model using the TensorFlow primitives discussed so far.\n", - "\n", - "In theory, this is pretty much all you need to use TensorFlow for your machine learning research.\n", - "In practice, particularly for neural networks, the higher level APIs like `tf.keras` will be much more convenient since it provides higher level building blocks (called \"layers\"), utilities to save and restore state, a suite of loss functions, a suite of optimization strategies etc. \n", - "\n", - "The [next tutorial](TODO) will cover these higher level APIs." - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "default_view": {}, - "name": "Training Models", - "provenance": [], - "version": "0.3.2", - "views": {} - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb deleted file mode 100644 index 4fe3a0e3f3..0000000000 --- a/tensorflow/contrib/eager/python/examples/notebooks/4_high_level.ipynb +++ /dev/null @@ -1,551 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "pwX7Fii1rwsJ" - }, - "outputs": [], - "source": [ - "import tensorflow as tf\n", - "tf.enable_eager_execution()\n", - "tfe = tf.contrib.eager\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "UEu3q4jmpKVT" - }, - "source": [ - "# High level API\n", - "\n", - "We recommend using `tf.keras` as a high-level API for building neural networks. That said, most TensorFlow APIs are usable with eager execution.\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "zSFfVVjkrrsI" - }, - "source": [ - "## Layers: common sets of useful operations\n", - "\n", - "Most of the time when writing code for machine learning models you want to operate at a higher level of abstraction than individual operations and manipulation of individual variables.\n", - "\n", - "Many machine learning models are expressible as the composition and stacking of relatively simple layers, and TensorFlow provides both a set of many common layers as a well as easy ways for you to write your own application-specific layers either from scratch or as the composition of existing layers.\n", - "\n", - "TensorFlow includes the full [Keras](https://keras.io) API in the tf.keras package, and the Keras layers are very useful when building your own models.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - } - }, - "colab_type": "code", - "id": "8PyXlPl-4TzQ" - }, - "outputs": [], - "source": [ - "# In the tf.keras.layers package, layers are objects. To construct a layer,\n", - "# simply construct the object. Most layers take as a first argument the number\n", - "# of output dimensions / channels.\n", - "layer = tf.keras.layers.Dense(100)\n", - "# The number of input dimensionss is often unnecessary, as it can be inferred\n", - "# the first time the layer is used, but it can be provided if you want to \n", - "# specify it manually, which is useful in some complex models.\n", - "layer = tf.keras.layers.Dense(10, input_shape=(None, 5))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Fn69xxPO5Psr" - }, - "source": [ - "The full list of pre-existing layers can be seen in [the documentation](https://www.tensorflow.org/api_docs/python/tf/keras/layers). It includes Dense (a fully-connected layer),\n", - "Conv2D, LSTM, BatchNormalization, Dropout, and many others." - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "height": 204 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 244, - "status": "ok", - "timestamp": 1527783641557, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "E3XKNknP5Mhb", - "outputId": "c5d52434-d980-4488-efa7-5660819d0207" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\u003ctf.Tensor: id=30, shape=(10, 10), dtype=float32, numpy=\n", - "array([[ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n", - " [ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]], dtype=float32)\u003e" - ] - }, - "execution_count": 3, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "# To use a layer, simply call it.\n", - "layer(tf.zeros([10, 5]))" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "height": 221 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 320, - "status": "ok", - "timestamp": 1527783642457, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "Wt_Nsv-L5t2s", - "outputId": "f0d96dce-0128-4080-bfe2-0ee6fbc0ad90" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "[\u003ctf.Variable 'dense_1/kernel:0' shape=(5, 10) dtype=float32, numpy=\n", - " array([[ 0.43788117, -0.62099844, -0.30525017, -0.59352523, 0.1783089 ,\n", - " 0.47078604, -0.23620895, -0.30482283, 0.01366901, -0.1288507 ],\n", - " [ 0.18407935, -0.56550485, 0.54180616, -0.42254075, 0.3702994 ,\n", - " 0.36705834, -0.29678228, 0.36660975, 0.36717761, 0.46269661],\n", - " [ 0.1709305 , -0.11529458, 0.32710236, 0.46300393, -0.62802851,\n", - " 0.51641601, 0.39624029, 0.26918125, -0.25196898, 0.21353298],\n", - " [ 0.35752094, 0.44161648, 0.61500639, -0.12653333, 0.41629118,\n", - " 0.36193585, 0.066082 , -0.59253877, 0.47318751, 0.17115968],\n", - " [-0.22554061, -0.17727301, 0.5525015 , 0.3678053 , -0.00454676,\n", - " 0.24066836, -0.53640735, 0.13792562, -0.10727292, 0.59708995]], dtype=float32)\u003e,\n", - " \u003ctf.Variable 'dense_1/bias:0' shape=(10,) dtype=float32, numpy=array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)\u003e]" - ] - }, - "execution_count": 4, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "# Layers have many useful methods. For example, you can inspect all variables\n", - "# in a layer by calling layer.variables. In this case a fully-connected layer\n", - "# will have variables for weights and biases.\n", - "layer.variables" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "height": 221 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 226, - "status": "ok", - "timestamp": 1527783643252, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "6ilvKjz8_4MQ", - "outputId": "f647fced-c2d7-41a3-c237-242036784665" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "(\u003ctf.Variable 'dense_1/kernel:0' shape=(5, 10) dtype=float32, numpy=\n", - " array([[ 0.43788117, -0.62099844, -0.30525017, -0.59352523, 0.1783089 ,\n", - " 0.47078604, -0.23620895, -0.30482283, 0.01366901, -0.1288507 ],\n", - " [ 0.18407935, -0.56550485, 0.54180616, -0.42254075, 0.3702994 ,\n", - " 0.36705834, -0.29678228, 0.36660975, 0.36717761, 0.46269661],\n", - " [ 0.1709305 , -0.11529458, 0.32710236, 0.46300393, -0.62802851,\n", - " 0.51641601, 0.39624029, 0.26918125, -0.25196898, 0.21353298],\n", - " [ 0.35752094, 0.44161648, 0.61500639, -0.12653333, 0.41629118,\n", - " 0.36193585, 0.066082 , -0.59253877, 0.47318751, 0.17115968],\n", - " [-0.22554061, -0.17727301, 0.5525015 , 0.3678053 , -0.00454676,\n", - " 0.24066836, -0.53640735, 0.13792562, -0.10727292, 0.59708995]], dtype=float32)\u003e,\n", - " \u003ctf.Variable 'dense_1/bias:0' shape=(10,) dtype=float32, numpy=array([ 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)\u003e)" - ] - }, - "execution_count": 5, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - "# The variables are also accessible through nice accessors\n", - "layer.kernel, layer.bias" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "O0kDbE54-5VS" - }, - "source": [ - "## Implementing custom layers\n", - "The best way to implement your own layer is extending the tf.keras.Layer class and implementing:\n", - " * `__init__` , where you can do all input-independent initialization\n", - " * `build`, where you know the shapes of the input tensors and can do the rest of the initialization\n", - " * `call`, where you do the forward computation\n", - "\n", - "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes requires to create the variables will need to be explicitly specified." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "height": 391 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 251, - "status": "ok", - "timestamp": 1527783661512, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "5Byl3n1k5kIy", - "outputId": "6e7f9285-649a-4132-82ce-73ea92f15862" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tf.Tensor(\n", - "[[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n", - " [ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]], shape=(10, 10), dtype=float32)\n", - "[\u003ctf.Variable 'my_dense_layer_1/kernel:0' shape=(5, 10) dtype=float32, numpy=\n", - "array([[-0.4011991 , 0.22458655, -0.33237562, -0.25117266, 0.33528614,\n", - " -0.01392961, 0.58580834, -0.16346583, 0.28465688, -0.47191954],\n", - " [-0.52922136, 0.22416979, -0.58209574, -0.60914612, 0.05226624,\n", - " -0.18325993, 0.5591442 , -0.24718609, 0.37148207, 0.40475875],\n", - " [ 0.16912812, -0.47618777, -0.38989353, 0.30105609, -0.08085585,\n", - " 0.44758242, 0.545829 , 0.51421839, 0.11063248, 0.20159996],\n", - " [ 0.34073615, -0.59835428, 0.06498981, -0.44489855, -0.34302285,\n", - " 0.20969599, 0.35527444, -0.03173476, -0.22227573, 0.09303057],\n", - " [ 0.41764337, -0.06435019, -0.52509922, -0.39957345, 0.56811184,\n", - " 0.23481232, -0.61666459, 0.31144124, -0.11532354, -0.42421889]], dtype=float32)\u003e]\n" - ] - } - ], - "source": [ - "class MyDenseLayer(tf.keras.layers.Layer):\n", - " def __init__(self, num_outputs):\n", - " super(MyDenseLayer, self).__init__()\n", - " self.num_outputs = num_outputs\n", - " \n", - " def build(self, input_shape):\n", - " self.kernel = self.add_variable(\"kernel\", \n", - " shape=[input_shape[-1].value, \n", - " self.num_outputs])\n", - " \n", - " def call(self, input):\n", - " return tf.matmul(input, self.kernel)\n", - " \n", - "layer = MyDenseLayer(10)\n", - "print(layer(tf.zeros([10, 5])))\n", - "print(layer.variables)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "tk8E2vY0-z4Z" - }, - "source": [ - "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`.\n", - "\n", - "Overall code is easier to read and maintain if it uses standard layers whenever possible, as other readers will be familiar with the behavior of standard layers. If you want to use a layer which is not present in tf.keras.layers or tf.contrib.layers, consider filing a [github issue](http://github.com/tensorflow/tensorflow/issues/new) or, even better, sending us a pull request!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "Qhg4KlbKrs3G" - }, - "source": [ - "## Models: composing layers\n", - "\n", - "Many interesting layer-like things in machine learning models are implemented by composing existing layers. For example, each residual block in a resnet is a composition of convolutions, batch normalizations, and a shortcut.\n", - "\n", - "The main class used when creating a layer-like thing which contains other layers is tf.keras.Model. Implementing one is done by inheriting from tf.keras.Model." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "height": 190 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 420, - "status": "ok", - "timestamp": 1527783698512, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "N30DTXiRASlb", - "outputId": "a8b23a8e-5cf9-4bbf-f93b-6c763d74e2b3" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tf.Tensor(\n", - "[[[[ 0. 0. 0.]\n", - " [ 0. 0. 0.]\n", - " [ 0. 0. 0.]]\n", - "\n", - " [[ 0. 0. 0.]\n", - " [ 0. 0. 0.]\n", - " [ 0. 0. 0.]]]], shape=(1, 2, 3, 3), dtype=float32)\n", - "['resnet_identity_block_1/conv2d_3/kernel:0', 'resnet_identity_block_1/conv2d_3/bias:0', 'resnet_identity_block_1/batch_normalization_3/gamma:0', 'resnet_identity_block_1/batch_normalization_3/beta:0', 'resnet_identity_block_1/conv2d_4/kernel:0', 'resnet_identity_block_1/conv2d_4/bias:0', 'resnet_identity_block_1/batch_normalization_4/gamma:0', 'resnet_identity_block_1/batch_normalization_4/beta:0', 'resnet_identity_block_1/conv2d_5/kernel:0', 'resnet_identity_block_1/conv2d_5/bias:0', 'resnet_identity_block_1/batch_normalization_5/gamma:0', 'resnet_identity_block_1/batch_normalization_5/beta:0', 'resnet_identity_block_1/batch_normalization_3/moving_mean:0', 'resnet_identity_block_1/batch_normalization_3/moving_variance:0', 'resnet_identity_block_1/batch_normalization_4/moving_mean:0', 'resnet_identity_block_1/batch_normalization_4/moving_variance:0', 'resnet_identity_block_1/batch_normalization_5/moving_mean:0', 'resnet_identity_block_1/batch_normalization_5/moving_variance:0']\n" - ] - } - ], - "source": [ - "class ResnetIdentityBlock(tf.keras.Model):\n", - " def __init__(self, kernel_size, filters):\n", - " super(ResnetIdentityBlock, self).__init__(name='')\n", - " filters1, filters2, filters3 = filters\n", - "\n", - " self.conv2a = tf.keras.layers.Conv2D(filters1, (1, 1))\n", - " self.bn2a = tf.keras.layers.BatchNormalization()\n", - "\n", - " self.conv2b = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same')\n", - " self.bn2b = tf.keras.layers.BatchNormalization()\n", - "\n", - " self.conv2c = tf.keras.layers.Conv2D(filters3, (1, 1))\n", - " self.bn2c = tf.keras.layers.BatchNormalization()\n", - "\n", - " def call(self, input_tensor, training=False):\n", - " x = self.conv2a(input_tensor)\n", - " x = self.bn2a(x, training=training)\n", - " x = tf.nn.relu(x)\n", - "\n", - " x = self.conv2b(x)\n", - " x = self.bn2b(x, training=training)\n", - " x = tf.nn.relu(x)\n", - "\n", - " x = self.conv2c(x)\n", - " x = self.bn2c(x, training=training)\n", - "\n", - " x += input_tensor\n", - " return tf.nn.relu(x)\n", - "\n", - " \n", - "block = ResnetIdentityBlock(1, [1, 2, 3])\n", - "print(block(tf.zeros([1, 2, 3, 3])))\n", - "print([x.name for x in block.variables])" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "wYfucVw65PMj" - }, - "source": [ - "Much of the time, however, models which compose many layers simply call one layer after the other. This can be done in very little code using tf.keras.Sequential" - ] - }, - { - "cell_type": "code", - "execution_count": 0, - "metadata": { - "colab": { - "autoexec": { - "startup": false, - "wait_interval": 0 - }, - "base_uri": "https://localhost:8080/", - "height": 153 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 361, - "status": "ok", - "timestamp": 1526674830777, - "user": { - "displayName": "Alexandre Passos", - "photoUrl": "//lh4.googleusercontent.com/-kmTTWXEgAPw/AAAAAAAAAAI/AAAAAAAAAC0/q_DoOzKGwds/s50-c-k-no/photo.jpg", - "userId": "108023195365833072773" - }, - "user_tz": 420 - }, - "id": "L9frk7Ur4uvJ", - "outputId": "882e9076-b6d9-4380-bb1e-7c6b57d54c39" - }, - "outputs": [ - { - "data": { - "text/plain": [ - "\u003ctf.Tensor: id=1423, shape=(1, 2, 3, 3), dtype=float32, numpy=\n", - "array([[[[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]],\n", - "\n", - " [[0., 0., 0.],\n", - " [0., 0., 0.],\n", - " [0., 0., 0.]]]], dtype=float32)\u003e" - ] - }, - "execution_count": 26, - "metadata": { - "tags": [] - }, - "output_type": "execute_result" - } - ], - "source": [ - " my_seq = tf.keras.Sequential([tf.keras.layers.Conv2D(1, (1, 1)),\n", - " tf.keras.layers.BatchNormalization(),\n", - " tf.keras.layers.Conv2D(2, 1, \n", - " padding='same'),\n", - " tf.keras.layers.BatchNormalization(),\n", - " tf.keras.layers.Conv2D(3, (1, 1)),\n", - " tf.keras.layers.BatchNormalization()])\n", - "my_seq(tf.zeros([1, 2, 3, 3]))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "colab_type": "text", - "id": "c5YwYcnuK-wc" - }, - "source": [ - "# Next steps\n", - "\n", - "Now you can go back to the previous notebook and adapt the linear regression example to use layers and models to be better structured." - ] - } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "default_view": {}, - "name": "4 - High level API - TensorFlow Eager.ipynb", - "provenance": [], - "version": "0.3.2", - "views": {} - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/tensorflow/contrib/eager/python/examples/notebooks/README.md b/tensorflow/contrib/eager/python/examples/notebooks/README.md new file mode 100644 index 0000000000..0d5ed84894 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/notebooks/README.md @@ -0,0 +1,11 @@ +## Research and experimentation + +Eager execution provides an imperative, define-by-run interface for advanced +operations. Write custom layers, forward passes, and training loops with auto +differentiation. Start with these notebooks, then read the +[eager execution guide](https://www.tensorflow.org/guide/eager). + +1. [Eager execution basics](./eager_basics.ipynb) +2. [Automatic differentiation and gradient tapes](./automatic_differentiation.ipynb) +3. [Custom training: basics](./custom_training.ipynb) +4. [Custom layers](./custom_layers.ipynb) diff --git a/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb new file mode 100644 index 0000000000..a18882fafa --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb @@ -0,0 +1,364 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "automatic_differentiation.ipynb", + "version": "0.3.2", + "views": {}, + "default_view": {}, + "provenance": [], + "private_outputs": true, + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "metadata": { + "id": "t09eeeR5prIJ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "##### Copyright 2018 The TensorFlow Authors." + ] + }, + { + "metadata": { + "id": "GCCk8_dHpuNf", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "cellView": "form" + }, + "cell_type": "code", + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "xh8WkEwWpnm7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Automatic differentiation and gradient tape" + ] + }, + { + "metadata": { + "id": "idv0bPeCp325", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "
\n", + "\n", + " Run in Google Colab\n", + "\n", + "View source on GitHub
" + ] + }, + { + "metadata": { + "id": "vDJ4XzMqodTy", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "In the previous tutorial we introduced `Tensor`s and operations on them. In this tutorial we will cover [automatic differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation), a key technique for optimizing machine learning models." + ] + }, + { + "metadata": { + "id": "GQJysDM__Qb0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup\n" + ] + }, + { + "metadata": { + "id": "OiMPZStlibBv", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "tf.enable_eager_execution()\n", + "\n", + "tfe = tf.contrib.eager # Shorthand for some symbols" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "1CLWJl0QliB0", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Derivatives of a function\n", + "\n", + "TensorFlow provides APIs for automatic differentiation - computing the derivative of a function. The way that more closely mimics the math is to encapsulate the computation in a Python function, say `f`, and use `tfe.gradients_function` to create a function that computes the derivatives of `f` with respect to its arguments. If you're familiar with [autograd](https://github.com/HIPS/autograd) for differentiating numpy functions, this will be familiar. For example: " + ] + }, + { + "metadata": { + "id": "9FViq92UX7P8", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "from math import pi\n", + "\n", + "def f(x):\n", + " return tf.square(tf.sin(x))\n", + "\n", + "assert f(pi/2).numpy() == 1.0\n", + "\n", + "\n", + "# grad_f will return a list of derivatives of f\n", + "# with respect to its arguments. Since f() has a single argument,\n", + "# grad_f will return a list with a single element.\n", + "grad_f = tfe.gradients_function(f)\n", + "assert tf.abs(grad_f(pi/2)[0]).numpy() < 1e-7" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "v9fPs8RyopCf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Higher-order gradients\n", + "\n", + "The same API can be used to differentiate as many times as you like:\n" + ] + }, + { + "metadata": { + "id": "3D0ZvnGYo0rW", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def f(x):\n", + " return tf.square(tf.sin(x))\n", + "\n", + "def grad(f):\n", + " return lambda x: tfe.gradients_function(f)(x)[0]\n", + "\n", + "x = tf.lin_space(-2*pi, 2*pi, 100) # 100 points between -2π and +2π\n", + "\n", + "import matplotlib.pyplot as plt\n", + "\n", + "plt.plot(x, f(x), label=\"f\")\n", + "plt.plot(x, grad(f)(x), label=\"first derivative\")\n", + "plt.plot(x, grad(grad(f))(x), label=\"second derivative\")\n", + "plt.plot(x, grad(grad(grad(f)))(x), label=\"third derivative\")\n", + "plt.legend()\n", + "plt.show()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-39gouo7mtgu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Gradient tapes\n", + "\n", + "Every differentiable TensorFlow operation has an associated gradient function. For example, the gradient function of `tf.square(x)` would be a function that returns `2.0 * x`. To compute the gradient of a user-defined function (like `f(x)` in the example above), TensorFlow first \"records\" all the operations applied to compute the output of the function. We call this record a \"tape\". It then uses that tape and the gradients functions associated with each primitive operation to compute the gradients of the user-defined function using [reverse mode differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation).\n", + "\n", + "Since operations are recorded as they are executed, Python control flow (using `if`s and `while`s for example) is naturally handled:\n", + "\n" + ] + }, + { + "metadata": { + "id": "MH0UfjympWf7", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def f(x, y):\n", + " output = 1\n", + " for i in range(y):\n", + " output = tf.multiply(output, x)\n", + " return output\n", + "\n", + "def g(x, y):\n", + " # Return the gradient of `f` with respect to it's first parameter\n", + " return tfe.gradients_function(f)(x, y)[0]\n", + "\n", + "assert f(3.0, 2).numpy() == 9.0 # f(x, 2) is essentially x * x\n", + "assert g(3.0, 2).numpy() == 6.0 # And its gradient will be 2 * x\n", + "assert f(4.0, 3).numpy() == 64.0 # f(x, 3) is essentially x * x * x\n", + "assert g(4.0, 3).numpy() == 48.0 # And its gradient will be 3 * x * x" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "aNmR5-jhpX2t", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "At times it may be inconvenient to encapsulate computation of interest into a function. For example, if you want the gradient of the output with respect to intermediate values computed in the function. In such cases, the slightly more verbose but explicit [tf.GradientTape](https://www.tensorflow.org/api_docs/python/tf/GradientTape) context is useful. All computation inside the context of a `tf.GradientTape` is \"recorded\".\n", + "\n", + "For example:" + ] + }, + { + "metadata": { + "id": "bAFeIE8EuVIq", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "x = tf.ones((2, 2))\n", + " \n", + "# TODO(b/78880779): Remove the 'persistent=True' argument and use\n", + "# a single t.gradient() call when the bug is resolved.\n", + "with tf.GradientTape(persistent=True) as t:\n", + " # TODO(ashankar): Explain with \"watch\" argument better?\n", + " t.watch(x)\n", + " y = tf.reduce_sum(x)\n", + " z = tf.multiply(y, y)\n", + "\n", + "# Use the same tape to compute the derivative of z with respect to the\n", + "# intermediate value y.\n", + "dz_dy = t.gradient(z, y)\n", + "assert dz_dy.numpy() == 8.0\n", + "\n", + "# Derivative of z with respect to the original input tensor x\n", + "dz_dx = t.gradient(z, x)\n", + "for i in [0, 1]:\n", + " for j in [0, 1]:\n", + " assert dz_dx[i][j].numpy() == 8.0" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "DK05KXrAAld3", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Higher-order gradients\n", + "\n", + "Operations inside of the `GradientTape` context manager are recorded for automatic differentiation. If gradients are computed in that context, then the gradient computation is recorded as well. As a result, the exact same API works for higher-order gradients as well. For example:" + ] + }, + { + "metadata": { + "id": "cPQgthZ7ugRJ", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# TODO(ashankar): Should we use the persistent tape here instead? Follow up on Tom and Alex's discussion\n", + "\n", + "x = tf.constant(1.0) # Convert the Python 1.0 to a Tensor object\n", + "\n", + "with tf.GradientTape() as t:\n", + " with tf.GradientTape() as t2:\n", + " t2.watch(x)\n", + " y = x * x * x\n", + " # Compute the gradient inside the 't' context manager\n", + " # which means the gradient computation is differentiable as well.\n", + " dy_dx = t2.gradient(y, x)\n", + "d2y_dx2 = t.gradient(dy_dx, x)\n", + "\n", + "assert dy_dx.numpy() == 3.0\n", + "assert d2y_dx2.numpy() == 6.0" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "4U1KKzUpNl58", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Next Steps\n", + "\n", + "In this tutorial we covered gradient computation in TensorFlow. With that we have enough of the primitives required to build an train neural networks, which we will cover in the [next tutorial](https://github.com/tensorflow/models/tree/master/official/contrib/eager/python/examples/notebooks/3_neural_networks.ipynb)." + ] + } + ] +} \ No newline at end of file diff --git a/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb new file mode 100644 index 0000000000..54fbf2a7e1 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb @@ -0,0 +1,399 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "custom_layers.ipynb", + "version": "0.3.2", + "views": {}, + "default_view": {}, + "provenance": [], + "private_outputs": true, + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + } + }, + "cells": [ + { + "metadata": { + "id": "tDnwEv8FtJm7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "##### Copyright 2018 The TensorFlow Authors." + ] + }, + { + "metadata": { + "id": "JlknJBWQtKkI", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "cellView": "form" + }, + "cell_type": "code", + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "60RdWsg1tETW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Custom layers" + ] + }, + { + "metadata": { + "id": "BcJg7Enms86w", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "
\n", + "\n", + " Run in Google Colab\n", + "\n", + "View source on GitHub
" + ] + }, + { + "metadata": { + "id": "UEu3q4jmpKVT", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "We recommend using `tf.keras` as a high-level API for building neural networks. That said, most TensorFlow APIs are usable with eager execution.\n" + ] + }, + { + "metadata": { + "id": "pwX7Fii1rwsJ", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "tfe = tf.contrib.eager\n", + "\n", + "tf.enable_eager_execution()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "zSFfVVjkrrsI", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Layers: common sets of useful operations\n", + "\n", + "Most of the time when writing code for machine learning models you want to operate at a higher level of abstraction than individual operations and manipulation of individual variables.\n", + "\n", + "Many machine learning models are expressible as the composition and stacking of relatively simple layers, and TensorFlow provides both a set of many common layers as a well as easy ways for you to write your own application-specific layers either from scratch or as the composition of existing layers.\n", + "\n", + "TensorFlow includes the full [Keras](https://keras.io) API in the tf.keras package, and the Keras layers are very useful when building your own models.\n" + ] + }, + { + "metadata": { + "id": "8PyXlPl-4TzQ", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# In the tf.keras.layers package, layers are objects. To construct a layer,\n", + "# simply construct the object. Most layers take as a first argument the number\n", + "# of output dimensions / channels.\n", + "layer = tf.keras.layers.Dense(100)\n", + "# The number of input dimensions is often unnecessary, as it can be inferred\n", + "# the first time the layer is used, but it can be provided if you want to \n", + "# specify it manually, which is useful in some complex models.\n", + "layer = tf.keras.layers.Dense(10, input_shape=(None, 5))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Fn69xxPO5Psr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "The full list of pre-existing layers can be seen in [the documentation](https://www.tensorflow.org/api_docs/python/tf/keras/layers). It includes Dense (a fully-connected layer),\n", + "Conv2D, LSTM, BatchNormalization, Dropout, and many others." + ] + }, + { + "metadata": { + "id": "E3XKNknP5Mhb", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# To use a layer, simply call it.\n", + "layer(tf.zeros([10, 5]))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Wt_Nsv-L5t2s", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Layers have many useful methods. For example, you can inspect all variables\n", + "# in a layer by calling layer.variables. In this case a fully-connected layer\n", + "# will have variables for weights and biases.\n", + "layer.variables" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "6ilvKjz8_4MQ", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# The variables are also accessible through nice accessors\n", + "layer.kernel, layer.bias" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "O0kDbE54-5VS", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Implementing custom layers\n", + "The best way to implement your own layer is extending the tf.keras.Layer class and implementing:\n", + " * `__init__` , where you can do all input-independent initialization\n", + " * `build`, where you know the shapes of the input tensors and can do the rest of the initialization\n", + " * `call`, where you do the forward computation\n", + "\n", + "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`. However, the advantage of creating them in `build` is that it enables late variable creation based on the shape of the inputs the layer will operate on. On the other hand, creating variables in `__init__` would mean that shapes required to create the variables will need to be explicitly specified." + ] + }, + { + "metadata": { + "id": "5Byl3n1k5kIy", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class MyDenseLayer(tf.keras.layers.Layer):\n", + " def __init__(self, num_outputs):\n", + " super(MyDenseLayer, self).__init__()\n", + " self.num_outputs = num_outputs\n", + " \n", + " def build(self, input_shape):\n", + " self.kernel = self.add_variable(\"kernel\", \n", + " shape=[input_shape[-1].value, \n", + " self.num_outputs])\n", + " \n", + " def call(self, input):\n", + " return tf.matmul(input, self.kernel)\n", + " \n", + "layer = MyDenseLayer(10)\n", + "print(layer(tf.zeros([10, 5])))\n", + "print(layer.variables)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "tk8E2vY0-z4Z", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Note that you don't have to wait until `build` is called to create your variables, you can also create them in `__init__`.\n", + "\n", + "Overall code is easier to read and maintain if it uses standard layers whenever possible, as other readers will be familiar with the behavior of standard layers. If you want to use a layer which is not present in tf.keras.layers or tf.contrib.layers, consider filing a [github issue](http://github.com/tensorflow/tensorflow/issues/new) or, even better, sending us a pull request!" + ] + }, + { + "metadata": { + "id": "Qhg4KlbKrs3G", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Models: composing layers\n", + "\n", + "Many interesting layer-like things in machine learning models are implemented by composing existing layers. For example, each residual block in a resnet is a composition of convolutions, batch normalizations, and a shortcut.\n", + "\n", + "The main class used when creating a layer-like thing which contains other layers is tf.keras.Model. Implementing one is done by inheriting from tf.keras.Model." + ] + }, + { + "metadata": { + "id": "N30DTXiRASlb", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class ResnetIdentityBlock(tf.keras.Model):\n", + " def __init__(self, kernel_size, filters):\n", + " super(ResnetIdentityBlock, self).__init__(name='')\n", + " filters1, filters2, filters3 = filters\n", + "\n", + " self.conv2a = tf.keras.layers.Conv2D(filters1, (1, 1))\n", + " self.bn2a = tf.keras.layers.BatchNormalization()\n", + "\n", + " self.conv2b = tf.keras.layers.Conv2D(filters2, kernel_size, padding='same')\n", + " self.bn2b = tf.keras.layers.BatchNormalization()\n", + "\n", + " self.conv2c = tf.keras.layers.Conv2D(filters3, (1, 1))\n", + " self.bn2c = tf.keras.layers.BatchNormalization()\n", + "\n", + " def call(self, input_tensor, training=False):\n", + " x = self.conv2a(input_tensor)\n", + " x = self.bn2a(x, training=training)\n", + " x = tf.nn.relu(x)\n", + "\n", + " x = self.conv2b(x)\n", + " x = self.bn2b(x, training=training)\n", + " x = tf.nn.relu(x)\n", + "\n", + " x = self.conv2c(x)\n", + " x = self.bn2c(x, training=training)\n", + "\n", + " x += input_tensor\n", + " return tf.nn.relu(x)\n", + "\n", + " \n", + "block = ResnetIdentityBlock(1, [1, 2, 3])\n", + "print(block(tf.zeros([1, 2, 3, 3])))\n", + "print([x.name for x in block.variables])" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wYfucVw65PMj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Much of the time, however, models which compose many layers simply call one layer after the other. This can be done in very little code using tf.keras.Sequential" + ] + }, + { + "metadata": { + "id": "L9frk7Ur4uvJ", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + " my_seq = tf.keras.Sequential([tf.keras.layers.Conv2D(1, (1, 1)),\n", + " tf.keras.layers.BatchNormalization(),\n", + " tf.keras.layers.Conv2D(2, 1, \n", + " padding='same'),\n", + " tf.keras.layers.BatchNormalization(),\n", + " tf.keras.layers.Conv2D(3, (1, 1)),\n", + " tf.keras.layers.BatchNormalization()])\n", + "my_seq(tf.zeros([1, 2, 3, 3]))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "c5YwYcnuK-wc", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Next steps\n", + "\n", + "Now you can go back to the previous notebook and adapt the linear regression example to use layers and models to be better structured." + ] + } + ] +} \ No newline at end of file diff --git a/tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb new file mode 100644 index 0000000000..0a781d2153 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb @@ -0,0 +1,478 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Custom training: basics", + "version": "0.3.2", + "views": {}, + "default_view": {}, + "provenance": [], + "private_outputs": true, + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "metadata": { + "id": "5rmpybwysXGV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "##### Copyright 2018 The TensorFlow Authors." + ] + }, + { + "metadata": { + "id": "m8y3rGtQsYP2", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "cellView": "form" + }, + "cell_type": "code", + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "hrXv0rU9sIma", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Custom training: basics" + ] + }, + { + "metadata": { + "id": "7S0BwJ_8sLu7", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "
\n", + "\n", + " Run in Google Colab\n", + "\n", + "View source on GitHub
" + ] + }, + { + "metadata": { + "id": "k2o3TTG4TFpt", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "In the previous tutorial we covered the TensorFlow APIs for automatic differentiation, a basic building block for machine learning.\n", + "In this tutorial we will use the TensorFlow primitives introduced in the prior tutorials to do some simple machine learning.\n", + "\n", + "TensorFlow also includes a higher-level neural networks API (`tf.keras`) which provides useful abstractions to reduce boilerplate. We strongly recommend those higher level APIs for people working with neural networks. However, in this short tutorial we cover neural network training from first principles to establish a strong foundation." + ] + }, + { + "metadata": { + "id": "3LXMVuV0VhDr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Setup" + ] + }, + { + "metadata": { + "id": "PJ64L90aVir3", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "import tensorflow as tf\n", + "tfe = tf.contrib.eager # Shorthand for some symbols\n", + "\n", + "tf.enable_eager_execution()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "eMAWbDJFVmMk", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Variables\n", + "\n", + "Tensors in TensorFlow are immutable stateless objects. Machine learning models, however, need to have changing state: as your model trains, the same code to compute predictions should behave differently over time (hopefully with a lower loss!). To represent this state which needs to change over the course of your computation, you can choose to rely on the fact that Python is a stateful programming language:\n" + ] + }, + { + "metadata": { + "id": "VkJwtLS_Jbn8", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "# Using python state\n", + "x = tf.zeros([10, 10])\n", + "x += 2 # This is equivalent to x = x + 2, which does not mutate the original\n", + " # value of x\n", + "print(x)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "wfneTXy7JcUz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "TensorFlow, however, has stateful operations built in, and these are often more pleasant to use than low-level Python representations of your state. To represent weights in a model, for example, it's often convenient and efficient to use TensorFlow variables.\n", + "\n", + "A Variable is an object which stores a value and, when used in a TensorFlow computation, will implicitly read from this stored value. There are operations (`tf.assign_sub`, `tf.scatter_update`, etc) which manipulate the value stored in a TensorFlow variable." + ] + }, + { + "metadata": { + "id": "itxmrMil6DQi", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "v = tfe.Variable(1.0)\n", + "assert v.numpy() == 1.0\n", + "\n", + "# Re-assign the value\n", + "v.assign(3.0)\n", + "assert v.numpy() == 3.0\n", + "\n", + "# Use `v` in a TensorFlow operation like tf.square() and reassign\n", + "v.assign(tf.square(v))\n", + "assert v.numpy() == 9.0" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-paSaeq1JzwC", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Computations using Variables are automatically traced when computing gradients. For Variables representing embeddings TensorFlow will do sparse updates by default, which are more computation and memory efficient.\n", + "\n", + "Using Variables is also a way to quickly let a reader of your code know that this piece of state is mutable." + ] + }, + { + "metadata": { + "id": "BMiFcDzE7Qu3", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Example: Fitting a linear model\n", + "\n", + "Let's now put the few concepts we have so far ---`Tensor`, `GradientTape`, `Variable` --- to build and train a simple model. This typically involves a few steps:\n", + "\n", + "1. Define the model.\n", + "2. Define a loss function.\n", + "3. Obtain training data.\n", + "4. Run through the training data and use an \"optimizer\" to adjust the variables to fit the data.\n", + "\n", + "In this tutorial, we'll walk through a trivial example of a simple linear model: `f(x) = x * W + b`, which has two variables - `W` and `b`. Furthermore, we'll synthesize data such that a well trained model would have `W = 3.0` and `b = 2.0`." + ] + }, + { + "metadata": { + "id": "gFzH64Jn9PIm", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Define the model\n", + "\n", + "Let's define a simple class to encapsulate the variables and the computation." + ] + }, + { + "metadata": { + "id": "_WRu7Pze7wk8", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "class Model(object):\n", + " def __init__(self):\n", + " # Initialize variable to (5.0, 0.0)\n", + " # In practice, these should be initialized to random values.\n", + " self.W = tfe.Variable(5.0)\n", + " self.b = tfe.Variable(0.0)\n", + " \n", + " def __call__(self, x):\n", + " return self.W * x + self.b\n", + " \n", + "model = Model()\n", + "\n", + "assert model(3.0).numpy() == 15.0" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "xa6j_yXa-j79", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Define a loss function\n", + "\n", + "A loss function measures how well the output of a model for a given input matches the desired output. Let's use the standard L2 loss." + ] + }, + { + "metadata": { + "id": "Y0ysUFGY924U", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def loss(predicted_y, desired_y):\n", + " return tf.reduce_mean(tf.square(predicted_y - desired_y))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "qutT_fkl_CBc", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Obtain training data\n", + "\n", + "Let's synthesize the training data with some noise." + ] + }, + { + "metadata": { + "id": "gxPTb-kt_N5m", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "TRUE_W = 3.0\n", + "TRUE_b = 2.0\n", + "NUM_EXAMPLES = 1000\n", + "\n", + "inputs = tf.random_normal(shape=[NUM_EXAMPLES])\n", + "noise = tf.random_normal(shape=[NUM_EXAMPLES])\n", + "outputs = inputs * TRUE_W + TRUE_b + noise" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "-50nq-wPBsAW", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Before we train the model let's visualize where the model stands right now. We'll plot the model's predictions in red and the training data in blue." + ] + }, + { + "metadata": { + "id": "_eb83LtrB4nt", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.scatter(inputs, outputs, c='b')\n", + "plt.scatter(inputs, model(inputs), c='r')\n", + "plt.show()\n", + "\n", + "print('Current loss: '),\n", + "print(loss(model(inputs), outputs).numpy())" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "sSDP-yeq_4jE", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Define a training loop\n", + "\n", + "We now have our network and our training data. Let's train it, i.e., use the training data to update the model's variables (`W` and `b`) so that the loss goes down using [gradient descent](https://en.wikipedia.org/wiki/Gradient_descent). There are many variants of the gradient descent scheme that are captured in `tf.train.Optimizer` implementations. We'd highly recommend using those implementations, but in the spirit of building from first principles, in this particular example we will implement the basic math ourselves." + ] + }, + { + "metadata": { + "id": "MBIACgdnA55X", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "def train(model, inputs, outputs, learning_rate):\n", + " with tf.GradientTape() as t:\n", + " current_loss = loss(model(inputs), outputs)\n", + " dW, db = t.gradient(current_loss, [model.W, model.b])\n", + " model.W.assign_sub(learning_rate * dW)\n", + " model.b.assign_sub(learning_rate * db)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "RwWPaJryD2aN", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "Finally, let's repeatedly run through the training data and see how `W` and `b` evolve." + ] + }, + { + "metadata": { + "id": "XdfkR223D9dW", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "model = Model()\n", + "\n", + "# Collect the history of W-values and b-values to plot later\n", + "Ws, bs = [], []\n", + "epochs = range(10)\n", + "for epoch in epochs:\n", + " Ws.append(model.W.numpy())\n", + " bs.append(model.b.numpy())\n", + " current_loss = loss(model(inputs), outputs)\n", + "\n", + " train(model, inputs, outputs, learning_rate=0.1)\n", + " print('Epoch %2d: W=%1.2f b=%1.2f, loss=%2.5f' %\n", + " (epoch, Ws[-1], bs[-1], current_loss))\n", + "\n", + "# Let's plot it all\n", + "plt.plot(epochs, Ws, 'r',\n", + " epochs, bs, 'b')\n", + "plt.plot([TRUE_W] * len(epochs), 'r--',\n", + " [TRUE_b] * len(epochs), 'b--')\n", + "plt.legend(['W', 'b', 'true W', 'true_b'])\n", + "plt.show()\n", + " " + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "vPnIVuaSJwWz", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Next Steps\n", + "\n", + "In this tutorial we covered `Variable`s and built and trained a simple linear model using the TensorFlow primitives discussed so far.\n", + "\n", + "In theory, this is pretty much all you need to use TensorFlow for your machine learning research.\n", + "In practice, particularly for neural networks, the higher level APIs like `tf.keras` will be much more convenient since it provides higher level building blocks (called \"layers\"), utilities to save and restore state, a suite of loss functions, a suite of optimization strategies etc. \n", + "\n", + "The [next tutorial](TODO) will cover these higher level APIs." + ] + } + ] +} \ No newline at end of file diff --git a/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb b/tensorflow/contrib/eager/python/examples/notebooks/eager_basics.ipynb similarity index 50% rename from tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb rename to tensorflow/contrib/eager/python/examples/notebooks/eager_basics.ipynb index 51d10a7784..b37a18c9a6 100644 --- a/tensorflow/contrib/eager/python/examples/notebooks/1_basics.ipynb +++ b/tensorflow/contrib/eager/python/examples/notebooks/eager_basics.ipynb @@ -1,27 +1,107 @@ { + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "eager_basics.ipynb", + "version": "0.3.2", + "views": {}, + "default_view": {}, + "provenance": [], + "private_outputs": true, + "collapsed_sections": [], + "toc_visible": true + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, "cells": [ { + "metadata": { + "id": "iPpI7RaYoZuE", + "colab_type": "text" + }, "cell_type": "markdown", + "source": [ + "##### Copyright 2018 The TensorFlow Authors." + ] + }, + { "metadata": { - "colab_type": "text", - "id": "U9i2Dsh-ziXr" + "id": "hro2InpHobKk", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "cellView": "form" }, + "cell_type": "code", + "source": [ + "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n", + "# you may not use this file except in compliance with the License.\n", + "# You may obtain a copy of the License at\n", + "#\n", + "# https://www.apache.org/licenses/LICENSE-2.0\n", + "#\n", + "# Unless required by applicable law or agreed to in writing, software\n", + "# distributed under the License is distributed on an \"AS IS\" BASIS,\n", + "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n", + "# See the License for the specific language governing permissions and\n", + "# limitations under the License." + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "U9i2Dsh-ziXr", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Eager execution basics" + ] + }, + { + "metadata": { + "id": "Hndw-YcxoOJK", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "
\n", + "\n", + " Run in Google Colab\n", + "\n", + "View source on GitHub
" + ] + }, + { + "metadata": { + "id": "6sILUVbHoSgH", + "colab_type": "text" + }, + "cell_type": "markdown", "source": [ - "# An introduction to TensorFlow\n", - "\n", "This is an introductory tutorial for using TensorFlow. It will cover:\n", "\n", "* Importing required packages\n", "* Creating and using Tensors\n", - "* Using GPU acceleration\n" + "* Using GPU acceleration\n", + "* Datasets" ] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "z1JcS5iBXMRO" + "id": "z1JcS5iBXMRO", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "## Import TensorFlow\n", "\n", @@ -30,32 +110,32 @@ ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { - "cellView": "code", + "id": "RlIWhyeLoYnG", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } }, - "colab_type": "code", - "id": "RlIWhyeLoYnG" + "cellView": "code" }, - "outputs": [], + "cell_type": "code", "source": [ "import tensorflow as tf\n", "\n", "tf.enable_eager_execution()" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "H9UySOPLXdaw" + "id": "H9UySOPLXdaw", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "## Tensors\n", "\n", @@ -63,46 +143,18 @@ ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { - "cellView": "code", + "id": "ngUe237Wt48W", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 - }, - "height": 125 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 320, - "status": "ok", - "timestamp": 1526420535530, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 + } }, - "id": "ngUe237Wt48W", - "outputId": "b1a1cd60-4eb3-443d-cd6b-68406390784e" + "cellView": "code" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tf.Tensor(3, shape=(), dtype=int32)\n", - "tf.Tensor([4 6], shape=(2,), dtype=int32)\n", - "tf.Tensor(25, shape=(), dtype=int32)\n", - "tf.Tensor(6, shape=(), dtype=int32)\n", - "tf.Tensor(aGVsbG8gd29ybGQ, shape=(), dtype=string)\n", - "tf.Tensor(13, shape=(), dtype=int32)\n" - ] - } - ], + "cell_type": "code", "source": [ "print(tf.add(1, 2))\n", "print(tf.add([1, 2], [3, 4]))\n", @@ -112,66 +164,46 @@ "\n", "# Operator overloading is also supported\n", "print(tf.square(2) + tf.square(3))" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "IDY4WsYRhP81" + "id": "IDY4WsYRhP81", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "Each Tensor has a shape and a datatype" ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { + "id": "srYWH1MdJNG7", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 - }, - "height": 53 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 215, - "status": "ok", - "timestamp": 1526420538162, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "srYWH1MdJNG7", - "outputId": "5e4ac41c-5115-4e50-eba0-42e249c16561" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(1, 2)\n", - "\u003cdtype: 'int32'\u003e\n" - ] + } } - ], + }, + "cell_type": "code", "source": [ "x = tf.matmul([[1]], [[2, 3]])\n", "print(x.shape)\n", "print(x.dtype)" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "eBPw8e8vrsom" + "id": "eBPw8e8vrsom", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "The most obvious differences between NumPy arrays and TensorFlow Tensors are:\n", "\n", @@ -180,11 +212,11 @@ ] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "Dwi1tdW3JBw6" + "id": "Dwi1tdW3JBw6", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "### NumPy Compatibility\n", "\n", @@ -197,52 +229,17 @@ ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { + "id": "lCUWzso6mbqR", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 - }, - "height": 251 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 238, - "status": "ok", - "timestamp": 1526420540562, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "lCUWzso6mbqR", - "outputId": "fd0a22bc-8249-49dd-fcbd-63161cc47e46" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "TensorFlow operations convert numpy arrays to Tensors automatically\n", - "tf.Tensor(\n", - "[[ 42. 42. 42.]\n", - " [ 42. 42. 42.]\n", - " [ 42. 42. 42.]], shape=(3, 3), dtype=float64)\n", - "And NumPy operations convert Tensors to numpy arrays automatically\n", - "[[ 43. 43. 43.]\n", - " [ 43. 43. 43.]\n", - " [ 43. 43. 43.]]\n", - "The .numpy() method explicitly converts a Tensor to a numpy array\n", - "[[ 42. 42. 42.]\n", - " [ 42. 42. 42.]\n", - " [ 42. 42. 42.]]\n" - ] + } } - ], + }, + "cell_type": "code", "source": [ "import numpy as np\n", "\n", @@ -258,14 +255,16 @@ "\n", "print(\"The .numpy() method explicitly converts a Tensor to a numpy array\")\n", "print(tensor.numpy())" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "PBNP8yTRfu_X" + "id": "PBNP8yTRfu_X", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "## GPU acceleration\n", "\n", @@ -273,42 +272,18 @@ ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { - "cellView": "code", + "id": "3Twf_Rw-gQFM", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 - }, - "height": 53 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 340, - "status": "ok", - "timestamp": 1526420543562, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 + } }, - "id": "3Twf_Rw-gQFM", - "outputId": "2239ae2b-adf3-4895-b1f3-464cf5361d1b" + "cellView": "code" }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Is there a GPU available: False\n", - "Is the Tensor on GPU #0: False\n" - ] - } - ], + "cell_type": "code", "source": [ "x = tf.random_uniform([3, 3])\n", "\n", @@ -317,26 +292,28 @@ "\n", "print(\"Is the Tensor on GPU #0: \"),\n", "print(x.device.endswith('GPU:0'))" - ] + ], + "execution_count": 0, + "outputs": [] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "vpgYzgVXW2Ud" + "id": "vpgYzgVXW2Ud", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "### Device Names\n", "\n", - "The `Tensor.device` property provides a fully qualified string name of the device hosting the contents of the Tensor. This name encodes a bunch of details, such as an identifier of the network address of the host on which this program is executing and the device within that host. This is required for distributed execution of TensorFlow programs, but we'll skip that for now. The string will end with `GPU:\u003cN\u003e` if the tensor is placed on the `N`-th tensor on the host." + "The `Tensor.device` property provides a fully qualified string name of the device hosting the contents of the Tensor. This name encodes a bunch of details, such as an identifier of the network address of the host on which this program is executing and the device within that host. This is required for distributed execution of TensorFlow programs, but we'll skip that for now. The string will end with `GPU:` if the tensor is placed on the `N`-th tensor on the host." ] }, { - "cell_type": "markdown", "metadata": { - "colab_type": "text", - "id": "ZWZQCimzuqyP" + "id": "ZWZQCimzuqyP", + "colab_type": "text" }, + "cell_type": "markdown", "source": [ "\n", "\n", @@ -346,41 +323,17 @@ ] }, { - "cell_type": "code", - "execution_count": 0, "metadata": { + "id": "RjkNZTuauy-Q", + "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 - }, - "height": 53 - }, - "colab_type": "code", - "executionInfo": { - "elapsed": 1762, - "status": "ok", - "timestamp": 1526420547562, - "user": { - "displayName": "", - "photoUrl": "", - "userId": "" - }, - "user_tz": 420 - }, - "id": "RjkNZTuauy-Q", - "outputId": "2e613293-ccac-4db2-b793-8ceb5b5adcfd" - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "On CPU:\n", - "10 loops, best of 3: 35.8 ms per loop\n" - ] + } } - ], + }, + "cell_type": "code", "source": [ "def time_matmul(x):\n", " %timeit tf.matmul(x, x)\n", @@ -398,32 +351,141 @@ " x = tf.random_uniform([1000, 1000])\n", " assert x.device.endswith(\"GPU:0\")\n", " time_matmul(x)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "o1K4dlhhHtQj", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "## Datasets\n", + "\n", + "This section demonstrates the use of the [`tf.data.Dataset` API](https://www.tensorflow.org/guide/datasets) to build pipelines to feed data to your model. It covers:\n", + "\n", + "* Creating a `Dataset`.\n", + "* Iteration over a `Dataset` with eager execution enabled.\n", + "\n", + "We recommend using the `Dataset`s API for building performant, complex input pipelines from simple, re-usable pieces that will feed your model's training or evaluation loops.\n", + "\n", + "If you're familiar with TensorFlow graphs, the API for constructing the `Dataset` object remains exactly the same when eager execution is enabled, but the process of iterating over elements of the dataset is slightly simpler.\n", + "You can use Python iteration over the `tf.data.Dataset` object and do not need to explicitly create an `tf.data.Iterator` object.\n", + "As a result, the discussion on iterators in the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets) is not relevant when eager execution is enabled." ] }, { + "metadata": { + "id": "zI0fmOynH-Ne", + "colab_type": "text" + }, "cell_type": "markdown", + "source": [ + "### Create a source `Dataset`\n", + "\n", + "Create a _source_ dataset using one of the factory functions like [`Dataset.from_tensors`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensors), [`Dataset.from_tensor_slices`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#from_tensor_slices) or using objects that read from files like [`TextLineDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TextLineDataset) or [`TFRecordDataset`](https://www.tensorflow.org/api_docs/python/tf/data/TFRecordDataset). See the [TensorFlow Guide](https://www.tensorflow.org/guide/datasets#reading_input_data) for more information." + ] + }, + { "metadata": { - "colab_type": "text", - "id": "YEOJTNiOvnpQ" + "id": "F04fVOHQIBiG", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } }, + "cell_type": "code", "source": [ - "## Next Steps\n", + "ds_tensors = tf.data.Dataset.from_tensor_slices([1, 2, 3, 4, 5, 6])\n", "\n", - "In this tutorial we covered the most fundamental concepts in TensorFlow - `Tensor`s, operations, and devices.\n", - "In [the next tutorial](https://github.com/tensorflow/models/tree/master/official/contrib/eager/python/examples/notebooks/2_gradients.ipynb) we will cover automatic differentiation - a building block required for training many machine learning models like neural networks." + "# Create a CSV file\n", + "import tempfile\n", + "_, filename = tempfile.mkstemp()\n", + "\n", + "with open(filename, 'w') as f:\n", + " f.write(\"\"\"Line 1\n", + "Line 2\n", + "Line 3\n", + " \"\"\")\n", + "\n", + "ds_file = tf.data.TextLineDataset(filename)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "vbxIhC-5IPdf", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Apply transformations\n", + "\n", + "Use the transformations functions like [`map`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#map), [`batch`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#batch), [`shuffle`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset#shuffle) etc. to apply transformations to the records of the dataset. See the [API documentation for `tf.data.Dataset`](https://www.tensorflow.org/api_docs/python/tf/data/Dataset) for details." ] + }, + { + "metadata": { + "id": "uXSDZWE-ISsd", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "ds_tensors = ds_tensors.map(tf.square).shuffle(2).batch(2)\n", + "\n", + "ds_file = ds_file.batch(2)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "A8X1GNfoIZKJ", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### Iterate\n", + "\n", + "When eager execution is enabled `Dataset` objects support iteration.\n", + "If you're familiar with the use of `Dataset`s in TensorFlow graphs, note that there is no need for calls to `Dataset.make_one_shot_iterator()` or `get_next()` calls." + ] + }, + { + "metadata": { + "id": "ws-WKRk5Ic6-", + "colab_type": "code", + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + } + }, + "cell_type": "code", + "source": [ + "print('Elements of ds_tensors:')\n", + "for x in ds_tensors:\n", + " print(x)\n", + "\n", + "print('\\nElements in ds_file:')\n", + "for x in ds_file:\n", + " print(x)" + ], + "execution_count": 0, + "outputs": [] } - ], - "metadata": { - "colab": { - "collapsed_sections": [], - "default_view": {}, - "name": "TensorFlow: An introduction", - "provenance": [], - "version": "0.3.2", - "views": {} - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} + ] +} \ No newline at end of file -- GitLab From 2b13b7ac7253e6f0d7d96855b1b3e7fee49277a7 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 3 Jul 2018 16:40:14 -0700 Subject: [PATCH 074/519] Update docs_src in 1.9 to match master --- tensorflow/docs_src/community/leftnav_files | 1 - tensorflow/docs_src/community/swift.md | 60 ------ tensorflow/docs_src/get_started/eager.md | 3 - tensorflow/docs_src/get_started/leftnav_files | 10 - .../docs_src/guide/custom_estimators.md | 8 +- .../docs_src/guide/datasets_for_estimators.md | 6 +- tensorflow/docs_src/guide/debugger.md | 30 ++- tensorflow/docs_src/guide/eager.md | 12 +- tensorflow/docs_src/guide/graphs.md | 2 +- tensorflow/docs_src/guide/keras.md | 24 ++- tensorflow/docs_src/guide/saved_model.md | 9 +- .../docs_src/guide/tensorboard_histograms.md | 4 +- tensorflow/docs_src/install/install_c.md | 2 +- tensorflow/docs_src/install/install_go.md | 2 +- tensorflow/docs_src/install/install_java.md | 24 +-- tensorflow/docs_src/install/install_linux.md | 24 ++- tensorflow/docs_src/install/install_mac.md | 13 +- .../docs_src/install/install_raspbian.md | 2 +- .../docs_src/install/install_sources.md | 36 ++-- .../docs_src/install/install_windows.md | 2 +- tensorflow/docs_src/mobile/leftnav_files | 1 + tensorflow/docs_src/mobile/linking_libs.md | 2 +- tensorflow/docs_src/mobile/mobile_intro.md | 3 +- tensorflow/docs_src/mobile/prepare_models.md | 4 +- .../docs_src/mobile/tflite/demo_android.md | 24 +-- tensorflow/docs_src/mobile/tflite/devguide.md | 9 +- tensorflow/docs_src/mobile/tflite/index.md | 17 +- .../docs_src/mobile/tflite/performance.md | 174 ++++++++++++++++++ .../docs_src/performance/quantization.md | 2 +- .../performance/xla/operation_semantics.md | 39 +++- .../{get_started => tutorials}/_index.yaml | 44 ++--- tensorflow/docs_src/tutorials/_toc.yaml | 93 ++++++++++ .../eager/custom_training_walkthrough.md | 3 + tensorflow/docs_src/tutorials/eager/index.md | 13 ++ .../docs_src/tutorials/image_retraining.md | 4 - .../tutorials/{ => images}/deep_cnn.md | 8 +- .../{ => images}/image_recognition.md | 1 - .../docs_src/tutorials/{ => images}/layers.md | 47 +---- tensorflow/docs_src/tutorials/index.md | 59 ------ .../keras}/basic_classification.md | 0 .../keras}/basic_regression.md | 0 .../keras}/basic_text_classification.md | 0 tensorflow/docs_src/tutorials/keras/index.md | 22 +++ .../keras}/overfit_and_underfit.md | 0 .../keras}/save_and_restore_models.md | 0 tensorflow/docs_src/tutorials/leftnav_files | 23 --- .../{get_started => tutorials}/next_steps.md | 0 .../tutorials/{ => non-ml}/mandelbrot.md | 0 .../docs_src/tutorials/{ => non-ml}/pdes.md | 3 +- .../{ => representation}/kernel_methods.md | 2 +- .../tutorials/{ => representation}/linear.md | 0 .../tutorials/{ => representation}/wide.md | 0 .../{ => representation}/wide_and_deep.md | 0 .../{ => representation}/word2vec.md | 0 tensorflow/docs_src/tutorials/seq2seq.md | 5 - .../{ => sequences}/audio_recognition.md | 0 .../tutorials/{ => sequences}/recurrent.md | 4 +- .../{ => sequences}/recurrent_quickdraw.md | 4 +- 58 files changed, 521 insertions(+), 363 deletions(-) delete mode 100644 tensorflow/docs_src/community/swift.md delete mode 100644 tensorflow/docs_src/get_started/eager.md delete mode 100644 tensorflow/docs_src/get_started/leftnav_files create mode 100644 tensorflow/docs_src/mobile/tflite/performance.md rename tensorflow/docs_src/{get_started => tutorials}/_index.yaml (77%) create mode 100644 tensorflow/docs_src/tutorials/_toc.yaml create mode 100644 tensorflow/docs_src/tutorials/eager/custom_training_walkthrough.md create mode 100644 tensorflow/docs_src/tutorials/eager/index.md delete mode 100644 tensorflow/docs_src/tutorials/image_retraining.md rename tensorflow/docs_src/tutorials/{ => images}/deep_cnn.md (98%) rename tensorflow/docs_src/tutorials/{ => images}/image_recognition.md (99%) rename tensorflow/docs_src/tutorials/{ => images}/layers.md (94%) delete mode 100644 tensorflow/docs_src/tutorials/index.md rename tensorflow/docs_src/{get_started => tutorials/keras}/basic_classification.md (100%) rename tensorflow/docs_src/{get_started => tutorials/keras}/basic_regression.md (100%) rename tensorflow/docs_src/{get_started => tutorials/keras}/basic_text_classification.md (100%) create mode 100644 tensorflow/docs_src/tutorials/keras/index.md rename tensorflow/docs_src/{get_started => tutorials/keras}/overfit_and_underfit.md (100%) rename tensorflow/docs_src/{get_started => tutorials/keras}/save_and_restore_models.md (100%) delete mode 100644 tensorflow/docs_src/tutorials/leftnav_files rename tensorflow/docs_src/{get_started => tutorials}/next_steps.md (100%) rename tensorflow/docs_src/tutorials/{ => non-ml}/mandelbrot.md (100%) mode change 100755 => 100644 rename tensorflow/docs_src/tutorials/{ => non-ml}/pdes.md (98%) mode change 100755 => 100644 rename tensorflow/docs_src/tutorials/{ => representation}/kernel_methods.md (99%) rename tensorflow/docs_src/tutorials/{ => representation}/linear.md (100%) rename tensorflow/docs_src/tutorials/{ => representation}/wide.md (100%) rename tensorflow/docs_src/tutorials/{ => representation}/wide_and_deep.md (100%) rename tensorflow/docs_src/tutorials/{ => representation}/word2vec.md (100%) delete mode 100644 tensorflow/docs_src/tutorials/seq2seq.md rename tensorflow/docs_src/tutorials/{ => sequences}/audio_recognition.md (100%) rename tensorflow/docs_src/tutorials/{ => sequences}/recurrent.md (98%) rename tensorflow/docs_src/tutorials/{ => sequences}/recurrent_quickdraw.md (98%) diff --git a/tensorflow/docs_src/community/leftnav_files b/tensorflow/docs_src/community/leftnav_files index 2bae60d9dd..0bd1f14de9 100644 --- a/tensorflow/docs_src/community/leftnav_files +++ b/tensorflow/docs_src/community/leftnav_files @@ -6,4 +6,3 @@ groups.md documentation.md style_guide.md benchmarks.md -swift.md diff --git a/tensorflow/docs_src/community/swift.md b/tensorflow/docs_src/community/swift.md deleted file mode 100644 index d1625d3b93..0000000000 --- a/tensorflow/docs_src/community/swift.md +++ /dev/null @@ -1,60 +0,0 @@ -

- -

- -# Swift for TensorFlow - -Welcome to the Swift for TensorFlow development community! - -Swift for TensorFlow is a new way to develop machine learning models. It -gives you the power of -[TensorFlow](https://www.tensorflow.org) directly -integrated into the [Swift programming language](https://swift.org/about). -With Swift, you can write the following imperative code, and Swift -automatically turns it into **a single TensorFlow Graph** and runs it -with the full performance of TensorFlow Sessions on CPU, GPU and -[TPU](https://cloud.google.com/tpu/docs/tpus). - -```swift -import TensorFlow - -var x = Tensor([[1, 2], [3, 4]]) - -for i in 1...5 { - x += x ⊗ x -} - -print(x) -``` - -Swift combines the flexibility of -[Eager Execution](https://www.tensorflow.org/programmers_guide/eager) with the -high performance of [Graphs and Sessions](https://www.tensorflow.org/programmers_guide/graphs). -Behind the scenes, Swift analyzes your Tensor code and automatically builds -graphs for you. Swift also catches type errors and shape mismatches before -running your code, and has [Automatic Differentiation](https://en.wikipedia.org/wiki/Automatic_differentiation) -built right in. We believe that machine learning tools are so important that -they deserve **a first-class language and a compiler**. - -Note: Swift for TensorFlow is an early stage research project. It has been -released to enable open source development and is not yet ready for general use -by machine learning developers. - -## Open Source - -We have released Swift for TensorFlow as an open-source project on GitHub! - -Our [documentation repository](https://github.com/tensorflow/swift) contains a -[project overview](https://github.com/tensorflow/swift/blob/master/docs/DesignOverview.md) -and [technical papers](https://github.com/tensorflow/swift/tree/master/docs) -explaining specific areas in depth. There are also instructions for [installing -pre-built packages](https://github.com/tensorflow/swift/blob/master/Installation.md) -(for macOS and Ubuntu) as well as a simple -[usage tutorial](https://github.com/tensorflow/swift/blob/master/Usage.md). - -Moving forward, we will use an open design model and all discussions will be -public. - -[Sign up here to join the community Google -group](https://groups.google.com/a/tensorflow.org/d/forum/swift), which we will -use for announcements and general discussion. diff --git a/tensorflow/docs_src/get_started/eager.md b/tensorflow/docs_src/get_started/eager.md deleted file mode 100644 index ddf239485a..0000000000 --- a/tensorflow/docs_src/get_started/eager.md +++ /dev/null @@ -1,3 +0,0 @@ -# Custom Training Walkthrough - -[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/r1.9.0/samples/core/get_started/eager.ipynb) diff --git a/tensorflow/docs_src/get_started/leftnav_files b/tensorflow/docs_src/get_started/leftnav_files deleted file mode 100644 index 99d2b2c3e1..0000000000 --- a/tensorflow/docs_src/get_started/leftnav_files +++ /dev/null @@ -1,10 +0,0 @@ -### Learn and use ML -basic_classification.md: Basic classification -basic_text_classification.md: Text classification -basic_regression.md: Regression -overfit_and_underfit.md -save_and_restore_models.md -next_steps.md - -### Research and experimentation -eager.md diff --git a/tensorflow/docs_src/guide/custom_estimators.md b/tensorflow/docs_src/guide/custom_estimators.md index fb20b35c12..a63e2bafb3 100644 --- a/tensorflow/docs_src/guide/custom_estimators.md +++ b/tensorflow/docs_src/guide/custom_estimators.md @@ -362,10 +362,10 @@ model's loss. This is the that will be optimized. We can calculate the loss by calling @{tf.losses.sparse_softmax_cross_entropy}. -The value returned by this function will be lowest, approximately 0, -probability of the correct class (at index `label`) is near 1.0. The loss value -returned is progressively larger as the probability of the correct class -decreases. +The value returned by this function will be approximately 0 at lowest, +when the probability of the correct class (at index `label`) is near 1.0. +The loss value returned is progressively larger as the probability of the +correct class decreases. This function returns the average over the whole batch. diff --git a/tensorflow/docs_src/guide/datasets_for_estimators.md b/tensorflow/docs_src/guide/datasets_for_estimators.md index b04af78cd8..b55a5731a4 100644 --- a/tensorflow/docs_src/guide/datasets_for_estimators.md +++ b/tensorflow/docs_src/guide/datasets_for_estimators.md @@ -76,9 +76,9 @@ Let's walk through the `train_input_fn()`. The function starts by using the @{tf.data.Dataset.from_tensor_slices} function to create a @{tf.data.Dataset} representing slices of the array. The array is sliced across the first dimension. For example, an array containing the -@{$tutorials/layers$mnist training data} has a shape of `(60000, 28, 28)`. -Passing this to `from_tensor_slices` returns a `Dataset` object containing -60000 slices, each one a 28x28 image. +MNIST training data has a shape of `(60000, 28, 28)`. Passing this to +`from_tensor_slices` returns a `Dataset` object containing 60000 slices, each one +a 28x28 image. The code that returns this `Dataset` is as follows: diff --git a/tensorflow/docs_src/guide/debugger.md b/tensorflow/docs_src/guide/debugger.md index 6bd941886d..8d78fe6fbd 100644 --- a/tensorflow/docs_src/guide/debugger.md +++ b/tensorflow/docs_src/guide/debugger.md @@ -17,7 +17,7 @@ how to use the graphical user interface (GUI) of tfdbg, i.e., the Note: The TensorFlow debugger uses a [curses](https://en.wikipedia.org/wiki/Curses_\(programming_library\))-based text user interface. On Mac OS X, the `ncurses` library is required and can be -installed with `brew install homebrew/dupes/ncurses`. On Windows, curses isn't as +installed with `brew install ncurses`. On Windows, curses isn't as well supported, so a [readline](https://en.wikipedia.org/wiki/GNU_Readline)-based interface can be used with tfdbg by installing `pyreadline` with `pip`. If you use Anaconda3, you can install it with a command such as @@ -33,8 +33,9 @@ and [`inf`s](https://en.wikipedia.org/wiki/Infinity), a frequently-encountered type of bug in TensorFlow model development. The following example is for users who use the low-level [`Session`](https://www.tensorflow.org/api_docs/python/tf/Session) API of -TensorFlow. A later section of this document describes how to use **tfdbg** -with a higher-level API, namely `Estimator`s. +TensorFlow. Later sections of this document describe how to use **tfdbg** +with higher-level APIs of TensorFlow, including `tf.estimator`, +`tf.keras` / `keras` and `tf.contrib.slim`. To *observe* such an issue, run the following command without the debugger (the source code can be found [here](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/debug/examples/debug_mnist.py)): @@ -209,6 +210,7 @@ Try the following commands at the `tfdbg>` prompt (referencing the code at | **`config`** | | **Set or show persistent TFDBG UI configuration.** | | | | `set` | Set the value of a config item: {`graph_recursion_depth`, `mouse_mode`}. | `config set graph_recursion_depth 3` | | | `show` | Show current persistent UI configuration. | `config show` | +| **`version`** | | **Print the version of TensorFlow and its key dependencies.** | `version` | | **`help`** | | **Print general help information** | `help` | | | `help ` | Print help for given command. | `help lt` | @@ -461,7 +463,6 @@ predict_results = classifier.predict(predict_input_fn, hooks=hooks) ``` [debug_tflearn_iris.py](https://www.tensorflow.org/code/tensorflow/python/debug/examples/debug_tflearn_iris.py), -based on [tf-learn's iris tutorial](https://www.tensorflow.org/versions/r1.8/get_started/tflearn), contains a full example of how to use the tfdbg with `Estimator`s. To run this example, do: @@ -477,20 +478,31 @@ for more details. ## Debugging Keras Models with TFDBG -To use TFDBG with [Keras](https://keras.io/), let the Keras backend use -a TFDBG-wrapped Session object. For example, to use the CLI wrapper: +To use TFDBG with +[tf.keras](https://www.tensorflow.org/api_docs/python/tf/keras), +let the Keras backend use a TFDBG-wrapped Session object. For example, to use +the CLI wrapper: ``` python import tensorflow as tf -from keras import backend as keras_backend from tensorflow.python import debug as tf_debug -keras_backend.set_session(tf_debug.LocalCLIDebugWrapperSession(tf.Session())) +tf.keras.backend.set_session(tf_debug.LocalCLIDebugWrapperSession(tf.Session())) # Define your keras model, called "model". -model.fit(...) # This will break into the TFDBG CLI. + +# Calls to `fit()`, 'evaluate()` and `predict()` methods will break into the +# TFDBG CLI. +model.fit(...) +model.evaluate(...) +model.predict(...) ``` +With minor modification, the preceding code example also works for the +[non-TensorFlow version of Keras](https://keras.io/) running against a +TensorFlow backend. You just need to replace `tf.keras.backend` with +`keras.backend`. + ## Debugging tf-slim with TFDBG TFDBG supports debugging of training and evaluation with diff --git a/tensorflow/docs_src/guide/eager.md b/tensorflow/docs_src/guide/eager.md index 00d02b4455..003ca265fe 100644 --- a/tensorflow/docs_src/guide/eager.md +++ b/tensorflow/docs_src/guide/eager.md @@ -149,16 +149,17 @@ it to implement your own layer: ```py class MySimpleLayer(tf.keras.layers.Layer): def __init__(self, output_units): + super(MySimpleLayer, self).__init__() self.output_units = output_units - def build(self, input): + def build(self, input_shape): # The build method gets called the first time your layer is used. # Creating variables on build() allows you to make their shape depend - # on the input shape and hence remove the need for the user to specify + # on the input shape and hence removes the need for the user to specify # full shapes. It is possible to create variables during __init__() if # you already know their full shapes. self.kernel = self.add_variable( - "kernel", [input.shape[-1], self.output_units]) + "kernel", [input_shape[-1], self.output_units]) def call(self, input): # Override call() instead of __call__ so we can perform some bookkeeping. @@ -315,9 +316,8 @@ for (batch, (images, labels)) in enumerate(dataset): The following example creates a multi-layer model that classifies the standard -[MNIST handwritten digits](https://www.tensorflow.org/tutorials/layers). It -demonstrates the optimizer and layer APIs to build trainable graphs in an eager -execution environment. +MNIST handwritten digits. It demonstrates the optimizer and layer APIs to build +trainable graphs in an eager execution environment. ### Train a model diff --git a/tensorflow/docs_src/guide/graphs.md b/tensorflow/docs_src/guide/graphs.md index e6246ef148..492f97c191 100644 --- a/tensorflow/docs_src/guide/graphs.md +++ b/tensorflow/docs_src/guide/graphs.md @@ -486,7 +486,7 @@ subgraph inside. ![](../images/mnist_deep.png) For more information about visualizing your TensorFlow application with -TensorBoard, see the [TensorBoard tutorial](../get_started/summaries_and_tensorboard.md). +TensorBoard, see the [TensorBoard guide](./summaries_and_tensorboard.md). ## Programming with multiple graphs diff --git a/tensorflow/docs_src/guide/keras.md b/tensorflow/docs_src/guide/keras.md index d584ebe945..1d846df104 100644 --- a/tensorflow/docs_src/guide/keras.md +++ b/tensorflow/docs_src/guide/keras.md @@ -221,7 +221,7 @@ To *evaluate* the inference-mode loss and metrics for the data provided: ```python model.evaluate(x, y, batch_size=32) -model.evaluate(dataset, steps=30 +model.evaluate(dataset, steps=30) ``` And to *predict* the output of the last layer in inference for the data provided, @@ -548,11 +548,9 @@ model.compile(optimizer=tf.train.RMSPropOptimizer(0.001), estimator = keras.estimator.model_to_estimator(model) ``` -Note: -* Enable [eager execution](./eager.md) for debugging +Note: Enable [eager execution](./eager.md) for debugging [Estimator input functions](./premade_estimators.md#create_input_functions) and inspecting data. -* Don't use batch normalization or try to finetune batch normalization models with estimators created from `tf.keras.estimator.model_to_estimator`. More details at [#17950](https://github.com/tensorflow/tensorflow/issues/17950) ### Multiple GPUs @@ -583,15 +581,6 @@ model.compile(loss='binary_crossentropy', optimizer=optimizer) model.summary() ``` -Convert the Keras model to a `tf.estimator.Estimator` instance: - -```python -keras_estimator = keras.estimator.model_to_estimator( - keras_model=model, - config=config, - model_dir='/tmp/model_dir') -``` - Define an *input pipeline*. The `input_fn` returns a `tf.data.Dataset` object used to distribute the data across multiple devices—with each device processing a slice of the input batch. @@ -617,6 +606,15 @@ strategy = tf.contrib.distribute.MirroredStrategy() config = tf.estimator.RunConfig(train_distribute=strategy) ``` +Convert the Keras model to a `tf.estimator.Estimator` instance: + +```python +keras_estimator = keras.estimator.model_to_estimator( + keras_model=model, + config=config, + model_dir='/tmp/model_dir') +``` + Finally, train the `Estimator` instance by providing the `input_fn` and `steps` arguments: diff --git a/tensorflow/docs_src/guide/saved_model.md b/tensorflow/docs_src/guide/saved_model.md index 27ef7bb0da..acc3d3ca0b 100644 --- a/tensorflow/docs_src/guide/saved_model.md +++ b/tensorflow/docs_src/guide/saved_model.md @@ -794,11 +794,12 @@ Here's the syntax: ``` usage: saved_model_cli run [-h] --dir DIR --tag_set TAG_SET --signature_def SIGNATURE_DEF_KEY [--inputs INPUTS] - [--input_exprs INPUT_EXPRS] [--outdir OUTDIR] + [--input_exprs INPUT_EXPRS] + [--input_examples INPUT_EXAMPLES] [--outdir OUTDIR] [--overwrite] [--tf_debug] ``` -The `run` command provides the following two ways to pass inputs to the model: +The `run` command provides the following three ways to pass inputs to the model: * `--inputs` option enables you to pass numpy ndarray in files. * `--input_exprs` option enables you to pass Python expressions. @@ -847,7 +848,7 @@ dictionary is stored in the pickle file and the value corresponding to the *variable_name* will be used. -#### `--inputs_exprs` +#### `--input_exprs` To pass inputs through Python expressions, specify the `--input_exprs` option. This can be useful for when you don't have data @@ -869,7 +870,7 @@ example: (Note that the `numpy` module is already available to you as `np`.) -#### `--inputs_examples` +#### `--input_examples` To pass `tf.train.Example` as inputs, specify the `--input_examples` option. For each input key, it takes a list of dictionary, where each dictionary is an diff --git a/tensorflow/docs_src/guide/tensorboard_histograms.md b/tensorflow/docs_src/guide/tensorboard_histograms.md index 918deda190..af8f2cadd1 100644 --- a/tensorflow/docs_src/guide/tensorboard_histograms.md +++ b/tensorflow/docs_src/guide/tensorboard_histograms.md @@ -13,8 +13,8 @@ TensorFlow has an op which is perfect for this purpose. As is usually the case with TensorBoard, we will ingest data using a summary op; in this case, ['tf.summary.histogram'](https://www.tensorflow.org/api_docs/python/tf/summary/histogram). -For a primer on how summaries work, please see the general -[TensorBoard tutorial](https://www.tensorflow.org/get_started/summaries_and_tensorboard). +For a primer on how summaries work, please see the +[TensorBoard guide](./summaries_and_tensorboard.md). Here is a code snippet that will generate some histogram summaries containing normally distributed data, where the mean of the distribution increases over diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 9aebf2bfa4..2901848745 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_64-1.9.0-rc2.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 1907355341..2c126df5aa 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc2.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index b9c9912816..692dfc9cef 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.9.0-rc2 + 1.9.0-rc0 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.9.0-rc2 + 1.9.0-rc0 @@ -124,12 +124,12 @@ instead: org.tensorflow libtensorflow - 1.9.0-rc2 + 1.9.0-rc0 org.tensorflow libtensorflow_jni_gpu - 1.9.0-rc2 + 1.9.0-rc0 ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc2.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc2.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,13 +175,13 @@ 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-1.9.0-rc2.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-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_64-1.9.0-rc2.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0-rc0.zip). 3. Extract this .zip file. - +__Note__: The native library (`tensorflow_jni.dll`) requires `msvcp140.dll` at runtime, which is included in the [Visual C++ 2015 Redistributable](https://www.microsoft.com/en-us/download/details.aspx?id=48145) package. ### Validate the installation @@ -227,7 +227,7 @@ 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: -
javac -cp libtensorflow-1.9.0-rc2.jar HelloTF.java
+
javac -cp libtensorflow-1.9.0-rc0.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.9.0-rc2.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.9.0-rc0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.9.0-rc2.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.9.0-rc0.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, 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 ae3d50ff39..f21c073a1b 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -339,9 +339,7 @@ Docker will download the TensorFlow binary image the first time you launch it. #### GPU support -Prior to installing TensorFlow with GPU support, ensure that your system meets all -[NVIDIA software requirements](#NVIDIARequirements). To launch a Docker container -with NVidia GPU support, enter a command of the following format: +To launch a Docker container with NVidia GPU support, enter a command of the following format (this [does not require any local CUDA installation](https://github.com/nvidia/nvidia-docker/wiki/CUDA#requirements)): 
 $ nvidia-docker run -it -p hostPort:containerPort TensorFlowGPUImage
@@ -438,7 +436,7 @@ Take the following steps to install TensorFlow in an Anaconda environment:
 
      
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp34-cp34m-linux_x86_64.whl

+     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
## Validate your installation @@ -491,7 +489,7 @@ TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). +To learn more, see the [TensorFlow tutorials](../tutorials/). ## TensorFlow GPU support @@ -678,14 +676,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -697,14 +695,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -716,14 +714,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
@@ -735,14 +733,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 3de6da1342..c6f0c17924 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: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl @@ -403,8 +403,7 @@ writing TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). - +To learn more, see the [TensorFlow tutorials](../tutorials/). ## Common installation problems @@ -518,7 +517,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
@@ -526,5 +525,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py2-none-a 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_raspbian.md b/tensorflow/docs_src/install/install_raspbian.md index 0caab6d335..46c4944ca7 100644 --- a/tensorflow/docs_src/install/install_raspbian.md +++ b/tensorflow/docs_src/install/install_raspbian.md @@ -230,7 +230,7 @@ problems, despite the log message. If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). +To learn more, see the [TensorFlow tutorials](../tutorials/). ## Common installation problems diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 3520f97c9a..fc1f6d05bd 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -81,7 +81,7 @@ or [macOS](#PrepareMac) - + ## Prepare environment for Linux Before building TensorFlow on Linux, install the following build @@ -289,17 +289,27 @@ Note: If you're only interested in building the libraries for the TensorFlow C or Java APIs, see [Build the C or Java libraries](#BuildCorJava), you do not need to build the pip package in that case. -To build a pip package for TensorFlow with CPU-only support, -you would typically invoke the following command: +### CPU-only support + +To build a pip package for TensorFlow with CPU-only support: + +
+$ bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
+
+ +To build a pip package for TensorFlow with CPU-only support for the Intel® MKL-DNN: 
-$ bazel build --config=opt //tensorflow/tools/pip_package:build_pip_package
+$ bazel build --config=mkl --config=opt //tensorflow/tools/pip_package:build_pip_package
-To build a pip package for TensorFlow with GPU support, -invoke the following command: +### GPU support + +To build a pip package for TensorFlow with GPU support: -
$ bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
+
+$ bazel build --config=opt --config=cuda //tensorflow/tools/pip_package:build_pip_package
+
**NOTE on gcc 5 or later:** the binary pip packages available on the TensorFlow website are built with gcc 4, which uses the older ABI. To @@ -328,10 +338,10 @@ 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 1.9.0rc2 on Linux: +for TensorFlow 1.9.0rc0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc2-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-py2-none-any.whl
## Validate your installation @@ -362,7 +372,7 @@ TensorFlow programs: 
Hello, TensorFlow!
-To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). +To learn more, see the [TensorFlow tutorials](../tutorials/). If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). @@ -373,9 +383,9 @@ The build and installation problems you encounter typically depend on the operating system. See the "Common installation problems" section of one of the following guides: - * @{$install_linux#CommonInstallationProblems$Installing TensorFlow on Linux} - * @{$install_mac#CommonInstallationProblems$Installing TensorFlow on Mac OS} - * @{$install_windows#CommonInstallationProblems$Installing TensorFlow on Windows} + * @{$install_linux#common_installation_problems$Installing TensorFlow on Linux} + * @{$install_mac#common_installation_problems$Installing TensorFlow on Mac OS} + * @{$install_windows#common_installation_problems$Installing TensorFlow on Windows} Beyond the errors documented in those two guides, the following table notes additional errors specific to building TensorFlow. Note that we diff --git a/tensorflow/docs_src/install/install_windows.md b/tensorflow/docs_src/install/install_windows.md index 7fe94f0bc3..7b7b17ce81 100644 --- a/tensorflow/docs_src/install/install_windows.md +++ b/tensorflow/docs_src/install/install_windows.md @@ -157,7 +157,7 @@ TensorFlow programs: If the system outputs an error message instead of a greeting, see [Common installation problems](#common_installation_problems). -To learn more, see [Get Started with TensorFlow](https://www.tensorflow.org/get_started). +To learn more, see the [TensorFlow tutorials](../tutorials/). ## Common installation problems diff --git a/tensorflow/docs_src/mobile/leftnav_files b/tensorflow/docs_src/mobile/leftnav_files index 585470d5f0..97340ef7e1 100644 --- a/tensorflow/docs_src/mobile/leftnav_files +++ b/tensorflow/docs_src/mobile/leftnav_files @@ -4,6 +4,7 @@ tflite/index.md tflite/devguide.md tflite/demo_android.md tflite/demo_ios.md +tflite/performance.md >>> ### TensorFlow Mobile mobile_intro.md diff --git a/tensorflow/docs_src/mobile/linking_libs.md b/tensorflow/docs_src/mobile/linking_libs.md index cf0db59021..efef5dd0da 100644 --- a/tensorflow/docs_src/mobile/linking_libs.md +++ b/tensorflow/docs_src/mobile/linking_libs.md @@ -27,7 +27,7 @@ called `libandroid_tensorflow_inference_java.jar`. There are three ways to include this functionality in your program: 1. Include the jcenter AAR which contains it, as in this - [example app](https://github.com/googlecodelabs/tensorflow-for-poets-2/blob/master/android/build.gradle#L59-L65) + [example app](https://github.com/googlecodelabs/tensorflow-for-poets-2/blob/master/android/tfmobile/build.gradle#L59-L65) 2. Download the nightly precompiled version from [ci.tensorflow.org](http://ci.tensorflow.org/view/Nightly/job/nightly-android/lastSuccessfulBuild/artifact/out/). diff --git a/tensorflow/docs_src/mobile/mobile_intro.md b/tensorflow/docs_src/mobile/mobile_intro.md index 241f01d460..baad443308 100644 --- a/tensorflow/docs_src/mobile/mobile_intro.md +++ b/tensorflow/docs_src/mobile/mobile_intro.md @@ -38,7 +38,8 @@ speech-driven interface, and many of these require on-device processing. Most of the time a user isn’t giving commands, and so streaming audio continuously to a remote server would be a waste of bandwidth, since it would mostly be silence or background noises. To solve this problem it’s common to have a small neural -network running on-device @{$tutorials/audio_recognition$listening out for a particular keyword}. +network running on-device +[listening out for a particular keyword](../tutorials/sequences/audio_recognition). Once that keyword has been spotted, the rest of the conversation can be transmitted over to the server for further processing if more computing power is needed. diff --git a/tensorflow/docs_src/mobile/prepare_models.md b/tensorflow/docs_src/mobile/prepare_models.md index 8b22c04d87..2b84dbb973 100644 --- a/tensorflow/docs_src/mobile/prepare_models.md +++ b/tensorflow/docs_src/mobile/prepare_models.md @@ -105,8 +105,8 @@ inline constants so everything’s in one file. To handle the conversion, you need the `freeze_graph.py` script, that’s held in [`tensorflow/python/tools/freeze_graph.py`](https://www.tensorflow.org/code/tensorflow/python/tools/freeze_graph.py). You’ll run it like this: - bazel build tensorflow/tools:freeze_graph - bazel-bin/tensorflow/tools/freeze_graph \ + bazel build tensorflow/python/tools:freeze_graph + bazel-bin/tensorflow/python/tools/freeze_graph \ --input_graph=/tmp/model/my_graph.pb \ --input_checkpoint=/tmp/model/model.ckpt-1000 \ --output_graph=/tmp/frozen_graph.pb \ diff --git a/tensorflow/docs_src/mobile/tflite/demo_android.md b/tensorflow/docs_src/mobile/tflite/demo_android.md index 7f2f8882a2..fdf0bcf3c1 100644 --- a/tensorflow/docs_src/mobile/tflite/demo_android.md +++ b/tensorflow/docs_src/mobile/tflite/demo_android.md @@ -1,7 +1,7 @@ # Android Demo App An example Android application using TensorFLow Lite is available -[on GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/app). +[on GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo). The demo is a sample camera app that classifies images continuously using either a quantized Mobilenet model or a floating point Inception-v3 model. To run the demo, a device running Android 5.0 ( API 21) or higher is required. @@ -44,20 +44,22 @@ app: Android Studio project. * Install all the Gradle extensions it requests. -To get a model, either: +Now you can build and run the demo app. -* Download the quantized [Mobilenet TensorFlow Lite model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip) - and 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](https://storage.googleapis.com/download.tensorflow.org/models/tflite/inception_v3_slim_2016_android_2017_11_10.zip) - and 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)
+The build process downloads the quantized [Mobilenet TensorFlow Lite model](https://storage.googleapis.com/download.tensorflow.org/models/tflite/mobilenet_v1_224_android_quant_2017_11_08.zip), and unzips it into the assets directory: `tensorflow/contrib/lite/java/demo/app/src/main/assets/`. + +Some additional details are available on the +[TF Lite Android App page](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/README.md). + +### Using other models + +To use a different model: +* Download the floating point [Inception-v3 model](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());`. -Now you can build and run the demo app. - ## Build TensorFlow Lite and the demo app from source diff --git a/tensorflow/docs_src/mobile/tflite/devguide.md b/tensorflow/docs_src/mobile/tflite/devguide.md index 4133bc172a..b168d6c183 100644 --- a/tensorflow/docs_src/mobile/tflite/devguide.md +++ b/tensorflow/docs_src/mobile/tflite/devguide.md @@ -54,10 +54,11 @@ both floating point and quantized inference. ### Train a custom model A developer may choose to train a custom model using Tensorflow (see the -@{$tutorials} for examples of building and training models). If you have already -written a model, the first step is to export this to a @{tf.GraphDef} file. This -is required because some formats do not store the model structure outside the -code, and we must communicate with other parts of the framework. See +[TensorFlow tutorials](../../tutorials/) for examples of building and training +models). If you have already written a model, the first step is to export this +to a @{tf.GraphDef} file. This is required because some formats do not store the +model structure outside the code, and we must communicate with other parts of the +framework. See [Exporting the Inference Graph](https://github.com/tensorflow/models/blob/master/research/slim/README.md) to create .pb file for the custom model. diff --git a/tensorflow/docs_src/mobile/tflite/index.md b/tensorflow/docs_src/mobile/tflite/index.md index 5622034827..3d1733024e 100644 --- a/tensorflow/docs_src/mobile/tflite/index.md +++ b/tensorflow/docs_src/mobile/tflite/index.md @@ -37,8 +37,9 @@ a custom (less-dynamic) memory allocator to ensure minimal load, initialization, and execution latency. TensorFlow Lite provides an interface to leverage hardware acceleration, if -available on the device. It does so via the Android Neural Networks library, -released as part of Android O-MR1. +available on the device. It does so via the +[Android Neural Networks API](https://developer.android.com/ndk/guides/neuralnetworks/index.html), +available on Android 8.1 (API level 27) and higher. ## Why do we need a new mobile-specific library? @@ -116,6 +117,10 @@ following: Wear](https://research.googleblog.com/2017/02/on-device-machine-intelligence.html) to all first-party and third-party apps. + Also see the complete list of + [TensorFlow Lite's supported models](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/g3doc/models.md), + including the model sizes, performance numbers, and downloadable model files. + - Quantized versions of the MobileNet model, which runs faster than the non-quantized (float) version on CPU. @@ -131,10 +136,10 @@ compatibility with this release. ## Getting Started We recommend you try out TensorFlow Lite with the pre-tested models indicated -above. If you have an existing mode, you will need to test whether your model is -compatible with both the converter and the supported operator set. To test your -model, see the [documentation on -GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite). +above. If you have an existing model, you will need to test whether your model +is compatible with both the converter and the supported operator set. To test +your model, see the +[documentation on GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite). ### Retrain Inception-V3 or MobileNet for a custom data set diff --git a/tensorflow/docs_src/mobile/tflite/performance.md b/tensorflow/docs_src/mobile/tflite/performance.md new file mode 100644 index 0000000000..79bacaaa1b --- /dev/null +++ b/tensorflow/docs_src/mobile/tflite/performance.md @@ -0,0 +1,174 @@ +# Performance + +This document lists TensorFlow Lite performance benchmarks when running well +known models on some Android and iOS devices. + +These performance benchmark numbers were generated with the +[Android TFLite benchmark binary](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark) +and the [iOS benchmark app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios). + +# Android performance benchmarks + +For Android benchmarks, the CPU affinity is set to use big cores on the device to +reduce variance (see [details](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark#reducing-variance-between-runs-on-android)). + +It assumes that models were download and unzipped to the +`/data/local/tmp/tflite_models` directory. The benchmark binary is built +using [these instructions](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark#on-android) +and assumed in the `/data/local/tmp` directory. + +To run the benchmark: + +``` +adb shell taskset ${CPU_MASK} /data/local/tmp/benchmark_model \ + --num_threads=1 \ + --graph=/data/local/tmp/tflite_models/${GRAPH} \ + --warmup_runs=1 \ + --num_runs=50 \ + --use_nnapi=false +``` + +Here, `${GRAPH}` is the name of model and `${CPU_MASK}` is the CPU affinity +chosen according to the following table: + +Device | CPU_MASK | +-------| ---------- +Pixel 2 | f0 | +Pixel xl | 0c | + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model NameDevice Mean inference time (std dev)
+ Mobilenet_1.0_224(float) + Pixel 2 166.5 ms (2.6 ms)
Pixel xl 122.9 ms (1.8 ms)
+ Mobilenet_1.0_224 (quant) + Pixel 2 69.5 ms (0.9 ms)
Pixel xl 78.9 ms (2.2 ms)
+ NASNet mobile + Pixel 2 273.8 ms (3.5 ms)
Pixel xl 210.8 ms (4.2 ms)
+ SqueezeNet + Pixel 2 234.0 ms (2.1 ms)
Pixel xl 158.0 ms (2.1 ms)
+ Inception_ResNet_V2 + Pixel 2 2846.0 ms (15.0 ms)
Pixel xl 1973.0 ms (15.0 ms)
+ Inception_V4 + Pixel 2 3180.0 ms (11.7 ms)
Pixel xl 2262.0 ms (21.0 ms)
+ +# iOS benchmarks + +To run iOS benchmarks, the [benchmark +app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios) +was modified to include the appropriate model and `benchmark_params.json` was +modified to set `num_threads` to 1. + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
Model NameDevice Mean inference time (std dev)
+ Mobilenet_1.0_224(float) + iPhone 8 32.2 ms (0.8 ms)
+ Mobilenet_1.0_224 (quant) + iPhone 8 24.4 ms (0.8 ms)
+ NASNet mobile + iPhone 8 60.3 ms (0.6 ms)
+ SqueezeNet + iPhone 8 44.3 (0.7 ms)
+ Inception_ResNet_V2 + iPhone 8562.4 ms (18.2 ms)
+ Inception_V4 + iPhone 8 661.0 ms (29.2 ms)
diff --git a/tensorflow/docs_src/performance/quantization.md b/tensorflow/docs_src/performance/quantization.md index 2fea02d861..c97f74139c 100644 --- a/tensorflow/docs_src/performance/quantization.md +++ b/tensorflow/docs_src/performance/quantization.md @@ -227,8 +227,8 @@ of 30.0f, and an 8-bit array, the quantized values represent the following: - +
QuantizedFloat
0-10.0
25530.0
12810.0
25530.0
Table 2: Example quantized value range diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md index 5887c3d88b..4c4f3f3934 100644 --- a/tensorflow/docs_src/performance/xla/operation_semantics.md +++ b/tensorflow/docs_src/performance/xla/operation_semantics.md @@ -581,12 +581,21 @@ Computes a sum across replicas. Arguments | Type | Semantics --------- | ------- | ----------------------------- `operand` | `XlaOp` | Array to sum across replicas. +| `replica_group_ids` | `int64` vector | Group ID for each replica. | The output shape is the same as the input shape. For example, if there are two replicas and the operand has the value `(1.0, 2.5)` and `(3.0, 5.25)` respectively on the two replicas, then the output value from this op will be `(4.0, 7.75)` on both replicas. +`replica_group_ids` identifies the group ID of each replica. The group ID must +either be empty (all replicas belong to a single group), or contain the same +number of elements as the number of replicas. For example, if +`replica_group_ids` = {0, 1, 2, 3, 0, 1, 2, 3} has eight replicas, there are +four subgroups of replica IDs: {0, 4}, {1, 5}, {2, 6}, and {3, 7}. The size of +each subgroup *must* be identical, so, for example, using: +`replica_group_ids` = {0, 1, 2, 0} for four replicas is invalid. + Computing the result of CrossReplicaSum requires having one input from each replica, so if one replica executes a CrossReplicaSum node more times than another, then the former replica will wait forever. Since the replicas are all @@ -1299,12 +1308,10 @@ See also : : : parameters of type T and M of : : : : arbitrary type : | `dimensions` | `int64` array | array of map dimensions | -| `static_operands` | sequence of M `XlaOp`s | M arrays of arbitrary type | Applies a scalar function over the given `operands` arrays, producing an array of the same dimensions where each element is the result of the mapped function -applied to the corresponding elements in the input arrays with `static_operands` -given as additional input to `computation`. +applied to the corresponding elements in the input arrays. The mapped function is an arbitrary computation with the restriction that it has N inputs of scalar type `T` and a single output with type `S`. The output has @@ -2003,13 +2010,35 @@ Slice(b, {2, 1}, {4, 3}) produces: See also [`XlaBuilder::Sort`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). -Sorts the elements in the operand. +There are two versions of the Sort instruction: a single-operand and a +two-operand version. `Sort(operand)` +Arguments | Type | Semantics +--------- | ------- | -------------------- +`operand` | `XlaOp` | The operand to sort. + +Sorts the elements in the operand in ascending order. The operand must be rank-1. +If the operand's elements have floating point type, and the operand contains +NaN elements, the order of elements in the output is implementation-defined. + +`Sort(key, value)` + +Sorts both the key and the value operands. The keys are sorted as in the +single-operand version. The values are sorted according to the order of their +corresponding keys. For example, if the inputs are `keys = [3, 1]` and +`values = [42, 50]`, then the output of the sort is the tuple `{[1, 3], [50, 42]}`. +The sort is not guaranteed to be stable, that is, if the keys array contains +duplicates, the order of their corresponding values may not be preserved. + Arguments | Type | Semantics --------- | ------- | ------------------- -`operand` | `XlaOp` | The operand to sort +`keys` | `XlaOp` | The sort keys. +`values` | `XlaOp` | The values to sort. + +The `keys` and `values` operand must both be rank-1, and must have the same +dimensions, but may have different element types. ## Transpose diff --git a/tensorflow/docs_src/get_started/_index.yaml b/tensorflow/docs_src/tutorials/_index.yaml similarity index 77% rename from tensorflow/docs_src/get_started/_index.yaml rename to tensorflow/docs_src/tutorials/_index.yaml index 4060804892..6fc8155669 100644 --- a/tensorflow/docs_src/get_started/_index.yaml +++ b/tensorflow/docs_src/tutorials/_index.yaml @@ -75,11 +75,11 @@ landing_page: TensorFlow Keras guide.

    -
  1. Basic classification
    2. -
  2. Text classification
    3. -
  3. Regression
    4. -
  4. Overfitting and underfitting
    5. -
  5. Save and load
    6. +
  6. Basic classification
    7. +
  7. Text classification
    8. +
  8. Regression
    9. +
  9. Overfitting and underfitting
    10. +
  10. Save and load
@@ -109,7 +109,7 @@ landing_page: model.evaluate(x_test, y_test) {% dynamic if request.tld != 'cn' %} - Run in a Notebook + Run in a Notebook {% dynamic endif %} - items: @@ -124,38 +124,38 @@ landing_page:
  1. {% dynamic if request.tld == 'cn' %} - Eager execution basics + Eager execution basics {% dynamic else %} - Eager execution basics + Eager execution basics {% dynamic endif %}
  2. {% dynamic if request.tld == 'cn' %} - Automatic differentiation and gradient tapes + Automatic differentiation and gradient tape {% dynamic else %} - Automatic differentiation and gradient tapes + Automatic differentiation and gradient tape {% dynamic endif %}
  3. {% dynamic if request.tld == 'cn' %} - Variables, models, and training + Custom training: basics {% dynamic else %} - Variables, models, and training + Custom training: basics {% dynamic endif %}
  4. {% dynamic if request.tld == 'cn' %} - Custom layers + Custom layers {% dynamic else %} - Custom layers + Custom layers {% dynamic endif %}
    5. -
  5. Custom training walkthrough
    6. +
  6. Custom training: walkthrough
  7. {% dynamic if request.tld == 'cn' %} Example: Neural machine translation w/ attention {% dynamic else %} - Example: Neural machine translation w/ attention + Example: Neural machine translation w/ attention {% dynamic endif %}
@@ -170,13 +170,15 @@ landing_page:

Estimators can train large models on multiple machines in a - production environment. Try the examples below and read the - Estimators guide. + production environment. Read the + Estimators guide for details.

    -
  1. How to build a simple text classifier with TF-Hub
    2. +
  2. Build a Convolutional Neural Network using Estimators
    3. +
  3. How to build a simple text classifier with TF-Hub
  4. Classifying Higgs boson processes
    5. -
  5. Wide and deep learning using estimators
    6. +
  6. Wide and deep learning using Estimators
    7. +
  7. Large-scale linear models
@@ -187,7 +189,7 @@ landing_page: - description: >

Google Colab: An easy way to learn and use TensorFlow

- Colaboratory + Colaboratory is a Google research project created to help disseminate machine learning education and research. It's a Jupyter notebook environment that requires no setup to use and runs entirely in the cloud. diff --git a/tensorflow/docs_src/tutorials/_toc.yaml b/tensorflow/docs_src/tutorials/_toc.yaml new file mode 100644 index 0000000000..d46d570a93 --- /dev/null +++ b/tensorflow/docs_src/tutorials/_toc.yaml @@ -0,0 +1,93 @@ +toc: +- title: Get started with TensorFlow + path: /tutorials/ + +- title: Learn and use ML + style: accordion + section: + - title: Overview + path: /tutorials/keras/ + - title: Basic classification + path: /tutorials/keras/basic_classification + - title: Text classification + path: /tutorials/keras/basic_text_classification + - title: Regression + path: /tutorials/keras/basic_regression + - title: Overfitting and underfitting + path: /tutorials/keras/overfit_and_underfit + - title: Save and restore models + path: /tutorials/keras/save_and_restore_models + +- title: Research and experimentation + style: accordion + section: + - title: Overview + path: /tutorials/eager/ + - title: Eager execution + path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/eager_intro.ipynb + status: external + - title: Automatic differentiation + path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb + status: external + - title: "Custom training: basics" + path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb + status: external + - title: Custom layers + path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb + status: external + - title: "Custom training: walkthrough" + path: /tutorials/eager/custom_training_walkthrough + - title: Neural machine translation + path: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb + status: external + +- title: Images + style: accordion + section: + - title: Build a CNN using Estimators + path: /tutorials/images/layers + - title: Image recognition + path: /tutorials/images/image_recognition + - title: Image retraining + path: /hub/tutorials/image_retraining + - title: Advanced CNN + path: /tutorials/images/deep_cnn + +- title: Sequences + style: accordion + section: + - title: Recurrent neural network + path: /tutorials/sequences/recurrent + - title: Drawing classification + path: /tutorials/sequences/recurrent_quickdraw + - title: Simple audio recognition + path: /tutorials/sequences/audio_recognition + - title: Neural machine translation + path: https://github.com/tensorflow/nmt + status: external + +- title: Data representation + style: accordion + section: + - title: Linear models + path: /tutorials/representation/wide + - title: Wide and deep learning + path: /tutorials/representation/wide_and_deep + - title: Vector representations of words + path: /tutorials/representation/word2vec + - title: Kernel methods + path: /tutorials/representation/kernel_methods + - title: Large-scale linear models + path: /tutorials/representation/linear + +- title: Non-ML + style: accordion + section: + - title: Mandelbrot set + path: /tutorials/non-ml/mandelbrot + - title: Partial differential equations + path: /tutorials/non-ml/pdes + +- break: True +- title: Next steps + path: /tutorials/next_steps diff --git a/tensorflow/docs_src/tutorials/eager/custom_training_walkthrough.md b/tensorflow/docs_src/tutorials/eager/custom_training_walkthrough.md new file mode 100644 index 0000000000..b45fbefac0 --- /dev/null +++ b/tensorflow/docs_src/tutorials/eager/custom_training_walkthrough.md @@ -0,0 +1,3 @@ +# Custom training: walkthrough + +[Colab notebook](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/get_started/eager.ipynb) diff --git a/tensorflow/docs_src/tutorials/eager/index.md b/tensorflow/docs_src/tutorials/eager/index.md new file mode 100644 index 0000000000..5445e0c343 --- /dev/null +++ b/tensorflow/docs_src/tutorials/eager/index.md @@ -0,0 +1,13 @@ +# Research and experimentation + +Eager execution provides an imperative, define-by-run interface for advanced +operations. Write custom layers, forward passes, and training loops with +auto differentiation. Start with these notebooks, then read the +[eager execution guide](../../guide/eager). + +1. [Eager execution](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/eager_intro.ipynb){:.external} +2. [Automatic differentiation and gradient tape](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/automatic_differentiation.ipynb){:.external} +3. [Custom training: basics](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/custom_training.ipynb){:.external} +4. [Custom layers](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/notebooks/custom_layers.ipynb){:.external} +5. [Custom training: walkthrough](/tutorials/eager/custom_training_walkthrough) +6. [Advanced example: Neural machine translation with attention](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb){:.external} diff --git a/tensorflow/docs_src/tutorials/image_retraining.md b/tensorflow/docs_src/tutorials/image_retraining.md deleted file mode 100644 index 27784eef9c..0000000000 --- a/tensorflow/docs_src/tutorials/image_retraining.md +++ /dev/null @@ -1,4 +0,0 @@ -# How to Retrain Inception's Final Layer for New Categories - -**NOTE: This tutorial has moved to** -https://github.com/tensorflow/hub/tree/master/docs/tutorials/image_retraining.md diff --git a/tensorflow/docs_src/tutorials/deep_cnn.md b/tensorflow/docs_src/tutorials/images/deep_cnn.md similarity index 98% rename from tensorflow/docs_src/tutorials/deep_cnn.md rename to tensorflow/docs_src/tutorials/images/deep_cnn.md index 44a32d9d1d..1590f15eb9 100644 --- a/tensorflow/docs_src/tutorials/deep_cnn.md +++ b/tensorflow/docs_src/tutorials/images/deep_cnn.md @@ -1,7 +1,4 @@ -# Convolutional Neural Networks - -> **NOTE:** This tutorial is intended for *advanced* users of TensorFlow -and assumes expertise and experience in machine learning. +# Advanced Convolutional Neural Networks ## Overview @@ -438,9 +435,6 @@ with a batch size of 64 and compare the training speed. ## Next Steps -[Congratulations!](https://www.youtube.com/watch?v=9bZkp7q19f0) You have -completed the CIFAR-10 tutorial. - If you are now interested in developing and training your own image classification system, we recommend forking this tutorial and replacing components to address your image classification problem. diff --git a/tensorflow/docs_src/tutorials/image_recognition.md b/tensorflow/docs_src/tutorials/images/image_recognition.md similarity index 99% rename from tensorflow/docs_src/tutorials/image_recognition.md rename to tensorflow/docs_src/tutorials/images/image_recognition.md index 332bcf54f0..432d470d0c 100644 --- a/tensorflow/docs_src/tutorials/image_recognition.md +++ b/tensorflow/docs_src/tutorials/images/image_recognition.md @@ -434,7 +434,6 @@ should be able to transfer some of that understanding to solving related problems. One way to perform transfer learning is to remove the final classification layer of the network and extract the [next-to-last layer of the CNN](https://arxiv.org/abs/1310.1531), in this case a 2048 dimensional vector. -There's a guide to doing this @{$image_retraining$in the how-to section}. ## Resources for Learning More diff --git a/tensorflow/docs_src/tutorials/layers.md b/tensorflow/docs_src/tutorials/images/layers.md similarity index 94% rename from tensorflow/docs_src/tutorials/layers.md rename to tensorflow/docs_src/tutorials/images/layers.md index 212e337637..12a215b50c 100644 --- a/tensorflow/docs_src/tutorials/layers.md +++ b/tensorflow/docs_src/tutorials/images/layers.md @@ -1,4 +1,4 @@ -# A Guide to TF Layers: Building a Convolutional Neural Network +# Build a Convolutional Neural Network using Estimators The TensorFlow @{tf.layers$`layers` module} provides a high-level API that makes it easy to construct a neural network. It provides methods that facilitate the @@ -470,51 +470,18 @@ as the loss metric. The following code calculates cross entropy when the model runs in either `TRAIN` or `EVAL` mode: ```python -onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10) -loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) +loss = tf.losses.sparse_softmax_cross_entropy(labels=labels, logits=logits) ``` Let's take a closer look at what's happening above. -Our `labels` tensor contains a list of predictions for our examples, e.g. `[1, -9, ...]`. In order to calculate cross-entropy, first we need to convert `labels` -to the corresponding -[one-hot encoding](https://www.quora.com/What-is-one-hot-encoding-and-when-is-it-used-in-data-science): +Our `labels` tensor contains a list of prediction indices for our examples, e.g. `[1, +9, ...]`. `logits` contains the linear outputs of our last layer. -```none -[[0, 1, 0, 0, 0, 0, 0, 0, 0, 0], - [0, 0, 0, 0, 0, 0, 0, 0, 0, 1], - ...] -``` - -We use the @{tf.one_hot} function -to perform this conversion. `tf.one_hot()` has two required arguments: - -* `indices`. The locations in the one-hot tensor that will have "on - values"—i.e., the locations of `1` values in the tensor shown above. -* `depth`. The depth of the one-hot tensor—i.e., the number of target classes. - Here, the depth is `10`. +`tf.losses.sparse_softmax_cross_entropy`, calculates the softmax crossentropy +(aka: categorical crossentropy, negative log-likelihood) from these two inputs +in an efficient, numerically stable way. -The following code creates the one-hot tensor for our labels, `onehot_labels`: - -```python -onehot_labels = tf.one_hot(indices=tf.cast(labels, tf.int32), depth=10) -``` - -Because `labels` contains a series of values from 0–9, `indices` is just our -`labels` tensor, with values cast to integers. The `depth` is `10` because we -have 10 possible target classes, one for each digit. - -Next, we compute cross-entropy of `onehot_labels` and the softmax of the -predictions from our logits layer. `tf.losses.softmax_cross_entropy()` takes -`onehot_labels` and `logits` as arguments, performs softmax activation on -`logits`, calculates cross-entropy, and returns our `loss` as a scalar `Tensor`: - -```python -loss = tf.losses.softmax_cross_entropy( - onehot_labels=onehot_labels, logits=logits) -``` ### Configure the Training Op diff --git a/tensorflow/docs_src/tutorials/index.md b/tensorflow/docs_src/tutorials/index.md deleted file mode 100644 index 6bd3a3a897..0000000000 --- a/tensorflow/docs_src/tutorials/index.md +++ /dev/null @@ -1,59 +0,0 @@ -# Tutorials - - -This section contains tutorials demonstrating how to do specific tasks -in TensorFlow. If you are new to TensorFlow, we recommend reading -[Get Started with TensorFlow](/get_started/). - -## Images - -These tutorials cover different aspects of image recognition: - - * @{$layers$MNIST}, which introduces convolutional neural networks (CNNs) and - demonstrates how to build a CNN in TensorFlow. - * @{$image_recognition}, which introduces the field of image recognition and - uses a pre-trained model (Inception) for recognizing images. - * @{$image_retraining}, which has a wonderfully self-explanatory title. - * @{$deep_cnn}, which demonstrates how to build a small CNN for recognizing - images. This tutorial is aimed at advanced TensorFlow users. - - -## Sequences - -These tutorials focus on machine learning problems dealing with sequence data. - - * @{$recurrent}, which demonstrates how to use a - recurrent neural network to predict the next word in a sentence. - * @{$seq2seq}, which demonstrates how to use a - sequence-to-sequence model to translate text from English to French. - * @{$recurrent_quickdraw} - builds a classification model for drawings, directly from the sequence of - pen strokes. - * @{$audio_recognition}, which shows how to - build a basic speech recognition network. - -## Data representation - -These tutorials demonstrate various data representations that can be used in -TensorFlow. - - * @{$wide}, uses - @{tf.feature_column$feature columns} to feed a variety of data types - to linear model, to solve a classification problem. - * @{$wide_and_deep}, builds on the - above linear model tutorial, adding a deep feed-forward neural network - component and a DNN-compatible data representation. - * @{$word2vec}, which demonstrates how to - create an embedding for words. - * @{$kernel_methods}, - which shows how to improve the quality of a linear model by using explicit - kernel mappings. - -## Non Machine Learning - -Although TensorFlow specializes in machine learning, the core of TensorFlow is -a powerful numeric computation system which you can also use to solve other -kinds of math problems. For example: - - * @{$mandelbrot} - * @{$pdes} diff --git a/tensorflow/docs_src/get_started/basic_classification.md b/tensorflow/docs_src/tutorials/keras/basic_classification.md similarity index 100% rename from tensorflow/docs_src/get_started/basic_classification.md rename to tensorflow/docs_src/tutorials/keras/basic_classification.md diff --git a/tensorflow/docs_src/get_started/basic_regression.md b/tensorflow/docs_src/tutorials/keras/basic_regression.md similarity index 100% rename from tensorflow/docs_src/get_started/basic_regression.md rename to tensorflow/docs_src/tutorials/keras/basic_regression.md diff --git a/tensorflow/docs_src/get_started/basic_text_classification.md b/tensorflow/docs_src/tutorials/keras/basic_text_classification.md similarity index 100% rename from tensorflow/docs_src/get_started/basic_text_classification.md rename to tensorflow/docs_src/tutorials/keras/basic_text_classification.md diff --git a/tensorflow/docs_src/tutorials/keras/index.md b/tensorflow/docs_src/tutorials/keras/index.md new file mode 100644 index 0000000000..9d42281c8f --- /dev/null +++ b/tensorflow/docs_src/tutorials/keras/index.md @@ -0,0 +1,22 @@ +# Learn and use machine learning + +This notebook collection is inspired by the book +*[Deep Learning with Python](https://books.google.com/books?id=Yo3CAQAACAAJ)*. +These tutorials use `tf.keras`, TensorFlow's high-level Python API for building +and training deep learning models. To learn more about using Keras with +TensorFlow, see the [TensorFlow Keras Guide](../../guide/keras). + +Publisher's note: *Deep Learning with Python* introduces the field of deep +learning using the Python language and the powerful Keras library. Written by +Keras creator and Google AI researcher François Chollet, this book builds your +understanding through intuitive explanations and practical examples. + +To learn about machine learning fundamentals and concepts, consider taking the +[Machine Learning Crash Course](https://developers.google.com/machine-learning/crash-course/). +Additional TensorFlow and machine learning resources are listed in [next steps](../next_steps). + +1. [Basic classification](./basic_classification) +2. [Text classification](./basic_text_classification) +3. [Regression](./basic_regression) +4. [Overfitting and underfitting](./overfit_and_underfit) +5. [Save and restore models](./save_and_restore_models) diff --git a/tensorflow/docs_src/get_started/overfit_and_underfit.md b/tensorflow/docs_src/tutorials/keras/overfit_and_underfit.md similarity index 100% rename from tensorflow/docs_src/get_started/overfit_and_underfit.md rename to tensorflow/docs_src/tutorials/keras/overfit_and_underfit.md diff --git a/tensorflow/docs_src/get_started/save_and_restore_models.md b/tensorflow/docs_src/tutorials/keras/save_and_restore_models.md similarity index 100% rename from tensorflow/docs_src/get_started/save_and_restore_models.md rename to tensorflow/docs_src/tutorials/keras/save_and_restore_models.md diff --git a/tensorflow/docs_src/tutorials/leftnav_files b/tensorflow/docs_src/tutorials/leftnav_files deleted file mode 100644 index 888052428f..0000000000 --- a/tensorflow/docs_src/tutorials/leftnav_files +++ /dev/null @@ -1,23 +0,0 @@ -index.md - -### Images -layers.md: MNIST -image_recognition.md: Image Recognition -image_retraining.md: Image Retraining -deep_cnn.md - -### Sequences -recurrent.md -seq2seq.md: Neural Machine Translation -recurrent_quickdraw.md: Drawing Classification -audio_recognition.md - -### Data Representation -wide.md: Linear Models -wide_and_deep.md: Wide & Deep Learning -word2vec.md -kernel_methods.md: Kernel Methods - -### Non-ML -mandelbrot.md -pdes.md diff --git a/tensorflow/docs_src/get_started/next_steps.md b/tensorflow/docs_src/tutorials/next_steps.md similarity index 100% rename from tensorflow/docs_src/get_started/next_steps.md rename to tensorflow/docs_src/tutorials/next_steps.md diff --git a/tensorflow/docs_src/tutorials/mandelbrot.md b/tensorflow/docs_src/tutorials/non-ml/mandelbrot.md old mode 100755 new mode 100644 similarity index 100% rename from tensorflow/docs_src/tutorials/mandelbrot.md rename to tensorflow/docs_src/tutorials/non-ml/mandelbrot.md diff --git a/tensorflow/docs_src/tutorials/pdes.md b/tensorflow/docs_src/tutorials/non-ml/pdes.md old mode 100755 new mode 100644 similarity index 98% rename from tensorflow/docs_src/tutorials/pdes.md rename to tensorflow/docs_src/tutorials/non-ml/pdes.md index 425e8d7084..b5a0fa834a --- a/tensorflow/docs_src/tutorials/pdes.md +++ b/tensorflow/docs_src/tutorials/non-ml/pdes.md @@ -135,7 +135,6 @@ for i in range(1000): DisplayArray(U.eval(), rng=[-0.1, 0.1]) ``` -![jpeg](../images/pde_output_2.jpg) +![jpeg](../../images/pde_output_2.jpg) Look! Ripples! - diff --git a/tensorflow/docs_src/tutorials/kernel_methods.md b/tensorflow/docs_src/tutorials/representation/kernel_methods.md similarity index 99% rename from tensorflow/docs_src/tutorials/kernel_methods.md rename to tensorflow/docs_src/tutorials/representation/kernel_methods.md index 205e2a2d2c..f3c232c511 100644 --- a/tensorflow/docs_src/tutorials/kernel_methods.md +++ b/tensorflow/docs_src/tutorials/representation/kernel_methods.md @@ -27,7 +27,7 @@ TensorFlow will provide support for sparse features at a later release. This tutorial uses [tf.contrib.learn](https://www.tensorflow.org/code/tensorflow/contrib/learn/python/learn) (TensorFlow's high-level Machine Learning API) Estimators for our ML models. -If you are not familiar with this API, [tf.estimator Quickstart](https://www.tensorflow.org/get_started/estimator) +If you are not familiar with this API, The [Estimator guide](../../guide/estimators.md) is a good place to start. We will use the MNIST dataset. The tutorial consists of the following steps: diff --git a/tensorflow/docs_src/tutorials/linear.md b/tensorflow/docs_src/tutorials/representation/linear.md similarity index 100% rename from tensorflow/docs_src/tutorials/linear.md rename to tensorflow/docs_src/tutorials/representation/linear.md diff --git a/tensorflow/docs_src/tutorials/wide.md b/tensorflow/docs_src/tutorials/representation/wide.md similarity index 100% rename from tensorflow/docs_src/tutorials/wide.md rename to tensorflow/docs_src/tutorials/representation/wide.md diff --git a/tensorflow/docs_src/tutorials/wide_and_deep.md b/tensorflow/docs_src/tutorials/representation/wide_and_deep.md similarity index 100% rename from tensorflow/docs_src/tutorials/wide_and_deep.md rename to tensorflow/docs_src/tutorials/representation/wide_and_deep.md diff --git a/tensorflow/docs_src/tutorials/word2vec.md b/tensorflow/docs_src/tutorials/representation/word2vec.md similarity index 100% rename from tensorflow/docs_src/tutorials/word2vec.md rename to tensorflow/docs_src/tutorials/representation/word2vec.md diff --git a/tensorflow/docs_src/tutorials/seq2seq.md b/tensorflow/docs_src/tutorials/seq2seq.md deleted file mode 100644 index 8928ba4f7d..0000000000 --- a/tensorflow/docs_src/tutorials/seq2seq.md +++ /dev/null @@ -1,5 +0,0 @@ -# Sequence-to-Sequence Models - -Please check out the -[tensorflow neural machine translation tutorial](https://github.com/tensorflow/nmt) -for building sequence-to-sequence models with the latest Tensorflow API. diff --git a/tensorflow/docs_src/tutorials/audio_recognition.md b/tensorflow/docs_src/tutorials/sequences/audio_recognition.md similarity index 100% rename from tensorflow/docs_src/tutorials/audio_recognition.md rename to tensorflow/docs_src/tutorials/sequences/audio_recognition.md diff --git a/tensorflow/docs_src/tutorials/recurrent.md b/tensorflow/docs_src/tutorials/sequences/recurrent.md similarity index 98% rename from tensorflow/docs_src/tutorials/recurrent.md rename to tensorflow/docs_src/tutorials/sequences/recurrent.md index 14da2c8785..715cc7856a 100644 --- a/tensorflow/docs_src/tutorials/recurrent.md +++ b/tensorflow/docs_src/tutorials/sequences/recurrent.md @@ -2,8 +2,8 @@ ## Introduction -Take a look at [this great article](https://colah.github.io/posts/2015-08-Understanding-LSTMs/) -for an introduction to recurrent neural networks and LSTMs in particular. +See [Understanding LSTM Networks](https://colah.github.io/posts/2015-08-Understanding-LSTMs/){:.external} +for an introduction to recurrent neural networks and LSTMs. ## Language Modeling diff --git a/tensorflow/docs_src/tutorials/recurrent_quickdraw.md b/tensorflow/docs_src/tutorials/sequences/recurrent_quickdraw.md similarity index 98% rename from tensorflow/docs_src/tutorials/recurrent_quickdraw.md rename to tensorflow/docs_src/tutorials/sequences/recurrent_quickdraw.md index 1afd861738..37bce5b76d 100644 --- a/tensorflow/docs_src/tutorials/recurrent_quickdraw.md +++ b/tensorflow/docs_src/tutorials/sequences/recurrent_quickdraw.md @@ -13,7 +13,7 @@ In this tutorial we'll show how to build an RNN-based recognizer for this problem. The model will use a combination of convolutional layers, LSTM layers, and a softmax output layer to classify the drawings: -

![RNN model structure](../images/quickdraw_model.png)
+
![RNN model structure](../../images/quickdraw_model.png)
The figure above shows the structure of the model that we will build in this tutorial. The input is a drawing that is encoded as a sequence of strokes of @@ -208,7 +208,7 @@ This data is then reformatted into a tensor of shape `[num_training_samples, max_length, 3]`. Then we determine the bounding box of the original drawing in screen coordinates and normalize the size such that the drawing has unit height. -
![Size normalization](../images/quickdraw_sizenormalization.png)
+
![Size normalization](../../images/quickdraw_sizenormalization.png)
Finally, we compute the differences between consecutive points and store these as a `VarLenFeature` in a -- GitLab From b46fde9a42f97d66535a2dde60642ce22473f80c Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 3 Jul 2018 16:56:01 -0700 Subject: [PATCH 075/519] fix rc2 --- 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 | 18 +++++++-------- tensorflow/docs_src/install/install_mac.md | 10 ++++----- .../docs_src/install/install_sources.md | 4 ++-- 6 files changed, 29 insertions(+), 29 deletions(-) diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 2901848745..9aebf2bfa4 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_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0-rc2.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 2c126df5aa..1907355341 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc2.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index 692dfc9cef..1fbdcc2b47 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.9.0-rc0 + 1.9.0-rc2 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.9.0-rc0 + 1.9.0-rc2 @@ -124,12 +124,12 @@ instead: org.tensorflow libtensorflow - 1.9.0-rc0 + 1.9.0-rc2 org.tensorflow libtensorflow_jni_gpu - 1.9.0-rc0 + 1.9.0-rc2 ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc2.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc0.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc2.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,10 +175,10 @@ 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-1.9.0-rc0.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc2.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_64-1.9.0-rc0.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0-rc2.zip). 3. Extract this .zip file. __Note__: The native library (`tensorflow_jni.dll`) requires `msvcp140.dll` at runtime, which is included in the [Visual C++ 2015 Redistributable](https://www.microsoft.com/en-us/download/details.aspx?id=48145) package. @@ -227,7 +227,7 @@ 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: -
javac -cp libtensorflow-1.9.0-rc0.jar HelloTF.java
+
javac -cp libtensorflow-1.9.0-rc2.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.9.0-rc0.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.9.0-rc2.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.9.0-rc0.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.9.0-rc2.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, 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 f21c073a1b..8efa166073 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -436,7 +436,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: 
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp34-cp34m-linux_x86_64.whl ## Validate your installation @@ -676,14 +676,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -695,14 +695,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -714,14 +714,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp35-cp35m-linux_x86_64.whl
@@ -733,14 +733,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc0-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index c6f0c17924..5b593d1ca9 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: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py2-none-any.whl @@ -517,7 +517,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py2-none-any.whl
@@ -525,5 +525,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py2-none-a 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc0-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index fc1f6d05bd..3801fc0f83 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -338,10 +338,10 @@ 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 1.9.0rc0 on Linux: +for TensorFlow 1.9.0rc2 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc0-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc2-py2-none-any.whl
## Validate your installation -- GitLab From 9f3bd2cf1eccdc76ed1934ade96c6cd4464bb8b2 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Fri, 6 Jul 2018 05:46:42 -0700 Subject: [PATCH 076/519] lint fix --- tensorflow/examples/tutorials/mnist/mnist_deep.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/tensorflow/examples/tutorials/mnist/mnist_deep.py b/tensorflow/examples/tutorials/mnist/mnist_deep.py index 47d2777813..5d8d8d84fe 100644 --- a/tensorflow/examples/tutorials/mnist/mnist_deep.py +++ b/tensorflow/examples/tutorials/mnist/mnist_deep.py @@ -170,7 +170,9 @@ def main(_): accuracy_l = [] for _ in range(20): batch = mnist.test.next_batch(500, shuffle=False) - accuracy_l.append(accuracy.eval(feed_dict={x: batch[0], y_: batch[1], keep_prob: 1.0})) + accuracy_l.append(accuracy.eval(feed_dict={x: batch[0], + y_: batch[1], + keep_prob: 1.0})) print('test accuracy %g' % numpy.mean(accuracy_l)) -- GitLab From d33bc55210478d58b858704bfa92316860b777fa Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Fri, 6 Jul 2018 09:27:31 -0700 Subject: [PATCH 077/519] Updating the version to 1.9.0 official. --- tensorflow/core/public/version.h | 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 | 18 +++++++-------- tensorflow/docs_src/install/install_mac.md | 10 ++++----- .../docs_src/install/install_sources.md | 4 ++-- tensorflow/tools/pip_package/setup.py | 2 +- 8 files changed, 31 insertions(+), 31 deletions(-) diff --git a/tensorflow/core/public/version.h b/tensorflow/core/public/version.h index 0e4a61ac1f..cea5e8ffb0 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 "-rc2" +#define TF_VERSION_SUFFIX "" #define TF_STR_HELPER(x) #x #define TF_STR(x) TF_STR_HELPER(x) diff --git a/tensorflow/docs_src/install/install_c.md b/tensorflow/docs_src/install/install_c.md index 9aebf2bfa4..362a03cd56 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_64-1.9.0-rc2.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-${OS}-x86_64-1.9.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_go.md b/tensorflow/docs_src/install/install_go.md index 1907355341..a4f2e5733b 100644 --- a/tensorflow/docs_src/install/install_go.md +++ b/tensorflow/docs_src/install/install_go.md @@ -38,7 +38,7 @@ steps to install this library and enable TensorFlow for Go: TF_TYPE="cpu" # Change to "gpu" for GPU support TARGET_DIRECTORY='/usr/local' curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0-rc2.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-${TF_TYPE}-$(go env GOOS)-x86_64-1.9.0.tar.gz" | sudo tar -C $TARGET_DIRECTORY -xz The `tar` command extracts the TensorFlow C library into the `lib` diff --git a/tensorflow/docs_src/install/install_java.md b/tensorflow/docs_src/install/install_java.md index b9c9912816..643c3b715f 100644 --- a/tensorflow/docs_src/install/install_java.md +++ b/tensorflow/docs_src/install/install_java.md @@ -36,7 +36,7 @@ following to the project's `pom.xml` to use the TensorFlow Java APIs: org.tensorflow tensorflow - 1.9.0-rc2 + 1.9.0 ``` @@ -65,7 +65,7 @@ As an example, these steps will create a Maven project that uses TensorFlow: org.tensorflow tensorflow - 1.9.0-rc2 + 1.9.0 @@ -124,12 +124,12 @@ instead: org.tensorflow libtensorflow - 1.9.0-rc2 + 1.9.0 org.tensorflow libtensorflow_jni_gpu - 1.9.0-rc2 + 1.9.0 ``` @@ -148,7 +148,7 @@ refer to the simpler instructions above instead. Take the following steps to install TensorFlow for Java on Linux or macOS: 1. Download - [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0-rc2.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0.jar), which is the TensorFlow Java Archive (JAR). 2. Decide whether you will run TensorFlow for Java on CPU(s) only or with @@ -167,7 +167,7 @@ Take the following steps to install TensorFlow for Java on Linux or macOS: OS=$(uname -s | tr '[:upper:]' '[:lower:]') mkdir -p ./jni curl -L \ - "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0-rc2.tar.gz" | + "https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-${TF_TYPE}-${OS}-x86_64-1.9.0.tar.gz" | tar -xz -C ./jni ### Install on Windows @@ -175,10 +175,10 @@ 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-1.9.0-rc2.jar), + [libtensorflow.jar](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow-1.9.0.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_64-1.9.0-rc2.zip). + [TensorFlow for Java on Windows](https://storage.googleapis.com/tensorflow/libtensorflow/libtensorflow_jni-cpu-windows-x86_64-1.9.0.zip). 3. Extract this .zip file. @@ -227,7 +227,7 @@ 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: -
javac -cp libtensorflow-1.9.0-rc2.jar HelloTF.java
+
javac -cp libtensorflow-1.9.0.jar HelloTF.java
### Running @@ -241,11 +241,11 @@ two files are available to the JVM: For example, the following command line executes the `HelloTF` program on Linux and macOS X: -
java -cp libtensorflow-1.9.0-rc2.jar:. -Djava.library.path=./jni HelloTF
+
java -cp libtensorflow-1.9.0.jar:. -Djava.library.path=./jni HelloTF
And the following command line executes the `HelloTF` program on Windows: -
java -cp libtensorflow-1.9.0-rc2.jar;. -Djava.library.path=jni HelloTF
+
java -cp libtensorflow-1.9.0.jar;. -Djava.library.path=jni HelloTF
If the program prints Hello from version, 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 ae3d50ff39..abec8ca072 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -438,7 +438,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: 
      (tensorflow)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp34-cp34m-linux_x86_64.whl
+ https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp34-cp34m-linux_x86_64.whl ## Validate your installation @@ -678,14 +678,14 @@ This section documents the relevant values for Linux installations. CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp27-none-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp27-none-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp27-none-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -697,14 +697,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp34-cp34m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp34-cp34m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp34-cp34m-linux_x86_64.whl
Note that GPU support requires the NVIDIA hardware and software described in @@ -716,14 +716,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp35-cp35m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp35-cp35m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp35-cp35m-linux_x86_64.whl
@@ -735,14 +735,14 @@ Note that GPU support requires the NVIDIA hardware and software described in CPU only: 
-https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/cpu/tensorflow-1.9.0-cp36-cp36m-linux_x86_64.whl
GPU support: 
-https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0rc2-cp36-cp36m-linux_x86_64.whl
+https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.9.0-cp36-cp36m-linux_x86_64.whl
diff --git a/tensorflow/docs_src/install/install_mac.md b/tensorflow/docs_src/install/install_mac.md index 3de6da1342..167d17adb4 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: 
 $ pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl If you encounter installation problems, see [Common Installation Problems](#common-installation-problems). @@ -242,7 +242,7 @@ take the following steps: issue the following command: 
 $ sudo pip3 install --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl If the preceding command fails, see [installation problems](#common-installation-problems). @@ -350,7 +350,7 @@ Take the following steps to install TensorFlow in an Anaconda environment: TensorFlow for Python 2.7: 
 (targetDirectory)$ pip install --ignore-installed --upgrade \
-     https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py2-none-any.whl
+ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py2-none-any.whl @@ -518,7 +518,7 @@ The value you specify depends on your Python version. 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py2-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py2-none-any.whl
@@ -526,5 +526,5 @@ https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py2-none-a 
-https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0rc2-py3-none-any.whl
+https://storage.googleapis.com/tensorflow/mac/cpu/tensorflow-1.9.0-py3-none-any.whl
diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 3520f97c9a..79da209928 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -328,10 +328,10 @@ 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 1.9.0rc2 on Linux: +for TensorFlow 1.9.0 on Linux: 
-$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0rc2-py2-none-any.whl
+$ sudo pip install /tmp/tensorflow_pkg/tensorflow-1.9.0-py2-none-any.whl
## Validate your installation diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 8c077580aa..dc9d059bab 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -45,7 +45,7 @@ DOCLINES = __doc__.split('\n') # 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 = '1.9.0-rc2' +_VERSION = '1.9.0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', -- GitLab From a522d458dacd3a34c4ff2e6b76556f623fe7dbd6 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Fri, 29 Jun 2018 22:43:22 -0700 Subject: [PATCH 078/519] Remove unused gcp and hdfs config flags, as these are on by default now. PiperOrigin-RevId: 202753310 --- tensorflow/tools/ci_build/ci_parameterized_build.sh | 2 +- tensorflow/tools/ci_build/ci_sanity.sh | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh index e621f85652..6aaeb14aee 100755 --- a/tensorflow/tools/ci_build/ci_parameterized_build.sh +++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh @@ -132,7 +132,7 @@ BAZEL_CMD="bazel test" BAZEL_BUILD_ONLY_CMD="bazel build" BAZEL_CLEAN_CMD="bazel clean" -DEFAULT_BAZEL_CONFIGS="--config=gcp --config=hdfs" +DEFAULT_BAZEL_CONFIGS="" PIP_CMD="${CI_BUILD_DIR}/builds/pip.sh" PIP_TEST_TUTORIALS_FLAG="--test_tutorials" diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index 05676f9551..0dd32ad1a8 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -543,7 +543,7 @@ SANITY_STEPS=("do_pylint PYTHON2" "do_pylint PYTHON3" "do_check_futures_test" "d SANITY_STEPS_DESC=("Python 2 pylint" "Python 3 pylint" "Check that python files have certain __future__ imports" "buildifier check" "bazel nobuild" "pip: license check for external dependencies" "C library: license check for external dependencies" "Java Native Library: license check for external dependencies" "Pip Smoke Test: Checking py_test dependencies exist in pip package" "Check load py_test: Check that BUILD files with py_test target properly load py_test" "Code Link Check: Check there are no broken links" "Test entries in /tensorflow/contrib/cmake/python_{modules|protos|protos_cc}.txt for validity and consistency" "Check file names for cases") INCREMENTAL_FLAG="" -DEFAULT_BAZEL_CONFIGS="--config=hdfs --config=gcp" +DEFAULT_BAZEL_CONFIGS="" # Parse command-line arguments BAZEL_FLAGS=${DEFAULT_BAZEL_CONFIGS} -- GitLab From 1107fb018307dfdc35fbe1c2d2f2a378c45aeb18 Mon Sep 17 00:00:00 2001 From: Tristan Rice Date: Wed, 20 Jun 2018 13:19:50 -0700 Subject: [PATCH 079/519] Cast: support casting to and from quantized types --- tensorflow/core/framework/tensor.h | 1 + tensorflow/core/kernels/cast_op.cc | 53 +++++++++++++++---- tensorflow/core/kernels/cast_op.h | 2 + tensorflow/core/kernels/cast_op_test.cc | 12 ++++- .../kernel_tests/distributions/util_test.py | 4 +- 5 files changed, 61 insertions(+), 11 deletions(-) diff --git a/tensorflow/core/framework/tensor.h b/tensorflow/core/framework/tensor.h index d2f2609d3b..1b19ab5da3 100644 --- a/tensorflow/core/framework/tensor.h +++ b/tensorflow/core/framework/tensor.h @@ -482,6 +482,7 @@ class Tensor { friend class VariableOp; // For access to set_shape friend class AutoReloadVariableOp; // For access to set_shape friend class TensorTestHelper; // For access to set_shape + friend class CastOpBase; // For access to set_dtype; friend class OpKernelContext; // For access to RefCountIsOne(). friend class ScopedAllocator; // For access to buf_. friend class XlaTensor; // For access to RefCountIsOne(). diff --git a/tensorflow/core/kernels/cast_op.cc b/tensorflow/core/kernels/cast_op.cc index 626db9131a..85b8a5ea6b 100644 --- a/tensorflow/core/kernels/cast_op.cc +++ b/tensorflow/core/kernels/cast_op.cc @@ -53,8 +53,39 @@ typedef Eigen::SyclDevice SYCLDevice; FN(arg0, std::complex) CastOpBase::CastOpBase(OpKernelConstruction* ctx) : OpKernel(ctx) { - OP_REQUIRES_OK(ctx, ctx->GetAttr("SrcT", &src_dtype_)); - OP_REQUIRES_OK(ctx, ctx->GetAttr("DstT", &dst_dtype_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("SrcT", &external_src_dtype_)); + + OP_REQUIRES_OK(ctx, ctx->GetAttr("DstT", &external_dst_dtype_)); + + // Quantized data types use the same underlying format as their non quantized + // version so we use the non quantized implementation for casting. + if (external_dst_dtype_ == DT_QUINT8) { + dst_dtype_ = DT_UINT8; + } else if (external_dst_dtype_ == DT_QINT8) { + dst_dtype_ = DT_INT8; + } else if (external_dst_dtype_ == DT_QINT32) { + dst_dtype_ = DT_INT32; + } else if (external_dst_dtype_ == DT_QINT16) { + dst_dtype_ = DT_INT16; + } else if (external_dst_dtype_ == DT_QUINT16) { + dst_dtype_ = DT_UINT16; + } else { + dst_dtype_ = external_dst_dtype_; + } + + if (external_src_dtype_ == DT_QUINT8) { + src_dtype_ = DT_UINT8; + } else if (external_src_dtype_ == DT_QINT8) { + src_dtype_ = DT_INT8; + } else if (external_src_dtype_ == DT_QINT32) { + src_dtype_ = DT_INT32; + } else if (external_src_dtype_ == DT_QINT16) { + src_dtype_ = DT_INT16; + } else if (external_src_dtype_ == DT_QUINT16) { + src_dtype_ = DT_UINT16; + } else { + src_dtype_ = external_src_dtype_; + } } void CastOpBase::Compute(OpKernelContext* ctx) { @@ -62,15 +93,19 @@ void CastOpBase::Compute(OpKernelContext* ctx) { if (work_ == nullptr) { ctx->set_output(0, inp); } else { + Tensor in; + in.UnsafeCopyFromInternal(inp, src_dtype_, inp.shape()); Tensor* out = nullptr; - OP_REQUIRES_OK(ctx, ctx->allocate_output(0, inp.shape(), &out)); - work_(ctx, inp, out); + OP_REQUIRES_OK(ctx, ctx->allocate_output(0, in.shape(), &out)); + out->set_dtype(dst_dtype_); + work_(ctx, in, out); + out->set_dtype(external_dst_dtype_); } } Status CastOpBase::Unimplemented() { - return errors::Unimplemented("Cast ", DataTypeString(src_dtype_), " to ", - DataTypeString(dst_dtype_), " is not supported"); + return errors::Unimplemented("Cast ", DataTypeString(external_src_dtype_), " to ", + DataTypeString(external_dst_dtype_), " is not supported"); } CpuCastOp::CpuCastOp(OpKernelConstruction* ctx) : CastOpBase(ctx) { @@ -78,7 +113,7 @@ CpuCastOp::CpuCastOp(OpKernelConstruction* ctx) : CastOpBase(ctx) { } Status CpuCastOp::Prepare() { - if (src_dtype_ == dst_dtype_) { + if (external_src_dtype_ == external_dst_dtype_) { work_ = nullptr; // Identity return Status::OK(); } @@ -127,7 +162,7 @@ class GpuCastOp : public CastOpBase { private: Status Prepare() { - if (src_dtype_ == dst_dtype_) { + if (external_src_dtype_ == external_dst_dtype_) { work_ = nullptr; // Identity return Status::OK(); } @@ -203,7 +238,7 @@ class SyclCastOp : public CastOpBase { private: Status Prepare() { - if (src_dtype_ == dst_dtype_) { + if (external_src_dtype_ == external_dst_dtype_) { work_ = nullptr; // Identity return Status::OK(); } diff --git a/tensorflow/core/kernels/cast_op.h b/tensorflow/core/kernels/cast_op.h index 16d2e0e0a5..aae1e7ff19 100644 --- a/tensorflow/core/kernels/cast_op.h +++ b/tensorflow/core/kernels/cast_op.h @@ -36,6 +36,8 @@ class CastOpBase : public OpKernel { protected: DataType src_dtype_; DataType dst_dtype_; + DataType external_src_dtype_; + DataType external_dst_dtype_; std::function work_ = nullptr; Status Unimplemented(); diff --git a/tensorflow/core/kernels/cast_op_test.cc b/tensorflow/core/kernels/cast_op_test.cc index 7da9d28a3d..b74dc25837 100644 --- a/tensorflow/core/kernels/cast_op_test.cc +++ b/tensorflow/core/kernels/cast_op_test.cc @@ -76,7 +76,12 @@ class CastOpTest : public OpsTestBase { TEST_CAST(in, half); \ TEST_CAST(in, float); \ TEST_CAST(in, double); \ - TEST_CAST(in, bfloat16); + TEST_CAST(in, bfloat16); \ + TEST_CAST(in, quint8); \ + TEST_CAST(in, qint8); \ + TEST_CAST(in, qint32); \ + TEST_CAST(in, qint16); \ + TEST_CAST(in, quint16); TEST_ALL_CASTS_FROM(uint8) TEST_ALL_CASTS_FROM(uint16) @@ -87,6 +92,11 @@ TEST_ALL_CASTS_FROM(half) TEST_ALL_CASTS_FROM(float) TEST_ALL_CASTS_FROM(double) TEST_ALL_CASTS_FROM(bfloat16) +TEST_ALL_CASTS_FROM(quint8) +TEST_ALL_CASTS_FROM(qint8) +TEST_ALL_CASTS_FROM(qint32) +TEST_ALL_CASTS_FROM(qint16) +TEST_ALL_CASTS_FROM(quint16) #undef TEST_ALL_CASTS_FROM #undef TEST_CAST diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py index 9d38ffcb4a..53f143abd6 100644 --- a/tensorflow/python/kernel_tests/distributions/util_test.py +++ b/tensorflow/python/kernel_tests/distributions/util_test.py @@ -311,8 +311,10 @@ class EmbedCheckCategoricalEventShapeTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def testUnsupportedDtype(self): with self.test_session(): + param = ops.convert_to_tensor( + np.ones([2**11+1]).astype(dtypes.qint16.as_numpy_dtype), + dtype=dtypes.qint16) with self.assertRaises(TypeError): - param = array_ops.ones([int(2**11+1)], dtype=dtypes.qint16) du.embed_check_categorical_event_shape(param) -- GitLab From a979fb29de16dff6495b51cb1363eea752b43513 Mon Sep 17 00:00:00 2001 From: AG Ramesh Date: Sun, 8 Jul 2018 20:38:26 -0700 Subject: [PATCH 080/519] Add reorder primitive reuse --- tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc | 10 +++------- tensorflow/core/kernels/mkl_conv_grad_input_ops.cc | 7 ++----- 2 files changed, 5 insertions(+), 12 deletions(-) diff --git a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc index 4e80f5acce..87849e48b8 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc @@ -872,12 +872,11 @@ class MklConv2DCustomBackpropFilterOp } // check if src and diff_dst need reorder - std::vector net; T *src_data = nullptr; if (fwd_src_md.data.format != conv2d_bwd_filter->GetSrcMemoryFormat()) { src.SetUsrMem(fwd_src_md, &src_tensor); src.CheckReorderToOpMem( - bwd_filter_pd->src_primitive_desc(), &net); + bwd_filter_pd->src_primitive_desc()); src_data = static_cast(src.GetOpMem().get_data_handle()); } else { src_data = static_cast(const_cast( @@ -889,14 +888,13 @@ class MklConv2DCustomBackpropFilterOp conv2d_bwd_filter->GetDiffDstMemoryFormat()) { diff_dst.SetUsrMem(diff_dst_md, &diff_dst_tensor); diff_dst.CheckReorderToOpMem( - bwd_filter_pd->diff_dst_primitive_desc(), &net); + bwd_filter_pd->diff_dst_primitive_desc()); diff_dst_data = static_cast( diff_dst.GetOpMem().get_data_handle()); } else { diff_dst_data = static_cast(const_cast( diff_dst_tensor.flat().data())); } - stream(stream::kind::eager).submit(net).wait(); // For backward filter, convert diff_filter back to Tensorflow layout // Here we prepare to reorder op memory back to user memory @@ -929,9 +927,7 @@ class MklConv2DCustomBackpropFilterOp // Reorder diff_filter back to Tensorflow layout if necessary if (diff_filter_reorder_required) { - std::vector net; - diff_filter.InsertReorderToUserMem(&net); - stream(stream::kind::eager).submit(net).wait(); + diff_filter.InsertReorderToUserMem(); } } catch (mkldnn::error& e) { string error_msg = "Status: " + std::to_string(e.status) + diff --git a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc index 0af4568b47..60a048779f 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc @@ -722,14 +722,12 @@ class MklConv2DCustomBackpropInputOp diff_src_tensor->flat().data())); // check if filter and diff_dst need reorder - std::vector net; T* filter_data = nullptr; if (fwd_filter_md.data.format != conv2d_bwd_input->GetFilterMemoryFormat()) { filter.SetUsrMem(fwd_filter_md, &filter_tensor); filter.CheckReorderToOpMem( - bwd_input_pd->weights_primitive_desc(), - &net); + bwd_input_pd->weights_primitive_desc()); filter_data = static_cast(filter.GetOpMem().get_data_handle()); } else { filter_data = static_cast(const_cast( @@ -741,14 +739,13 @@ class MklConv2DCustomBackpropInputOp conv2d_bwd_input->GetDiffDstMemoryFormat()) { diff_dst.SetUsrMem(diff_dst_md, &diff_dst_tensor); diff_dst.CheckReorderToOpMem( - bwd_input_pd->diff_dst_primitive_desc(), &net); + bwd_input_pd->diff_dst_primitive_desc()); diff_dst_data = static_cast( diff_dst.GetOpMem().get_data_handle()); } else { diff_dst_data = static_cast(const_cast( diff_dst_tensor.flat().data())); } - stream(stream::kind::eager).submit(net).wait(); // execute convolution input bwd conv2d_bwd_input->Execute(diff_src_data, filter_data, diff_dst_data); -- GitLab From 1466b2eb0b545241b3abd5bcf229bcb0494da6b2 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Mon, 4 Jun 2018 15:58:18 +0000 Subject: [PATCH 081/519] Add complex support for tan At the moment tan in tensorflow supports float and double, but not complex types. This fix adds the support for tan, and removes related TODO in the test Signed-off-by: Yong Tang --- tensorflow/core/kernels/cwise_op_tan.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/cwise_op_tan.cc b/tensorflow/core/kernels/cwise_op_tan.cc index c1a25767d3..ce90145c70 100644 --- a/tensorflow/core/kernels/cwise_op_tan.cc +++ b/tensorflow/core/kernels/cwise_op_tan.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/core/kernels/cwise_ops_common.h" namespace tensorflow { -REGISTER2(UnaryOp, CPU, "Tan", functor::tan, float, double); +REGISTER4(UnaryOp, CPU, "Tan", functor::tan, float, double, complex64, complex128); #if GOOGLE_CUDA REGISTER2(UnaryOp, GPU, "Tan", functor::tan, float, double); -- GitLab From 9249690173fe4bf46f2fc170f3e68f5496d7609c Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Mon, 4 Jun 2018 15:59:36 +0000 Subject: [PATCH 082/519] Remove TODO and enable complex for tan in test case. Signed-off-by: Yong Tang --- tensorflow/cc/gradients/math_grad_test.cc | 6 +----- 1 file changed, 1 insertion(+), 5 deletions(-) diff --git a/tensorflow/cc/gradients/math_grad_test.cc b/tensorflow/cc/gradients/math_grad_test.cc index fd7b6fe662..1c9bdff5e1 100644 --- a/tensorflow/cc/gradients/math_grad_test.cc +++ b/tensorflow/cc/gradients/math_grad_test.cc @@ -475,11 +475,7 @@ TEST_F(CWiseUnaryGradTest, Tan_Complex) { auto x_fn = [this](const int i) { return CRV({{1, 0}, {0, 1}, {2, -1}, {1, 2}, {3, 4}}); }; - // TODO(kbsriram) - // Enable when tan kernel supports complex inputs - if (false) { - TestCWiseGrad(TAN, x_fn); - } + TestCWiseGrad(TAN, x_fn); } TEST_F(CWiseUnaryGradTest, Atan) { -- GitLab From 00dbde11f428953cc34c933051692178820d28e1 Mon Sep 17 00:00:00 2001 From: SneakyFish5 <32284796+SneakyFish5@users.noreply.github.com> Date: Wed, 11 Jul 2018 15:35:19 -0500 Subject: [PATCH 083/519] Fix copied buffer --- tensorflow/contrib/lite/allocation.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/allocation.cc b/tensorflow/contrib/lite/allocation.cc index c42622ff02..6aa4b57d7c 100644 --- a/tensorflow/contrib/lite/allocation.cc +++ b/tensorflow/contrib/lite/allocation.cc @@ -99,7 +99,7 @@ FileCopyAllocation::FileCopyAllocation(const char* filename, filename); return; } - copied_buffer_ = std::move(buffer); + copied_buffer_.reset(const_cast(buffer.release())); } FileCopyAllocation::~FileCopyAllocation() {} -- GitLab From 571d3dc5747e04fe0a80be185e64532cf74e1fb0 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Wed, 11 Jul 2018 23:54:26 -0700 Subject: [PATCH 084/519] Let segmenter able to remove ineligible input/output nodes. --- .../contrib/tensorrt/convert/convert_graph.cc | 9 +- .../contrib/tensorrt/convert/convert_nodes.cc | 8 + .../contrib/tensorrt/convert/convert_nodes.h | 4 + .../contrib/tensorrt/segment/segment.cc | 186 +++++-- tensorflow/contrib/tensorrt/segment/segment.h | 20 +- .../contrib/tensorrt/segment/segment_test.cc | 460 +++++++----------- 6 files changed, 338 insertions(+), 349 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 1c4fd4a0ce..359fac36f5 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -107,8 +107,10 @@ bool IsTensorRTCandidate(const tensorflow::Node* node) { // TODO(ben,jie): ... }; // LINT.ThenChange(//tensorflow/contrib/tensorrt/convert/convert_nodes.h) - return (candidate_ops.count(node->type_string()) || - PluginFactoryTensorRT::GetInstance()->IsPlugin(node->type_string())); + if (!candidate_ops.count(node->type_string()) && + !PluginFactoryTensorRT::GetInstance()->IsPlugin(node->type_string())) { + return false; + } } tensorflow::Status BuildNodeMap( @@ -720,7 +722,8 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { segment_options.minimum_segment_size = params.minimum_segment_size; tensorflow::tensorrt::segment::SegmentNodesVector initial_segments; TF_RETURN_IF_ERROR(tensorrt::segment::SegmentGraph( - &graph, IsTensorRTCandidate, segment_options, &initial_segments)); + &graph, IsTensorRTCandidate, IsTensorRTInputCandidate, + IsTensorRTOutputCandidate, segment_options, &initial_segments)); if (initial_segments.size() > 1) { VLOG(0) << "MULTIPLE tensorrt candidate conversion: " << initial_segments.size(); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 146b9c7344..8f6656e4ad 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2391,6 +2391,14 @@ tensorflow::Status ConvertSegmentToGraphDef( return tensorflow::Status::OK(); } +bool IsTensorRTInputCandidate(const tensorflow::Node* node) { + return true; +} + +bool IsTensorRTOutputCandidate(const tensorflow::Node* node) { + return true; +} + } // namespace convert } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 7684d8d4a2..872ba6a080 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -128,6 +128,10 @@ tensorflow::Status ConvertGraphDefToEngine( TrtUniquePtrType* engine, bool* convert_successfully); +bool IsTensorRTInputCandidate(const tensorflow::Node* node); + +bool IsTensorRTOutputCandidate(const tensorflow::Node* node); + } // namespace convert } // namespace tensorrt } // namespace tensorflow diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc index cc42913eca..5c0898b29a 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.cc +++ b/tensorflow/contrib/tensorrt/segment/segment.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/segment/segment.h" +#include #include #include #include @@ -32,6 +33,7 @@ namespace tensorflow { namespace tensorrt { namespace segment { using ::tensorflow::strings::StrAppend; + // A simple graph representation to mirror tensorflow::Graph. This structure // helps saving memory since segmenter modifies the graph in place, preventing // the need to create a copy of the graph. It is composed of edges and nodes. @@ -215,7 +217,7 @@ namespace { bool CheckCycles(const std::unique_ptr& g, const SimpleNode* src, const std::vector& start) { - // copied from TF ReverseDFS. + // Copied from TF ReverseDFS, which only works for tensorflow::Graph. struct Work { SimpleNode* node; bool leave; // Are we entering or leaving n? @@ -269,6 +271,24 @@ bool CanContractEdge(const SimpleEdge* edge, // 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 + // + // TODO(aaroey): there are several problems with the current approach: + // 1. src->dst->src, this is not detected but it should be; + // 2. src->dst->...(any node sequence that doesn't contain src)...->dst, this + // is detected but it should not be. + // + // Note that it's fine that dst connects back to src indirectly (i.e. through + // a path with length > 1 that consists of intermedia nodes other than src). + // While loops is one example. + // + // The goal is to make sure that the trt subgraph: + // 1. has no loops (i.e. is a DAG), and + // 2. if there is a path in the subgraph from X to Y (X and Y are both nodes + // in the subgraph), then all paths from X to Y are in the subgraph. + // + // To achieve this goal, the correct way seems to be: + // 1. remove any direct edge from src->dst; + // 2. detect if src can reach dst, if so they cannot be merged. std::vector dfs_start_nodes; for (SimpleNode* node : dst->in_nodes()) { if (node != src) { @@ -276,8 +296,8 @@ bool CanContractEdge(const SimpleEdge* edge, } } - bool is_cycle = CheckCycles(graph, src, dfs_start_nodes); - return !is_cycle; + const bool has_cycle = CheckCycles(graph, src, dfs_start_nodes); + return !has_cycle; } } // namespace @@ -342,22 +362,20 @@ void ContractEdge(SimpleEdge* edge, SimpleGraph* graph, } tensorflow::Status SegmentGraph( - const tensorflow::GraphDef& gdef, - const std::function& 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)); - return SegmentGraph(&graph, candidate_fn, options, segments); -} - -tensorflow::Status SegmentGraph( - tensorflow::Graph* tf_graph, + const tensorflow::Graph* tf_graph, const std::function& candidate_fn, + const std::function& input_candidate_fn, + const std::function& output_candidate_fn, const SegmentOptions& options, SegmentNodesVector* segments) { + // Steps: + // 1. run the segmentation algorithm to find all the segments, which uses + // candidate_fn to determine the candidates segment nodes; + // 2. for each segments, remove the nodes that are inputs/outputs of the + // segment but are not eligible, using input/output_candidate_fn to + // determine the eligibilities; + // 3. convert the segment into expected return format and return the result. + + // --------------------------------- Step 1 --------------------------------- auto graph = std::unique_ptr(new SimpleGraph(tf_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 @@ -372,14 +390,19 @@ tensorflow::Status SegmentGraph( 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. + // 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. For exaample, consider graph with node {A, B, C, D} and edges + // {A->B, A->C, B->D, C->D), where A, B, D are trt compatible but C is not, so + // in theory we can choose to contract either A, B or B, D but not both, but + // here it always choose to contract B, D. + // + // 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 tforder; tensorflow::GetPostOrder(*tf_graph, &tforder); // use postorder implementation from tensorflow and construct mirror in @@ -392,13 +415,11 @@ tensorflow::Status SegmentGraph( for (const SimpleNode* node : order) { // All output nodes of 'node' have been visited... VLOG(2) << "Trying node " << node->name() << " id=" << node->id(); - // '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. @@ -416,7 +437,6 @@ tensorflow::Status SegmentGraph( VLOG(2) << "... ... not a TRT candidate"; continue; } - if (CanContractEdge(out_edge, graph)) { VLOG(2) << "... ... can contract"; contract_edges.insert(out_edge); @@ -424,11 +444,9 @@ tensorflow::Status SegmentGraph( 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()) { @@ -457,11 +475,22 @@ tensorflow::Status SegmentGraph( // Collect the segments/subgraphs. Each subgraph is represented by a // set of the names of the nodes in that subgraph. - std::unordered_map> sg_map; + + // A map from the segment identifier (currently the name of the root node of + // the segment tree) to the segment nodes set. + std::unordered_map> sg_map; + + // A map from the segment identifier (currently the name of the root node of + // the segment tree) to the device names that the nodes in the segment are + // assigned to. + // + // TODO(aaroey): nodes assigned to different devices should not be merged, + // fix this. std::unordered_map> device_maps; + for (auto& u : node_segments) { if ((u.Value() != nullptr) && (u.ParentValue() != nullptr)) { - sg_map[u.ParentValue()->name()].insert(u.Value()->name()); + sg_map[u.ParentValue()->name()].insert(u.Value()->tf_node()); auto tf_node = u.Value()->tf_node(); // has_assigned_device_name() is expected to return true // when called from optimization pass. However, since graph @@ -482,25 +511,104 @@ tensorflow::Status SegmentGraph( } } + // --------------------------------- Step 2 --------------------------------- + // Remove ineligible input/output nodes. + for (auto& itr : sg_map) { + std::set& segment_nodes = itr.second; + VLOG(1) << "Segment original size: " << segment_nodes.size(); + while (true) { + std::deque in_nodes_que, out_nodes_que; + // Find an input node that is not eligible and add it to the queue. + // Nodes that has no incoming edges should not be treated as "input", + // as there are really no inputs to them. Similar for output nodes. + for (auto node : segment_nodes) { + bool added = false; + for (const tensorflow::Edge* edge : node->in_edges()) { + if (!edge->IsControlEdge() && !edge->src()->IsSource() && + !segment_nodes.count(edge->src())) { // 'node' is an input node. + if (!input_candidate_fn(node)) { + in_nodes_que.push_back(node); + added = true; + break; + } + } + } + if (added) continue; // Only adding the node once to either queue. + for (const tensorflow::Edge* edge : node->out_edges()) { + if (!edge->dst()->IsSink() && !edge->IsControlEdge() && + !segment_nodes.count(edge->dst())) { // 'node' is an output node. + if (!output_candidate_fn(node)) { + out_nodes_que.push_back(node); + break; + } + } + } + } + if (in_nodes_que.empty() && out_nodes_que.empty()) { + // No more ineligible input/output nodes. + break; + } + // Now for each ineligible node, remove all of its inputs or outputs from + // the subgraph. + // + // It can be proven that, if the original subgraph: + // 1. is a DAG, and + // 2. all paths between two nodes in the subgraph are all inside the + // subgraph + // then after doing this operation the resulting subgraph will keep the + // same properties 1 and 2. + // + // For simplicity we use heuristics: for input nodes remove all its + // input, for output nodes remove all its output. In this way, for common + // cases the number of removed nodes should be minimum. + auto remove_nodes = [&segment_nodes]( + bool is_input_nodes, + std::deque* que) { + // Run a BFS on the queue to find all the input/output nodes. + std::set visited; + while (!que->empty()) { + auto node = que->front(); + que->pop_front(); + if (!visited.insert(node).second) continue; + segment_nodes.erase(node); + for (auto in : is_input_nodes ? node->in_nodes() : node->out_nodes()) { + if (segment_nodes.count(in)) { + que->push_back(in); + VLOG(2) << "Need to remove node " << in->name() + << " because one of its " + << (is_input_nodes ? "output" : "input") + << " nodes in the graph was removed: " << node->name(); + } + } + } + }; + remove_nodes(true, &in_nodes_que); + remove_nodes(false, &out_nodes_que); + } + VLOG(1) << "Segment new size: " << segment_nodes.size(); + } + + // --------------------------------- Step 3 --------------------------------- // Convert the segments into the expected return format for (const auto& itr : sg_map) { - const auto& segment_node_names = itr.second; + const std::set& segment_nodes = itr.second; if (VLOG_IS_ON(1)) { string s; - for (const auto& name : segment_node_names) { - s += " " + name; - } - VLOG(1) << "Segment " << segments->size() << ":" << s; + for (auto node : segment_nodes) s += " " + node->name(); + VLOG(1) << "Segment " << segments->size() << ": " << s; } // Don't use small segments. - if (static_cast(segment_node_names.size()) < + if (static_cast(segment_nodes.size()) < options.minimum_segment_size) { VLOG(1) << "Segment " << segments->size() << " has only " - << segment_node_names.size() << " nodes, dropping"; + << segment_nodes.size() << " nodes, dropping"; continue; } + // TODO(sami): Make segmenter placement aware once trtscopes are in place + std::set segment_node_names; + for (auto node : itr.second) segment_node_names.insert(node->name()); const auto& dev_itr = device_maps.find(itr.first); if (dev_itr == device_maps.end() || dev_itr->second.empty()) { VLOG(1) << "No device assigned to segment " << segments->size(); diff --git a/tensorflow/contrib/tensorrt/segment/segment.h b/tensorflow/contrib/tensorrt/segment/segment.h index 81b4bfe49f..ab75135054 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.h +++ b/tensorflow/contrib/tensorrt/segment/segment.h @@ -40,22 +40,6 @@ struct SegmentOptions { std::set 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. -// -// TODO(aaroey): remove this method. -tensorflow::Status SegmentGraph( - const tensorflow::GraphDef& gdef, - const std::function& candidate_fn, - const SegmentOptions& options, SegmentNodesVector* segments); - // Get the subgraphs of a graph that can be handled by TensorRT. // // @param graph tensorflow::Graph of the network @@ -66,8 +50,10 @@ tensorflow::Status SegmentGraph( // all the NodeDefs in that subgraph. // @return the status. tensorflow::Status SegmentGraph( - tensorflow::Graph* tf_graph, + const tensorflow::Graph* tf_graph, const std::function& candidate_fn, + const std::function& input_candidate_fn, + const std::function& output_candidate_fn, const SegmentOptions& options, SegmentNodesVector* segments); } // namespace segment diff --git a/tensorflow/contrib/tensorrt/segment/segment_test.cc b/tensorflow/contrib/tensorrt/segment/segment_test.cc index f5b2d258d7..a43cf4f416 100644 --- a/tensorflow/contrib/tensorrt/segment/segment_test.cc +++ b/tensorflow/contrib/tensorrt/segment/segment_test.cc @@ -14,350 +14,230 @@ 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/cc/framework/scope.h" +#include "tensorflow/cc/ops/standard_ops.h" +#include "tensorflow/core/graph/testlib.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/logging.h" #include "tensorflow/core/platform/test.h" #include "tensorflow/core/platform/types.h" +#include "tensorflow/core/public/session.h" namespace tensorflow { namespace tensorrt { namespace segment { namespace test { +namespace ops = ::tensorflow::ops; 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); - + protected: std::function MakeCandidateFn( - const std::set& node_names); + const std::set& node_names) { + return [node_names](const tensorflow::Node* node) -> bool { + return node_names.find(node->name()) != node_names.end(); + }; + } - 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); + void RunTest(const tensorflow::Graph* graph, + const std::set& candidates, + const std::set& input_candidates, + const std::set& output_candidates, + const std::vector>& expected_segments) { + SegmentNodesVector segments; + TF_EXPECT_OK(SegmentGraph( + graph, MakeCandidateFn(candidates), MakeCandidateFn(input_candidates), + MakeCandidateFn(output_candidates), default_options_, &segments)); + ValidateSegment(segments, expected_segments); + } + + void ValidateSegment(const SegmentNodesVector& segments, + const std::vector>& expected_segments) { + EXPECT_EQ(expected_segments.size(), segments.size()); + for (int i = 0; i < segments.size(); ++i) { + const auto& segment_node_names = segments[i].first; + const auto& expected = expected_segments[i]; + for (const auto& name : expected) { + EXPECT_TRUE(segment_node_names.count(name)) + << "Segment " << i << " is missing expected node: " << name; + } + if (segment_node_names.size() == expected.size()) continue; + for (const auto& name : segment_node_names) { + EXPECT_TRUE(expected.count(name)) + << "Unexpected node found in segment " << i << ": " << name; + } + } + } 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 SegmentTest::MakeCandidateFn( - const std::set& node_names) { - return [node_names](const tensorflow::Node* 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; +std::set operator-(const std::set& lhs, const string& rhs) { + std::set result = lhs; + CHECK(result.erase(rhs)); + return result; } 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()); - + Scope s = Scope::NewRootScope(); + tensorflow::Graph g(OpRegistry::Global()); + TF_EXPECT_OK(s.ToGraph(&g)); // Expect no segments/subgraphs. - EXPECT_TRUE(segments.empty()); - TF_DeleteGraph(graph); + RunTest(&g, {}, {}, {}, {}); } TEST_F(SegmentTest, Simple) { - TF_Status* s = TF_NewStatus(); - TF_Graph* graph = TF_NewGraph(); - // feed - // // || + // // \\ // add0 add1 - // | | / + // | \ / // | add2 - // | / || + // | / \\ // add3 add4 - // | / + // \ / // - // - 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 expected{"add0", "add1", "add2", "add3", "add4"}; - for (const auto& ex : expected) { - EXPECT_TRUE(segments[0].first.find(ex) != segments[0].first.end()) - << "Missing expected node " << ex; - } - TF_DeleteGraph(graph); - TF_DeleteStatus(s); + Scope s = Scope::NewRootScope(); + auto feed = ops::Placeholder(s.WithOpName("feed"), DT_FLOAT); + auto add0 = ops::Add(s.WithOpName("add0"), feed, feed); + auto add1 = ops::Add(s.WithOpName("add1"), feed, feed); + auto add2 = ops::Add(s.WithOpName("add2"), add0, add1); + auto add3 = ops::Add(s.WithOpName("add3"), add0, add2); + auto add4 = ops::Add(s.WithOpName("add4"), add2, add2); + tensorflow::Graph g(OpRegistry::Global()); + TF_EXPECT_OK(s.ToGraph(&g)); + + // All Add operations are candidates, and we expect all of them to be + // collapsed into a single segment + const std::set all_adds = {"add0", "add1", "add2", "add3", "add4"}; + RunTest(&g, all_adds, all_adds, all_adds, {all_adds}); + + // Make add1 not a candidate, and we expect all other Add operations to be + // collapsed into a single segment + auto without_add1 = all_adds - "add1"; + RunTest(&g, without_add1, without_add1, without_add1, {without_add1}); + + // Make add1 not a candidate and add2 not an input candidate, and we expect + // add0 and add2 are removed from the segment. + auto without_add2 = all_adds - "add2"; + RunTest(&g, without_add1, without_add2, without_add1, {{"add3", "add4"}}); + + // Making add2 not an input candidate itself won't affect anything. + RunTest(&g, all_adds, without_add2, all_adds, {all_adds}); + + // Making add1 not an input candidate. + RunTest(&g, all_adds, without_add1, all_adds, {without_add1}); + + // Making add3 not an output candidate doesn't affect anything, since it's + // output is sink. + auto without_add3 = all_adds - "add3"; + RunTest(&g, all_adds, all_adds, without_add3, {all_adds}); } 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 - // | / + // \ / // - // - 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); + Scope s = Scope::NewRootScope(); + auto feed = ops::Placeholder(s.WithOpName("feed"), DT_FLOAT); + auto add0 = ops::Add(s.WithOpName("add0"), feed, feed); + auto add1 = ops::Add(s.WithOpName("add1"), feed, feed); + auto add2 = ops::Add(s.WithOpName("add2"), add0, add1); + auto add3 = ops::Add(s.WithOpName("add3"), add0, add2); + auto add4 = ops::Add(s.WithOpName("add4"), add2, add2); + tensorflow::Graph g(OpRegistry::Global()); + TF_EXPECT_OK(s.ToGraph(&g)); + + // add2 is not a TRT candidate so there should be no segments generated. + const std::set without_add2 = {"add0", "add1", "add3", "add4"}; + RunTest(&g, without_add2, without_add2, without_add2, {}); } 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 - // | | / - // - // - 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 expected0{"add6", "add8"}; - for (const auto& ex : expected0) { - EXPECT_TRUE(segments[0].first.find(ex) != segments[0].first.end()) - << "Missing expected node " << ex; - } - - std::vector expected1{"add0", "add1", "add2", "add3"}; - for (const auto& ex : expected1) { - EXPECT_TRUE(segments[1].first.find(ex) != segments[1].first.end()) - << "Missing expected node " << ex; - } - TF_DeleteGraph(graph); - TF_DeleteStatus(s); + // feed + // // || \\ + // add0 add1 add7 + // | \ / / \\ + // | add2 / \\ + // | || \ | || + // | || add5 add8 + // | / \ / \ / + // add3 add4 add6 + // \ | / + // + Scope s = Scope::NewRootScope(); + auto feed = ops::Placeholder(s.WithOpName("feed"), DT_FLOAT); + auto add0 = ops::Add(s.WithOpName("add0"), feed, feed); + auto add1 = ops::Add(s.WithOpName("add1"), feed, feed); + auto add7 = ops::Add(s.WithOpName("add7"), feed, feed); + auto add2 = ops::Add(s.WithOpName("add2"), add0, add1); + auto add5 = ops::Add(s.WithOpName("add5"), add2, add7); + auto add8 = ops::Add(s.WithOpName("add8"), add7, add7); + auto add3 = ops::Add(s.WithOpName("add3"), add0, add2); + auto add4 = ops::Add(s.WithOpName("add4"), add2, add5); + auto add6 = ops::Add(s.WithOpName("add6"), add5, add8); + tensorflow::Graph g(OpRegistry::Global()); + TF_EXPECT_OK(s.ToGraph(&g)); + + const std::set all_adds = {"add0", "add1", "add2", "add3", "add4", + "add5", "add6", "add7", "add8"}; + // Make add5 not a TRT candidate, and we expect two segments. + auto without_add5 = all_adds - "add5"; + RunTest(&g, without_add5, without_add5, without_add5, + {{"add6", "add8"}, {"add0", "add1", "add2", "add3"}}); + + // Make add8 not a candidate and add6 not an input candidate, then all direct + // and indirect inputs of add6 will be removed from the segment. + auto without_add8 = all_adds - "add8"; + auto without_add6 = all_adds - "add6"; + RunTest(&g, without_add8, without_add6, all_adds, {{"add3", "add4"}}); + + // Make add3 not a candidate and add0 not an output candidate, then all + // direct and indirect outputs of add0 will be removed from the segment. + auto without_add3 = all_adds - "add3"; + auto without_add0 = all_adds - "add0"; + RunTest(&g, without_add3, all_adds, without_add0, {{"add1", "add7", "add8"}}); } 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 // || // - // - 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 expected0{"add3", "add4", "add5", "add6", "add7"}; - for (const auto& ex : expected0) { - EXPECT_TRUE(segments[0].first.find(ex) != segments[0].first.end()) - << "Missing expected node " << ex; - } - - std::vector expected1{"add0", "add1"}; - for (const auto& ex : expected1) { - EXPECT_TRUE(segments[1].first.find(ex) != segments[1].first.end()) - << "Missing expected node " << ex; - } - TF_DeleteGraph(graph); - TF_DeleteStatus(s); + Scope s = Scope::NewRootScope(); + auto feed = ops::Placeholder(s.WithOpName("feed"), DT_FLOAT); + auto add0 = ops::Add(s.WithOpName("add0"), feed, feed); + auto add1 = ops::Add(s.WithOpName("add1"), add0, add0); + auto add2 = ops::Add(s.WithOpName("add2"), add1, add1); + auto add3 = ops::Add(s.WithOpName("add3"), add2, add2); + auto add4 = ops::Add(s.WithOpName("add4"), add0, add0); + auto add5 = ops::Add(s.WithOpName("add5"), add4, add4); + auto add6 = ops::Add(s.WithOpName("add6"), add5, add5); + auto add7 = ops::Add(s.WithOpName("add7"), add3, add6); + tensorflow::Graph g(OpRegistry::Global()); + TF_EXPECT_OK(s.ToGraph(&g)); + + // Make add2 not a TRT candidate, and we expect 2 segments. + const std::set all_adds = {"add0", "add1", "add2", "add3", + "add4", "add5", "add6", "add7"}; + RunTest(&g, all_adds - "add2", all_adds, all_adds, + {{"add3", "add4", "add5", "add6", "add7"}, {"add0", "add1"}}); } } // namespace test -- GitLab From 8e3001f5e7ffa71b565c4c67a5e2c800fa1ab7af Mon Sep 17 00:00:00 2001 From: Dmitry Klimenkov Date: Thu, 12 Jul 2018 15:56:12 +0300 Subject: [PATCH 085/519] Putting batch_axis,seq_axis instead batch_dim, seq_dim --- tensorflow/python/ops/rnn.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index deba133fb9..aea01a8081 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -427,14 +427,14 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, if seq_lengths is not None: return array_ops.reverse_sequence( input=input_, seq_lengths=seq_lengths, - seq_dim=seq_dim, batch_dim=batch_dim) + seq_axis=seq_dim, batch_axis=batch_dim) else: return array_ops.reverse(input_, axis=[seq_dim]) with vs.variable_scope("bw") as bw_scope: inputs_reverse = _reverse( inputs, seq_lengths=sequence_length, - seq_dim=time_dim, batch_dim=batch_dim) + seq_axis=time_dim, batch_axis=batch_dim) tmp, output_state_bw = dynamic_rnn( cell=cell_bw, inputs=inputs_reverse, sequence_length=sequence_length, initial_state=initial_state_bw, dtype=dtype, @@ -443,7 +443,7 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, output_bw = _reverse( tmp, seq_lengths=sequence_length, - seq_dim=time_dim, batch_dim=batch_dim) + seq_axis=time_dim, batch_axis=batch_dim) outputs = (output_fw, output_bw) output_states = (output_state_fw, output_state_bw) -- GitLab From 6fa928715865eceda6f16db6f6b43e2a40d39b64 Mon Sep 17 00:00:00 2001 From: Nishidha Panpaliya Date: Thu, 12 Jul 2018 15:09:07 +0000 Subject: [PATCH 086/519] Fix for issue# 20361 - changed the test cases' inputs and expected such that it passes on both x86 and ppc64le --- .../lite/kernels/resize_bilinear_test.cc | 24 +++++++++---------- 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc b/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc index 10caffea03..747cbb4ea2 100644 --- a/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc +++ b/tensorflow/contrib/lite/kernels/resize_bilinear_test.cc @@ -247,7 +247,7 @@ TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches8Bit) { 3, 6, // 9, 12, // 4, 10, // - 10, 16 // + 12, 16 // }); m.SetSize({3, 3}); m.Invoke(); @@ -256,8 +256,8 @@ TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches8Bit) { 7, 9, 10, // 9, 11, 12, // 4, 8, 10, // - 8, 12, 14, // - 10, 13, 16, // + 9, 12, 14, // + 12, 14, 16, // }))); ResizeBilinearOpModel const_m({TensorType_UINT8, {2, 2, 2, 1}}, {3, 3}); @@ -265,7 +265,7 @@ TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches8Bit) { 3, 6, // 9, 12, // 4, 10, // - 10, 16 // + 12, 16 // }); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ @@ -273,8 +273,8 @@ TEST(ResizeBilinearOpTest, TwoDimensionalResizeWithTwoBatches8Bit) { 7, 9, 10, // 9, 11, 12, // 4, 8, 10, // - 8, 12, 14, // - 10, 13, 16, // + 9, 12, 14, // + 12, 14, 16, // }))); } @@ -282,26 +282,26 @@ TEST(ResizeBilinearOpTest, ThreeDimensionalResize8Bit) { ResizeBilinearOpModel m({TensorType_UINT8, {1, 2, 2, 2}}); m.SetInput({ 3, 4, 6, 10, // - 9, 10, 12, 16, // + 10, 12, 14, 16, // }); m.SetSize({3, 3}); m.Invoke(); EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 4, 5, 8, 6, 10, // - 7, 8, 9, 12, 10, 14, // - 9, 10, 11, 13, 12, 16, // + 7, 9, 10, 12, 11, 14, // + 10, 12, 12, 14, 14, 16, // }))); ResizeBilinearOpModel const_m({TensorType_UINT8, {1, 2, 2, 2}}, {3, 3}); const_m.SetInput({ 3, 4, 6, 10, // - 9, 10, 12, 16, // + 10, 12, 14, 16, // }); const_m.Invoke(); EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ 3, 4, 5, 8, 6, 10, // - 7, 8, 9, 12, 10, 14, // - 9, 10, 11, 13, 12, 16, // + 7, 9, 10, 12, 11, 14, // + 10, 12, 12, 14, 14, 16, // }))); } } // namespace -- GitLab From 86f632e29810fa93db559f882567b9569dabfad5 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Thu, 12 Jul 2018 15:22:03 -0700 Subject: [PATCH 087/519] Implement the input/output edge validaters --- .../contrib/tensorrt/convert/convert_graph.cc | 46 ++--- .../contrib/tensorrt/convert/convert_nodes.cc | 165 ++++++++++++------ .../contrib/tensorrt/convert/convert_nodes.h | 26 ++- .../contrib/tensorrt/segment/segment.cc | 8 +- tensorflow/contrib/tensorrt/segment/segment.h | 4 +- .../contrib/tensorrt/segment/segment_test.cc | 21 ++- 6 files changed, 182 insertions(+), 88 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 359fac36f5..ba01eaabc2 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -107,10 +107,8 @@ bool IsTensorRTCandidate(const tensorflow::Node* node) { // TODO(ben,jie): ... }; // LINT.ThenChange(//tensorflow/contrib/tensorrt/convert/convert_nodes.h) - if (!candidate_ops.count(node->type_string()) && - !PluginFactoryTensorRT::GetInstance()->IsPlugin(node->type_string())) { - return false; - } + return (candidate_ops.count(node->type_string()) || + PluginFactoryTensorRT::GetInstance()->IsPlugin(node->type_string())); } tensorflow::Status BuildNodeMap( @@ -280,7 +278,8 @@ tensorflow::Status GetEngineInfo( subgraph_node_ids.push_back(node_id); for (const auto edge : node->in_edges()) { auto input_node = edge->src(); - if (segment_nodes.count(input_node->name()) == 0) { + if (segment_nodes.count(input_node->name()) == 0 && + !edge->IsControlEdge() && !input_node->IsSource()) { // Add constant input node into the segment. We don't care if it has // other output edges going into other engines or TF nodes. Since we add // it only to the subsegment node list, not the subsegment itself, it @@ -288,7 +287,7 @@ tensorflow::Status GetEngineInfo( // will prune it out. if (input_node->type_string() == "Const") { subgraph_node_ids.push_back(input_node->id()); - } else if (!edge->IsControlEdge() && !input_node->IsSource()) { + } else { string s(input_node->name()); StrAppend(&s, ":", edge->src_output()); VLOG(1) << "Input edge = " << s; @@ -351,9 +350,9 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, nvinfer1::IGpuAllocator* alloc, int max_batch_size) { const auto& info = infos.at(pos); - std::vector out_shapes; - std::vector input_shapes; - std::vector shapes; + std::vector output_shape_protos; + std::vector input_shape_protos; + std::vector input_shapes; std::vector inputs; std::vector out_types; VLOG(1) << "Processing " << info.engine_name; @@ -366,11 +365,11 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, tensorflow::TensorShapeProto out_shape; // shape of the output node inside segment conn.inside_shape.AsProto(&out_shape); - if (out_shapes.size() <= conn.port_number) { - out_shapes.resize(conn.port_number + 1); + if (output_shape_protos.size() <= conn.port_number) { + output_shape_protos.resize(conn.port_number + 1); out_types.resize(conn.port_number + 1); } - out_shapes.at(conn.port_number) = out_shape; + output_shape_protos.at(conn.port_number) = out_shape; out_types.at(conn.port_number) = conn.connection_type; continue; } @@ -378,12 +377,12 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, // Set the shapes and data types of input edge. tensorflow::TensorShapeProto in_shape; conn.outside_shape.AsProto(&in_shape); - if (input_shapes.size() <= conn.port_number) { + if (input_shape_protos.size() <= conn.port_number) { + input_shape_protos.resize(conn.port_number + 1); input_shapes.resize(conn.port_number + 1); - shapes.resize(conn.port_number + 1); } - input_shapes.at(conn.port_number) = in_shape; - shapes.at(conn.port_number) = conn.outside_shape; + input_shape_protos.at(conn.port_number) = in_shape; + input_shapes.at(conn.port_number) = conn.outside_shape; string input_node = conn.outside_node_name; int input_port = conn.outside_port; @@ -411,6 +410,8 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, VLOG(1) << "Engine Input " << input_node << ":" << input_port << " -> " << info.engine_name << ":" << inputs.size(); // Skip duplicate inputs. + // TODO(aaroey): use std::find instead. GetEngineInfo already remove + // duplicate connections, so here we should never find any duplicate? bool new_input = true; for (const auto& inp : inputs) { if (inp.node == input_node && inp.index == input_port) { @@ -438,8 +439,8 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, TF_RETURN_IF_ERROR(ConvertGraphDefToEngine( info.segment_graph_def, info.precision_mode == INT8MODE ? FP32MODE : info.precision_mode, - max_batch_size, info.max_workspace_size_bytes, shapes, &trt_logger, - alloc, /*calibrator=*/nullptr, &engine, + max_batch_size, info.max_workspace_size_bytes, input_shapes, + &trt_logger, alloc, /*calibrator=*/nullptr, &engine, /*convert_successfully=*/nullptr)); TrtUniquePtrType engine_data(engine->serialize()); segment_string = @@ -487,8 +488,8 @@ tensorflow::Status CreateTRTNode(tensorflow::Graph* graph, } tensorflow::NodeDef trt_node; tensorflow::Status status = - node_builder.Attr("input_shapes", input_shapes) - .Attr("output_shapes", out_shapes) + node_builder.Attr("input_shapes", input_shape_protos) + .Attr("output_shapes", output_shape_protos) .Attr("static_engine", info.engine_type == EngineInfo::EngineType::TRTStatic) .Attr("segment_funcdef_name", @@ -705,6 +706,7 @@ std::pair GetDeviceAndAllocator( } // Entry function from optimization pass. +// TODO(aaeory): parameter should use pointer type. tensorflow::Status ConvertAfterShapes(ConversionParams& params) { // Convert graphdef to graph. tensorflow::FunctionLibraryDefinition flib(tensorflow::OpRegistry::Global(), @@ -722,8 +724,8 @@ tensorflow::Status ConvertAfterShapes(ConversionParams& params) { segment_options.minimum_segment_size = params.minimum_segment_size; tensorflow::tensorrt::segment::SegmentNodesVector initial_segments; TF_RETURN_IF_ERROR(tensorrt::segment::SegmentGraph( - &graph, IsTensorRTCandidate, IsTensorRTInputCandidate, - IsTensorRTOutputCandidate, segment_options, &initial_segments)); + &graph, IsTensorRTCandidate, InputEdgeValidator(*params.graph_properties), + OutputEdgeValidator(), segment_options, &initial_segments)); if (initial_segments.size() > 1) { VLOG(0) << "MULTIPLE tensorrt candidate conversion: " << initial_segments.size(); diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 8f6656e4ad..c49e26ea4e 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" #include +#include #include #include #include @@ -57,7 +58,6 @@ namespace tensorflow { namespace tensorrt { namespace convert { using ::tensorflow::str_util::Split; - using ::tensorflow::strings::StrAppend; using ::tensorflow::strings::StrCat; @@ -77,11 +77,63 @@ inline tensorflow::Status ConvertDType(tensorflow::DataType tf_dtype, break; default: return tensorflow::errors::InvalidArgument( - "Unsupported data type " + tensorflow::DataTypeString(tf_dtype)); + "Unsupported data type ", tensorflow::DataTypeString(tf_dtype)); } return tensorflow::Status::OK(); } +void GetInputProperties(const grappler::GraphProperties& graph_properties, + const Node* outside_node, const int out_port, + PartialTensorShape* shape, + tensorflow::DataType* dtype) { + if (graph_properties.HasOutputProperties(outside_node->name())) { + auto output_params = + graph_properties.GetOutputProperties(outside_node->name()); + auto out_shape = output_params.at(out_port); + *dtype = out_shape.dtype(); + *shape = out_shape.shape(); + } else { + VLOG(0) << "Unknown output shape" << outside_node->name(); + *dtype = outside_node->output_type(out_port); + } +} + +void GetOutputProperties(const grappler::GraphProperties& graph_properties, + const Node* outside_node, const int in_port, + PartialTensorShape* shape, + tensorflow::DataType* dtype) { + if (graph_properties.HasInputProperties(outside_node->name())) { + auto input_params = + graph_properties.GetInputProperties(outside_node->name()); + auto in_shape = input_params.at(in_port); + *dtype = in_shape.dtype(); + *shape = in_shape.shape(); + } else { + *dtype = outside_node->input_type(in_port); + } +} + +tensorflow::Status ValidateInputProperties(const PartialTensorShape& shape, + const tensorflow::DataType dtype, + nvinfer1::DataType* trt_dtype) { + TF_RETURN_IF_ERROR(ConvertDType(dtype, trt_dtype)); + if (shape.dims() < 0) { + return tensorflow::errors::InvalidArgument( + "Input tensor rank is unknown."); + } + if (shape.dims() > 8) { + return tensorflow::errors::OutOfRange( + "Input tensor rank is greater than 8."); + } + for (int d = 1; d < shape.dims(); ++d) { + if (shape.dim_size(d) < 0) { + return tensorflow::errors::InvalidArgument( + "Input tensor has a unknow non-batch dimemension at dim ", d); + } + } + return Status::OK(); +} + inline nvinfer1::Dims GetTensorShape(const tensorflow::Tensor& tensor) { nvinfer1::Dims dims; dims.nbDims = tensor.dims(); @@ -2177,25 +2229,22 @@ tensorflow::Status ConvertGraphDefToEngine( (node_def.op() == "Placeholder")) { nvinfer1::DimsCHW input_dim_pseudo_chw; for (int i = 0; i < 8; i++) input_dim_pseudo_chw.d[i] = 0; - nvinfer1::DataType dtype(nvinfer1::DataType::kFLOAT); - auto type_status = - ConvertDType(node_def.attr().at("dtype").type(), &dtype); - if (type_status != tensorflow::Status::OK()) { - LOG(WARNING) << "Type conversion failed for " << node_name; - return type_status; - } int32 slot_number = -1; - if (!tensorflow::strings::safe_strto32(node_name.c_str() + 8, - &slot_number)) { - LOG(ERROR) << "Failed to parse slot number from " << node_name - << " +8= " << node_name.c_str() + 8; + if (!tensorflow::strings::safe_strto32( + node_name.c_str() + strlen(kInputPHName), &slot_number)) { + return tensorflow::errors::InvalidArgument( + "Failed to parse slot number from ", node_name); } + nvinfer1::DataType dtype; auto shape = input_shapes.at(slot_number); - if (shape.dims() > 8) { - LOG(ERROR) << "Tensor rank is greater than 8 for " << node_name - << " at input slot " << slot_number; - return tensorflow::errors::OutOfRange( - "Input tensor rank is greater than 8"); + auto status = ValidateInputProperties( + shape, node_def.attr().at("dtype").type(), &dtype); + if (!status.ok()) { + const string error_message = StrCat( + "Validation failed for ", node_name, " and input slot ", + slot_number, ": ", status.error_message()); + LOG(WARNING) << error_message; + return Status(status.code(), error_message); } if (VLOG_IS_ON(1)) { string dim_str("dims="); @@ -2226,10 +2275,10 @@ tensorflow::Status ConvertGraphDefToEngine( } else if (tensorflow::str_util::StartsWith(node_name, kOutputPHName) && (node_def.op() == "Identity")) { int32 slot_number = -1; - if (!tensorflow::strings::safe_strto32(node_name.c_str() + 9, - &slot_number)) { - LOG(ERROR) << "Failed to parse slot number from " << node_name - << " +9=" << node_name.c_str() + 9; + if (!tensorflow::strings::safe_strto32( + node_name.c_str() + strlen(kOutputPHName), &slot_number)) { + return tensorflow::errors::InvalidArgument( + "Failed to parse slot number from ", node_name); } if (output_tensors.size() <= slot_number) { output_tensors.resize(slot_number + 1); @@ -2288,38 +2337,20 @@ tensorflow::Status ConvertSegmentToGraphDef( "Cannot find node with id ", connection.outside_id, " in the graph."); } // Updates the shape and data types of input/output connections. - tensorflow::DataType input_type = tensorflow::DT_FLOAT; + tensorflow::DataType dtype; tensorflow::PartialTensorShape partial_shape; if (connection.is_input_edge) { - if (graph_properties.HasOutputProperties(connection.outside_node_name)) { - auto output_params = - graph_properties.GetOutputProperties(connection.outside_node_name); - auto out_shape = output_params.at(connection.outside_port); - input_type = out_shape.dtype(); - std::vector dims; - partial_shape = out_shape.shape(); - connection.outside_shape = partial_shape; - } else { - VLOG(0) << "Unknown output shape" << outside_node->name(); - input_type = graph->FindNodeId(connection.outside_id) - ->output_type(connection.outside_port); - } - connection.connection_type = input_type; - - } else { // output edge - if (graph_properties.HasInputProperties(connection.outside_node_name)) { - auto input_params = - graph_properties.GetInputProperties(connection.outside_node_name); - auto in_shape = input_params.at(connection.outside_port); - input_type = in_shape.dtype(); - partial_shape = in_shape.shape(); - connection.inside_shape = partial_shape; - } else { - input_type = graph->FindNodeId(connection.inside_id) - ->output_type(connection.outside_port); - } - connection.connection_type = input_type; + GetInputProperties(graph_properties, + graph->FindNodeId(connection.outside_id), + connection.outside_port, &partial_shape, &dtype); + + } else { + GetOutputProperties(graph_properties, + graph->FindNodeId(connection.outside_id), + connection.outside_port, &partial_shape, &dtype); } + connection.outside_shape = partial_shape; + connection.connection_type = dtype; // Add dummy input/output nodes to the segment graphdef. if (connection.is_input_edge) { @@ -2335,7 +2366,7 @@ tensorflow::Status ConvertSegmentToGraphDef( auto seg_node = segment_def->add_node(); tensorflow::NodeDefBuilder builder(node_name, "Placeholder"); auto status = builder.Attr("shape", partial_shape) - .Attr("dtype", input_type) + .Attr("dtype", dtype) .Finalize(seg_node); VLOG(1) << "Constructing input " << node_name << " for the edge " << connection.outside_node_name << ":" << connection.outside_port @@ -2353,7 +2384,7 @@ tensorflow::Status ConvertSegmentToGraphDef( marker_nodes.insert(node_name); auto seg_node = segment_def->add_node(); tensorflow::NodeDefBuilder builder(node_name, "Identity"); - auto status = builder.Input(connection.inside_node_name, 0, input_type) + auto status = builder.Input(connection.inside_node_name, 0, dtype) .Finalize(seg_node); VLOG(1) << "Constructing output " << node_name << " for the edge " << connection.inside_node_name << ":" << connection.inside_port @@ -2391,11 +2422,35 @@ tensorflow::Status ConvertSegmentToGraphDef( return tensorflow::Status::OK(); } -bool IsTensorRTInputCandidate(const tensorflow::Node* node) { +bool InputEdgeValidator::operator()(const tensorflow::Edge* in_edge) const { + if (in_edge->IsControlEdge()) return true; + PartialTensorShape shape; + tensorflow::DataType dtype; + GetInputProperties(graph_properties_, in_edge->src(), in_edge->src_output(), + &shape, &dtype); + nvinfer1::DataType trt_dtype; + Status status = ValidateInputProperties(shape, dtype, &trt_dtype); + if (!status.ok()) { + VLOG(2) << "--> Need to remove input node " << in_edge->dst()->name() + << ": " << status; + return false; + } + if (shape.dims() < 3 && in_edge->src()->type_string() != "Const") { + VLOG(2) << "--> Need to remove input node " << in_edge->dst()->name() + << " which has an input at port " << in_edge->dst_input() + << " with #dim<3 and is not a const: " << shape; + return false; + } return true; } -bool IsTensorRTOutputCandidate(const tensorflow::Node* node) { +bool OutputEdgeValidator::operator()(const tensorflow::Edge* out_edge) const { + if (out_edge->IsControlEdge()) return true; + if (out_edge->src()->type_string() == "Const") { + VLOG(2) << "--> Need to remove output node " << out_edge->src()->name() + << " which is a Const."; + return false; + } return true; } diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 872ba6a080..64337eee84 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -104,6 +104,8 @@ struct EngineInfo { // topological order. // - segment_def: the output GraphDef, whose non-input/output nodedefs will be // sorted in topological order. +// +// TODO(aaroey): add tests to validate these properties. tensorflow::Status ConvertSegmentToGraphDef( const tensorflow::Graph* graph, const tensorflow::grappler::GraphProperties& graph_properties, @@ -128,9 +130,29 @@ tensorflow::Status ConvertGraphDefToEngine( TrtUniquePtrType* engine, bool* convert_successfully); -bool IsTensorRTInputCandidate(const tensorflow::Node* node); +// Helper class for the segmenter to determine whether an input edge to the TRT +// segment is valid. +class InputEdgeValidator { + public: + InputEdgeValidator(const grappler::GraphProperties& graph_properties) + : graph_properties_(graph_properties) {} + + // Return true if the specified edge is eligible to be an input edge of the + // TRT segment. + bool operator()(const tensorflow::Edge* in_edge) const; -bool IsTensorRTOutputCandidate(const tensorflow::Node* node); + private: + const grappler::GraphProperties& graph_properties_; +}; + +// Helper class for the segmenter to determine whether an output edge from the +// TRT segment is valid. +class OutputEdgeValidator { + public: + // Return true if the specified edge is eligible to be an output edge of the + // TRT segment. + bool operator()(const tensorflow::Edge* out_edge) const; +}; } // namespace convert } // namespace tensorrt diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc index 5c0898b29a..92807bed14 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.cc +++ b/tensorflow/contrib/tensorrt/segment/segment.cc @@ -364,8 +364,8 @@ void ContractEdge(SimpleEdge* edge, SimpleGraph* graph, tensorflow::Status SegmentGraph( const tensorflow::Graph* tf_graph, const std::function& candidate_fn, - const std::function& input_candidate_fn, - const std::function& output_candidate_fn, + const std::function& input_candidate_fn, + const std::function& output_candidate_fn, const SegmentOptions& options, SegmentNodesVector* segments) { // Steps: // 1. run the segmentation algorithm to find all the segments, which uses @@ -526,7 +526,7 @@ tensorflow::Status SegmentGraph( for (const tensorflow::Edge* edge : node->in_edges()) { if (!edge->IsControlEdge() && !edge->src()->IsSource() && !segment_nodes.count(edge->src())) { // 'node' is an input node. - if (!input_candidate_fn(node)) { + if (!input_candidate_fn(edge)) { in_nodes_que.push_back(node); added = true; break; @@ -537,7 +537,7 @@ tensorflow::Status SegmentGraph( for (const tensorflow::Edge* edge : node->out_edges()) { if (!edge->dst()->IsSink() && !edge->IsControlEdge() && !segment_nodes.count(edge->dst())) { // 'node' is an output node. - if (!output_candidate_fn(node)) { + if (!output_candidate_fn(edge)) { out_nodes_que.push_back(node); break; } diff --git a/tensorflow/contrib/tensorrt/segment/segment.h b/tensorflow/contrib/tensorrt/segment/segment.h index ab75135054..8c44eb782a 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.h +++ b/tensorflow/contrib/tensorrt/segment/segment.h @@ -52,8 +52,8 @@ struct SegmentOptions { tensorflow::Status SegmentGraph( const tensorflow::Graph* tf_graph, const std::function& candidate_fn, - const std::function& input_candidate_fn, - const std::function& output_candidate_fn, + const std::function& input_candidate_fn, + const std::function& output_candidate_fn, const SegmentOptions& options, SegmentNodesVector* segments); } // namespace segment diff --git a/tensorflow/contrib/tensorrt/segment/segment_test.cc b/tensorflow/contrib/tensorrt/segment/segment_test.cc index a43cf4f416..432e7b1c04 100644 --- a/tensorflow/contrib/tensorrt/segment/segment_test.cc +++ b/tensorflow/contrib/tensorrt/segment/segment_test.cc @@ -41,15 +41,30 @@ class SegmentTest : public ::testing::Test { }; } + std::function MakeInputEdgeCandidateFn( + const std::set& node_names) { + return [node_names](const tensorflow::Edge* in_edge) -> bool { + return node_names.find(in_edge->dst()->name()) != node_names.end(); + }; + } + + std::function MakeOutputEdgeCandidateFn( + const std::set& node_names) { + return [node_names](const tensorflow::Edge* out_edge) -> bool { + return node_names.find(out_edge->src()->name()) != node_names.end(); + }; + } + void RunTest(const tensorflow::Graph* graph, const std::set& candidates, const std::set& input_candidates, const std::set& output_candidates, const std::vector>& expected_segments) { SegmentNodesVector segments; - TF_EXPECT_OK(SegmentGraph( - graph, MakeCandidateFn(candidates), MakeCandidateFn(input_candidates), - MakeCandidateFn(output_candidates), default_options_, &segments)); + TF_EXPECT_OK(SegmentGraph(graph, MakeCandidateFn(candidates), + MakeInputEdgeCandidateFn(input_candidates), + MakeOutputEdgeCandidateFn(output_candidates), + default_options_, &segments)); ValidateSegment(segments, expected_segments); } -- GitLab From 7fe0ae12ec42eca1ea07d93bbd63de394743a018 Mon Sep 17 00:00:00 2001 From: Pradeep Banavara Date: Thu, 12 Jul 2018 21:52:13 -0400 Subject: [PATCH 088/519] Fix: #12686 SoftmaxCrossEntropyWithLogits Committing in a new PR as the old PR has too many commit files --- tensorflow/cc/gradients/nn_grad.cc | 94 ++++++++++++++++++++++--- tensorflow/cc/gradients/nn_grad_test.cc | 29 ++++++-- 2 files changed, 109 insertions(+), 14 deletions(-) diff --git a/tensorflow/cc/gradients/nn_grad.cc b/tensorflow/cc/gradients/nn_grad.cc index c73482d5f4..dc6477e59d 100644 --- a/tensorflow/cc/gradients/nn_grad.cc +++ b/tensorflow/cc/gradients/nn_grad.cc @@ -47,6 +47,81 @@ Status SoftmaxGrad(const Scope& scope, const Operation& op, } REGISTER_GRADIENT_OP("Softmax", SoftmaxGrad); +bool IsZero(const Scope& scope, Output grad) { + std::array zero_op_type_names{{"ZerosLike", "Zeros"}}; + string op_type_name = grad.op().node()->type_string(); + for (auto& zero_op_type_name: zero_op_type_names) { + if (op_type_name == zero_op_type_name) { + return true; + } + } + // the Operation we were provided is not named something obvious + // we need to actually look at its contents. + // the original python code did this by calling a utility function called + // tensor_util.constant_value. When you dig into tensor_tuil.constant_value + // it is a large number of 'if' statements that measure certain edge cases + // where it is possible to get the value of the tensor without actually + // evaluating it. There are many kinds of tensors that can not have this + // done. + // There is no C++ equivalent to tensor_util.constant_value so we do nothing + // for the moment. + return false; +} + +Output BroadcastMul(const Scope& scope, Output vec, Output mat) { + /* Multiply after broadcasting vec to match dimensions of mat. + Args: + vec: A 1-D tensor of dimension [D0] + mat: A 2-D tensor of dimesnion [D0, D1] + + Returns: + A tensor of dimension [D0, D1], the result fo vec * mat + we use an element for element multiply here. + */ + auto reshaped = ExpandDims(scope, vec, -1); + return Multiply(scope, reshaped, mat); +} + +Status SoftmaxCrossEntropyWithLogitsGrad(const Scope& scope, + const Operation& op, + const std::vector& grad_inputs, + std::vector* grad_outputs) { + // Softmax gradient with cross entropy logits function + // We multiply the backprop for cost with the gradients - op.output[1] + // There is no gradient for labels + auto logits = + op.input(0); // the outputs of the network are at + // input index 0. The "truth" labels are at index 1. + auto softmax_grad = op.output(1); + + // The documentation for ops::SoftmaxCrossEntropyWithLogits says + // loss is the output at index 0, and backprop is the output at index 1 + auto grad_loss = grad_inputs[0]; + auto grad_grad = grad_inputs[1]; + + auto grad = BroadcastMul(scope, grad_loss, softmax_grad); + if (!IsZero(scope, grad_grad)) { + std::vector axis; + auto logitsSoftmax = Softmax(scope, logits); + + auto grad_gradExpand = ExpandDims(scope, grad_grad, 1); + auto logitsSoftMaxExpand = ExpandDims(scope, logitsSoftmax, 2); + auto matMulResult = + BatchMatMul(scope, grad_gradExpand, logitsSoftMaxExpand); + axis.push_back(1); + auto squeezeResult = Squeeze(scope, matMulResult, Squeeze::Axis(axis)); + auto subtractionResult = Subtract(scope, grad_grad, squeezeResult); + auto multiplyResult = Multiply(scope, subtractionResult, logitsSoftmax); + grad = Add(scope, grad, multiplyResult); + } + auto minusLogSoftmax = Multiply(scope, LogSoftmax(scope, logits), -1.0f); + grad_outputs->push_back(grad); + grad_outputs->push_back(BroadcastMul(scope, grad_loss, minusLogSoftmax)); + return scope.status(); +} +REGISTER_GRADIENT_OP("SoftmaxCrossEntropyWithLogits", + SoftmaxCrossEntropyWithLogitsGrad); + Status LogSoftmaxGrad(const Scope& scope, const Operation& op, const std::vector& grad_inputs, std::vector* grad_outputs) { @@ -195,9 +270,9 @@ Status MaxPool3DGradHelper(const Scope& scope, const Operation& op, TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); MaxPool3DGrad::Attrs grad_attrs; - auto dx = MaxPool3DGrad(scope, op.input(0), op.output(0), grad_inputs[0], - ksize, strides, padding, - grad_attrs.DataFormat(data_format)); + auto dx = + MaxPool3DGrad(scope, op.input(0), op.output(0), grad_inputs[0], ksize, + strides, padding, grad_attrs.DataFormat(data_format)); grad_outputs->push_back(dx); return scope.status(); } @@ -216,10 +291,9 @@ Status AvgPoolGradHelper(const Scope& scope, const Operation& op, TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); internal::AvgPoolGrad::Attrs grad_attrs; - auto dx = - internal::AvgPoolGrad(scope, Shape(scope, op.input(0)), grad_inputs[0], - ksize, strides, padding, - grad_attrs.DataFormat(data_format)); + auto dx = internal::AvgPoolGrad(scope, Shape(scope, op.input(0)), + grad_inputs[0], ksize, strides, padding, + grad_attrs.DataFormat(data_format)); grad_outputs->push_back(dx); return scope.status(); } @@ -238,9 +312,9 @@ Status AvgPool3DGradHelper(const Scope& scope, const Operation& op, TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "padding", &padding)); TF_RETURN_IF_ERROR(GetNodeAttr(attrs, "data_format", &data_format)); AvgPool3DGrad::Attrs grad_attrs; - auto dx = AvgPool3DGrad(scope, Shape(scope, op.input(0)), grad_inputs[0], - ksize, strides, padding, - grad_attrs.DataFormat(data_format)); + auto dx = + AvgPool3DGrad(scope, Shape(scope, op.input(0)), grad_inputs[0], ksize, + strides, padding, grad_attrs.DataFormat(data_format)); grad_outputs->push_back(dx); return scope.status(); } diff --git a/tensorflow/cc/gradients/nn_grad_test.cc b/tensorflow/cc/gradients/nn_grad_test.cc index b4d457a9d1..f26a7e99e6 100644 --- a/tensorflow/cc/gradients/nn_grad_test.cc +++ b/tensorflow/cc/gradients/nn_grad_test.cc @@ -25,6 +25,8 @@ limitations under the License. namespace tensorflow { namespace { +using ops::AvgPool; +using ops::AvgPool3D; using ops::BiasAdd; using ops::Conv2D; using ops::Elu; @@ -33,11 +35,9 @@ using ops::FractionalMaxPool; 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::MaxPoolV2; using ops::Placeholder; using ops::Relu; using ops::Relu6; @@ -111,6 +111,27 @@ TEST_F(NNGradTest, SoftmaxGrad) { RunTest(x, shape, y, shape); } +TEST_F(NNGradTest, SoftmaxCrossEntropyWithLogitsGrad) { + TensorShape logitsShape( + {5, 3}); // batch size of 5,3 possible labels (classes), + // logits is what is produced by a network + // they are compared to labels which are the truth + TensorShape lossShape( + {5}); // batch size of 5, 1 value for each entry in the batch + // loss is the difference between logits and labels + + auto logits = Placeholder(scope_, DT_FLOAT, + Placeholder::Shape(logitsShape)); // estimation + auto labels = + Placeholder(scope_, DT_FLOAT, Placeholder::Shape(logitsShape)); // truth + auto y = + tensorflow::ops::SoftmaxCrossEntropyWithLogits(scope_, logits, labels); + // Please note the reversal of the backprop and loss orders. A separate issue + // #18734 has been opened for this. + RunTest({logits, labels}, {logitsShape, logitsShape}, {y.backprop, y.loss}, + {logitsShape, lossShape}); +} + TEST_F(NNGradTest, LogSoftmaxGrad) { TensorShape shape({5, 3}); auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(shape)); @@ -253,7 +274,7 @@ TEST_F(NNGradTest, AvgPool3DGradHelper) { RunTest(x, x_shape, y, y_shape); } -TEST_F(NNGradTest, LRN){ +TEST_F(NNGradTest, LRN) { TensorShape x_shape({1, 1, 2, 1}); auto x = Placeholder(scope_, DT_FLOAT, Placeholder::Shape(x_shape)); auto y = LRN(scope_, x); -- GitLab From e3e434d966ba5f800ba73ca688a851aa878c5463 Mon Sep 17 00:00:00 2001 From: Jie Date: Fri, 13 Jul 2018 01:02:12 -0700 Subject: [PATCH 089/519] [tftrt update] Added python tests for converter functions Added BUILD for python tests --- tensorflow/contrib/tensorrt/BUILD | 33 ++++ .../contrib/tensorrt/test/base_unit_test.py | 118 +++++++++++ .../tensorrt/test/batch_matmul_test.py | 97 +++++++++ .../tensorrt/test/biasadd_matmul_test.py | 116 +++++++++++ .../binary_tensor_weight_broadcast_test.py | 148 ++++++++++++++ .../tensorrt/test/concatenation_test.py | 87 +++++++++ .../tensorrt/test/const_broadcast_test.py | 75 +++++++ .../multi_connection_neighbor_engine_test.py | 101 ++++++++++ .../tensorrt/test/neighboring_engine_test.py | 78 ++++++++ tensorflow/contrib/tensorrt/test/run_test.py | 184 ++++++++++++++++++ .../contrib/tensorrt/test/unary_test.py | 125 ++++++++++++ .../contrib/tensorrt/test/unit_tests.py | 67 +++++++ tensorflow/contrib/tensorrt/test/utilities.py | 30 +++ .../tensorrt/test/vgg_block_nchw_test.py | 85 ++++++++ .../contrib/tensorrt/test/vgg_block_test.py | 76 ++++++++ 15 files changed, 1420 insertions(+) create mode 100644 tensorflow/contrib/tensorrt/test/base_unit_test.py create mode 100644 tensorflow/contrib/tensorrt/test/batch_matmul_test.py create mode 100644 tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py create mode 100644 tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py create mode 100644 tensorflow/contrib/tensorrt/test/concatenation_test.py create mode 100644 tensorflow/contrib/tensorrt/test/const_broadcast_test.py create mode 100644 tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py create mode 100644 tensorflow/contrib/tensorrt/test/neighboring_engine_test.py create mode 100644 tensorflow/contrib/tensorrt/test/run_test.py create mode 100644 tensorflow/contrib/tensorrt/test/unary_test.py create mode 100644 tensorflow/contrib/tensorrt/test/unit_tests.py create mode 100644 tensorflow/contrib/tensorrt/test/utilities.py create mode 100644 tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py create mode 100644 tensorflow/contrib/tensorrt/test/vgg_block_test.py diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index adda0b758b..d957ca0861 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -341,6 +341,39 @@ py_test( ], ) +py_test( + name = "converter_unit_tests", + srcs = [ + "test/base_unit_test.py", + "test/batch_matmul_test.py", + "test/biasadd_matmul_test.py", + "test/binary_tensor_weight_broadcast_test.py", + "test/concatenation_test.py", + "test/const_broadcast_test.py", + "test/multi_connection_neighbor_engine_test.py", + "test/neighboring_engine_test.py", + "test/run_test.py", + "test/unary_test.py", + "test/unit_tests.py", + "test/utilities.py", + "test/vgg_block_nchw_test.py", + "test/vgg_block_test.py", + ], + main = "test/unit_tests.py", + srcs_version = "PY2AND3", + tags = [ + "manual", + "notap", + ], + deps = [ + ":init_py", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:layers", + "//tensorflow/python:training", + ], +) + cc_library( name = "utils", hdrs = ["convert/utils.h"], diff --git a/tensorflow/contrib/tensorrt/test/base_unit_test.py b/tensorflow/contrib/tensorrt/test/base_unit_test.py new file mode 100644 index 0000000000..8a6c648ab6 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/base_unit_test.py @@ -0,0 +1,118 @@ +# 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. +# ============================================================================== +"""Base class to facilitate development of integration tests.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + + +class BaseUnitTest(object): + """Base class for unit tests in TF-TRT""" + + def __init__(self, log_file='log.txt'): + self.static_mode_list = {} + self.dynamic_mode_list = {} + self.dummy_input = None + self.get_network = None + self.expect_nb_nodes = None + self.test_name = None + self.log_file = log_file + self.ckpt = None + self.allclose_rtol = 0.01 + self.allclose_atol = 0.01 + self.allclose_equal_nan = True + # saves out graphdef + self.debug = False + # require node count check fail leads to test failure + self.check_node_count = False + + def run(self, run_test_context): + run_test_context.run_test(self.get_network, self.static_mode_list, + self.dynamic_mode_list, self.dummy_input, + self.ckpt) + return self.log_result(run_test_context) + + def log_result(self, run_test_result): + log = open(self.log_file, 'a') + log.write(("================= model: %s\n") % (self.test_name)) + + if self.debug: + open(self.test_name + "_native.pb", + 'wb').write(run_test_result.native_network.SerializeToString()) + all_success = True + if len(run_test_result.tftrt_conversion_flag) != 0: + log.write(" -- static_mode\n") + for static_mode in run_test_result.tftrt_conversion_flag: + if self.debug: + open(self.test_name + "_" + static_mode + ".pb", + 'wb').write(run_test_result.tftrt[static_mode].SerializeToString()) + log.write(" ----\n") + log.write((" mode: [%s]\n") % (static_mode)) + if run_test_result.tftrt_conversion_flag[static_mode]: + if run_test_result.tftrt_nb_nodes[static_mode] != self.expect_nb_nodes: + log.write( + ("[WARNING]: converted node number does not match (%d,%d,%d)!!!\n" + ) % (run_test_result.tftrt_nb_nodes[static_mode], + self.expect_nb_nodes, run_test_result.native_nb_nodes)) + if self.check_node_count: + all_success = False + + if np.array_equal(run_test_result.tftrt_result[static_mode], + run_test_result.native_result): + log.write(" output: equal\n") + elif np.allclose( + run_test_result.tftrt_result[static_mode], + run_test_result.native_result, + atol=self.allclose_atol, + rtol=self.allclose_rtol, + equal_nan=self.allclose_equal_nan): + log.write(" output: allclose\n") + else: + diff = run_test_result.tftrt_result[static_mode] - run_test_result.native_result + log.write("[ERROR]: output does not match!!!\n") + log.write("max diff: " + str(np.max(diff))) + log.write("\ntftrt:\n") + log.write(str(run_test_result.tftrt_result[static_mode])) + log.write("\nnative:\n") + log.write(str(run_test_result.native_result)) + log.write("\ndiff:\n") + log.write(str(diff)) + all_success = False + else: + log.write("[ERROR]: conversion failed!!!\n") + all_success = False + + if len(run_test_result.tftrt_dynamic_conversion_flag) != 0: + log.write(" -- dynamic_mode\n") + for dynamic_mode in run_test_result.tftrt_dynamic_conversion_flag: + log.write("\n ----\n") + log.write((" mode: [%s]\n") % (dynamic_mode)) + if run_test_result.tftrt_dynamic_conversion_flag[dynamic_mode]: + if np.array_equal(run_test_result.tftrt_dynamic_result[dynamic_mode], + run_test_result.native_result): + log.write(" output: equal\n") + elif np.allclose(run_test_result.tftrt_dynamic_result[dynamic_mode], + run_test_result.native_result): + log.write(" output: allclose\n") + else: + log.write("[ERROR]: output does not match!!!\n") + all_success = False + else: + log.write("[ERROR]: conversion failed!!!\n") + all_success = False + return all_success diff --git a/tensorflow/contrib/tensorrt/test/batch_matmul_test.py b/tensorflow/contrib/tensorrt/test/batch_matmul_test.py new file mode 100644 index 0000000000..3c83a3a562 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/batch_matmul_test.py @@ -0,0 +1,97 @@ +# 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. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session +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 variable_scope +from tensorflow.python.ops import variables +from tensorflow.python.ops import init_ops +from tensorflow.python.ops import gen_array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.training import training +from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest +from tensorflow.contrib.tensorrt.test.utilities import get_all_variables + + +class BatchMatMulTest(BaseUnitTest): + """Testing BatchMatMul in TF-TRT conversion""" + + def __init__(self, log_file='log.txt'): + super(BatchMatMulTest, self).__init__() + self.static_mode_list = {"FP32", "FP16"} + self.debug = True + self.dynamic_mode_list = {} + self.inp_dims = (12, 5, 8, 12) + self.dummy_input = np.random.random_sample(self.inp_dims) + self.get_network = self.matmul_test + self.expect_nb_nodes = 16 + self.log_file = log_file + self.test_name = self.__class__.__name__ + self.ckpt = "./tmp.ckpt" + sess = session.Session() + + def matmul_test(self): + g = ops.Graph() + gpu_options = config_pb2.GPUOptions() + sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) + with g.as_default(): + x = array_ops.placeholder( + dtype=dtypes.float32, shape=self.inp_dims, name="input") + + b = constant_op.constant( + np.random.randn(12, 5, 12, 7), dtype=dtypes.float32) + x1 = math_ops.matmul(x, b) + b = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtypes.float32) + x1 = x1 + b + + var = variable_scope.get_variable( + "test", [12, 5, 12, 7], + dtype=dtypes.float32, + initializer=init_ops.truncated_normal_initializer) + x2 = math_ops.matmul(x, var) + b = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtypes.float32) + x2 = x2 * b + + var = variable_scope.get_variable( + "test2", [12, 84], + dtype=dtypes.float32, + initializer=init_ops.truncated_normal_initializer) + c = gen_array_ops.reshape(x, [12, 40, 12]) + b = gen_array_ops.reshape(var, [12, 12, 7]) + x3 = math_ops.matmul(c, b) + b = constant_op.constant(np.random.randn(40, 1), dtype=dtypes.float32) + x3 = x3 + b + x3 = gen_array_ops.reshape(x3, [12, 5, 8, 7]) + + out = x3 + x1 + array_ops.squeeze(out, name="output") + + with session.Session(config=sessconfig, graph=g) as sess: + names_var_list = get_all_variables(sess) + saver = training.Saver(names_var_list) + sess.run(variables.global_variables_initializer()) + saver.save(sess, self.ckpt) + return g.as_graph_def() diff --git a/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py b/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py new file mode 100644 index 0000000000..1ac6f5cb6a --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py @@ -0,0 +1,116 @@ +# 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. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.core.protobuf import config_pb2 +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 nn +from tensorflow.python.ops import gen_array_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.layers import core +from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest + + +class BiasaddMatMulTest(BaseUnitTest): + """Testing BiasAdd MatMul in TF-TRT conversion""" + + def __init__(self, log_file='log.txt'): + super(BiasaddMatMulTest, self).__init__() + self.static_mode_list = {"FP32", "FP16"} + self.debug = True + self.dynamic_mode_list = {} + self.inp_dims = (48, 12) + self.dummy_input = np.random.random_sample(self.inp_dims) + self.get_network = self.matmul_test + self.expect_nb_nodes = 53 + self.log_file = log_file + self.test_name = self.__class__.__name__ + + def matmul_test(self): + g = ops.Graph() + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) + with g.as_default(): + x = array_ops.placeholder( + dtype=dtypes.float32, shape=self.inp_dims, name="input") + + b = constant_op.constant(np.random.randn(12, 4), dtype=dtypes.float32) + x1 = math_ops.matmul(x, b) + b = constant_op.constant(np.random.randn(1, 4), dtype=dtypes.float32) + x1 = x1 + b + + b = constant_op.constant(np.random.randn(48, 4), dtype=dtypes.float32) + x2 = math_ops.matmul(x, b, transpose_a=True) + x2 = gen_array_ops.reshape(x2, [48, 1]) + + b = constant_op.constant(np.random.randn(4, 12), dtype=dtypes.float32) + x3 = math_ops.matmul(x, b, transpose_b=True) + + b = constant_op.constant(np.random.randn(16, 48), dtype=dtypes.float32) + x4 = math_ops.matmul(x, b, transpose_b=True, transpose_a=True) + x4 = gen_array_ops.reshape(x4, [48, 4]) + + x5 = gen_array_ops.reshape(x, [4, 12, 12]) + x5 = core.flatten(x5) + b = constant_op.constant(np.random.randn(144, 48), dtype=dtypes.float32) + x5 = math_ops.matmul(x5, b) + b = constant_op.constant(np.random.randn(48), dtype=dtypes.float32) + x5 = nn.bias_add(x5, b) + x5 = gen_array_ops.reshape(x5, [48, 4]) + + x6 = gen_array_ops.reshape(x, [4, 12, 12]) + b = constant_op.constant(np.random.randn(12), dtype=dtypes.float32) + x6 = nn.bias_add(x6, b, data_format="NHWC") + x6 = gen_array_ops.reshape(x6, [48, -1]) + + x7 = gen_array_ops.reshape(x, [4, 12, 3, 4]) + b = constant_op.constant(np.random.randn(4), dtype=dtypes.float32) + x7 = nn.bias_add(x7, b, data_format="NHWC") + x7 = gen_array_ops.reshape(x7, [48, -1]) + + x8 = gen_array_ops.reshape(x, [4, 12, 3, 2, 2]) + b = constant_op.constant(np.random.randn(2), dtype=dtypes.float32) + x8 = nn.bias_add(x8, b, data_format="NHWC") + x8 = gen_array_ops.reshape(x8, [48, -1]) + + x9 = gen_array_ops.reshape(x, [4, 12, 3, 2, 2]) + b = constant_op.constant(np.random.randn(3), dtype=dtypes.float32) + x9 = nn.bias_add(x9, b, data_format="NCHW") + x9 = gen_array_ops.reshape(x9, [48, -1]) + + x10 = gen_array_ops.reshape(x, [4, 12, 3, 4]) + b = constant_op.constant(np.random.randn(12), dtype=dtypes.float32) + x10 = nn.bias_add(x10, b, data_format="NCHW") + x10 = gen_array_ops.reshape(x10, [48, -1]) + + x11 = gen_array_ops.reshape(x, [4, 12, 12]) + b = constant_op.constant(np.random.randn(4), dtype=dtypes.float32) + x11 = nn.bias_add(x11, b, data_format="NCHW") + x11 = gen_array_ops.reshape(x11, [48, -1]) + + out = array_ops.concat( + [x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11], axis=-1) + out = array_ops.squeeze(out, name="output") + + return g.as_graph_def() diff --git a/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py b/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py new file mode 100644 index 0000000000..5233a493d0 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py @@ -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. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.core.protobuf import config_pb2 +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 gen_array_ops +from tensorflow.python.ops import math_ops +from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest + + +class BinaryTensorWeightBroadcastTest(BaseUnitTest): + """unit tests for scale & elementwise layers in TF-TRT""" + + def __init__(self, log_file='log.txt'): + super(BinaryTensorWeightBroadcastTest, self).__init__() + self.static_mode_list = {"FP32", "FP16"} + self.debug = True + self.dynamic_mode_list = {} + self.inp_dims = (10, 24, 24, 20) + self.dummy_input = np.random.random_sample(self.inp_dims) + self.get_network = self.get_simple_graph_def + self.expect_nb_nodes = 35 + self.log_file = log_file + self.test_name = self.__class__.__name__ + self.allclose_rtol = 0.1 + self.allclose_atol = 0.05 + + def get_simple_graph_def(self): + g = ops.Graph() + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) + with g.as_default(): + x = array_ops.placeholder( + dtype=dtypes.float32, shape=self.inp_dims, name="input") + + # scale + a = constant_op.constant(np.random.randn(1), dtype=dtypes.float32) + f = x + a + x = math_ops.sigmoid(f) + + # scale + a = constant_op.constant(np.random.randn(1), dtype=dtypes.float32) + f = a + x + x = math_ops.sigmoid(f) + + # scale + a = constant_op.constant(np.random.randn(24, 1, 1), dtype=dtypes.float32) + f = x + a + x = math_ops.sigmoid(f) + + # scale + a = constant_op.constant(np.random.randn(24, 1, 1), dtype=dtypes.float32) + f = a + x + x = math_ops.sigmoid(f) + + # scale + a = constant_op.constant( + np.random.randn(24, 24, 20), dtype=dtypes.float32) + f = a + x + x = math_ops.sigmoid(f) + + # scale + a = constant_op.constant( + np.random.randn(24, 24, 20), dtype=dtypes.float32) + f = x + a + x = math_ops.sigmoid(f) + + # elementwise + a = constant_op.constant(np.random.randn(20), dtype=dtypes.float32) + f = x + a + x = math_ops.sigmoid(f) + + # elementwise + a = constant_op.constant(np.random.randn(20), dtype=dtypes.float32) + f = a + x + x = math_ops.sigmoid(f) + + # elementwise + a = constant_op.constant( + np.random.randn(1, 24, 1, 1), dtype=dtypes.float32) + f = a + x + x = math_ops.sigmoid(f) + + # elementwise + a = constant_op.constant( + np.random.randn(1, 24, 1, 1), dtype=dtypes.float32) + f = x + a + x = math_ops.sigmoid(f) + + # elementwise + a = constant_op.constant( + np.random.randn(1, 24, 24, 1), dtype=dtypes.float32) + f = a + x + x = math_ops.sigmoid(f) + + # elementwise + a = constant_op.constant( + np.random.randn(1, 24, 24, 1), dtype=dtypes.float32) + f = x + a + x = math_ops.sigmoid(f) + + # elementwise + a = constant_op.constant( + np.random.randn(1, 24, 24, 20), dtype=dtypes.float32) + f = a + x + x = math_ops.sigmoid(f) + + # elementwise + a = constant_op.constant( + np.random.randn(1, 24, 24, 20), dtype=dtypes.float32) + f = x + a + x = math_ops.sigmoid(f) + + # elementwise + a = constant_op.constant(np.random.randn(24, 20), dtype=dtypes.float32) + f = a + x + x = math_ops.sigmoid(f) + + # elementwise + a = constant_op.constant(np.random.randn(24, 20), dtype=dtypes.float32) + f = x + a + x = math_ops.sigmoid(f) + + gen_array_ops.reshape(x, [5, -1], name="output") + + return g.as_graph_def() diff --git a/tensorflow/contrib/tensorrt/test/concatenation_test.py b/tensorflow/contrib/tensorrt/test/concatenation_test.py new file mode 100644 index 0000000000..de0817d2e8 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/concatenation_test.py @@ -0,0 +1,87 @@ +# 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. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.core.protobuf import config_pb2 +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 gen_array_ops +from tensorflow.python.ops import gen_math_ops +from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest + + +class ConcatenationTest(BaseUnitTest): + """Testing Concatenation in TF-TRT conversion""" + + def __init__(self, log_file='log.txt'): + super(ConcatenationTest, self).__init__() + self.static_mode_list = {"FP32", "FP16"} + self.debug = True + self.dynamic_mode_list = {} + self.inp_dims = (2, 3, 3, 1) + self.dummy_input = np.random.random_sample(self.inp_dims) + self.get_network = self.get_simple_graph_def + self.expect_nb_nodes = 4 + self.log_file = log_file + self.test_name = self.__class__.__name__ + + def get_simple_graph_def(self): + g = ops.Graph() + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) + with g.as_default(): + x = array_ops.placeholder( + dtype=dtypes.float32, shape=self.inp_dims, name="input") + + # scale + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtypes.float32) + r1 = x / a + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtypes.float32) + r2 = a / x + a = constant_op.constant(np.random.randn(1, 3, 1), dtype=dtypes.float32) + r3 = a + x + a = constant_op.constant(np.random.randn(1, 3, 1), dtype=dtypes.float32) + r4 = x * a + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtypes.float32) + r5 = x - a + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtypes.float32) + r6 = a - x + a = constant_op.constant(np.random.randn(3, 1), dtype=dtypes.float32) + r7 = x - a + a = constant_op.constant(np.random.randn(3, 1), dtype=dtypes.float32) + r8 = a - x + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtypes.float32) + r9 = gen_math_ops.maximum(x, a) + a = constant_op.constant(np.random.randn(3, 1), dtype=dtypes.float32) + r10 = gen_math_ops.minimum(a, x) + a = constant_op.constant(np.random.randn(3), dtype=dtypes.float32) + r11 = x * a + a = constant_op.constant(np.random.randn(1), dtype=dtypes.float32) + r12 = a * x + concat1 = array_ops.concat([r1, r2, r3, r4, r5, r6], axis=-1) + concat2 = array_ops.concat([r7, r8, r9, r10, r11, r12], axis=3) + x = array_ops.concat([concat1, concat2], axis=-1) + + gen_array_ops.reshape(x, [2, -1], name="output") + + return g.as_graph_def() diff --git a/tensorflow/contrib/tensorrt/test/const_broadcast_test.py b/tensorflow/contrib/tensorrt/test/const_broadcast_test.py new file mode 100644 index 0000000000..74d39d9015 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/const_broadcast_test.py @@ -0,0 +1,75 @@ +# 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 + +from tensorflow.core.protobuf import config_pb2 +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 nn +from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest + + +class ConstBroadcastTest(BaseUnitTest): + """Testing Constant broadcasting in TF-TRT""" + + def __init__(self, log_file='log.txt'): + super(ConstBroadcastTest, self).__init__() + self.static_mode_list = {"FP32", "FP16"} + self.debug = True + self.dynamic_mode_list = {} + self.inp_dims = (5, 12, 12, 2) + self.dummy_input = np.random.random_sample(self.inp_dims) + self.get_network = self.conv_broadcast + self.expect_nb_nodes = 7 + self.log_file = log_file + self.test_name = self.__class__.__name__ + self.allclose_rtol = 0.05 + self.allclose_atol = 0.05 + + def conv_broadcast(self): + g = ops.Graph() + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) + with g.as_default(): + x = array_ops.placeholder( + dtype=dtypes.float32, shape=self.inp_dims, name="input") + filt1 = constant_op.constant( + 1, shape=(3, 3, 2, 1), dtype=dtypes.float32, name='filt1') + y1 = nn.conv2d(x, filt1, strides=[1, 1, 1, 1], padding='SAME', name='y1') + z1 = nn.relu(y1, name='z1') + filt2 = constant_op.constant( + np.random.randn(9), + shape=(3, 3, 1, 1), + dtype=dtypes.float32, + name='filt2') + y2 = nn.conv2d(z1, filt2, strides=[1, 1, 1, 1], padding='SAME', name='y2') + z2 = nn.relu(y2, name='z') + filt3 = constant_op.constant( + np.random.randn(3, 3, 1, 1), + shape=(3, 3, 1, 1), + dtype=dtypes.float32, + name='filt3') + y3 = nn.conv2d(z2, filt3, strides=[1, 1, 1, 1], padding='SAME', name='y3') + z = nn.relu(y3, name='output') + + return g.as_graph_def() diff --git a/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py b/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py new file mode 100644 index 0000000000..291b4d16c1 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py @@ -0,0 +1,101 @@ +# 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. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.core.protobuf import config_pb2 +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 nn +from tensorflow.python.ops import gen_math_ops +from tensorflow.python.ops import math_ops +from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest + + +class MultiConnectionNeighborEngineTest(BaseUnitTest): + """Multi connection neighboring nodes wiring tests in TF-TRT""" + + def __init__(self, log_file='log.txt'): + super(MultiConnectionNeighborEngineTest, self).__init__() + self.static_mode_list = {"FP32", "FP16"} + self.debug = True + self.dynamic_mode_list = {} + self.inp_dims = (2, 3, 7, 5) + self.dummy_input = np.random.normal(1.0, 0.5, self.inp_dims) + self.get_network = self.neighboring_tensor_test + self.expect_nb_nodes = 7 + self.log_file = log_file + self.test_name = self.__class__.__name__ + self.allclose_rtol = 0.05 + self.allclose_atol = 0.05 + + def neighboring_tensor_test(self): + g = ops.Graph() + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) + with g.as_default(): + x = array_ops.placeholder( + dtype=dtypes.float32, shape=self.inp_dims, name="input") + e = constant_op.constant( + np.random.normal(.05, .005, [3, 2, 3, 4]), + name="weights", + dtype=dtypes.float32) + conv = nn.conv2d( + input=x, + filter=e, + data_format="NCHW", + strides=[1, 1, 1, 1], + padding="VALID", + name="conv") + b = constant_op.constant( + np.random.normal(2.0, 1.0, [1, 4, 1, 1]), + name="bias", + dtype=dtypes.float32) + t = conv + b + + b = constant_op.constant( + np.random.normal(5.0, 1.0, [1, 4, 1, 1]), + name="bias", + dtype=dtypes.float32) + q = conv - b + edge = math_ops.sigmoid(q) + + b = constant_op.constant( + np.random.normal(5.0, 1.0, [1, 4, 1, 1]), + name="bias", + dtype=dtypes.float32) + d = b + conv + edge3 = math_ops.sigmoid(d) + + c = constant_op.constant( + np.random.normal(1.0, 1.0, [1, 4, 1, 1]), + name="bias", + dtype=dtypes.float32) + edge1 = gen_math_ops.tan(conv) + t = t - edge1 + q = q + edge + t = t + q + t = t + d + t = t - edge3 + array_ops.squeeze(t, name="output") + + return g.as_graph_def() diff --git a/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py b/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py new file mode 100644 index 0000000000..f916db3504 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py @@ -0,0 +1,78 @@ +# 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. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.core.protobuf import config_pb2 +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 nn +from tensorflow.python.ops import gen_math_ops +from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest + + +class NeighboringEngineTest(BaseUnitTest): + """Neighboring node wiring tests in TF-TRT conversion""" + + def __init__(self, log_file='log.txt'): + super(NeighboringEngineTest, self).__init__() + self.static_mode_list = {"FP32", "FP16"} + self.debug = True + self.dynamic_mode_list = {} + self.inp_dims = (2, 3, 7, 5) + self.dummy_input = np.random.random_sample(self.inp_dims) + self.get_network = self.neighboring_tensor_test + self.expect_nb_nodes = 5 + self.log_file = log_file + self.test_name = self.__class__.__name__ + self.allclose_rtol = 0.05 + self.allclose_atol = 0.05 + + def neighboring_tensor_test(self): + g = ops.Graph() + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) + with g.as_default(): + x = array_ops.placeholder( + dtype=dtypes.float32, shape=self.inp_dims, name="input") + e = constant_op.constant( + np.random.normal(.3, 0.05, [3, 2, 3, 4]), + name="weights", + dtype=dtypes.float32) + conv = nn.conv2d( + input=x, + filter=e, + data_format="NCHW", + strides=[1, 1, 1, 1], + padding="VALID", + name="conv") + b = constant_op.constant( + np.random.normal(1.0, 1.0, [1, 4, 1, 1]), + name="bias", + dtype=dtypes.float32) + t = conv * b + + e = gen_math_ops.tan(conv) + t = t - e + array_ops.squeeze(t, name="output") + + return g.as_graph_def() diff --git a/tensorflow/contrib/tensorrt/test/run_test.py b/tensorflow/contrib/tensorrt/test/run_test.py new file mode 100644 index 0000000000..4d109cc378 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/run_test.py @@ -0,0 +1,184 @@ +# 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 convert and execute TF-TensorRT graph.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib import tensorrt as trt +from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 +from tensorflow.python.client import session +from tensorflow.python.framework import importer +from tensorflow.python.framework import ops +from tensorflow.python.training import training +from tensorflow.contrib.tensorrt.test.utilities import get_all_variables + +OUTPUT_NODE = "output" +INPUT_NODE = "input" +CALIB_COUNT = 5 # calibration iteration + + +class RunTest: + """base class to run TR-TRT conversion and execution""" + + def __init__(self): + self.clean() + + def __enter__(self): + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + self.clean() + + def clean(self): + self.tftrt = {} + self.tftrt_conversion_flag = {} + self.tftrt_nb_nodes = {} + self.tftrt_result = {} + self.tftrt_dynamic_conversion_flag = {} + self.tftrt_dynamic_result = {} + self.check_file = None + self.native_network = None + + def run_test(self, + network, + static_mode_list, + dynamic_mode_list, + dummy_input, + file_name=None): + self.native_network = network() + success = True + initialization = False + if file_name != None: + initialization = True + self.check_file = file_name + self.native_result, self.native_nb_nodes = self.execute_graph( + self.native_network, dummy_input, initialization) + for mode in static_mode_list: + try: + self.run_static_convert_network(mode, dummy_input, initialization) + self.tftrt_conversion_flag[mode] = True + except Exception as inst: + self.tftrt_conversion_flag[mode] = False + success = False + for mode in dynamic_mode_list: + try: + self.run_dynamic_convert_network(mode, dummy_input, initialization) + self.tftrt_dynamic_conversion_flag[mode] = True + except Exception as inst: + self.tftrt_dynamic_conversion_flag[mode] = False + success = False + return success + + def run_dynamic_convert_network(self, mode, dummy_input, initialization=True): + inp_dims = dummy_input.shape + if mode == "FP32" or mode == "FP16": + opt_config = rewriter_config_pb2.RewriterConfig() + opt_config.optimizers.extend(["constfold", "layout"]) + custom_op = opt_config.custom_optimizers.add() + custom_op.name = "TensorRTOptimizer" + custom_op.parameter_map["minimum_segment_size"].i = 3 + custom_op.parameter_map["precision_mode"].s = mode + custom_op.parameter_map["max_batch_size"].i = inp_dims[0] + custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 + print(custom_op) + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + graph_options = config_pb2.GraphOptions(rewrite_options=opt_config) + sessconfig = config_pb2.ConfigProto( + gpu_options=gpu_options, graph_options=graph_options) + print(sessconfig) + g = ops.Graph() + ops.reset_default_graph() + with g.as_default(): + inp, out = importer.import_graph_def( + graph_def=self.native_network, return_elements=["input", "output"]) + inp = inp.outputs[0] + out = out.outputs[0] + with session.Session(config=sessconfig, graph=g) as sess: + if (initialization): + names_var_list = get_all_variables(sess) + saver = training.Saver(names_var_list) + saver.restore(sess, self.check_file) + self.tftrt_dynamic_result[mode] = sess.run(out, {inp: dummy_input}) + else: + raise Exception("dynamic op mode: " + mode + " not supported") + + def run_static_convert_network(self, mode, dummy_input, initialization=True): + inp_dims = dummy_input.shape + if mode == "FP32" or mode == "FP16" or mode == "INT8": + trt_graph = trt.create_inference_graph( + input_graph_def=self.native_network, + outputs=[OUTPUT_NODE], + max_batch_size=inp_dims[0], + max_workspace_size_bytes=1 << 25, + precision_mode=mode, # TRT Engine precision "FP32","FP16" or "INT8" + minimum_segment_size=2 # minimum number of nodes in an engine + ) + if mode == "INT8": + _ = self.execute_calibration(trt_graph, dummy_input, initialization) + trt_graph = trt.calib_graph_to_infer_graph(trt_graph) + trt_result, nb_nodes = self.execute_graph(trt_graph, dummy_input, + initialization) + self.tftrt[mode] = trt_graph + self.tftrt_nb_nodes[mode] = nb_nodes + self.tftrt_result[mode] = trt_result + else: + raise Exception("mode: " + mode + " not supported") + + def execute_graph(self, gdef, dummy_input, initialization=True): + """Run given graphdef once.""" + gpu_options = config_pb2.GPUOptions() + sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) + ops.reset_default_graph() + g = ops.Graph() + nb_nodes = 0 + with g.as_default(): + inp, out = importer.import_graph_def( + graph_def=gdef, return_elements=[INPUT_NODE, OUTPUT_NODE], name="") + nb_nodes = len(g.get_operations()) + inp = inp.outputs[0] + out = out.outputs[0] + with session.Session(config=sessconfig, graph=g) as sess: + if (initialization): + names_var_list = get_all_variables(sess) + saver = training.Saver(names_var_list) + saver.restore(sess, self.check_file) + val = sess.run(out, {inp: dummy_input}) + return val, nb_nodes + + # Use real data that is representative of the inference dataset + # for calibration. For this test script it is random data. + def execute_calibration(self, gdef, dummy_input, initialization=True): + """Run given calibration graph multiple times.""" + gpu_options = config_pb2.GPUOptions() + ops.reset_default_graph() + g = ops.Graph() + with g.as_default(): + inp, out = importer.import_graph_def( + graph_def=gdef, return_elements=[INPUT_NODE, OUTPUT_NODE], name="") + inp = inp.outputs[0] + out = out.outputs[0] + with session.Session( + config=config_pb2.ConfigProto(gpu_options=gpu_options), + graph=g) as sess: + if (initialization): + names_var_list = get_all_variables(sess) + saver = training.Saver(names_var_list) + saver.restore(sess, self.check_file) + for _ in range(CALIB_COUNT): + val = sess.run(out, {inp: dummy_input}) + return val diff --git a/tensorflow/contrib/tensorrt/test/unary_test.py b/tensorflow/contrib/tensorrt/test/unary_test.py new file mode 100644 index 0000000000..a054939ce2 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/unary_test.py @@ -0,0 +1,125 @@ +# 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. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session +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 variable_scope +from tensorflow.python.ops import variables +from tensorflow.python.ops import init_ops +from tensorflow.python.ops import gen_array_ops +from tensorflow.python.ops import gen_math_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.training import training +from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest +from tensorflow.contrib.tensorrt.test.utilities import get_all_variables + + +class UnaryTest(BaseUnitTest): + """Unit tests for unary operations in TF-TRT""" + + def __init__(self, log_file='log.txt'): + super(UnaryTest, self).__init__() + self.static_mode_list = {"FP32", "FP16"} + self.debug = True + self.dynamic_mode_list = {} + self.inp_dims = (12, 5, 8, 1, 1, 12) + self.dummy_input = np.random.random_sample(self.inp_dims) + self.get_network = self.unary_test + self.expect_nb_nodes = 17 + self.log_file = log_file + self.test_name = self.__class__.__name__ + self.ckpt = "./tmp.ckpt" + + def unary_test(self): + g = ops.Graph() + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) + with g.as_default(): + x = array_ops.placeholder( + dtype=dtypes.float32, shape=self.inp_dims, name="input") + q = math_ops.abs(x) + q = q + 1.0 + q = gen_math_ops.exp(q) + q = gen_math_ops.log(q) + q = array_ops.squeeze(q, axis=-2) + q = math_ops.abs(q) + q = q + 2.2 + q = gen_math_ops.sqrt(q) + q = gen_math_ops.rsqrt(q) + q = math_ops.negative(q) + q = array_ops.squeeze(q, axis=3) + q = math_ops.abs(q) + q = q + 3.0 + a = gen_math_ops.reciprocal(q) + + x = constant_op.constant(np.random.randn(5, 8, 12), dtype=dtypes.float32) + q = math_ops.abs(x) + q = q + 2.0 + q = gen_math_ops.exp(q) + q = gen_math_ops.log(q) + q = math_ops.abs(q) + q = q + 2.1 + q = gen_math_ops.sqrt(q) + q = gen_math_ops.rsqrt(q) + q = math_ops.negative(q) + q = math_ops.abs(q) + q = q + 4.0 + b = gen_math_ops.reciprocal(q) + + # TODO(jie): this one will break, broadcasting on batch. + x = variable_scope.get_variable( + "test", [12, 40, 12], + dtype=dtypes.float32, + initializer=init_ops.truncated_normal_initializer) + x = gen_array_ops.reshape(x, [12, 5, 8, 1, 12, 1, 1]) + q = math_ops.abs(x) + q = q + 5.0 + q = gen_math_ops.exp(q) + q = array_ops.squeeze(q, axis=[-1, -2, 3]) + q = gen_math_ops.log(q) + q = math_ops.abs(q) + q = q + 5.1 + q = gen_array_ops.reshape(q, [12, 5, 1, 1, 8, 1, 12]) + q = array_ops.squeeze(q, axis=[5, 2, 3]) + q = gen_math_ops.sqrt(q) + q = math_ops.abs(q) + q = q + 5.2 + q = gen_math_ops.rsqrt(q) + q = math_ops.negative(q) + q = math_ops.abs(q) + q = q + 5.3 + c = gen_math_ops.reciprocal(q) + + q = a * b + q = q / c + array_ops.squeeze(q, name="output") + + with session.Session(config=sessconfig, graph=g) as sess: + names_var_list = get_all_variables(sess) + saver = training.Saver(names_var_list) + sess.run(variables.global_variables_initializer()) + saver.save(sess, self.ckpt) + return g.as_graph_def() diff --git a/tensorflow/contrib/tensorrt/test/unit_tests.py b/tensorflow/contrib/tensorrt/test/unit_tests.py new file mode 100644 index 0000000000..ac6e3b13ee --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/unit_tests.py @@ -0,0 +1,67 @@ +# 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 execute and log all integration tests.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.tensorrt.test.batch_matmul_test import BatchMatMulTest +from tensorflow.contrib.tensorrt.test.biasadd_matmul_test import BiasaddMatMulTest +from tensorflow.contrib.tensorrt.test.binary_tensor_weight_broadcast_test import BinaryTensorWeightBroadcastTest +from tensorflow.contrib.tensorrt.test.concatenation_test import ConcatenationTest +from tensorflow.contrib.tensorrt.test.multi_connection_neighbor_engine_test import MultiConnectionNeighborEngineTest +from tensorflow.contrib.tensorrt.test.neighboring_engine_test import NeighboringEngineTest +from tensorflow.contrib.tensorrt.test.unary_test import UnaryTest +from tensorflow.contrib.tensorrt.test.vgg_block_nchw_test import VGGBlockNCHWTest +from tensorflow.contrib.tensorrt.test.vgg_block_test import VGGBlockTest +from tensorflow.contrib.tensorrt.test.const_broadcast_test import ConstBroadcastTest + +from tensorflow.contrib.tensorrt.test.run_test import RunTest + +tests = 0 +passed_test = 0 + +failed_list = [] +test_list = [] + +test_list.append(BatchMatMulTest()) +test_list.append(BiasaddMatMulTest()) +test_list.append(BinaryTensorWeightBroadcastTest()) +test_list.append(ConcatenationTest()) +test_list.append(NeighboringEngineTest()) +test_list.append(UnaryTest()) +test_list.append(VGGBlockNCHWTest()) +test_list.append(VGGBlockTest()) +test_list.append(MultiConnectionNeighborEngineTest()) +test_list.append(ConstBroadcastTest()) + +for test in test_list: + test.debug = True + test.check_node_count = False + with RunTest() as context: + tests += 1 + if test.run(context): + passed_test += 1 + else: + failed_list.append(test.test_name) + print("Failed test: %s\n", test.test_name) + +if passed_test == tests: + print("Passed\n") +else: + print(("%d out of %d passed\n -- failed list:") % (passed_test, tests)) + for test in failed_list: + print(" - " + test) diff --git a/tensorflow/contrib/tensorrt/test/utilities.py b/tensorflow/contrib/tensorrt/test/utilities.py new file mode 100644 index 0000000000..0ea5f5b883 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/utilities.py @@ -0,0 +1,30 @@ +# 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 script for TF-TensorRT integration tests.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.ops import variables + + +def get_all_variables(sess): + var_names = sess.run(variables.report_uninitialized_variables()) + names_var_list = {} + for name in var_names: + names_var_list[name] = sess.graph.get_tensor_by_name(name + ":0") + print(var_names) + return names_var_list diff --git a/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py b/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py new file mode 100644 index 0000000000..9a759eb994 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py @@ -0,0 +1,85 @@ +# 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. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.core.protobuf import config_pb2 +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 nn +from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import nn_impl +from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest + + +class VGGBlockNCHWTest(BaseUnitTest): + """single vgg layer in NCHW unit tests in TF-TRT""" + + def __init__(self, log_file='log.txt'): + super(VGGBlockNCHWTest, self).__init__() + self.static_mode_list = {"FP32", "FP16"} + self.debug = True + self.dynamic_mode_list = {} + self.inp_dims = (5, 2, 8, 8) + self.dummy_input = np.random.random_sample(self.inp_dims) + self.get_network = self.get_simple_graph_def + self.expect_nb_nodes = 3 + self.log_file = log_file + self.test_name = self.__class__.__name__ + + def get_simple_graph_def(self): + g = ops.Graph() + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) + with g.as_default(): + x = array_ops.placeholder( + dtype=dtypes.float32, shape=self.inp_dims, name="input") + x, mean_x, var_x = nn_impl.fused_batch_norm( + x, + np.random.randn(2).astype(np.float32), + np.random.randn(2).astype(np.float32), + mean=np.random.randn(2).astype(np.float32), + variance=np.random.randn(2).astype(np.float32), + data_format="NCHW", + is_training=False) + e = constant_op.constant( + np.random.randn(1, 1, 2, 6), name="weights", dtype=dtypes.float32) + conv = nn.conv2d( + input=x, + filter=e, + data_format="NCHW", + strides=[1, 1, 2, 2], + padding="SAME", + name="conv") + b = constant_op.constant( + np.random.randn(6), name="bias", dtype=dtypes.float32) + t = nn.bias_add(conv, b, data_format="NCHW", name="biasAdd") + relu = nn.relu(t, "relu") + idty = array_ops.identity(relu, "ID") + v = nn_ops.max_pool( + idty, [1, 1, 2, 2], [1, 1, 2, 2], + "VALID", + data_format="NCHW", + name="max_pool") + array_ops.squeeze(v, name="output") + + return g.as_graph_def() diff --git a/tensorflow/contrib/tensorrt/test/vgg_block_test.py b/tensorflow/contrib/tensorrt/test/vgg_block_test.py new file mode 100644 index 0000000000..04176d58ca --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/vgg_block_test.py @@ -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. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.core.protobuf import config_pb2 +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 nn +from tensorflow.python.ops import nn_ops +from tensorflow.python.ops import nn_impl +from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest + + +class VGGBlockTest(BaseUnitTest): + """single vgg layer test in TF-TRT conversion""" + + def __init__(self, log_file='log.txt'): + super(VGGBlockTest, self).__init__() + self.static_mode_list = {"FP32", "FP16"} + self.debug = True + self.dynamic_mode_list = {} + self.inp_dims = (5, 8, 8, 2) + self.dummy_input = np.random.random_sample(self.inp_dims) + self.get_network = self.get_simple_graph_def + self.expect_nb_nodes = 7 + self.log_file = log_file + self.test_name = self.__class__.__name__ + + def get_simple_graph_def(self): + g = ops.Graph() + gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) + sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) + with g.as_default(): + x = array_ops.placeholder( + dtype=dtypes.float32, shape=self.inp_dims, name="input") + x, mean_x, var_x = nn_impl.fused_batch_norm( + x, + np.random.randn(2).astype(np.float32), + np.random.randn(2).astype(np.float32), + mean=np.random.randn(2).astype(np.float32), + variance=np.random.randn(2).astype(np.float32), + is_training=False) + e = constant_op.constant( + np.random.randn(1, 1, 2, 6), name="weights", dtype=dtypes.float32) + conv = nn.conv2d( + input=x, filter=e, strides=[1, 2, 2, 1], padding="SAME", name="conv") + b = constant_op.constant( + np.random.randn(6), name="bias", dtype=dtypes.float32) + t = nn.bias_add(conv, b, name="biasAdd") + relu = nn.relu(t, "relu") + idty = array_ops.identity(relu, "ID") + v = nn_ops.max_pool( + idty, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") + array_ops.squeeze(v, name="output") + + return g.as_graph_def() -- GitLab From 8e6fce0fc7bd541b3af1b27dac0e9cf682360b65 Mon Sep 17 00:00:00 2001 From: Dmitry Klimenkov Date: Fri, 13 Jul 2018 16:44:18 +0300 Subject: [PATCH 090/519] some rnn.py fixes were incorrect --- tensorflow/python/ops/rnn.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index aea01a8081..6e1c23f928 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -434,7 +434,7 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, with vs.variable_scope("bw") as bw_scope: inputs_reverse = _reverse( inputs, seq_lengths=sequence_length, - seq_axis=time_dim, batch_axis=batch_dim) + seq_dim=time_dim, batch_dim=batch_dim) tmp, output_state_bw = dynamic_rnn( cell=cell_bw, inputs=inputs_reverse, sequence_length=sequence_length, initial_state=initial_state_bw, dtype=dtype, @@ -443,7 +443,7 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, output_bw = _reverse( tmp, seq_lengths=sequence_length, - seq_axis=time_dim, batch_axis=batch_dim) + seq_dim=time_dim, batch_dim=batch_dim) outputs = (output_fw, output_bw) output_states = (output_state_fw, output_state_bw) -- GitLab From e8c44d765146d228ee88e46b1e1f1e8fb3894818 Mon Sep 17 00:00:00 2001 From: Dmitry Klimenkov Date: Fri, 13 Jul 2018 18:30:49 +0300 Subject: [PATCH 091/519] rename arguments from seq_dim to seq_axis, batch_dim to batch_axis in _reverse(...) function --- tensorflow/python/ops/rnn.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index 6e1c23f928..7521d59a60 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -423,18 +423,18 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, time_dim = 0 batch_dim = 1 - def _reverse(input_, seq_lengths, seq_dim, batch_dim): + def _reverse(input_, seq_lengths, seq_axis, batch_axis): if seq_lengths is not None: return array_ops.reverse_sequence( input=input_, seq_lengths=seq_lengths, - seq_axis=seq_dim, batch_axis=batch_dim) + seq_axis=seq_axis, batch_axis=batch_axis) else: - return array_ops.reverse(input_, axis=[seq_dim]) + return array_ops.reverse(input_, axis=[seq_axis]) with vs.variable_scope("bw") as bw_scope: inputs_reverse = _reverse( inputs, seq_lengths=sequence_length, - seq_dim=time_dim, batch_dim=batch_dim) + seq_axis=time_dim, batch_axis=batch_dim) tmp, output_state_bw = dynamic_rnn( cell=cell_bw, inputs=inputs_reverse, sequence_length=sequence_length, initial_state=initial_state_bw, dtype=dtype, @@ -443,7 +443,7 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, output_bw = _reverse( tmp, seq_lengths=sequence_length, - seq_dim=time_dim, batch_dim=batch_dim) + seq_axis=time_dim, batch_axis=batch_dim) outputs = (output_fw, output_bw) output_states = (output_state_fw, output_state_bw) -- GitLab From ee7c2fc000d5640468343ec93b4878d1334d481c Mon Sep 17 00:00:00 2001 From: Dmitry Klimenkov Date: Fri, 13 Jul 2018 19:21:10 +0300 Subject: [PATCH 092/519] changed time_dim, batch_dim to time_axis, batch_axis accordingly --- tensorflow/python/ops/rnn.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index 7521d59a60..7096e0dd84 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -417,11 +417,11 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, # Backward direction if not time_major: - time_dim = 1 - batch_dim = 0 + time_axis = 1 + batch_axis = 0 else: - time_dim = 0 - batch_dim = 1 + time_axis = 0 + batch_axis = 1 def _reverse(input_, seq_lengths, seq_axis, batch_axis): if seq_lengths is not None: @@ -434,7 +434,7 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, with vs.variable_scope("bw") as bw_scope: inputs_reverse = _reverse( inputs, seq_lengths=sequence_length, - seq_axis=time_dim, batch_axis=batch_dim) + seq_axis=time_axis, batch_axis=batch_axis) tmp, output_state_bw = dynamic_rnn( cell=cell_bw, inputs=inputs_reverse, sequence_length=sequence_length, initial_state=initial_state_bw, dtype=dtype, @@ -443,7 +443,7 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, output_bw = _reverse( tmp, seq_lengths=sequence_length, - seq_axis=time_dim, batch_axis=batch_dim) + seq_axis=time_axis, batch_axis=batch_axis) outputs = (output_fw, output_bw) output_states = (output_state_fw, output_state_bw) -- GitLab From 94b19f2a168c9cbd6e577a73580495a202738f9b Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sun, 15 Jul 2018 16:10:58 +0000 Subject: [PATCH 093/519] Use FastBoundsCheck in ArgMax kernel op This fix updates ArgMax kernel implementation to use FastBoundsCheck for improved performance, and keep consistency with other places in tf. Signed-off-by: Yong Tang --- tensorflow/core/kernels/argmax_op.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/argmax_op.cc b/tensorflow/core/kernels/argmax_op.cc index 49cd997fed..adc573e40c 100644 --- a/tensorflow/core/kernels/argmax_op.cc +++ b/tensorflow/core/kernels/argmax_op.cc @@ -59,7 +59,7 @@ class ArgOp : public OpKernel { int axis = dim < 0 ? dim + input_dims : dim; - OP_REQUIRES(context, axis >= 0 && axis < input_dims, + OP_REQUIRES(context, FastBoundsCheck(axis, input_dims), errors::InvalidArgument("Expected dimension in the range [", -input_dims, ", ", input_dims, "), but got ", dim)); -- GitLab From 05f41cb39598fcdcd4510cdf1c1bf4b4c9fe4868 Mon Sep 17 00:00:00 2001 From: Jon Perl Date: Sun, 15 Jul 2018 19:57:10 -0400 Subject: [PATCH 094/519] Test the timer in InMemoryEvaluatorHook This failing test demonstrates a bug that all iterations are running --- tensorflow/contrib/estimator/python/estimator/hooks_test.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/estimator/python/estimator/hooks_test.py b/tensorflow/contrib/estimator/python/estimator/hooks_test.py index 95ae971852..e094dade6a 100644 --- a/tensorflow/contrib/estimator/python/estimator/hooks_test.py +++ b/tensorflow/contrib/estimator/python/estimator/hooks_test.py @@ -102,6 +102,7 @@ class InMemoryEvaluatorHookTest(test.TestCase): self.assertTrue(os.path.isdir(estimator.eval_dir())) step_keyword_to_value = summary_step_keyword_to_value_mapping( estimator.eval_dir()) + # 4.5 = sum(range(10))/10 # before training self.assertEqual(4.5, step_keyword_to_value[0]['mean_of_features']) @@ -110,6 +111,7 @@ class InMemoryEvaluatorHookTest(test.TestCase): self.assertEqual(4.5, step_keyword_to_value[8]['mean_of_features']) # end self.assertEqual(4.5, step_keyword_to_value[10]['mean_of_features']) + self.assertEqual([0, 4, 8, 10], list(step_keyword_to_value.keys())) def test_uses_latest_variable_value(self): -- GitLab From fe666a2bd2d99dfbdfe696e9d0424eb1aa236ece Mon Sep 17 00:00:00 2001 From: Jon Perl Date: Sun, 15 Jul 2018 19:58:24 -0400 Subject: [PATCH 095/519] Fix timer in InMemoryEvaluatorHook --- tensorflow/contrib/estimator/python/estimator/hooks.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/estimator/python/estimator/hooks.py b/tensorflow/contrib/estimator/python/estimator/hooks.py index ddd6aa442f..caadafdfa6 100644 --- a/tensorflow/contrib/estimator/python/estimator/hooks.py +++ b/tensorflow/contrib/estimator/python/estimator/hooks.py @@ -189,7 +189,7 @@ class InMemoryEvaluatorHook(training.SessionRunHook): init_fn=feed_variables, copy_from_scaffold=self._scaffold) with self._graph.as_default(): - return self._estimator._evaluate_run( + self._estimator._evaluate_run( checkpoint_path=None, scaffold=scaffold, update_op=self._update_op, -- GitLab From 602b03d5fb521aef6561a5c131075d915907357e Mon Sep 17 00:00:00 2001 From: Paul Woitaschek Date: Mon, 16 Jul 2018 21:37:42 +0200 Subject: [PATCH 096/519] Update AndroidManifest.xml --- tensorflow/contrib/lite/java/AndroidManifest.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/java/AndroidManifest.xml b/tensorflow/contrib/lite/java/AndroidManifest.xml index f954bba739..c3849e6868 100644 --- a/tensorflow/contrib/lite/java/AndroidManifest.xml +++ b/tensorflow/contrib/lite/java/AndroidManifest.xml @@ -4,7 +4,7 @@ + android:targetSdkVersion="19" /> -- GitLab From 455c931c252fbeed421b2ba3750743d6c7ad968d Mon Sep 17 00:00:00 2001 From: Shashi <1162712+shashishekhar@users.noreply.github.com> Date: Mon, 16 Jul 2018 12:39:56 -0700 Subject: [PATCH 097/519] Update allocation.cc --- tensorflow/contrib/lite/allocation.cc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/lite/allocation.cc b/tensorflow/contrib/lite/allocation.cc index 6aa4b57d7c..d85b3b12f7 100644 --- a/tensorflow/contrib/lite/allocation.cc +++ b/tensorflow/contrib/lite/allocation.cc @@ -99,6 +99,8 @@ FileCopyAllocation::FileCopyAllocation(const char* filename, filename); return; } + // Versions of GCC before 6.2.0 don't support std::move from non-const char[] to + // const char[] unique_ptrs. copied_buffer_.reset(const_cast(buffer.release())); } -- GitLab From b05a47dd10872871d08a5c7a3ee97e49fdf64098 Mon Sep 17 00:00:00 2001 From: Shashi <1162712+shashishekhar@users.noreply.github.com> Date: Mon, 16 Jul 2018 12:41:19 -0700 Subject: [PATCH 098/519] Update allocation.cc --- tensorflow/contrib/lite/allocation.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/lite/allocation.cc b/tensorflow/contrib/lite/allocation.cc index d85b3b12f7..b19efca842 100644 --- a/tensorflow/contrib/lite/allocation.cc +++ b/tensorflow/contrib/lite/allocation.cc @@ -99,8 +99,8 @@ FileCopyAllocation::FileCopyAllocation(const char* filename, filename); return; } - // Versions of GCC before 6.2.0 don't support std::move from non-const char[] to - // const char[] unique_ptrs. + // Versions of GCC before 6.2.0 don't support std::move from non-const + // char[] to const char[] unique_ptrs. copied_buffer_.reset(const_cast(buffer.release())); } -- GitLab From bd6c04a86cd77d1b969d88fd243c80b7780b6db3 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Mon, 16 Jul 2018 14:48:20 -0700 Subject: [PATCH 099/519] Refactor tf_trt_integration_test so we can extend it to other graphs. --- tensorflow/contrib/tensorrt/BUILD | 28 +- tensorflow/contrib/tensorrt/test/base_test.py | 125 +++++++ .../contrib/tensorrt/test/base_unit_test.py | 118 ------ tensorflow/contrib/tensorrt/test/run_test.py | 184 ---------- .../tensorrt/test/tf_trt_integration_test.py | 347 ------------------ .../test/tf_trt_integration_test_base.py | 293 +++++++++++++++ .../contrib/tensorrt/test/unit_tests.py | 67 ---- tensorflow/contrib/tensorrt/test/utilities.py | 30 -- 8 files changed, 433 insertions(+), 759 deletions(-) create mode 100644 tensorflow/contrib/tensorrt/test/base_test.py delete mode 100644 tensorflow/contrib/tensorrt/test/base_unit_test.py delete mode 100644 tensorflow/contrib/tensorrt/test/run_test.py delete mode 100644 tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py create mode 100644 tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py delete mode 100644 tensorflow/contrib/tensorrt/test/unit_tests.py delete mode 100644 tensorflow/contrib/tensorrt/test/utilities.py diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index d957ca0861..7aed241fd0 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -11,7 +11,6 @@ exports_files(["LICENSE"]) load( "//tensorflow:tensorflow.bzl", - "py_test", "tf_cc_test", "tf_copts", "tf_cuda_library", @@ -20,6 +19,7 @@ load( "tf_gen_op_libs", "tf_gen_op_wrapper_py", ) +load("//tensorflow:tensorflow.bzl", "cuda_py_tests") 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") @@ -33,7 +33,6 @@ tf_cuda_cc_test( size = "small", srcs = ["tensorrt_test.cc"], tags = [ - "manual", "notap", ], deps = [ @@ -311,7 +310,6 @@ tf_cuda_cc_test( size = "small", srcs = ["plugin/trt_plugin_factory_test.cc"], tags = [ - "manual", "notap", ], deps = [ @@ -325,15 +323,9 @@ tf_cuda_cc_test( ]), ) -py_test( - name = "tf_trt_integration_test", - srcs = ["test/tf_trt_integration_test.py"], - main = "test/tf_trt_integration_test.py", - srcs_version = "PY2AND3", - tags = [ - "manual", - "notap", - ], +py_library( + name = "tf_trt_integration_test_base", + srcs = ["test/tf_trt_integration_test_base.py"], deps = [ ":init_py", "//tensorflow/python:client_testlib", @@ -341,6 +333,17 @@ py_test( ], ) +cuda_py_tests( + name = "tf_trt_integration_test", + srcs = ["test/base_test.py"], + additional_deps = [ + ":tf_trt_integration_test_base", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_test_lib", + ], + prefix = "integration_test", +) + py_test( name = "converter_unit_tests", srcs = [ @@ -362,7 +365,6 @@ py_test( main = "test/unit_tests.py", srcs_version = "PY2AND3", tags = [ - "manual", "notap", ], deps = [ diff --git a/tensorflow/contrib/tensorrt/test/base_test.py b/tensorflow/contrib/tensorrt/test/base_test.py new file mode 100644 index 0000000000..4b9e6d668f --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/base_test.py @@ -0,0 +1,125 @@ +# 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. +# ============================================================================== +"""Basic tests for TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +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 ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import nn +from tensorflow.python.ops import nn_ops +from tensorflow.python.platform import test +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test + + +# TODO(aaroey): test graph with different dtypes. +def _GetSingleEngineGraphDef(dtype=dtypes.float32): + """Create a graph containing single segment.""" + input_dims = [100, 24, 24, 2] + g = ops.Graph() + with g.as_default(): + inp = array_ops.placeholder( + dtype=dtype, shape=[None] + input_dims[1:], name=trt_test.INPUT_NAME) + with g.device("/GPU:0"): + conv_filter = constant_op.constant( + [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], + name="weights", + dtype=dtype) + conv = nn.conv2d( + input=inp, + filter=conv_filter, + strides=[1, 2, 2, 1], + padding="SAME", + name="conv") + bias = constant_op.constant( + [4., 1.5, 2., 3., 5., 7.], name="bias", dtype=dtype) + added = nn.bias_add(conv, bias, name="bias_add") + relu = nn.relu(added, "relu") + identity = array_ops.identity(relu, "identity") + pool = nn_ops.max_pool( + identity, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") + array_ops.squeeze(pool, name=trt_test.OUTPUT_NAME) + return trt_test.TfTrtIntegrationTestParams( + graph_name="SimpleSingleEngine", + gdef=g.as_graph_def(), + input_dims=input_dims, + num_expected_engines=1, + expected_output_dims=(100, 6, 6, 6), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +# TODO(aaroey): test graph with different dtypes. +def _GetMultiEngineGraphDef(dtype=dtypes.float32): + """Create a graph containing multiple segment.""" + input_dims = [100, 24, 24, 2] + g = ops.Graph() + with g.as_default(): + inp = array_ops.placeholder( + dtype=dtype, shape=[None] + input_dims[1:], name=trt_test.INPUT_NAME) + with g.device("/GPU:0"): + conv_filter = constant_op.constant( + [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], + name="weights", + dtype=dtype) + conv = nn.conv2d( + input=inp, + filter=conv_filter, + strides=[1, 2, 2, 1], + padding="SAME", + name="conv") + c1 = constant_op.constant( + np.random.randn(input_dims[0], 12, 12, 6), dtype=dtype) + p = conv * c1 + c2 = constant_op.constant( + np.random.randn(input_dims[0], 12, 12, 6), dtype=dtype) + q = conv / c2 + + edge = trt_test.TRT_INCOMPATIBLE_OP(q) + edge /= edge + r = edge + edge + + p -= edge + q *= edge + s = p + q + s -= r + array_ops.squeeze(s, name=trt_test.OUTPUT_NAME) + return trt_test.TfTrtIntegrationTestParams( + graph_name="SimpleMultipleEngines", + gdef=g.as_graph_def(), + input_dims=input_dims, + num_expected_engines=2, + expected_output_dims=(100, 12, 12, 6), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + + +class BaseTest(trt_test.TfTrtIntegrationTestBase): + """Class to test Tensorflow-TensorRT integration.""" + pass + + +if __name__ == "__main__": + # TODO(aaroey): add a large complex graph to test. + trt_test.AddTests(BaseTest, + [_GetSingleEngineGraphDef(), + _GetMultiEngineGraphDef()]) + test.main() diff --git a/tensorflow/contrib/tensorrt/test/base_unit_test.py b/tensorflow/contrib/tensorrt/test/base_unit_test.py deleted file mode 100644 index 8a6c648ab6..0000000000 --- a/tensorflow/contrib/tensorrt/test/base_unit_test.py +++ /dev/null @@ -1,118 +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. -# ============================================================================== -"""Base class to facilitate development of integration tests.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import numpy as np - - -class BaseUnitTest(object): - """Base class for unit tests in TF-TRT""" - - def __init__(self, log_file='log.txt'): - self.static_mode_list = {} - self.dynamic_mode_list = {} - self.dummy_input = None - self.get_network = None - self.expect_nb_nodes = None - self.test_name = None - self.log_file = log_file - self.ckpt = None - self.allclose_rtol = 0.01 - self.allclose_atol = 0.01 - self.allclose_equal_nan = True - # saves out graphdef - self.debug = False - # require node count check fail leads to test failure - self.check_node_count = False - - def run(self, run_test_context): - run_test_context.run_test(self.get_network, self.static_mode_list, - self.dynamic_mode_list, self.dummy_input, - self.ckpt) - return self.log_result(run_test_context) - - def log_result(self, run_test_result): - log = open(self.log_file, 'a') - log.write(("================= model: %s\n") % (self.test_name)) - - if self.debug: - open(self.test_name + "_native.pb", - 'wb').write(run_test_result.native_network.SerializeToString()) - all_success = True - if len(run_test_result.tftrt_conversion_flag) != 0: - log.write(" -- static_mode\n") - for static_mode in run_test_result.tftrt_conversion_flag: - if self.debug: - open(self.test_name + "_" + static_mode + ".pb", - 'wb').write(run_test_result.tftrt[static_mode].SerializeToString()) - log.write(" ----\n") - log.write((" mode: [%s]\n") % (static_mode)) - if run_test_result.tftrt_conversion_flag[static_mode]: - if run_test_result.tftrt_nb_nodes[static_mode] != self.expect_nb_nodes: - log.write( - ("[WARNING]: converted node number does not match (%d,%d,%d)!!!\n" - ) % (run_test_result.tftrt_nb_nodes[static_mode], - self.expect_nb_nodes, run_test_result.native_nb_nodes)) - if self.check_node_count: - all_success = False - - if np.array_equal(run_test_result.tftrt_result[static_mode], - run_test_result.native_result): - log.write(" output: equal\n") - elif np.allclose( - run_test_result.tftrt_result[static_mode], - run_test_result.native_result, - atol=self.allclose_atol, - rtol=self.allclose_rtol, - equal_nan=self.allclose_equal_nan): - log.write(" output: allclose\n") - else: - diff = run_test_result.tftrt_result[static_mode] - run_test_result.native_result - log.write("[ERROR]: output does not match!!!\n") - log.write("max diff: " + str(np.max(diff))) - log.write("\ntftrt:\n") - log.write(str(run_test_result.tftrt_result[static_mode])) - log.write("\nnative:\n") - log.write(str(run_test_result.native_result)) - log.write("\ndiff:\n") - log.write(str(diff)) - all_success = False - else: - log.write("[ERROR]: conversion failed!!!\n") - all_success = False - - if len(run_test_result.tftrt_dynamic_conversion_flag) != 0: - log.write(" -- dynamic_mode\n") - for dynamic_mode in run_test_result.tftrt_dynamic_conversion_flag: - log.write("\n ----\n") - log.write((" mode: [%s]\n") % (dynamic_mode)) - if run_test_result.tftrt_dynamic_conversion_flag[dynamic_mode]: - if np.array_equal(run_test_result.tftrt_dynamic_result[dynamic_mode], - run_test_result.native_result): - log.write(" output: equal\n") - elif np.allclose(run_test_result.tftrt_dynamic_result[dynamic_mode], - run_test_result.native_result): - log.write(" output: allclose\n") - else: - log.write("[ERROR]: output does not match!!!\n") - all_success = False - else: - log.write("[ERROR]: conversion failed!!!\n") - all_success = False - return all_success diff --git a/tensorflow/contrib/tensorrt/test/run_test.py b/tensorflow/contrib/tensorrt/test/run_test.py deleted file mode 100644 index 4d109cc378..0000000000 --- a/tensorflow/contrib/tensorrt/test/run_test.py +++ /dev/null @@ -1,184 +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. -# ============================================================================== -"""script to convert and execute TF-TensorRT graph.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib import tensorrt as trt -from tensorflow.core.protobuf import config_pb2 -from tensorflow.core.protobuf import rewriter_config_pb2 -from tensorflow.python.client import session -from tensorflow.python.framework import importer -from tensorflow.python.framework import ops -from tensorflow.python.training import training -from tensorflow.contrib.tensorrt.test.utilities import get_all_variables - -OUTPUT_NODE = "output" -INPUT_NODE = "input" -CALIB_COUNT = 5 # calibration iteration - - -class RunTest: - """base class to run TR-TRT conversion and execution""" - - def __init__(self): - self.clean() - - def __enter__(self): - return self - - def __exit__(self, exc_type, exc_val, exc_tb): - self.clean() - - def clean(self): - self.tftrt = {} - self.tftrt_conversion_flag = {} - self.tftrt_nb_nodes = {} - self.tftrt_result = {} - self.tftrt_dynamic_conversion_flag = {} - self.tftrt_dynamic_result = {} - self.check_file = None - self.native_network = None - - def run_test(self, - network, - static_mode_list, - dynamic_mode_list, - dummy_input, - file_name=None): - self.native_network = network() - success = True - initialization = False - if file_name != None: - initialization = True - self.check_file = file_name - self.native_result, self.native_nb_nodes = self.execute_graph( - self.native_network, dummy_input, initialization) - for mode in static_mode_list: - try: - self.run_static_convert_network(mode, dummy_input, initialization) - self.tftrt_conversion_flag[mode] = True - except Exception as inst: - self.tftrt_conversion_flag[mode] = False - success = False - for mode in dynamic_mode_list: - try: - self.run_dynamic_convert_network(mode, dummy_input, initialization) - self.tftrt_dynamic_conversion_flag[mode] = True - except Exception as inst: - self.tftrt_dynamic_conversion_flag[mode] = False - success = False - return success - - def run_dynamic_convert_network(self, mode, dummy_input, initialization=True): - inp_dims = dummy_input.shape - if mode == "FP32" or mode == "FP16": - opt_config = rewriter_config_pb2.RewriterConfig() - opt_config.optimizers.extend(["constfold", "layout"]) - custom_op = opt_config.custom_optimizers.add() - custom_op.name = "TensorRTOptimizer" - custom_op.parameter_map["minimum_segment_size"].i = 3 - custom_op.parameter_map["precision_mode"].s = mode - custom_op.parameter_map["max_batch_size"].i = inp_dims[0] - custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 - print(custom_op) - gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - graph_options = config_pb2.GraphOptions(rewrite_options=opt_config) - sessconfig = config_pb2.ConfigProto( - gpu_options=gpu_options, graph_options=graph_options) - print(sessconfig) - g = ops.Graph() - ops.reset_default_graph() - with g.as_default(): - inp, out = importer.import_graph_def( - graph_def=self.native_network, return_elements=["input", "output"]) - inp = inp.outputs[0] - out = out.outputs[0] - with session.Session(config=sessconfig, graph=g) as sess: - if (initialization): - names_var_list = get_all_variables(sess) - saver = training.Saver(names_var_list) - saver.restore(sess, self.check_file) - self.tftrt_dynamic_result[mode] = sess.run(out, {inp: dummy_input}) - else: - raise Exception("dynamic op mode: " + mode + " not supported") - - def run_static_convert_network(self, mode, dummy_input, initialization=True): - inp_dims = dummy_input.shape - if mode == "FP32" or mode == "FP16" or mode == "INT8": - trt_graph = trt.create_inference_graph( - input_graph_def=self.native_network, - outputs=[OUTPUT_NODE], - max_batch_size=inp_dims[0], - max_workspace_size_bytes=1 << 25, - precision_mode=mode, # TRT Engine precision "FP32","FP16" or "INT8" - minimum_segment_size=2 # minimum number of nodes in an engine - ) - if mode == "INT8": - _ = self.execute_calibration(trt_graph, dummy_input, initialization) - trt_graph = trt.calib_graph_to_infer_graph(trt_graph) - trt_result, nb_nodes = self.execute_graph(trt_graph, dummy_input, - initialization) - self.tftrt[mode] = trt_graph - self.tftrt_nb_nodes[mode] = nb_nodes - self.tftrt_result[mode] = trt_result - else: - raise Exception("mode: " + mode + " not supported") - - def execute_graph(self, gdef, dummy_input, initialization=True): - """Run given graphdef once.""" - gpu_options = config_pb2.GPUOptions() - sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) - ops.reset_default_graph() - g = ops.Graph() - nb_nodes = 0 - with g.as_default(): - inp, out = importer.import_graph_def( - graph_def=gdef, return_elements=[INPUT_NODE, OUTPUT_NODE], name="") - nb_nodes = len(g.get_operations()) - inp = inp.outputs[0] - out = out.outputs[0] - with session.Session(config=sessconfig, graph=g) as sess: - if (initialization): - names_var_list = get_all_variables(sess) - saver = training.Saver(names_var_list) - saver.restore(sess, self.check_file) - val = sess.run(out, {inp: dummy_input}) - return val, nb_nodes - - # Use real data that is representative of the inference dataset - # for calibration. For this test script it is random data. - def execute_calibration(self, gdef, dummy_input, initialization=True): - """Run given calibration graph multiple times.""" - gpu_options = config_pb2.GPUOptions() - ops.reset_default_graph() - g = ops.Graph() - with g.as_default(): - inp, out = importer.import_graph_def( - graph_def=gdef, return_elements=[INPUT_NODE, OUTPUT_NODE], name="") - inp = inp.outputs[0] - out = out.outputs[0] - with session.Session( - config=config_pb2.ConfigProto(gpu_options=gpu_options), - graph=g) as sess: - if (initialization): - names_var_list = get_all_variables(sess) - saver = training.Saver(names_var_list) - saver.restore(sess, self.check_file) - for _ in range(CALIB_COUNT): - val = sess.run(out, {inp: dummy_input}) - return val diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py deleted file mode 100644 index d9c41f90d0..0000000000 --- a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test.py +++ /dev/null @@ -1,347 +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. -# ============================================================================== -"""Script to test TF-TensorRT integration.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from collections import namedtuple -import itertools -import warnings -import numpy as np -import six - -from tensorflow.contrib import tensorrt as trt -from tensorflow.core.protobuf import config_pb2 -from tensorflow.core.protobuf import rewriter_config_pb2 -from tensorflow.python.framework import constant_op -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import importer -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 math_ops -from tensorflow.python.ops import nn -from tensorflow.python.ops import nn_ops -from tensorflow.python.platform import test - -INPUT_NAME = "input" -OUTPUT_NAME = "output" -INPUT_DIMS = [100, 24, 24, 2] -MODE_FP32 = "FP32" -MODE_FP16 = "FP16" -MODE_INT8 = "INT8" - -if six.PY2: - to_bytes = lambda s: s - to_string = lambda s: s -else: - to_bytes = lambda s: s.encode("utf-8", errors="surrogateescape") - to_string = lambda s: s.decode("utf-8") - - -# TODO(aaroey): test graph with different dtypes. -def GetSingleEngineGraphDef(dtype=dtypes.float32): - """Create a graph containing single segment.""" - g = ops.Graph() - with g.as_default(): - inp = array_ops.placeholder( - dtype=dtype, shape=[None] + INPUT_DIMS[1:], name=INPUT_NAME) - with g.device("/GPU:0"): - conv_filter = constant_op.constant( - [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], - name="weights", - dtype=dtype) - conv = nn.conv2d( - input=inp, - filter=conv_filter, - strides=[1, 2, 2, 1], - padding="SAME", - name="conv") - bias = constant_op.constant( - [4., 1.5, 2., 3., 5., 7.], name="bias", dtype=dtype) - added = nn.bias_add(conv, bias, name="bias_add") - relu = nn.relu(added, "relu") - identity = array_ops.identity(relu, "identity") - pool = nn_ops.max_pool( - identity, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") - array_ops.squeeze(pool, name=OUTPUT_NAME) - return g.as_graph_def() - - -# TODO(aaroey): test graph with different dtypes. -def GetMultiEngineGraphDef(dtype=dtypes.float32): - """Create a graph containing multiple segment.""" - g = ops.Graph() - with g.as_default(): - inp = array_ops.placeholder( - dtype=dtype, shape=[None] + INPUT_DIMS[1:], name=INPUT_NAME) - with g.device("/GPU:0"): - conv_filter = constant_op.constant( - [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], - name="weights", - dtype=dtype) - conv = nn.conv2d( - input=inp, - filter=conv_filter, - strides=[1, 2, 2, 1], - padding="SAME", - name="conv") - c1 = constant_op.constant( - np.random.randn(INPUT_DIMS[0], 12, 12, 6), dtype=dtype) - p = conv * c1 - c2 = constant_op.constant( - np.random.randn(INPUT_DIMS[0], 12, 12, 6), dtype=dtype) - q = conv / c2 - - edge = math_ops.sin(q) - edge /= edge - r = edge + edge - - p -= edge - q *= edge - s = p + q - s -= r - array_ops.squeeze(s, name=OUTPUT_NAME) - return g.as_graph_def() - - -TestGraph = namedtuple("TestGraph", - ["gdef", "num_expected_engines", "expected_output_dims"]) - -TEST_GRAPHS = { - "SingleEngineGraph": - TestGraph( - gdef=GetSingleEngineGraphDef(), - num_expected_engines=1, - expected_output_dims=(100, 6, 6, 6)), - "MultiEngineGraph": - TestGraph( - gdef=GetMultiEngineGraphDef(), - num_expected_engines=2, - expected_output_dims=(100, 12, 12, 6)), - # TODO(aaroey): add a large complex graph to test. -} - - -class TfTrtIntegrationTest(test_util.TensorFlowTestCase): - """Class to test Tensorflow-TensorRT integration.""" - - def setUp(self): - """Setup method.""" - super(TfTrtIntegrationTest, self).setUp() - warnings.simplefilter("always") - self._input = np.random.random_sample(INPUT_DIMS) - - def _GetConfigProto(self, - use_optimizer, - precision_mode=None, - is_dynamic_op=None): - if use_optimizer: - rewriter_cfg = rewriter_config_pb2.RewriterConfig() - rewriter_cfg.optimizers.extend(["constfold", "layout"]) - custom_op = rewriter_cfg.custom_optimizers.add() - custom_op.name = "TensorRTOptimizer" - custom_op.parameter_map["minimum_segment_size"].i = 3 - custom_op.parameter_map["max_batch_size"].i = self._input.shape[0] - custom_op.parameter_map["is_dynamic_op"].b = is_dynamic_op - custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 - custom_op.parameter_map["precision_mode"].s = to_bytes(precision_mode) - graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_cfg) - else: - graph_options = config_pb2.GraphOptions() - - gpu_options = config_pb2.GPUOptions() - if trt.trt_convert.get_linked_tensorrt_version()[0] == 3: - gpu_options.per_process_gpu_memory_fraction = 0.50 - - config = config_pb2.ConfigProto( - gpu_options=gpu_options, graph_options=graph_options) - return config - - def _RunGraph(self, graph_key, gdef, input_data, config, num_runs=2): - """Run given graphdef multiple times.""" - g = ops.Graph() - with g.as_default(): - inp, out = importer.import_graph_def( - graph_def=gdef, return_elements=[INPUT_NAME, OUTPUT_NAME], name="") - inp = inp.outputs[0] - out = out.outputs[0] - with self.test_session( - graph=g, config=config, use_gpu=True, force_gpu=True) as sess: - val = None - # Defaults to 2 runs to verify result across multiple runs is same. - for _ in range(num_runs): - new_val = sess.run(out, {inp: input_data}) - self.assertEquals(TEST_GRAPHS[graph_key].expected_output_dims, - new_val.shape) - if val is not None: - self.assertAllEqual(new_val, val) - val = new_val - return val - - # Use real data that is representative of the inference dataset - # for calibration. For this test script it is random data. - def _RunCalibration(self, graph_key, gdef, input_data, config): - """Run calibration on given graph.""" - return self._RunGraph(graph_key, gdef, input_data, config, 30) - - def _GetTrtGraph(self, gdef, precision_mode, is_dynamic_op): - """Return trt converted graph.""" - return trt.create_inference_graph( - input_graph_def=gdef, - outputs=[OUTPUT_NAME], - max_batch_size=self._input.shape[0], - max_workspace_size_bytes=1 << 25, - precision_mode=precision_mode, - minimum_segment_size=2, - is_dynamic_op=is_dynamic_op) - - def _VerifyGraphDef(self, - graph_key, - gdef, - precision_mode=None, - is_calibrated=None, - dynamic_engine=None): - num_engines = 0 - for n in gdef.node: - if n.op == "TRTEngineOp": - num_engines += 1 - self.assertNotEqual("", n.attr["serialized_segment"].s) - self.assertNotEqual("", n.attr["segment_funcdef_name"].s) - self.assertEquals(n.attr["precision_mode"].s, precision_mode) - self.assertEquals(n.attr["static_engine"].b, not dynamic_engine) - if precision_mode == MODE_INT8 and is_calibrated: - self.assertNotEqual("", n.attr["calibration_data"].s) - else: - self.assertEquals("", n.attr["calibration_data"].s) - if precision_mode is None: - self.assertEquals(num_engines, 0) - else: - self.assertEquals(num_engines, - TEST_GRAPHS[graph_key].num_expected_engines) - - def _RunTest(self, graph_key, use_optimizer, precision_mode, - dynamic_infer_engine, dynamic_calib_engine): - assert precision_mode in [MODE_FP32, MODE_FP16, MODE_INT8] - input_gdef = TEST_GRAPHS[graph_key].gdef - self._VerifyGraphDef(graph_key, input_gdef) - - # Get reference result without running trt. - config_no_trt = self._GetConfigProto(False) - print("Running original graph w/o trt, config:\n%s" % str(config_no_trt)) - ref_result = self._RunGraph(graph_key, input_gdef, self._input, - config_no_trt) - - # Run calibration if necessary. - if precision_mode == MODE_INT8: - - calib_config = self._GetConfigProto(use_optimizer, precision_mode, - dynamic_calib_engine) - print("Running calibration graph, config:\n%s" % str(calib_config)) - if use_optimizer: - self.assertTrue(False) - # TODO(aaroey): uncomment this and get infer_gdef when this mode is - # supported. - # result = self._RunCalibration(graph_key, input_gdef, self._input, - # calib_config) - else: - calib_gdef = self._GetTrtGraph(input_gdef, precision_mode, - dynamic_calib_engine) - self._VerifyGraphDef(graph_key, calib_gdef, precision_mode, False, - dynamic_calib_engine) - result = self._RunCalibration(graph_key, calib_gdef, self._input, - calib_config) - infer_gdef = trt.calib_graph_to_infer_graph(calib_gdef) - self._VerifyGraphDef(graph_key, infer_gdef, precision_mode, True, - dynamic_calib_engine) - self.assertAllClose(ref_result, result, rtol=1.e-03) - else: - infer_gdef = input_gdef - - # Run inference. - infer_config = self._GetConfigProto(use_optimizer, precision_mode, - dynamic_infer_engine) - print("Running final inference graph, config:\n%s" % str(infer_config)) - if use_optimizer: - result = self._RunGraph(graph_key, infer_gdef, self._input, infer_config) - else: - trt_infer_gdef = self._GetTrtGraph(infer_gdef, precision_mode, - dynamic_infer_engine) - self._VerifyGraphDef(graph_key, trt_infer_gdef, precision_mode, True, - dynamic_infer_engine) - result = self._RunGraph(graph_key, trt_infer_gdef, self._input, - infer_config) - self.assertAllClose(ref_result, result, rtol=1.e-03) - - def testIdempotence(self): - # Test that applying tensorrt optimizer or offline conversion tools multiple - # times to the same graph will result in same graph. - # TODO(aaroey): implement this. - pass - - -def GetTests(): - - def _GetTest(g, u, p, i, c): - - def _Test(self): - print("Running test with parameters: graph_key=%s, use_optimizer=%s, " - "precision_mode=%s, dynamic_infer_engine=%s, " - "dynamic_calib_engine=%s" % (g, u, p, i, c)) - self._RunTest(g, u, p, i, c) - - return _Test - - use_optimizer_options = [False, True] - precision_mode_options = [MODE_FP32, MODE_FP16, MODE_INT8] - dynamic_infer_engine_options = [False, True] - dynamic_calib_engine_options = [False, True] - for (graph_key, use_optimizer, precision_mode, - dynamic_infer_engine, dynamic_calib_engine) in itertools.product( - TEST_GRAPHS, use_optimizer_options, precision_mode_options, - dynamic_infer_engine_options, dynamic_calib_engine_options): - if precision_mode == MODE_INT8: - if not dynamic_calib_engine and dynamic_infer_engine: - # TODO(aaroey): test this case, the conversion from static calibration - # engine to dynamic inference engine should be a noop. - continue - if use_optimizer: - # TODO(aaroey): if use_optimizer is True we need to get the inference - # graphdef using custom python wrapper class, which is not currently - # supported yet. - continue - if not dynamic_calib_engine: - # TODO(aaroey): construction of static calibration engine is not - # supported yet. - continue - if dynamic_calib_engine and not dynamic_infer_engine: - # TODO(aaroey): construction of static inference engine using dynamic - # calibration engine is not supported yet. - continue - else: # In non int8 mode. - if dynamic_calib_engine: - # dynamic_calib_engine doesn't affect non-int8 modes, so just let - # related tests run once on dynamic_calib_engine=False. - continue - yield _GetTest(graph_key, use_optimizer, precision_mode, - dynamic_infer_engine, dynamic_calib_engine) - - -if __name__ == "__main__": - for index, t in enumerate(GetTests()): - setattr(TfTrtIntegrationTest, "testTfTRT_" + str(index), t) - test.main() diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py new file mode 100644 index 0000000000..980cc87366 --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py @@ -0,0 +1,293 @@ +# 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 to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from collections import namedtuple +import itertools +import warnings +import numpy as np +import re +import six + +from tensorflow.contrib import tensorrt as trt +from tensorflow.core.protobuf import config_pb2 +from tensorflow.core.protobuf import rewriter_config_pb2 +from tensorflow.python.framework import importer +from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util +from tensorflow.python.ops import math_ops +from tensorflow.python.platform import test +from tensorflow.python.platform import tf_logging as logging + +TfTrtIntegrationTestParams = namedtuple("TfTrtIntegrationTestParams", [ + "graph_name", "gdef", "input_dims", "num_expected_engines", + "expected_output_dims", "allclose_atol", "allclose_rtol" +]) + +INPUT_NAME = "input" +OUTPUT_NAME = "output" +TRT_INCOMPATIBLE_OP = math_ops.sin +PRECISION_MODES = ["FP32", "FP16", "INT8"] + + +def IsQuantizationMode(mode): + return mode == "INT8" + + +class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): + """Class to test Tensorflow-TensorRT integration.""" + + def _ToBytes(self, s): + if six.PY2: + return s + else: + return s.encode("utf-8") + + def _ToString(self, s): + if six.PY2: + return s + else: + return s.decode("utf-8") + + def setUp(self): + """Setup method.""" + super(TfTrtIntegrationTestBase, self).setUp() + warnings.simplefilter("always") + + def _GetConfigProto(self, + params, + use_optimizer, + precision_mode=None, + is_dynamic_op=None): + """Get config proto based on specific settings.""" + if use_optimizer: + rewriter_cfg = rewriter_config_pb2.RewriterConfig() + rewriter_cfg.optimizers.extend(["constfold", "layout"]) + custom_op = rewriter_cfg.custom_optimizers.add() + custom_op.name = "TensorRTOptimizer" + custom_op.parameter_map["minimum_segment_size"].i = 3 + custom_op.parameter_map["max_batch_size"].i = params.input_dims[0] + custom_op.parameter_map["is_dynamic_op"].b = is_dynamic_op + custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 + custom_op.parameter_map["precision_mode"].s = self._ToBytes( + precision_mode) + graph_options = config_pb2.GraphOptions(rewrite_options=rewriter_cfg) + else: + graph_options = config_pb2.GraphOptions() + + gpu_options = config_pb2.GPUOptions() + if trt.trt_convert.get_linked_tensorrt_version()[0] == 3: + gpu_options.per_process_gpu_memory_fraction = 0.50 + + config = config_pb2.ConfigProto( + gpu_options=gpu_options, graph_options=graph_options) + return config + + def _RunGraph(self, params, gdef, input_data, config, num_runs=2): + """Run given graphdef multiple times.""" + g = ops.Graph() + with g.as_default(): + inp, out = importer.import_graph_def( + graph_def=gdef, return_elements=[INPUT_NAME, OUTPUT_NAME], name="") + inp = inp.outputs[0] + out = out.outputs[0] + with self.test_session( + graph=g, config=config, use_gpu=True, force_gpu=True) as sess: + val = None + # Defaults to 2 runs to verify result across multiple runs is same. + for _ in range(num_runs): + new_val = sess.run(out, {inp: input_data}) + self.assertEquals(params.expected_output_dims, new_val.shape) + if val is not None: + self.assertAllEqual(new_val, val) + val = new_val + return val + + # Use real data that is representative of the inference dataset + # for calibration. For this test script it is random data. + def _RunCalibration(self, params, gdef, input_data, config): + """Run calibration on given graph.""" + return self._RunGraph(params, gdef, input_data, config, 30) + + def _GetTrtGraphDef(self, params, gdef, precision_mode, is_dynamic_op): + """Return trt converted graphdef.""" + return trt.create_inference_graph( + input_graph_def=gdef, + outputs=[OUTPUT_NAME], + max_batch_size=params.input_dims[0], + max_workspace_size_bytes=1 << 25, + precision_mode=precision_mode, + minimum_segment_size=2, + is_dynamic_op=is_dynamic_op) + + def _VerifyGraphDef(self, + params, + gdef, + precision_mode=None, + is_calibrated=None, + dynamic_engine=None): + num_engines = 0 + for n in gdef.node: + if n.op == "TRTEngineOp": + num_engines += 1 + self.assertNotEqual("", n.attr["serialized_segment"].s) + self.assertNotEqual("", n.attr["segment_funcdef_name"].s) + self.assertEquals(n.attr["precision_mode"].s, precision_mode) + self.assertEquals(n.attr["static_engine"].b, not dynamic_engine) + if IsQuantizationMode(precision_mode) and is_calibrated: + self.assertNotEqual("", n.attr["calibration_data"].s) + else: + self.assertEquals("", n.attr["calibration_data"].s) + if precision_mode is None: + self.assertEquals(num_engines, 0) + else: + self.assertEquals(num_engines, params.num_expected_engines) + + def _RunTest(self, params, use_optimizer, precision_mode, + dynamic_infer_engine, dynamic_calib_engine): + assert precision_mode in PRECISION_MODES + inp = np.random.random_sample(params.input_dims) + input_gdef = params.gdef + self._VerifyGraphDef(params, input_gdef) + + # Get reference result without running trt. + config_no_trt = self._GetConfigProto(params, False) + logging.info("Running original graph w/o trt, config:\n%s", + str(config_no_trt)) + ref_result = self._RunGraph(params, input_gdef, inp, config_no_trt) + + # Run calibration if necessary. + if IsQuantizationMode(precision_mode): + + calib_config = self._GetConfigProto(params, use_optimizer, precision_mode, + dynamic_calib_engine) + logging.info("Running calibration graph, config:\n%s", str(calib_config)) + if use_optimizer: + self.assertTrue(False) + # TODO(aaroey): uncomment this and get infer_gdef when this mode is + # supported. + # result = self._RunCalibration(params, input_gdef, inp, calib_config) + else: + calib_gdef = self._GetTrtGraphDef(params, input_gdef, precision_mode, + dynamic_calib_engine) + self._VerifyGraphDef(params, calib_gdef, precision_mode, False, + dynamic_calib_engine) + result = self._RunCalibration(params, calib_gdef, inp, calib_config) + infer_gdef = trt.calib_graph_to_infer_graph(calib_gdef) + self._VerifyGraphDef(params, infer_gdef, precision_mode, True, + dynamic_calib_engine) + + self.assertAllClose( + ref_result, + result, + atol=params.allclose_atol, + rtol=params.allclose_rtol) + else: + infer_gdef = input_gdef + + # Run inference. + infer_config = self._GetConfigProto(params, use_optimizer, precision_mode, + dynamic_infer_engine) + logging.info("Running final inference graph, config:\n%s", + str(infer_config)) + if use_optimizer: + result = self._RunGraph(params, infer_gdef, inp, infer_config) + else: + trt_infer_gdef = self._GetTrtGraphDef(params, infer_gdef, precision_mode, + dynamic_infer_engine) + self._VerifyGraphDef(params, trt_infer_gdef, precision_mode, True, + dynamic_infer_engine) + result = self._RunGraph(params, trt_infer_gdef, inp, infer_config) + + self.assertAllClose( + ref_result, + result, + atol=params.allclose_atol, + rtol=params.allclose_rtol) + + def testIdempotence(self): + # Test that applying tensorrt optimizer or offline conversion tools multiple + # times to the same graph will result in same graph. + # TODO(aaroey): implement this. + pass + + +def AddTests(test_class, params_list): + + def _GetTest(params, use_optimizer, precision_mode, dynamic_infer_engine, + dynamic_calib_engine): + + def _Test(self): + logging.info( + "Running test with parameters: graph_name=%s, " + "use_optimizer=%s, precision_mode=%s, " + "dynamic_infer_engine=%s, dynamic_calib_engine=%s", params.graph_name, + use_optimizer, precision_mode, dynamic_infer_engine, + dynamic_calib_engine) + self._RunTest(params, use_optimizer, precision_mode, dynamic_infer_engine, + dynamic_calib_engine) + + return _Test + + use_optimizer_options = [False, True] + dynamic_infer_engine_options = [False, True] + dynamic_calib_engine_options = [False, True] + for (params, use_optimizer, precision_mode, + dynamic_infer_engine, dynamic_calib_engine) in itertools.product( + params_list, use_optimizer_options, PRECISION_MODES, + dynamic_infer_engine_options, dynamic_calib_engine_options): + if IsQuantizationMode(precision_mode): + if not dynamic_calib_engine and dynamic_infer_engine: + # TODO(aaroey): test this case, the conversion from static calibration + # engine to dynamic inference engine should be a noop. + continue + if use_optimizer: + # TODO(aaroey): if use_optimizer is True we need to get the inference + # graphdef using custom python wrapper class, which is not currently + # supported yet. + continue + if not dynamic_calib_engine: + # TODO(aaroey): construction of static calibration engine is not + # supported yet. + continue + if dynamic_calib_engine and not dynamic_infer_engine: + # TODO(aaroey): construction of static inference engine using dynamic + # calibration engine is not supported yet. + continue + else: # In non int8 mode. + if dynamic_calib_engine: + # dynamic_calib_engine doesn't affect non-int8 modes, so just let + # related tests run once on dynamic_calib_engine=False. + continue + + conversion = "OptimizerConversion" if use_optimizer else "ToolConversion" + infer_engine_type = ("DynamicInferEngine" + if dynamic_infer_engine else "StaticInferEngine") + calib_engine_type = "" + if precision_mode == "INT8": + calib_engine_type = ("DynamicCalibEngine" + if dynamic_calib_engine else "StaticCalibEngine") + test_name = "%s_%s_%s_%s%s" % (re.sub( + "[^a-zA-Z0-9]+", "", params.graph_name), conversion, precision_mode, + infer_engine_type, ("_" + calib_engine_type) + if len(calib_engine_type) else "") + setattr( + test_class, "testTfTRT_" + test_name, + _GetTest(params, use_optimizer, precision_mode, dynamic_infer_engine, + dynamic_calib_engine)) diff --git a/tensorflow/contrib/tensorrt/test/unit_tests.py b/tensorflow/contrib/tensorrt/test/unit_tests.py deleted file mode 100644 index ac6e3b13ee..0000000000 --- a/tensorflow/contrib/tensorrt/test/unit_tests.py +++ /dev/null @@ -1,67 +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. -# ============================================================================== -"""Script to execute and log all integration tests.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.contrib.tensorrt.test.batch_matmul_test import BatchMatMulTest -from tensorflow.contrib.tensorrt.test.biasadd_matmul_test import BiasaddMatMulTest -from tensorflow.contrib.tensorrt.test.binary_tensor_weight_broadcast_test import BinaryTensorWeightBroadcastTest -from tensorflow.contrib.tensorrt.test.concatenation_test import ConcatenationTest -from tensorflow.contrib.tensorrt.test.multi_connection_neighbor_engine_test import MultiConnectionNeighborEngineTest -from tensorflow.contrib.tensorrt.test.neighboring_engine_test import NeighboringEngineTest -from tensorflow.contrib.tensorrt.test.unary_test import UnaryTest -from tensorflow.contrib.tensorrt.test.vgg_block_nchw_test import VGGBlockNCHWTest -from tensorflow.contrib.tensorrt.test.vgg_block_test import VGGBlockTest -from tensorflow.contrib.tensorrt.test.const_broadcast_test import ConstBroadcastTest - -from tensorflow.contrib.tensorrt.test.run_test import RunTest - -tests = 0 -passed_test = 0 - -failed_list = [] -test_list = [] - -test_list.append(BatchMatMulTest()) -test_list.append(BiasaddMatMulTest()) -test_list.append(BinaryTensorWeightBroadcastTest()) -test_list.append(ConcatenationTest()) -test_list.append(NeighboringEngineTest()) -test_list.append(UnaryTest()) -test_list.append(VGGBlockNCHWTest()) -test_list.append(VGGBlockTest()) -test_list.append(MultiConnectionNeighborEngineTest()) -test_list.append(ConstBroadcastTest()) - -for test in test_list: - test.debug = True - test.check_node_count = False - with RunTest() as context: - tests += 1 - if test.run(context): - passed_test += 1 - else: - failed_list.append(test.test_name) - print("Failed test: %s\n", test.test_name) - -if passed_test == tests: - print("Passed\n") -else: - print(("%d out of %d passed\n -- failed list:") % (passed_test, tests)) - for test in failed_list: - print(" - " + test) diff --git a/tensorflow/contrib/tensorrt/test/utilities.py b/tensorflow/contrib/tensorrt/test/utilities.py deleted file mode 100644 index 0ea5f5b883..0000000000 --- a/tensorflow/contrib/tensorrt/test/utilities.py +++ /dev/null @@ -1,30 +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. -# ============================================================================== -"""Utilities script for TF-TensorRT integration tests.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -from tensorflow.python.ops import variables - - -def get_all_variables(sess): - var_names = sess.run(variables.report_uninitialized_variables()) - names_var_list = {} - for name in var_names: - names_var_list[name] = sess.graph.get_tensor_by_name(name + ":0") - print(var_names) - return names_var_list -- GitLab From 653b290777cff0d46a669bef0e67c995c762d99d Mon Sep 17 00:00:00 2001 From: Jon Perl Date: Mon, 16 Jul 2018 18:17:10 -0400 Subject: [PATCH 100/519] Make InMemoryEvaluatorHook test agnostic to different platforms keys() order --- tensorflow/contrib/estimator/python/estimator/hooks_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/estimator/python/estimator/hooks_test.py b/tensorflow/contrib/estimator/python/estimator/hooks_test.py index e094dade6a..ee88d5ecf5 100644 --- a/tensorflow/contrib/estimator/python/estimator/hooks_test.py +++ b/tensorflow/contrib/estimator/python/estimator/hooks_test.py @@ -111,7 +111,7 @@ class InMemoryEvaluatorHookTest(test.TestCase): self.assertEqual(4.5, step_keyword_to_value[8]['mean_of_features']) # end self.assertEqual(4.5, step_keyword_to_value[10]['mean_of_features']) - self.assertEqual([0, 4, 8, 10], list(step_keyword_to_value.keys())) + self.assertEqual(set([0, 4, 8, 10]), set(step_keyword_to_value.keys())) def test_uses_latest_variable_value(self): -- GitLab From 545458669207ab1ffbbb531c40f709a22130043f Mon Sep 17 00:00:00 2001 From: Clayne Robison Date: Mon, 16 Jul 2018 17:14:26 -0700 Subject: [PATCH 101/519] Adding support for MKL builds with AVX2. MKL-DNN ignores the compiler switches and takes code paths based on the platform detected at runtime. This commit will add support for avx2 instructions to the rest of TensorFlow. --- .../ci_build/linux/mkl/build-dev-container.sh | 25 +++++++++++++++++++ 1 file changed, 25 insertions(+) diff --git a/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh b/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh index ad22ebe4eb..1d9c832d66 100755 --- a/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh +++ b/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh @@ -34,6 +34,9 @@ echo "TF_DOCKER_BUILD_DEVEL_BRANCH=${TF_DOCKER_BUILD_DEVEL_BRANCH}" echo "TF_DOCKER_BUILD_IMAGE_NAME=${TF_DOCKER_BUILD_IMAGE_NAME}" echo "TF_DOCKER_BUILD_VERSION=${TF_DOCKER_BUILD_VERSION}" +# Build containers for AVX +#"TF_BAZEL_BUILD_OPTIONS": "'{}'" (default build option= avx) + # build the python 2 container and whl TF_DOCKER_BUILD_TYPE="MKL" \ TF_DOCKER_BUILD_IS_DEVEL="YES" \ @@ -49,5 +52,27 @@ TF_DOCKER_BUILD_TYPE="MKL" \ TF_DOCKER_BUILD_IMAGE_NAME="${TF_DOCKER_BUILD_IMAGE_NAME}" \ TF_DOCKER_BUILD_VERSION="${TF_DOCKER_BUILD_VERSION}" \ TF_DOCKER_BUILD_PYTHON_VERSION="PYTHON3" \ + ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh + +# Build containers for AVX2 +TF_BAZEL_BUILD_OPTIONS="--config=mkl --copt=-mavx2 --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0" + +# build the python 2 container and whl +TF_DOCKER_BUILD_TYPE="MKL" \ + TF_DOCKER_BUILD_IS_DEVEL="YES" \ + TF_DOCKER_BUILD_DEVEL_BRANCH="${TF_DOCKER_BUILD_DEVEL_BRANCH}" \ + TF_DOCKER_BUILD_IMAGE_NAME="${TF_DOCKER_BUILD_IMAGE_NAME}" \ + TF_DOCKER_BUILD_VERSION="${TF_DOCKER_BUILD_VERSION}-avx2" \ + TF_BAZEL_BUILD_OPTIONS="${TF_BAZEL_BUILD_OPTIONS}" \ ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh +# build the python 3 container and whl +TF_DOCKER_BUILD_TYPE="MKL" \ + TF_DOCKER_BUILD_IS_DEVEL="YES" \ + TF_DOCKER_BUILD_DEVEL_BRANCH="${TF_DOCKER_BUILD_DEVEL_BRANCH}" \ + TF_DOCKER_BUILD_IMAGE_NAME="${TF_DOCKER_BUILD_IMAGE_NAME}" \ + TF_DOCKER_BUILD_VERSION="${TF_DOCKER_BUILD_VERSION}-avx2" \ + TF_DOCKER_BUILD_PYTHON_VERSION="PYTHON3" \ + TF_BAZEL_BUILD_OPTIONS="${TF_BAZEL_BUILD_OPTIONS}" \ + ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh + -- GitLab From 6b7198b495ae7a4acd9604dbeda41e7855f97bdd Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Mon, 16 Jul 2018 20:00:48 -0700 Subject: [PATCH 102/519] Refactor the test base class so each subclass can define its own graph. --- tensorflow/contrib/tensorrt/test/base_test.py | 163 +++++++++--------- .../test/tf_trt_integration_test_base.py | 73 +++++--- 2 files changed, 126 insertions(+), 110 deletions(-) diff --git a/tensorflow/contrib/tensorrt/test/base_test.py b/tensorflow/contrib/tensorrt/test/base_test.py index 4b9e6d668f..f057e377cb 100644 --- a/tensorflow/contrib/tensorrt/test/base_test.py +++ b/tensorflow/contrib/tensorrt/test/base_test.py @@ -30,96 +30,93 @@ from tensorflow.python.platform import test from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test -# TODO(aaroey): test graph with different dtypes. -def _GetSingleEngineGraphDef(dtype=dtypes.float32): - """Create a graph containing single segment.""" - input_dims = [100, 24, 24, 2] - g = ops.Graph() - with g.as_default(): - inp = array_ops.placeholder( - dtype=dtype, shape=[None] + input_dims[1:], name=trt_test.INPUT_NAME) - with g.device("/GPU:0"): - conv_filter = constant_op.constant( - [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], - name="weights", - dtype=dtype) - conv = nn.conv2d( - input=inp, - filter=conv_filter, - strides=[1, 2, 2, 1], - padding="SAME", - name="conv") - bias = constant_op.constant( - [4., 1.5, 2., 3., 5., 7.], name="bias", dtype=dtype) - added = nn.bias_add(conv, bias, name="bias_add") - relu = nn.relu(added, "relu") - identity = array_ops.identity(relu, "identity") - pool = nn_ops.max_pool( - identity, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") - array_ops.squeeze(pool, name=trt_test.OUTPUT_NAME) - return trt_test.TfTrtIntegrationTestParams( - graph_name="SimpleSingleEngine", - gdef=g.as_graph_def(), - input_dims=input_dims, - num_expected_engines=1, - expected_output_dims=(100, 6, 6, 6), - allclose_atol=1.e-03, - allclose_rtol=1.e-03) +class SimpleSingleEngineGraphDefTest(trt_test.TfTrtIntegrationTestBase): + def GetParams(self): + """Create a graph containing single segment.""" + # TODO(aaroey): test graph with different dtypes. + dtype = dtypes.float32 + input_dims = [100, 24, 24, 2] + g = ops.Graph() + with g.as_default(): + inp = array_ops.placeholder( + dtype=dtype, shape=[None] + input_dims[1:], name=self.input_name) + with g.device("/GPU:0"): + conv_filter = constant_op.constant( + [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], + name="weights", + dtype=dtype) + conv = nn.conv2d( + input=inp, + filter=conv_filter, + strides=[1, 2, 2, 1], + padding="SAME", + name="conv") + bias = constant_op.constant( + [4., 1.5, 2., 3., 5., 7.], name="bias", dtype=dtype) + added = nn.bias_add(conv, bias, name="bias_add") + relu = nn.relu(added, "relu") + identity = array_ops.identity(relu, "identity") + pool = nn_ops.max_pool( + identity, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") + array_ops.squeeze(pool, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_dims=input_dims, + num_expected_engines=1, + expected_output_dims=(100, 6, 6, 6), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) -# TODO(aaroey): test graph with different dtypes. -def _GetMultiEngineGraphDef(dtype=dtypes.float32): - """Create a graph containing multiple segment.""" - input_dims = [100, 24, 24, 2] - g = ops.Graph() - with g.as_default(): - inp = array_ops.placeholder( - dtype=dtype, shape=[None] + input_dims[1:], name=trt_test.INPUT_NAME) - with g.device("/GPU:0"): - conv_filter = constant_op.constant( - [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], - name="weights", - dtype=dtype) - conv = nn.conv2d( - input=inp, - filter=conv_filter, - strides=[1, 2, 2, 1], - padding="SAME", - name="conv") - c1 = constant_op.constant( - np.random.randn(input_dims[0], 12, 12, 6), dtype=dtype) - p = conv * c1 - c2 = constant_op.constant( - np.random.randn(input_dims[0], 12, 12, 6), dtype=dtype) - q = conv / c2 - edge = trt_test.TRT_INCOMPATIBLE_OP(q) - edge /= edge - r = edge + edge +class SimpleMultiEngineGraphDefTest(trt_test.TfTrtIntegrationTestBase): - p -= edge - q *= edge - s = p + q - s -= r - array_ops.squeeze(s, name=trt_test.OUTPUT_NAME) - return trt_test.TfTrtIntegrationTestParams( - graph_name="SimpleMultipleEngines", - gdef=g.as_graph_def(), - input_dims=input_dims, - num_expected_engines=2, - expected_output_dims=(100, 12, 12, 6), - allclose_atol=1.e-03, - allclose_rtol=1.e-03) + def GetParams(self): + """Create a graph containing multiple segment.""" + # TODO(aaroey): test graph with different dtypes. + dtype = dtypes.float32 + input_dims = [100, 24, 24, 2] + g = ops.Graph() + with g.as_default(): + inp = array_ops.placeholder( + dtype=dtype, shape=[None] + input_dims[1:], name=self.input_name) + with g.device("/GPU:0"): + conv_filter = constant_op.constant( + [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], + name="weights", + dtype=dtype) + conv = nn.conv2d( + input=inp, + filter=conv_filter, + strides=[1, 2, 2, 1], + padding="SAME", + name="conv") + c1 = constant_op.constant( + np.random.randn(input_dims[0], 12, 12, 6), dtype=dtype) + p = conv * c1 + c2 = constant_op.constant( + np.random.randn(input_dims[0], 12, 12, 6), dtype=dtype) + q = conv / c2 + edge = self.trt_incompatible_op(q) + edge /= edge + r = edge + edge -class BaseTest(trt_test.TfTrtIntegrationTestBase): - """Class to test Tensorflow-TensorRT integration.""" - pass + p -= edge + q *= edge + s = p + q + s -= r + array_ops.squeeze(s, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_dims=input_dims, + num_expected_engines=2, + expected_output_dims=(100, 12, 12, 6), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) +# TODO(aaroey): add a large complex graph to test. + if __name__ == "__main__": - # TODO(aaroey): add a large complex graph to test. - trt_test.AddTests(BaseTest, - [_GetSingleEngineGraphDef(), - _GetMultiEngineGraphDef()]) test.main() diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py index 980cc87366..b1dc7b649f 100644 --- a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py +++ b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py @@ -22,7 +22,6 @@ from collections import namedtuple import itertools import warnings import numpy as np -import re import six from tensorflow.contrib import tensorrt as trt @@ -36,23 +35,36 @@ from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging TfTrtIntegrationTestParams = namedtuple("TfTrtIntegrationTestParams", [ - "graph_name", "gdef", "input_dims", "num_expected_engines", - "expected_output_dims", "allclose_atol", "allclose_rtol" + "gdef", "input_dims", "num_expected_engines", "expected_output_dims", + "allclose_atol", "allclose_rtol" ]) -INPUT_NAME = "input" -OUTPUT_NAME = "output" -TRT_INCOMPATIBLE_OP = math_ops.sin PRECISION_MODES = ["FP32", "FP16", "INT8"] -def IsQuantizationMode(mode): +def _IsQuantizationMode(mode): return mode == "INT8" class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): """Class to test Tensorflow-TensorRT integration.""" + @property + def input_name(self): + return "input" + + @property + def output_name(self): + return "output" + + @property + def trt_incompatible_op(self): + return math_ops.sin + + @property + def precision_modes(self): + return ["FP32", "FP16", "INT8"] + def _ToBytes(self, s): if six.PY2: return s @@ -70,6 +82,10 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): super(TfTrtIntegrationTestBase, self).setUp() warnings.simplefilter("always") + def GetParams(self): + """Return a TfTrtIntegrationTestParams for test, implemented by subclass.""" + raise NotImplementedError() + def _GetConfigProto(self, params, use_optimizer, @@ -104,7 +120,9 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): g = ops.Graph() with g.as_default(): inp, out = importer.import_graph_def( - graph_def=gdef, return_elements=[INPUT_NAME, OUTPUT_NAME], name="") + graph_def=gdef, + return_elements=[self.input_name, self.output_name], + name="") inp = inp.outputs[0] out = out.outputs[0] with self.test_session( @@ -129,7 +147,7 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): """Return trt converted graphdef.""" return trt.create_inference_graph( input_graph_def=gdef, - outputs=[OUTPUT_NAME], + outputs=[self.output_name], max_batch_size=params.input_dims[0], max_workspace_size_bytes=1 << 25, precision_mode=precision_mode, @@ -150,7 +168,7 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): self.assertNotEqual("", n.attr["segment_funcdef_name"].s) self.assertEquals(n.attr["precision_mode"].s, precision_mode) self.assertEquals(n.attr["static_engine"].b, not dynamic_engine) - if IsQuantizationMode(precision_mode) and is_calibrated: + if _IsQuantizationMode(precision_mode) and is_calibrated: self.assertNotEqual("", n.attr["calibration_data"].s) else: self.assertEquals("", n.attr["calibration_data"].s) @@ -173,7 +191,7 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): ref_result = self._RunGraph(params, input_gdef, inp, config_no_trt) # Run calibration if necessary. - if IsQuantizationMode(precision_mode): + if _IsQuantizationMode(precision_mode): calib_config = self._GetConfigProto(params, use_optimizer, precision_mode, dynamic_calib_engine) @@ -228,18 +246,17 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): pass -def AddTests(test_class, params_list): +def _AddTests(test_class): - def _GetTest(params, use_optimizer, precision_mode, dynamic_infer_engine, + def _GetTest(use_optimizer, precision_mode, dynamic_infer_engine, dynamic_calib_engine): def _Test(self): + params = self.GetParams() logging.info( - "Running test with parameters: graph_name=%s, " - "use_optimizer=%s, precision_mode=%s, " - "dynamic_infer_engine=%s, dynamic_calib_engine=%s", params.graph_name, - use_optimizer, precision_mode, dynamic_infer_engine, - dynamic_calib_engine) + "Running test with parameters: use_optimizer=%s, precision_mode=%s, " + "dynamic_infer_engine=%s, dynamic_calib_engine=%s", use_optimizer, + precision_mode, dynamic_infer_engine, dynamic_calib_engine) self._RunTest(params, use_optimizer, precision_mode, dynamic_infer_engine, dynamic_calib_engine) @@ -248,11 +265,11 @@ def AddTests(test_class, params_list): use_optimizer_options = [False, True] dynamic_infer_engine_options = [False, True] dynamic_calib_engine_options = [False, True] - for (params, use_optimizer, precision_mode, + for (use_optimizer, precision_mode, dynamic_infer_engine, dynamic_calib_engine) in itertools.product( - params_list, use_optimizer_options, PRECISION_MODES, - dynamic_infer_engine_options, dynamic_calib_engine_options): - if IsQuantizationMode(precision_mode): + use_optimizer_options, PRECISION_MODES, dynamic_infer_engine_options, + dynamic_calib_engine_options): + if _IsQuantizationMode(precision_mode): if not dynamic_calib_engine and dynamic_infer_engine: # TODO(aaroey): test this case, the conversion from static calibration # engine to dynamic inference engine should be a noop. @@ -283,11 +300,13 @@ def AddTests(test_class, params_list): if precision_mode == "INT8": calib_engine_type = ("DynamicCalibEngine" if dynamic_calib_engine else "StaticCalibEngine") - test_name = "%s_%s_%s_%s%s" % (re.sub( - "[^a-zA-Z0-9]+", "", params.graph_name), conversion, precision_mode, - infer_engine_type, ("_" + calib_engine_type) - if len(calib_engine_type) else "") + test_name = "%s_%s_%s%s" % (conversion, precision_mode, infer_engine_type, + ("_" + calib_engine_type) + if len(calib_engine_type) else "") setattr( test_class, "testTfTRT_" + test_name, - _GetTest(params, use_optimizer, precision_mode, dynamic_infer_engine, + _GetTest(use_optimizer, precision_mode, dynamic_infer_engine, dynamic_calib_engine)) + + +_AddTests(TfTrtIntegrationTestBase) -- GitLab From e02fbb25784498b44e73d9370da65a3f23f6de15 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Tue, 17 Jul 2018 08:02:04 -0700 Subject: [PATCH 103/519] Fix review comments and formatting issues. --- .../contrib/tensorrt/convert/convert_graph.cc | 4 ++-- .../contrib/tensorrt/convert/convert_nodes.cc | 17 +++++++++-------- 2 files changed, 11 insertions(+), 10 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_graph.cc b/tensorflow/contrib/tensorrt/convert/convert_graph.cc index 3b42a5ee96..8a0e4caa9c 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_graph.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_graph.cc @@ -49,9 +49,9 @@ limitations under the License. #include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/types.h" -#include "tensorflow/core/protobuf/config.pb.h" // NOLINT +#include "tensorflow/core/protobuf/config.pb.h" // NOLINT #include "tensorflow/core/protobuf/device_properties.pb.h" // NOLINT -#include "tensorflow/core/protobuf/rewriter_config.pb.h" // NOLINT +#include "tensorflow/core/protobuf/rewriter_config.pb.h" // NOLINT #include "tensorflow/core/util/device_name_utils.h" #if GOOGLE_CUDA diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index e4ffc230e4..4dee51e1e8 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -125,8 +125,8 @@ void GetInputProperties(const grappler::GraphProperties& graph_properties, void GetOutputProperties(const grappler::GraphProperties& graph_properties, const Node* outside_node, const int in_port, - PartialTensorShape* shape, - tensorflow::DataType* dtype) { + PartialTensorShape* shape, + tensorflow::DataType* dtype) { if (graph_properties.HasInputProperties(outside_node->name())) { auto input_params = graph_properties.GetInputProperties(outside_node->name()); @@ -141,10 +141,11 @@ void GetOutputProperties(const grappler::GraphProperties& graph_properties, tensorflow::Status ValidateInputProperties(const PartialTensorShape& shape, const tensorflow::DataType dtype, nvinfer1::DataType* trt_dtype) { + // TODO(aaroey): some of these checks also apply to IsTensorRTCandidate(), so + // put them there instead. TF_RETURN_IF_ERROR(ConvertDType(dtype, trt_dtype)); if (shape.dims() < 0) { - return tensorflow::errors::InvalidArgument( - "Input tensor rank is unknown."); + return tensorflow::errors::InvalidArgument("Input tensor rank is unknown."); } if (shape.dims() > 8) { return tensorflow::errors::OutOfRange( @@ -153,7 +154,7 @@ tensorflow::Status ValidateInputProperties(const PartialTensorShape& shape, for (int d = 1; d < shape.dims(); ++d) { if (shape.dim_size(d) < 0) { return tensorflow::errors::InvalidArgument( - "Input tensor has a unknow non-batch dimemension at dim ", d); + "Input tensor has a unknown non-batch dimemension at dim ", d); } } return Status::OK(); @@ -2703,9 +2704,9 @@ tensorflow::Status ConvertGraphDefToEngine( auto status = ValidateInputProperties( shape, node_def.attr().at("dtype").type(), &dtype); if (!status.ok()) { - const string error_message = StrCat( - "Validation failed for ", node_name, " and input slot ", - slot_number, ": ", status.error_message()); + const string error_message = + StrCat("Validation failed for ", node_name, " and input slot ", + slot_number, ": ", status.error_message()); LOG(WARNING) << error_message; return Status(status.code(), error_message); } -- GitLab From e94b49718f800276d2c045349db4480bbe12dd6b Mon Sep 17 00:00:00 2001 From: Paul Woitaschek Date: Tue, 17 Jul 2018 21:03:38 +0200 Subject: [PATCH 104/519] Update tflite_convert.py --- tensorflow/contrib/lite/python/tflite_convert.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/python/tflite_convert.py b/tensorflow/contrib/lite/python/tflite_convert.py index 9bd1f4f76e..d17482e601 100644 --- a/tensorflow/contrib/lite/python/tflite_convert.py +++ b/tensorflow/contrib/lite/python/tflite_convert.py @@ -257,7 +257,7 @@ def run_main(_): parser.add_argument( "--input_arrays", type=str, - help="Names of the output arrays, comma-separated.") + help="Names of the input arrays, comma-separated.") parser.add_argument( "--input_shapes", type=str, -- GitLab From dee0908764c391b275c9eac737f6e480fc8e8310 Mon Sep 17 00:00:00 2001 From: Clayne Robison Date: Tue, 17 Jul 2018 21:36:45 -0700 Subject: [PATCH 105/519] Adding better support for avx- and avx2-class instruction sets. -march=sandybridge enables MMX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, POPCNT, AVX, AES and PCLMUL instruction set support. See https://gcc.gnu.org/onlinedocs/gcc-5.4.0/gcc/x86-Options.html#x86-Options -march=haswell enables MOVBE, MMX, SSE, SSE2, SSE3, SSSE3, SSE4.1, SSE4.2, POPCNT, AVX, AVX2, AES, PCLMUL, FSGSBASE, RDRND, FMA, BMI, BMI2 and F16C instruction set support. See https://gcc.gnu.org/onlinedocs/gcc-5.4.0/gcc/x86-Options.html#x86-Options --- tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh b/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh index 1d9c832d66..7de58ef625 100755 --- a/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh +++ b/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh @@ -35,7 +35,8 @@ echo "TF_DOCKER_BUILD_IMAGE_NAME=${TF_DOCKER_BUILD_IMAGE_NAME}" echo "TF_DOCKER_BUILD_VERSION=${TF_DOCKER_BUILD_VERSION}" # Build containers for AVX -#"TF_BAZEL_BUILD_OPTIONS": "'{}'" (default build option= avx) +# Include the instructions for sandybridge and later, but tune for ivybridge +TF_BAZEL_BUILD_OPTIONS="--config=mkl --copt=-march=sandybridge --copt=-mtune=ivybridge --copt=-O3 --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0" # build the python 2 container and whl TF_DOCKER_BUILD_TYPE="MKL" \ @@ -55,7 +56,8 @@ TF_DOCKER_BUILD_TYPE="MKL" \ ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh # Build containers for AVX2 -TF_BAZEL_BUILD_OPTIONS="--config=mkl --copt=-mavx2 --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0" +# Include the instructions for haswell and later, but tune for broadwell +TF_BAZEL_BUILD_OPTIONS="--config=mkl --copt=-march=haswell --copt=-mtune=broadwell --copt=-O3 --cxxopt=-D_GLIBCXX_USE_CXX11_ABI=0" # build the python 2 container and whl TF_DOCKER_BUILD_TYPE="MKL" \ -- GitLab From f843a6210073ddda131a9fbef520539e1329fcd7 Mon Sep 17 00:00:00 2001 From: Jongmin Park Date: Wed, 18 Jul 2018 16:44:20 +0900 Subject: [PATCH 106/519] Edit a link to notebooks directory Bottom of this document, a link to **notebooks directory** who has tutorial misslinked. Need to change from `..` to `../notebooks`. --- tensorflow/tools/docker/notebooks/1_hello_tensorflow.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/docker/notebooks/1_hello_tensorflow.ipynb b/tensorflow/tools/docker/notebooks/1_hello_tensorflow.ipynb index 0633b03259..8fa871ef77 100644 --- a/tensorflow/tools/docker/notebooks/1_hello_tensorflow.ipynb +++ b/tensorflow/tools/docker/notebooks/1_hello_tensorflow.ipynb @@ -665,7 +665,7 @@ "source": [ "## What's next?\n", "\n", - "This has been a gentle introduction to TensorFlow, focused on what TensorFlow is and the very basics of doing anything in TensorFlow. If you'd like more, the next tutorial in the series is Getting Started with TensorFlow, also available in the [notebooks directory](..)." + "This has been a gentle introduction to TensorFlow, focused on what TensorFlow is and the very basics of doing anything in TensorFlow. If you'd like more, the next tutorial in the series is Getting Started with TensorFlow, also available in the [notebooks directory](../notebooks)." ] } ], -- GitLab From be8184f8e002576aa2ef3274436dea68e9173c5f Mon Sep 17 00:00:00 2001 From: Kenneth Blomqvist Date: Wed, 18 Jul 2018 15:44:31 +0300 Subject: [PATCH 107/519] Fix extract image patches float type issue --- tensorflow/python/ops/array_grad.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/ops/array_grad.py b/tensorflow/python/ops/array_grad.py index fe459a96b9..a2b5f77f91 100644 --- a/tensorflow/python/ops/array_grad.py +++ b/tensorflow/python/ops/array_grad.py @@ -790,7 +790,7 @@ def _ExtractImagePatchesGrad(op, grad): sp_mat = sparse_tensor.SparseTensor( array_ops.constant(idx, dtype=ops.dtypes.int64), - array_ops.ones((len(idx),), dtype=ops.dtypes.float32), sp_shape) + array_ops.ones((len(idx),), dtype=grad.dtype), sp_shape) jac = sparse_ops.sparse_tensor_dense_matmul(sp_mat, grad_flat) -- GitLab From aa7960d9187131039b7122f66e60dd89dd5a90bb Mon Sep 17 00:00:00 2001 From: wim glenn Date: Wed, 18 Jul 2018 10:12:16 -0500 Subject: [PATCH 108/519] Recommend the user site, no sudo --- tensorflow/docs_src/install/install_linux.md | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 7534d0fac1..0d9b6af093 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -65,7 +65,7 @@ We *recommend* using `pip` version 8.1 or higher. If using a release before version 8.1, upgrade `pip`: 
-  sudo pip install -U pip
+  pip install -U pip
If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is @@ -198,7 +198,7 @@ We *recommend* using `pip` version 8.1 or higher. If using a release before version 8.1, upgrade `pip`: 
-  sudo pip install -U pip
+  pip install -U pip
If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is @@ -220,8 +220,8 @@ Choose one of the available TensorFlow packages for installation: And use `pip` to install the package for Python 2 or 3: 
-  sudo pip install -U tensorflow   # Python 2.7
-  sudo pip3 install -U tensorflow  # Python 3.n
+  pip install -U --user tensorflow   # Python 2.7
+  pip3 install -U --user tensorflow  # Python 3.n
Use `pip list` to show the packages installed on the system. @@ -239,8 +239,8 @@ If the above steps failed, try installing the TensorFlow binary using the remote URL of the `pip` package: 
-  sudo pip install --upgrade remote-pkg-URL   # Python 2.7
-  sudo pip3 install --upgrade remote-pkg-URL  # Python 3.n
+  pip install --user --upgrade remote-pkg-URL   # Python 2.7
+  pip3 install --user --upgrade remote-pkg-URL  # Python 3.n
The remote-pkg-URL depends on the operating system, Python version, @@ -255,8 +255,8 @@ encounter problems. To uninstall TensorFlow on your system, use one of following commands: 
-  sudo pip uninstall tensorflow   # for Python 2.7
-  sudo pip3 uninstall tensorflow  # for Python 3.n
+  pip uninstall tensorflow   # for Python 2.7
+  pip3 uninstall tensorflow  # for Python 3.n
-- GitLab From cb297f637e461839ff85c4557116fa90003daec7 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Wed, 18 Jul 2018 08:42:13 -0700 Subject: [PATCH 109/519] Add support for multiple inputs. --- tensorflow/contrib/tensorrt/test/base_test.py | 12 ++++-- .../test/tf_trt_integration_test_base.py | 40 ++++++++++--------- 2 files changed, 29 insertions(+), 23 deletions(-) diff --git a/tensorflow/contrib/tensorrt/test/base_test.py b/tensorflow/contrib/tensorrt/test/base_test.py index f057e377cb..5ec7c7094e 100644 --- a/tensorflow/contrib/tensorrt/test/base_test.py +++ b/tensorflow/contrib/tensorrt/test/base_test.py @@ -36,11 +36,12 @@ class SimpleSingleEngineGraphDefTest(trt_test.TfTrtIntegrationTestBase): """Create a graph containing single segment.""" # TODO(aaroey): test graph with different dtypes. dtype = dtypes.float32 + input_name = "input" input_dims = [100, 24, 24, 2] g = ops.Graph() with g.as_default(): inp = array_ops.placeholder( - dtype=dtype, shape=[None] + input_dims[1:], name=self.input_name) + dtype=dtype, shape=[None] + input_dims[1:], name=input_name) with g.device("/GPU:0"): conv_filter = constant_op.constant( [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], @@ -62,7 +63,8 @@ class SimpleSingleEngineGraphDefTest(trt_test.TfTrtIntegrationTestBase): array_ops.squeeze(pool, name=self.output_name) return trt_test.TfTrtIntegrationTestParams( gdef=g.as_graph_def(), - input_dims=input_dims, + input_names=[input_name], + input_dims=[input_dims], num_expected_engines=1, expected_output_dims=(100, 6, 6, 6), allclose_atol=1.e-03, @@ -75,11 +77,12 @@ class SimpleMultiEngineGraphDefTest(trt_test.TfTrtIntegrationTestBase): """Create a graph containing multiple segment.""" # TODO(aaroey): test graph with different dtypes. dtype = dtypes.float32 + input_name = "input" input_dims = [100, 24, 24, 2] g = ops.Graph() with g.as_default(): inp = array_ops.placeholder( - dtype=dtype, shape=[None] + input_dims[1:], name=self.input_name) + dtype=dtype, shape=[None] + input_dims[1:], name=input_name) with g.device("/GPU:0"): conv_filter = constant_op.constant( [[[[1., 0.5, 4., 6., 0.5, 1.], [1., 0.5, 1., 1., 0.5, 1.]]]], @@ -109,7 +112,8 @@ class SimpleMultiEngineGraphDefTest(trt_test.TfTrtIntegrationTestBase): array_ops.squeeze(s, name=self.output_name) return trt_test.TfTrtIntegrationTestParams( gdef=g.as_graph_def(), - input_dims=input_dims, + input_names=[input_name], + input_dims=[input_dims], num_expected_engines=2, expected_output_dims=(100, 12, 12, 6), allclose_atol=1.e-03, diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py index 0f3b1eb37d..d7a7c8d998 100644 --- a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py +++ b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py @@ -35,8 +35,8 @@ from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging TfTrtIntegrationTestParams = namedtuple("TfTrtIntegrationTestParams", [ - "gdef", "input_dims", "num_expected_engines", "expected_output_dims", - "allclose_atol", "allclose_rtol" + "gdef", "input_names", "input_dims", "num_expected_engines", + "expected_output_dims", "allclose_atol", "allclose_rtol" ]) PRECISION_MODES = ["FP32", "FP16", "INT8"] @@ -49,10 +49,6 @@ def _IsQuantizationMode(mode): class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): """Class to test Tensorflow-TensorRT integration.""" - @property - def input_name(self): - return "input" - @property def output_name(self): return "output" @@ -98,7 +94,8 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): custom_op = rewriter_cfg.custom_optimizers.add() custom_op.name = "TensorRTOptimizer" custom_op.parameter_map["minimum_segment_size"].i = 3 - custom_op.parameter_map["max_batch_size"].i = params.input_dims[0] + custom_op.parameter_map["max_batch_size"].i = max( + [dims[0] for dims in params.input_dims]) custom_op.parameter_map["is_dynamic_op"].b = is_dynamic_op custom_op.parameter_map["max_workspace_size_bytes"].i = 1 << 25 custom_op.parameter_map["precision_mode"].s = self._ToBytes( @@ -117,20 +114,23 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): def _RunGraph(self, params, gdef, input_data, config, num_runs=2): """Run given graphdef multiple times.""" + assert len(params.input_names) == len(input_data) g = ops.Graph() with g.as_default(): - inp, out = importer.import_graph_def( + io_ops = importer.import_graph_def( graph_def=gdef, - return_elements=[self.input_name, self.output_name], + return_elements=params.input_names + [self.output_name], name="") - inp = inp.outputs[0] - out = out.outputs[0] + inp = [i.outputs[0] for i in io_ops[:-1]] + assert len(inp) == len(input_data) + out = io_ops[-1].outputs[0] with self.test_session( graph=g, config=config, use_gpu=True, force_gpu=True) as sess: val = None # Defaults to 2 runs to verify result across multiple runs is same. for _ in range(num_runs): - new_val = sess.run(out, {inp: input_data}) + new_val = sess.run(out, + {inp[i]: input_data[i] for i in range(len(inp))}) self.assertEquals(params.expected_output_dims, new_val.shape) if val is not None: self.assertAllEqual(new_val, val) @@ -148,7 +148,7 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): return trt.create_inference_graph( input_graph_def=gdef, outputs=[self.output_name], - max_batch_size=params.input_dims[0], + max_batch_size=max([dims[0] for dims in params.input_dims]), max_workspace_size_bytes=1 << 25, precision_mode=precision_mode, minimum_segment_size=2, @@ -180,7 +180,7 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): def _RunTest(self, params, use_optimizer, precision_mode, dynamic_infer_engine, dynamic_calib_engine): assert precision_mode in PRECISION_MODES - inp = np.random.random_sample(params.input_dims) + input_data = [np.random.random_sample(dims) for dims in params.input_dims] input_gdef = params.gdef self._VerifyGraphDef(params, input_gdef) @@ -188,7 +188,7 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): config_no_trt = self._GetConfigProto(params, False) logging.info("Running original graph w/o trt, config:\n%s", str(config_no_trt)) - ref_result = self._RunGraph(params, input_gdef, inp, config_no_trt) + ref_result = self._RunGraph(params, input_gdef, input_data, config_no_trt) # Run calibration if necessary. if _IsQuantizationMode(precision_mode): @@ -200,13 +200,15 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): self.assertTrue(False) # TODO(aaroey): uncomment this and get infer_gdef when this mode is # supported. - # result = self._RunCalibration(params, input_gdef, inp, calib_config) + # result = self._RunCalibration(params, input_gdef, input_data, + # calib_config) else: calib_gdef = self._GetTrtGraphDef(params, input_gdef, precision_mode, dynamic_calib_engine) self._VerifyGraphDef(params, calib_gdef, precision_mode, False, dynamic_calib_engine) - result = self._RunCalibration(params, calib_gdef, inp, calib_config) + result = self._RunCalibration(params, calib_gdef, input_data, + calib_config) infer_gdef = trt.calib_graph_to_infer_graph(calib_gdef) self._VerifyGraphDef(params, infer_gdef, precision_mode, True, dynamic_calib_engine) @@ -225,13 +227,13 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): logging.info("Running final inference graph, config:\n%s", str(infer_config)) if use_optimizer: - result = self._RunGraph(params, infer_gdef, inp, infer_config) + result = self._RunGraph(params, infer_gdef, input_data, infer_config) else: trt_infer_gdef = self._GetTrtGraphDef(params, infer_gdef, precision_mode, dynamic_infer_engine) self._VerifyGraphDef(params, trt_infer_gdef, precision_mode, True, dynamic_infer_engine) - result = self._RunGraph(params, trt_infer_gdef, inp, infer_config) + result = self._RunGraph(params, trt_infer_gdef, input_data, infer_config) self.assertAllClose( ref_result, -- GitLab From 0e6bb6e3358a741bd995cb9b0055091c6b42a632 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Wed, 18 Jul 2018 10:25:37 -0700 Subject: [PATCH 110/519] Set plugin_converter_ and enable tests for custom_plugin_example. --- tensorflow/contrib/tensorrt/convert/convert_nodes.cc | 2 ++ tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD | 5 ++--- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc index 65fef27533..49e825151a 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.cc +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.cc @@ -2588,6 +2588,8 @@ void Converter::register_op_converters() { op_registry_["BatchMatMul"] = ConvertBatchMatMul; op_registry_["TopKV2"] = ConvertTopK; #endif + + plugin_converter_ = ConvertPlugin; } } // namespace diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD index a89cf3ab8b..1ef1c3de75 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD @@ -111,8 +111,7 @@ cuda_py_test( "//tensorflow/python:tf_optimizer", ], tags = [ - "manual", - "noguitar", - "notap", + "no_windows", + "nomac", ], ) -- GitLab From 2a0958455799601068db054c130fa9573e7c1e22 Mon Sep 17 00:00:00 2001 From: Michael Case Date: Wed, 18 Jul 2018 10:37:01 -0700 Subject: [PATCH 111/519] Remove usage of remove_undocumented from core parallel_for. remove_undocumented is causing issues with our pip tests. remove_undocumented is not used anywhere else in core TF code and we have a new mechanism for annotating the public TF API. --- tensorflow/python/ops/parallel_for/__init__.py | 10 ---------- 1 file changed, 10 deletions(-) diff --git a/tensorflow/python/ops/parallel_for/__init__.py b/tensorflow/python/ops/parallel_for/__init__.py index b49d865968..dd8bc6d487 100644 --- a/tensorflow/python/ops/parallel_for/__init__.py +++ b/tensorflow/python/ops/parallel_for/__init__.py @@ -23,13 +23,3 @@ from tensorflow.python.ops.parallel_for.control_flow_ops import for_loop from tensorflow.python.ops.parallel_for.control_flow_ops import pfor from tensorflow.python.ops.parallel_for.gradients import batch_jacobian from tensorflow.python.ops.parallel_for.gradients import jacobian -from tensorflow.python.util.all_util import remove_undocumented - -_allowed_symbols = [ - 'pfor', - 'for_loop', - 'jacobian', - 'batch_jacobian', -] - -remove_undocumented(__name__, _allowed_symbols) -- GitLab From 34b588e35dae4f36ce205b713bc0a47e98097585 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Wed, 18 Jul 2018 17:37:45 +0000 Subject: [PATCH 112/519] Add additional shape validation for QuantizedAdd This fix add additional shape validation for QuantizedAdd with min_x, min_y, max_x, max_y. Additional unit tests have been added in math_ops_test.cc. Signed-off-by: Yong Tang --- tensorflow/core/ops/math_ops.cc | 7 +++++++ tensorflow/core/ops/math_ops_test.cc | 15 +++++++++++++++ 2 files changed, 22 insertions(+) diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index 4b0591c6e8..386ae9635a 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -1504,6 +1504,13 @@ REGISTER_OP("QuantizedAdd") .SetIsCommutative() .SetShapeFn([](InferenceContext* c) { TF_RETURN_IF_ERROR(shape_inference::BroadcastBinaryOpShapeFn(c)); + // min_x, max_x, min_y, max_y should be scalar. + ShapeHandle unused; + TF_RETURN_IF_ERROR(c->WithRank(c->input(2), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(3), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(4), 0, &unused)); + TF_RETURN_IF_ERROR(c->WithRank(c->input(5), 0, &unused)); + c->set_output(1, c->Scalar()); c->set_output(2, c->Scalar()); return Status::OK(); diff --git a/tensorflow/core/ops/math_ops_test.cc b/tensorflow/core/ops/math_ops_test.cc index 25dc033065..23f1538912 100644 --- a/tensorflow/core/ops/math_ops_test.cc +++ b/tensorflow/core/ops/math_ops_test.cc @@ -543,4 +543,19 @@ TEST(MathOpsTest, HistogramFixedWidth_ShapeFn) { INFER_OK(op, "[?];[2];[]", "[?]"); INFER_OK(op, "[?];[2];?", "[?]"); } + +TEST(MathOpsTest, QuantizedAdd_ShapeFn) { + ShapeInferenceTestOp op("QuantizedAdd"); + + INFER_OK(op, "?;?;?;?;?;?", "?;[];[]"); + INFER_OK(op, "?;?;[];[];[];[]", "?;[];[]"); + INFER_OK(op, "[1,2];?;[];[];[];[]", "?;[];[]"); + INFER_OK(op, "[];[2];[];[];[];[]", "[d1_0];[];[]"); + + // Rank checks on input scalars. + INFER_ERROR("must be rank 0", op, "?;?;[1];?;?;?"); + INFER_ERROR("must be rank 0", op, "?;?;?;[2];?;?"); + INFER_ERROR("must be rank 0", op, "?;?;?;?;[3];?"); + INFER_ERROR("must be rank 0", op, "?;?;?;?;?;[4]"); +} } // end namespace tensorflow -- GitLab From a10592ef7741d858466a980239fc95e65d7c66b6 Mon Sep 17 00:00:00 2001 From: Geoffrey Irving Date: Wed, 11 Jul 2018 13:05:14 -0700 Subject: [PATCH 113/519] Improve error messages for gather_nd and scatter_nd Use SliceDebugString to produce nice error messages using multidimensional indexes. --- tensorflow/core/kernels/gather_nd_op.cc | 7 ++++--- tensorflow/core/kernels/scatter_nd_op.cc | 6 ++++-- tensorflow/core/kernels/scatter_nd_op_test.cc | 2 +- tensorflow/python/kernel_tests/gather_nd_op_test.py | 12 ++++-------- .../python/kernel_tests/scatter_nd_ops_test.py | 4 ++-- 5 files changed, 15 insertions(+), 16 deletions(-) diff --git a/tensorflow/core/kernels/gather_nd_op.cc b/tensorflow/core/kernels/gather_nd_op.cc index 4e53291b7f..e50b7fe3bf 100644 --- a/tensorflow/core/kernels/gather_nd_op.cc +++ b/tensorflow/core/kernels/gather_nd_op.cc @@ -188,12 +188,13 @@ Status DoGatherNd(OpKernelContext* c, const Tensor& params, // bad_i will only return >= 0 on CPUs right now. if (bad_i >= 0) { + auto shape = indices.shape(); + shape.RemoveLastDims(1); return errors::InvalidArgument( - "flat indices[", bad_i, ", :] = [", + "indices", SliceDebugString(shape, bad_i), " = [", str_util::Join( gtl::ArraySlice(&indices_mat(bad_i, 0), indices_nd), ", "), - "] does not index into param (shape: ", params.shape().DebugString(), - ")."); + "] does not index into param shape ", params.shape().DebugString()); } } return Status::OK(); diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc index e1fc2ea128..5f300fb64d 100644 --- a/tensorflow/core/kernels/scatter_nd_op.cc +++ b/tensorflow/core/kernels/scatter_nd_op.cc @@ -537,11 +537,13 @@ Status DoScatterNd(OpKernelContext* c, const Tensor& indices, } } if (bad_i >= 0) { + auto slice_shape = indices.shape(); + slice_shape.RemoveLastDims(1); return errors::InvalidArgument( - "Invalid indices: ", SliceDebugString(indices.shape(), bad_i), " = [", + "indices", SliceDebugString(slice_shape, bad_i), " = [", str_util::Join( gtl::ArraySlice(&indices_flat(bad_i, 0), slice_dim), ", "), - "] does not index into ", shape.DebugString()); + "] does not index into shape ", shape.DebugString()); } return Status::OK(); } diff --git a/tensorflow/core/kernels/scatter_nd_op_test.cc b/tensorflow/core/kernels/scatter_nd_op_test.cc index c134a8dd5b..95ecc69c95 100644 --- a/tensorflow/core/kernels/scatter_nd_op_test.cc +++ b/tensorflow/core/kernels/scatter_nd_op_test.cc @@ -185,7 +185,7 @@ TEST_F(ScatterNdUpdateOpTest, Error_IndexOutOfRange) { {100, 101, 102, 777, 778, 779, 10000, 10001, 10002}); Status s = RunOpKernel(); EXPECT_TRUE(str_util::StrContains( - s.ToString(), "Invalid indices: [2,0] = [99] does not index into [5,3]")) + s.ToString(), "indices[2] = [99] does not index into shape [5,3]")) << s; } diff --git a/tensorflow/python/kernel_tests/gather_nd_op_test.py b/tensorflow/python/kernel_tests/gather_nd_op_test.py index 58e2a8ac2a..c0b419e1d1 100644 --- a/tensorflow/python/kernel_tests/gather_nd_op_test.py +++ b/tensorflow/python/kernel_tests/gather_nd_op_test.py @@ -203,8 +203,7 @@ class GatherNdTest(test.TestCase): indices = [[[0], [7]]] # Make this one higher rank gather_nd = array_ops.gather_nd(params, indices) with self.assertRaisesOpError( - r"flat indices\[1, :\] = \[7\] does not index into param " - r"\(shape: \[3\]\)"): + r"indices\[0,1\] = \[7\] does not index into param shape \[3\]"): gather_nd.eval() def _disabledTestBadIndicesGPU(self): @@ -217,8 +216,7 @@ class GatherNdTest(test.TestCase): indices = [[[0], [7]]] # Make this one higher rank gather_nd = array_ops.gather_nd(params, indices) with self.assertRaisesOpError( - r"flat indices\[1, :\] = \[7\] does not index into param " - r"\(shape: \[3\]\)"): + r"indices\[0,1\] = \[7\] does not index into param shape \[3\]"): gather_nd.eval() def testBadIndicesWithSlicesCPU(self): @@ -227,8 +225,7 @@ class GatherNdTest(test.TestCase): indices = [[[0], [0], [1]]] # Make this one higher rank gather_nd = array_ops.gather_nd(params, indices) with self.assertRaisesOpError( - r"flat indices\[2, :\] = \[1\] does not index into param " - r"\(shape: \[1,3\]\)"): + r"indices\[0,2\] = \[1\] does not index into param shape \[1,3\]"): gather_nd.eval() def _disabledTestBadIndicesWithSlicesGPU(self): @@ -241,8 +238,7 @@ class GatherNdTest(test.TestCase): indices = [[[0], [0], [1]]] # Make this one higher rank gather_nd = array_ops.gather_nd(params, indices) with self.assertRaisesOpError( - r"flat indices\[2, :\] = \[1\] does not index into param " - r"\(shape: \[1,3\]\)"): + r"indices\[0,2\] = \[1\] does not index into param shape \[1,3\]"): gather_nd.eval() def testGradientsRank2Elements(self): diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py index f9b9c77bbf..c31499e52d 100644 --- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py @@ -268,12 +268,12 @@ class StatefulScatterNdTest(test.TestCase): # Test some out of range errors. indices = np.array([[-1], [0], [5]]) with self.assertRaisesOpError( - r"Invalid indices: \[0,0\] = \[-1\] does not index into \[6\]"): + r"indices\[0\] = \[-1\] does not index into shape \[6\]"): op(ref, indices, updates).eval() indices = np.array([[2], [0], [6]]) with self.assertRaisesOpError( - r"Invalid indices: \[2,0\] = \[6\] does not index into \[6\]"): + r"indices\[2\] = \[6\] does not index into shape \[6\]"): op(ref, indices, updates).eval() def testRank3ValidShape(self): -- GitLab From 1ce63a90ed6a346d62db653ba0ec43accead797d Mon Sep 17 00:00:00 2001 From: Clayne Robison Date: Wed, 18 Jul 2018 14:43:38 -0700 Subject: [PATCH 114/519] Passing full sandybridge build params to the container build script --- tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh b/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh index 7de58ef625..a1d91a6123 100755 --- a/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh +++ b/tensorflow/tools/ci_build/linux/mkl/build-dev-container.sh @@ -44,6 +44,7 @@ TF_DOCKER_BUILD_TYPE="MKL" \ TF_DOCKER_BUILD_DEVEL_BRANCH="${TF_DOCKER_BUILD_DEVEL_BRANCH}" \ TF_DOCKER_BUILD_IMAGE_NAME="${TF_DOCKER_BUILD_IMAGE_NAME}" \ TF_DOCKER_BUILD_VERSION="${TF_DOCKER_BUILD_VERSION}" \ + TF_BAZEL_BUILD_OPTIONS="${TF_BAZEL_BUILD_OPTIONS}" \ ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh # build the python 3 container and whl @@ -53,6 +54,7 @@ TF_DOCKER_BUILD_TYPE="MKL" \ TF_DOCKER_BUILD_IMAGE_NAME="${TF_DOCKER_BUILD_IMAGE_NAME}" \ TF_DOCKER_BUILD_VERSION="${TF_DOCKER_BUILD_VERSION}" \ TF_DOCKER_BUILD_PYTHON_VERSION="PYTHON3" \ + TF_BAZEL_BUILD_OPTIONS="${TF_BAZEL_BUILD_OPTIONS}" \ ${WORKSPACE}/tensorflow/tools/docker/parameterized_docker_build.sh # Build containers for AVX2 -- GitLab From 7648454c49b397fba7fc2c73c5d7d2149af3481a Mon Sep 17 00:00:00 2001 From: Sunitha Kambhampati Date: Wed, 18 Jul 2018 15:37:21 -0700 Subject: [PATCH 115/519] Add SQLITE_OPEN_URI flag to sqlite open to support db_uri and add a unit test --- .../contrib/summary/summary_ops_test.py | 19 +++++++++++++++++++ tensorflow/core/lib/db/sqlite.cc | 1 + 2 files changed, 20 insertions(+) diff --git a/tensorflow/contrib/summary/summary_ops_test.py b/tensorflow/contrib/summary/summary_ops_test.py index 3e41e3d0b4..77b1c93ff2 100644 --- a/tensorflow/contrib/summary/summary_ops_test.py +++ b/tensorflow/contrib/summary/summary_ops_test.py @@ -17,9 +17,12 @@ from __future__ import division from __future__ import print_function import os +import pathlib import tempfile import time +import sqlite3 + import numpy as np import six @@ -275,6 +278,22 @@ class EagerFileTest(test_util.TensorFlowTestCase): class EagerDbTest(summary_test_util.SummaryDbTest): + def testDbURIOpen(self): + tmpdb_path = os.path.join(self.get_temp_dir(), 'tmpDbURITest.sqlite') + tmpdb_uri = pathlib.Path(tmpdb_path).as_uri() + tmpdb_writer = summary_ops.create_db_writer( + tmpdb_uri, + "experimentA", + "run1", + "user1") + with summary_ops.always_record_summaries(): + with tmpdb_writer.as_default(): + summary_ops.scalar('t1', 2.0) + tmpdb = sqlite3.connect(tmpdb_path) + num = get_one(tmpdb, 'SELECT count(*) FROM Tags WHERE tag_name = "t1"') + self.assertEqual(num, 1) + tmpdb.close() + def testIntegerSummaries(self): step = training_util.create_global_step() writer = self.create_db_writer() diff --git a/tensorflow/core/lib/db/sqlite.cc b/tensorflow/core/lib/db/sqlite.cc index cb6943379d..cf11f3a331 100644 --- a/tensorflow/core/lib/db/sqlite.cc +++ b/tensorflow/core/lib/db/sqlite.cc @@ -112,6 +112,7 @@ Status EnvPragma(Sqlite* db, const char* pragma, const char* var) { /* static */ Status Sqlite::Open(const string& path, int flags, Sqlite** db) { flags |= SQLITE_OPEN_PRIVATECACHE; + flags |= SQLITE_OPEN_URI; sqlite3* sqlite = nullptr; int rc = sqlite3_open_v2(path.c_str(), &sqlite, flags, nullptr); if (rc != SQLITE_OK) { -- GitLab From 2e9d8fd8a27cf716ce91d1022fc3154cecad9e1d Mon Sep 17 00:00:00 2001 From: Jie Date: Wed, 18 Jul 2018 20:47:56 -0700 Subject: [PATCH 116/519] [tftrt unit tests] 1. unit tests refactored to tf_trt_integration_test style 2. disabled failed test (tests passed in static conversion but failed in dynamic conversion) 3. comment on _VerifyGraphDef: we should cover cases for failed conversion in dynamic conversion, where func def (TF fallback) is used. --- tensorflow/contrib/tensorrt/BUILD | 44 ++----- .../tensorrt/test/batch_matmul_test.py | 107 +++++++----------- .../tensorrt/test/biasadd_matmul_test.py | 80 +++++++------ .../binary_tensor_weight_broadcast_test.py | 95 ++++++---------- .../tensorrt/test/concatenation_test.py | 68 ++++++----- .../tensorrt/test/const_broadcast_test.py | 48 ++++---- .../multi_connection_neighbor_engine_test.py | 54 ++++----- .../tensorrt/test/neighboring_engine_test.py | 49 ++++---- .../test/tf_trt_integration_test_base.py | 3 + .../contrib/tensorrt/test/unary_test.py | 66 +++++------ .../tensorrt/test/vgg_block_nchw_test.py | 44 ++++--- .../contrib/tensorrt/test/vgg_block_test.py | 44 ++++--- 12 files changed, 303 insertions(+), 399 deletions(-) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index dd2554c81e..fa47f51b66 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -335,48 +335,28 @@ py_library( cuda_py_tests( name = "tf_trt_integration_test", - srcs = ["test/base_test.py"], - additional_deps = [ - ":tf_trt_integration_test_base", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_test_lib", - ], - prefix = "integration_test", - tags = [ - "no_windows", - "nomac", - ], -) - -py_test( - name = "converter_unit_tests", srcs = [ - "test/base_unit_test.py", - "test/batch_matmul_test.py", - "test/biasadd_matmul_test.py", + "test/base_test.py", + #"test/batch_matmul_test.py", + #"test/biasadd_matmul_test.py", "test/binary_tensor_weight_broadcast_test.py", "test/concatenation_test.py", "test/const_broadcast_test.py", "test/multi_connection_neighbor_engine_test.py", "test/neighboring_engine_test.py", - "test/run_test.py", "test/unary_test.py", - "test/unit_tests.py", - "test/utilities.py", - "test/vgg_block_nchw_test.py", - "test/vgg_block_test.py", + #"test/vgg_block_nchw_test.py", + #"test/vgg_block_test.py", ], - main = "test/unit_tests.py", - srcs_version = "PY2AND3", - tags = [ - "notap", - ], - deps = [ - ":init_py", + additional_deps = [ + ":tf_trt_integration_test_base", "//tensorflow/python:client_testlib", "//tensorflow/python:framework_test_lib", - "//tensorflow/python:layers", - "//tensorflow/python:training", + ], + prefix = "integration_test", + tags = [ + "no_windows", + "nomac", ], ) diff --git a/tensorflow/contrib/tensorrt/test/batch_matmul_test.py b/tensorflow/contrib/tensorrt/test/batch_matmul_test.py index 3c83a3a562..163af54184 100644 --- a/tensorflow/contrib/tensorrt/test/batch_matmul_test.py +++ b/tensorflow/contrib/tensorrt/test/batch_matmul_test.py @@ -20,78 +20,59 @@ from __future__ import print_function import numpy as np -from tensorflow.core.protobuf import config_pb2 -from tensorflow.python.client import session 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 variable_scope -from tensorflow.python.ops import variables -from tensorflow.python.ops import init_ops from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import math_ops -from tensorflow.python.training import training -from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest -from tensorflow.contrib.tensorrt.test.utilities import get_all_variables +from tensorflow.python.platform import test +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test -class BatchMatMulTest(BaseUnitTest): - """Testing BatchMatMul in TF-TRT conversion""" +class BatchMatMulTest(trt_test.TfTrtIntegrationTestBase): - def __init__(self, log_file='log.txt'): - super(BatchMatMulTest, self).__init__() - self.static_mode_list = {"FP32", "FP16"} - self.debug = True - self.dynamic_mode_list = {} - self.inp_dims = (12, 5, 8, 12) - self.dummy_input = np.random.random_sample(self.inp_dims) - self.get_network = self.matmul_test - self.expect_nb_nodes = 16 - self.log_file = log_file - self.test_name = self.__class__.__name__ - self.ckpt = "./tmp.ckpt" - sess = session.Session() - - def matmul_test(self): + def GetParams(self): + """Testing conversion of BatchMatMul in TF-TRT conversion""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [12, 5, 8, 12] + w1_name = "matmul_w1" + w1_dims = [12, 5, 12, 7] + w2_name = "matmul_w2" + w2_dims = [12, 12, 7] g = ops.Graph() - gpu_options = config_pb2.GPUOptions() - sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) with g.as_default(): - x = array_ops.placeholder( - dtype=dtypes.float32, shape=self.inp_dims, name="input") - - b = constant_op.constant( - np.random.randn(12, 5, 12, 7), dtype=dtypes.float32) - x1 = math_ops.matmul(x, b) - b = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtypes.float32) - x1 = x1 + b - - var = variable_scope.get_variable( - "test", [12, 5, 12, 7], - dtype=dtypes.float32, - initializer=init_ops.truncated_normal_initializer) - x2 = math_ops.matmul(x, var) - b = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtypes.float32) - x2 = x2 * b - - var = variable_scope.get_variable( - "test2", [12, 84], - dtype=dtypes.float32, - initializer=init_ops.truncated_normal_initializer) - c = gen_array_ops.reshape(x, [12, 40, 12]) - b = gen_array_ops.reshape(var, [12, 12, 7]) - x3 = math_ops.matmul(c, b) - b = constant_op.constant(np.random.randn(40, 1), dtype=dtypes.float32) - x3 = x3 + b - x3 = gen_array_ops.reshape(x3, [12, 5, 8, 7]) - - out = x3 + x1 - array_ops.squeeze(out, name="output") + inp = array_ops.placeholder( + dtype=dtype, shape=[None] + input_dims[1:], name=input_name) + w1 = array_ops.placeholder( + dtype=dtype, shape=w1_dims, name=w1_name) + w2 = array_ops.placeholder( + dtype=dtype, shape=w2_dims, name=w2_name) + with g.device("/GPU:0"): + b = constant_op.constant( + np.random.randn(12, 5, 12, 7), dtype=dtype) + c = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype) + d = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype) + x1 = math_ops.matmul(inp, b) + x1 = x1 + c + x2 = math_ops.matmul(inp, w1) + x2 = x2 * d + e = gen_array_ops.reshape(inp, [12, 40, 12]) + x3 = math_ops.matmul(e, w2) + f = constant_op.constant(np.random.randn(40, 1), dtype=dtype) + x3 = x3 + f + x3 = gen_array_ops.reshape(x3, [12, 5, 8, 7]) + out = x1 + x2 + x3 + array_ops.squeeze(out, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name, w1_name, w2_name], + input_dims=[input_dims, w1_dims, w2_dims], + num_expected_engines=1, + expected_output_dims=(12, 5, 8, 7), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) - with session.Session(config=sessconfig, graph=g) as sess: - names_var_list = get_all_variables(sess) - saver = training.Saver(names_var_list) - sess.run(variables.global_variables_initializer()) - saver.save(sess, self.ckpt) - return g.as_graph_def() +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py b/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py index 1ac6f5cb6a..9b153ada05 100644 --- a/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py +++ b/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py @@ -20,7 +20,6 @@ from __future__ import print_function import numpy as np -from tensorflow.core.protobuf import config_pb2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -28,89 +27,86 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import math_ops -from tensorflow.python.layers import core -from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest - - -class BiasaddMatMulTest(BaseUnitTest): - """Testing BiasAdd MatMul in TF-TRT conversion""" - - def __init__(self, log_file='log.txt'): - super(BiasaddMatMulTest, self).__init__() - self.static_mode_list = {"FP32", "FP16"} - self.debug = True - self.dynamic_mode_list = {} - self.inp_dims = (48, 12) - self.dummy_input = np.random.random_sample(self.inp_dims) - self.get_network = self.matmul_test - self.expect_nb_nodes = 53 - self.log_file = log_file - self.test_name = self.__class__.__name__ - - def matmul_test(self): +from tensorflow.python.platform import test +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test + + +class BiasaddMatMulTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Testing conversion of BiasAdd MatMul in TF-TRT conversion""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [48, 12] g = ops.Graph() - gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) with g.as_default(): x = array_ops.placeholder( - dtype=dtypes.float32, shape=self.inp_dims, name="input") + dtype=dtype, shape=input_dims, name=input_name) - b = constant_op.constant(np.random.randn(12, 4), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(12, 4), dtype=dtype) x1 = math_ops.matmul(x, b) - b = constant_op.constant(np.random.randn(1, 4), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(1, 4), dtype=dtype) x1 = x1 + b - b = constant_op.constant(np.random.randn(48, 4), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(48, 4), dtype=dtype) x2 = math_ops.matmul(x, b, transpose_a=True) x2 = gen_array_ops.reshape(x2, [48, 1]) - b = constant_op.constant(np.random.randn(4, 12), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(4, 12), dtype=dtype) x3 = math_ops.matmul(x, b, transpose_b=True) - b = constant_op.constant(np.random.randn(16, 48), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(16, 48), dtype=dtype) x4 = math_ops.matmul(x, b, transpose_b=True, transpose_a=True) x4 = gen_array_ops.reshape(x4, [48, 4]) - x5 = gen_array_ops.reshape(x, [4, 12, 12]) - x5 = core.flatten(x5) - b = constant_op.constant(np.random.randn(144, 48), dtype=dtypes.float32) + x5 = gen_array_ops.reshape(x, [4, 144]) + b = constant_op.constant(np.random.randn(144, 48), dtype=dtype) x5 = math_ops.matmul(x5, b) - b = constant_op.constant(np.random.randn(48), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(48), dtype=dtype) x5 = nn.bias_add(x5, b) x5 = gen_array_ops.reshape(x5, [48, 4]) x6 = gen_array_ops.reshape(x, [4, 12, 12]) - b = constant_op.constant(np.random.randn(12), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(12), dtype=dtype) x6 = nn.bias_add(x6, b, data_format="NHWC") x6 = gen_array_ops.reshape(x6, [48, -1]) x7 = gen_array_ops.reshape(x, [4, 12, 3, 4]) - b = constant_op.constant(np.random.randn(4), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(4), dtype=dtype) x7 = nn.bias_add(x7, b, data_format="NHWC") x7 = gen_array_ops.reshape(x7, [48, -1]) x8 = gen_array_ops.reshape(x, [4, 12, 3, 2, 2]) - b = constant_op.constant(np.random.randn(2), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(2), dtype=dtype) x8 = nn.bias_add(x8, b, data_format="NHWC") x8 = gen_array_ops.reshape(x8, [48, -1]) x9 = gen_array_ops.reshape(x, [4, 12, 3, 2, 2]) - b = constant_op.constant(np.random.randn(3), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(3), dtype=dtype) x9 = nn.bias_add(x9, b, data_format="NCHW") x9 = gen_array_ops.reshape(x9, [48, -1]) x10 = gen_array_ops.reshape(x, [4, 12, 3, 4]) - b = constant_op.constant(np.random.randn(12), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(12), dtype=dtype) x10 = nn.bias_add(x10, b, data_format="NCHW") x10 = gen_array_ops.reshape(x10, [48, -1]) x11 = gen_array_ops.reshape(x, [4, 12, 12]) - b = constant_op.constant(np.random.randn(4), dtype=dtypes.float32) + b = constant_op.constant(np.random.randn(4), dtype=dtype) x11 = nn.bias_add(x11, b, data_format="NCHW") x11 = gen_array_ops.reshape(x11, [48, -1]) out = array_ops.concat( [x1, x2, x3, x4, x5, x6, x7, x8, x9, x10, x11], axis=-1) - out = array_ops.squeeze(out, name="output") - - return g.as_graph_def() + out = array_ops.squeeze(out, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=7, + expected_output_dims=(48, 89), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py b/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py index 5233a493d0..e80712731d 100644 --- a/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py +++ b/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py @@ -20,129 +20,108 @@ from __future__ import print_function import numpy as np -from tensorflow.core.protobuf import config_pb2 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 gen_array_ops from tensorflow.python.ops import math_ops -from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest +from tensorflow.python.platform import test +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test -class BinaryTensorWeightBroadcastTest(BaseUnitTest): - """unit tests for scale & elementwise layers in TF-TRT""" +class BinaryTensorWeightBroadcastTest(trt_test.TfTrtIntegrationTestBase): - def __init__(self, log_file='log.txt'): - super(BinaryTensorWeightBroadcastTest, self).__init__() - self.static_mode_list = {"FP32", "FP16"} - self.debug = True - self.dynamic_mode_list = {} - self.inp_dims = (10, 24, 24, 20) - self.dummy_input = np.random.random_sample(self.inp_dims) - self.get_network = self.get_simple_graph_def - self.expect_nb_nodes = 35 - self.log_file = log_file - self.test_name = self.__class__.__name__ - self.allclose_rtol = 0.1 - self.allclose_atol = 0.05 - - def get_simple_graph_def(self): + def GetParams(self): + """unit tests for scale & elementwise layers in TF-TRT""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [10, 24, 24, 20] g = ops.Graph() - gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) with g.as_default(): x = array_ops.placeholder( - dtype=dtypes.float32, shape=self.inp_dims, name="input") - + dtype=dtype, shape=input_dims, name=input_name) # scale - a = constant_op.constant(np.random.randn(1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(1), dtype=dtype) f = x + a x = math_ops.sigmoid(f) - # scale - a = constant_op.constant(np.random.randn(1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(1), dtype=dtype) f = a + x x = math_ops.sigmoid(f) - # scale - a = constant_op.constant(np.random.randn(24, 1, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(24, 1, 1), dtype=dtype) f = x + a x = math_ops.sigmoid(f) - # scale - a = constant_op.constant(np.random.randn(24, 1, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(24, 1, 1), dtype=dtype) f = a + x x = math_ops.sigmoid(f) - # scale a = constant_op.constant( - np.random.randn(24, 24, 20), dtype=dtypes.float32) + np.random.randn(24, 24, 20), dtype=dtype) f = a + x x = math_ops.sigmoid(f) - # scale a = constant_op.constant( - np.random.randn(24, 24, 20), dtype=dtypes.float32) + np.random.randn(24, 24, 20), dtype=dtype) f = x + a x = math_ops.sigmoid(f) - # elementwise - a = constant_op.constant(np.random.randn(20), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(20), dtype=dtype) f = x + a x = math_ops.sigmoid(f) - # elementwise - a = constant_op.constant(np.random.randn(20), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(20), dtype=dtype) f = a + x x = math_ops.sigmoid(f) - # elementwise a = constant_op.constant( - np.random.randn(1, 24, 1, 1), dtype=dtypes.float32) + np.random.randn(1, 24, 1, 1), dtype=dtype) f = a + x x = math_ops.sigmoid(f) - # elementwise a = constant_op.constant( - np.random.randn(1, 24, 1, 1), dtype=dtypes.float32) + np.random.randn(1, 24, 1, 1), dtype=dtype) f = x + a x = math_ops.sigmoid(f) - # elementwise a = constant_op.constant( - np.random.randn(1, 24, 24, 1), dtype=dtypes.float32) + np.random.randn(1, 24, 24, 1), dtype=dtype) f = a + x x = math_ops.sigmoid(f) - # elementwise a = constant_op.constant( - np.random.randn(1, 24, 24, 1), dtype=dtypes.float32) + np.random.randn(1, 24, 24, 1), dtype=dtype) f = x + a x = math_ops.sigmoid(f) - # elementwise a = constant_op.constant( - np.random.randn(1, 24, 24, 20), dtype=dtypes.float32) + np.random.randn(1, 24, 24, 20), dtype=dtype) f = a + x x = math_ops.sigmoid(f) - # elementwise a = constant_op.constant( - np.random.randn(1, 24, 24, 20), dtype=dtypes.float32) + np.random.randn(1, 24, 24, 20), dtype=dtype) f = x + a x = math_ops.sigmoid(f) - # elementwise - a = constant_op.constant(np.random.randn(24, 20), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(24, 20), dtype=dtype) f = a + x x = math_ops.sigmoid(f) - # elementwise - a = constant_op.constant(np.random.randn(24, 20), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(24, 20), dtype=dtype) f = x + a x = math_ops.sigmoid(f) - - gen_array_ops.reshape(x, [5, -1], name="output") - - return g.as_graph_def() + gen_array_ops.reshape(x, [5, -1], name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=16, + expected_output_dims=(5, 23040), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/concatenation_test.py b/tensorflow/contrib/tensorrt/test/concatenation_test.py index de0817d2e8..cf0bfeeb00 100644 --- a/tensorflow/contrib/tensorrt/test/concatenation_test.py +++ b/tensorflow/contrib/tensorrt/test/concatenation_test.py @@ -20,68 +20,64 @@ from __future__ import print_function import numpy as np -from tensorflow.core.protobuf import config_pb2 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 gen_array_ops from tensorflow.python.ops import gen_math_ops -from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest +from tensorflow.python.platform import test +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test -class ConcatenationTest(BaseUnitTest): - """Testing Concatenation in TF-TRT conversion""" +class ConcatenationTest(trt_test.TfTrtIntegrationTestBase): - def __init__(self, log_file='log.txt'): - super(ConcatenationTest, self).__init__() - self.static_mode_list = {"FP32", "FP16"} - self.debug = True - self.dynamic_mode_list = {} - self.inp_dims = (2, 3, 3, 1) - self.dummy_input = np.random.random_sample(self.inp_dims) - self.get_network = self.get_simple_graph_def - self.expect_nb_nodes = 4 - self.log_file = log_file - self.test_name = self.__class__.__name__ - - def get_simple_graph_def(self): + def GetParams(self): + """Testing Concatenation in TF-TRT conversion""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [2, 3, 3, 1] g = ops.Graph() - gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) with g.as_default(): x = array_ops.placeholder( - dtype=dtypes.float32, shape=self.inp_dims, name="input") - + dtype=dtype, shape=input_dims, name=input_name) # scale - a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype) r1 = x / a - a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype) r2 = a / x - a = constant_op.constant(np.random.randn(1, 3, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(1, 3, 1), dtype=dtype) r3 = a + x - a = constant_op.constant(np.random.randn(1, 3, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(1, 3, 1), dtype=dtype) r4 = x * a - a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype) r5 = x - a - a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype) r6 = a - x - a = constant_op.constant(np.random.randn(3, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(3, 1), dtype=dtype) r7 = x - a - a = constant_op.constant(np.random.randn(3, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(3, 1), dtype=dtype) r8 = a - x - a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype) r9 = gen_math_ops.maximum(x, a) - a = constant_op.constant(np.random.randn(3, 1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(3, 1), dtype=dtype) r10 = gen_math_ops.minimum(a, x) - a = constant_op.constant(np.random.randn(3), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(3), dtype=dtype) r11 = x * a - a = constant_op.constant(np.random.randn(1), dtype=dtypes.float32) + a = constant_op.constant(np.random.randn(1), dtype=dtype) r12 = a * x concat1 = array_ops.concat([r1, r2, r3, r4, r5, r6], axis=-1) concat2 = array_ops.concat([r7, r8, r9, r10, r11, r12], axis=3) x = array_ops.concat([concat1, concat2], axis=-1) + gen_array_ops.reshape(x, [2, -1], name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=1, + expected_output_dims=(2, 126), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) - gen_array_ops.reshape(x, [2, -1], name="output") - - return g.as_graph_def() +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/const_broadcast_test.py b/tensorflow/contrib/tensorrt/test/const_broadcast_test.py index 74d39d9015..97f5580ac0 100644 --- a/tensorflow/contrib/tensorrt/test/const_broadcast_test.py +++ b/tensorflow/contrib/tensorrt/test/const_broadcast_test.py @@ -20,56 +20,52 @@ from __future__ import print_function import numpy as np -from tensorflow.core.protobuf import config_pb2 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 nn -from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest +from tensorflow.python.platform import test +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test -class ConstBroadcastTest(BaseUnitTest): - """Testing Constant broadcasting in TF-TRT""" +class ConstBroadcastTest(trt_test.TfTrtIntegrationTestBase): - def __init__(self, log_file='log.txt'): - super(ConstBroadcastTest, self).__init__() - self.static_mode_list = {"FP32", "FP16"} - self.debug = True - self.dynamic_mode_list = {} - self.inp_dims = (5, 12, 12, 2) - self.dummy_input = np.random.random_sample(self.inp_dims) - self.get_network = self.conv_broadcast - self.expect_nb_nodes = 7 - self.log_file = log_file - self.test_name = self.__class__.__name__ - self.allclose_rtol = 0.05 - self.allclose_atol = 0.05 - - def conv_broadcast(self): + def GetParams(self): + """unit test for Constant broadcasting in TF-TRT""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [5, 12, 12, 2] g = ops.Graph() - gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) with g.as_default(): x = array_ops.placeholder( - dtype=dtypes.float32, shape=self.inp_dims, name="input") + dtype=dtype, shape=input_dims, name=input_name) filt1 = constant_op.constant( - 1, shape=(3, 3, 2, 1), dtype=dtypes.float32, name='filt1') + 0.3, shape=(3, 3, 2, 1), dtype=dtype, name='filt1') y1 = nn.conv2d(x, filt1, strides=[1, 1, 1, 1], padding='SAME', name='y1') z1 = nn.relu(y1, name='z1') filt2 = constant_op.constant( np.random.randn(9), shape=(3, 3, 1, 1), - dtype=dtypes.float32, + dtype=dtype, name='filt2') y2 = nn.conv2d(z1, filt2, strides=[1, 1, 1, 1], padding='SAME', name='y2') z2 = nn.relu(y2, name='z') filt3 = constant_op.constant( np.random.randn(3, 3, 1, 1), shape=(3, 3, 1, 1), - dtype=dtypes.float32, + dtype=dtype, name='filt3') y3 = nn.conv2d(z2, filt3, strides=[1, 1, 1, 1], padding='SAME', name='y3') z = nn.relu(y3, name='output') + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=1, + expected_output_dims=(5, 12, 12, 1), + allclose_atol=1.e-02, + allclose_rtol=1.e-02) - return g.as_graph_def() +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py b/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py index 291b4d16c1..e62f9e479e 100644 --- a/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py +++ b/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py @@ -20,7 +20,6 @@ from __future__ import print_function import numpy as np -from tensorflow.core.protobuf import config_pb2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -28,37 +27,25 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn from tensorflow.python.ops import gen_math_ops from tensorflow.python.ops import math_ops -from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest +from tensorflow.python.platform import test +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test -class MultiConnectionNeighborEngineTest(BaseUnitTest): - """Multi connection neighboring nodes wiring tests in TF-TRT""" +class MultiConnectionNeighborEngineTest(trt_test.TfTrtIntegrationTestBase): - def __init__(self, log_file='log.txt'): - super(MultiConnectionNeighborEngineTest, self).__init__() - self.static_mode_list = {"FP32", "FP16"} - self.debug = True - self.dynamic_mode_list = {} - self.inp_dims = (2, 3, 7, 5) - self.dummy_input = np.random.normal(1.0, 0.5, self.inp_dims) - self.get_network = self.neighboring_tensor_test - self.expect_nb_nodes = 7 - self.log_file = log_file - self.test_name = self.__class__.__name__ - self.allclose_rtol = 0.05 - self.allclose_atol = 0.05 - - def neighboring_tensor_test(self): + def GetParams(self): + """unit test for multi connection neighboring nodes wiring tests in TF-TRT""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [2, 3, 7, 5] g = ops.Graph() - gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) with g.as_default(): x = array_ops.placeholder( - dtype=dtypes.float32, shape=self.inp_dims, name="input") + dtype=dtype, shape=input_dims, name=input_name) e = constant_op.constant( np.random.normal(.05, .005, [3, 2, 3, 4]), name="weights", - dtype=dtypes.float32) + dtype=dtype) conv = nn.conv2d( input=x, filter=e, @@ -69,33 +56,42 @@ class MultiConnectionNeighborEngineTest(BaseUnitTest): b = constant_op.constant( np.random.normal(2.0, 1.0, [1, 4, 1, 1]), name="bias", - dtype=dtypes.float32) + dtype=dtype) t = conv + b b = constant_op.constant( np.random.normal(5.0, 1.0, [1, 4, 1, 1]), name="bias", - dtype=dtypes.float32) + dtype=dtype) q = conv - b edge = math_ops.sigmoid(q) b = constant_op.constant( np.random.normal(5.0, 1.0, [1, 4, 1, 1]), name="bias", - dtype=dtypes.float32) + dtype=dtype) d = b + conv edge3 = math_ops.sigmoid(d) c = constant_op.constant( np.random.normal(1.0, 1.0, [1, 4, 1, 1]), name="bias", - dtype=dtypes.float32) + dtype=dtype) edge1 = gen_math_ops.tan(conv) t = t - edge1 q = q + edge t = t + q t = t + d t = t - edge3 - array_ops.squeeze(t, name="output") + array_ops.squeeze(t, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=2, + expected_output_dims=(2, 4, 5, 4), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) - return g.as_graph_def() +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py b/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py index f916db3504..bbe8823552 100644 --- a/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py +++ b/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py @@ -20,44 +20,31 @@ from __future__ import print_function import numpy as np -from tensorflow.core.protobuf import config_pb2 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 nn from tensorflow.python.ops import gen_math_ops -from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest +from tensorflow.python.platform import test +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test -class NeighboringEngineTest(BaseUnitTest): - """Neighboring node wiring tests in TF-TRT conversion""" +class NeighboringEngineTest(trt_test.TfTrtIntegrationTestBase): - def __init__(self, log_file='log.txt'): - super(NeighboringEngineTest, self).__init__() - self.static_mode_list = {"FP32", "FP16"} - self.debug = True - self.dynamic_mode_list = {} - self.inp_dims = (2, 3, 7, 5) - self.dummy_input = np.random.random_sample(self.inp_dims) - self.get_network = self.neighboring_tensor_test - self.expect_nb_nodes = 5 - self.log_file = log_file - self.test_name = self.__class__.__name__ - self.allclose_rtol = 0.05 - self.allclose_atol = 0.05 - - def neighboring_tensor_test(self): + def GetParams(self): + """Neighboring node wiring tests in TF-TRT conversion""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [2, 3, 7, 5] g = ops.Graph() - gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) with g.as_default(): x = array_ops.placeholder( - dtype=dtypes.float32, shape=self.inp_dims, name="input") + dtype=dtype, shape=input_dims, name=input_name) e = constant_op.constant( np.random.normal(.3, 0.05, [3, 2, 3, 4]), name="weights", - dtype=dtypes.float32) + dtype=dtype) conv = nn.conv2d( input=x, filter=e, @@ -68,11 +55,19 @@ class NeighboringEngineTest(BaseUnitTest): b = constant_op.constant( np.random.normal(1.0, 1.0, [1, 4, 1, 1]), name="bias", - dtype=dtypes.float32) + dtype=dtype) t = conv * b - e = gen_math_ops.tan(conv) t = t - e - array_ops.squeeze(t, name="output") + array_ops.squeeze(t, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=2, + expected_output_dims=(2, 4, 5, 4), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) - return g.as_graph_def() +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py index 02a9280542..48890ad413 100644 --- a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py +++ b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py @@ -162,6 +162,9 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): dynamic_engine=None): num_engines = 0 for n in gdef.node: + # TODO(jie): we should have coverage for failed conversion (TF fallback). + # where the conversion will fail and we shouldn't count this engine as the + # converted engines. if n.op == "TRTEngineOp": num_engines += 1 self.assertNotEqual(self._ToBytes(""), n.attr["serialized_segment"].s) diff --git a/tensorflow/contrib/tensorrt/test/unary_test.py b/tensorflow/contrib/tensorrt/test/unary_test.py index a054939ce2..4c10c50e85 100644 --- a/tensorflow/contrib/tensorrt/test/unary_test.py +++ b/tensorflow/contrib/tensorrt/test/unary_test.py @@ -20,46 +20,31 @@ from __future__ import print_function import numpy as np -from tensorflow.core.protobuf import config_pb2 -from tensorflow.python.client import session 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 variable_scope -from tensorflow.python.ops import variables -from tensorflow.python.ops import init_ops from tensorflow.python.ops import gen_array_ops from tensorflow.python.ops import gen_math_ops from tensorflow.python.ops import math_ops -from tensorflow.python.training import training -from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest -from tensorflow.contrib.tensorrt.test.utilities import get_all_variables +from tensorflow.python.platform import test +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test -class UnaryTest(BaseUnitTest): - """Unit tests for unary operations in TF-TRT""" +class UnaryTest(trt_test.TfTrtIntegrationTestBase): + - def __init__(self, log_file='log.txt'): - super(UnaryTest, self).__init__() - self.static_mode_list = {"FP32", "FP16"} - self.debug = True - self.dynamic_mode_list = {} - self.inp_dims = (12, 5, 8, 1, 1, 12) - self.dummy_input = np.random.random_sample(self.inp_dims) - self.get_network = self.unary_test - self.expect_nb_nodes = 17 - self.log_file = log_file - self.test_name = self.__class__.__name__ - self.ckpt = "./tmp.ckpt" - - def unary_test(self): + def GetParams(self): + """unit test for unary operations in TF-TRT""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [12, 5, 8, 1, 1, 12] + input2_name = "input_2" + input2_dims = [12, 5, 8, 1, 12, 1, 1] g = ops.Graph() - gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) with g.as_default(): x = array_ops.placeholder( - dtype=dtypes.float32, shape=self.inp_dims, name="input") + dtype=dtype, shape=input_dims, name=input_name) q = math_ops.abs(x) q = q + 1.0 q = gen_math_ops.exp(q) @@ -75,7 +60,7 @@ class UnaryTest(BaseUnitTest): q = q + 3.0 a = gen_math_ops.reciprocal(q) - x = constant_op.constant(np.random.randn(5, 8, 12), dtype=dtypes.float32) + x = constant_op.constant(np.random.randn(5, 8, 12), dtype=dtype) q = math_ops.abs(x) q = q + 2.0 q = gen_math_ops.exp(q) @@ -90,11 +75,8 @@ class UnaryTest(BaseUnitTest): b = gen_math_ops.reciprocal(q) # TODO(jie): this one will break, broadcasting on batch. - x = variable_scope.get_variable( - "test", [12, 40, 12], - dtype=dtypes.float32, - initializer=init_ops.truncated_normal_initializer) - x = gen_array_ops.reshape(x, [12, 5, 8, 1, 12, 1, 1]) + x = array_ops.placeholder( + dtype=dtype, shape=input2_dims, name=input2_name) q = math_ops.abs(x) q = q + 5.0 q = gen_math_ops.exp(q) @@ -115,11 +97,15 @@ class UnaryTest(BaseUnitTest): q = a * b q = q / c - array_ops.squeeze(q, name="output") + array_ops.squeeze(q, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name, input2_name], + input_dims=[input_dims, input2_dims], + num_expected_engines=5, + expected_output_dims=(12, 5, 8, 12), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) - with session.Session(config=sessconfig, graph=g) as sess: - names_var_list = get_all_variables(sess) - saver = training.Saver(names_var_list) - sess.run(variables.global_variables_initializer()) - saver.save(sess, self.ckpt) - return g.as_graph_def() +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py b/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py index 9a759eb994..3621c13bc9 100644 --- a/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py +++ b/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py @@ -20,7 +20,6 @@ from __future__ import print_function import numpy as np -from tensorflow.core.protobuf import config_pb2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -28,31 +27,21 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn from tensorflow.python.ops import nn_ops from tensorflow.python.ops import nn_impl -from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest +from tensorflow.python.platform import test +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test -class VGGBlockNCHWTest(BaseUnitTest): - """single vgg layer in NCHW unit tests in TF-TRT""" +class VGGBlockNCHWTest(trt_test.TfTrtIntegrationTestBase): - def __init__(self, log_file='log.txt'): - super(VGGBlockNCHWTest, self).__init__() - self.static_mode_list = {"FP32", "FP16"} - self.debug = True - self.dynamic_mode_list = {} - self.inp_dims = (5, 2, 8, 8) - self.dummy_input = np.random.random_sample(self.inp_dims) - self.get_network = self.get_simple_graph_def - self.expect_nb_nodes = 3 - self.log_file = log_file - self.test_name = self.__class__.__name__ - - def get_simple_graph_def(self): + def GetParams(self): + """single vgg layer in NCHW unit tests in TF-TRT""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [5, 2, 8, 8] g = ops.Graph() - gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) with g.as_default(): x = array_ops.placeholder( - dtype=dtypes.float32, shape=self.inp_dims, name="input") + dtype=dtype, shape=input_dims, name=input_name) x, mean_x, var_x = nn_impl.fused_batch_norm( x, np.random.randn(2).astype(np.float32), @@ -62,7 +51,7 @@ class VGGBlockNCHWTest(BaseUnitTest): data_format="NCHW", is_training=False) e = constant_op.constant( - np.random.randn(1, 1, 2, 6), name="weights", dtype=dtypes.float32) + np.random.randn(1, 1, 2, 6), name="weights", dtype=dtype) conv = nn.conv2d( input=x, filter=e, @@ -71,7 +60,7 @@ class VGGBlockNCHWTest(BaseUnitTest): padding="SAME", name="conv") b = constant_op.constant( - np.random.randn(6), name="bias", dtype=dtypes.float32) + np.random.randn(6), name="bias", dtype=dtype) t = nn.bias_add(conv, b, data_format="NCHW", name="biasAdd") relu = nn.relu(t, "relu") idty = array_ops.identity(relu, "ID") @@ -81,5 +70,14 @@ class VGGBlockNCHWTest(BaseUnitTest): data_format="NCHW", name="max_pool") array_ops.squeeze(v, name="output") + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=1, + expected_output_dims=(5, 6, 2, 2), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) - return g.as_graph_def() +if __name__ == "__main__": + test.main() diff --git a/tensorflow/contrib/tensorrt/test/vgg_block_test.py b/tensorflow/contrib/tensorrt/test/vgg_block_test.py index 04176d58ca..1ef32fe52f 100644 --- a/tensorflow/contrib/tensorrt/test/vgg_block_test.py +++ b/tensorflow/contrib/tensorrt/test/vgg_block_test.py @@ -20,7 +20,6 @@ from __future__ import print_function import numpy as np -from tensorflow.core.protobuf import config_pb2 from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -28,31 +27,21 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import nn from tensorflow.python.ops import nn_ops from tensorflow.python.ops import nn_impl -from tensorflow.contrib.tensorrt.test.base_unit_test import BaseUnitTest +from tensorflow.python.platform import test +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test -class VGGBlockTest(BaseUnitTest): - """single vgg layer test in TF-TRT conversion""" +class VGGBlockTest(trt_test.TfTrtIntegrationTestBase): - def __init__(self, log_file='log.txt'): - super(VGGBlockTest, self).__init__() - self.static_mode_list = {"FP32", "FP16"} - self.debug = True - self.dynamic_mode_list = {} - self.inp_dims = (5, 8, 8, 2) - self.dummy_input = np.random.random_sample(self.inp_dims) - self.get_network = self.get_simple_graph_def - self.expect_nb_nodes = 7 - self.log_file = log_file - self.test_name = self.__class__.__name__ - - def get_simple_graph_def(self): + def GetParams(self): + """single vgg layer test in TF-TRT conversion""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [5, 8, 8, 2] g = ops.Graph() - gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.50) - sessconfig = config_pb2.ConfigProto(gpu_options=gpu_options) with g.as_default(): x = array_ops.placeholder( - dtype=dtypes.float32, shape=self.inp_dims, name="input") + dtype=dtype, shape=input_dims, name=input_name) x, mean_x, var_x = nn_impl.fused_batch_norm( x, np.random.randn(2).astype(np.float32), @@ -61,16 +50,25 @@ class VGGBlockTest(BaseUnitTest): variance=np.random.randn(2).astype(np.float32), is_training=False) e = constant_op.constant( - np.random.randn(1, 1, 2, 6), name="weights", dtype=dtypes.float32) + np.random.randn(1, 1, 2, 6), name="weights", dtype=dtype) conv = nn.conv2d( input=x, filter=e, strides=[1, 2, 2, 1], padding="SAME", name="conv") b = constant_op.constant( - np.random.randn(6), name="bias", dtype=dtypes.float32) + np.random.randn(6), name="bias", dtype=dtype) t = nn.bias_add(conv, b, name="biasAdd") relu = nn.relu(t, "relu") idty = array_ops.identity(relu, "ID") v = nn_ops.max_pool( idty, [1, 2, 2, 1], [1, 2, 2, 1], "VALID", name="max_pool") array_ops.squeeze(v, name="output") + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=1, + expected_output_dims=(5, 2, 2, 6), + allclose_atol=1.e-03, + allclose_rtol=1.e-03) - return g.as_graph_def() +if __name__ == "__main__": + test.main() -- GitLab From 3e5dbd6a34e3a069f27e33de341e5f8d4cfdd7b4 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Wed, 18 Jul 2018 22:24:34 -0700 Subject: [PATCH 117/519] Fix a bug where plugin factory didn't reset the unique_ptr but release it (thus cause mem leak). --- tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc | 3 --- 1 file changed, 3 deletions(-) diff --git a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc index 2bc591484d..cccc912262 100644 --- a/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc +++ b/tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.cc @@ -65,9 +65,6 @@ bool PluginFactoryTensorRT::RegisterPlugin( void PluginFactoryTensorRT::DestroyPlugins() { tensorflow::mutex_lock lock(instance_m_); - for (auto& owned_plugin_ptr : owned_plugins_) { - owned_plugin_ptr.release(); - } owned_plugins_.clear(); } -- GitLab From 9a87590da3876b38af946ab363c9d94b8d46e0f9 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Wed, 18 Jul 2018 22:43:41 -0700 Subject: [PATCH 118/519] Temporarily fix the undefined symbols problem --- tensorflow/core/graph/algorithm.cc | 16 ++++++++++++++++ tensorflow/core/graph/algorithm.h | 22 ++++++++++++++++------ 2 files changed, 32 insertions(+), 6 deletions(-) diff --git a/tensorflow/core/graph/algorithm.cc b/tensorflow/core/graph/algorithm.cc index 9b4200e0b4..548096078f 100644 --- a/tensorflow/core/graph/algorithm.cc +++ b/tensorflow/core/graph/algorithm.cc @@ -23,6 +23,12 @@ limitations under the License. namespace tensorflow { +void DFS(const Graph& g, const std::function& enter, + const std::function& leave, + const NodeComparator& stable_comparator) { + DFS(g, enter, leave, stable_comparator, {}); +} + void DFS(const Graph& g, const std::function& enter, const std::function& leave, const NodeComparator& stable_comparator, @@ -162,6 +168,11 @@ void ReverseDFSFrom(const Graph& g, gtl::ArraySlice start, ReverseDFSFromHelper(g, start, enter, leave, stable_comparator); } +void GetPostOrder(const Graph& g, std::vector* order, + const NodeComparator& stable_comparator) { + GetPostOrder(g, order, stable_comparator, {}); +} + void GetPostOrder(const Graph& g, std::vector* order, const NodeComparator& stable_comparator, const EdgeFilter& edge_filter) { @@ -170,6 +181,11 @@ void GetPostOrder(const Graph& g, std::vector* order, edge_filter); } +void GetReversePostOrder(const Graph& g, std::vector* order, + const NodeComparator& stable_comparator) { + GetReversePostOrder(g, order, stable_comparator, {}); +} + void GetReversePostOrder(const Graph& g, std::vector* order, const NodeComparator& stable_comparator, const EdgeFilter& edge_filter) { diff --git a/tensorflow/core/graph/algorithm.h b/tensorflow/core/graph/algorithm.h index 5bbbc6f6dc..7d8a3456e4 100644 --- a/tensorflow/core/graph/algorithm.h +++ b/tensorflow/core/graph/algorithm.h @@ -52,8 +52,12 @@ struct NodeComparatorName { // If edge_filter is set then ignores edges for which edge_filter returns false. extern void DFS(const Graph& g, const std::function& enter, const std::function& leave, - const NodeComparator& stable_comparator = {}, - const EdgeFilter& edge_filter = {}); + const NodeComparator& stable_comparator = {}); + +extern void DFS(const Graph& g, const std::function& enter, + const std::function& leave, + const NodeComparator& stable_comparator, + const EdgeFilter& edge_filter); // Perform a reverse depth-first-search on g starting at the sink node. // If enter is not empty, calls enter(n) before visiting any parents of n. @@ -91,8 +95,11 @@ extern void ReverseDFSFrom(const Graph& g, gtl::ArraySlice start, // // REQUIRES: order is not NULL. void GetPostOrder(const Graph& g, std::vector* order, - const NodeComparator& stable_comparator = {}, - const EdgeFilter& edge_filter = {}); + const NodeComparator& stable_comparator = {}); + +void GetPostOrder(const Graph& g, std::vector* order, + const NodeComparator& stable_comparator, + const EdgeFilter& edge_filter); // Stores in *order the reverse post-order numbering of all nodes // If stable_comparator is set, a stable ordering of visit is achieved by @@ -100,8 +107,11 @@ void GetPostOrder(const Graph& g, std::vector* order, // // If edge_filter is set then ignores edges for which edge_filter returns false. void GetReversePostOrder(const Graph& g, std::vector* order, - const NodeComparator& stable_comparator = {}, - const EdgeFilter& edge_filter = {}); + const NodeComparator& stable_comparator); + +void GetReversePostOrder(const Graph& g, std::vector* order, + const NodeComparator& stable_comparator, + const EdgeFilter& edge_filter); // Prune nodes in "g" that are not in some path from the source node // to any node in 'nodes'. Returns true if changes were made to the graph. -- GitLab From 694dd61fcdfb2019af3e28b4151d93bdd690c94a Mon Sep 17 00:00:00 2001 From: cheerss Date: Thu, 19 Jul 2018 14:57:43 +0800 Subject: [PATCH 119/519] Update keras.md tf.keras.models should load model configuration with `model_from_json` or `model_from_yaml`, not `from_json` or `from_yaml` --- tensorflow/docs_src/guide/keras.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/docs_src/guide/keras.md b/tensorflow/docs_src/guide/keras.md index 1d846df104..2330fa03c7 100644 --- a/tensorflow/docs_src/guide/keras.md +++ b/tensorflow/docs_src/guide/keras.md @@ -467,13 +467,13 @@ JSON and YAML serialization formats: json_string = model.to_json() # Recreate the model (freshly initialized) -fresh_model = keras.models.from_json(json_string) +fresh_model = keras.models.model_from_json(json_string) # Serializes a model to YAML format yaml_string = model.to_yaml() # Recreate the model -fresh_model = keras.models.from_yaml(yaml_string) +fresh_model = keras.models.model_from_yaml(yaml_string) ``` Caution: Subclassed models are not serializable because their architecture is -- GitLab From a847fc55483a6300079e14f3a8c05963fc2c3337 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Wed, 18 Jul 2018 23:58:55 -0700 Subject: [PATCH 120/519] Fix deserializeCudaEngine to take the real plugin factory pointer, introduced by #19871 --- tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 54009179a8..646d62483f 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -15,9 +15,11 @@ limitations under the License. #include "tensorflow/contrib/tensorrt/kernels/trt_engine_op.h" #include + #include "tensorflow/contrib/tensorrt/convert/convert_nodes.h" #include "tensorflow/contrib/tensorrt/convert/utils.h" #include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include "tensorflow/contrib/tensorrt/plugin/trt_plugin_factory.h" #include "tensorflow/contrib/tensorrt/resources/trt_resource_manager.h" #include "tensorflow/contrib/tensorrt/resources/trt_resources.h" #include "tensorflow/core/framework/graph_to_functiondef.h" @@ -457,7 +459,8 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, #endif TrtUniquePtrType static_engine( infer->deserializeCudaEngine(serialized_segment_.c_str(), - serialized_segment_.size(), nullptr)); + serialized_segment_.size(), + PluginFactoryTensorRT::GetInstance())); auto raw_static_engine = static_engine.get(); const auto max_batch_size = raw_static_engine->getMaxBatchSize(); engine_map_[max_batch_size] = { -- GitLab From 6e94fd7b5eebc506202e1ae3b6c0de73e3b727bf Mon Sep 17 00:00:00 2001 From: Adrian Kuegel Date: Thu, 19 Jul 2018 00:58:11 -0700 Subject: [PATCH 121/519] Move Bitonic sort emitter logic into a helper file. This will allow to reuse it for the CPU backend. Also move the method EmitOperandArrayLoopNest to the LoopNest class. This makes more sense than having it as part of IrEmitter. PiperOrigin-RevId: 205200030 --- tensorflow/compiler/xla/service/gpu/BUILD | 1 + .../compiler/xla/service/gpu/ir_emitter.cc | 203 +----------------- .../compiler/xla/service/gpu/ir_emitter.h | 11 - tensorflow/compiler/xla/service/llvm_ir/BUILD | 14 ++ .../compiler/xla/service/llvm_ir/llvm_loop.cc | 30 +++ .../compiler/xla/service/llvm_ir/llvm_loop.h | 11 + .../compiler/xla/service/llvm_ir/sort_util.cc | 201 +++++++++++++++++ .../compiler/xla/service/llvm_ir/sort_util.h | 34 +++ 8 files changed, 298 insertions(+), 207 deletions(-) create mode 100644 tensorflow/compiler/xla/service/llvm_ir/sort_util.cc create mode 100644 tensorflow/compiler/xla/service/llvm_ir/sort_util.h diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index a043795a21..ca39797e81 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -170,6 +170,7 @@ cc_library( "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/compiler/xla/service/llvm_ir:loop_emitter", + "//tensorflow/compiler/xla/service/llvm_ir:sort_util", "//tensorflow/compiler/xla/service/llvm_ir:tuple_ops", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index a08b72e3af..449a18e710 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -37,6 +37,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h" +#include "tensorflow/compiler/xla/service/llvm_ir/sort_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h" #include "tensorflow/compiler/xla/service/name_uniquer.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -44,7 +45,6 @@ limitations under the License. #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/window_util.h" -#include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/lib/core/errors.h" namespace xla { @@ -125,135 +125,14 @@ Status IrEmitter::HandleGetTupleElement(HloInstruction* get_tuple_element) { } Status IrEmitter::HandleSort(HloInstruction* sort) { - auto keys = sort->operand(0); auto values = sort->operand_count() > 1 ? sort->operand(1) : nullptr; if (values != nullptr) { // TODO(b/26783907): Also sort the values by their corresponding key. return Unimplemented("Key/Value Sort is not implemented on GPU"); } int dimension_to_sort = sort->dimensions(0); - const llvm_ir::IrArray& keys_array = GetIrArray(*keys, *sort); - const llvm_ir::IrArray& target_array = GetIrArray(*sort, *sort); - - const Shape& keys_shape = keys->shape(); - - // TODO(b/26783907): This case can probably be avoided with the Algebraic - // Simplifier. - if (ShapeUtil::IsScalar(keys_shape)) { - return Status::OK(); - } - - // Create loop nests which loop through the operand dimensions. The sort - // dimension is handled in three separate innermost loops which perform the - // sorting. - llvm_ir::ForLoopNest loop_nest(IrName(sort), &ir_builder_); - llvm_ir::IrArray::Index keys_index = EmitOperandArrayLoopNest( - keys_array, dimension_to_sort, "keys", &loop_nest); - - // 'compare_keys_index' is the index of the element that 'keys_index' should - // be compared to. - llvm_ir::IrArray::Index compare_keys_index(keys_index.GetType()); - for (size_t dimension = 0; dimension < keys_index.size(); ++dimension) { - if (dimension != dimension_to_sort) { - compare_keys_index.push_back(keys_index[dimension]); - } else { - compare_keys_index.push_back(nullptr); - } - } - - // Create the sorting loops which do the sorting. - int64 dimension_to_sort_bound = keys_shape.dimensions(dimension_to_sort); - std::unique_ptr stages_loop = loop_nest.AddLoop( - /*start_index=*/0, - /*end_index=*/ - tensorflow::Log2Ceiling64(dimension_to_sort_bound), - /*suffix=*/"sort_stages"); - std::unique_ptr mask_loop = loop_nest.AddLoop( - /*suffix=*/"mask", - /*start_index=*/keys_index.GetConstantWithIndexType(0), - /*end_index=*/stages_loop->GetIndVarValue()); - std::unique_ptr compare_loop = loop_nest.AddLoop( - /*start_index=*/0, - /*end_index=*/dimension_to_sort_bound, - /*suffix=*/"compare"); - - // Naive C++ code for the inner loops (without parallelization): - // - // for (int64 stage = 0; stage < Log2Ceiling(dimension_to_sort_bound); - // ++stage) { - // int64 first_xor_mask = (1LL << (stage + 1)) - 1; - // for (int64 i = 0; i < dimension_to_sort_bound; ++i) { - // int64 j = i ^ first_xor_mask; - // if (i < j && j < dimension_to_sort_bound) { - // int64 min_key = std::min(keys[i], keys[j]); - // keys[j] = std::max(keys[i], keys[j]); - // keys[i] = min_key; - // } - // } - // for (int64 mask = 0; mask < stage; ++mask) { - // int64 later_xor_mask = (1LL << (stage - (mask + 1)); - // for (int64 i = 0; i < dimension_to_sort_bound; ++i) { - // int64 j = i ^ later_xor_mask; - // if (i < j && j < dimension_to_sort_bound) { - // int64 min_key = std::min(keys[i], keys[j]); - // keys[j] = std::max(keys[i], keys[j]); - // keys[i] = min_key; - // } - // } - // } - // } - // - // This follows the algorithm described on Wikipedia: - // https://en.wikipedia.org/wiki/Bitonic_sorter - - SetToFirstInsertPoint(stages_loop->GetBodyBasicBlock(), &ir_builder_); - // The first xor mask of a stage is 2^(stage + 1) - 1. - auto first_xor_mask = ir_builder_.CreateSub( - ir_builder_.CreateShl( - keys_index.GetConstantWithIndexType(1), - ir_builder_.CreateAdd(stages_loop->GetIndVarValue(), - keys_index.GetConstantWithIndexType(1))), - keys_index.GetConstantWithIndexType(1)); - std::unique_ptr first_compare_loop = - llvm_ir::ForLoop::EmitForLoop( - /*prefix=*/"first_compare", - /*start_index=*/keys_index.GetConstantWithIndexType(0), - /*end_index=*/ - keys_index.GetConstantWithIndexType( - keys_shape.dimensions(dimension_to_sort)), - /*step=*/keys_index.GetConstantWithIndexType(1), - /*ir_builder=*/&ir_builder_); - - SetToFirstInsertPoint(first_compare_loop->GetBodyBasicBlock(), &ir_builder_); - // 'first_compare_loop' iterates through the 'dimension_to_sort'. - keys_index[dimension_to_sort] = first_compare_loop->GetIndVarValue(); - compare_keys_index[dimension_to_sort] = ir_builder_.CreateXor( - first_compare_loop->GetIndVarValue(), first_xor_mask); - EmitCompareLoop(dimension_to_sort, keys_index, compare_keys_index, - target_array); - - SetToFirstInsertPoint(compare_loop->GetPreheaderBasicBlock(), &ir_builder_); - // The later masks of a stage are 2^(stage - (mask_loop_ind_var + 1)). - auto later_xor_mask = ir_builder_.CreateShl( - keys_index.GetConstantWithIndexType(1), - ir_builder_.CreateSub( - stages_loop->GetIndVarValue(), - ir_builder_.CreateAdd(mask_loop->GetIndVarValue(), - keys_index.GetConstantWithIndexType(1)))); - - SetToFirstInsertPoint(compare_loop->GetBodyBasicBlock(), &ir_builder_); - // 'compare_loop' iterates through the 'dimension_to_sort'. - keys_index[dimension_to_sort] = compare_loop->GetIndVarValue(); - compare_keys_index[dimension_to_sort] = - ir_builder_.CreateXor(compare_loop->GetIndVarValue(), later_xor_mask); - EmitCompareLoop(dimension_to_sort, keys_index, compare_keys_index, - target_array); - - // Set the IR builder insert point to the exit basic block of the outer most - // loop. This ensures later instructions are inserted after this loop nest. - ir_builder_.SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); - - return Status::OK(); + return llvm_ir::EmitSortInPlace(dimension_to_sort, GetIrArray(*sort, *sort), + IrName(sort), &ir_builder_); } Status IrEmitter::HandleSend(HloInstruction*) { @@ -527,44 +406,6 @@ Status IrEmitter::EmitAtomicOperationUsingCAS(const HloComputation& computation, return Status::OK(); } -void IrEmitter::EmitCompareLoop( - int64 dimension_to_sort, const llvm_ir::IrArray::Index& keys_index, - const llvm_ir::IrArray::Index& compare_keys_index, - const llvm_ir::IrArray& keys_array) { - // TODO(b/26783907): parallelize this loop. - - // if (is_smaller_index && - // compare_keys[dimension_to_sort] < dimension_to_sort_bound) - llvm::Value* is_smaller_index = ir_builder_.CreateICmpSLT( - keys_index[dimension_to_sort], compare_keys_index[dimension_to_sort]); - int64 dimension_to_sort_bound = - keys_array.GetShape().dimensions(dimension_to_sort); - auto if_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateAnd( - is_smaller_index, - ir_builder_.CreateICmpSLT( - compare_keys_index[dimension_to_sort], - keys_index.GetConstantWithIndexType(dimension_to_sort_bound))), - "smaller_comparison_index", &ir_builder_, /*emit_else=*/false); - SetToFirstInsertPoint(if_data.true_block, &ir_builder_); - auto key1 = keys_array.EmitReadArrayElement(keys_index, &ir_builder_); - auto key2 = keys_array.EmitReadArrayElement(compare_keys_index, &ir_builder_); - auto key_type = keys_array.GetShape().element_type(); - auto comparison = - primitive_util::IsFloatingPointType(key_type) - // TODO(b/26783907): Figure out how to handle NaNs. - ? ir_builder_.CreateFCmp(llvm::FCmpInst::FCMP_ULT, key1, key2) - : ir_builder_.CreateICmp( - primitive_util::IsSignedIntegralType(key_type) - ? llvm::ICmpInst::ICMP_SLT - : llvm::ICmpInst::ICMP_ULT, - key1, key2); - auto min_key = ir_builder_.CreateSelect(comparison, key1, key2); - auto max_key = ir_builder_.CreateSelect(comparison, key2, key1); - keys_array.EmitWriteArrayElement(keys_index, min_key, &ir_builder_); - keys_array.EmitWriteArrayElement(compare_keys_index, max_key, &ir_builder_); -} - Status IrEmitter::EmitAtomicOperationForNestedComputation( const HloComputation& computation, llvm::Value* output_address, llvm::Value* source_address) { @@ -691,10 +532,10 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { // operand dimensions. The reduction dimension of the LHS and RHS are handled // in a separate innermost loop which performs the sum of products. llvm_ir::ForLoopNest loop_nest(IrName(dot), &ir_builder_); - llvm_ir::IrArray::Index lhs_index = EmitOperandArrayLoopNest( - lhs_array, lhs_reduction_dimension, "lhs", &loop_nest); - llvm_ir::IrArray::Index rhs_index = EmitOperandArrayLoopNest( - rhs_array, rhs_reduction_dimension, "rhs", &loop_nest); + llvm_ir::IrArray::Index lhs_index = loop_nest.EmitOperandArrayLoopNest( + lhs_array, /*dimension_to_skip=*/lhs_reduction_dimension, "lhs"); + llvm_ir::IrArray::Index rhs_index = loop_nest.EmitOperandArrayLoopNest( + rhs_array, /*dimension_to_skip=*/rhs_reduction_dimension, "rhs"); // Create the reduction loop which does the sum of products reduction. std::unique_ptr reduction_loop = loop_nest.AddLoop( @@ -943,36 +784,6 @@ Status IrEmitter::HandleBatchNormGrad(HloInstruction*) { "to a cudnn CustomCall using CudnnBatchNormRewriter."); } -llvm_ir::IrArray::Index IrEmitter::EmitOperandArrayLoopNest( - const llvm_ir::IrArray& operand_array, int64 reduction_dimension, - tensorflow::StringPiece name_suffix, llvm_ir::ForLoopNest* loop_nest) { - // Prepares the dimension list we will use to emit the loop nest. Outermost - // loops are added first. Add loops in major-to-minor order, and skip the - // reduction dimension. - std::vector dimensions; - const Shape& shape = operand_array.GetShape(); - for (int i = 0; i < LayoutUtil::MinorToMajor(shape).size(); ++i) { - int64 dimension = LayoutUtil::Major(shape.layout(), i); - if (dimension != reduction_dimension) { - dimensions.push_back(dimension); - } - } - - // Create loop nest with one for-loop for each dimension of the - // output. - llvm_ir::IrArray::Index index = - loop_nest->AddLoopsForShapeOnDimensions(shape, dimensions, name_suffix); - // Verify every dimension except the reduction dimension was set in the index. - for (size_t dimension = 0; dimension < index.size(); ++dimension) { - if (dimension == reduction_dimension) { - DCHECK_EQ(nullptr, index[dimension]); - } else { - DCHECK_NE(nullptr, index[dimension]); - } - } - return index; -} - StatusOr IrEmitter::ComputeNestedElement( const HloComputation& computation, tensorflow::gtl::ArraySlice parameter_elements) { diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.h b/tensorflow/compiler/xla/service/gpu/ir_emitter.h index e9ad4a752b..77e48d729c 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.h @@ -171,17 +171,6 @@ class IrEmitter : public DfsHloVisitorWithDefault { const HloModuleConfig& hlo_module_config_; private: - // Emits a series of nested loops for iterating over an operand array in the - // dot operation. Loops are constructed in major to minor dimension layout - // order. No loop is emitted for the given reduction_dimension. The function - // returns an IrArray index for the given operand_array containing the indvars - // of the loops. All dimensions of the index are filled except for the - // reduction dimension. name_suffix is the string to append to the names of - // LLVM constructs (eg, basic blocks) constructed by this method. - llvm_ir::IrArray::Index EmitOperandArrayLoopNest( - const llvm_ir::IrArray& operand_array, int64 reduction_dimension, - tensorflow::StringPiece name_suffix, llvm_ir::ForLoopNest* loop_nest); - // A helper method for EmitAtomicOperationForNestedComputation. Certain // computations, such as floating-point addition and integer maximization, can // be simply implemented using an LLVM atomic instruction. If "computation" is diff --git a/tensorflow/compiler/xla/service/llvm_ir/BUILD b/tensorflow/compiler/xla/service/llvm_ir/BUILD index c14a5bfb53..462be543bc 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/BUILD +++ b/tensorflow/compiler/xla/service/llvm_ir/BUILD @@ -180,6 +180,20 @@ cc_library( ], ) +cc_library( + name = "sort_util", + srcs = ["sort_util.cc"], + hdrs = ["sort_util.h"], + deps = [ + ":ir_array", + ":llvm_loop", + ":llvm_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/core:lib", + "@llvm//:core", + ], +) + cc_library( name = "tuple_ops", srcs = ["tuple_ops.cc"], diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc index c9ae7d3afd..1227534779 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc @@ -262,5 +262,35 @@ IrArray::Index ForLoopNest::AddLoopsForShapeOnDimensions( return index; } +IrArray::Index ForLoopNest::EmitOperandArrayLoopNest( + const llvm_ir::IrArray& operand_array, int64 dimension_to_skip, + tensorflow::StringPiece name_suffix) { + // Prepares the dimension list we will use to emit the loop nest. Outermost + // loops are added first. Add loops in major-to-minor order, and skip the + // 'dimension_to_skip' dimension. + std::vector dimensions; + const Shape& shape = operand_array.GetShape(); + for (int64 dimension : LayoutUtil::MinorToMajor(shape)) { + if (dimension != dimension_to_skip) { + dimensions.push_back(dimension); + } + } + + // Create loop nest with one for-loop for each dimension of the + // output. + llvm_ir::IrArray::Index index = + AddLoopsForShapeOnDimensions(shape, dimensions, name_suffix); + // Verify every dimension except the 'dimension_to_skip' dimension was set in + // the index. + for (size_t dimension = 0; dimension < index.size(); ++dimension) { + if (dimension == dimension_to_skip) { + DCHECK_EQ(nullptr, index[dimension]); + } else { + DCHECK_NE(nullptr, index[dimension]); + } + } + return index; +} + } // namespace llvm_ir } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h index 0dd5b9d3b2..b3266022db 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h @@ -248,6 +248,17 @@ class ForLoopNest { const Shape& shape, tensorflow::gtl::ArraySlice dimensions, tensorflow::StringPiece suffix); + // Emits a series of nested loops for iterating over an operand array. Loops + // are constructed in major to minor dimension layout order. No loop is + // emitted for the given 'dimension_to_skip'. The function returns an IrArray + // index for the given operand_array containing the indvars of the loops. All + // dimensions of the index are filled except for 'dimension_to_skip'. + // name_suffix is the string to append to the names of LLVM constructs (eg, + // basic blocks) constructed by this method. + IrArray::Index EmitOperandArrayLoopNest(const llvm_ir::IrArray& operand_array, + int64 dimension_to_skip, + tensorflow::StringPiece name_suffix); + // Convenience methods which return particular basic blocks of the outermost // or innermost loops. These methods return nullptr if no loops have been // added yet. diff --git a/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc b/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc new file mode 100644 index 0000000000..16a9a5aaeb --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/sort_util.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 "tensorflow/compiler/xla/service/llvm_ir/sort_util.h" + +// IWYU pragma: no_include "llvm/IR/Intrinsics.gen.inc" +#include "llvm/IR/BasicBlock.h" +#include "llvm/IR/Constants.h" +#include "llvm/IR/Instructions.h" +#include "tensorflow/compiler/xla/primitive_util.h" +#include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" +#include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" +#include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/core/lib/core/bits.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { +namespace llvm_ir { + +namespace { +// Adds the inner comparison loop where we compare elements pointed to by +// 'keys_index' and 'compare_keys_index'. +void EmitCompareLoop(int64 dimension_to_sort, + const llvm_ir::IrArray::Index& keys_index, + const llvm_ir::IrArray::Index& compare_keys_index, + const llvm_ir::IrArray& keys_array, + llvm::IRBuilder<>* ir_builder) { + // TODO(b/26783907): parallelize this loop. + + // if (is_smaller_index && + // compare_keys[dimension_to_sort] < dimension_to_sort_bound) + llvm::Value* is_smaller_index = ir_builder->CreateICmpSLT( + keys_index[dimension_to_sort], compare_keys_index[dimension_to_sort]); + int64 dimension_to_sort_bound = + keys_array.GetShape().dimensions(dimension_to_sort); + auto if_data = llvm_ir::EmitIfThenElse( + ir_builder->CreateAnd( + is_smaller_index, + ir_builder->CreateICmpSLT( + compare_keys_index[dimension_to_sort], + keys_index.GetConstantWithIndexType(dimension_to_sort_bound))), + "smaller_comparison_index", ir_builder, /*emit_else=*/false); + SetToFirstInsertPoint(if_data.true_block, ir_builder); + auto key1 = keys_array.EmitReadArrayElement(keys_index, ir_builder); + auto key2 = keys_array.EmitReadArrayElement(compare_keys_index, ir_builder); + auto key_type = keys_array.GetShape().element_type(); + auto comparison = + primitive_util::IsFloatingPointType(key_type) + // TODO(b/26783907): Figure out how to handle NaNs. + ? ir_builder->CreateFCmp(llvm::FCmpInst::FCMP_ULT, key1, key2) + : ir_builder->CreateICmp( + primitive_util::IsSignedIntegralType(key_type) + ? llvm::ICmpInst::ICMP_SLT + : llvm::ICmpInst::ICMP_ULT, + key1, key2); + auto min_key = ir_builder->CreateSelect(comparison, key1, key2); + auto max_key = ir_builder->CreateSelect(comparison, key2, key1); + keys_array.EmitWriteArrayElement(keys_index, min_key, ir_builder); + keys_array.EmitWriteArrayElement(compare_keys_index, max_key, ir_builder); +} +} // namespace + +Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, + tensorflow::StringPiece name, + llvm::IRBuilder<>* ir_builder) { + const Shape& keys_shape = keys_array.GetShape(); + + // TODO(b/26783907): This case can probably be avoided with the Algebraic + // Simplifier. + if (ShapeUtil::IsScalar(keys_shape)) { + return Status::OK(); + } + + // Create loop nests which loop through the operand dimensions. The sort + // dimension is handled in three separate innermost loops which perform the + // sorting. + ForLoopNest loop_nest(name, ir_builder); + IrArray::Index keys_index = + loop_nest.EmitOperandArrayLoopNest(keys_array, dimension_to_sort, "keys"); + + // 'compare_keys_index' is the index of the element that 'keys_index' should + // be compared to. + IrArray::Index compare_keys_index(keys_index.GetType()); + for (size_t dimension = 0; dimension < keys_index.size(); ++dimension) { + if (dimension != dimension_to_sort) { + compare_keys_index.push_back(keys_index[dimension]); + } else { + compare_keys_index.push_back(nullptr); + } + } + + // Create the sorting loops which do the sorting. + int64 dimension_to_sort_bound = keys_shape.dimensions(dimension_to_sort); + std::unique_ptr stages_loop = loop_nest.AddLoop( + /*start_index=*/0, + /*end_index=*/ + tensorflow::Log2Ceiling64(dimension_to_sort_bound), + /*suffix=*/"sort_stages"); + std::unique_ptr mask_loop = loop_nest.AddLoop( + /*suffix=*/"mask", + /*start_index=*/keys_index.GetConstantWithIndexType(0), + /*end_index=*/stages_loop->GetIndVarValue()); + std::unique_ptr compare_loop = loop_nest.AddLoop( + /*start_index=*/0, + /*end_index=*/dimension_to_sort_bound, + /*suffix=*/"compare"); + + // Naive C++ code for the inner loops (without parallelization): + // + // for (int64 stage = 0; stage < Log2Ceiling(dimension_to_sort_bound); + // ++stage) { + // int64 first_xor_mask = (1LL << (stage + 1)) - 1; + // for (int64 i = 0; i < dimension_to_sort_bound; ++i) { + // int64 j = i ^ first_xor_mask; + // if (i < j && j < dimension_to_sort_bound) { + // int64 min_key = std::min(keys[i], keys[j]); + // keys[j] = std::max(keys[i], keys[j]); + // keys[i] = min_key; + // } + // } + // for (int64 mask = 0; mask < stage; ++mask) { + // int64 later_xor_mask = (1LL << (stage - (mask + 1)); + // for (int64 i = 0; i < dimension_to_sort_bound; ++i) { + // int64 j = i ^ later_xor_mask; + // if (i < j && j < dimension_to_sort_bound) { + // int64 min_key = std::min(keys[i], keys[j]); + // keys[j] = std::max(keys[i], keys[j]); + // keys[i] = min_key; + // } + // } + // } + // } + // + // This follows the algorithm described on Wikipedia: + // https://en.wikipedia.org/wiki/Bitonic_sorter + + SetToFirstInsertPoint(stages_loop->GetBodyBasicBlock(), ir_builder); + // The first xor mask of a stage is 2^(stage + 1) - 1. + auto first_xor_mask = ir_builder->CreateSub( + ir_builder->CreateShl( + keys_index.GetConstantWithIndexType(1), + ir_builder->CreateAdd(stages_loop->GetIndVarValue(), + keys_index.GetConstantWithIndexType(1))), + keys_index.GetConstantWithIndexType(1)); + std::unique_ptr first_compare_loop = ForLoop::EmitForLoop( + /*prefix=*/"first_compare", + /*start_index=*/keys_index.GetConstantWithIndexType(0), + /*end_index=*/ + keys_index.GetConstantWithIndexType(dimension_to_sort_bound), + /*step=*/keys_index.GetConstantWithIndexType(1), + /*ir_builder=*/ir_builder); + + SetToFirstInsertPoint(first_compare_loop->GetBodyBasicBlock(), ir_builder); + // 'first_compare_loop' iterates through the 'dimension_to_sort'. + keys_index[dimension_to_sort] = first_compare_loop->GetIndVarValue(); + compare_keys_index[dimension_to_sort] = ir_builder->CreateXor( + first_compare_loop->GetIndVarValue(), first_xor_mask); + EmitCompareLoop(dimension_to_sort, keys_index, compare_keys_index, keys_array, + ir_builder); + + SetToFirstInsertPoint(compare_loop->GetPreheaderBasicBlock(), ir_builder); + // The later masks of a stage are 2^(stage - (mask_loop_ind_var + 1)). + auto later_xor_mask = ir_builder->CreateShl( + keys_index.GetConstantWithIndexType(1), + ir_builder->CreateSub( + stages_loop->GetIndVarValue(), + ir_builder->CreateAdd(mask_loop->GetIndVarValue(), + keys_index.GetConstantWithIndexType(1)))); + + SetToFirstInsertPoint(compare_loop->GetBodyBasicBlock(), ir_builder); + // 'compare_loop' iterates through the 'dimension_to_sort'. + keys_index[dimension_to_sort] = compare_loop->GetIndVarValue(); + compare_keys_index[dimension_to_sort] = + ir_builder->CreateXor(compare_loop->GetIndVarValue(), later_xor_mask); + EmitCompareLoop(dimension_to_sort, keys_index, compare_keys_index, keys_array, + ir_builder); + + // Set the IR builder insert point to the exit basic block of the outer most + // loop. This ensures later instructions are inserted after this loop nest. + ir_builder->SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); + + return Status::OK(); +} + +} // namespace llvm_ir +} // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/sort_util.h b/tensorflow/compiler/xla/service/llvm_ir/sort_util.h new file mode 100644 index 0000000000..fc45bfab12 --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/sort_util.h @@ -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. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_SORT_UTIL_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_SORT_UTIL_H_ + +#include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" +#include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { +namespace llvm_ir { +// Emits llvm IR to sort the 'dimension_to_sort' dimension of 'keys_array' into +// ascending order. +Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, + tensorflow::StringPiece name, + llvm::IRBuilder<>* ir_builder); +} // namespace llvm_ir +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_SORT_UTIL_H_ -- GitLab From c818bf016d4b48838d943338fd0c8581ab95ada1 Mon Sep 17 00:00:00 2001 From: Thomas Joerg Date: Thu, 19 Jul 2018 01:23:33 -0700 Subject: [PATCH 122/519] [XLA:GPU] Make the scalar_mul_computation in the test fixture actually use 'multiply'. PiperOrigin-RevId: 205202802 --- tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc index a6dc635b52..49b075be5e 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc @@ -40,7 +40,7 @@ const char kModulePrefix[] = R"( scalar_mul_computation { scalar_lhs.1 = f32[] parameter(0) scalar_rhs.1 = f32[] parameter(1) - ROOT mul.1 = f32[] add(scalar_lhs.1, scalar_rhs.1) + ROOT mul.1 = f32[] multiply(scalar_lhs.1, scalar_rhs.1) })"; TEST_F(MultiOutputFusionTest, MultiOutputFusionSiblingReduceAndReduceFusion) { -- GitLab From dbf10397ed25001f03ed0eac879e328f625fe0d1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 03:53:59 -0700 Subject: [PATCH 123/519] A deleter class that calls RefCounted::Unref, and a unique_ptr alias RefCountPtr that uses this deleter. This class can be used to automate the management of ref-owned objects. PiperOrigin-RevId: 205217510 --- tensorflow/core/lib/core/refcount.h | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/tensorflow/core/lib/core/refcount.h b/tensorflow/core/lib/core/refcount.h index eb41f9ff36..87bcfec411 100644 --- a/tensorflow/core/lib/core/refcount.h +++ b/tensorflow/core/lib/core/refcount.h @@ -17,6 +17,8 @@ limitations under the License. #define TENSORFLOW_LIB_CORE_REFCOUNT_H_ #include +#include + #include "tensorflow/core/platform/logging.h" namespace tensorflow { @@ -58,6 +60,15 @@ class RefCounted { void operator=(const RefCounted&) = delete; }; +// A deleter class to form a std::unique_ptr that unrefs objects. +struct RefCountDeleter { + void operator()(tensorflow::core::RefCounted* o) const { o->Unref(); } +}; + +// A unique_ptr that unrefs the owned object on destruction. +template +using RefCountPtr = std::unique_ptr; + // Helper class to unref an object when out-of-scope. class ScopedUnref { public: -- GitLab From bdd4871cd0d3159f709de1588532096d32db1390 Mon Sep 17 00:00:00 2001 From: vilmar-hillow Date: Thu, 19 Jul 2018 14:52:13 +0300 Subject: [PATCH 124/519] Typo in tf.Session fixed --- tensorflow/docs_src/performance/performance_guide.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/performance/performance_guide.md b/tensorflow/docs_src/performance/performance_guide.md index cb0f5ca924..dafacbe379 100644 --- a/tensorflow/docs_src/performance/performance_guide.md +++ b/tensorflow/docs_src/performance/performance_guide.md @@ -464,7 +464,7 @@ equal to the number of physical cores rather than logical cores. config = tf.ConfigProto() config.intra_op_parallelism_threads = 44 config.inter_op_parallelism_threads = 44 - tf.session(config=config) + tf.Session(config=config) ``` -- GitLab From 68353db31b981057d612a46571ee244a2aca6840 Mon Sep 17 00:00:00 2001 From: Thomas Joerg Date: Thu, 19 Jul 2018 05:23:36 -0700 Subject: [PATCH 125/519] [XLA:GPU] Ignore fp precision for multi-output fusion. This allows fusing producers with fp16 outputs into reduce fusions which are always fp32. PiperOrigin-RevId: 205224989 --- .../xla/service/gpu/multi_output_fusion.cc | 40 ++++++++++- .../service/gpu/multi_output_fusion_test.cc | 70 +++++++++++++++++++ 2 files changed, 107 insertions(+), 3 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc index ea661b3c2c..f95fbb01f9 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc @@ -23,6 +23,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" @@ -71,7 +72,6 @@ bool GpuMultiOutputFusion::ShapesCompatibleForFusion(HloInstruction* instr1, // In that case, the operand of the reduce needs to have the same shape // as the other tuple operands, but also we need to compare the output // shapes of the reduces. - // TODO(tjoerg): Allow differences in fp precision. auto* element_instr_1 = get_element_instr(instr1); auto* element_instr_2 = get_element_instr(instr2); if (element_instr_1->opcode() == HloOpcode::kReduce && @@ -80,8 +80,8 @@ bool GpuMultiOutputFusion::ShapesCompatibleForFusion(HloInstruction* instr1, return false; } // The elementwise output shapes must be the same (including layout). - return ShapeUtil::Equal(get_element_shape(element_instr_1), - get_element_shape(element_instr_2)); + return ShapeUtil::EqualIgnoringFpPrecision( + get_element_shape(element_instr_1), get_element_shape(element_instr_2)); } namespace { @@ -107,6 +107,27 @@ bool IsInputFusibleReduction(HloInstruction* instr) { return IsReductionToVector(*instr); } } + +// The code emitted for reduction suffers from poor data locality if the layouts +// of input parameters differ. In such situtations it is beneficial not to fuse. +// We consider input params with maximum rank only. Params with smaller ranks +// will be broadcasted and have not been observed to cause data locality issues. +// TODO(b/110927656): Improve reduce emitters to remove this limitation. +bool ReduceFriendlyInputLayouts(HloInstruction* instr) { + int64 max_rank = 0; + const Layout* max_rank_layout; + for (HloInstruction* param : instr->fused_parameters()) { + if (ShapeUtil::Rank(param->shape()) > max_rank) { + max_rank = ShapeUtil::Rank(param->shape()); + max_rank_layout = ¶m->shape().layout(); + } + } + return c_all_of(instr->fused_parameters(), [&](HloInstruction* param) { + return (ShapeUtil::Rank(param->shape()) < max_rank) || + (LayoutUtil::Equal(param->shape().layout(), *max_rank_layout)); + }); +} + } // namespace bool GpuMultiOutputFusion::IsFusible(HloInstruction* instr) { @@ -173,29 +194,41 @@ bool GpuMultiOutputFusion::DoProducerConsumerMultiOutputFusion() { // fusions operands. for (HloInstruction* consumer : computation()->MakeInstructionPostOrder()) { if (consumer->user_count() == 0) { + VLOG(3) << consumer->name() << " has no users."; continue; } if (!IsInputFusibleReduction(consumer)) { + VLOG(3) << consumer->name() << " is not an input-fusable reduction."; continue; } + VLOG(3) << consumer->name() + << " is a fusion candidate. Looking for fuseable operands."; auto consumer_operands = consumer->operands(); for (size_t i = 0; i < consumer_operands.size(); ++i) { HloInstruction* producer = consumer_operands[i]; if (!producer->IsFusable()) { + VLOG(3) << producer->name() << " is not fusable."; continue; } const bool is_loop_fusion = producer->opcode() == HloOpcode::kFusion && producer->fusion_kind() == HloInstruction::FusionKind::kLoop; if (!is_loop_fusion) { + VLOG(3) << producer->name() << " is not a loop fusion."; continue; } if (!ShapesCompatibleForFusion(producer, consumer)) { + VLOG(3) << producer->name() << " has an incompatible shape."; + continue; + } + if (!ReduceFriendlyInputLayouts(producer)) { + VLOG(3) << producer->name() << " has inputs with mixed layouts."; continue; } // If we have already decided to fuse this producer, skip it. if (ContainsKey(to_fuse, producer)) { + VLOG(3) << producer->name() << " will be fused with another consumer."; continue; } // Do not fuse a producer if the other operands of the fusion are @@ -204,6 +237,7 @@ bool GpuMultiOutputFusion::DoProducerConsumerMultiOutputFusion() { return producer != operand && reachability()->IsReachable(producer, operand); })) { + VLOG(3) << producer->name() << " would introduce a cycle when fused."; break; } to_fuse.insert(producer); diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc index 49b075be5e..451e49f23a 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc @@ -349,5 +349,75 @@ TEST_F(MultiOutputFusionTest, ProducerConsumerFusionDoNotFuseLoopReduceFusion) { ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); } +TEST_F(MultiOutputFusionTest, + ProducerConsumerFusionFp16LoopFusionAndReduceFusion) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + fused_select { + p1.1 = f16[2,2,2]{2,1,0} parameter(1) + c0 = f16[] constant(0) + broadcast = f16[2,2,2]{2,1,0} broadcast(f16[] c0), dimensions={} + greater-than = pred[2,2,2]{2,1,0} greater-than(f32[2,2,2]{2,1,0} p1.1, f32[2,2,2]{2,1,0} broadcast) + p0.1 = f16[2,2,2]{2,1,0} parameter(0) + ROOT select = f16[2,2,2]{2,1,0} select(pred[2,2,2]{2,1,0} greater-than, f16[2,2,2]{2,1,0} p0.1, f16[2,2,2]{2,1,0} broadcast) + } + fused_reduce { + p0.2 = f16[2,2,2]{2,1,0} parameter(0) + convert = f32[2,2,2]{2,1,0} convert(p0.2) + c1 = f32[] constant(0) + r1 = f32[2,2]{1,0} reduce(convert, c1), dimensions={2}, to_apply=scalar_add_computation + mul = f32[2,2,2]{2,1,0} multiply(convert, convert) + r2 = f32[2,2]{1,0} reduce(mul, c1), dimensions={2}, to_apply=scalar_add_computation + ROOT tuple = (f32[2,2]{1,0}, f32[2,2]{1,0}) tuple(r1, r2) + } + ENTRY reduce { + p0 = f16[2,2,2]{2,1,0} parameter(0) + p1 = f16[2,2,2]{2,1,0} parameter(1) + select = f16[2,2,2]{2,1,0} fusion(p0, p1), kind=kLoop, calls=fused_select + fusion = (f32[2,2]{1,0}, f32[2,2]{1,0}) fusion(select), kind=kInput, calls=fused_reduce + gte0 = f32[2,2]{1,0} get-tuple-element(fusion), index=0 + gte1 = f32[2,2]{1,0} get-tuple-element(fusion), index=1 + ROOT root = (f32[2,2]{1,0}, f32[2,2]{1,0}, f16[2,2,2]{2,1,0}) tuple(gte1, gte1, select) + })")) + .ValueOrDie(); + ASSERT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + const HloInstruction* root = module->entry_computation()->root_instruction(); + EXPECT_THAT(root, op::Tuple(op::GetTupleElement(), op::GetTupleElement(), + op::GetTupleElement())); + const HloInstruction* fusion = root->operand(0)->operand(0); + ASSERT_TRUE(fusion->IsMultiOutputFusion()); + EXPECT_THAT(fusion->fused_expression_root(), + op::Tuple(op::Reduce(), op::Reduce(), op::Select())); +} + +TEST_F(MultiOutputFusionTest, + ProducerConsumerFusionReduceUnfriendlyLoopFusion) { + auto module = ParseHloString(tensorflow::strings::StrCat(kModulePrefix, R"( + mixed_input_layouts_computation { + p0.1 = f16[128,1024,32,32]{1,3,2,0} parameter(0) + p1.1 = f16[128,1024,32,32]{3,2,1,0} parameter(1) + copy = f16[128,1024,32,32]{1,3,2,0} copy(p1.1) + c0 = f16[] constant(0) + broadcast = f16[128,1024,32,32]{1,3,2,0} broadcast(c0), dimensions={} + greater-than = pred[128,1024,32,32]{1,3,2,0} greater-than(copy, broadcast) + ROOT root = f16[128,1024,32,32]{1,3,2,0} select(greater-than, p0.1, broadcast) + } + fused_reduce { + p0.2 = f16[128,1024,32,32]{1,3,2,0} parameter(0) + convert = f32[128,1024,32,32]{1,3,2,0} convert(p0.2) + c0.2 = f32[] constant(0) + ROOT reduce = f32[1024]{0} reduce(convert, c0.2), dimensions={0,2,3}, to_apply=scalar_add_computation + } + ENTRY reduce { + p0 = f16[128,1024,32,32]{3,2,1,0} parameter(0) + p1 = f16[128,1024,32,32]{1,3,2,0} parameter(1) + loop_fusion = f16[128,1024,32,32]{1,3,2,0} fusion(p0, p1), kind=kLoop, calls=mixed_input_layouts_computation + reduce_fusion = f32[1024]{0} fusion(loop_fusion), kind=kInput, calls=fused_reduce + ROOT root = (f32[1024]{0}, f16[128,1024,32,32]{1,3,2,0}) tuple(reduce_fusion, loop_fusion) + })")) + .ValueOrDie(); + ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); +} + } // namespace gpu } // namespace xla -- GitLab From 4e3b7baca38aa93657272b2e80128d0552247f87 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 06:07:49 -0700 Subject: [PATCH 126/519] Docstrings in compiler.py PiperOrigin-RevId: 205228977 --- tensorflow/contrib/autograph/pyct/compiler.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/autograph/pyct/compiler.py b/tensorflow/contrib/autograph/pyct/compiler.py index c172ab21f6..c90a5e89c2 100644 --- a/tensorflow/contrib/autograph/pyct/compiler.py +++ b/tensorflow/contrib/autograph/pyct/compiler.py @@ -71,7 +71,16 @@ def _build_source_map(node, code): def ast_to_source(node, indentation=' '): - """Return the source code of given AST.""" + """Return the source code of given AST. + + Args: + node: The code to compile, as an AST object. + indentation: The string to use for indentation. + + Returns: + code: The source code generated from the AST object + source_mapping: A mapping between the user and AutoGraph generated code. + """ original_node = node if isinstance(node, gast.AST): node = gast.gast_to_ast(node) @@ -105,7 +114,8 @@ def ast_to_object(node, exit. Returns: - A module object containing the compiled source code. + compiled_node: A module object containing the compiled source code. + source: The source code of the compiled object Raises: ValueError: If ag_source_map__ is already in the namespace of the compiled node. -- GitLab From 616a42e95e179c8300b8983e1c534bc03097a869 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Thu, 19 Jul 2018 06:58:35 -0700 Subject: [PATCH 127/519] Revert "Temporarily fix the undefined symbols problem" This reverts commit 9a87590da3876b38af946ab363c9d94b8d46e0f9. --- tensorflow/core/graph/algorithm.cc | 16 ---------------- tensorflow/core/graph/algorithm.h | 22 ++++++---------------- 2 files changed, 6 insertions(+), 32 deletions(-) diff --git a/tensorflow/core/graph/algorithm.cc b/tensorflow/core/graph/algorithm.cc index 548096078f..9b4200e0b4 100644 --- a/tensorflow/core/graph/algorithm.cc +++ b/tensorflow/core/graph/algorithm.cc @@ -23,12 +23,6 @@ limitations under the License. namespace tensorflow { -void DFS(const Graph& g, const std::function& enter, - const std::function& leave, - const NodeComparator& stable_comparator) { - DFS(g, enter, leave, stable_comparator, {}); -} - void DFS(const Graph& g, const std::function& enter, const std::function& leave, const NodeComparator& stable_comparator, @@ -168,11 +162,6 @@ void ReverseDFSFrom(const Graph& g, gtl::ArraySlice start, ReverseDFSFromHelper(g, start, enter, leave, stable_comparator); } -void GetPostOrder(const Graph& g, std::vector* order, - const NodeComparator& stable_comparator) { - GetPostOrder(g, order, stable_comparator, {}); -} - void GetPostOrder(const Graph& g, std::vector* order, const NodeComparator& stable_comparator, const EdgeFilter& edge_filter) { @@ -181,11 +170,6 @@ void GetPostOrder(const Graph& g, std::vector* order, edge_filter); } -void GetReversePostOrder(const Graph& g, std::vector* order, - const NodeComparator& stable_comparator) { - GetReversePostOrder(g, order, stable_comparator, {}); -} - void GetReversePostOrder(const Graph& g, std::vector* order, const NodeComparator& stable_comparator, const EdgeFilter& edge_filter) { diff --git a/tensorflow/core/graph/algorithm.h b/tensorflow/core/graph/algorithm.h index 7d8a3456e4..5bbbc6f6dc 100644 --- a/tensorflow/core/graph/algorithm.h +++ b/tensorflow/core/graph/algorithm.h @@ -52,12 +52,8 @@ struct NodeComparatorName { // If edge_filter is set then ignores edges for which edge_filter returns false. extern void DFS(const Graph& g, const std::function& enter, const std::function& leave, - const NodeComparator& stable_comparator = {}); - -extern void DFS(const Graph& g, const std::function& enter, - const std::function& leave, - const NodeComparator& stable_comparator, - const EdgeFilter& edge_filter); + const NodeComparator& stable_comparator = {}, + const EdgeFilter& edge_filter = {}); // Perform a reverse depth-first-search on g starting at the sink node. // If enter is not empty, calls enter(n) before visiting any parents of n. @@ -95,11 +91,8 @@ extern void ReverseDFSFrom(const Graph& g, gtl::ArraySlice start, // // REQUIRES: order is not NULL. void GetPostOrder(const Graph& g, std::vector* order, - const NodeComparator& stable_comparator = {}); - -void GetPostOrder(const Graph& g, std::vector* order, - const NodeComparator& stable_comparator, - const EdgeFilter& edge_filter); + const NodeComparator& stable_comparator = {}, + const EdgeFilter& edge_filter = {}); // Stores in *order the reverse post-order numbering of all nodes // If stable_comparator is set, a stable ordering of visit is achieved by @@ -107,11 +100,8 @@ void GetPostOrder(const Graph& g, std::vector* order, // // If edge_filter is set then ignores edges for which edge_filter returns false. void GetReversePostOrder(const Graph& g, std::vector* order, - const NodeComparator& stable_comparator); - -void GetReversePostOrder(const Graph& g, std::vector* order, - const NodeComparator& stable_comparator, - const EdgeFilter& edge_filter); + const NodeComparator& stable_comparator = {}, + const EdgeFilter& edge_filter = {}); // Prune nodes in "g" that are not in some path from the source node // to any node in 'nodes'. Returns true if changes were made to the graph. -- GitLab From 1e1f3b0c69aa716834be55e311e512363107f1df Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sun, 15 Jul 2018 15:41:06 +0000 Subject: [PATCH 128/519] Update or-tools to v6.7.2 This fix updates or-tools from 253f795 (dated 03/21/2017) to the latest versioned release version of v6.7.2 Signed-off-by: Yong Tang --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 4b4f31813c..0b2bf83259 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -144,13 +144,13 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "ortools_archive", urls = [ - "https://mirror.bazel.build/github.com/google/or-tools/archive/253f7955c6a1fd805408fba2e42ac6d45b312d15.tar.gz", + "https://mirror.bazel.build/github.com/google/or-tools/archive/v6.7.2.tar.gz", # Please uncomment me, when the next upgrade happens. Then # remove the whitelist entry in third_party/repo.bzl. - # "https://github.com/google/or-tools/archive/253f7955c6a1fd805408fba2e42ac6d45b312d15.tar.gz", + # "https://github.com/google/or-tools/archive/v6.7.2.tar.gz", ], - sha256 = "932075525642b04ac6f1b50589f1df5cd72ec2f448b721fd32234cf183f0e755", - strip_prefix = "or-tools-253f7955c6a1fd805408fba2e42ac6d45b312d15/src", + sha256 = "d025a95f78b5fc5eaa4da5f395f23d11c23cf7dbd5069f1f627f002de87b86b9", + strip_prefix = "or-tools-6.7.2/src", build_file = clean_dep("//third_party:ortools.BUILD"), ) -- GitLab From 4ef7d900fb11d3e361b62a4a6c8a645fbdb8efb7 Mon Sep 17 00:00:00 2001 From: Yong Tang Date: Sun, 15 Jul 2018 15:42:15 +0000 Subject: [PATCH 129/519] Remove unneeded comments Signed-off-by: Yong Tang --- tensorflow/workspace.bzl | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 0b2bf83259..06916e924c 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -145,9 +145,7 @@ def tf_workspace(path_prefix="", tf_repo_name=""): name = "ortools_archive", urls = [ "https://mirror.bazel.build/github.com/google/or-tools/archive/v6.7.2.tar.gz", - # Please uncomment me, when the next upgrade happens. Then - # remove the whitelist entry in third_party/repo.bzl. - # "https://github.com/google/or-tools/archive/v6.7.2.tar.gz", + "https://github.com/google/or-tools/archive/v6.7.2.tar.gz", ], sha256 = "d025a95f78b5fc5eaa4da5f395f23d11c23cf7dbd5069f1f627f002de87b86b9", strip_prefix = "or-tools-6.7.2/src", -- GitLab From cb299834dbe8469f8b54c129e6831e42eed399a2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 07:10:21 -0700 Subject: [PATCH 130/519] Adds class name to the multi_label per class metrics when label_vocabulary is provided. PiperOrigin-RevId: 205235131 --- .../contrib/estimator/python/estimator/head.py | 16 +++++++++++++--- .../estimator/python/estimator/head_test.py | 14 ++++++++------ .../python/estimator/canned/metric_keys.py | 5 +++++ 3 files changed, 26 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/estimator/python/estimator/head.py b/tensorflow/contrib/estimator/python/estimator/head.py index c9d86ef4ab..34f765d565 100644 --- a/tensorflow/contrib/estimator/python/estimator/head.py +++ b/tensorflow/contrib/estimator/python/estimator/head.py @@ -943,20 +943,30 @@ class _MultiLabelHead(head_lib._Head): # pylint:disable=protected-access class_probabilities = array_ops.slice( probabilities, begin=begin, size=size) class_labels = array_ops.slice(labels, begin=begin, size=size) - prob_key = keys.PROBABILITY_MEAN_AT_CLASS % class_id + if self._label_vocabulary is None: + prob_key = keys.PROBABILITY_MEAN_AT_CLASS % class_id + else: + prob_key = ( + keys.PROBABILITY_MEAN_AT_NAME % self._label_vocabulary[class_id]) metric_ops[head_lib._summary_key(self._name, prob_key)] = ( # pylint:disable=protected-access head_lib._predictions_mean( # pylint:disable=protected-access predictions=class_probabilities, weights=weights, name=prob_key)) - auc_key = keys.AUC_AT_CLASS % class_id + if self._label_vocabulary is None: + auc_key = keys.AUC_AT_CLASS % class_id + else: + auc_key = keys.AUC_AT_NAME % self._label_vocabulary[class_id] metric_ops[head_lib._summary_key(self._name, auc_key)] = ( # pylint:disable=protected-access head_lib._auc( # pylint:disable=protected-access labels=class_labels, predictions=class_probabilities, weights=weights, name=auc_key)) - auc_pr_key = keys.AUC_PR_AT_CLASS % class_id + if self._label_vocabulary is None: + auc_pr_key = keys.AUC_PR_AT_CLASS % class_id + else: + auc_pr_key = keys.AUC_PR_AT_NAME % self._label_vocabulary[class_id] metric_ops[head_lib._summary_key(self._name, auc_pr_key)] = ( # pylint:disable=protected-access head_lib._auc( # pylint:disable=protected-access labels=class_labels, diff --git a/tensorflow/contrib/estimator/python/estimator/head_test.py b/tensorflow/contrib/estimator/python/estimator/head_test.py index 7b884402d4..2d367adb47 100644 --- a/tensorflow/contrib/estimator/python/estimator/head_test.py +++ b/tensorflow/contrib/estimator/python/estimator/head_test.py @@ -694,12 +694,14 @@ class MultiLabelHead(test.TestCase): # this assert tests that the algorithm remains consistent. keys.AUC: 0.3333, keys.AUC_PR: 0.7639, - keys.PROBABILITY_MEAN_AT_CLASS % 0: np.sum(_sigmoid(logits[:, 0])) / 2., - keys.AUC_AT_CLASS % 0: 0., - keys.AUC_PR_AT_CLASS % 0: 1., - keys.PROBABILITY_MEAN_AT_CLASS % 1: np.sum(_sigmoid(logits[:, 1])) / 2., - keys.AUC_AT_CLASS % 1: 1., - keys.AUC_PR_AT_CLASS % 1: 1., + keys.PROBABILITY_MEAN_AT_NAME % 'a': + np.sum(_sigmoid(logits[:, 0])) / 2., + keys.AUC_AT_NAME % 'a': 0., + keys.AUC_PR_AT_NAME % 'a': 1., + keys.PROBABILITY_MEAN_AT_NAME % 'b': + np.sum(_sigmoid(logits[:, 1])) / 2., + keys.AUC_AT_NAME % 'b': 1., + keys.AUC_PR_AT_NAME % 'b': 1., } self._test_eval( diff --git a/tensorflow/python/estimator/canned/metric_keys.py b/tensorflow/python/estimator/canned/metric_keys.py index 4f7c849ba4..9d49240fea 100644 --- a/tensorflow/python/estimator/canned/metric_keys.py +++ b/tensorflow/python/estimator/canned/metric_keys.py @@ -47,3 +47,8 @@ class MetricKeys(object): PROBABILITY_MEAN_AT_CLASS = 'probability_mean/class%d' AUC_AT_CLASS = 'auc/class%d' AUC_PR_AT_CLASS = 'auc_precision_recall/class%d' + + # The following require a class name applied. + PROBABILITY_MEAN_AT_NAME = 'probability_mean/%s' + AUC_AT_NAME = 'auc/%s' + AUC_PR_AT_NAME = 'auc_precision_recall/%s' -- GitLab From 5bf5f09450f153b1f35030e91b18bf56499a85d7 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Thu, 19 Jul 2018 07:26:50 -0700 Subject: [PATCH 131/519] Consistently use `--upgrade` instead of `-U` --- tensorflow/docs_src/install/install_linux.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 0d9b6af093..84f7cc1c31 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -65,7 +65,7 @@ We *recommend* using `pip` version 8.1 or higher. If using a release before version 8.1, upgrade `pip`: 
-  pip install -U pip
+  pip install --upgrade pip
If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is @@ -102,7 +102,7 @@ When the Virtualenv is activated, the shell prompt displays as `(venv) $`. Within the active virtual environment, upgrade `pip`: 
-(venv)$ pip install -U pip
+(venv)$ pip install --upgrade pip
You can install other Python packages within the virtual environment without @@ -120,7 +120,7 @@ Choose one of the available TensorFlow packages for installation: Within an active Virtualenv environment, use `pip` to install the package: 
-  pip install -U tensorflow
+  pip install --upgrade tensorflow
Use `pip list` to show the packages installed in the virtual environment. @@ -198,7 +198,7 @@ We *recommend* using `pip` version 8.1 or higher. If using a release before version 8.1, upgrade `pip`: 
-  pip install -U pip
+  pip install --upgrade pip
If not using Ubuntu and [setuptools](https://pypi.org/project/setuptools/) is @@ -220,8 +220,8 @@ Choose one of the available TensorFlow packages for installation: And use `pip` to install the package for Python 2 or 3: 
-  pip install -U --user tensorflow   # Python 2.7
-  pip3 install -U --user tensorflow  # Python 3.n
+  pip install --upgrade --user tensorflow   # Python 2.7
+  pip3 install -upgrade --user tensorflow  # Python 3.n
Use `pip list` to show the packages installed on the system. -- GitLab From 7b0c47e59e2af9d28b453609497548cb4fbdc6df Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Thu, 19 Jul 2018 07:36:49 -0700 Subject: [PATCH 132/519] fix typo I added. --- tensorflow/docs_src/install/install_linux.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/install/install_linux.md b/tensorflow/docs_src/install/install_linux.md index 84f7cc1c31..b0106ad481 100644 --- a/tensorflow/docs_src/install/install_linux.md +++ b/tensorflow/docs_src/install/install_linux.md @@ -221,7 +221,7 @@ And use `pip` to install the package for Python 2 or 3: 
   pip install --upgrade --user tensorflow   # Python 2.7
-  pip3 install -upgrade --user tensorflow  # Python 3.n
+  pip3 install --upgrade --user tensorflow  # Python 3.n
Use `pip list` to show the packages installed on the system. -- GitLab From 7c2e16f92a13762a50d37049bd8c80fc439b03ab Mon Sep 17 00:00:00 2001 From: James Keeling Date: Thu, 19 Jul 2018 07:47:51 -0700 Subject: [PATCH 133/519] Fix argument comment in c_api_function_test.cc PiperOrigin-RevId: 205239285 --- tensorflow/c/c_api_function_test.cc | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/c/c_api_function_test.cc b/tensorflow/c/c_api_function_test.cc index 610274696f..f7ca219c89 100644 --- a/tensorflow/c/c_api_function_test.cc +++ b/tensorflow/c/c_api_function_test.cc @@ -1516,7 +1516,8 @@ void DefineStatefulFunction(const char* name, TF_Function** func) { TF_Output inputs[] = {}; TF_Output outputs[] = {{random, 0}}; - *func = TF_GraphToFunction(func_graph.get(), name, /*append_hash=*/false, -1, + *func = TF_GraphToFunction(func_graph.get(), name, + /*append_hash_to_fn_name=*/false, -1, /*opers=*/nullptr, 0, inputs, 1, outputs, /*output_names=*/nullptr, /*opts=*/nullptr, "", s.get()); -- GitLab From da12c366bd55cd797e433f78034d3848736ebe6f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 08:09:45 -0700 Subject: [PATCH 134/519] Imported from GitHub PR #20884 Add int32/string k/v support for tf.contrib.lookup.HashTable This fix tries to address the issue raised in #20869 where there were no int32/string k/v support for tf.contrib.lookup.HashTable. This fix adds the int32/string for the kernel. This fix fixes #20869. Signed-off-by: Yong Tang Copybara import of the project: - 2cdbb1e05e349b985ace42abd9a6a5140fa38b9f Add int32/string k/v support for tf.contrib.lookup.HashTa... by Yong Tang - 54653810d18ea28c4d9fb82736b3529e84d1128c Add test case for int32/string(k/v) of tf.contrib.lookup.... by Yong Tang - 27599c2915cde87a3c550c8876519e95b3f828db Fix string vs byte mismatch in python 3 by Yong Tang - b58849aac511dcc0fc95218e011e945d3dac86b5 Merge 27599c2915cde87a3c550c8876519e95b3f828db into e7278... by Yong Tang COPYBARA_INTEGRATE_REVIEW=https://github.com/tensorflow/tensorflow/pull/20884 from yongtang:20869-tf.contrib.lookup.HashTable 27599c2915cde87a3c550c8876519e95b3f828db PiperOrigin-RevId: 205242516 --- tensorflow/contrib/lookup/lookup_ops_test.py | 15 +++++++++++++++ tensorflow/core/kernels/lookup_table_op.cc | 1 + 2 files changed, 16 insertions(+) diff --git a/tensorflow/contrib/lookup/lookup_ops_test.py b/tensorflow/contrib/lookup/lookup_ops_test.py index 889accdd5a..8d510ede58 100644 --- a/tensorflow/contrib/lookup/lookup_ops_test.py +++ b/tensorflow/contrib/lookup/lookup_ops_test.py @@ -280,6 +280,21 @@ class HashTableOpTest(test.TestCase): table.init.run() self.assertAllEqual(3, table.size().eval()) + def testHashTableInt32String(self): + with self.test_session(): + default_val = "n/a" + keys = constant_op.constant([0, 1, 2], dtypes.int32) + values = constant_op.constant(["brain", "salad", "surgery"]) + table = lookup.HashTable( + lookup.KeyValueTensorInitializer(keys, values), default_val) + table.init.run() + + input_tensor = constant_op.constant([0, 1, -1]) + output = table.lookup(input_tensor) + + result = output.eval() + self.assertAllEqual([b"brain", b"salad", b"n/a"], result) + class MutableHashTableOpTest(test.TestCase): diff --git a/tensorflow/core/kernels/lookup_table_op.cc b/tensorflow/core/kernels/lookup_table_op.cc index 57b7798ba0..07e754a6ef 100644 --- a/tensorflow/core/kernels/lookup_table_op.cc +++ b/tensorflow/core/kernels/lookup_table_op.cc @@ -822,6 +822,7 @@ REGISTER_KERNEL(int64, float); REGISTER_KERNEL(string, string); REGISTER_KERNEL(string, bool); REGISTER_KERNEL(int32, int32); +REGISTER_KERNEL(int32, string); #undef REGISTER_KERNEL -- GitLab From 6f0e971c30654b02e3ed2f1bc4d3f09b584668e7 Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Thu, 19 Jul 2018 08:21:34 -0700 Subject: [PATCH 135/519] [TF:XLA] Rename xla::Diagonal to xla::GetMatrixDiagonal. Fix its handling of rectangular matrices. Switch the TF DiagPart and MatrixDiagPart operators to use GetMatrixDiagonal. Extend CreateScalar{And,Or}Computation to support non-PRED types. PiperOrigin-RevId: 205244201 --- tensorflow/compiler/tf2xla/kernels/diag_op.cc | 105 ++---------------- tensorflow/compiler/tf2xla/lib/scatter.cc | 2 +- .../compiler/tf2xla/lib/triangular_solve.cc | 4 +- .../compiler/xla/client/lib/arithmetic.cc | 12 +- .../compiler/xla/client/lib/arithmetic.h | 6 +- tensorflow/compiler/xla/client/lib/numeric.cc | 21 ++-- tensorflow/compiler/xla/client/lib/numeric.h | 6 +- .../compiler/xla/client/lib/numeric_test.cc | 26 ++++- tensorflow/compiler/xla/tests/reduce_test.cc | 43 ++++++- 9 files changed, 100 insertions(+), 125 deletions(-) diff --git a/tensorflow/compiler/tf2xla/kernels/diag_op.cc b/tensorflow/compiler/tf2xla/kernels/diag_op.cc index 6dec414c53..22cda27567 100644 --- a/tensorflow/compiler/tf2xla/kernels/diag_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/diag_op.cc @@ -123,8 +123,6 @@ class DiagPartOp : public XlaOpKernel { explicit DiagPartOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::XlaBuilder* builder = ctx->builder(); - const TensorShape input_shape = ctx->InputShape(0); auto dims = input_shape.dim_sizes(); @@ -150,37 +148,13 @@ class DiagPartOp : public XlaOpKernel { new_dims.push_back(dims[i]); } - xla::XlaOp diag = ctx->Input(0); - - // TODO(b/30878775): use Slice with strides when supported, in place of - // the Pad -> Reshape -> Slice. - - // Picture: - // [[1, 0, 0, 0] pad and reshape to [[1, 0, 0, 0, 0], - // [0, 2, 0, 0] =================> [2, 0, 0, 0, 0], - // [0, 0, 3, 0] [3, 0, 0, 0, 0], - // [0, 0, 0, 4]] [4, 0, 0, 0, 0]] - // and then slice out the first column. - - // Flattens the input to 1D. - int64 size = input_shape.num_elements(); - diag = xla::Reshape(diag, {size}); - - // Adds padding after the last element of 'new_size'. - xla::PaddingConfig config; - auto* dim = config.add_dimensions(); - dim->set_edge_padding_high(new_size); - auto zero = XlaHelpers::Zero(builder, input_type(0)); - diag = xla::Pad(diag, zero, config); - - // Reshapes so the diagonal is now in the first column. - diag = xla::Reshape(diag, {new_size, new_size + 1}); + xla::XlaOp input = ctx->Input(0); - // Slices out the first column and reshapes to the final shape. - diag = xla::Slice(diag, {0, 0}, {new_size, 1}, {1, 1}); - diag = xla::Reshape(diag, new_dims); + xla::XlaOp output = xla::Reshape( + xla::GetMatrixDiagonal(xla::Reshape(input, {new_size, new_size})), + new_dims); - ctx->SetOutput(0, diag); + ctx->SetOutput(0, output); } }; @@ -220,8 +194,6 @@ class MatrixDiagPartOp : public XlaOpKernel { explicit MatrixDiagPartOp(OpKernelConstruction* ctx) : XlaOpKernel(ctx) {} void Compile(XlaOpKernelContext* ctx) override { - xla::XlaBuilder* builder = ctx->builder(); - const TensorShape input_shape = ctx->InputShape(0); auto dims = input_shape.dim_sizes(); @@ -229,71 +201,8 @@ class MatrixDiagPartOp : public XlaOpKernel { errors::InvalidArgument("Expected 2 <= dims, got shape ", input_shape.DebugString())); - xla::XlaOp diag = ctx->Input(0); - - int last_dim = dims.size() - 1; - int64 last_dim_size = dims[last_dim]; - - // The smaller of the last two dimension sizes. - int64 smaller_dim_size = std::min(dims[last_dim - 1], dims[last_dim]); - - // TODO(b/30878775): use Slice with strides when supported, in place of - // the Pad -> Reshape -> Slice. - - // Picture: for each 2D matrix in the tensor's last two dimensions: - // [[1, 0, 0, 0] pad and reshape to [[1, 0, 0, 0, 0], - // [0, 2, 0, 0] =================> [2, 0, 0, 0, 0], - // [0, 0, 3, 0]] [3, 0, 0, 0, 0], - // and then slice out the first column. - // - // Another example, with tall and narrow input. - // [[1, 0] pad and reshape to [[1, 0, 0], - // [0, 2] =================> [2, 0, 0]] - // [0, 0] - // [0, 0]] - - // Collapses the last two dimensions. - std::vector flattened_dims(dims.begin(), dims.end() - 1); - flattened_dims.back() *= dims.back(); - diag = xla::Reshape(diag, flattened_dims); - - // Slices or pads the last dimension to 'target_size'. - int64 actual_size = flattened_dims.back(); - int64 target_size = smaller_dim_size * (last_dim_size + 1); - if (actual_size < target_size) { - xla::PaddingConfig config = - xla::MakeNoPaddingConfig(flattened_dims.size()); - auto* dim = config.mutable_dimensions(flattened_dims.size() - 1); - dim->set_edge_padding_high(target_size - actual_size); - auto zero = XlaHelpers::Zero(builder, input_type(0)); - diag = xla::Pad(diag, zero, config); - } else if (actual_size > target_size) { - std::vector start(flattened_dims.size(), 0); - std::vector limits(flattened_dims.begin(), flattened_dims.end()); - std::vector strides(flattened_dims.size(), 1); - limits[flattened_dims.size() - 1] = target_size; - diag = xla::Slice(diag, start, limits, strides); - } - - // Reshape so the target values are in the first position of the last - // dimension. - std::vector unflattened_dims(dims.begin(), dims.end()); - dims[last_dim - 1] = smaller_dim_size; - dims[last_dim] = last_dim_size + 1; - diag = xla::Reshape(diag, dims); - - // Slices out the first column and reshapes to the final shape. - std::vector start(dims.size(), 0); - std::vector limits(dims.begin(), dims.end()); - std::vector strides(dims.size(), 1); - limits[last_dim] = 1; - diag = xla::Slice(diag, start, limits, strides); - - // Collapses away the last dimension. - dims.pop_back(); - diag = xla::Reshape(diag, dims); - - ctx->SetOutput(0, diag); + xla::XlaOp input = ctx->Input(0); + ctx->SetOutput(0, xla::GetMatrixDiagonal(input)); } }; diff --git a/tensorflow/compiler/tf2xla/lib/scatter.cc b/tensorflow/compiler/tf2xla/lib/scatter.cc index 6a5be1c2be..739032fef7 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.cc +++ b/tensorflow/compiler/tf2xla/lib/scatter.cc @@ -132,7 +132,7 @@ xla::StatusOr XlaScatter( // Discard updates with negative indices, since some users expect this. auto index_in_range = xla::ReduceAll( xla::Le(zero_index, index), xla::ConstantR0(body_builder, true), - xla::CreateScalarAndComputation(body_builder)); + xla::CreateScalarAndComputation(xla::PRED, body_builder)); // Make the index in bounds to prevent implementation defined behavior. index = xla::Max(index, zero_index); diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc index e405f8dfaa..a2dd5a0d57 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc @@ -325,7 +325,7 @@ xla::XlaOp TriangularSolveLeftLooking(xla::XlaOp a, xla::XlaOp b, } // Rescale the input to be unit triangular - auto diag = Diagonal(a); + auto diag = xla::GetMatrixDiagonal(a); xla::XlaOp scaled_a; std::vector broadcast_dimensions(ndims - 1); std::iota(broadcast_dimensions.begin(), broadcast_dimensions.end(), 0); @@ -490,7 +490,7 @@ xla::XlaOp TriangularSolveRightLooking(xla::XlaOp a, xla::XlaOp b, } // Rescale the input to be unit triangular - auto diag = Diagonal(a); + auto diag = xla::GetMatrixDiagonal(a); xla::XlaOp scaled_a; std::vector broadcast_dimensions(ndims - 1); std::iota(broadcast_dimensions.begin(), broadcast_dimensions.end(), 0); diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.cc b/tensorflow/compiler/xla/client/lib/arithmetic.cc index 978fc40f34..de1d785e19 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.cc +++ b/tensorflow/compiler/xla/client/lib/arithmetic.cc @@ -94,16 +94,18 @@ XlaComputation CreateScalarMinComputation(PrimitiveType type, }); } -XlaComputation CreateScalarAndComputation(XlaBuilder* builder) { +XlaComputation CreateScalarAndComputation(PrimitiveType type, + XlaBuilder* builder) { return CreateScalarComputation( - "and", PRED, builder, + "and", type, builder, [](XlaBuilder* b, const XlaOp& lhs, const XlaOp& rhs) { return And(lhs, rhs); }); } -XlaComputation CreateScalarOrComputation(XlaBuilder* builder) { - return CreateScalarComputation("or", PRED, builder, +XlaComputation CreateScalarOrComputation(PrimitiveType type, + XlaBuilder* builder) { + return CreateScalarComputation("or", type, builder, [](XlaBuilder* b, const XlaOp& lhs, const XlaOp& rhs) { return Or(lhs, rhs); }); } @@ -112,7 +114,7 @@ XlaOp Any(XlaOp predicates) { XlaBuilder* builder = predicates.builder(); return builder->ReportErrorOrReturn([&]() -> StatusOr { auto f = ConstantR0(builder, false); - XlaComputation logical_or = CreateScalarOrComputation(builder); + XlaComputation logical_or = CreateScalarOrComputation(PRED, builder); TF_ASSIGN_OR_RETURN(const Shape& predicates_shape, builder->GetShape(predicates)); std::vector all_dimensions(ShapeUtil::Rank(predicates_shape)); diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.h b/tensorflow/compiler/xla/client/lib/arithmetic.h index d0b916e8c8..8367e09450 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.h +++ b/tensorflow/compiler/xla/client/lib/arithmetic.h @@ -45,10 +45,12 @@ XlaComputation CreateScalarMinComputation(PrimitiveType type, XlaBuilder* builder); // Creates a scalar logical AND computation and returns it. -XlaComputation CreateScalarAndComputation(XlaBuilder* builder); +XlaComputation CreateScalarAndComputation(PrimitiveType type, + XlaBuilder* builder); // Creates a scalar logical OR computation and returns it. -XlaComputation CreateScalarOrComputation(XlaBuilder* builder); +XlaComputation CreateScalarOrComputation(PrimitiveType type, + XlaBuilder* builder); // Returns whether any predicate in "predicates" is set. // diff --git a/tensorflow/compiler/xla/client/lib/numeric.cc b/tensorflow/compiler/xla/client/lib/numeric.cc index cdbeb189f4..a6e460aa75 100644 --- a/tensorflow/compiler/xla/client/lib/numeric.cc +++ b/tensorflow/compiler/xla/client/lib/numeric.cc @@ -79,25 +79,30 @@ XlaOp IdentityMatrix(XlaBuilder* builder, PrimitiveType type, int64 m, return ConvertElementType(indicator, type); } -XlaOp Diagonal(XlaOp x) { +XlaOp GetMatrixDiagonal(XlaOp x) { XlaBuilder* builder = x.builder(); return builder->ReportErrorOrReturn([&]() -> StatusOr { TF_ASSIGN_OR_RETURN(Shape shape, builder->GetShape(x)); const int64 n_dims = ShapeUtil::Rank(shape); TF_RET_CHECK(n_dims >= 2); - const int64 n = shape.dimensions(n_dims - 1); const int64 m = shape.dimensions(n_dims - 2); + const int64 n = shape.dimensions(n_dims - 1); tensorflow::gtl::ArraySlice major_dims( AsInt64Slice(shape.dimensions()), /*pos=*/0, /*len=*/n_dims - 2); auto a = Iota(builder, U32, n); auto b = Iota(builder, U32, m); - auto indicator = Eq(a, Broadcast(b, {n}), /*broadcast_dimensions=*/{0}); + auto indicator = Eq(b, Broadcast(a, {m}), /*broadcast_dimensions=*/{0}); auto mask = Broadcast(indicator, major_dims); - XlaComputation add = - CreateScalarAddComputation(shape.element_type(), builder); - auto diag = Reduce(Select(mask, x, Zeros(builder, shape)), ScalarLike(x, 0), - add, {n_dims - 1}); - return diag; + + // TPUs don't support S64 add reduction at the moment. But fortunately + // OR-reductions work just as well for integers. + XlaComputation reducer = + primitive_util::IsIntegralType(shape.element_type()) + ? CreateScalarOrComputation(shape.element_type(), builder) + : CreateScalarAddComputation(shape.element_type(), builder); + + return Reduce(Select(mask, x, Zeros(builder, shape)), ScalarLike(x, 0), + reducer, {m >= n ? n_dims - 2 : n_dims - 1}); }); } diff --git a/tensorflow/compiler/xla/client/lib/numeric.h b/tensorflow/compiler/xla/client/lib/numeric.h index 3ec084636b..e9037b722c 100644 --- a/tensorflow/compiler/xla/client/lib/numeric.h +++ b/tensorflow/compiler/xla/client/lib/numeric.h @@ -29,8 +29,10 @@ XlaOp Iota(XlaBuilder* builder, PrimitiveType type, int64 size); // else. XlaOp IdentityMatrix(XlaBuilder* builder, PrimitiveType type, int64 m, int64 n); -// Get the diagonals of the last two dimensions. -XlaOp Diagonal(XlaOp x); +// Get the diagonals of the last two dimensions. If 'x' has shape +// [..., M, N], then the output has shape [..., min(M, N)], containing the +// diagonal elements (i.e., with indices [..., i, i]). +XlaOp GetMatrixDiagonal(XlaOp x); } // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/numeric_test.cc b/tensorflow/compiler/xla/client/lib/numeric_test.cc index bc8a73e9d7..bfea3f539d 100644 --- a/tensorflow/compiler/xla/client/lib/numeric_test.cc +++ b/tensorflow/compiler/xla/client/lib/numeric_test.cc @@ -24,7 +24,11 @@ limitations under the License. namespace xla { namespace { -using NumericTest = ClientLibraryTestBase; +class NumericTest : public ClientLibraryTestBase { + protected: + template + void TestMatrixDiagonal(); +}; XLA_TEST_F(NumericTest, Iota) { XlaBuilder builder(TestName()); @@ -33,5 +37,25 @@ XLA_TEST_F(NumericTest, Iota) { ComputeAndCompareR1(&builder, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {}); } +template +void NumericTest::TestMatrixDiagonal() { + XlaBuilder builder("GetMatrixDiagonal"); + Array3D input(2, 3, 4); + input.FillIota(0); + + XlaOp a; + auto a_data = CreateR3Parameter(input, 0, "a", &builder, &a); + GetMatrixDiagonal(a); + Array2D expected({{0, 5, 10}, {12, 17, 22}}); + + ComputeAndCompareR2(&builder, expected, {a_data.get()}); +} + +XLA_TEST_F(NumericTest, GetMatrixDiagonal_S32) { TestMatrixDiagonal(); } + +XLA_TEST_F(NumericTest, GetMatrixDiagonal_S64) { TestMatrixDiagonal(); } + +XLA_TEST_F(NumericTest, GetMatrixDiagonal_F32) { TestMatrixDiagonal(); } + } // namespace } // namespace xla diff --git a/tensorflow/compiler/xla/tests/reduce_test.cc b/tensorflow/compiler/xla/tests/reduce_test.cc index 1407fca72f..e4a8ddf86a 100644 --- a/tensorflow/compiler/xla/tests/reduce_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_test.cc @@ -125,10 +125,10 @@ class ReduceTest : public ClientLibraryTestBase { XlaComputation reduce; if (and_reduce) { init_value = ConstantR0(&builder, true); - reduce = CreateScalarAndComputation(&builder); + reduce = CreateScalarAndComputation(PRED, &builder); } else { init_value = ConstantR0(&builder, false); - reduce = CreateScalarOrComputation(&builder); + reduce = CreateScalarOrComputation(PRED, &builder); } Reduce(pred_values, init_value, reduce, /*dimensions_to_reduce=*/{0}); @@ -163,10 +163,10 @@ class ReduceTest : public ClientLibraryTestBase { XlaComputation reduce_op; if (and_reduce) { init_value = ConstantR0(&builder, true); - reduce_op = CreateScalarAndComputation(&builder); + reduce_op = CreateScalarAndComputation(PRED, &builder); } else { init_value = ConstantR0(&builder, false); - reduce_op = CreateScalarOrComputation(&builder); + reduce_op = CreateScalarOrComputation(PRED, &builder); } Reduce(input_pred, init_value, reduce_op, @@ -798,13 +798,17 @@ XLA_TEST_F(ReduceTest, VectorizedReduce_Min) { XLA_TEST_F(ReduceTest, VectorizedReduce_BooleanAnd) { RunVectorizedReduceTestForType( - static_cast(CreateScalarAndComputation), + static_cast([](XlaBuilder* builder) { + return CreateScalarAndComputation(PRED, builder); + }), [](bool a, bool b) { return a && b; }, true); } XLA_TEST_F(ReduceTest, VectorizedReduce_BooleanOr) { RunVectorizedReduceTestForType( - static_cast(CreateScalarOrComputation), + static_cast([](XlaBuilder* builder) { + return CreateScalarOrComputation(PRED, builder); + }), [](bool a, bool b) { return a || b; }, false); } @@ -963,5 +967,32 @@ XLA_TEST_F(ReduceTest, ReduceIdentity) { ErrorSpec(0.0001)); } +XLA_TEST_F(ReduceTest, AndReduceU64) { + XlaBuilder builder(TestName()); + Array2D initializer = {{0x123456789ABCDEF0LL, 0x3BCDEF12A4567890LL}, + {0XFFFFFFFFFFFFFFD6LL, 101}, + {1, 0XFFFFFFFFFFFFFFFFLL}}; + auto reducer = CreateScalarAndComputation(U64, &builder); + auto m = ConstantR2FromArray2D(&builder, initializer); + Reduce(m, ConstantR0(&builder, 0xFFFFFFFFFFFFFFFFLL), reducer, {1}); + + std::vector expected = {0x1204461080145890LL, 68, 1}; + ComputeAndCompareR1(&builder, expected, {}); +} + +XLA_TEST_F(ReduceTest, OrReduceU64) { + XlaBuilder builder(TestName()); + Array2D initializer = {{0x123456789ABCDEF0LL, 0x3BCDEF12A4567890LL}, + {0xFFFFFFFFFFFFFFD6LL, 101}, + {1, 0xCAFEBEEFABABABABLL}}; + auto reducer = CreateScalarOrComputation(U64, &builder); + auto m = ConstantR2FromArray2D(&builder, initializer); + Reduce(m, ConstantR0(&builder, 0), reducer, {1}); + + std::vector expected = {0X3BFDFF7ABEFEFEF0LL, 0XFFFFFFFFFFFFFFF7LL, + 0xCAFEBEEFABABABABLL}; + ComputeAndCompareR1(&builder, expected, {}); +} + } // namespace } // namespace xla -- GitLab From daafc6571a4817b1313b7c243fbd35e3a9f12dab Mon Sep 17 00:00:00 2001 From: David Majnemer Date: Thu, 19 Jul 2018 08:51:32 -0700 Subject: [PATCH 136/519] [XLA] Don't use Pow for simple expressions Using Pow to handle squaring or taking the reciprocal is overkill, Pow is not going to be as accurate as the straightforward formulation without relying on optimization in the compiler or the Pow implementation to kick in. PiperOrigin-RevId: 205247912 --- tensorflow/compiler/xla/client/lib/math.cc | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/tensorflow/compiler/xla/client/lib/math.cc b/tensorflow/compiler/xla/client/lib/math.cc index a6d606f944..0221de7672 100644 --- a/tensorflow/compiler/xla/client/lib/math.cc +++ b/tensorflow/compiler/xla/client/lib/math.cc @@ -25,11 +25,9 @@ XlaOp Sqrt(XlaOp operand) { return Pow(operand, ScalarLike(operand, 0.5)); } XlaOp Rsqrt(XlaOp operand) { return Pow(operand, ScalarLike(operand, -0.5)); } -XlaOp Square(XlaOp operand) { return Pow(operand, ScalarLike(operand, 2.0)); } +XlaOp Square(XlaOp operand) { return operand * operand; } -XlaOp Reciprocal(XlaOp operand) { - return Pow(operand, ScalarLike(operand, -1.0)); -} +XlaOp Reciprocal(XlaOp operand) { return ScalarLike(operand, 1.0) / operand; } namespace { -- GitLab From e9e48b963b1ad1274ad8a0ad7d07d7fa990fe6b9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 08:52:49 -0700 Subject: [PATCH 137/519] Update `reader` dependencies such that the SavedModel loader still works on mobile. PiperOrigin-RevId: 205248073 --- tensorflow/cc/saved_model/BUILD | 13 ++++++++++++- 1 file changed, 12 insertions(+), 1 deletion(-) diff --git a/tensorflow/cc/saved_model/BUILD b/tensorflow/cc/saved_model/BUILD index 730b1b669b..3d3895c8fa 100644 --- a/tensorflow/cc/saved_model/BUILD +++ b/tensorflow/cc/saved_model/BUILD @@ -39,9 +39,20 @@ cc_library( hdrs = ["reader.h"], deps = [ ":constants", + ] + if_not_mobile([ + # TODO(b/111634734): :lib and :protos_all contain dependencies that + # cannot be built on mobile platforms. Instead, include the appropriate + # tf_lib depending on the build platform. "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", - ], + ]) + if_mobile([ + # Mobile-friendly SavedModel proto. See go/portable-proto for more info. + "//tensorflow/core:saved_model_portable_proto", + ]) + if_android([ + "//tensorflow/core:android_tensorflow_lib", + ]) + if_ios([ + "//tensorflow/core:ios_tensorflow_lib", + ]), ) tf_cc_test( -- GitLab From 2509b3a2152c8dda9fff8ed58f414c1316fa5379 Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Thu, 19 Jul 2018 08:56:07 -0700 Subject: [PATCH 138/519] eager guide: s/tfe.Checkpoint/tf.train.Checkpoint/ PiperOrigin-RevId: 205248470 --- .../contrib/eager/python/examples/gan/mnist.py | 5 ++--- .../eager/python/examples/rnn_ptb/rnn_ptb.py | 2 +- tensorflow/docs_src/guide/eager.md | 16 ++++++++-------- third_party/examples/eager/spinn/spinn.py | 2 +- 4 files changed, 12 insertions(+), 13 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/gan/mnist.py b/tensorflow/contrib/eager/python/examples/gan/mnist.py index b33243021b..9a42179299 100644 --- a/tensorflow/contrib/eager/python/examples/gan/mnist.py +++ b/tensorflow/contrib/eager/python/examples/gan/mnist.py @@ -29,7 +29,6 @@ import time import tensorflow as tf -import tensorflow.contrib.eager as tfe from tensorflow.examples.tutorials.mnist import input_data layers = tf.keras.layers @@ -265,7 +264,7 @@ def train_one_epoch(generator, discriminator, generator_optimizer, def main(_): (device, data_format) = ('/gpu:0', 'channels_first') - if FLAGS.no_gpu or tfe.num_gpus() <= 0: + if FLAGS.no_gpu or tf.contrib.eager.num_gpus() <= 0: (device, data_format) = ('/cpu:0', 'channels_last') print('Using device %s, and data format %s.' % (device, data_format)) @@ -291,7 +290,7 @@ def main(_): latest_cpkt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir) if latest_cpkt: print('Using latest checkpoint at ' + latest_cpkt) - checkpoint = tfe.Checkpoint(**model_objects) + checkpoint = tf.train.Checkpoint(**model_objects) # Restore variables on creation if a checkpoint exists. checkpoint.restore(latest_cpkt) 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 d64bf5354e..15776c694e 100644 --- a/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py +++ b/tensorflow/contrib/eager/python/examples/rnn_ptb/rnn_ptb.py @@ -315,7 +315,7 @@ def main(_): FLAGS.hidden_dim, FLAGS.num_layers, FLAGS.dropout, use_cudnn_rnn) optimizer = tf.train.GradientDescentOptimizer(learning_rate) - checkpoint = tfe.Checkpoint( + checkpoint = tf.train.Checkpoint( learning_rate=learning_rate, model=model, # GradientDescentOptimizer has no state to checkpoint, but noting it # here lets us swap in an optimizer that does. diff --git a/tensorflow/docs_src/guide/eager.md b/tensorflow/docs_src/guide/eager.md index 42ad9652f8..3b54d6d2bb 100644 --- a/tensorflow/docs_src/guide/eager.md +++ b/tensorflow/docs_src/guide/eager.md @@ -504,13 +504,13 @@ with tf.device("gpu:0"): ### Object-based saving -`tfe.Checkpoint` can save and restore `tf.Variable`s to and from +`tf.train.Checkpoint` can save and restore `tf.Variable`s to and from checkpoints: ```py x = tf.Variable(10.) -checkpoint = tfe.Checkpoint(x=x) # save as "x" +checkpoint = tf.train.Checkpoint(x=x) # save as "x" x.assign(2.) # Assign a new value to the variables and save. save_path = checkpoint.save('./ckpt/') @@ -523,18 +523,18 @@ checkpoint.restore(save_path) print(x) # => 2.0 ``` -To save and load models, `tfe.Checkpoint` stores the internal state of objects, +To save and load models, `tf.train.Checkpoint` stores the internal state of objects, without requiring hidden variables. To record the state of a `model`, -an `optimizer`, and a global step, pass them to a `tfe.Checkpoint`: +an `optimizer`, and a global step, pass them to a `tf.train.Checkpoint`: ```py model = MyModel() optimizer = tf.train.AdamOptimizer(learning_rate=0.001) checkpoint_dir = ‘/path/to/model_dir’ checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt") -root = tfe.Checkpoint(optimizer=optimizer, - model=model, - optimizer_step=tf.train.get_or_create_global_step()) +root = tf.train.Checkpoint(optimizer=optimizer, + model=model, + optimizer_step=tf.train.get_or_create_global_step()) root.save(file_prefix=checkpoint_prefix) # or @@ -824,7 +824,7 @@ gives you eager's interactive experimentation and debuggability with the distributed performance benefits of graph execution. Write, debug, and iterate in eager execution, then import the model graph for -production deployment. Use `tfe.Checkpoint` to save and restore model +production deployment. Use `tf.train.Checkpoint` to save and restore model variables, this allows movement between eager and graph execution environments. See the examples in: [tensorflow/contrib/eager/python/examples](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples). diff --git a/third_party/examples/eager/spinn/spinn.py b/third_party/examples/eager/spinn/spinn.py index c242ef3fdd..de63ebe9e6 100644 --- a/third_party/examples/eager/spinn/spinn.py +++ b/third_party/examples/eager/spinn/spinn.py @@ -626,7 +626,7 @@ def train_or_infer_spinn(embed, model = SNLIClassifier(config, embed) global_step = tf.train.get_or_create_global_step() trainer = SNLIClassifierTrainer(model, config.lr) - checkpoint = tfe.Checkpoint(trainer=trainer, global_step=global_step) + checkpoint = tf.train.Checkpoint(trainer=trainer, global_step=global_step) checkpoint.restore(tf.train.latest_checkpoint(config.logdir)) if inference_sentence_pair: -- GitLab From 15f3a087693a75243962b346d31a013d15990921 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Thu, 19 Jul 2018 08:57:50 -0700 Subject: [PATCH 139/519] Add a few more links to the notebook. PiperOrigin-RevId: 205248656 --- tensorflow/contrib/autograph/README.md | 17 +++++++++++++---- 1 file changed, 13 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/autograph/README.md b/tensorflow/contrib/autograph/README.md index 679ab48e5c..cc54da4daa 100644 --- a/tensorflow/contrib/autograph/README.md +++ b/tensorflow/contrib/autograph/README.md @@ -1,6 +1,6 @@ # AutoGraph -IMPORTANT: AutoGraph is alpha software, and under active development. Expect rough edges and bugs, but if you try it, we appreciate early feedback! We'd also love contributions ([please see our contributing guidelines](CONTRIBUTING.md) and our [style guide](STYLE_GUIDE.md)). +IMPORTANT: AutoGraph is beta software, and under active development. Expect rough edges and bugs, but if you try it, we appreciate early feedback! We'd also love contributions ([please see our contributing guidelines](CONTRIBUTING.md) and our [style guide](STYLE_GUIDE.md)). AutoGraph is a Python to TensorFlow compiler. @@ -68,12 +68,21 @@ Then import the `autograph` module from `tf.contrib`: from tensorflow.contrib import autograph as ag ``` -### Interactive demo notebooks +### Related links -For more extensive examples, check out these interactive notebooks: +Articles: - * [RNN trained using Keras and Estimators](https://colab.sandbox.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/rnn_keras_estimator.ipynb) + * [TensorFlow blog post](https://medium.com/tensorflow/autograph-converts-python-into-tensorflow-graphs-b2a871f87ec7) + +Interactive notebooks: + + * [Quick guide](https://colab.research.google.com/github/tensorflow/models/blob/master/samples/core/guide/autograph.ipynb) + * [RNN trained using Keras and Estimators](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/rnn_keras_estimator.ipynb) * [Demo from the TF Dev Summit 2018](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb) + * [Basic control flow speed test](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_collatz_speed_test.ipynb) + * [MNIST training speed test](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/ag_vs_eager_mnist_speed_test.ipynb) + * [Basic algorithm samples](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb) + * [Introductory workshop support notebook](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/autograph/examples/notebooks/workshop.ipynb) ## Using with annotations -- GitLab From e240aa301afc57a63366638af4cf92c823e8084a Mon Sep 17 00:00:00 2001 From: Eugene Brevdo Date: Thu, 19 Jul 2018 09:09:45 -0700 Subject: [PATCH 140/519] Add tf.bool support back to tf.scatter_nd. PiperOrigin-RevId: 205250376 --- tensorflow/core/kernels/scatter_nd_op.cc | 4 ++++ .../core/kernels/scatter_nd_op_cpu_impl.h | 7 +++--- tensorflow/core/ops/array_ops.cc | 2 +- .../kernel_tests/scatter_nd_ops_test.py | 23 +++++++++++++++++++ 4 files changed, 32 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/kernels/scatter_nd_op.cc b/tensorflow/core/kernels/scatter_nd_op.cc index e1fc2ea128..c44753e25e 100644 --- a/tensorflow/core/kernels/scatter_nd_op.cc +++ b/tensorflow/core/kernels/scatter_nd_op.cc @@ -277,6 +277,9 @@ TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ND_ADD_SUB_CPU); TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ND_UPDATE_CPU); TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ND_CPU); TF_CALL_string(REGISTER_SCATTER_ND_CPU); +TF_CALL_bool(REGISTER_SCATTER_ND_ADD_SUB_CPU); +TF_CALL_bool(REGISTER_SCATTER_ND_UPDATE_CPU); +TF_CALL_bool(REGISTER_SCATTER_ND_CPU); // Registers GPU kernels. #if GOOGLE_CUDA @@ -309,6 +312,7 @@ TF_CALL_complex128(REGISTER_SCATTER_ND_ALL_GPU); TF_CALL_int32(REGISTER_SCATTER_ND_ADD_SUB_SYCL); TF_CALL_int32(REGISTER_SCATTER_ND_UPDATE_SYCL); +TF_CALL_bool(REGISTER_SCATTER_ND_UPDATE_SYCL); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_ADD_SUB_SYCL); TF_CALL_GPU_NUMBER_TYPES_NO_HALF(REGISTER_SCATTER_ND_UPDATE_SYCL); #undef REGISTER_SCATTER_ND_ADD_SUB_SYCL diff --git a/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h b/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h index 7cfffa20c5..472f5a3547 100644 --- a/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h +++ b/tensorflow/core/kernels/scatter_nd_op_cpu_impl.h @@ -161,15 +161,16 @@ struct ScatterNdFunctor { TF_CALL_ALL_TYPES(REGISTER_SCATTER_ND_UPDATE); REGISTER_SCATTER_ND_INDEX(string, scatter_nd_op::UpdateOp::ADD); -TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ND_MATH) - +TF_CALL_NUMBER_TYPES(REGISTER_SCATTER_ND_MATH); +TF_CALL_bool(REGISTER_SCATTER_ND_MATH); #undef REGISTER_SCATTER_ND_MATH #undef REGISTER_SCATTER_ND_UPDATE #undef REGISTER_SCATTER_ND_INDEX #undef REGISTER_SCATTER_ND_FULL -#ifdef TENSORFLOW_USE_SYCL // Implementation of update functor for SYCL. +#ifdef TENSORFLOW_USE_SYCL + template struct ScatterNdFunctor { Index operator()( diff --git a/tensorflow/core/ops/array_ops.cc b/tensorflow/core/ops/array_ops.cc index 02989f8d3d..d6ae75473f 100644 --- a/tensorflow/core/ops/array_ops.cc +++ b/tensorflow/core/ops/array_ops.cc @@ -2881,7 +2881,7 @@ REGISTER_OP("ScatterNdNonAliasingAdd") .Input("indices: Tindices") .Input("updates: T") .Output("output: T") - .Attr("T: numbertype") + .Attr("T: {numbertype, bool}") .Attr("Tindices: {int32, int64}") .SetShapeFn(shape_inference::ScatterNdUpdateShape); diff --git a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py index f9b9c77bbf..080319f6e8 100644 --- a/tensorflow/python/kernel_tests/scatter_nd_ops_test.py +++ b/tensorflow/python/kernel_tests/scatter_nd_ops_test.py @@ -369,6 +369,29 @@ class ScatterNdTest(test.TestCase): del input_ # input_ is not used in scatter_nd return array_ops.scatter_nd(indices, updates, shape) + @test_util.run_in_graph_and_eager_modes + def testBool(self): + indices = constant_op.constant( + [[4], [3], [1], [7]], dtype=dtypes.int32) + updates = constant_op.constant( + [False, True, False, True], dtype=dtypes.bool) + expected = np.array( + [False, False, False, True, False, False, False, True]) + scatter = self.scatter_nd(indices, updates, shape=(8,)) + result = self.evaluate(scatter) + self.assertAllEqual(expected, result) + + # Same indice is updated twice by same value. + indices = constant_op.constant( + [[4], [3], [3], [7]], dtype=dtypes.int32) + updates = constant_op.constant( + [False, True, True, True], dtype=dtypes.bool) + expected = np.array([ + False, False, False, True, False, False, False, True]) + scatter = self.scatter_nd(indices, updates, shape=(8,)) + result = self.evaluate(scatter) + self.assertAllEqual(expected, result) + @test_util.run_in_graph_and_eager_modes def testInvalidShape(self): # TODO(apassos) figure out how to unify these errors -- GitLab From b81144bbfb9a7887b0cefb2802ef3cc6df4860ce Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 09:18:03 -0700 Subject: [PATCH 141/519] Update ops-related pbtxt files. PiperOrigin-RevId: 205251410 --- .../core/ops/compat/ops_history.v1.pbtxt | 55 +++++++++++++++++++ tensorflow/core/ops/ops.pbtxt | 1 + 2 files changed, 56 insertions(+) diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt index e91089e627..69351cd392 100644 --- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt +++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt @@ -55242,6 +55242,61 @@ op { } } } +op { + name: "ScatterNdNonAliasingAdd" + input_arg { + name: "input" + type_attr: "T" + } + input_arg { + name: "indices" + type_attr: "Tindices" + } + input_arg { + name: "updates" + type_attr: "T" + } + output_arg { + name: "output" + type_attr: "T" + } + attr { + name: "T" + type: "type" + allowed_values { + list { + type: DT_FLOAT + type: DT_DOUBLE + type: DT_INT32 + type: DT_UINT8 + type: DT_INT16 + type: DT_INT8 + type: DT_COMPLEX64 + type: DT_INT64 + type: DT_QINT8 + type: DT_QUINT8 + type: DT_QINT32 + type: DT_BFLOAT16 + type: DT_UINT16 + type: DT_COMPLEX128 + type: DT_HALF + type: DT_UINT32 + type: DT_UINT64 + type: DT_BOOL + } + } + } + attr { + name: "Tindices" + type: "type" + allowed_values { + list { + type: DT_INT32 + type: DT_INT64 + } + } + } +} op { name: "ScatterNdSub" input_arg { diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index 6f07dd612e..978bb0bbf4 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -26182,6 +26182,7 @@ op { type: DT_HALF type: DT_UINT32 type: DT_UINT64 + type: DT_BOOL } } } -- GitLab From 7ce817b2b386787b504c7652cc8877135409a8f4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 10:12:15 -0700 Subject: [PATCH 142/519] Rework Add / Sub kernels signatures. PiperOrigin-RevId: 205259359 --- tensorflow/contrib/lite/kernels/add.cc | 85 +- .../contrib/lite/kernels/internal/common.h | 133 +++ .../internal/optimized/legacy_optimized_ops.h | 239 +++++ .../internal/optimized/optimized_ops.h | 802 ++++------------ .../internal/reference/legacy_reference_ops.h | 234 +++++ .../internal/reference/reference_ops.h | 907 +++++++++--------- .../contrib/lite/kernels/internal/types.h | 112 ++- tensorflow/contrib/lite/kernels/sub.cc | 68 +- 8 files changed, 1452 insertions(+), 1128 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/add.cc b/tensorflow/contrib/lite/kernels/add.cc index f44d531cbf..af9b5c7013 100644 --- a/tensorflow/contrib/lite/kernels/add.cc +++ b/tensorflow/contrib/lite/kernels/add.cc @@ -110,15 +110,12 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { QuantizeMultiplierSmallerThanOneExp( real_input1_multiplier, &data->input1_multiplier, &data->input1_shift); - data->input1_shift *= -1; QuantizeMultiplierSmallerThanOneExp( real_input2_multiplier, &data->input2_multiplier, &data->input2_shift); - data->input2_shift *= -1; QuantizeMultiplierSmallerThanOneExp( real_output_multiplier, &data->output_multiplier, &data->output_shift); - data->output_shift *= -1; CalculateActivationRangeUint8(params->activation, output, &data->output_activation_min, @@ -152,14 +149,14 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { CheckedLog2(output->params.scale, &output_scale_log2_rounded); TF_LITE_ENSURE(context, output_scale_is_pot); - data->input1_shift = output_scale_log2_rounded - input1_scale_log2_rounded; - data->input2_shift = output_scale_log2_rounded - input2_scale_log2_rounded; + data->input1_shift = input1_scale_log2_rounded - output_scale_log2_rounded; + data->input2_shift = input2_scale_log2_rounded - output_scale_log2_rounded; // Shifting of one input is supported. The graph quantization should ensure // that the other input matches the output. TF_LITE_ENSURE(context, data->input1_shift == 0 || data->input2_shift == 0); - TF_LITE_ENSURE(context, data->input1_shift >= 0); - TF_LITE_ENSURE(context, data->input2_shift >= 0); + TF_LITE_ENSURE(context, data->input1_shift <= 0); + TF_LITE_ENSURE(context, data->input2_shift <= 0); CalculateActivationRangeQuantized(context, params->activation, output, &data->output_activation_min, @@ -173,24 +170,27 @@ template void EvalAdd(TfLiteContext* context, TfLiteNode* node, TfLiteAddParams* params, const OpData* data, const TfLiteTensor* input1, const TfLiteTensor* input2, TfLiteTensor* output) { -#define TF_LITE_ADD(type, opname, data_type) \ - data_type output_activation_min, output_activation_max; \ - CalculateActivationRange(params->activation, &output_activation_min, \ - &output_activation_max); \ - type::opname(GetTensorData(input1), GetTensorDims(input1), \ - GetTensorData(input2), GetTensorDims(input2), \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_ADD(type, opname, data_type) \ + data_type output_activation_min, output_activation_max; \ + CalculateActivationRange(params->activation, &output_activation_min, \ + &output_activation_max); \ + tflite::ArithmeticParams op_params; \ + SetActivationParams(output_activation_min, output_activation_max, \ + &op_params); \ + type::opname(op_params, GetTensorShape(input1), \ + GetTensorData(input1), GetTensorShape(input2), \ + GetTensorData(input2), GetTensorShape(output), \ + GetTensorData(output)) if (output->type == kTfLiteInt32) { if (kernel_type == kReference) { if (data->requires_broadcast) { - TF_LITE_ADD(reference_ops, BroadcastAdd, int32_t); + TF_LITE_ADD(reference_ops, BroadcastAdd4DSlow, int32_t); } else { TF_LITE_ADD(reference_ops, Add, int32_t); } } else { if (data->requires_broadcast) { - TF_LITE_ADD(optimized_ops, BroadcastAdd, int32_t); + TF_LITE_ADD(optimized_ops, BroadcastAdd4DSlow, int32_t); } else { TF_LITE_ADD(optimized_ops, Add, int32_t); } @@ -198,13 +198,13 @@ void EvalAdd(TfLiteContext* context, TfLiteNode* node, TfLiteAddParams* params, } else if (output->type == kTfLiteFloat32) { if (kernel_type == kReference) { if (data->requires_broadcast) { - TF_LITE_ADD(reference_ops, BroadcastAdd, float); + TF_LITE_ADD(reference_ops, BroadcastAdd4DSlow, float); } else { TF_LITE_ADD(reference_ops, Add, float); } } else { if (data->requires_broadcast) { - TF_LITE_ADD(optimized_ops, BroadcastAdd, float); + TF_LITE_ADD(optimized_ops, BroadcastAdd4DSlow, float); } else { TF_LITE_ADD(optimized_ops, Add, float); } @@ -220,30 +220,43 @@ TfLiteStatus EvalAddQuantized(TfLiteContext* context, TfLiteNode* node, const TfLiteTensor* input2, TfLiteTensor* output) { if (output->type == kTfLiteUInt8) { -#define TF_LITE_ADD(type, opname) \ - type::opname( \ - data->left_shift, GetTensorData(input1), GetTensorDims(input1), \ - data->input1_offset, data->input1_multiplier, data->input1_shift, \ - GetTensorData(input2), GetTensorDims(input2), \ - data->input2_offset, data->input2_multiplier, data->input2_shift, \ - data->output_offset, data->output_multiplier, data->output_shift, \ - data->output_activation_min, data->output_activation_max, \ - GetTensorData(output), GetTensorDims(output)); +#define TF_LITE_ADD(type, opname) \ + tflite::ArithmeticParams op_params; \ + op_params.left_shift = data->left_shift; \ + op_params.input1_offset = data->input1_offset; \ + op_params.input1_multiplier = data->input1_multiplier; \ + op_params.input1_shift = data->input1_shift; \ + op_params.input2_offset = data->input2_offset; \ + op_params.input2_multiplier = data->input2_multiplier; \ + op_params.input2_shift = data->input2_shift; \ + op_params.output_offset = data->output_offset; \ + op_params.output_multiplier = data->output_multiplier; \ + op_params.output_shift = data->output_shift; \ + SetActivationParams(data->output_activation_min, \ + data->output_activation_max, &op_params); \ + type::opname(op_params, GetTensorShape(input1), \ + GetTensorData(input1), GetTensorShape(input2), \ + GetTensorData(input2), GetTensorShape(output), \ + GetTensorData(output)) // The quantized version of Add doesn't support activations, so we // always use BroadcastAdd. if (kernel_type == kReference) { - TF_LITE_ADD(reference_ops, BroadcastAdd); + TF_LITE_ADD(reference_ops, BroadcastAdd4DSlow); } else { - TF_LITE_ADD(optimized_ops, BroadcastAdd); + TF_LITE_ADD(optimized_ops, BroadcastAdd4DSlow); } #undef TF_LITE_ADD } else if (output->type == kTfLiteInt16) { -#define TF_LITE_ADD(type, opname) \ - type::opname(GetTensorData(input1), GetTensorDims(input1), \ - data->input1_shift, GetTensorData(input2), \ - GetTensorDims(input2), data->input2_shift, \ - data->output_activation_min, data->output_activation_max, \ - GetTensorData(output), GetTensorDims(output)); +#define TF_LITE_ADD(type, opname) \ + tflite::ArithmeticParams op_params; \ + op_params.input1_shift = data->input1_shift; \ + op_params.input2_shift = data->input2_shift; \ + SetActivationParams(data->output_activation_min, \ + data->output_activation_max, &op_params); \ + type::opname(op_params, GetTensorShape(input1), \ + GetTensorData(input1), GetTensorShape(input2), \ + GetTensorData(input2), GetTensorShape(output), \ + GetTensorData(output)) // The quantized version of Add doesn't support activations, so we // always use BroadcastAdd. if (kernel_type == kReference) { diff --git a/tensorflow/contrib/lite/kernels/internal/common.h b/tensorflow/contrib/lite/kernels/internal/common.h index b86ca49c11..310a8980e6 100644 --- a/tensorflow/contrib/lite/kernels/internal/common.h +++ b/tensorflow/contrib/lite/kernels/internal/common.h @@ -127,6 +127,139 @@ int CountLeadingZeros(T integer_input) { return leading_zeros; } +// DO NOT USE THIS STRUCT FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING +// BROADCASTING. +// +// NdArrayDesc describes the shape and memory layout of an N-dimensional +// rectangular array of numbers. +// +// NdArrayDesc is basically identical to Dims defined in types.h. +// However, as Dims is to be deprecated, this class exists as an adaptor +// to enable simple unoptimized implementations of element-wise broadcasting +// operations. +template +struct NdArrayDesc { + // The "extent" of each dimension. Indices along dimension d must be in the + // half-open interval [0, extents[d]). + int extents[N]; + + // The number of *elements* (not bytes) between consecutive indices of each + // dimension. + int strides[N]; +}; + +// DO NOT USE THIS FUNCTION FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING +// BROADCASTING. +// +// Same as Offset(), except takes as NdArrayDesc instead of Dims. +inline int SubscriptToIndex(const NdArrayDesc<4>& desc, int i0, int i1, int i2, + int i3) { + TFLITE_DCHECK(i0 >= 0 && i0 < desc.extents[0]); + TFLITE_DCHECK(i1 >= 0 && i1 < desc.extents[1]); + TFLITE_DCHECK(i2 >= 0 && i2 < desc.extents[2]); + TFLITE_DCHECK(i3 >= 0 && i3 < desc.extents[3]); + return i0 * desc.strides[0] + i1 * desc.strides[1] + i2 * desc.strides[2] + + i3 * desc.strides[3]; +} + +// Given the dimensions of the operands for an element-wise binary broadcast, +// adjusts them so that they can be directly iterated over with simple loops. +// Returns the adjusted dims as instances of NdArrayDesc in 'desc0_out' and +// 'desc1_out'. 'desc0_out' and 'desc1_out' cannot be nullptr. +// +// This function assumes that the two input shapes are compatible up to +// broadcasting and the shorter one has already been prepended with 1s to be the +// same length. E.g., if shape0 is (1, 16, 16, 64) and shape1 is (1, 64), +// shape1 must already have been prepended to be (1, 1, 1, 64). Recall that +// Dims refer to shapes in reverse order. In this case, input0_dims will be +// (64, 16, 16, 1) and input1_dims will be (64, 1, 1, 1). +// +// When two shapes are compatible up to broadcasting, for each dimension d, +// the input extents are either equal, or one of them is 1. +// +// This function performs the following for each dimension d: +// - If the extents are equal, then do nothing since the loop that walks over +// both of the input arrays is correct. +// - Otherwise, one (and only one) of the extents must be 1. Say extent0 is 1 +// and extent1 is e1. Then set extent0 to e1 and stride0 *to 0*. This allows +// array0 to be referenced *at any index* in dimension d and still access the +// same slice. +template +inline void NdArrayDescsForElementwiseBroadcast(const Dims& input0_dims, + const Dims& input1_dims, + NdArrayDesc* desc0_out, + NdArrayDesc* desc1_out) { + TFLITE_DCHECK(desc0_out != nullptr); + TFLITE_DCHECK(desc1_out != nullptr); + + // Copy dims to desc. + for (int i = 0; i < N; ++i) { + desc0_out->extents[i] = input0_dims.sizes[i]; + desc0_out->strides[i] = input0_dims.strides[i]; + desc1_out->extents[i] = input1_dims.sizes[i]; + desc1_out->strides[i] = input1_dims.strides[i]; + } + + // Walk over each dimension. If the extents are equal do nothing. + // Otherwise, set the desc with extent 1 to have extent equal to the other and + // stride 0. + for (int i = 0; i < N; ++i) { + const int extent0 = ArraySize(input0_dims, i); + const int extent1 = ArraySize(input1_dims, i); + if (extent0 != extent1) { + if (extent0 == 1) { + desc0_out->strides[i] = 0; + desc0_out->extents[i] = extent1; + } else { + TFLITE_DCHECK_EQ(extent1, 1); + desc1_out->strides[i] = 0; + desc1_out->extents[i] = extent0; + } + } + } +} + +template +inline void NdArrayDescsForElementwiseBroadcast( + const RuntimeShape& input0_shape, const RuntimeShape& input1_shape, + NdArrayDesc* desc0_out, NdArrayDesc* desc1_out) { + TFLITE_DCHECK(desc0_out != nullptr); + TFLITE_DCHECK(desc1_out != nullptr); + + auto extended_input0_shape = RuntimeShape::ExtendedShape(N, input0_shape); + auto extended_input1_shape = RuntimeShape::ExtendedShape(N, input1_shape); + + // Copy dims to desc, calculating strides. + int desc0_stride = 1; + int desc1_stride = 1; + for (int i = N - 1; i >= 0; --i) { + desc0_out->extents[i] = extended_input0_shape.Dims(i); + desc0_out->strides[i] = desc0_stride; + desc0_stride *= extended_input0_shape.Dims(i); + desc1_out->extents[i] = extended_input1_shape.Dims(i); + desc1_out->strides[i] = desc1_stride; + desc1_stride *= extended_input1_shape.Dims(i); + } + + // Walk over each dimension. If the extents are equal do nothing. + // Otherwise, set the desc with extent 1 to have extent equal to the other and + // stride 0. + for (int i = 0; i < N; ++i) { + const int extent0 = extended_input0_shape.Dims(i); + const int extent1 = extended_input1_shape.Dims(i); + if (extent0 != extent1) { + if (extent0 == 1) { + desc0_out->strides[i] = 0; + desc0_out->extents[i] = extent1; + } else { + TFLITE_DCHECK_EQ(extent1, 1); + desc1_out->strides[i] = 0; + desc1_out->extents[i] = extent0; + } + } + } +} + } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_COMMON_H_ diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h index 6db41d7961..d5503073a7 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/legacy_optimized_ops.h @@ -55,6 +55,245 @@ inline void Relu(const float* input_data, const Dims<4>& input_dims, DimsToShape(output_dims)); } +// legacy, for compatibility with old checked-in code +template +void Add(const float* input1_data, const Dims<4>& input1_dims, + const float* input2_data, const Dims<4>& input2_dims, + float* output_data, const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +inline void Add(int left_shift, const uint8* input1_data, + const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, + const uint8* input2_data, const Dims<4>& input2_dims, + int32 input2_offset, int32 input2_multiplier, int input2_shift, + int32 output_offset, int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + + tflite::ArithmeticParams op_params; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void Add(const int32* input1_data, const Dims<4>& input1_dims, + const int32* input2_data, const Dims<4>& input2_dims, + int32* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Add/int32"); + TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); + + tflite::ArithmeticParams op_params; + op_params.quantized_activation_min = std::numeric_limits::min(); + op_params.quantized_activation_max = std::numeric_limits::max(); + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T output_activation_min, T output_activation_max, + T* output_data, const Dims<4>& output_dims) { + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + BroadcastAdd4DSlow(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +template +inline void BroadcastAdd(int left_shift, const uint8* input1_data, + const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, + const uint8* input2_data, const Dims<4>& input2_dims, + int32 input2_offset, int32 input2_multiplier, + int input2_shift, int32 output_offset, + int32 output_multiplier, int output_shift, + int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + + tflite::ArithmeticParams op_params; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + BroadcastAdd4DSlow(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +template +inline void BroadcastAddFivefold( + int y0, int y1, int y2, int y3, int y4, int left_shift, + const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, const uint8* input2_data, + const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, + int input2_shift, int32 output_offset, int32 output_multiplier, + int output_shift, int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + tflite::ArithmeticParams op_params; + op_params.broadcast_category = + tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + op_params.broadcast_shape[4] = y0; + op_params.broadcast_shape[3] = y1; + op_params.broadcast_shape[2] = y2; + op_params.broadcast_shape[1] = y3; + op_params.broadcast_shape[0] = y4; + BroadcastAddFivefold(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +// legacy, for compatibility with old checked-in code +template +void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T* output_data, const Dims<4>& output_dims) { + T output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + BroadcastAdd(input1_data, input1_dims, input2_data, input2_dims, + output_activation_min, output_activation_max, output_data, + output_dims); +} + +template +inline void Add(const int16* input1_data, const Dims<4>& input1_dims, + int input1_shift, const int16* input2_data, + const Dims<4>& input2_dims, int input2_shift, + int16 output_activation_min, int16 output_activation_max, + int16* output_data, const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, -32768); + TFLITE_DCHECK_EQ(output_activation_max, 32767); + } + + tflite::ArithmeticParams op_params; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +inline void Sub(const float* input1_data, const Dims<4>& input1_dims, + const float* input2_data, const Dims<4>& input2_dims, + float* output_data, const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(FusedActivationFunctionType::kNone, + &output_activation_min, &output_activation_max); + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + Sub(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void Sub(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, + const Dims<4>& input2_dims, T* output_data, + const Dims<4>& output_dims) { + T output_activation_min, output_activation_max; + GetActivationMinMax(FusedActivationFunctionType::kNone, + &output_activation_min, &output_activation_max); + tflite::ArithmeticParams op_params; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Sub(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + inline void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride_width, int stride_height, int pad_width, int pad_height, int kwidth, int kheight, diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h index 2f73036e03..78567d52ea 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/optimized_ops.h @@ -42,10 +42,12 @@ namespace optimized_ops { // Unoptimized reference ops: using reference_ops::ArgMax; using reference_ops::ArgMinMax; +using reference_ops::BroadcastAdd4DSlow; using reference_ops::BroadcastGreater; using reference_ops::BroadcastGreaterEqual; using reference_ops::BroadcastLess; using reference_ops::BroadcastLessEqual; +using reference_ops::BroadcastSub4DSlow; using reference_ops::Concatenation; using reference_ops::DepthConcatenation; using reference_ops::Dequantize; @@ -217,98 +219,6 @@ SaturatingRoundingMultiplyByPOTParam( SaturatingRoundingMultiplyByPOTParam(a.raw(), exponent)); } -// DO NOT USE THIS STRUCT FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING ELEMENT-WISE -// BROADCASTING. -// -// NdArrayDesc describes the shape and memory layout of an N-dimensional -// rectangular array of numbers. -// -// NdArrayDesc is basically identical to Dims defined in types.h. -// However, as Dims is to be deprecated, this class exists as an adaptor -// to enable simple unoptimized implementations of element-wise broadcasting -// operations. -template -struct NdArrayDesc { - // The "extent" of each dimension. Indices along dimension d must be in the - // half-open interval [0, extents[d]). - int extents[N]; - - // The number of *elements* (not bytes) between consecutive indices of each - // dimension. - int strides[N]; -}; - -// DO NOT USE THIS FUNCTION FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING -// ELEMENT-WISE BROADCASTING. -// -// Same as Offset(), except takes as NdArrayDesc instead of Dims. -inline int SubscriptToIndex(const NdArrayDesc<4>& desc, int i0, int i1, int i2, - int i3) { - TFLITE_DCHECK(i0 >= 0 && i0 < desc.extents[0]); - TFLITE_DCHECK(i1 >= 0 && i1 < desc.extents[1]); - TFLITE_DCHECK(i2 >= 0 && i2 < desc.extents[2]); - TFLITE_DCHECK(i3 >= 0 && i3 < desc.extents[3]); - return i0 * desc.strides[0] + i1 * desc.strides[1] + i2 * desc.strides[2] + - i3 * desc.strides[3]; -} - -// Given the dimensions of the operands for an element-wise binary broadcast, -// adjusts them so that they can be directly iterated over with simple loops. -// Returns the adjusted dims as instances of NdArrayDesc in 'desc0_out' and -// 'desc1_out'. 'desc0_out' and 'desc1_out' cannot be nullptr. -// -// This function assumes that the two input shapes are compatible up to -// broadcasting and the shorter one has already been prepended with 1s to be the -// same length. E.g., if shape0 is (1, 16, 16, 64) and shape1 is (1, 64), -// shape1 must already have been prepended to be (1, 1, 1, 64). Recall that -// Dims refer to shapes in reverse order. In this case, input0_dims will be -// (64, 16, 16, 1) and input1_dims will be (64, 1, 1, 1). -// -// When two shapes are compatible up to broadcasting, for each dimension d, -// the input extents are either equal, or one of them is 1. -// -// This function performs the following for each dimension d: -// - If the extents are equal, then do nothing since the loop that walks over -// both of the input arrays is correct. -// - Otherwise, one (and only one) of the extents must be 1. Say extent0 is 1 -// and extent1 is e1. Then set extent0 to e1 and stride0 *to 0*. This allows -// array0 to be referenced *at any index* in dimension d and still access the -// same slice. -template -inline void NdArrayDescsForElementwiseBroadcast(const Dims& input0_dims, - const Dims& input1_dims, - NdArrayDesc* desc0_out, - NdArrayDesc* desc1_out) { - TFLITE_DCHECK(desc0_out != nullptr); - TFLITE_DCHECK(desc1_out != nullptr); - - // Copy dims to desc. - for (int i = 0; i < N; ++i) { - desc0_out->extents[i] = input0_dims.sizes[i]; - desc0_out->strides[i] = input0_dims.strides[i]; - desc1_out->extents[i] = input1_dims.sizes[i]; - desc1_out->strides[i] = input1_dims.strides[i]; - } - - // Walk over each dimension. If the extents are equal do nothing. - // Otherwise, set the desc with extent 1 to have extent equal to the other and - // stride 0. - for (int i = 0; i < N; ++i) { - const int extent0 = ArraySize(input0_dims, i); - const int extent1 = ArraySize(input1_dims, i); - if (extent0 != extent1) { - if (extent0 == 1) { - desc0_out->strides[i] = 0; - desc0_out->extents[i] = extent1; - } else { - TFLITE_DCHECK_EQ(extent1, 1); - desc1_out->strides[i] = 0; - desc1_out->extents[i] = extent0; - } - } - } -} - inline bool AreSameDims(const Dims<4>& dims1, const Dims<4>& dims2) { for (int i = 0; i < 4; i++) { if (dims1.sizes[i] != dims2.sizes[i]) { @@ -2478,20 +2388,17 @@ inline void L2Normalization(const uint8* input_data, } } -inline void Add(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const float* input1_data, + const RuntimeShape& input2_shape, const float* input2_data, + const RuntimeShape& output_shape, float* output_data) { gemmlowp::ScopedProfilingLabel label("Add"); - TFLITE_DCHECK(IsPackedWithoutStrides(input1_dims)); - TFLITE_DCHECK(IsPackedWithoutStrides(input2_dims)); - TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); int i = 0; - const int size = MatchingFlatSize(input1_dims, input2_dims, output_dims); + const int size = MatchingFlatSize(input1_shape, input2_shape, output_shape); #ifdef USE_NEON - const auto activation_min = vdupq_n_f32(output_activation_min); - const auto activation_max = vdupq_n_f32(output_activation_max); + const auto activation_min = vdupq_n_f32(params.float_activation_min); + const auto activation_max = vdupq_n_f32(params.float_activation_max); for (; i <= size - 16; i += 16) { auto a10 = vld1q_f32(input1_data + i); auto a11 = vld1q_f32(input1_data + i + 4); @@ -2530,29 +2437,26 @@ inline void Add(const float* input1_data, const Dims<4>& input1_dims, for (; i < size; i++) { auto x = input1_data[i] + input2_data[i]; - output_data[i] = ActivationFunctionWithMinMax(x, output_activation_min, - output_activation_max); + output_data[i] = ActivationFunctionWithMinMax( + x, params.float_activation_min, params.float_activation_max); } } // Element-wise add that can often be used for inner loop of broadcast add as // well as the non-broadcast add. -inline void AddElementwise(int size, int left_shift, const uint8* input1_data, - int32 input1_offset, int32 input1_multiplier, - int input1_shift, const uint8* input2_data, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data) { +inline void AddElementwise(int size, const ArithmeticParams& params, + const uint8* input1_data, const uint8* input2_data, + uint8* output_data) { int i = 0; - TFLITE_DCHECK_GT(input1_offset, -256); - TFLITE_DCHECK_GT(input2_offset, -256); - TFLITE_DCHECK_LT(input1_offset, 256); - TFLITE_DCHECK_LT(input2_offset, 256); + TFLITE_DCHECK_GT(params.input1_offset, -256); + TFLITE_DCHECK_GT(params.input2_offset, -256); + TFLITE_DCHECK_LT(params.input1_offset, 256); + TFLITE_DCHECK_LT(params.input2_offset, 256); #ifdef USE_NEON - const auto output_activation_min_vector = vdup_n_u8(output_activation_min); - const auto output_activation_max_vector = vdup_n_u8(output_activation_max); + const auto output_activation_min_vector = + vdup_n_u8(params.quantized_activation_min); + const auto output_activation_max_vector = + vdup_n_u8(params.quantized_activation_max); for (; i <= size - 8; i += 8) { const auto input1_val_original = vld1_u8(input1_data + i); const auto input2_val_original = vld1_u8(input2_data + i); @@ -2561,9 +2465,9 @@ inline void AddElementwise(int size, int left_shift, const uint8* input1_data, const auto input2_val_s16 = vreinterpretq_s16_u16(vmovl_u8(input2_val_original)); const auto input1_val = - vaddq_s16(input1_val_s16, vdupq_n_s16(input1_offset)); + vaddq_s16(input1_val_s16, vdupq_n_s16(params.input1_offset)); const auto input2_val = - vaddq_s16(input2_val_s16, vdupq_n_s16(input2_offset)); + vaddq_s16(input2_val_s16, vdupq_n_s16(params.input2_offset)); const auto input1_val_high = vget_high_s16(input1_val); const auto input1_val_low = vget_low_s16(input1_val); const auto input2_val_high = vget_high_s16(input2_val); @@ -2572,32 +2476,32 @@ inline void AddElementwise(int size, int left_shift, const uint8* input1_data, auto x12 = vmovl_s16(input1_val_high); auto x21 = vmovl_s16(input2_val_low); auto x22 = vmovl_s16(input2_val_high); - const auto left_shift_dup = vdupq_n_s32(left_shift); + const auto left_shift_dup = vdupq_n_s32(params.left_shift); x11 = vshlq_s32(x11, left_shift_dup); x12 = vshlq_s32(x12, left_shift_dup); x21 = vshlq_s32(x21, left_shift_dup); x22 = vshlq_s32(x22, left_shift_dup); - x11 = vqrdmulhq_n_s32(x11, input1_multiplier); - x12 = vqrdmulhq_n_s32(x12, input1_multiplier); - x21 = vqrdmulhq_n_s32(x21, input2_multiplier); - x22 = vqrdmulhq_n_s32(x22, input2_multiplier); - const auto input1_shift_dup = vdupq_n_s32(-input1_shift); - const auto input2_shift_dup = vdupq_n_s32(-input2_shift); + x11 = vqrdmulhq_n_s32(x11, params.input1_multiplier); + x12 = vqrdmulhq_n_s32(x12, params.input1_multiplier); + x21 = vqrdmulhq_n_s32(x21, params.input2_multiplier); + x22 = vqrdmulhq_n_s32(x22, params.input2_multiplier); + const auto input1_shift_dup = vdupq_n_s32(params.input1_shift); + const auto input2_shift_dup = vdupq_n_s32(params.input2_shift); x11 = vshlq_s32(x11, input1_shift_dup); x12 = vshlq_s32(x12, input1_shift_dup); x21 = vshlq_s32(x21, input2_shift_dup); x22 = vshlq_s32(x22, input2_shift_dup); auto s1 = vaddq_s32(x11, x21); auto s2 = vaddq_s32(x12, x22); - s1 = vqrdmulhq_n_s32(s1, output_multiplier); - s2 = vqrdmulhq_n_s32(s2, output_multiplier); + s1 = vqrdmulhq_n_s32(s1, params.output_multiplier); + s2 = vqrdmulhq_n_s32(s2, params.output_multiplier); using gemmlowp::RoundingDivideByPOT; - s1 = RoundingDivideByPOT(s1, output_shift); - s2 = RoundingDivideByPOT(s2, output_shift); + s1 = RoundingDivideByPOT(s1, -params.output_shift); + s2 = RoundingDivideByPOT(s2, -params.output_shift); const auto s1_narrowed = vmovn_s32(s1); const auto s2_narrowed = vmovn_s32(s2); const auto s = vaddq_s16(vcombine_s16(s1_narrowed, s2_narrowed), - vdupq_n_s16(output_offset)); + vdupq_n_s16(params.output_offset)); const auto clamped = vmax_u8(output_activation_min_vector, vmin_u8(output_activation_max_vector, vqmovun_s16(s))); @@ -2606,101 +2510,74 @@ inline void AddElementwise(int size, int left_shift, const uint8* input1_data, #endif // NEON for (; i < size; ++i) { - const int32 input1_val = input1_offset + input1_data[i]; - const int32 input2_val = input2_offset + input2_data[i]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); + const int32 input1_val = params.input1_offset + input1_data[i]; + const int32 input2_val = params.input2_offset + input2_data[i]; + const int32 shifted_input1_val = input1_val * (1 << params.left_shift); + const int32 shifted_input2_val = input2_val * (1 << params.left_shift); const int32 scaled_input1_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); + shifted_input1_val, params.input1_multiplier, params.input1_shift); const int32 scaled_input2_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); + shifted_input2_val, params.input2_multiplier, params.input2_shift); const int32 raw_sum = scaled_input1_val + scaled_input2_val; const int32 raw_output = MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, output_multiplier, kReverseShift * output_shift) + - output_offset; - const int32 clamped_output = std::min( - output_activation_max, std::max(output_activation_min, raw_output)); + raw_sum, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32 clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); output_data[i] = static_cast(clamped_output); } } -// legacy, for compatibility with old checked-in code -template -void Add(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float* output_data, const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - Add(input1_data, input1_dims, input2_data, input2_dims, output_activation_min, - output_activation_max, output_data, output_dims); -} - -template -inline void Add(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, int input2_shift, - int32 output_offset, int32 output_multiplier, int output_shift, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const uint8* input1_data, + const RuntimeShape& input2_shape, const uint8* input2_data, + const RuntimeShape& output_shape, uint8* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); gemmlowp::ScopedProfilingLabel label("Add/8bit"); - const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); - TFLITE_DCHECK(IsPackedWithoutStrides(input1_dims)); - TFLITE_DCHECK(IsPackedWithoutStrides(input2_dims)); - TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); - - TFLITE_DCHECK_GT(input1_offset, -256); - TFLITE_DCHECK_GT(input2_offset, -256); - TFLITE_DCHECK_LT(input1_offset, 256); - TFLITE_DCHECK_LT(input2_offset, 256); - AddElementwise(flat_size, left_shift, input1_data, input1_offset, - input1_multiplier, input1_shift, input2_data, input2_offset, - input2_multiplier, input2_shift, output_offset, - output_multiplier, output_shift, output_activation_min, - output_activation_max, output_data); + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); + + TFLITE_DCHECK_GT(params.input1_offset, -256); + TFLITE_DCHECK_GT(params.input2_offset, -256); + TFLITE_DCHECK_LT(params.input1_offset, 256); + TFLITE_DCHECK_LT(params.input2_offset, 256); + AddElementwise(flat_size, params, input1_data, input2_data, output_data); } -inline void Add(const int16* input1_data, const Dims<4>& input1_dims, - int input1_shift, const int16* input2_data, - const Dims<4>& input2_dims, int input2_shift, - int16 output_activation_min, int16 output_activation_max, - int16* output_data, const Dims<4>& output_dims) { +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int16* input1_data, + const RuntimeShape& input2_shape, const int16* input2_data, + const RuntimeShape& output_shape, int16* output_data) { gemmlowp::ScopedProfilingLabel label("Add/Int16"); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); - const int flat_size = MatchingFlatSize(output_dims, input1_dims, input2_dims); + const int input1_shift = params.input1_shift; + const int flat_size = + MatchingFlatSize(output_shape, input1_shape, input2_shape); + const int16 output_activation_min = params.quantized_activation_min; + const int16 output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK(input1_shift == 0 || input2_shift == 0); - TFLITE_DCHECK_GE(input1_shift, 0); - TFLITE_DCHECK_GE(input2_shift, 0); + TFLITE_DCHECK(input1_shift == 0 || params.input2_shift == 0); + TFLITE_DCHECK_LE(input1_shift, 0); + TFLITE_DCHECK_LE(params.input2_shift, 0); const int16* not_shift_input = input1_shift == 0 ? input1_data : input2_data; const int16* shift_input = input1_shift == 0 ? input2_data : input1_data; - const int input_shift = input1_shift == 0 ? input2_shift : input1_shift; + const int input_right_shift = + input1_shift == 0 ? -params.input2_shift : -input1_shift; for (int i = 0; i < flat_size; i++) { // F0 uses 0 integer bits, range [-1, 1]. using F0 = gemmlowp::FixedPoint; F0 input_ready_scaled = F0::FromRaw(not_shift_input[i]); - F0 scaled_input = - F0::FromRaw(gemmlowp::RoundingDivideByPOT(shift_input[i], input_shift)); + F0 scaled_input = F0::FromRaw( + gemmlowp::RoundingDivideByPOT(shift_input[i], input_right_shift)); F0 result = gemmlowp::SaturatingAdd(scaled_input, input_ready_scaled); const int16 raw_output = result.raw(); const int16 clamped_output = std::min( @@ -2709,195 +2586,59 @@ inline void Add(const int16* input1_data, const Dims<4>& input1_dims, } } -inline void Add(const int32* input1_data, const Dims<4>& input1_dims, - const int32* input2_data, const Dims<4>& input2_dims, - int32 output_activation_min, int32 output_activation_max, - int32* output_data, const Dims<4>& output_dims) { +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int32* input1_data, + const RuntimeShape& input2_shape, const int32* input2_data, + const RuntimeShape& output_shape, int32* output_data) { gemmlowp::ScopedProfilingLabel label("Add/int32"); - const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); - for (int i = 0; i < flat_size; ++i) { - output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] + input2_data[i], output_activation_min, - output_activation_max); - } -} - -template -inline void Add(const int16* input1_data, const Dims<4>& input1_dims, - int input1_shift, const int16* input2_data, - const Dims<4>& input2_dims, int input2_shift, - int16 output_activation_min, int16 output_activation_max, - int16* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, -32768); - TFLITE_DCHECK_EQ(output_activation_max, 32767); - } - - Add(input1_data, input1_dims, input1_shift, input2_data, input2_dims, - input2_shift, output_activation_min, output_activation_max, output_data, - output_dims); -} - -template -void Add(const int32* input1_data, const Dims<4>& input1_dims, - const int32* input2_data, const Dims<4>& input2_dims, - int32* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Add/int32"); - TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); - - auto input1_map = MapAsVector(input1_data, input1_dims); - auto input2_map = MapAsVector(input2_data, input2_dims); - auto output_map = MapAsVector(output_data, output_dims); - if (AreSameDims(input1_dims, input2_dims)) { + auto input1_map = MapAsVector(input1_data, input1_shape); + auto input2_map = MapAsVector(input2_data, input2_shape); + auto output_map = MapAsVector(output_data, output_shape); + if (input1_shape == input2_shape) { output_map.array() = input1_map.array() + input2_map.array(); - } else if (FlatSize(input2_dims) == 1) { + } else if (input2_shape.FlatSize() == 1) { auto scalar = input2_data[0]; output_map.array() = input1_map.array() + scalar; - } else if (FlatSize(input1_dims) == 1) { + } else if (input1_shape.FlatSize() == 1) { auto scalar = input1_data[0]; output_map.array() = scalar + input2_map.array(); } else { // Should not come here. TFLITE_DCHECK(false); } + output_map = output_map.cwiseMax(params.quantized_activation_min); + output_map = output_map.cwiseMin(params.quantized_activation_max); } -// TODO(jiawen): We can implement BroadcastAdd on buffers of arbitrary -// dimensionality if the runtime code does a single loop over one dimension -// that handles broadcasting as the base case. The code generator would then -// generate max(D1, D2) nested for loops. -// TODO(benoitjacob): BroadcastAdd is intentionally duplicated from -// reference_ops.h. Once an optimized version is implemented and NdArrayDesc -// is no longer referenced in this file, move NdArrayDesc from types.h to -// reference_ops.h. -template -void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T output_activation_min, T output_activation_max, - T* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastAdd"); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] + - input2_data[SubscriptToIndex(desc2, c, x, y, b)], - output_activation_min, output_activation_max); - } - } - } - } -} - -// legacy, for compatibility with old checked-in code -template -void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T* output_data, const Dims<4>& output_dims) { - T output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - BroadcastAdd(input1_data, input1_dims, input2_data, input2_dims, - output_activation_min, output_activation_max, output_data, - output_dims); -} - -inline void BroadcastAdd(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastAddGeneric/8bit"); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - const int32 input1_val = - input1_offset + input1_data[SubscriptToIndex(desc1, c, x, y, b)]; - const int32 input2_val = - input2_offset + input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); - const int32 scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); - const int32 scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); - const int32 raw_sum = scaled_input1_val + scaled_input2_val; - const int32 raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, output_multiplier, kReverseShift * output_shift) + - output_offset; - const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - output_data[Offset(output_dims, c, x, y, b)] = - static_cast(clamped_output); - } - } - } - } -} - -inline void BroadcastAddFivefold( - int y0, int y1, int y2, int y3, int y4, int left_shift, - const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, const uint8* input2_data, - const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, int32 output_multiplier, - int output_shift, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { +inline void BroadcastAddFivefold(const ArithmeticParams& unswitched_params, + const RuntimeShape& unswitched_input1_shape, + const uint8* unswitched_input1_data, + const RuntimeShape& unswitched_input2_shape, + const uint8* unswitched_input2_data, + const RuntimeShape& output_shape, + uint8* output_data) { gemmlowp::ScopedProfilingLabel label("BroadcastAddFivefold/8bit"); + ArithmeticParams switched_params = unswitched_params; + switched_params.input1_offset = unswitched_params.input2_offset; + switched_params.input1_multiplier = unswitched_params.input2_multiplier; + switched_params.input1_shift = unswitched_params.input2_shift; + switched_params.input2_offset = unswitched_params.input1_offset; + switched_params.input2_multiplier = unswitched_params.input1_multiplier; + switched_params.input2_shift = unswitched_params.input1_shift; + + const bool use_unswitched = + unswitched_params.broadcast_category == + tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; + + const ArithmeticParams& params = + use_unswitched ? unswitched_params : switched_params; + const uint8* input1_data = + use_unswitched ? unswitched_input1_data : unswitched_input2_data; + const uint8* input2_data = + use_unswitched ? unswitched_input2_data : unswitched_input1_data; + // Fivefold nested loops. The second input resets its position for each // iteration of the second loop. The first input resets its position at the // beginning of the fourth loop. The innermost loop is an elementwise add of @@ -2905,82 +2646,29 @@ inline void BroadcastAddFivefold( uint8* output_data_ptr = output_data; const uint8* input1_data_ptr = input1_data; const uint8* input2_data_reset = input2_data; - for (int i4 = 0; i4 < y4; ++i4) { + int y0 = params.broadcast_shape[0]; + int y1 = params.broadcast_shape[1]; + int y2 = params.broadcast_shape[2]; + int y3 = params.broadcast_shape[3]; + int y4 = params.broadcast_shape[4]; + for (int i0 = 0; i0 < y0; ++i0) { const uint8* input2_data_ptr; - for (int i3 = 0; i3 < y3; ++i3) { + for (int i1 = 0; i1 < y1; ++i1) { input2_data_ptr = input2_data_reset; for (int i2 = 0; i2 < y2; ++i2) { - for (int i1 = 0; i1 < y1; ++i1) { - AddElementwise( - y0, left_shift, input1_data_ptr, input1_offset, input1_multiplier, - input1_shift, input2_data_ptr, input2_offset, input2_multiplier, - input2_shift, output_offset, output_multiplier, output_shift, - output_activation_min, output_activation_max, output_data_ptr); - input2_data_ptr += y0; - output_data_ptr += y0; + for (int i3 = 0; i3 < y3; ++i3) { + AddElementwise(y4, params, input1_data_ptr, input2_data_ptr, + output_data_ptr); + input2_data_ptr += y4; + output_data_ptr += y4; } - input1_data_ptr += y0; + input1_data_ptr += y4; } } input2_data_reset = input2_data_ptr; } } -template -inline void BroadcastAdd(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - BroadcastAdd(left_shift, input1_data, input1_dims, input1_offset, - input1_multiplier, input1_shift, input2_data, input2_dims, - input2_offset, input2_multiplier, input2_shift, output_offset, - output_multiplier, output_shift, output_activation_min, - output_activation_max, output_data, output_dims); -} - -template -inline void BroadcastAddFivefold( - int y0, int y1, int y2, int y3, int y4, int left_shift, - const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, const uint8* input2_data, - const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, int32 output_multiplier, - int output_shift, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - BroadcastAddFivefold(y0, y1, y2, y3, y4, left_shift, input1_data, input1_dims, - input1_offset, input1_multiplier, input1_shift, - input2_data, input2_dims, input2_offset, - input2_multiplier, input2_shift, output_offset, - output_multiplier, output_shift, output_activation_min, - output_activation_max, output_data, output_dims); -} - inline void Mul(const float* input1_data, const Dims<4>& input1_dims, const float* input2_data, const Dims<4>& input2_dims, float output_activation_min, float output_activation_max, @@ -3305,135 +2993,78 @@ void BroadcastDiv(const T* input1_data, const Dims<4>& input1_dims, } // TODO(aselle): This is not actually optimized yet. -inline void Sub(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float output_activation_min, float output_activation_max, - float* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Sub"); - const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); +inline void SubNonBroadcast(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + gemmlowp::ScopedProfilingLabel label("SubNonBroadcast"); + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); for (int i = 0; i < flat_size; ++i) { output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] - input2_data[i], output_activation_min, - output_activation_max); + input1_data[i] - input2_data[i], params.float_activation_min, + params.float_activation_max); } } -inline void Sub(const int32* input1_data, const Dims<4>& input1_dims, - const int32* input2_data, const Dims<4>& input2_dims, - int32 output_activation_min, int32 output_activation_max, - int32* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Sub/int32"); - const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); +inline void SubWithActivation(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int32* input1_data, + const RuntimeShape& input2_shape, + const int32* input2_data, + const RuntimeShape& output_shape, + int32* output_data) { + gemmlowp::ScopedProfilingLabel label("SubWithActivation/int32"); + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, input2_shape); for (int i = 0; i < flat_size; ++i) { output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] - input2_data[i], output_activation_min, - output_activation_max); + input1_data[i] - input2_data[i], params.quantized_activation_min, + params.quantized_activation_max); } } -// TODO(jiawen): We can implement BroadcastSub on buffers of arbitrary -// dimensionality if the runtime code does a single loop over one dimension -// that handles broadcasting as the base case. The code generator would then -// generate max(D1, D2) nested for loops. -// TODO(benoitjacob): BroadcastSub is intentionally duplicated from -// reference_ops.h. Once an optimized version is implemented and NdArrayDesc -// is no longer referenced in this file, move NdArrayDesc from types.h to -// reference_ops.h. -template -void BroadcastSub(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T output_activation_min, T output_activation_max, - T* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastSub"); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = - ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] - - input2_data[SubscriptToIndex(desc2, c, x, y, b)], - output_activation_min, output_activation_max); - } - } - } +inline void SubWithActivation(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + gemmlowp::ScopedProfilingLabel label("SubWithActivation/float"); + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, input2_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] - input2_data[i], params.float_activation_min, + params.float_activation_max); } } -inline void BroadcastSub(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastSub/8bit"); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); +template +void Sub(const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, + T* output_data) { + gemmlowp::ScopedProfilingLabel label("Sub"); - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - const int32 input1_val = - input1_offset + input1_data[SubscriptToIndex(desc1, c, x, y, b)]; - const int32 input2_val = - input2_offset + input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); - const int32 scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); - const int32 scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); - const int32 raw_sub = scaled_input1_val - scaled_input2_val; - const int32 raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sub, output_multiplier, kReverseShift * output_shift) + - output_offset; - const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - output_data[Offset(output_dims, c, x, y, b)] = - static_cast(clamped_output); - } - } - } + auto input1_map = MapAsVector(input1_data, input1_shape); + auto input2_map = MapAsVector(input2_data, input2_shape); + auto output_map = MapAsVector(output_data, output_shape); + if (input1_shape == input2_shape) { + output_map.array() = input1_map.array() - input2_map.array(); + } else if (input1_shape.FlatSize() == 1) { + auto scalar = input1_data[0]; + output_map.array() = scalar - input2_map.array(); + } else if (input2_shape.FlatSize() == 1) { + auto scalar = input2_data[0]; + output_map.array() = input1_map.array() - scalar; + } else { + BroadcastSub4DSlow(params, input1_shape, input1_data, input2_shape, + input2_data, output_shape, output_data); } } @@ -5875,63 +5506,6 @@ inline void Slice(const T* input_data, const Dims<4>& input_dims, } } -template -void GenericBroadcastSub(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("GenericBroadcastSub"); - - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = - input1_data[SubscriptToIndex(desc1, c, x, y, b)] - - input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - } - } - } - } -} - -template -void Sub(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, - const Dims<4>& input2_dims, T* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("Sub"); - - auto input1_map = MapAsVector(input1_data, input1_dims); - auto input2_map = MapAsVector(input2_data, input2_dims); - auto output_map = MapAsVector(output_data, output_dims); - if (AreSameDims(input1_dims, input2_dims)) { - output_map.array() = input1_map.array() - input2_map.array(); - } else if (FlatSize(input1_dims) == 1) { - auto scalar = input1_data[0]; - output_map.array() = scalar - input2_map.array(); - } else if (FlatSize(input2_dims) == 1) { - auto scalar = input2_data[0]; - output_map.array() = input1_map.array() - scalar; - } else { - GenericBroadcastSub(input1_data, input1_dims, input2_data, input2_dims, - output_data, output_dims); - } -} - template void TensorFlowMinimum(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, T* output_data, diff --git a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h index f715d34bc1..bcf5e4e4f6 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/legacy_reference_ops.h @@ -63,6 +63,240 @@ inline void Relu6(const float* input_data, const Dims<4>& input_dims, DimsToShape(output_dims)); } +template +inline void Add(int left_shift, const uint8* input1_data, + const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, + const uint8* input2_data, const Dims<4>& input2_dims, + int32 input2_offset, int32 input2_multiplier, int input2_shift, + int32 output_offset, int32 output_multiplier, int output_shift, + int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + + tflite::ArithmeticParams op_params; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void Add(const int32* input1_data, const Dims<4>& input1_dims, + const int32* input2_data, const Dims<4>& input2_dims, + int32* output_data, const Dims<4>& output_dims) { + gemmlowp::ScopedProfilingLabel label("Add/int32"); + TFLITE_DCHECK(Ac == FusedActivationFunctionType::kNone); + + tflite::ArithmeticParams op_params; + op_params.quantized_activation_min = std::numeric_limits::min(); + op_params.quantized_activation_max = std::numeric_limits::max(); + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +inline void BroadcastAdd(int left_shift, const uint8* input1_data, + const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, + const uint8* input2_data, const Dims<4>& input2_dims, + int32 input2_offset, int32 input2_multiplier, + int input2_shift, int32 output_offset, + int32 output_multiplier, int output_shift, + int32 output_activation_min, + int32 output_activation_max, uint8* output_data, + const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + + tflite::ArithmeticParams op_params; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + BroadcastAdd4DSlow(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +template +void Add(const float* input1_data, const Dims<4>& input1_dims, + const float* input2_data, const Dims<4>& input2_dims, + float* output_data, const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T output_activation_min, T output_activation_max, + T* output_data, const Dims<4>& output_dims) { + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + BroadcastAdd4DSlow(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +template +inline void BroadcastAddFivefold( + int y0, int y1, int y2, int y3, int y4, int left_shift, + const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, + int32 input1_multiplier, int input1_shift, const uint8* input2_data, + const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, + int input2_shift, int32 output_offset, int32 output_multiplier, + int output_shift, int32 output_activation_min, int32 output_activation_max, + uint8* output_data, const Dims<4>& output_dims) { + constexpr int kReverseShift = -1; + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, 0); + TFLITE_DCHECK_EQ(output_activation_max, 255); + } + tflite::ArithmeticParams op_params; + op_params.broadcast_category = + tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; + op_params.left_shift = left_shift; + op_params.input1_offset = input1_offset; + op_params.input1_multiplier = input1_multiplier; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_offset = input2_offset; + op_params.input2_multiplier = input2_multiplier; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.output_offset = output_offset; + op_params.output_multiplier = output_multiplier; + op_params.output_shift = kReverseShift * output_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + op_params.broadcast_shape[4] = y0; + op_params.broadcast_shape[3] = y1; + op_params.broadcast_shape[2] = y2; + op_params.broadcast_shape[1] = y3; + op_params.broadcast_shape[0] = y4; + BroadcastAddFivefold(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, + DimsToShape(output_dims), output_data); +} + +// legacy, for compatibility with old checked-in code +template +void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, + const T* input2_data, const Dims<4>& input2_dims, + T* output_data, const Dims<4>& output_dims) { + T output_activation_min, output_activation_max; + GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); + + BroadcastAdd(input1_data, input1_dims, input2_data, input2_dims, + output_activation_min, output_activation_max, output_data, + output_dims); +} + +template +inline void Add(const int16* input1_data, const Dims<4>& input1_dims, + int input1_shift, const int16* input2_data, + const Dims<4>& input2_dims, int input2_shift, + int16 output_activation_min, int16 output_activation_max, + int16* output_data, const Dims<4>& output_dims) { + static_assert(Ac == FusedActivationFunctionType::kNone || + Ac == FusedActivationFunctionType::kRelu || + Ac == FusedActivationFunctionType::kRelu6 || + Ac == FusedActivationFunctionType::kRelu1, + ""); + TFLITE_DCHECK_LE(output_activation_min, output_activation_max); + if (Ac == FusedActivationFunctionType::kNone) { + TFLITE_DCHECK_EQ(output_activation_min, -32768); + TFLITE_DCHECK_EQ(output_activation_max, 32767); + } + + tflite::ArithmeticParams op_params; + op_params.input1_shift = kReverseShift * input1_shift; + op_params.input2_shift = kReverseShift * input2_shift; + op_params.quantized_activation_min = output_activation_min; + op_params.quantized_activation_max = output_activation_max; + Add(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +inline void Sub(const float* input1_data, const Dims<4>& input1_dims, + const float* input2_data, const Dims<4>& input2_dims, + float* output_data, const Dims<4>& output_dims) { + float output_activation_min, output_activation_max; + GetActivationMinMax(FusedActivationFunctionType::kNone, + &output_activation_min, &output_activation_max); + tflite::ArithmeticParams op_params; + op_params.float_activation_min = output_activation_min; + op_params.float_activation_max = output_activation_max; + Sub(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + +template +void Sub(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, + const Dims<4>& input2_dims, T* output_data, + const Dims<4>& output_dims) { + tflite::ArithmeticParams op_params; + op_params.quantized_activation_min = std::numeric_limits::min(); + op_params.quantized_activation_max = std::numeric_limits::max(); + Sub(op_params, DimsToShape(input1_dims), input1_data, + DimsToShape(input2_dims), input2_data, DimsToShape(output_dims), + output_data); +} + inline void AveragePool(const float* input_data, const Dims<4>& input_dims, int stride_width, int stride_height, int pad_width, int pad_height, int kwidth, int kheight, diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 04f61c7434..10e23f0b41 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -158,98 +158,6 @@ SaturatingRoundingMultiplyByPOTParam( SaturatingRoundingMultiplyByPOTParam(a.raw(), exponent)); } -// DO NOT USE THIS STRUCT FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING ELEMENT-WISE -// BROADCASTING. -// -// NdArrayDesc describes the shape and memory layout of an N-dimensional -// rectangular array of numbers. -// -// NdArrayDesc is basically identical to Dims defined in types.h. -// However, as Dims is to be deprecated, this class exists as an adaptor -// to enable simple unoptimized implementations of element-wise broadcasting -// operations. -template -struct NdArrayDesc { - // The "extent" of each dimension. Indices along dimension d must be in the - // half-open interval [0, extents[d]). - int extents[N]; - - // The number of *elements* (not bytes) between consecutive indices of each - // dimension. - int strides[N]; -}; - -// DO NOT USE THIS FUNCTION FOR NEW FUNCTIONALITY BEYOND IMPLEMENTING -// ELEMENT-WISE BROADCASTING. -// -// Same as Offset(), except takes as NdArrayDesc instead of Dims. -inline int SubscriptToIndex(const NdArrayDesc<4>& desc, int i0, int i1, int i2, - int i3) { - TFLITE_DCHECK(i0 >= 0 && i0 < desc.extents[0]); - TFLITE_DCHECK(i1 >= 0 && i1 < desc.extents[1]); - TFLITE_DCHECK(i2 >= 0 && i2 < desc.extents[2]); - TFLITE_DCHECK(i3 >= 0 && i3 < desc.extents[3]); - return i0 * desc.strides[0] + i1 * desc.strides[1] + i2 * desc.strides[2] + - i3 * desc.strides[3]; -} - -// Given the dimensions of the operands for an element-wise binary broadcast, -// adjusts them so that they can be directly iterated over with simple loops. -// Returns the adjusted dims as instances of NdArrayDesc in 'desc0_out' and -// 'desc1_out'. 'desc0_out' and 'desc1_out' cannot be nullptr. -// -// This function assumes that the two input shapes are compatible up to -// broadcasting and the shorter one has already been prepended with 1s to be the -// same length. E.g., if shape0 is (1, 16, 16, 64) and shape1 is (1, 64), -// shape1 must already have been prepended to be (1, 1, 1, 64). Recall that -// Dims refer to shapes in reverse order. In this case, input0_dims will be -// (64, 16, 16, 1) and input1_dims will be (64, 1, 1, 1). -// -// When two shapes are compatible up to broadcasting, for each dimension d, -// the input extents are either equal, or one of them is 1. -// -// This function performs the following for each dimension d: -// - If the extents are equal, then do nothing since the loop that walks over -// both of the input arrays is correct. -// - Otherwise, one (and only one) of the extents must be 1. Say extent0 is 1 -// and extent1 is e1. Then set extent0 to e1 and stride0 *to 0*. This allows -// array0 to be referenced *at any index* in dimension d and still access the -// same slice. -template -inline void NdArrayDescsForElementwiseBroadcast(const Dims& input0_dims, - const Dims& input1_dims, - NdArrayDesc* desc0_out, - NdArrayDesc* desc1_out) { - TFLITE_DCHECK(desc0_out != nullptr); - TFLITE_DCHECK(desc1_out != nullptr); - - // Copy dims to desc. - for (int i = 0; i < N; ++i) { - desc0_out->extents[i] = input0_dims.sizes[i]; - desc0_out->strides[i] = input0_dims.strides[i]; - desc1_out->extents[i] = input1_dims.sizes[i]; - desc1_out->strides[i] = input1_dims.strides[i]; - } - - // Walk over each dimension. If the extents are equal do nothing. - // Otherwise, set the desc with extent 1 to have extent equal to the other and - // stride 0. - for (int i = 0; i < N; ++i) { - const int extent0 = ArraySize(input0_dims, i); - const int extent1 = ArraySize(input1_dims, i); - if (extent0 != extent1) { - if (extent0 == 1) { - desc0_out->strides[i] = 0; - desc0_out->extents[i] = extent1; - } else { - TFLITE_DCHECK_EQ(extent1, 1); - desc1_out->strides[i] = 0; - desc1_out->extents[i] = extent0; - } - } - } -} - 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, @@ -1065,114 +973,108 @@ inline void L2Normalization(const uint8* input_data, } template -inline void Add(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T output_activation_min, T output_activation_max, - T* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); for (int i = 0; i < flat_size; ++i) { output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] + input2_data[i], output_activation_min, - output_activation_max); + input1_data[i] + input2_data[i], params.quantized_activation_min, + params.quantized_activation_max); } } -// legacy, for compatibility with old checked-in code -template -void Add(const float* input1_data, const Dims<4>& input1_dims, - const float* input2_data, const Dims<4>& input2_dims, - float* output_data, const Dims<4>& output_dims) { - float output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); - - Add(input1_data, input1_dims, input2_data, input2_dims, output_activation_min, - output_activation_max, output_data, output_dims); +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const float* input1_data, + const RuntimeShape& input2_shape, const float* input2_data, + const RuntimeShape& output_shape, float* output_data) { + const int size = MatchingFlatSize(input1_shape, input2_shape, output_shape); + for (int i = 0; i < size; i++) { + auto x = input1_data[i] + input2_data[i]; + output_data[i] = ActivationFunctionWithMinMax( + x, params.float_activation_min, params.float_activation_max); + } } -template -inline void Add(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, int input2_shift, - int32 output_offset, int32 output_multiplier, int output_shift, - int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - const int batches = - MatchingArraySize(input1_dims, 3, input2_dims, 3, output_dims, 3); - const int height = - MatchingArraySize(input1_dims, 2, input2_dims, 2, output_dims, 2); - const int width = - MatchingArraySize(input1_dims, 1, input2_dims, 1, output_dims, 1); - const int depth = - MatchingArraySize(input1_dims, 0, input2_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) { - for (int c = 0; c < depth; ++c) { - const int32 input1_val = - input1_offset + input1_data[Offset(input1_dims, c, x, y, b)]; - const int32 input2_val = - input2_offset + input2_data[Offset(input2_dims, c, x, y, b)]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); - const int32 scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); - const int32 scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); - const int32 raw_sum = scaled_input1_val + scaled_input2_val; - const int32 raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, output_multiplier, kReverseShift * output_shift) + - output_offset; - const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - output_data[Offset(output_dims, c, x, y, b)] = - static_cast(clamped_output); - } - } - } +// Element-wise add that can often be used for inner loop of broadcast add as +// well as the non-broadcast add. +inline void AddElementwise(int size, const ArithmeticParams& params, + const uint8* input1_data, const uint8* input2_data, + uint8* output_data) { + TFLITE_DCHECK_GT(params.input1_offset, -256); + TFLITE_DCHECK_GT(params.input2_offset, -256); + TFLITE_DCHECK_LT(params.input1_offset, 256); + TFLITE_DCHECK_LT(params.input2_offset, 256); + + for (int i = 0; i < size; ++i) { + const int32 input1_val = params.input1_offset + input1_data[i]; + const int32 input2_val = params.input2_offset + input2_data[i]; + const int32 shifted_input1_val = input1_val * (1 << params.left_shift); + const int32 shifted_input2_val = input2_val * (1 << params.left_shift); + const int32 scaled_input1_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input1_val, params.input1_multiplier, params.input1_shift); + const int32 scaled_input2_val = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + shifted_input2_val, params.input2_multiplier, params.input2_shift); + const int32 raw_sum = scaled_input1_val + scaled_input2_val; + const int32 raw_output = + MultiplyByQuantizedMultiplierSmallerThanOneExp( + raw_sum, params.output_multiplier, params.output_shift) + + params.output_offset; + const int32 clamped_output = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[i] = static_cast(clamped_output); } } -inline void Add(const int16* input1_data, const Dims<4>& input1_dims, - int input1_shift, const int16* input2_data, - const Dims<4>& input2_dims, int input2_shift, - int16 output_activation_min, int16 output_activation_max, - int16* output_data, const Dims<4>& output_dims) { - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const uint8* input1_data, + const RuntimeShape& input2_shape, const uint8* input2_data, + const RuntimeShape& output_shape, uint8* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); - const int flat_size = MatchingFlatSize(output_dims, input1_dims, input2_dims); + TFLITE_DCHECK_GT(params.input1_offset, -256); + TFLITE_DCHECK_GT(params.input2_offset, -256); + TFLITE_DCHECK_LT(params.input1_offset, 256); + TFLITE_DCHECK_LT(params.input2_offset, 256); + AddElementwise(flat_size, params, input1_data, input2_data, output_data); +} + +inline void Add(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const int16* input1_data, + const RuntimeShape& input2_shape, const int16* input2_data, + const RuntimeShape& output_shape, int16* output_data) { + TFLITE_DCHECK_LE(params.quantized_activation_min, + params.quantized_activation_max); + + const int input1_shift = params.input1_shift; + const int flat_size = + MatchingFlatSize(output_shape, input1_shape, input2_shape); + const int16 output_activation_min = params.quantized_activation_min; + const int16 output_activation_max = params.quantized_activation_max; - TFLITE_DCHECK(input1_shift == 0 || input2_shift == 0); - TFLITE_DCHECK_GE(input1_shift, 0); - TFLITE_DCHECK_GE(input2_shift, 0); + TFLITE_DCHECK(input1_shift == 0 || params.input2_shift == 0); + TFLITE_DCHECK_LE(input1_shift, 0); + TFLITE_DCHECK_LE(params.input2_shift, 0); const int16* not_shift_input = input1_shift == 0 ? input1_data : input2_data; const int16* shift_input = input1_shift == 0 ? input2_data : input1_data; - const int input_shift = input1_shift == 0 ? input2_shift : input1_shift; + const int input_right_shift = + input1_shift == 0 ? -params.input2_shift : -input1_shift; for (int i = 0; i < flat_size; i++) { // F0 uses 0 integer bits, range [-1, 1]. using F0 = gemmlowp::FixedPoint; F0 input_ready_scaled = F0::FromRaw(not_shift_input[i]); - F0 scaled_input = - F0::FromRaw(gemmlowp::RoundingDivideByPOT(shift_input[i], input_shift)); + F0 scaled_input = F0::FromRaw( + gemmlowp::RoundingDivideByPOT(shift_input[i], input_right_shift)); F0 result = gemmlowp::SaturatingAdd(scaled_input, input_ready_scaled); const int16 raw_output = result.raw(); const int16 clamped_output = std::min( @@ -1181,42 +1083,28 @@ inline void Add(const int16* input1_data, const Dims<4>& input1_dims, } } -template -inline void Add(const int16* input1_data, const Dims<4>& input1_dims, - int input1_shift, const int16* input2_data, - const Dims<4>& input2_dims, int input2_shift, - int16 output_activation_min, int16 output_activation_max, - int16* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, -32768); - TFLITE_DCHECK_EQ(output_activation_max, 32767); - } - - Add(input1_data, input1_dims, input1_shift, input2_data, input2_dims, - input2_shift, output_activation_min, output_activation_max, output_data, - output_dims); -} - // TODO(jiawen): We can implement BroadcastAdd on buffers of arbitrary // dimensionality if the runtime code does a single loop over one dimension // that handles broadcasting as the base case. The code generator would then // generate max(D1, D2) nested for loops. -template -void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T output_activation_min, T output_activation_max, - T* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastAdd"); - +// TODO(benoitjacob): BroadcastAdd is intentionally duplicated from +// reference_ops.h. Once an optimized version is implemented and NdArrayDesc +// is no longer referenced in this file, move NdArrayDesc from types.h to +// reference_ops.h. +inline void BroadcastAdd4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/float"); NdArrayDesc<4> desc1; NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); // In Tensorflow, the dimensions are canonically named (batch_number, row, // col, channel), with extents (batches, height, width, depth), with the @@ -1229,49 +1117,77 @@ void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, // We name our variables by their Tensorflow convention, but generate C code // nesting loops such that the innermost loop has the smallest stride for the // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] + - input2_data[SubscriptToIndex(desc2, c, x, y, b)], - output_activation_min, output_activation_max); + input1_data[SubscriptToIndex(desc1, b, y, x, c)] + + input2_data[SubscriptToIndex(desc2, b, y, x, c)], + params.float_activation_min, params.float_activation_max); } } } } } -// legacy, for compatibility with old checked-in code -template -void BroadcastAdd(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T* output_data, const Dims<4>& output_dims) { - T output_activation_min, output_activation_max; - GetActivationMinMax(Ac, &output_activation_min, &output_activation_max); +inline void BroadcastAdd4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int32* input1_data, + const RuntimeShape& input2_shape, + const int32* input2_data, + const RuntimeShape& output_shape, + int32* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/int32"); + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); - BroadcastAdd(input1_data, input1_dims, input2_data, input2_dims, - output_activation_min, output_activation_max, output_data, - output_dims); + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = + ActivationFunctionWithMinMax( + input1_data[SubscriptToIndex(desc1, b, y, x, c)] + + input2_data[SubscriptToIndex(desc2, b, y, x, c)], + params.quantized_activation_min, + params.quantized_activation_max); + } + } + } + } } -inline void BroadcastAdd(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastAdd/8bit"); - +inline void BroadcastAdd4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const uint8* input1_data, + const RuntimeShape& input2_shape, + const uint8* input2_data, + const RuntimeShape& output_shape, + uint8* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/uint8"); NdArrayDesc<4> desc1; NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); // In Tensorflow, the dimensions are canonically named (batch_number, row, // col, channel), with extents (batches, height, width, depth), with the @@ -1284,33 +1200,37 @@ inline void BroadcastAdd(int left_shift, const uint8* input1_data, // We name our variables by their Tensorflow convention, but generate C code // nesting loops such that the innermost loop has the smallest stride for the // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { const int32 input1_val = - input1_offset + input1_data[SubscriptToIndex(desc1, c, x, y, b)]; + params.input1_offset + + input1_data[SubscriptToIndex(desc1, b, y, x, c)]; const int32 input2_val = - input2_offset + input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); + params.input2_offset + + input2_data[SubscriptToIndex(desc2, b, y, x, c)]; + const int32 shifted_input1_val = + input1_val * (1 << params.left_shift); + const int32 shifted_input2_val = + input2_val * (1 << params.left_shift); const int32 scaled_input1_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); + shifted_input1_val, params.input1_multiplier, + params.input1_shift); const int32 scaled_input2_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); + shifted_input2_val, params.input2_multiplier, + params.input2_shift); const int32 raw_sum = scaled_input1_val + scaled_input2_val; const int32 raw_output = MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, output_multiplier, kReverseShift * output_shift) + - output_offset; + raw_sum, params.output_multiplier, params.output_shift) + + params.output_offset; const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - output_data[Offset(output_dims, c, x, y, b)] = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[Offset(extended_output_shape, b, y, x, c)] = static_cast(clamped_output); } } @@ -1318,117 +1238,62 @@ inline void BroadcastAdd(int left_shift, const uint8* input1_data, } } -inline void BroadcastAddFivefold( - int y0, int y1, int y2, int y3, int y4, int left_shift, - const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, const uint8* input2_data, - const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, int32 output_multiplier, - int output_shift, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastAddFivefold/8bit"); - - int sb1 = y0; - int sa2 = y0; - int sb2 = y0 * y1; - int sa3 = y0 * y2; - int sa4 = y0 * y2 * y3; - int sb4 = y0 * y1 * y2; - +inline void BroadcastAddFivefold(const ArithmeticParams& unswitched_params, + const RuntimeShape& unswitched_input1_shape, + const uint8* unswitched_input1_data, + const RuntimeShape& unswitched_input2_shape, + const uint8* unswitched_input2_data, + const RuntimeShape& output_shape, + uint8* output_data) { + ArithmeticParams switched_params = unswitched_params; + switched_params.input1_offset = unswitched_params.input2_offset; + switched_params.input1_multiplier = unswitched_params.input2_multiplier; + switched_params.input1_shift = unswitched_params.input2_shift; + switched_params.input2_offset = unswitched_params.input1_offset; + switched_params.input2_multiplier = unswitched_params.input1_multiplier; + switched_params.input2_shift = unswitched_params.input1_shift; + + const bool use_unswitched = + unswitched_params.broadcast_category == + tflite::BroadcastableOpCategory::kFirstInputBroadcastsFast; + + const ArithmeticParams& params = + use_unswitched ? unswitched_params : switched_params; + const uint8* input1_data = + use_unswitched ? unswitched_input1_data : unswitched_input2_data; + const uint8* input2_data = + use_unswitched ? unswitched_input2_data : unswitched_input1_data; + + // Fivefold nested loops. The second input resets its position for each + // iteration of the second loop. The first input resets its position at the + // beginning of the fourth loop. The innermost loop is an elementwise add of + // sections of the arrays. uint8* output_data_ptr = output_data; - for (int i4 = 0; i4 < y4; ++i4) { - for (int i3 = 0; i3 < y3; ++i3) { + const uint8* input1_data_ptr = input1_data; + const uint8* input2_data_reset = input2_data; + int y0 = params.broadcast_shape[0]; + int y1 = params.broadcast_shape[1]; + int y2 = params.broadcast_shape[2]; + int y3 = params.broadcast_shape[3]; + int y4 = params.broadcast_shape[4]; + for (int i0 = 0; i0 < y0; ++i0) { + const uint8* input2_data_ptr; + for (int i1 = 0; i1 < y1; ++i1) { + input2_data_ptr = input2_data_reset; for (int i2 = 0; i2 < y2; ++i2) { - for (int i1 = 0; i1 < y1; ++i1) { - for (int i0 = 0; i0 < y0; ++i0) { - const int32 input1_val = - input1_offset + - input1_data[i4 * sa4 + i3 * sa3 + i2 * sa2 + i0]; - const int32 input2_val = - input2_offset + - input2_data[i4 * sb4 + i2 * sb2 + i1 * sb1 + i0]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); - const int32 scaled_input1_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); - const int32 scaled_input2_val = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); - const int32 raw_sum = scaled_input1_val + scaled_input2_val; - const int32 raw_output = - MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sum, output_multiplier, kReverseShift * output_shift) + - output_offset; - const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - *output_data_ptr = static_cast(clamped_output); - ++output_data_ptr; - } + for (int i3 = 0; i3 < y3; ++i3) { + AddElementwise(y4, params, input1_data_ptr, input2_data_ptr, + output_data_ptr); + input2_data_ptr += y4; + output_data_ptr += y4; } + input1_data_ptr += y4; } } + input2_data_reset = input2_data_ptr; } } -template -inline void BroadcastAdd(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - BroadcastAdd(left_shift, input1_data, input1_dims, input1_offset, - input1_multiplier, input1_shift, input2_data, input2_dims, - input2_offset, input2_multiplier, input2_shift, output_offset, - output_multiplier, output_shift, output_activation_min, - output_activation_max, output_data, output_dims); -} - -template -inline void BroadcastAddFivefold( - int y0, int y1, int y2, int y3, int y4, int left_shift, - const uint8* input1_data, const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, const uint8* input2_data, - const Dims<4>& input2_dims, int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, int32 output_multiplier, - int output_shift, int32 output_activation_min, int32 output_activation_max, - uint8* output_data, const Dims<4>& output_dims) { - static_assert(Ac == FusedActivationFunctionType::kNone || - Ac == FusedActivationFunctionType::kRelu || - Ac == FusedActivationFunctionType::kRelu6 || - Ac == FusedActivationFunctionType::kRelu1, - ""); - TFLITE_DCHECK_LE(output_activation_min, output_activation_max); - if (Ac == FusedActivationFunctionType::kNone) { - TFLITE_DCHECK_EQ(output_activation_min, 0); - TFLITE_DCHECK_EQ(output_activation_max, 255); - } - BroadcastAddFivefold(y0, y1, y2, y3, y4, left_shift, input1_data, input1_dims, - input1_offset, input1_multiplier, input1_shift, - input2_data, input2_dims, input2_offset, - input2_multiplier, input2_shift, output_offset, - output_multiplier, output_shift, output_activation_min, - output_activation_max, output_data, output_dims); -} - template inline void Mul(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, const Dims<4>& input2_dims, @@ -1667,16 +1532,35 @@ inline void Div(const T* input1_data, const Dims<4>& input1_dims, } } -template -inline void Sub(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T output_activation_min, T output_activation_max, - T* output_data, const Dims<4>& output_dims) { - const int flat_size = MatchingFlatSize(input1_dims, input2_dims, output_dims); +inline void SubNonBroadcast(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); for (int i = 0; i < flat_size; ++i) { output_data[i] = ActivationFunctionWithMinMax( - input1_data[i] - input2_data[i], output_activation_min, - output_activation_max); + input1_data[i] - input2_data[i], params.float_activation_min, + params.float_activation_max); + } +} + +inline void SubNonBroadcast(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int32* input1_data, + const RuntimeShape& input2_shape, + const int32* input2_data, + const RuntimeShape& output_shape, + int32* output_data) { + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, output_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] - input2_data[i], params.quantized_activation_min, + params.quantized_activation_max); } } @@ -1684,16 +1568,24 @@ inline void Sub(const T* input1_data, const Dims<4>& input1_dims, // dimensionality if the runtime code does a single loop over one dimension // that handles broadcasting as the base case. The code generator would then // generate max(D1, D2) nested for loops. -template -void BroadcastSub(const T* input1_data, const Dims<4>& input1_dims, - const T* input2_data, const Dims<4>& input2_dims, - T output_activation_min, T output_activation_max, - T* output_data, const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastSub"); - +// TODO(benoitjacob): BroadcastSub is intentionally duplicated from +// reference_ops.h. Once an optimized version is implemented and NdArrayDesc +// is no longer referenced in this file, move NdArrayDesc from types.h to +// reference_ops.h. +inline void BroadcastSub4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/float"); NdArrayDesc<4> desc1; NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); // In Tensorflow, the dimensions are canonically named (batch_number, row, // col, channel), with extents (batches, height, width, depth), with the @@ -1706,36 +1598,35 @@ void BroadcastSub(const T* input1_data, const Dims<4>& input1_dims, // We name our variables by their Tensorflow convention, but generate C code // nesting loops such that the innermost loop has the smallest stride for the // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = ActivationFunctionWithMinMax( - input1_data[SubscriptToIndex(desc1, c, x, y, b)] - - input2_data[SubscriptToIndex(desc2, c, x, y, b)], - output_activation_min, output_activation_max); + input1_data[SubscriptToIndex(desc1, b, y, x, c)] - + input2_data[SubscriptToIndex(desc2, b, y, x, c)], + params.float_activation_min, params.float_activation_max); } } } } } -inline void BroadcastSub(int left_shift, const uint8* input1_data, - const Dims<4>& input1_dims, int32 input1_offset, - int32 input1_multiplier, int input1_shift, - const uint8* input2_data, const Dims<4>& input2_dims, - int32 input2_offset, int32 input2_multiplier, - int input2_shift, int32 output_offset, - int32 output_multiplier, int output_shift, - int32 output_activation_min, - int32 output_activation_max, uint8* output_data, - const Dims<4>& output_dims) { - gemmlowp::ScopedProfilingLabel label("BroadcastSub/8bit"); - +inline void BroadcastSub4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const uint8* input1_data, + const RuntimeShape& input2_shape, + const uint8* input2_data, + const RuntimeShape& output_shape, + uint8* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/uint8"); NdArrayDesc<4> desc1; NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); // In Tensorflow, the dimensions are canonically named (batch_number, row, // col, channel), with extents (batches, height, width, depth), with the @@ -1748,33 +1639,37 @@ inline void BroadcastSub(int left_shift, const uint8* input1_data, // We name our variables by their Tensorflow convention, but generate C code // nesting loops such that the innermost loop has the smallest stride for the // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { const int32 input1_val = - input1_offset + input1_data[SubscriptToIndex(desc1, c, x, y, b)]; + params.input1_offset + + input1_data[SubscriptToIndex(desc1, b, y, x, c)]; const int32 input2_val = - input2_offset + input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - const int32 shifted_input1_val = input1_val * (1 << left_shift); - const int32 shifted_input2_val = input2_val * (1 << left_shift); + params.input2_offset + + input2_data[SubscriptToIndex(desc2, b, y, x, c)]; + const int32 shifted_input1_val = + input1_val * (1 << params.left_shift); + const int32 shifted_input2_val = + input2_val * (1 << params.left_shift); const int32 scaled_input1_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input1_val, input1_multiplier, - kReverseShift * input1_shift); + shifted_input1_val, params.input1_multiplier, + params.input1_shift); const int32 scaled_input2_val = MultiplyByQuantizedMultiplierSmallerThanOneExp( - shifted_input2_val, input2_multiplier, - kReverseShift * input2_shift); + shifted_input2_val, params.input2_multiplier, + params.input2_shift); const int32 raw_sub = scaled_input1_val - scaled_input2_val; const int32 raw_output = MultiplyByQuantizedMultiplierSmallerThanOneExp( - raw_sub, output_multiplier, kReverseShift * output_shift) + - output_offset; + raw_sub, params.output_multiplier, params.output_shift) + + params.output_offset; const int32 clamped_output = - std::min(output_activation_max, - std::max(output_activation_min, raw_output)); - output_data[Offset(output_dims, c, x, y, b)] = + std::min(params.quantized_activation_max, + std::max(params.quantized_activation_min, raw_output)); + output_data[Offset(extended_output_shape, b, y, x, c)] = static_cast(clamped_output); } } @@ -1782,6 +1677,156 @@ inline void BroadcastSub(int left_shift, const uint8* input1_data, } } +inline void BroadcastSub4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int32* input1_data, + const RuntimeShape& input2_shape, + const int32* input2_data, + const RuntimeShape& output_shape, + int32* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/int32"); + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = + ActivationFunctionWithMinMax( + input1_data[SubscriptToIndex(desc1, b, y, x, c)] - + input2_data[SubscriptToIndex(desc2, b, y, x, c)], + params.quantized_activation_min, + params.quantized_activation_max); + } + } + } + } +} + +template +void BroadcastSub4DSlow(const ArithmeticParams& params, + const RuntimeShape& input1_shape, const T* input1_data, + const RuntimeShape& input2_shape, const T* input2_data, + const RuntimeShape& output_shape, T* output_data) { + gemmlowp::ScopedProfilingLabel label("BroadcastAdd4DSlow/templated"); + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = + ActivationFunctionWithMinMax( + input1_data[SubscriptToIndex(desc1, b, y, x, c)] - + input2_data[SubscriptToIndex(desc2, b, y, x, c)], + params.quantized_activation_min, + params.quantized_activation_max); + } + } + } + } +} + +template +void Sub(const ArithmeticParams& params, const RuntimeShape& input1_shape, + const T* input1_data, const RuntimeShape& input2_shape, + const T* input2_data, const RuntimeShape& output_shape, + T* output_data) { + NdArrayDesc<4> desc1; + NdArrayDesc<4> desc2; + NdArrayDescsForElementwiseBroadcast(input1_shape, input2_shape, &desc1, + &desc2); + RuntimeShape extended_output_shape = + RuntimeShape::ExtendedShape(4, output_shape); + + // In Tensorflow, the dimensions are canonically named (batch_number, row, + // col, channel), with extents (batches, height, width, depth), with the + // trailing dimension changing most rapidly (channels has the smallest stride, + // typically 1 element). + // + // In generated C code, we store arrays with the dimensions reversed. The + // first dimension has smallest stride. + // + // We name our variables by their Tensorflow convention, but generate C code + // nesting loops such that the innermost loop has the smallest stride for the + // best cache behavior. + for (int b = 0; b < extended_output_shape.Dims(0); ++b) { + for (int y = 0; y < extended_output_shape.Dims(1); ++y) { + for (int x = 0; x < extended_output_shape.Dims(2); ++x) { + for (int c = 0; c < extended_output_shape.Dims(3); ++c) { + output_data[Offset(extended_output_shape, b, y, x, c)] = + input1_data[SubscriptToIndex(desc1, b, y, x, c)] - + input2_data[SubscriptToIndex(desc2, b, y, x, c)]; + } + } + } + } +} + +inline void SubWithActivation(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const int32* input1_data, + const RuntimeShape& input2_shape, + const int32* input2_data, + const RuntimeShape& output_shape, + int32* output_data) { + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, input2_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] - input2_data[i], params.quantized_activation_min, + params.quantized_activation_max); + } +} + +inline void SubWithActivation(const ArithmeticParams& params, + const RuntimeShape& input1_shape, + const float* input1_data, + const RuntimeShape& input2_shape, + const float* input2_data, + const RuntimeShape& output_shape, + float* output_data) { + const int flat_size = + MatchingFlatSize(input1_shape, input2_shape, input2_shape); + for (int i = 0; i < flat_size; ++i) { + output_data[i] = ActivationFunctionWithMinMax( + input1_data[i] - input2_data[i], params.float_activation_min, + params.float_activation_max); + } +} + template void Concatenation(int concat_dim, const Scalar* const* input_data, const Dims<4>* const* input_dims, int inputs_count, @@ -3717,38 +3762,6 @@ inline void Mean(const T* input_data, const Dims<4>& input_dims, } } -template -void Sub(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, - const Dims<4>& input2_dims, T* output_data, - const Dims<4>& output_dims) { - NdArrayDesc<4> desc1; - NdArrayDesc<4> desc2; - NdArrayDescsForElementwiseBroadcast(input1_dims, input2_dims, &desc1, &desc2); - - // In Tensorflow, the dimensions are canonically named (batch_number, row, - // col, channel), with extents (batches, height, width, depth), with the - // trailing dimension changing most rapidly (channels has the smallest stride, - // typically 1 element). - // - // In generated C code, we store arrays with the dimensions reversed. The - // first dimension has smallest stride. - // - // We name our variables by their Tensorflow convention, but generate C code - // nesting loops such that the innermost loop has the smallest stride for the - // best cache behavior. - for (int b = 0; b < ArraySize(output_dims, 3); ++b) { - for (int y = 0; y < ArraySize(output_dims, 2); ++y) { - for (int x = 0; x < ArraySize(output_dims, 1); ++x) { - for (int c = 0; c < ArraySize(output_dims, 0); ++c) { - output_data[Offset(output_dims, c, x, y, b)] = - input1_data[SubscriptToIndex(desc1, c, x, y, b)] - - input2_data[SubscriptToIndex(desc2, c, x, y, b)]; - } - } - } - } -} - template void TensorFlowMinimum(const T* input1_data, const Dims<4>& input1_dims, const T* input2_data, T* output_data, diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index 737cfb69c9..fe113dfdd3 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -119,6 +119,8 @@ class RuntimeShape { // larger shapes are separately allocated. static constexpr int kMaxSmallSize = 4; + RuntimeShape& operator=(RuntimeShape const&) = delete; + RuntimeShape() : size_(0) {} explicit RuntimeShape(int dimensions_count) : size_(dimensions_count) { @@ -135,6 +137,20 @@ class RuntimeShape { BuildFrom(init_list); } + // Avoid using this constructor. We should be able to delete it when C++17 + // rolls out. + RuntimeShape(RuntimeShape const& other) : size_(other.DimensionsCount()) { + if (size_ > kMaxSmallSize) { + dims_pointer_ = new int32[size_]; + } + std::memcpy(DimsData(), other.DimsData(), sizeof(int32) * size_); + } + + bool operator==(const RuntimeShape& comp) const { + return this->size_ == comp.size_ && + std::memcmp(DimsData(), comp.DimsData(), size_ * sizeof(int32)) == 0; + } + ~RuntimeShape() { if (size_ > kMaxSmallSize) { delete[] dims_pointer_; @@ -191,6 +207,16 @@ class RuntimeShape { } } + // This will probably be factored out. Old code made substantial use of 4-D + // shapes, and so this function is used to extend smaller shapes. Note that + // (a) as Dims<4>-dependent code is eliminated, the reliance on this should be + // reduced, and (b) some kernels are stricly 4-D, but then the shapes of their + // inputs should already be 4-D, so this function should not be needed. + inline static RuntimeShape ExtendedShape(int new_shape_size, + const RuntimeShape& shape) { + return RuntimeShape(new_shape_size, shape, 1); + } + inline void BuildFrom(const std::initializer_list init_list) { BuildFrom>(init_list); } @@ -208,7 +234,25 @@ class RuntimeShape { return buffer_size; } + bool operator!=(const RuntimeShape& comp) const { return !((*this) == comp); } + private: + // For use only by ExtendFrom(), written to guarantee (return-value) copy + // elision in C++17. + // This creates a shape padded to the desired size with the specified value. + RuntimeShape(int new_shape_size, const RuntimeShape& shape, int pad_value) + : size_(0) { + TFLITE_CHECK_GE(new_shape_size, shape.DimensionsCount()); + TFLITE_CHECK_LE(new_shape_size, kMaxSmallSize); + Resize(new_shape_size); + const int size_increase = new_shape_size - shape.DimensionsCount(); + for (int i = 0; i < size_increase; ++i) { + SetDim(i, pad_value); + } + std::memcpy(DimsData() + size_increase, shape.DimsData(), + sizeof(int32) * shape.DimensionsCount()); + } + int32 size_; union { int32 dims_[kMaxSmallSize]; @@ -364,6 +408,7 @@ inline int RequiredBufferSizeForDims(const Dims<4>& dims) { // arrays. inline int MatchingFlatSize(const RuntimeShape& shape, const RuntimeShape& check_shape_0) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); const int dims_count = shape.DimensionsCount(); for (int i = 0; i < dims_count; ++i) { TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); @@ -374,6 +419,7 @@ inline int MatchingFlatSize(const RuntimeShape& shape, inline int MatchingFlatSize(const RuntimeShape& shape, const RuntimeShape& check_shape_0, const RuntimeShape& check_shape_1) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); const int dims_count = shape.DimensionsCount(); for (int i = 0; i < dims_count; ++i) { TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); @@ -385,6 +431,7 @@ inline int MatchingFlatSize(const RuntimeShape& shape, const RuntimeShape& check_shape_0, const RuntimeShape& check_shape_1, const RuntimeShape& check_shape_2) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); const int dims_count = shape.DimensionsCount(); for (int i = 0; i < dims_count; ++i) { TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); @@ -397,6 +444,7 @@ inline int MatchingFlatSize(const RuntimeShape& shape, const RuntimeShape& check_shape_1, const RuntimeShape& check_shape_2, const RuntimeShape& check_shape_3) { + TFLITE_DCHECK_EQ(shape.DimensionsCount(), check_shape_0.DimensionsCount()); const int dims_count = shape.DimensionsCount(); for (int i = 0; i < dims_count; ++i) { TFLITE_DCHECK_EQ(shape.Dims(i), check_shape_0.Dims(i)); @@ -601,14 +649,74 @@ struct PoolParams { int stride_width; int filter_height; int filter_width; - // uint8, etc, inference params. + // uint8, etc, activation params. int32 quantized_activation_min; int32 quantized_activation_max; - // float inference params. + // float activation params. float float_activation_min; float float_activation_max; }; +enum class BroadcastableOpCategory : uint8 { + kNone, + kNonBroadcast, // Matching input shapes. + kFirstInputBroadcastsFast, // Fivefold nested loops. + kSecondInputBroadcastsFast, // Fivefold nested loops. + kGenericBroadcast, // Fall-back. +}; + +// For Add, Sub, Mul ops. +struct ArithmeticParams { + // Shape dependent / common to data / op types. + BroadcastableOpCategory broadcast_category; + // uint8 inference params. + int32 input1_offset; + int32 input2_offset; + int32 output_offset; + int32 output_multiplier; + int output_shift; + // Add / Sub, not Mul, uint8 inference params. + int left_shift; + int32 input1_multiplier; + int input1_shift; + int32 input2_multiplier; + int input2_shift; + // uint8, etc, activation params. + int32 quantized_activation_min; + int32 quantized_activation_max; + // float activation params. + float float_activation_min; + float float_activation_max; + + // Processed output dimensions. + // Let input "a" be the one that broadcasts in the faster-changing dimension. + // Then, after coalescing, for shapes {a0, a1, a2, a3, a4} and + // {b0, b1, b2, b3, b4}, + // broadcast_shape[4] = b0 = a0. + // broadcast_shape[3] = b1; a1 = 1. + // broadcast_shape[2] = b2 = a2. + // broadcast_shape[1] = a3; b3 = 1. + // broadcast_shape[0] = b4 = a4. + int broadcast_shape[5]; +}; + +template +inline void SetActivationParams(T min, T max, ArithmeticParams* params); + +template <> +inline void SetActivationParams(float min, float max, + ArithmeticParams* params) { + params->float_activation_min = min; + params->float_activation_max = max; +} + +template <> +inline void SetActivationParams(int32 min, int32 max, + ArithmeticParams* params) { + params->quantized_activation_min = min; + params->quantized_activation_max = max; +} + } // namespace tflite #endif // TENSORFLOW_CONTRIB_LITE_KERNELS_INTERNAL_TYPES_H_ diff --git a/tensorflow/contrib/lite/kernels/sub.cc b/tensorflow/contrib/lite/kernels/sub.cc index 541c85f756..77a1f59689 100644 --- a/tensorflow/contrib/lite/kernels/sub.cc +++ b/tensorflow/contrib/lite/kernels/sub.cc @@ -81,40 +81,43 @@ template void EvalSub(TfLiteContext* context, TfLiteNode* node, TfLiteSubParams* params, const OpData* data, const TfLiteTensor* input1, const TfLiteTensor* input2, TfLiteTensor* output) { -#define TF_LITE_SUB(type, opname, data_type) \ - data_type output_activation_min, output_activation_max; \ - CalculateActivationRange(params->activation, &output_activation_min, \ - &output_activation_max); \ - type::opname(GetTensorData(input1), GetTensorDims(input1), \ - GetTensorData(input2), GetTensorDims(input2), \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)) +#define TF_LITE_SUB(type, opname, data_type) \ + data_type output_activation_min, output_activation_max; \ + CalculateActivationRange(params->activation, &output_activation_min, \ + &output_activation_max); \ + tflite::ArithmeticParams op_params; \ + SetActivationParams(output_activation_min, output_activation_max, \ + &op_params); \ + type::opname(op_params, GetTensorShape(input1), \ + GetTensorData(input1), GetTensorShape(input2), \ + GetTensorData(input2), GetTensorShape(output), \ + GetTensorData(output)) if (output->type == kTfLiteInt32) { if (kernel_type == kReference) { if (data->requires_broadcast) { - TF_LITE_SUB(reference_ops, BroadcastSub, int32_t); + TF_LITE_SUB(reference_ops, BroadcastSub4DSlow, int32_t); } else { - TF_LITE_SUB(reference_ops, Sub, int32_t); + TF_LITE_SUB(reference_ops, SubWithActivation, int32_t); } } else { if (data->requires_broadcast) { - TF_LITE_SUB(optimized_ops, BroadcastSub, int32_t); + TF_LITE_SUB(optimized_ops, BroadcastSub4DSlow, int32_t); } else { - TF_LITE_SUB(optimized_ops, Sub, int32_t); + TF_LITE_SUB(optimized_ops, SubWithActivation, int32_t); } } } else if (output->type == kTfLiteFloat32) { if (kernel_type == kReference) { if (data->requires_broadcast) { - TF_LITE_SUB(reference_ops, BroadcastSub, float); + TF_LITE_SUB(reference_ops, BroadcastSub4DSlow, float); } else { - TF_LITE_SUB(reference_ops, Sub, float); + TF_LITE_SUB(reference_ops, SubWithActivation, float); } } else { if (data->requires_broadcast) { - TF_LITE_SUB(optimized_ops, BroadcastSub, float); + TF_LITE_SUB(optimized_ops, BroadcastSub4DSlow, float); } else { - TF_LITE_SUB(optimized_ops, Sub, float); + TF_LITE_SUB(optimized_ops, SubWithActivation, float); } } } @@ -143,36 +146,43 @@ void EvalQuantized(TfLiteContext* context, TfLiteNode* node, int input1_shift; QuantizeMultiplierSmallerThanOneExp(real_input1_multiplier, &input1_multiplier, &input1_shift); - input1_shift *= -1; int32 input2_multiplier; int input2_shift; QuantizeMultiplierSmallerThanOneExp(real_input2_multiplier, &input2_multiplier, &input2_shift); - input2_shift *= -1; int32 output_multiplier; int output_shift; QuantizeMultiplierSmallerThanOneExp(real_output_multiplier, &output_multiplier, &output_shift); - output_shift *= -1; int32 output_activation_min, output_activation_max; CalculateActivationRangeUint8(params->activation, output, &output_activation_min, &output_activation_max); -#define TF_LITE_SUB(type, opname) \ - type::opname(left_shift, GetTensorData(input1), \ - GetTensorDims(input1), input1_offset, input1_multiplier, \ - input1_shift, GetTensorData(input2), \ - GetTensorDims(input2), input2_offset, input2_multiplier, \ - input2_shift, output_offset, output_multiplier, output_shift, \ - output_activation_min, output_activation_max, \ - GetTensorData(output), GetTensorDims(output)); +#define TF_LITE_SUB(type, opname) \ + tflite::ArithmeticParams op_params; \ + op_params.left_shift = left_shift; \ + op_params.input1_offset = input1_offset; \ + op_params.input1_multiplier = input1_multiplier; \ + op_params.input1_shift = input1_shift; \ + op_params.input2_offset = input2_offset; \ + op_params.input2_multiplier = input2_multiplier; \ + op_params.input2_shift = input2_shift; \ + op_params.output_offset = output_offset; \ + op_params.output_multiplier = output_multiplier; \ + op_params.output_shift = output_shift; \ + SetActivationParams(output_activation_min, output_activation_max, \ + &op_params); \ + type::opname(op_params, GetTensorShape(input1), \ + GetTensorData(input1), GetTensorShape(input2), \ + GetTensorData(input2), GetTensorShape(output), \ + GetTensorData(output)) // The quantized version of Sub doesn't support activations, so we // always use BroadcastSub. if (kernel_type == kReference) { - TF_LITE_SUB(reference_ops, BroadcastSub); + TF_LITE_SUB(reference_ops, BroadcastSub4DSlow); } else { - TF_LITE_SUB(optimized_ops, BroadcastSub); + TF_LITE_SUB(optimized_ops, BroadcastSub4DSlow); } #undef TF_LITE_SUB } -- GitLab From 8eb773d6c23de29dccfc3cf3da441a8552ed13ed Mon Sep 17 00:00:00 2001 From: Michael Kuperstein Date: Thu, 19 Jul 2018 10:30:58 -0700 Subject: [PATCH 143/519] [XLA] Better shape size validation for sparse arrays. PiperOrigin-RevId: 205262376 --- tensorflow/compiler/xla/shape_util.cc | 51 ++++++++++++++++----------- 1 file changed, 31 insertions(+), 20 deletions(-) diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index f4668c0f55..6480148336 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -883,40 +883,51 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { } int64 shape_size = [&shape]() { - int64 shape_size; if (LayoutUtil::IsSparseArray(shape)) { - shape_size = LayoutUtil::MaxSparseElements(shape.layout()); - if (shape_size < 0) { - return shape_size; + int64 max_sparse_elements = LayoutUtil::MaxSparseElements(shape.layout()); + if (max_sparse_elements < 0) { + return max_sparse_elements; } - shape_size = MultiplyWithoutOverflow(shape_size, ShapeUtil::Rank(shape)); - if (shape_size < 0) { - return shape_size; + int64 sparse_elements_size = MultiplyWithoutOverflow( + max_sparse_elements, ByteSizeOfPrimitiveType(shape.element_type())); + if (sparse_elements_size < 0) { + return sparse_elements_size; } - shape_size = MultiplyWithoutOverflow(shape_size, sizeof(int64)); - if (shape_size < 0) { - return shape_size; + int64 sparse_indices_size = + MultiplyWithoutOverflow(max_sparse_elements, ShapeUtil::Rank(shape)); + if (sparse_indices_size < 0) { + return sparse_indices_size; + } + sparse_indices_size = + MultiplyWithoutOverflow(sparse_indices_size, sizeof(int64)); + if (sparse_indices_size < 0) { + return sparse_indices_size; + } + // At this point, both sparse_indices_size and sparse_elements_size are + // non-negative, so we can easily check if adding them wraps. + if (static_cast(sparse_elements_size) + + static_cast(sparse_indices_size) > + INT64_MAX) { + return static_cast(-1); } } - shape_size = 1; - // This is intentionally unconditional: even if the shape is sparse, we want // to verify the densified version has a reasonable size. + int64 dense_shape_size = 1; if (shape.dimensions().empty()) { - return shape_size; + return dense_shape_size; } for (int64 dim : shape.dimensions()) { - shape_size = MultiplyWithoutOverflow(shape_size, dim); - if (shape_size < 0) { - return shape_size; + dense_shape_size = MultiplyWithoutOverflow(dense_shape_size, dim); + if (dense_shape_size < 0) { + return dense_shape_size; } } - shape_size = MultiplyWithoutOverflow( - shape_size, ByteSizeOfPrimitiveType(shape.element_type())); - - return shape_size; + dense_shape_size = MultiplyWithoutOverflow( + dense_shape_size, ByteSizeOfPrimitiveType(shape.element_type())); + return dense_shape_size; }(); if (shape_size < 0) { -- GitLab From 9dfa333cc8e78b9a533562448d67d48ec568622d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 10:41:29 -0700 Subject: [PATCH 144/519] Small text change PiperOrigin-RevId: 205264291 --- .../eager/python/examples/generative_examples/dcgan.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb index 43c8c355dc..232f9a8ef0 100644 --- a/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb +++ b/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb @@ -31,7 +31,7 @@ "\n", "On a colab GPU(Tesla K80), the model takes around 40 seconds per epoch to train.\n", "\n", - "Below is the output generated after training the generator and discriminator models for 100 epochs.\n", + "Below is the output generated after training the generator and discriminator models for 150 epochs.\n", "\n", "![sample output](https://tensorflow.org/images/gan/dcgan.gif)" ] -- GitLab From 1044888430b34353f54266bf0674144dfe675687 Mon Sep 17 00:00:00 2001 From: Rohan Jain Date: Thu, 19 Jul 2018 10:44:24 -0700 Subject: [PATCH 145/519] Making the Eager iterator use the new copy_to_device. This CL gets rid of the forced placement of all eager datasets / iterators on the CPU since now we can have some datasets on the GPU. PiperOrigin-RevId: 205264791 --- .../data/python/ops/prefetching_ops.py | 5 ++ tensorflow/contrib/eager/python/datasets.py | 64 +++---------------- tensorflow/python/data/ops/iterator_ops.py | 26 ++++---- 3 files changed, 26 insertions(+), 69 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/prefetching_ops.py b/tensorflow/contrib/data/python/ops/prefetching_ops.py index 50212d3b52..45abd6376c 100644 --- a/tensorflow/contrib/data/python/ops/prefetching_ops.py +++ b/tensorflow/contrib/data/python/ops/prefetching_ops.py @@ -480,6 +480,11 @@ class _CopyToDeviceDataset(dataset_ops.Dataset): self._finalize_func = _remote_finalize_func self._finalize_captured_args = _remote_finalize_func.captured_inputs + + g = ops.get_default_graph() + _remote_init_func.add_to_graph(g) + _remote_next_func.add_to_graph(g) + _remote_finalize_func.add_to_graph(g) # pylint: enable=protected-scope # The one_shot_iterator implementation needs a 0 arg _make_dataset function diff --git a/tensorflow/contrib/eager/python/datasets.py b/tensorflow/contrib/eager/python/datasets.py index 58c548d798..e31dbbe80f 100644 --- a/tensorflow/contrib/eager/python/datasets.py +++ b/tensorflow/contrib/eager/python/datasets.py @@ -18,33 +18,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import threading - from tensorflow.contrib.data.python.ops import prefetching_ops from tensorflow.python.data.ops import iterator_ops -from tensorflow.python.data.util import nest -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.ops import gen_dataset_ops -from tensorflow.python.ops import resource_variable_ops from tensorflow.python.training.checkpointable import base as checkpointable from tensorflow.python.training.saver import BaseSaverBuilder -_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 Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase): """An iterator producing tf.Tensor objects from a tf.data.Dataset. @@ -80,38 +61,18 @@ class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase): "`tf.contrib.eager.Iterator`. Use `for ... in dataset:` to iterate " "over the dataset instead.") - super(Iterator, self).__init__(dataset) if not context.context().device_spec.device_type: is_remote_device = False else: is_remote_device = context.context().device_spec.device_type != "CPU" - self._buffer_resource_handle = None if is_remote_device: - with ops.device("/device:CPU:0"): - iter_string_handle = gen_dataset_ops.iterator_to_string_handle( - self._resource) - - @function.Defun(dtypes.string) - def remote_fn(h): - remote_iterator = iterator_ops.Iterator.from_string_handle( - h, self.output_types, self.output_shapes, self.output_classes) - return remote_iterator.get_next() - - remote_fn.add_to_graph(None) - target = constant_op.constant("/device:CPU:0") - with ops.device(self._device): - self._buffer_resource_handle = prefetching_ops.function_buffering_resource( # pylint: disable=line-too-long - string_arg=iter_string_handle, - output_types=self._flat_output_types, - f=remote_fn, - target_device=target, - buffer_size=10, - container="", - shared_name=_generate_shared_name( - "contrib_eager_iterator_function_buffer_resource")) - self._buffer_resource_deleter = resource_variable_ops.EagerResourceDeleter( # pylint: disable=line-too-long - handle=self._buffer_resource_handle, - handle_device=self._device) + with ops.device(None): + # Let the placer figure out where to place the various functions etc. + # created by the CopyToDeviceDataset. + dataset = dataset.apply(prefetching_ops.copy_to_device( + context.context().device_name)) + dataset = dataset.prefetch(1) + super(Iterator, self).__init__(dataset) def _next_internal(self): """Returns a nested structure of `tf.Tensor`s containing the next element. @@ -120,16 +81,7 @@ class Iterator(iterator_ops.EagerIterator, checkpointable.CheckpointableBase): # that there is no more data to iterate over. # TODO(b/77291417): Fix with context.execution_mode(context.SYNC): - if self._buffer_resource_handle is not None: - with ops.device(self._device): - ret = prefetching_ops.function_buffering_resource_get_next( - function_buffer_resource=self._buffer_resource_handle, - output_types=self._flat_output_types) - return sparse.deserialize_sparse_tensors( - nest.pack_sequence_as(self._output_types, ret), self._output_types, - self._output_shapes, self._output_classes) - else: - return super(Iterator, self)._next_internal() + return super(Iterator, self)._next_internal() # TODO(shivaniagrawal): Expose checkpointable stateful objects from dataset # attributes(potential). diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py index 35de2f2841..f0784ed3d0 100644 --- a/tensorflow/python/data/ops/iterator_ops.py +++ b/tensorflow/python/data/ops/iterator_ops.py @@ -499,23 +499,23 @@ class EagerIterator(object): "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"): - ds_variant = dataset._as_variant_tensor() # pylint: disable=protected-access - self._output_classes = dataset.output_classes - self._output_types = dataset.output_types - self._output_shapes = dataset.output_shapes - self._flat_output_types = nest.flatten( - sparse.as_dense_types(self._output_types, self._output_classes)) - self._flat_output_shapes = nest.flatten( - sparse.as_dense_shapes(self._output_shapes, self._output_classes)) + self._device = context.context().device_name + ds_variant = dataset._as_variant_tensor() # pylint: disable=protected-access + self._output_classes = dataset.output_classes + self._output_types = dataset.output_types + self._output_shapes = dataset.output_shapes + self._flat_output_types = nest.flatten( + sparse.as_dense_types(self._output_types, self._output_classes)) + self._flat_output_shapes = nest.flatten( + sparse.as_dense_shapes(self._output_shapes, self._output_classes)) + with ops.colocate_with(ds_variant): self._resource = gen_dataset_ops.anonymous_iterator( output_types=self._flat_output_types, output_shapes=self._flat_output_shapes) gen_dataset_ops.make_iterator(ds_variant, self._resource) - # Delete the resource when this object is deleted - self._resource_deleter = resource_variable_ops.EagerResourceDeleter( - handle=self._resource, handle_device="/device:CPU:0") - self._device = context.context().device_name + # Delete the resource when this object is deleted + self._resource_deleter = resource_variable_ops.EagerResourceDeleter( + handle=self._resource, handle_device=self._device) def __iter__(self): return self -- GitLab From 88c520b3f12a1ee5e63d9f05094ca9f84700ea6e Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Thu, 19 Jul 2018 11:02:05 -0700 Subject: [PATCH 146/519] Merges variable_scope.variable and tf.Variable PiperOrigin-RevId: 205267974 --- .../python/ops/resource_variable_ops.py | 13 ----- tensorflow/python/ops/variable_scope.py | 54 +++++------------- tensorflow/python/ops/variables.py | 57 +++++++++++++++---- .../python/training/checkpointable/util.py | 2 +- 4 files changed, 63 insertions(+), 63 deletions(-) diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py index 5979b76ff2..1f56ad25bf 100644 --- a/tensorflow/python/ops/resource_variable_ops.py +++ b/tensorflow/python/ops/resource_variable_ops.py @@ -1294,16 +1294,3 @@ def is_resource_variable(var): """"Returns True if `var` is to be considered a ResourceVariable.""" return isinstance(var, ResourceVariable) or hasattr( var, "_should_act_as_resource_variable") - - -_DEFAULT_USE_RESOURCE = False - - -def _default_variable_creator(_, *args, **kwds): - use_resource = kwds.pop("use_resource", _DEFAULT_USE_RESOURCE) - use_resource = use_resource or context.executing_eagerly() - if use_resource: - return ResourceVariable(*args, **kwds) - return variables.RefVariable(*args, **kwds) - -variables.default_variable_creator = _default_variable_creator diff --git a/tensorflow/python/ops/variable_scope.py b/tensorflow/python/ops/variable_scope.py index 0f37dcc027..aca44bcd44 100644 --- a/tensorflow/python/ops/variable_scope.py +++ b/tensorflow/python/ops/variable_scope.py @@ -2349,7 +2349,10 @@ def default_variable_creator(next_creator=None, **kwargs): validate_shape = kwargs.get("validate_shape", True) caching_device = kwargs.get("caching_device", None) name = kwargs.get("name", None) + variable_def = kwargs.get("variable_def", None) dtype = kwargs.get("dtype", None) + expected_shape = kwargs.get("expected_shape", None) + import_scope = kwargs.get("import_scope", None) constraint = kwargs.get("constraint", None) use_resource = kwargs.get("use_resource", None) @@ -2360,23 +2363,24 @@ def default_variable_creator(next_creator=None, **kwargs): if use_resource is None: use_resource = get_variable_scope().use_resource - if use_resource or (use_resource is None and context.executing_eagerly()): + use_resource = use_resource or context.executing_eagerly() + if use_resource: return resource_variable_ops.ResourceVariable( initial_value=initial_value, trainable=trainable, collections=collections, validate_shape=validate_shape, caching_device=caching_device, name=name, dtype=dtype, - constraint=constraint) - elif not use_resource and context.executing_eagerly(): - raise RuntimeError( - "VariableScope should use resource variable when eager execution is" - " enabled, but use_resource is False." - ) + constraint=constraint, variable_def=variable_def, + import_scope=import_scope) else: - return variables.Variable( + return variables.RefVariable( initial_value=initial_value, trainable=trainable, collections=collections, validate_shape=validate_shape, caching_device=caching_device, name=name, dtype=dtype, - constraint=constraint) + constraint=constraint, variable_def=variable_def, + expected_shape=expected_shape, import_scope=import_scope) + + +variables.default_variable_creator = default_variable_creator def _make_getter(captured_getter, captured_previous): @@ -2384,36 +2388,8 @@ def _make_getter(captured_getter, captured_previous): return lambda **kwargs: captured_getter(captured_previous, **kwargs) -def variable(initial_value=None, - trainable=None, - collections=None, - validate_shape=True, - caching_device=None, - name=None, - dtype=None, - constraint=None, - use_resource=None, - synchronization=VariableSynchronization.AUTO, - aggregation=VariableAggregation.NONE): - previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs) - for getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access - previous_getter = _make_getter(getter, previous_getter) - - # Reset `aggregation` that is explicitly set as `None` to the enum None value. - if aggregation is None: - aggregation = VariableAggregation.NONE - return previous_getter( - initial_value=initial_value, - trainable=trainable, - collections=collections, - validate_shape=validate_shape, - caching_device=caching_device, - name=name, - dtype=dtype, - constraint=constraint, - use_resource=use_resource, - synchronization=synchronization, - aggregation=aggregation) +# TODO(apassos) remove forwarding symbol +variable = variables.Variable @tf_contextlib.contextmanager diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py index 6bb2d6f669..d03d93beeb 100644 --- a/tensorflow/python/ops/variables.py +++ b/tensorflow/python/ops/variables.py @@ -40,15 +40,15 @@ from tensorflow.python.util.deprecation import deprecated from tensorflow.python.util.tf_export import tf_export -def default_variable_creator(_, *args, **kwds): - del args, kwds - raise NotImplementedError("resource_variable_ops needs to be imported") +def default_variable_creator(_, **kwds): + del kwds + raise NotImplementedError("variable_scope needs to be imported") def _make_getter(captured_getter, captured_previous): """To avoid capturing loop variables.""" - def getter(*args, **kwargs): - return captured_getter(captured_previous, *args, **kwargs) + def getter(**kwargs): + return captured_getter(captured_previous, **kwargs) return getter @@ -86,11 +86,48 @@ class VariableAggregation(enum.Enum): class VariableMetaclass(type): """Metaclass to allow construction of tf.Variable to be overridden.""" + def _variable_call(cls, + initial_value=None, + trainable=None, + collections=None, + validate_shape=True, + caching_device=None, + name=None, + variable_def=None, + dtype=None, + expected_shape=None, + import_scope=None, + constraint=None, + use_resource=None, + synchronization=VariableSynchronization.AUTO, + aggregation=VariableAggregation.NONE): + """Call on Variable class. Useful to force the signature.""" + previous_getter = lambda **kwargs: default_variable_creator(None, **kwargs) + for getter in ops.get_default_graph()._variable_creator_stack: # pylint: disable=protected-access + previous_getter = _make_getter(getter, previous_getter) + + # Reset `aggregation` that is explicitly set as `None` to the enum NONE. + if aggregation is None: + aggregation = VariableAggregation.NONE + return previous_getter( + initial_value=initial_value, + trainable=trainable, + collections=collections, + validate_shape=validate_shape, + caching_device=caching_device, + name=name, + variable_def=variable_def, + dtype=dtype, + expected_shape=expected_shape, + import_scope=import_scope, + constraint=constraint, + use_resource=use_resource, + synchronization=synchronization, + aggregation=aggregation) + def __call__(cls, *args, **kwargs): if cls is Variable: - previous_getter = lambda *a, **k: default_variable_creator(None, *a, **k) - # TODO(apassos) use a stack of getters here - return previous_getter(*args, **kwargs) + return cls._variable_call(*args, **kwargs) else: return super(VariableMetaclass, cls).__call__(*args, **kwargs) @@ -650,8 +687,8 @@ class Variable(six.with_metaclass(VariableMetaclass, @staticmethod def from_proto(variable_def, import_scope=None): """Returns a `Variable` object created from `variable_def`.""" - return Variable(variable_def=variable_def, - import_scope=import_scope) + return RefVariable(variable_def=variable_def, + import_scope=import_scope) class SaveSliceInfo(object): """Information on how to save this Variable as a slice. diff --git a/tensorflow/python/training/checkpointable/util.py b/tensorflow/python/training/checkpointable/util.py index 6ae5765b13..686232fe27 100644 --- a/tensorflow/python/training/checkpointable/util.py +++ b/tensorflow/python/training/checkpointable/util.py @@ -747,7 +747,7 @@ def capture_dependencies(template): initial_value=initializer, name=name, **inner_kwargs) - if name.startswith(name_prefix): + if name is not None and name.startswith(name_prefix): scope_stripped_name = name[len(name_prefix) + 1:] if not checkpointable_parent: return template._add_variable_with_custom_getter( # pylint: disable=protected-access -- GitLab From 109ae67a7e99e3dcb4d93cc22df5b3912f4558c9 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Thu, 19 Jul 2018 11:27:39 -0700 Subject: [PATCH 147/519] Expose each device's incarnation via `Session.list_devices()`. PiperOrigin-RevId: 205273020 --- tensorflow/c/c_api.cc | 1 + tensorflow/c/c_api.h | 7 +++++++ tensorflow/python/client/session.py | 14 ++++++++++--- .../client/session_list_devices_test.py | 8 ++++++- tensorflow/python/client/session_test.py | 21 ++++++++++++------- tensorflow/python/client/tf_session.i | 5 +++++ 6 files changed, 44 insertions(+), 12 deletions(-) diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc index 5c218d3f25..a3003953a3 100644 --- a/tensorflow/c/c_api.cc +++ b/tensorflow/c/c_api.cc @@ -963,6 +963,7 @@ TF_DEVICELIST_METHOD(const char*, TF_DeviceListName, name().c_str(), nullptr); TF_DEVICELIST_METHOD(const char*, TF_DeviceListType, device_type().c_str(), nullptr); TF_DEVICELIST_METHOD(int64_t, TF_DeviceListMemoryBytes, memory_limit(), -1); +TF_DEVICELIST_METHOD(uint64_t, TF_DeviceListIncarnation, incarnation(), 0); #undef TF_DEVICELIST_METHOD diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h index 1eb75ef11f..fddc09d45e 100644 --- a/tensorflow/c/c_api.h +++ b/tensorflow/c/c_api.h @@ -1521,6 +1521,13 @@ TF_CAPI_EXPORT extern const char* TF_DeviceListType(const TF_DeviceList* list, TF_CAPI_EXPORT extern int64_t TF_DeviceListMemoryBytes( const TF_DeviceList* list, int index, TF_Status* status); +// Retrieve the incarnation number of a given device. +// +// If index is out of bounds, an error code will be set in the status object, +// and 0 will be returned. +TF_CAPI_EXPORT extern uint64_t TF_DeviceListIncarnation( + const TF_DeviceList* list, int index, TF_Status* status); + // -------------------------------------------------------------------------- // Load plugins containing custom ops and kernels diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index e037925961..8ede6ab54c 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -540,10 +540,11 @@ class _DeviceAttributes(object): (in bytes). """ - def __init__(self, name, device_type, memory_limit_bytes): + def __init__(self, name, device_type, memory_limit_bytes, incarnation): self._name = device.canonical_name(name) self._device_type = device_type self._memory_limit_bytes = memory_limit_bytes + self._incarnation = incarnation @property def name(self): @@ -557,11 +558,16 @@ class _DeviceAttributes(object): def memory_limit_bytes(self): return self._memory_limit_bytes + @property + def incarnation(self): + return self._incarnation + def __repr__(self): - return '_DeviceAttributes(%s, %s, %d)' % ( + return '_DeviceAttributes(%s, %s, %d, %d)' % ( self.name, self.device_type, self.memory_limit_bytes, + self.incarnation, ) @@ -658,7 +664,9 @@ class BaseSession(SessionInterface): name = tf_session.TF_DeviceListName(raw_device_list, i) device_type = tf_session.TF_DeviceListType(raw_device_list, i) memory = tf_session.TF_DeviceListMemoryBytes(raw_device_list, i) - device_list.append(_DeviceAttributes(name, device_type, memory)) + incarnation = tf_session.TF_DeviceListIncarnation(raw_device_list, i) + device_list.append( + _DeviceAttributes(name, device_type, memory, incarnation)) tf_session.TF_DeleteDeviceList(raw_device_list) return device_list diff --git a/tensorflow/python/client/session_list_devices_test.py b/tensorflow/python/client/session_list_devices_test.py index c5d82c213a..dd381c689f 100644 --- a/tensorflow/python/client/session_list_devices_test.py +++ b/tensorflow/python/client/session_list_devices_test.py @@ -37,6 +37,8 @@ class SessionListDevicesTest(test_util.TensorFlowTestCase): devices = sess.list_devices() self.assertTrue('/job:localhost/replica:0/task:0/device:CPU:0' in set( [d.name for d in devices]), devices) + # All valid device incarnations must be non-zero. + self.assertTrue(all(d.incarnation != 0 for d in devices)) def testInvalidDeviceNumber(self): opts = tf_session.TF_NewSessionOptions() @@ -54,6 +56,8 @@ class SessionListDevicesTest(test_util.TensorFlowTestCase): devices = sess.list_devices() self.assertTrue('/job:local/replica:0/task:0/device:CPU:0' in set( [d.name for d in devices]), devices) + # All valid device incarnations must be non-zero. + self.assertTrue(all(d.incarnation != 0 for d in devices)) def testListDevicesClusterSpecPropagation(self): server1 = server_lib.Server.create_local_server() @@ -67,11 +71,13 @@ class SessionListDevicesTest(test_util.TensorFlowTestCase): config = config_pb2.ConfigProto(cluster_def=cluster_def) with session.Session(server1.target, config=config) as sess: devices = sess.list_devices() - device_names = set([d.name for d in devices]) + device_names = set(d.name for d in devices) self.assertTrue( '/job:worker/replica:0/task:0/device:CPU:0' in device_names) self.assertTrue( '/job:worker/replica:0/task:1/device:CPU:0' in device_names) + # All valid device incarnations must be non-zero. + self.assertTrue(all(d.incarnation != 0 for d in devices)) if __name__ == '__main__': diff --git a/tensorflow/python/client/session_test.py b/tensorflow/python/client/session_test.py index b72e029d1c..052be68385 100644 --- a/tensorflow/python/client/session_test.py +++ b/tensorflow/python/client/session_test.py @@ -35,6 +35,7 @@ from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session from tensorflow.python.framework import common_shapes from tensorflow.python.framework import constant_op +from tensorflow.python.framework import device as framework_device_lib from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import function @@ -104,18 +105,20 @@ class SessionTest(test_util.TensorFlowTestCase): copy_val) def testManyCPUs(self): - # TODO(keveman): Implement ListDevices and test for the number of - # devices returned by ListDevices. with session.Session( config=config_pb2.ConfigProto(device_count={ - 'CPU': 2 - })): + 'CPU': 2, 'GPU': 0 + })) as sess: inp = constant_op.constant(10.0, name='W1') self.assertAllEqual(inp.eval(), 10.0) + devices = sess.list_devices() + self.assertEqual(2, len(devices)) + for device in devices: + self.assertEqual('CPU', framework_device_lib.DeviceSpec.from_string( + device.name).device_type) + def testPerSessionThreads(self): - # TODO(keveman): Implement ListDevices and test for the number of - # devices returned by ListDevices. with session.Session( config=config_pb2.ConfigProto(use_per_session_threads=True)): inp = constant_op.constant(10.0, name='W1') @@ -1868,19 +1871,21 @@ class SessionTest(test_util.TensorFlowTestCase): def testDeviceAttributes(self): attrs = session._DeviceAttributes( - '/job:worker/replica:0/task:3/device:CPU:2', 'TYPE', 1337) + '/job:worker/replica:0/task:3/device:CPU:2', 'TYPE', 1337, 1000000) self.assertEqual(1337, attrs.memory_limit_bytes) self.assertEqual('/job:worker/replica:0/task:3/device:CPU:2', attrs.name) self.assertEqual('TYPE', attrs.device_type) + self.assertEqual(1000000, attrs.incarnation) str_repr = '%s' % attrs self.assertTrue(str_repr.startswith('_DeviceAttributes'), str_repr) def testDeviceAttributesCanonicalization(self): attrs = session._DeviceAttributes('/job:worker/replica:0/task:3/cpu:1', - 'TYPE', 1337) + 'TYPE', 1337, 1000000) self.assertEqual(1337, attrs.memory_limit_bytes) self.assertEqual('/job:worker/replica:0/task:3/device:CPU:1', attrs.name) self.assertEqual('TYPE', attrs.device_type) + self.assertEqual(1000000, attrs.incarnation) str_repr = '%s' % attrs self.assertTrue(str_repr.startswith('_DeviceAttributes'), str_repr) diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i index 985cb90436..1cdd8e0b6a 100644 --- a/tensorflow/python/client/tf_session.i +++ b/tensorflow/python/client/tf_session.i @@ -138,6 +138,11 @@ tensorflow::ImportNumpy(); $result = PyLong_FromLongLong($1); } +// Convert TF_DeviceListIncarnation uint64_t output to Python integer +%typemap(out) uint64_t { + $result = PyLong_FromUnsignedLongLong($1); +} + // We use TF_OperationGetControlInputs_wrapper instead of // TF_OperationGetControlInputs %ignore TF_OperationGetControlInputs; -- GitLab From 8571b4f3d2d06caefd8f714d7ea98a3701ea3a96 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 11:37:18 -0700 Subject: [PATCH 148/519] Internal Change PiperOrigin-RevId: 205274579 --- tensorflow/contrib/lite/delegates/eager/BUILD | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/tensorflow/contrib/lite/delegates/eager/BUILD b/tensorflow/contrib/lite/delegates/eager/BUILD index 9f31ffdf67..03a4b7bf1d 100644 --- a/tensorflow/contrib/lite/delegates/eager/BUILD +++ b/tensorflow/contrib/lite/delegates/eager/BUILD @@ -42,10 +42,6 @@ cc_library( name = "delegate_data", srcs = ["delegate_data.cc"], hdrs = ["delegate_data.h"], - tags = [ - "no_oss", - "tflite_not_portable", - ], deps = [ ":buffer_map", "//tensorflow/core:core_cpu", @@ -59,6 +55,7 @@ cc_test( size = "small", srcs = ["delegate_data_test.cc"], tags = [ + "no_oss", "tflite_not_portable", ], deps = [ -- GitLab From 3d6c8b8aae8433b16af61097641b9958e679fb3d Mon Sep 17 00:00:00 2001 From: Smit Hinsu Date: Thu, 19 Jul 2018 11:49:44 -0700 Subject: [PATCH 149/519] Assert closeness of output values instead of equality in layout optimizer tests using convolutions Convolution output may differ across convolution algorithms and is not guaranteed to match exactly. PiperOrigin-RevId: 205276671 --- tensorflow/python/grappler/layout_optimizer_test.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/grappler/layout_optimizer_test.py b/tensorflow/python/grappler/layout_optimizer_test.py index 7d07c77c79..8cc971c61d 100644 --- a/tensorflow/python/grappler/layout_optimizer_test.py +++ b/tensorflow/python/grappler/layout_optimizer_test.py @@ -1340,7 +1340,7 @@ class LayoutOptimizerTest(test.TestCase): expected_num_transposes = 2 self.assertEqual(expected_num_transposes, num_transposes) self._assert_trans_nhwc_to_nchw('Conv2D-0', nodes) - self.assertAllEqual(output_val_ref, output_val) + self.assertAllClose(output_val_ref, output_val, atol=1e-3) def testLoop(self): if test.is_gpu_available(cuda_only=True): -- GitLab From cb11b60da0e2d8e2730e9bb096f40aa2ed1f2b56 Mon Sep 17 00:00:00 2001 From: Mark Daoust Date: Thu, 19 Jul 2018 11:50:09 -0700 Subject: [PATCH 150/519] Add redirects to point api duplicates to the canonical doc location. PiperOrigin-RevId: 205276722 --- tensorflow/tools/docs/generate.py | 5 ++++ tensorflow/tools/docs/generate_lib.py | 30 ++++++++++++++++++++-- tensorflow/tools/docs/generate_lib_test.py | 13 +++++++++- 3 files changed, 45 insertions(+), 3 deletions(-) diff --git a/tensorflow/tools/docs/generate.py b/tensorflow/tools/docs/generate.py index fc93085e3e..f96887e4c7 100644 --- a/tensorflow/tools/docs/generate.py +++ b/tensorflow/tools/docs/generate.py @@ -31,6 +31,11 @@ if __name__ == '__main__': doc_generator = generate_lib.DocGenerator() doc_generator.add_output_dir_argument() doc_generator.add_src_dir_argument() + doc_generator.argument_parser.add_argument( + '--site_api_path', + type=str, default='api_docs/python', + help='The path from the site-root to api_docs' + 'directory for this project') # This doc generator works on the TensorFlow codebase. Since this script lives # at tensorflow/tools/docs, and all code is defined somewhere inside diff --git a/tensorflow/tools/docs/generate_lib.py b/tensorflow/tools/docs/generate_lib.py index e7634cd5dc..4f70a69364 100644 --- a/tensorflow/tools/docs/generate_lib.py +++ b/tensorflow/tools/docs/generate_lib.py @@ -55,7 +55,8 @@ def write_docs(output_dir, parser_config, yaml_toc, root_title='TensorFlow', - search_hints=True): + search_hints=True, + site_api_path=None): """Write previously extracted docs to disk. Write a docs page for each symbol included in the indices of parser_config to @@ -73,6 +74,8 @@ def write_docs(output_dir, root_title: The title name for the root level index.md. search_hints: (bool) include meta-data search hints at the top of each output file. + site_api_path: Used to write the api-duplicates _redirects.yaml file. if + None (the default) the file is not generated. Raises: ValueError: if `output_dir` is not an absolute path @@ -92,6 +95,9 @@ def write_docs(output_dir, # - symbol name(string):pathname (string) symbol_to_file = {} + # Collect redirects for an api _redirects.yaml file. + redirects = ['redirects:\n'] + # Parse and write Markdown pages, resolving cross-links (@{symbol}). for full_name, py_object in six.iteritems(parser_config.index): parser_config.reference_resolver.current_doc_full_name = full_name @@ -150,6 +156,25 @@ def write_docs(output_dir, raise OSError( 'Cannot write documentation for %s to %s' % (full_name, directory)) + if site_api_path: + duplicates = parser_config.duplicates.get(full_name, []) + if not duplicates: + continue + + duplicates = [item for item in duplicates if item != full_name] + template = ('- from: /{}\n' + ' to: /{}\n') + for dup in duplicates: + from_path = os.path.join(site_api_path, dup.replace('.', '/')) + to_path = os.path.join(site_api_path, full_name.replace('.', '/')) + redirects.append( + template.format(from_path, to_path)) + + if site_api_path: + api_redirects_path = os.path.join(output_dir, '_redirects.yaml') + with open(api_redirects_path, 'w') as redirect_file: + redirect_file.write(''.join(redirects)) + if yaml_toc: # Generate table of contents @@ -608,7 +633,8 @@ class DocGenerator(object): parser_config, yaml_toc=self.yaml_toc, root_title=root_title, - search_hints=getattr(flags, 'search_hints', True)) + search_hints=getattr(flags, 'search_hints', True), + site_api_path=getattr(flags, 'site_api_path', None)) # Replace all the @{} references in files under `FLAGS.src_dir` replace_refs(flags.src_dir, flags.output_dir, reference_resolver, '*.md') diff --git a/tensorflow/tools/docs/generate_lib_test.py b/tensorflow/tools/docs/generate_lib_test.py index 7a6f9fd9f7..de18b13254 100644 --- a/tensorflow/tools/docs/generate_lib_test.py +++ b/tensorflow/tools/docs/generate_lib_test.py @@ -107,7 +107,18 @@ class GenerateTest(googletest.TestCase): output_dir = googletest.GetTempDir() - generate_lib.write_docs(output_dir, parser_config, yaml_toc=True) + generate_lib.write_docs(output_dir, parser_config, yaml_toc=True, + site_api_path='api_docs/python') + + # Check redirects + redirects_file = os.path.join(output_dir, '_redirects.yaml') + self.assertTrue(os.path.exists(redirects_file)) + with open(redirects_file) as f: + redirects = f.read() + self.assertEqual(redirects.split(), [ + 'redirects:', '-', 'from:', '/api_docs/python/tf/test_function', 'to:', + '/api_docs/python/tf/TestModule/test_function' + ]) # Make sure that the right files are written to disk. self.assertTrue(os.path.exists(os.path.join(output_dir, 'index.md'))) -- GitLab From cb5f7b3f72bd113ffa8a5179ce289e3ec90fb908 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Thu, 19 Jul 2018 12:06:19 -0700 Subject: [PATCH 151/519] Fix python formatting issues. --- tensorflow/contrib/tensorrt/BUILD | 8 +++--- .../tensorrt/test/batch_matmul_test.py | 10 +++---- .../tensorrt/test/biasadd_matmul_test.py | 4 +-- .../binary_tensor_weight_broadcast_test.py | 28 +++++++------------ .../tensorrt/test/concatenation_test.py | 4 +-- .../tensorrt/test/const_broadcast_test.py | 13 ++++----- .../multi_connection_neighbor_engine_test.py | 20 ++++--------- .../tensorrt/test/neighboring_engine_test.py | 12 +++----- .../test/tf_trt_integration_test_base.py | 3 +- .../contrib/tensorrt/test/unary_test.py | 5 ++-- .../tensorrt/test/vgg_block_nchw_test.py | 7 ++--- .../contrib/tensorrt/test/vgg_block_test.py | 7 ++--- 12 files changed, 47 insertions(+), 74 deletions(-) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index fa47f51b66..dea9c0a4ae 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -337,16 +337,16 @@ cuda_py_tests( name = "tf_trt_integration_test", srcs = [ "test/base_test.py", - #"test/batch_matmul_test.py", - #"test/biasadd_matmul_test.py", + # "test/batch_matmul_test.py", + # "test/biasadd_matmul_test.py", "test/binary_tensor_weight_broadcast_test.py", "test/concatenation_test.py", "test/const_broadcast_test.py", "test/multi_connection_neighbor_engine_test.py", "test/neighboring_engine_test.py", "test/unary_test.py", - #"test/vgg_block_nchw_test.py", - #"test/vgg_block_test.py", + # "test/vgg_block_nchw_test.py", + # "test/vgg_block_test.py", ], additional_deps = [ ":tf_trt_integration_test_base", diff --git a/tensorflow/contrib/tensorrt/test/batch_matmul_test.py b/tensorflow/contrib/tensorrt/test/batch_matmul_test.py index 163af54184..e47daae3ee 100644 --- a/tensorflow/contrib/tensorrt/test/batch_matmul_test.py +++ b/tensorflow/contrib/tensorrt/test/batch_matmul_test.py @@ -45,13 +45,10 @@ class BatchMatMulTest(trt_test.TfTrtIntegrationTestBase): with g.as_default(): inp = array_ops.placeholder( dtype=dtype, shape=[None] + input_dims[1:], name=input_name) - w1 = array_ops.placeholder( - dtype=dtype, shape=w1_dims, name=w1_name) - w2 = array_ops.placeholder( - dtype=dtype, shape=w2_dims, name=w2_name) + w1 = array_ops.placeholder(dtype=dtype, shape=w1_dims, name=w1_name) + w2 = array_ops.placeholder(dtype=dtype, shape=w2_dims, name=w2_name) with g.device("/GPU:0"): - b = constant_op.constant( - np.random.randn(12, 5, 12, 7), dtype=dtype) + b = constant_op.constant(np.random.randn(12, 5, 12, 7), dtype=dtype) c = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype) d = constant_op.constant(np.random.randn(5, 1, 1), dtype=dtype) x1 = math_ops.matmul(inp, b) @@ -74,5 +71,6 @@ class BatchMatMulTest(trt_test.TfTrtIntegrationTestBase): allclose_atol=1.e-03, allclose_rtol=1.e-03) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py b/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py index 9b153ada05..2de99c5d5c 100644 --- a/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py +++ b/tensorflow/contrib/tensorrt/test/biasadd_matmul_test.py @@ -40,8 +40,7 @@ class BiasaddMatMulTest(trt_test.TfTrtIntegrationTestBase): input_dims = [48, 12] g = ops.Graph() with g.as_default(): - x = array_ops.placeholder( - dtype=dtype, shape=input_dims, name=input_name) + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) b = constant_op.constant(np.random.randn(12, 4), dtype=dtype) x1 = math_ops.matmul(x, b) @@ -108,5 +107,6 @@ class BiasaddMatMulTest(trt_test.TfTrtIntegrationTestBase): allclose_atol=1.e-03, allclose_rtol=1.e-03) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py b/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py index e80712731d..0a3e00afb0 100644 --- a/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py +++ b/tensorflow/contrib/tensorrt/test/binary_tensor_weight_broadcast_test.py @@ -39,8 +39,7 @@ class BinaryTensorWeightBroadcastTest(trt_test.TfTrtIntegrationTestBase): input_dims = [10, 24, 24, 20] g = ops.Graph() with g.as_default(): - x = array_ops.placeholder( - dtype=dtype, shape=input_dims, name=input_name) + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) # scale a = constant_op.constant(np.random.randn(1), dtype=dtype) f = x + a @@ -58,13 +57,11 @@ class BinaryTensorWeightBroadcastTest(trt_test.TfTrtIntegrationTestBase): f = a + x x = math_ops.sigmoid(f) # scale - a = constant_op.constant( - np.random.randn(24, 24, 20), dtype=dtype) + a = constant_op.constant(np.random.randn(24, 24, 20), dtype=dtype) f = a + x x = math_ops.sigmoid(f) # scale - a = constant_op.constant( - np.random.randn(24, 24, 20), dtype=dtype) + a = constant_op.constant(np.random.randn(24, 24, 20), dtype=dtype) f = x + a x = math_ops.sigmoid(f) # elementwise @@ -76,33 +73,27 @@ class BinaryTensorWeightBroadcastTest(trt_test.TfTrtIntegrationTestBase): f = a + x x = math_ops.sigmoid(f) # elementwise - a = constant_op.constant( - np.random.randn(1, 24, 1, 1), dtype=dtype) + a = constant_op.constant(np.random.randn(1, 24, 1, 1), dtype=dtype) f = a + x x = math_ops.sigmoid(f) # elementwise - a = constant_op.constant( - np.random.randn(1, 24, 1, 1), dtype=dtype) + a = constant_op.constant(np.random.randn(1, 24, 1, 1), dtype=dtype) f = x + a x = math_ops.sigmoid(f) # elementwise - a = constant_op.constant( - np.random.randn(1, 24, 24, 1), dtype=dtype) + a = constant_op.constant(np.random.randn(1, 24, 24, 1), dtype=dtype) f = a + x x = math_ops.sigmoid(f) # elementwise - a = constant_op.constant( - np.random.randn(1, 24, 24, 1), dtype=dtype) + a = constant_op.constant(np.random.randn(1, 24, 24, 1), dtype=dtype) f = x + a x = math_ops.sigmoid(f) # elementwise - a = constant_op.constant( - np.random.randn(1, 24, 24, 20), dtype=dtype) + a = constant_op.constant(np.random.randn(1, 24, 24, 20), dtype=dtype) f = a + x x = math_ops.sigmoid(f) # elementwise - a = constant_op.constant( - np.random.randn(1, 24, 24, 20), dtype=dtype) + a = constant_op.constant(np.random.randn(1, 24, 24, 20), dtype=dtype) f = x + a x = math_ops.sigmoid(f) # elementwise @@ -123,5 +114,6 @@ class BinaryTensorWeightBroadcastTest(trt_test.TfTrtIntegrationTestBase): allclose_atol=1.e-03, allclose_rtol=1.e-03) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/tensorrt/test/concatenation_test.py b/tensorflow/contrib/tensorrt/test/concatenation_test.py index cf0bfeeb00..222167f8d5 100644 --- a/tensorflow/contrib/tensorrt/test/concatenation_test.py +++ b/tensorflow/contrib/tensorrt/test/concatenation_test.py @@ -39,8 +39,7 @@ class ConcatenationTest(trt_test.TfTrtIntegrationTestBase): input_dims = [2, 3, 3, 1] g = ops.Graph() with g.as_default(): - x = array_ops.placeholder( - dtype=dtype, shape=input_dims, name=input_name) + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) # scale a = constant_op.constant(np.random.randn(3, 1, 1), dtype=dtype) r1 = x / a @@ -79,5 +78,6 @@ class ConcatenationTest(trt_test.TfTrtIntegrationTestBase): allclose_atol=1.e-03, allclose_rtol=1.e-03) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/tensorrt/test/const_broadcast_test.py b/tensorflow/contrib/tensorrt/test/const_broadcast_test.py index 97f5580ac0..6e5f546fc7 100644 --- a/tensorflow/contrib/tensorrt/test/const_broadcast_test.py +++ b/tensorflow/contrib/tensorrt/test/const_broadcast_test.py @@ -34,21 +34,17 @@ class ConstBroadcastTest(trt_test.TfTrtIntegrationTestBase): def GetParams(self): """unit test for Constant broadcasting in TF-TRT""" dtype = dtypes.float32 - input_name = "input" + input_name = 'input' input_dims = [5, 12, 12, 2] g = ops.Graph() with g.as_default(): - x = array_ops.placeholder( - dtype=dtype, shape=input_dims, name=input_name) + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) filt1 = constant_op.constant( 0.3, shape=(3, 3, 2, 1), dtype=dtype, name='filt1') y1 = nn.conv2d(x, filt1, strides=[1, 1, 1, 1], padding='SAME', name='y1') z1 = nn.relu(y1, name='z1') filt2 = constant_op.constant( - np.random.randn(9), - shape=(3, 3, 1, 1), - dtype=dtype, - name='filt2') + np.random.randn(9), shape=(3, 3, 1, 1), dtype=dtype, name='filt2') y2 = nn.conv2d(z1, filt2, strides=[1, 1, 1, 1], padding='SAME', name='y2') z2 = nn.relu(y2, name='z') filt3 = constant_op.constant( @@ -67,5 +63,6 @@ class ConstBroadcastTest(trt_test.TfTrtIntegrationTestBase): allclose_atol=1.e-02, allclose_rtol=1.e-02) -if __name__ == "__main__": + +if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py b/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py index e62f9e479e..7fa798fb45 100644 --- a/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py +++ b/tensorflow/contrib/tensorrt/test/multi_connection_neighbor_engine_test.py @@ -40,8 +40,7 @@ class MultiConnectionNeighborEngineTest(trt_test.TfTrtIntegrationTestBase): input_dims = [2, 3, 7, 5] g = ops.Graph() with g.as_default(): - x = array_ops.placeholder( - dtype=dtype, shape=input_dims, name=input_name) + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) e = constant_op.constant( np.random.normal(.05, .005, [3, 2, 3, 4]), name="weights", @@ -54,29 +53,21 @@ class MultiConnectionNeighborEngineTest(trt_test.TfTrtIntegrationTestBase): padding="VALID", name="conv") b = constant_op.constant( - np.random.normal(2.0, 1.0, [1, 4, 1, 1]), - name="bias", - dtype=dtype) + np.random.normal(2.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype) t = conv + b b = constant_op.constant( - np.random.normal(5.0, 1.0, [1, 4, 1, 1]), - name="bias", - dtype=dtype) + np.random.normal(5.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype) q = conv - b edge = math_ops.sigmoid(q) b = constant_op.constant( - np.random.normal(5.0, 1.0, [1, 4, 1, 1]), - name="bias", - dtype=dtype) + np.random.normal(5.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype) d = b + conv edge3 = math_ops.sigmoid(d) c = constant_op.constant( - np.random.normal(1.0, 1.0, [1, 4, 1, 1]), - name="bias", - dtype=dtype) + np.random.normal(1.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype) edge1 = gen_math_ops.tan(conv) t = t - edge1 q = q + edge @@ -93,5 +84,6 @@ class MultiConnectionNeighborEngineTest(trt_test.TfTrtIntegrationTestBase): allclose_atol=1.e-03, allclose_rtol=1.e-03) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py b/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py index bbe8823552..439af81664 100644 --- a/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py +++ b/tensorflow/contrib/tensorrt/test/neighboring_engine_test.py @@ -39,12 +39,9 @@ class NeighboringEngineTest(trt_test.TfTrtIntegrationTestBase): input_dims = [2, 3, 7, 5] g = ops.Graph() with g.as_default(): - x = array_ops.placeholder( - dtype=dtype, shape=input_dims, name=input_name) + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) e = constant_op.constant( - np.random.normal(.3, 0.05, [3, 2, 3, 4]), - name="weights", - dtype=dtype) + np.random.normal(.3, 0.05, [3, 2, 3, 4]), name="weights", dtype=dtype) conv = nn.conv2d( input=x, filter=e, @@ -53,9 +50,7 @@ class NeighboringEngineTest(trt_test.TfTrtIntegrationTestBase): padding="VALID", name="conv") b = constant_op.constant( - np.random.normal(1.0, 1.0, [1, 4, 1, 1]), - name="bias", - dtype=dtype) + np.random.normal(1.0, 1.0, [1, 4, 1, 1]), name="bias", dtype=dtype) t = conv * b e = gen_math_ops.tan(conv) t = t - e @@ -69,5 +64,6 @@ class NeighboringEngineTest(trt_test.TfTrtIntegrationTestBase): allclose_atol=1.e-03, allclose_rtol=1.e-03) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py index 48890ad413..6e12e7e026 100644 --- a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py +++ b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py @@ -169,7 +169,8 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): num_engines += 1 self.assertNotEqual(self._ToBytes(""), n.attr["serialized_segment"].s) self.assertNotEqual(self._ToBytes(""), n.attr["segment_funcdef_name"].s) - self.assertEqual(self._ToBytes(precision_mode), n.attr["precision_mode"].s) + self.assertEqual( + self._ToBytes(precision_mode), n.attr["precision_mode"].s) self.assertEqual(not dynamic_engine, n.attr["static_engine"].b) if _IsQuantizationMode(precision_mode) and is_calibrated: self.assertNotEqual(self._ToBytes(""), n.attr["calibration_data"].s) diff --git a/tensorflow/contrib/tensorrt/test/unary_test.py b/tensorflow/contrib/tensorrt/test/unary_test.py index 4c10c50e85..7395c4a311 100644 --- a/tensorflow/contrib/tensorrt/test/unary_test.py +++ b/tensorflow/contrib/tensorrt/test/unary_test.py @@ -32,7 +32,6 @@ from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt class UnaryTest(trt_test.TfTrtIntegrationTestBase): - def GetParams(self): """unit test for unary operations in TF-TRT""" @@ -43,8 +42,7 @@ class UnaryTest(trt_test.TfTrtIntegrationTestBase): input2_dims = [12, 5, 8, 1, 12, 1, 1] g = ops.Graph() with g.as_default(): - x = array_ops.placeholder( - dtype=dtype, shape=input_dims, name=input_name) + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) q = math_ops.abs(x) q = q + 1.0 q = gen_math_ops.exp(q) @@ -107,5 +105,6 @@ class UnaryTest(trt_test.TfTrtIntegrationTestBase): allclose_atol=1.e-03, allclose_rtol=1.e-03) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py b/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py index 3621c13bc9..0e28afeaf4 100644 --- a/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py +++ b/tensorflow/contrib/tensorrt/test/vgg_block_nchw_test.py @@ -40,8 +40,7 @@ class VGGBlockNCHWTest(trt_test.TfTrtIntegrationTestBase): input_dims = [5, 2, 8, 8] g = ops.Graph() with g.as_default(): - x = array_ops.placeholder( - dtype=dtype, shape=input_dims, name=input_name) + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) x, mean_x, var_x = nn_impl.fused_batch_norm( x, np.random.randn(2).astype(np.float32), @@ -59,8 +58,7 @@ class VGGBlockNCHWTest(trt_test.TfTrtIntegrationTestBase): strides=[1, 1, 2, 2], padding="SAME", name="conv") - b = constant_op.constant( - np.random.randn(6), name="bias", dtype=dtype) + b = constant_op.constant(np.random.randn(6), name="bias", dtype=dtype) t = nn.bias_add(conv, b, data_format="NCHW", name="biasAdd") relu = nn.relu(t, "relu") idty = array_ops.identity(relu, "ID") @@ -79,5 +77,6 @@ class VGGBlockNCHWTest(trt_test.TfTrtIntegrationTestBase): allclose_atol=1.e-03, allclose_rtol=1.e-03) + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/tensorrt/test/vgg_block_test.py b/tensorflow/contrib/tensorrt/test/vgg_block_test.py index 1ef32fe52f..f2aacddd87 100644 --- a/tensorflow/contrib/tensorrt/test/vgg_block_test.py +++ b/tensorflow/contrib/tensorrt/test/vgg_block_test.py @@ -40,8 +40,7 @@ class VGGBlockTest(trt_test.TfTrtIntegrationTestBase): input_dims = [5, 8, 8, 2] g = ops.Graph() with g.as_default(): - x = array_ops.placeholder( - dtype=dtype, shape=input_dims, name=input_name) + x = array_ops.placeholder(dtype=dtype, shape=input_dims, name=input_name) x, mean_x, var_x = nn_impl.fused_batch_norm( x, np.random.randn(2).astype(np.float32), @@ -53,8 +52,7 @@ class VGGBlockTest(trt_test.TfTrtIntegrationTestBase): np.random.randn(1, 1, 2, 6), name="weights", dtype=dtype) conv = nn.conv2d( input=x, filter=e, strides=[1, 2, 2, 1], padding="SAME", name="conv") - b = constant_op.constant( - np.random.randn(6), name="bias", dtype=dtype) + b = constant_op.constant(np.random.randn(6), name="bias", dtype=dtype) t = nn.bias_add(conv, b, name="biasAdd") relu = nn.relu(t, "relu") idty = array_ops.identity(relu, "ID") @@ -70,5 +68,6 @@ class VGGBlockTest(trt_test.TfTrtIntegrationTestBase): allclose_atol=1.e-03, allclose_rtol=1.e-03) + if __name__ == "__main__": test.main() -- GitLab From f330a1c8925a4a33bd0ea451656cfd80772979c3 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Thu, 19 Jul 2018 12:11:20 -0700 Subject: [PATCH 152/519] Intern predicate pointers This is a performance optimization. PiperOrigin-RevId: 205280010 --- tensorflow/compiler/jit/deadness_analysis.cc | 158 +++++++++++-------- 1 file changed, 89 insertions(+), 69 deletions(-) diff --git a/tensorflow/compiler/jit/deadness_analysis.cc b/tensorflow/compiler/jit/deadness_analysis.cc index b2d119029a..d81e5fe900 100644 --- a/tensorflow/compiler/jit/deadness_analysis.cc +++ b/tensorflow/compiler/jit/deadness_analysis.cc @@ -44,10 +44,6 @@ class Predicate { enum class Kind { kAnd, kOr, kNot, kSymbol }; virtual string ToString() const = 0; - virtual bool operator==(const Predicate& other) const = 0; - virtual bool operator!=(const Predicate& other) const { - return !(*this == other); - } int64 hash() const { return hash_; } virtual Kind kind() const = 0; @@ -58,6 +54,8 @@ class Predicate { private: const int64 hash_; + + TF_DISALLOW_COPY_AND_ASSIGN(Predicate); }; int64 HashPredicateSequence(Predicate::Kind kind, @@ -69,19 +67,6 @@ int64 HashPredicateSequence(Predicate::Kind kind, return hash; } -bool PredicateSequenceEqual(gtl::ArraySlice lhs, - gtl::ArraySlice rhs) { - if (lhs.size() != rhs.size()) { - return false; - } - for (int64 i = 0; i < lhs.size(); i++) { - if (*lhs[i] != *rhs[i]) { - return false; - } - } - return true; -} - // Represents a logical conjunction of a set of predicates. class AndPredicate : public Predicate { public: @@ -102,17 +87,9 @@ class AndPredicate : public Predicate { return strings::StrCat("(", str_util::Join(operands_str, " & "), ")"); } - bool operator==(const Predicate& other) const override { - return other.kind() == Kind::kAnd && - PredicateSequenceEqual( - dynamic_cast(other).operands(), operands()); - } - Kind kind() const override { return Kind::kAnd; } - const tensorflow::gtl::ArraySlice operands() const { - return operands_; - } + const gtl::ArraySlice operands() const { return operands_; } private: std::vector operands_; @@ -138,16 +115,8 @@ class OrPredicate : public Predicate { return strings::StrCat("(", str_util::Join(operands_str, " | "), ")"); } - bool operator==(const Predicate& other) const override { - return other.kind() == Kind::kOr && - PredicateSequenceEqual( - dynamic_cast(other).operands(), operands()); - } - Kind kind() const override { return Kind::kOr; } - const tensorflow::gtl::ArraySlice operands() const { - return operands_; - } + const gtl::ArraySlice operands() const { return operands_; } private: std::vector operands_; @@ -164,11 +133,6 @@ class NotPredicate : public Predicate { return strings::StrCat("~", operand()->ToString()); } - bool operator==(const Predicate& other) const override { - return other.kind() == Kind::kNot && - *dynamic_cast(other).operand() == *operand(); - } - Kind kind() const override { return Kind::kNot; } Predicate* operand() const { return operand_; } @@ -188,14 +152,6 @@ class SymbolPredicate : public Predicate { must_be_true_(must_be_true) {} string ToString() const override { return tensor_id_.ToString(); } - bool operator==(const Predicate& other) const override { - return other.kind() == Kind::kSymbol && - must_be_true() == - dynamic_cast(other).must_be_true() && - dynamic_cast(other).tensor_id() == - tensor_id(); - } - Kind kind() const override { return Kind::kSymbol; } // If `must_be_true()` is true this SymbolPredicate represents the proposition @@ -225,16 +181,37 @@ class PredicateFactory { Predicate* MakeAndPredicate(gtl::ArraySlice operands) { return MakeAndOrImpl(operands, /*is_and=*/true); } + Predicate* MakeOrPredicate(gtl::ArraySlice operands) { return MakeAndOrImpl(operands, /*is_and=*/false); } Predicate* MakeNotPredicate(Predicate* pred) { - return Make(pred); + SignatureForNot signature = pred; + auto it = interned_not_instances_.find(signature); + if (it == interned_not_instances_.end()) { + std::unique_ptr new_pred = Make(pred); + Predicate* new_pred_ptr = new_pred.get(); + interned_not_instances_.emplace(signature, std::move(new_pred)); + return new_pred_ptr; + } else { + return it->second.get(); + } } Predicate* MakeSymbolPredicate(TensorId tensor_id, bool must_be_true) { - return Make(tensor_id, must_be_true); + SignatureForSymbol signature = {tensor_id, must_be_true}; + auto it = interned_symbol_instances_.find(signature); + if (it == interned_symbol_instances_.end()) { + std::unique_ptr new_pred = + Make(tensor_id, must_be_true); + Predicate* new_pred_ptr = new_pred.get(); + interned_symbol_instances_.emplace(std::move(signature), + std::move(new_pred)); + return new_pred_ptr; + } else { + return it->second.get(); + } } Predicate* MakeTrue() { return MakeAndPredicate({}); } @@ -242,29 +219,53 @@ class PredicateFactory { private: template - Predicate* Make(Args... args) { - std::unique_ptr pred( + std::unique_ptr Make(Args&&... args) { + return std::unique_ptr( new PredicateT(std::forward(args)...)); - predicate_storage_.emplace_back(std::move(pred)); - return predicate_storage_.back().get(); } Predicate* MakeAndOrImpl(gtl::ArraySlice operands, bool is_and); - struct PredicatePtrHash { - size_t operator()(const Predicate* pred) const { return pred->hash(); } + // Predicate instances are interned, meaning that there is only a single + // instance of a Predicate object with a given content. This makes checking + // for structural equality super-cheap -- we can just compare pointers. + // + // We intern predicates by maintaining a map from the content of a Predicate + // to the only instance of said predicate we allow to exist in the + // interned_and_or_instances_, interned_not_instances_ and + // interned_symbol_instances_ fields. These maps also double up as storage + // for the owning pointers to predicate instances. + + using SignatureForAndOr = + std::pair>; + using SignatureForNot = Predicate*; + using SignatureForSymbol = std::pair; + + struct HashSignatureForAndOr { + size_t operator()(const SignatureForAndOr& signature) const { + size_t hash = ::tensorflow::hash()(signature.first); + for (Predicate* p : signature.second) { + hash = Hash64Combine(hash, ::tensorflow::hash()(p)); + } + return hash; + } }; - struct PredicatePtrEq { - size_t operator()(const Predicate* a, const Predicate* b) const { - return *a == *b; + struct HashSignatureForSymbol { + size_t operator()(const SignatureForSymbol& signature) const { + return Hash64Combine(SafeTensorId::Hasher()(signature.first), + ::tensorflow::hash()(signature.second)); } }; - using PredicateSet = - gtl::FlatSet; - - std::vector> predicate_storage_; + gtl::FlatMap, + HashSignatureForAndOr> + interned_and_or_instances_; + gtl::FlatMap> + interned_not_instances_; + gtl::FlatMap, + HashSignatureForSymbol> + interned_symbol_instances_; }; // Common code to create AndPredicate or OrPredicate instances. @@ -272,7 +273,7 @@ Predicate* PredicateFactory::MakeAndOrImpl(gtl::ArraySlice operands, bool is_and) { Predicate::Kind pred_kind = is_and ? Predicate::Kind::kAnd : Predicate::Kind::kOr; - PredicateSet simplified_ops_set; + gtl::FlatSet simplified_ops_set; std::vector simplified_ops; for (Predicate* op : operands) { // Simplify A&A => A and A|A => A. @@ -300,7 +301,7 @@ Predicate* PredicateFactory::MakeAndOrImpl(gtl::ArraySlice operands, } // Simplify "A&~A=>False" and "A|~A=>True". - PredicateSet negated_ops; + gtl::FlatSet negated_ops; for (Predicate* op : simplified_ops) { if (op->kind() == Predicate::Kind::kNot) { negated_ops.insert(dynamic_cast(*op).operand()); @@ -317,8 +318,26 @@ Predicate* PredicateFactory::MakeAndOrImpl(gtl::ArraySlice operands, simplified_ops.begin(), simplified_ops.end(), [](Predicate* a, Predicate* b) { return a->hash() < b->hash(); }); - return is_and ? Make(std::move(simplified_ops)) - : Make(std::move(simplified_ops)); + auto it = interned_and_or_instances_.find({pred_kind, simplified_ops}); + if (it == interned_and_or_instances_.end()) { + simplified_ops.shrink_to_fit(); + // NB! Because we'll use a non-owning reference to simplified_ops in the + // key for interned_and_or_instances_ we need to be careful to std::move() + // it all the way through. + gtl::ArraySlice operands_slice = simplified_ops; + std::unique_ptr new_pred = + is_and ? Make(std::move(simplified_ops)) + : Make(std::move(simplified_ops)); + + Predicate* new_pred_ptr = new_pred.get(); + CHECK(interned_and_or_instances_ + .emplace(SignatureForAndOr(pred_kind, operands_slice), + std::move(new_pred)) + .second); + return new_pred_ptr; + } else { + return it->second.get(); + } } class DeadnessAnalysisImpl : public DeadnessAnalysis { @@ -491,8 +510,9 @@ bool DeadnessAnalysisImpl::HasInputsWithMismatchingDeadness(const Node& node) { // Today we just compare the predicates for equality (with some // canonicalization/simplification happening before) but we could be more - // sophisticated here if need be. - if (pred != nullptr && *pred != *it->second) { + // sophisticated here if need be. Comparing pointers is sufficient because + // we intern Predicate instances by their content. + if (pred != nullptr && pred != it->second) { if (vlog_) { VLOG(2) << "HasInputsWithMismatchingDeadness(" << node.name() << ") -> true"; -- GitLab From bbe9364b22ce78f0ab4ec03895bd7178802ef893 Mon Sep 17 00:00:00 2001 From: Michael Case Date: Thu, 19 Jul 2018 12:16:26 -0700 Subject: [PATCH 153/519] Internal Change. PiperOrigin-RevId: 205280699 --- tensorflow/contrib/estimator/BUILD | 204 ++++------------------------- tensorflow/python/estimator/BUILD | 8 +- 2 files changed, 29 insertions(+), 183 deletions(-) diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD index 11d40f5982..1aa3df8d8d 100644 --- a/tensorflow/contrib/estimator/BUILD +++ b/tensorflow/contrib/estimator/BUILD @@ -28,7 +28,7 @@ py_library( ":multi_head", ":replicate_model_fn", ":rnn", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -54,22 +54,10 @@ py_test( deps = [ ":baseline", ":head", - "//tensorflow/python:check_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform", - "//tensorflow/python:session", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variables", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:export_export", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:numpy_io", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", "//third_party/py/numpy", "@six_archive//:six", ], @@ -96,11 +84,8 @@ py_test( ], deps = [ ":boosted_trees", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:numpy_io", - "//tensorflow/python/feature_column", "//third_party/py/numpy", ], ) @@ -110,7 +95,7 @@ py_library( srcs = ["python/estimator/dnn.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:nn", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", "//tensorflow/python/estimator:dnn", ], @@ -129,16 +114,11 @@ py_test( deps = [ ":dnn", ":head", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:platform", - "//tensorflow/python:summary", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:dnn_testing_utils", "//tensorflow/python/estimator:export_export", "//tensorflow/python/estimator:numpy_io", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", "//third_party/py/numpy", "@six_archive//:six", ], @@ -149,7 +129,7 @@ py_library( srcs = ["python/estimator/dnn_linear_combined.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:nn", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", "//tensorflow/python/estimator:dnn_linear_combined", ], @@ -168,18 +148,12 @@ py_test( deps = [ ":dnn_linear_combined", ":head", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:nn", - "//tensorflow/python:platform", - "//tensorflow/python:summary", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:dnn_testing_utils", "//tensorflow/python/estimator:export_export", "//tensorflow/python/estimator:linear_testing_utils", "//tensorflow/python/estimator:numpy_io", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", "//third_party/py/numpy", "@six_archive//:six", ], @@ -192,10 +166,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:clip_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:util", @@ -211,18 +182,11 @@ py_test( tags = ["notsan"], # b/62863147 deps = [ ":extenders", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/contrib/data/python/ops:dataset_ops", "//tensorflow/contrib/predictor", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:framework_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:training", - "//tensorflow/python:variables", "//tensorflow/python/estimator:estimator_py", "//tensorflow/python/estimator:linear", - "//tensorflow/python/feature_column", "//third_party/py/numpy", ], ) @@ -246,21 +210,11 @@ py_test( tags = ["notsan"], # b/62863147 deps = [ ":export", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:metrics", - "//tensorflow/python:parsing_ops", - "//tensorflow/python:session", - "//tensorflow/python:state_ops", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python:variables", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", "//tensorflow/python/estimator:export_export", "//tensorflow/python/estimator:export_output", "//tensorflow/python/estimator:model_fn", - "//tensorflow/python/saved_model:loader", - "//tensorflow/python/saved_model:tag_constants", ], ) @@ -271,25 +225,12 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//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", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:export_output", "//tensorflow/python/estimator:head", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:signature_constants", ], ) @@ -300,25 +241,10 @@ py_test( srcs_version = "PY2AND3", deps = [ ":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", - "//tensorflow/python:dtypes", - "//tensorflow/python:errors", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:string_ops", - "//tensorflow/python:training", - "//tensorflow/python:variables", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:signature_constants", "//third_party/py/numpy", "@six_archive//:six", ], @@ -331,8 +257,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:framework_ops", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:estimator_py", ], ) @@ -345,10 +270,7 @@ py_test( tags = ["notsan"], deps = [ ":hooks", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:training", - "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:estimator_py", "//third_party/py/numpy", "@six_archive//:six", @@ -377,16 +299,11 @@ py_test( deps = [ ":head", ":linear", - "//tensorflow/python:client_testlib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:platform", - "//tensorflow/python:summary", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:export_export", "//tensorflow/python/estimator:linear_testing_utils", "//tensorflow/python/estimator:numpy_io", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", "//third_party/py/numpy", "@six_archive//:six", ], @@ -399,8 +316,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:framework_ops", - "//tensorflow/python:util", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:dnn", "//tensorflow/python/estimator:linear", ], @@ -413,9 +329,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":logit_fns", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:session", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:model_fn", ], ) @@ -427,18 +341,11 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:array_ops", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:summary", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:export_output", "//tensorflow/python/estimator:head", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:model_fn", - "//tensorflow/python/saved_model:signature_constants", "@six_archive//:six", ], ) @@ -451,15 +358,10 @@ py_test( deps = [ ":head", ":multi_head", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:client_testlib", - "//tensorflow/python:constant_op", - "//tensorflow/python:framework_ops", - "//tensorflow/python:string_ops", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:metric_keys", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/saved_model:signature_constants", "//third_party/py/numpy", "@six_archive//:six", ], @@ -472,24 +374,10 @@ py_library( ], srcs_version = "PY2AND3", deps = [ - "//tensorflow/core:protos_all_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:device", - "//tensorflow/python:device_lib", - "//tensorflow/python:framework_ops", - "//tensorflow/python:math_ops", - "//tensorflow/python:platform", - "//tensorflow/python:sparse_ops", - "//tensorflow/python:sparse_tensor", - "//tensorflow/python:state_ops", - "//tensorflow/python:training", - "//tensorflow/python:util", - "//tensorflow/python:variable_scope", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator:export_output", "//tensorflow/python/estimator:model_fn", "//tensorflow/python/estimator:util", - "//tensorflow/python/ops/losses", "@six_archive//:six", ], ) @@ -500,6 +388,7 @@ cuda_py_test( srcs = ["python/estimator/replicate_model_fn_test.py"], additional_deps = [ "@absl_py//absl/testing:parameterized", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", "//tensorflow/python/estimator:dnn", "//tensorflow/python/estimator:export_export", @@ -508,21 +397,6 @@ cuda_py_test( "//tensorflow/python/estimator:numpy_io", "//tensorflow/python/estimator:optimizers", "//tensorflow/python/estimator:prediction_keys", - "//tensorflow/python/feature_column", - "//tensorflow/python/ops/losses", - "//tensorflow/python/saved_model:signature_constants", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework_for_generated_wrappers", - "//tensorflow/python:framework_test_lib", - "//tensorflow/python:math_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:platform", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", - "//tensorflow/python:variables", ":replicate_model_fn", ], tags = [ @@ -538,22 +412,11 @@ py_library( srcs_version = "PY2AND3", deps = [ ":extenders", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/contrib/feature_column:feature_column_py", - "//tensorflow/python:array_ops", - "//tensorflow/python:check_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:init_ops", - "//tensorflow/python:layers", - "//tensorflow/python:partitioned_variables", - "//tensorflow/python:rnn", - "//tensorflow/python:rnn_cell", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variable_scope", "//tensorflow/python/estimator", "//tensorflow/python/estimator:head", "//tensorflow/python/estimator:optimizers", - "//tensorflow/python/feature_column", "@six_archive//:six", ], ) @@ -572,21 +435,10 @@ py_test( deps = [ ":head", ":rnn", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/contrib/data", - "//tensorflow/core:protos_all_py", - "//tensorflow/python:check_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:lib", - "//tensorflow/python:math_ops", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", - "//tensorflow/python:variables", "//tensorflow/python/estimator:numpy_io", "//tensorflow/python/estimator:parsing_utils", - "//tensorflow/python/feature_column", "//third_party/py/numpy", "@six_archive//:six", ], @@ -597,13 +449,7 @@ py_library( srcs = ["python/estimator/early_stopping.py"], srcs_version = "PY2AND3", deps = [ - "//tensorflow/python:dtypes", - "//tensorflow/python:framework_ops", - "//tensorflow/python:init_ops", - "//tensorflow/python:platform", - "//tensorflow/python:state_ops", - "//tensorflow/python:summary", - "//tensorflow/python:training", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", ], ) @@ -614,7 +460,7 @@ py_test( srcs_version = "PY2AND3", deps = [ ":early_stopping", - "//tensorflow/python:client_testlib", + "//tensorflow:tensorflow_py_no_contrib", "//tensorflow/python/estimator", "@absl_py//absl/testing:parameterized", ], diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index 6c415b1bf2..fd46163050 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -40,9 +40,9 @@ py_library( srcs_version = "PY2AND3", deps = [ ":gc", + ":metric_keys", + ":util", "//tensorflow:tensorflow_py_no_contrib", - "//tensorflow/python/estimator:metric_keys", - "//tensorflow/python/estimator:util", ], ) @@ -683,9 +683,9 @@ py_test( ], deps = [ ":keras", + ":numpy_io", + ":run_config", "//tensorflow:tensorflow_py_no_contrib", - "//tensorflow/python/estimator:numpy_io", - "//tensorflow/python/estimator:run_config", "//third_party/py/numpy", ], ) -- GitLab From e9869ece182be721dc07fe8ecb7c7288f2fce90f Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Thu, 19 Jul 2018 12:19:37 -0700 Subject: [PATCH 154/519] [tf.data] Adding `tf.contrib.data.reduce_dataset` which can be used to reduce a dataset to a single element. PiperOrigin-RevId: 205281140 --- tensorflow/contrib/data/__init__.py | 2 + .../contrib/data/python/kernel_tests/BUILD | 2 + .../kernel_tests/get_single_element_test.py | 82 +++++++++++++------ tensorflow/contrib/data/python/ops/BUILD | 3 + .../data/python/ops/get_single_element.py | 30 +++++++ 5 files changed, 94 insertions(+), 25 deletions(-) diff --git a/tensorflow/contrib/data/__init__.py b/tensorflow/contrib/data/__init__.py index 675330716b..7878e46e88 100644 --- a/tensorflow/contrib/data/__init__.py +++ b/tensorflow/contrib/data/__init__.py @@ -52,6 +52,7 @@ See @{$guide/datasets$Importing Data} for an overview. @@prefetch_to_device @@read_batch_features @@rejection_resample +@@reduce_dataset @@sample_from_datasets @@scan @@shuffle_and_repeat @@ -77,6 +78,7 @@ 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.get_single_element import reduce_dataset from tensorflow.contrib.data.python.ops.grouping import bucket_by_sequence_length from tensorflow.contrib.data.python.ops.grouping import group_by_reducer from tensorflow.contrib.data.python.ops.grouping import group_by_window diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index f805027727..036dc795bb 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -121,6 +121,7 @@ py_test( srcs = ["get_single_element_test.py"], deps = [ "//tensorflow/contrib/data/python/ops:get_single_element", + "//tensorflow/contrib/data/python/ops:grouping", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:constant_op", @@ -128,6 +129,7 @@ py_test( "//tensorflow/python:errors", "//tensorflow/python:sparse_tensor", "//tensorflow/python/data/ops:dataset_ops", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py b/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py index 87b7c6ddb7..e6883d53e0 100644 --- a/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/get_single_element_test.py @@ -17,9 +17,12 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized +import numpy as np + from tensorflow.contrib.data.python.ops import get_single_element +from tensorflow.contrib.data.python.ops import grouping 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.framework import sparse_tensor @@ -27,40 +30,69 @@ from tensorflow.python.ops import array_ops from tensorflow.python.platform import test -class GetSingleElementTest(test.TestCase): +class GetSingleElementTest(test.TestCase, parameterized.TestCase): - def testGetSingleElement(self): - skip_value = array_ops.placeholder(dtypes.int64, shape=[]) - take_value = array_ops.placeholder_with_default( - constant_op.constant(1, dtype=dtypes.int64), shape=[]) + @parameterized.named_parameters( + ("Zero", 0, 1), + ("Five", 5, 1), + ("Ten", 10, 1), + ("Empty", 100, 1, errors.InvalidArgumentError, "Dataset was empty."), + ("MoreThanOne", 0, 2, errors.InvalidArgumentError, + "Dataset had more than one element."), + ) + def testGetSingleElement(self, skip, take, error=None, error_msg=None): + skip_t = array_ops.placeholder(dtypes.int64, shape=[]) + take_t = array_ops.placeholder(dtypes.int64, shape=[]) def make_sparse(x): x_1d = array_ops.reshape(x, [1]) x_2d = array_ops.reshape(x, [1, 1]) return sparse_tensor.SparseTensor(x_2d, x_1d, x_1d) - dataset = (dataset_ops.Dataset.range(100) - .skip(skip_value) - .map(lambda x: (x * x, make_sparse(x))) - .take(take_value)) - + dataset = dataset_ops.Dataset.range(100).skip(skip_t).map( + lambda x: (x * x, make_sparse(x))).take(take_t) element = get_single_element.get_single_element(dataset) with self.test_session() as sess: - for x in [0, 5, 10]: - dense_val, sparse_val = sess.run(element, feed_dict={skip_value: x}) - self.assertEqual(x * x, dense_val) - self.assertAllEqual([[x]], sparse_val.indices) - self.assertAllEqual([x], sparse_val.values) - self.assertAllEqual([x], sparse_val.dense_shape) - - with self.assertRaisesRegexp(errors.InvalidArgumentError, - "Dataset was empty."): - sess.run(element, feed_dict={skip_value: 100}) - - with self.assertRaisesRegexp(errors.InvalidArgumentError, - "Dataset had more than one element."): - sess.run(element, feed_dict={skip_value: 0, take_value: 2}) + if error is None: + dense_val, sparse_val = sess.run( + element, feed_dict={ + skip_t: skip, + take_t: take + }) + self.assertEqual(skip * skip, dense_val) + self.assertAllEqual([[skip]], sparse_val.indices) + self.assertAllEqual([skip], sparse_val.values) + self.assertAllEqual([skip], sparse_val.dense_shape) + else: + with self.assertRaisesRegexp(error, error_msg): + sess.run(element, feed_dict={skip_t: skip, take_t: take}) + + @parameterized.named_parameters( + ("SumZero", 0), + ("SumOne", 1), + ("SumFive", 5), + ("SumTen", 10), + ) + def testReduceDataset(self, stop): + def init_fn(_): + return np.int64(0) + + def reduce_fn(state, value): + return state + value + + def finalize_fn(state): + return state + + sum_reducer = grouping.Reducer(init_fn, reduce_fn, finalize_fn) + + stop_t = array_ops.placeholder(dtypes.int64, shape=[]) + dataset = dataset_ops.Dataset.range(stop_t) + element = get_single_element.reduce_dataset(dataset, sum_reducer) + + with self.test_session() as sess: + value = sess.run(element, feed_dict={stop_t: stop}) + self.assertEqual(stop * (stop - 1) / 2, value) if __name__ == "__main__": diff --git a/tensorflow/contrib/data/python/ops/BUILD b/tensorflow/contrib/data/python/ops/BUILD index 160d7fe22a..1ad021ea03 100644 --- a/tensorflow/contrib/data/python/ops/BUILD +++ b/tensorflow/contrib/data/python/ops/BUILD @@ -28,10 +28,12 @@ py_library( srcs = ["get_single_element.py"], srcs_version = "PY2AND3", deps = [ + ":grouping", "//tensorflow/python:dataset_ops_gen", "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/data/util:nest", "//tensorflow/python/data/util:sparse", + "//third_party/py/numpy", ], ) @@ -129,6 +131,7 @@ py_library( "//tensorflow/python/data/util:convert", "//tensorflow/python/data/util:nest", "//tensorflow/python/data/util:sparse", + "//third_party/py/numpy", ], ) diff --git a/tensorflow/contrib/data/python/ops/get_single_element.py b/tensorflow/contrib/data/python/ops/get_single_element.py index 0f4cd8e20c..ef9284456e 100644 --- a/tensorflow/contrib/data/python/ops/get_single_element.py +++ b/tensorflow/contrib/data/python/ops/get_single_element.py @@ -17,6 +17,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import numpy as np + +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.data.util import sparse @@ -68,3 +71,30 @@ def get_single_element(dataset): return sparse.deserialize_sparse_tensors( nested_ret, dataset.output_types, dataset.output_shapes, dataset.output_classes) + + +def reduce_dataset(dataset, reducer): + """Returns the result of reducing the `dataset` using `reducer`. + + Args: + dataset: A @{tf.data.Dataset} object. + reducer: A @{tf.contrib.data.Reducer} object representing the reduce logic. + + Returns: + A nested structure of @{tf.Tensor} objects, corresponding to the result + of reducing `dataset` using `reducer`. + + Raises: + TypeError: if `dataset` is not a `tf.data.Dataset` object. + """ + if not isinstance(dataset, dataset_ops.Dataset): + raise TypeError("`dataset` must be a `tf.data.Dataset` object.") + + # The sentinel dataset is used in case the reduced dataset is empty. + sentinel_dataset = dataset_ops.Dataset.from_tensors( + reducer.finalize_func(reducer.init_func(np.int64(0)))) + reduced_dataset = dataset.apply( + grouping.group_by_reducer(lambda x: np.int64(0), reducer)) + + return get_single_element( + reduced_dataset.concatenate(sentinel_dataset).take(1)) -- GitLab From 8ec87f55008982eb939d963c1d4a4ff7ef9ab3d3 Mon Sep 17 00:00:00 2001 From: Smit Hinsu Date: Thu, 19 Jul 2018 13:06:41 -0700 Subject: [PATCH 155/519] Mark tensorflow_lingvo directory as internal to tensorflow PiperOrigin-RevId: 205288716 --- tensorflow/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 518c2b0489..0b08f2093d 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -415,6 +415,7 @@ package_group( "//learning/meta_rank/...", "//tensorflow/...", "//tensorflow_fold/llgtm/...", + "//tensorflow_lingvo/...", "//third_party/py/tensor2tensor/...", ], ) -- GitLab From afae286739dbfd6339cde505ae573f2776b80afc Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 13:21:37 -0700 Subject: [PATCH 156/519] [XLA] add XLA math library primitives to local Python client PiperOrigin-RevId: 205291033 --- .../xla/python/local_computation_builder.cc | 24 +++++++++++++++---- .../xla/python/local_computation_builder.h | 24 +++++++++++++++---- .../xla/python/local_computation_builder.i | 24 +++++++++++++++---- tensorflow/compiler/xla/python/xla_client.py | 18 +++++++++++++- 4 files changed, 77 insertions(+), 13 deletions(-) diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index 66b1c08a39..f25348e735 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -617,6 +617,8 @@ _FORWARD_BINOP(Xor) _FORWARD_BINOP(ShiftLeft) _FORWARD_BINOP(ShiftRightArithmetic) _FORWARD_BINOP(ShiftRightLogical) +_FORWARD_BINOP(Atan2) +_FORWARD_BINOP(Pow) _FORWARD_UNOP(Not) _FORWARD_UNOP(Abs) _FORWARD_UNOP(Exp) @@ -630,13 +632,27 @@ _FORWARD_UNOP(Sign) _FORWARD_UNOP(Cos) _FORWARD_UNOP(Sin) _FORWARD_UNOP(Tanh) -_FORWARD_UNOP(Sqrt) -_FORWARD_UNOP(Square) -_FORWARD_BINOP(Pow) _FORWARD_UNOP(IsFinite) -_FORWARD_UNOP(Reciprocal) _FORWARD_UNOP(Neg) _FORWARD_UNOP(Sort) +_FORWARD_UNOP(Sqrt) +_FORWARD_UNOP(Rsqrt) +_FORWARD_UNOP(Square) +_FORWARD_UNOP(Reciprocal) +_FORWARD_UNOP(Erfc) +_FORWARD_UNOP(Erf) +_FORWARD_UNOP(ErfInv) +_FORWARD_UNOP(Lgamma) +_FORWARD_UNOP(Digamma) +_FORWARD_UNOP(Acos) +_FORWARD_UNOP(Asin) +_FORWARD_UNOP(Atan) +_FORWARD_UNOP(Tan) +_FORWARD_UNOP(Acosh) +_FORWARD_UNOP(Asinh) +_FORWARD_UNOP(Atanh) +_FORWARD_UNOP(Cosh) +_FORWARD_UNOP(Sinh) #undef _FORWARD #undef _FORWARD_UNOP diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index 17ad044578..0e0d8ac29a 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -336,6 +336,8 @@ class LocalComputationBuilder { _FORWARD_BINOP(ShiftLeft) _FORWARD_BINOP(ShiftRightArithmetic) _FORWARD_BINOP(ShiftRightLogical) + _FORWARD_BINOP(Atan2) + _FORWARD_BINOP(Pow) _FORWARD_UNOP(Not) _FORWARD_UNOP(Abs) _FORWARD_UNOP(Exp) @@ -349,13 +351,27 @@ class LocalComputationBuilder { _FORWARD_UNOP(Cos) _FORWARD_UNOP(Sin) _FORWARD_UNOP(Tanh) - _FORWARD_UNOP(Sqrt) - _FORWARD_UNOP(Square) - _FORWARD_BINOP(Pow) _FORWARD_UNOP(IsFinite) - _FORWARD_UNOP(Reciprocal) _FORWARD_UNOP(Neg) _FORWARD_UNOP(Sort) + _FORWARD_UNOP(Sqrt) + _FORWARD_UNOP(Rsqrt) + _FORWARD_UNOP(Square) + _FORWARD_UNOP(Reciprocal) + _FORWARD_UNOP(Erfc) + _FORWARD_UNOP(Erf) + _FORWARD_UNOP(ErfInv) + _FORWARD_UNOP(Lgamma) + _FORWARD_UNOP(Digamma) + _FORWARD_UNOP(Acos) + _FORWARD_UNOP(Asin) + _FORWARD_UNOP(Atan) + _FORWARD_UNOP(Tan) + _FORWARD_UNOP(Acosh) + _FORWARD_UNOP(Asinh) + _FORWARD_UNOP(Atanh) + _FORWARD_UNOP(Cosh) + _FORWARD_UNOP(Sinh) #undef _FORWARD #undef _FORWARD_UNOP diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index 42bf76e5d8..eeccbd7cfa 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -1005,13 +1005,29 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Cos; %unignore xla::swig::LocalComputationBuilder::Sin; %unignore xla::swig::LocalComputationBuilder::Tanh; -%unignore xla::swig::LocalComputationBuilder::Sqrt; -%unignore xla::swig::LocalComputationBuilder::Square; -%unignore xla::swig::LocalComputationBuilder::Pow; +%unignore xla::swig::LocalComputationBuilder::Atan2; %unignore xla::swig::LocalComputationBuilder::IsFinite; -%unignore xla::swig::LocalComputationBuilder::Reciprocal; +%unignore xla::swig::LocalComputationBuilder::Pow; %unignore xla::swig::LocalComputationBuilder::Neg; %unignore xla::swig::LocalComputationBuilder::Sort; +%unignore xla::swig::LocalComputationBuilder::Sqrt; +%unignore xla::swig::LocalComputationBuilder::Rsqrt; +%unignore xla::swig::LocalComputationBuilder::Square; +%unignore xla::swig::LocalComputationBuilder::Reciprocal; +%unignore xla::swig::LocalComputationBuilder::Erfc; +%unignore xla::swig::LocalComputationBuilder::Erf; +%unignore xla::swig::LocalComputationBuilder::ErfInv; +%unignore xla::swig::LocalComputationBuilder::Lgamma; +%unignore xla::swig::LocalComputationBuilder::Digamma; +%unignore xla::swig::LocalComputationBuilder::Acos; +%unignore xla::swig::LocalComputationBuilder::Asin; +%unignore xla::swig::LocalComputationBuilder::Atan; +%unignore xla::swig::LocalComputationBuilder::Tan; +%unignore xla::swig::LocalComputationBuilder::Acosh; +%unignore xla::swig::LocalComputationBuilder::Asinh; +%unignore xla::swig::LocalComputationBuilder::Atanh; +%unignore xla::swig::LocalComputationBuilder::Cosh; +%unignore xla::swig::LocalComputationBuilder::Sinh; %unignore xla::swig::DestructureLocalShapedBufferTuple; %unignore xla::swig::DeleteLocalShapedBuffer; %unignore xla::swig::DeleteLocalComputation; diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index f93d7bda2d..ef043e4ca0 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -99,12 +99,27 @@ _UNARY_OPS = [ 'Cos', 'Sin', 'Tanh', + 'IsFinite', 'Sqrt', + 'Rsqrt', 'Square', - 'IsFinite', 'Reciprocal', 'Neg', 'Sort', + 'Erf', + 'Erfc', + 'ErfInv', + 'Lgamma', + 'Digamma', + 'Acos', + 'Asin', + 'Atan', + 'Tan', + 'Acosh', + 'Asinh', + 'Atanh', + 'Cosh', + 'Sinh', ] _BINARY_OPS = [ @@ -128,6 +143,7 @@ _BINARY_OPS = [ 'ShiftLeft', 'ShiftRightArithmetic', 'ShiftRightLogical', + 'Atan2', ] -- GitLab From 6de6c2c8c00d947f08c40f37f563b35292dddf48 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Thu, 19 Jul 2018 13:25:25 -0700 Subject: [PATCH 157/519] Use std::unique_ptr in BFCAllocator::AllocationRegion. PiperOrigin-RevId: 205291721 --- tensorflow/core/common_runtime/bfc_allocator.h | 10 +++------- 1 file changed, 3 insertions(+), 7 deletions(-) diff --git a/tensorflow/core/common_runtime/bfc_allocator.h b/tensorflow/core/common_runtime/bfc_allocator.h index 52aedb1e9c..cd8ff6e5c0 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.h +++ b/tensorflow/core/common_runtime/bfc_allocator.h @@ -191,18 +191,14 @@ class BFCAllocator : public VisitableAllocator { DCHECK_EQ(0, memory_size % kMinAllocationSize); const size_t n_handles = (memory_size + kMinAllocationSize - 1) / kMinAllocationSize; - handles_ = new ChunkHandle[n_handles]; + handles_.reset(new ChunkHandle[n_handles]); for (size_t i = 0; i < n_handles; i++) { handles_[i] = kInvalidChunkHandle; } } - AllocationRegion() {} - - ~AllocationRegion() { delete[] handles_; } - + AllocationRegion() = default; AllocationRegion(AllocationRegion&& other) { Swap(other); } - AllocationRegion& operator=(AllocationRegion&& other) { Swap(other); return *this; @@ -241,7 +237,7 @@ class BFCAllocator : public VisitableAllocator { // Array of size "memory_size / kMinAllocationSize". It is // indexed by (p-base) / kMinAllocationSize, contains ChunkHandle // for the memory allocation represented by "p" - ChunkHandle* handles_ = nullptr; + std::unique_ptr handles_; TF_DISALLOW_COPY_AND_ASSIGN(AllocationRegion); }; -- GitLab From c7aabf29c098176fdd4cbb4d32327e989505b054 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 13:33:09 -0700 Subject: [PATCH 158/519] Stop itemizing TF Lite tests to run in continuous builds. PiperOrigin-RevId: 205292867 --- .../tools/ci_build/ci_parameterized_build.sh | 31 +---------------- .../ci_build/linux/cpu/run_py3_contrib.sh | 33 +------------------ 2 files changed, 2 insertions(+), 62 deletions(-) diff --git a/tensorflow/tools/ci_build/ci_parameterized_build.sh b/tensorflow/tools/ci_build/ci_parameterized_build.sh index 08e2c3edd2..5115be8c6d 100755 --- a/tensorflow/tools/ci_build/ci_parameterized_build.sh +++ b/tensorflow/tools/ci_build/ci_parameterized_build.sh @@ -150,36 +150,7 @@ BAZEL_TARGET="//tensorflow/... -//tensorflow/compiler/..." if [[ -n "$TF_SKIP_CONTRIB_TESTS" ]]; then BAZEL_TARGET="$BAZEL_TARGET -//tensorflow/contrib/..." else - BAZEL_TARGET="${BAZEL_TARGET} -//tensorflow/contrib/lite/..." - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:context_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:framework" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:interpreter_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:model_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/toco:toco" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:simple_memory_arena_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite:string_util_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:activations_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:add_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:basic_rnn_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:concatenation_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:conv_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:depthwise_conv_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:embedding_lookup_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:embedding_lookup_sparse_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:fully_connected_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:hashtable_lookup_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:local_response_norm_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:lsh_projection_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:lstm_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:l2norm_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:mul_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:pooling_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:reshape_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:resize_bilinear_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:skip_gram_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:softmax_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:space_to_depth_test" - BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/kernels:svdf_test" + BAZEL_TARGET="${BAZEL_TARGET} //tensorflow/contrib/lite/..." fi TUT_TEST_DATA_DIR="/tmp/tf_tutorial_test_data" diff --git a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh index 2b68de3c5b..f6fa9251d4 100755 --- a/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh +++ b/tensorflow/tools/ci_build/linux/cpu/run_py3_contrib.sh @@ -34,35 +34,4 @@ yes "" | $PYTHON_BIN_PATH configure.py bazel test --test_tag_filters=-no_oss,-oss_serial,-gpu,-benchmark-test -k \ --jobs=${N_JOBS} --test_timeout 300,450,1200,3600 --config=opt \ --test_size_filters=small,medium --test_output=errors -- \ - //tensorflow/contrib/... \ - -//tensorflow/contrib/lite/... \ - //tensorflow/contrib/lite:context_test \ - //tensorflow/contrib/lite:framework \ - //tensorflow/contrib/lite:interpreter_test \ - //tensorflow/contrib/lite:model_test \ - //tensorflow/contrib/lite/toco:toco \ - //tensorflow/contrib/lite:simple_memory_arena_test \ - //tensorflow/contrib/lite:string_util_test \ - //tensorflow/contrib/lite/kernels:activations_test \ - //tensorflow/contrib/lite/kernels:add_test \ - //tensorflow/contrib/lite/kernels:basic_rnn_test \ - //tensorflow/contrib/lite/kernels:concatenation_test \ - //tensorflow/contrib/lite/kernels:conv_test \ - //tensorflow/contrib/lite/kernels:depthwise_conv_test \ - //tensorflow/contrib/lite/kernels:embedding_lookup_test \ - //tensorflow/contrib/lite/kernels:embedding_lookup_sparse_test \ - //tensorflow/contrib/lite/kernels:fully_connected_test \ - //tensorflow/contrib/lite/testing:generated_zip_tests \ - //tensorflow/contrib/lite/kernels:hashtable_lookup_test \ - //tensorflow/contrib/lite/kernels:local_response_norm_test \ - //tensorflow/contrib/lite/kernels:lsh_projection_test \ - //tensorflow/contrib/lite/kernels:lstm_test \ - //tensorflow/contrib/lite/kernels:l2norm_test \ - //tensorflow/contrib/lite/kernels:mul_test \ - //tensorflow/contrib/lite/kernels:pooling_test \ - //tensorflow/contrib/lite/kernels:reshape_test \ - //tensorflow/contrib/lite/kernels:resize_bilinear_test \ - //tensorflow/contrib/lite/kernels:skip_gram_test \ - //tensorflow/contrib/lite/kernels:softmax_test \ - //tensorflow/contrib/lite/kernels:space_to_depth_test \ - //tensorflow/contrib/lite/kernels:svdf_test + //tensorflow/contrib/... -- GitLab From 661ad6be85fa611fa297bc8b8bacef752bef7ffc Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Thu, 19 Jul 2018 13:34:14 -0700 Subject: [PATCH 159/519] [TF:XLA] Bump open source llvm revision to r337441 PiperOrigin-RevId: 205293013 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 4b4f31813c..2c8658fc59 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -487,11 +487,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/10c3b3d15ed6a788ac12221b784caf81fb8248b5.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/10c3b3d15ed6a788ac12221b784caf81fb8248b5.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/62b518b75a780a3bc75982cbe54b0e7bc262aa6e.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/62b518b75a780a3bc75982cbe54b0e7bc262aa6e.tar.gz", ], - sha256 = "a9feb6b47267c30fd7c19ebfdf4dbde6757054f716fa77c09bcb1106799c3253", - strip_prefix = "llvm-10c3b3d15ed6a788ac12221b784caf81fb8248b5", + sha256 = "51ab0edcf7dde0207f5cf141aec16b14fcac5290112cdf1ea671a2757f719f8b", + strip_prefix = "llvm-62b518b75a780a3bc75982cbe54b0e7bc262aa6e", build_file = clean_dep("//third_party/llvm:llvm.autogenerated.BUILD"), ) -- GitLab From 58fdd0dfce6d4c71fa7d381190987ccad33da0b6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 13:35:40 -0700 Subject: [PATCH 160/519] Test io::RecordWriter.flush() Requires changing flush_mode from default Z_NO_FLUSH See tensorflow/core/lib/io/zlib_compression_options.h PiperOrigin-RevId: 205293231 --- tensorflow/core/BUILD | 1 + .../core/lib/io/record_reader_writer_test.cc | 84 +++++++++++++++++++ 2 files changed, 85 insertions(+) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 514713bb96..fc12027291 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -3226,6 +3226,7 @@ tf_cc_tests( ":test", ":test_main", "//third_party/eigen3", + "@zlib_archive//:zlib", ], ) diff --git a/tensorflow/core/lib/io/record_reader_writer_test.cc b/tensorflow/core/lib/io/record_reader_writer_test.cc index 95ac040602..c36c909399 100644 --- a/tensorflow/core/lib/io/record_reader_writer_test.cc +++ b/tensorflow/core/lib/io/record_reader_writer_test.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/core/lib/io/record_reader.h" #include "tensorflow/core/lib/io/record_writer.h" +#include #include #include "tensorflow/core/platform/env.h" @@ -33,6 +34,89 @@ static std::vector BufferSizes() { 12, 13, 14, 15, 16, 17, 18, 19, 20, 65536}; } +namespace { + +io::RecordReaderOptions GetMatchingReaderOptions( + const io::RecordWriterOptions& options) { + if (options.compression_type == io::RecordWriterOptions::ZLIB_COMPRESSION) { + return io::RecordReaderOptions::CreateRecordReaderOptions("ZLIB"); + } + return io::RecordReaderOptions::CreateRecordReaderOptions(""); +} + +uint64 GetFileSize(const string& fname) { + Env* env = Env::Default(); + uint64 fsize; + TF_CHECK_OK(env->GetFileSize(fname, &fsize)); + return fsize; +} + +void VerifyFlush(const io::RecordWriterOptions& options) { + std::vector records = { + "abcdefghijklmnopqrstuvwxyz", + "ZYXWVUTSRQPONMLKJIHGFEDCBA0123456789!@#$%^&*()", + "G5SyohOL9UmXofSOOwWDrv9hoLLMYPJbG9r38t3uBRcHxHj2PdKcPDuZmKW62RIY", + "aaaaaaaaaaaaaaaaaaaaaaaaaa", + }; + + Env* env = Env::Default(); + string fname = testing::TmpDir() + "/record_reader_writer_flush_test"; + + std::unique_ptr file; + TF_CHECK_OK(env->NewWritableFile(fname, &file)); + io::RecordWriter writer(file.get(), options); + + std::unique_ptr read_file; + TF_CHECK_OK(env->NewRandomAccessFile(fname, &read_file)); + io::RecordReaderOptions read_options = GetMatchingReaderOptions(options); + io::RecordReader reader(read_file.get(), read_options); + + EXPECT_EQ(GetFileSize(fname), 0); + for (size_t i = 0; i < records.size(); i++) { + uint64 start_size = GetFileSize(fname); + + // Write a new record. + TF_EXPECT_OK(writer.WriteRecord(records[i])); + TF_CHECK_OK(writer.Flush()); + TF_CHECK_OK(file->Flush()); + + // Verify that file size has changed after file flush. + uint64 new_size = GetFileSize(fname); + EXPECT_GT(new_size, start_size); + + // Verify that file has all records written so far and no more. + uint64 offset = 0; + string record; + for (size_t j = 0; j <= i; j++) { + // Check that j'th record is written correctly. + TF_CHECK_OK(reader.ReadRecord(&offset, &record)); + EXPECT_EQ(record, records[j]); + } + + // Verify that file has no more records. + CHECK_EQ(reader.ReadRecord(&offset, &record).code(), error::OUT_OF_RANGE); + } +} + +} // namespace + +TEST(RecordReaderWriterTest, TestFlush) { + io::RecordWriterOptions options; + VerifyFlush(options); +} + +TEST(RecordReaderWriterTest, TestZlibSyncFlush) { + io::RecordWriterOptions options; + options.compression_type = io::RecordWriterOptions::ZLIB_COMPRESSION; + // The default flush_mode is Z_NO_FLUSH and only writes to the file when the + // buffer is full or the file is closed, which makes testing harder. + // By using Z_SYNC_FLUSH the test can verify Flush does write out records of + // approximately the right size at the right times. + options.zlib_options.flush_mode = Z_SYNC_FLUSH; + + VerifyFlush(options); +} + TEST(RecordReaderWriterTest, TestBasics) { Env* env = Env::Default(); string fname = testing::TmpDir() + "/record_reader_writer_test"; -- GitLab From 1b21235444eb12429ee41d185b6f594778f7c30a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 13:48:50 -0700 Subject: [PATCH 161/519] Improve Windows build process After this change, the windows build steps should be like: https://docs.google.com/document/d/1oVYzPJVv8r5N9PecqwG74rY_QbqPH70IxBouBHdq5EI/edit?usp=sharing PiperOrigin-RevId: 205295588 --- configure.py | 59 ++++++++++++++----- .../windows/cpu/pip/build_tf_windows.sh | 10 +--- .../windows/gpu/pip/build_tf_windows.sh | 10 +--- .../tools/pip_package/build_pip_package.sh | 6 +- 4 files changed, 54 insertions(+), 31 deletions(-) diff --git a/configure.py b/configure.py index c482628ec8..60fe54b2f6 100644 --- a/configure.py +++ b/configure.py @@ -882,7 +882,7 @@ def set_tf_cudnn_version(environ_cp): default_cudnn_path = environ_cp.get('CUDA_TOOLKIT_PATH') ask_cudnn_path = (r'Please specify the location where cuDNN %s library is ' 'installed. Refer to README.md for more details. [Default' - ' is %s]:') % (tf_cudnn_version, default_cudnn_path) + ' is %s]: ') % (tf_cudnn_version, default_cudnn_path) cudnn_install_path = get_from_env_or_user_or_default( environ_cp, 'CUDNN_INSTALL_PATH', ask_cudnn_path, default_cudnn_path) @@ -1201,7 +1201,7 @@ def set_tf_cuda_compute_capabilities(environ_cp): 'https://developer.nvidia.com/cuda-gpus.\nPlease' ' note that each additional compute ' 'capability significantly increases your ' - 'build time and binary size. [Default is: %s]' % + 'build time and binary size. [Default is: %s]: ' % default_cuda_compute_capabilities) tf_cuda_compute_capabilities = get_from_env_or_user_or_default( environ_cp, 'TF_CUDA_COMPUTE_CAPABILITIES', @@ -1402,14 +1402,36 @@ def set_build_strip_flag(): write_to_bazelrc('build --strip=always') -def set_windows_build_flags(): - if is_windows(): - # The non-monolithic build is not supported yet - write_to_bazelrc('build --config monolithic') - # Suppress warning messages - write_to_bazelrc('build --copt=-w --host_copt=-w') - # Output more verbose information when something goes wrong - write_to_bazelrc('build --verbose_failures') +def set_windows_build_flags(environ_cp): + """Set Windows specific build options.""" + # The non-monolithic build is not supported yet + write_to_bazelrc('build --config monolithic') + # Suppress warning messages + write_to_bazelrc('build --copt=-w --host_copt=-w') + # Output more verbose information when something goes wrong + write_to_bazelrc('build --verbose_failures') + # The host and target platforms are the same in Windows build. So we don't + # have to distinct them. This avoids building the same targets twice. + write_to_bazelrc('build --distinct_host_configuration=false') + # Enable short object file path to avoid long path issue on Windows. + # TODO(pcloudy): Remove this flag when upgrading Bazel to 0.16.0 + # Short object file path will be enabled by default. + write_to_bazelrc('build --experimental_shortened_obj_file_path=true') + + if get_var( + environ_cp, 'TF_OVERRIDE_EIGEN_STRONG_INLINE', 'Eigen strong inline', + True, + ('Would you like to override eigen strong inline for some C++ ' + 'compilation to reduce the compiling time?'), + 'Eigen strong inline overridden.', + 'Not overriding eigen strong inline, ' + 'some compilations could take more than 20 mins.'): + # Due to a known MSVC compiler issue + # https://github.com/tensorflow/tensorflow/issues/10521 + # Overriding eigen strong inline speeds up the compiling of + # conv_grad_ops_3d.cc and conv_ops_3d.cc by 20 minutes, + # but this also hurts the performance. Let users decide what they want. + write_to_bazelrc('build --define=override_eigen_strong_inline=true') def config_info_line(name, help_text): @@ -1537,7 +1559,8 @@ def main(): set_grpc_build_flags() set_cc_opt_flags(environ_cp) set_build_strip_flag() - set_windows_build_flags() + if is_windows(): + set_windows_build_flags(environ_cp) if get_var( environ_cp, 'TF_SET_ANDROID_WORKSPACE', 'android workspace', @@ -1549,11 +1572,15 @@ def main(): create_android_ndk_rule(environ_cp) create_android_sdk_rule(environ_cp) - print('Preconfigured Bazel build configs. You can use any of the below by ' - 'adding "--config=<>" to your build command. See tools/bazel.rc for ' - 'more details.') - config_info_line('mkl', 'Build with MKL support.') - config_info_line('monolithic', 'Config for mostly static monolithic build.') + # On Windows, we don't have MKL support and the build is always monolithic. + # So no need to print the following message. + # TODO(pcloudy): remove the following if check when they make sense on Windows + if not is_windows(): + print('Preconfigured Bazel build configs. You can use any of the below by ' + 'adding "--config=<>" to your build command. See tools/bazel.rc for ' + 'more details.') + config_info_line('mkl', 'Build with MKL support.') + config_info_line('monolithic', 'Config for mostly static monolithic build.') if __name__ == '__main__': main() diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index 61dec249f3..dc7ea1dc57 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -67,16 +67,12 @@ for ARG in "$@"; do done if [[ "$release_build" != 1 ]]; then - # --define=override_eigen_strong_inline=true speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc + # Overriding eigen strong inline speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc # by 20 minutes. See https://github.com/tensorflow/tensorflow/issues/10521 - # Because this hurts the performance of TF, we don't enable it in release build. - echo "build --define=override_eigen_strong_inline=true" >> "${TMP_BAZELRC}" + # Because this hurts the performance of TF, we don't override it in release build. + export TF_OVERRIDE_EIGEN_STRONG_INLINE=0 fi -# The host and target platforms are the same in Windows build. So we don't have -# to distinct them. This helps avoid building the same targets twice. -echo "build --distinct_host_configuration=false" >> "${TMP_BAZELRC}" - # Enable short object file path to avoid long path issue on Windows. echo "startup --output_user_root=${TMPDIR}" >> "${TMP_BAZELRC}" diff --git a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh index e232306653..a4175a0e81 100644 --- a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh @@ -67,16 +67,12 @@ for ARG in "$@"; do done if [[ "$release_build" != 1 ]]; then - # --define=override_eigen_strong_inline=true speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc + # Overriding eigen strong inline speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc # by 20 minutes. See https://github.com/tensorflow/tensorflow/issues/10521 - # Because this hurts the performance of TF, we don't enable it in release build. - echo "build --define=override_eigen_strong_inline=true" >> "${TMP_BAZELRC}" + # Because this hurts the performance of TF, we don't override it in release build. + export TF_OVERRIDE_EIGEN_STRONG_INLINE=0 fi -# The host and target platforms are the same in Windows build. So we don't have -# to distinct them. This helps avoid building the same targets twice. -echo "build --distinct_host_configuration=false" >> "${TMP_BAZELRC}" - # Enable short object file path to avoid long path issue on Windows. echo "startup --output_user_root=${TMPDIR}" >> "${TMP_BAZELRC}" diff --git a/tensorflow/tools/pip_package/build_pip_package.sh b/tensorflow/tools/pip_package/build_pip_package.sh index 4101b34a11..ca40f2eaa8 100755 --- a/tensorflow/tools/pip_package/build_pip_package.sh +++ b/tensorflow/tools/pip_package/build_pip_package.sh @@ -17,8 +17,12 @@ set -e +function is_absolute { + [[ "$1" = /* ]] || [[ "$1" =~ ^[a-zA-Z]:[/\\].* ]] +} + function real_path() { - [[ $1 = /* ]] && echo "$1" || echo "$PWD/${1#./}" + is_absolute "$1" && echo "$1" || echo "$PWD/${1#./}" } function cp_external() { -- GitLab From 498fed9be6cc556b08c1d3ffd31565497daaa8c1 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Thu, 19 Jul 2018 13:59:20 -0700 Subject: [PATCH 162/519] Automated rollback of commit 8ec87f55008982eb939d963c1d4a4ff7ef9ab3d3 PiperOrigin-RevId: 205297172 --- tensorflow/BUILD | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 0b08f2093d..518c2b0489 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -415,7 +415,6 @@ package_group( "//learning/meta_rank/...", "//tensorflow/...", "//tensorflow_fold/llgtm/...", - "//tensorflow_lingvo/...", "//third_party/py/tensor2tensor/...", ], ) -- GitLab From 6e02d79ba0179a23679e65b31405c591726bc552 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 14:34:20 -0700 Subject: [PATCH 163/519] Make count metric consistent with other metrics by converting variable to tensor (_aggregate_variable() returns Tensor). PiperOrigin-RevId: 205303531 --- .../contrib/metrics/python/ops/metric_ops.py | 18 ++++++++++++------ .../metrics/python/ops/metric_ops_test.py | 5 +++++ 2 files changed, 17 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops.py b/tensorflow/contrib/metrics/python/ops/metric_ops.py index b14202ff9e..a328670526 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops.py @@ -3715,6 +3715,7 @@ def count(values, name=None): """Computes the number of examples, or sum of `weights`. + This metric keeps track of the denominator in `tf.metrics.mean`. When evaluating some metric (e.g. mean) on one or more subsets of the data, this auxiliary metric is useful for keeping track of how many examples there are in each subset. @@ -3741,15 +3742,21 @@ def count(values, ValueError: If `weights` is not `None` and its shape doesn't match `values`, or if either `metrics_collections` or `updates_collections` are not a list or tuple. + RuntimeError: If eager execution is enabled. """ + if context.executing_eagerly(): + raise RuntimeError('tf.contrib.metrics.count is not supported when eager ' + 'execution is enabled.') with variable_scope.variable_scope(name, 'count', (values, weights)): + count_ = metrics_impl.metric_variable([], dtypes.float32, name='count') if weights is None: num_values = math_ops.to_float(array_ops.size(values)) else: - _, _, weights = metrics_impl._remove_squeezable_dimensions( # pylint: disable=protected-access + values = math_ops.to_float(values) + values, _, weights = metrics_impl._remove_squeezable_dimensions( # pylint: disable=protected-access predictions=values, labels=None, weights=weights) @@ -3758,15 +3765,14 @@ def count(values, num_values = math_ops.reduce_sum(weights) with ops.control_dependencies([values]): - update_op = state_ops.assign_add(count_, num_values) + update_count_op = state_ops.assign_add(count_, num_values) - if metrics_collections: - ops.add_to_collections(metrics_collections, count_) + count_ = metrics_impl._aggregate_variable(count_, metrics_collections) # pylint: disable=protected-access if updates_collections: - ops.add_to_collections(updates_collections, update_op) + ops.add_to_collections(updates_collections, update_count_op) - return count_, update_op + return count_, update_count_op def cohen_kappa(labels, diff --git a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py index a09fc4abd4..401fedcbed 100644 --- a/tensorflow/contrib/metrics/python/ops/metric_ops_test.py +++ b/tensorflow/contrib/metrics/python/ops/metric_ops_test.py @@ -6854,6 +6854,11 @@ class CountTest(test.TestCase): array_ops.ones([4, 3]), updates_collections=[my_collection_name]) self.assertListEqual(ops.get_collection(my_collection_name), [update_op]) + def testReturnType(self): + c, op = metrics.count(array_ops.ones([4, 3])) + self.assertTrue(isinstance(c, ops.Tensor)) + self.assertTrue(isinstance(op, ops.Operation) or isinstance(op, ops.Tensor)) + def testBasic(self): with self.test_session() as sess: values_queue = data_flow_ops.FIFOQueue( -- GitLab From 056c971faf024d38160f0e593f37b16be66da666 Mon Sep 17 00:00:00 2001 From: Youlong Cheng Date: Thu, 19 Jul 2018 14:55:15 -0700 Subject: [PATCH 164/519] PUBLIC: Relax batch_size checking for broadcast mode and fix a bug in broadcast mode. RELNOTES: n/a PiperOrigin-RevId: 205307114 --- tensorflow/contrib/tpu/python/tpu/tpu_context.py | 9 ++++++--- tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 3 ++- 2 files changed, 8 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_context.py b/tensorflow/contrib/tpu/python/tpu/tpu_context.py index 211c59cb90..750e677263 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_context.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_context.py @@ -595,7 +595,8 @@ class _InternalTPUContext(object): raise ValueError(message) if mode == model_fn_lib.ModeKeys.TRAIN: - if self._train_batch_size % num_replicas != 0: + if (self._train_batch_size % num_replicas != 0 and + not self.is_input_broadcast_with_iterators()): raise ValueError( 'train batch size {} must be divisible by number of replicas {}' .format(self._train_batch_size, num_replicas)) @@ -605,7 +606,8 @@ class _InternalTPUContext(object): raise ValueError( 'eval_batch_size in TPUEstimator constructor cannot be `None`' 'if .evaluate is running on TPU.') - if self._eval_batch_size % num_replicas != 0: + if (self._eval_batch_size % num_replicas != 0 and + not self.is_input_broadcast_with_iterators()): raise ValueError( 'eval batch size {} must be divisible by number of replicas {}' .format(self._eval_batch_size, num_replicas)) @@ -619,7 +621,8 @@ class _InternalTPUContext(object): raise ValueError( 'predict_batch_size in TPUEstimator constructor should not be ' '`None` if .predict is running on TPU.') - if self._predict_batch_size % num_replicas != 0: + if (self._predict_batch_size % num_replicas != 0 and + not self.is_input_broadcast_with_iterators()): raise ValueError( 'predict batch size {} must be divisible by number of replicas {}' .format(self._predict_batch_size, num_replicas)) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index c7cd7896e0..73dfefd19c 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -858,7 +858,8 @@ def generate_broadcast_enqueue_ops_fn(ctx, input_fn, inputs_structure_recorder, if ctx.mode == model_fn_lib.ModeKeys.PREDICT: raise TypeError('Mode PREDICT not yet supported in BROADCAST mode.') - hooks.append(inputs.dataset_initializer_hook()) + if is_dataset: + hooks.append(inputs.dataset_initializer_hook()) num_replicas_per_host = ctx.num_of_replicas_per_host def tpu_ordinal_function_impl(replica_id): -- GitLab From 5f34e4ded7ac519c4102029eb2f22fb9b4b27aba Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Thu, 19 Jul 2018 15:04:57 -0700 Subject: [PATCH 165/519] Cleanups to BFCAllocator::FreeAndMaybeCoalesce. No functional change. PiperOrigin-RevId: 205308949 --- .../core/common_runtime/bfc_allocator.cc | 48 +++++++------------ 1 file changed, 16 insertions(+), 32 deletions(-) diff --git a/tensorflow/core/common_runtime/bfc_allocator.cc b/tensorflow/core/common_runtime/bfc_allocator.cc index 9cda17867b..f8ca039d15 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.cc +++ b/tensorflow/core/common_runtime/bfc_allocator.cc @@ -465,49 +465,33 @@ void BFCAllocator::FreeAndMaybeCoalesce(BFCAllocator::ChunkHandle h) { Chunk* c = ChunkFromHandle(h); CHECK(c->in_use() && (c->bin_num == kInvalidBinNum)); - // Mark the chunk as no longer in use + // Mark the chunk as no longer in use. c->allocation_id = -1; // Updates the stats. stats_.bytes_in_use -= c->size; - // This chunk is no longer in-use, consider coalescing the chunk - // with adjacent chunks. - ChunkHandle chunk_to_reassign = h; + ChunkHandle coalesced_chunk = h; - // If the next chunk is free, coalesce the two - if (c->next != kInvalidChunkHandle) { - Chunk* cnext = ChunkFromHandle(c->next); - if (!cnext->in_use()) { - // VLOG(8) << "Chunk at " << cnext->ptr << " merging with c " << - // c->ptr; - - chunk_to_reassign = h; - - // Deletes c->next - RemoveFreeChunkFromBin(c->next); - Merge(h, ChunkFromHandle(h)->next); - } + // If the next chunk is free, merge it into c and delete it. + if (c->next != kInvalidChunkHandle && !ChunkFromHandle(c->next)->in_use()) { + // VLOG(8) << "Merging c->next " << ChunkFromHandle(c->next)->ptr + // << " with c " << c->ptr; + RemoveFreeChunkFromBin(c->next); + Merge(h, c->next); } - // If the previous chunk is free, coalesce the two - c = ChunkFromHandle(h); - if (c->prev != kInvalidChunkHandle) { - Chunk* cprev = ChunkFromHandle(c->prev); - if (!cprev->in_use()) { - // VLOG(8) << "Chunk at " << c->ptr << " merging into c->prev " - // << cprev->ptr; + // If the previous chunk is free, merge c into it and delete c. + if (c->prev != kInvalidChunkHandle && !ChunkFromHandle(c->prev)->in_use()) { + // VLOG(8) << "Merging c " << c->ptr << " into c->prev " + // << ChunkFromHandle(c->prev)->ptr; - chunk_to_reassign = c->prev; - - // Deletes c - RemoveFreeChunkFromBin(c->prev); - Merge(ChunkFromHandle(h)->prev, h); - c = ChunkFromHandle(h); - } + coalesced_chunk = c->prev; + RemoveFreeChunkFromBin(c->prev); + Merge(c->prev, h); } - InsertFreeChunkIntoBin(chunk_to_reassign); + InsertFreeChunkIntoBin(coalesced_chunk); } void BFCAllocator::AddAllocVisitor(Visitor visitor) { -- GitLab From 6860bf17aa7fc4a4ff323d8913cee6dccd54a52b Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Thu, 19 Jul 2018 15:22:22 -0700 Subject: [PATCH 166/519] Fix dependency issues. --- tensorflow/contrib/tensorrt/BUILD | 4 +++- .../tensorrt/test/tf_trt_integration_test_base.py | 12 ++++++------ 2 files changed, 9 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index dea9c0a4ae..317041b87d 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -153,6 +153,7 @@ py_library( ], srcs_version = "PY2AND3", deps = [ + ":tf_trt_integration_test_base", ":trt_convert_py", ":trt_ops_py", "//tensorflow/python:errors", @@ -327,7 +328,8 @@ py_library( name = "tf_trt_integration_test_base", srcs = ["test/tf_trt_integration_test_base.py"], deps = [ - ":init_py", + ":trt_convert_py", + ":trt_ops_py", "//tensorflow/python:client_testlib", "//tensorflow/python:framework_test_lib", ], diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py index 6e12e7e026..560dc256fa 100644 --- a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py +++ b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py @@ -24,14 +24,14 @@ import warnings import numpy as np import six -from tensorflow.contrib import tensorrt as trt +from tensorflow.contrib.tensorrt.python.ops import trt_engine_op +from tensorflow.contrib.tensorrt.python import trt_convert from tensorflow.core.protobuf import config_pb2 from tensorflow.core.protobuf import rewriter_config_pb2 from tensorflow.python.framework import importer from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.ops import math_ops -from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging TfTrtIntegrationTestParams = namedtuple("TfTrtIntegrationTestParams", [ @@ -105,7 +105,7 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): graph_options = config_pb2.GraphOptions() gpu_options = config_pb2.GPUOptions() - if trt.trt_convert.get_linked_tensorrt_version()[0] == 3: + if trt_convert.get_linked_tensorrt_version()[0] == 3: gpu_options.per_process_gpu_memory_fraction = 0.50 config = config_pb2.ConfigProto( @@ -145,7 +145,7 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): def _GetTrtGraphDef(self, params, gdef, precision_mode, is_dynamic_op): """Return trt converted graphdef.""" - return trt.create_inference_graph( + return trt_convert.create_inference_graph( input_graph_def=gdef, outputs=[self.output_name], max_batch_size=max([dims[0] for dims in params.input_dims]), @@ -213,7 +213,7 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): dynamic_calib_engine) result = self._RunCalibration(params, calib_gdef, input_data, calib_config) - infer_gdef = trt.calib_graph_to_infer_graph(calib_gdef) + infer_gdef = trt_convert.calib_graph_to_infer_graph(calib_gdef) self._VerifyGraphDef(params, infer_gdef, precision_mode, True, dynamic_calib_engine) @@ -320,5 +320,5 @@ def _AddTests(test_class): dynamic_calib_engine)) -if trt.is_tensorrt_enabled(): +if trt_convert.is_tensorrt_enabled(): _AddTests(TfTrtIntegrationTestBase) -- GitLab From 97f89dcd6ec02d40f6aae1d5ce5ffa377a40c110 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Thu, 19 Jul 2018 15:26:32 -0700 Subject: [PATCH 167/519] [XLA:CPU] Don't create dead constants in WhileLoopSinking We run this in a fixpoint pipeline together with DCE, dead constants mean that the fixpoint will never be reached. PiperOrigin-RevId: 205312251 --- .../service/while_loop_constant_sinking.cc | 6 +++ .../while_loop_constant_sinking_test.cc | 45 +++++++++++++++++++ 2 files changed, 51 insertions(+) diff --git a/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc b/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc index 10fc4958fa..62af45128a 100644 --- a/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc +++ b/tensorflow/compiler/xla/service/while_loop_constant_sinking.cc @@ -61,6 +61,12 @@ StatusOr WhileLoopConstantSinking::TrySinkingConstantsIntoWhileBody( WhileUtil::GetInvariantGTEsForWhileBody(*while_body)) { int64 index = invariant_gte->tuple_index(); const HloInstruction& invariant_value = *init_value.operand(index); + + // Should have at least one user that's not while_body_root. + if (invariant_gte->user_count() <= 1) { + continue; + } + if (invariant_value.opcode() == HloOpcode::kConstant) { auto* constant_instr = while_body->AddInstruction(invariant_value.Clone(/*suffix=*/".sunk")); diff --git a/tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc b/tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc index 393e758038..266039d2ff 100644 --- a/tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc +++ b/tensorflow/compiler/xla/service/while_loop_constant_sinking_test.cc @@ -196,5 +196,50 @@ ENTRY entry { op::GetTupleElement(op::Parameter(0)), op::GetTupleElement(op::Parameter(0)))); } + +TEST_F(WhileLoopConstantSinkingTest, DontCreateDeadConstant) { + const char* const hlo_string = R"( +HloModule ModuleWithWhile + +body { + p_body = (f32[2],f32[2]) parameter(0) + p_body.0 = f32[2] get-tuple-element((f32[2],f32[2]) p_body), index=0 + p_body.1 = f32[2] get-tuple-element((f32[2],f32[2]) p_body), index=1 + + outfeed = token[] outfeed(p_body.0) + ROOT root = (f32[2],f32[2],f32[2]) tuple(p_body.0, p_body.1, p_body.1) +} + +condition { + p_cond = (f32[2],f32[2]) parameter(0) + ROOT result = pred[] constant(true) +} + +ENTRY entry { + const_0 = f32[2] constant({1, 2}) + const_1 = f32[2] constant({2, 1}) + while_init = (f32[2],f32[2]) tuple(const_0, const_1) + ROOT while = (f32[2],f32[2],f32[2]) while(while_init), condition=condition, + body=body +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseHloString(hlo_string)); + + TF_ASSERT_OK_AND_ASSIGN(bool changed, + WhileLoopConstantSinking{}.Run(module.get())); + ASSERT_TRUE(changed); + + auto* while_body = module->GetComputationWithName("body"); + EXPECT_THAT(while_body->root_instruction(), + op::Tuple(op::GetTupleElement(), op::GetTupleElement(), + op::GetTupleElement())); + for (const HloInstruction* inst : while_body->instructions()) { + if (inst->opcode() == HloOpcode::kConstant) { + EXPECT_GT(inst->user_count(), 0); + } + } +} } // namespace } // namespace xla -- GitLab From c0d998e3a772b0021b6283145e2aa24b701a0e7f Mon Sep 17 00:00:00 2001 From: Toby Boyd Date: Tue, 17 Jul 2018 14:26:44 -0700 Subject: [PATCH 168/519] Link lib and header where ./configure expects --- tensorflow/tools/ci_build/Dockerfile.gpu | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu b/tensorflow/tools/ci_build/Dockerfile.gpu index 7591ecc04e..46538d9ec7 100644 --- a/tensorflow/tools/ci_build/Dockerfile.gpu +++ b/tensorflow/tools/ci_build/Dockerfile.gpu @@ -22,6 +22,11 @@ RUN /install/install_golang.sh COPY install/.bazelrc /etc/bazel.bazelrc ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH +# Link NCCL libray and header where the build script expects them. +RUN mkdir /usr/local/cuda-9.0/lib && \ + ln -s /usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/local/cuda/lib/libnccl.so.2 && \ + ln -s /usr/include/nccl.h /usr/local/cuda/include/nccl.h + # Configure the build for our CUDA configuration. ENV TF_NEED_CUDA 1 ENV TF_CUDA_COMPUTE_CAPABILITIES 3.0 -- GitLab From c519794c7cca51d2c75aa53b56a1448804f68647 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Thu, 19 Jul 2018 15:32:00 -0700 Subject: [PATCH 169/519] Disable the plugin test by default. --- tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD index 1ef1c3de75..69058c5826 100644 --- a/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD +++ b/tensorflow/contrib/tensorrt/custom_plugin_examples/BUILD @@ -111,7 +111,10 @@ cuda_py_test( "//tensorflow/python:tf_optimizer", ], tags = [ + "manual", "no_windows", + "noguitar", "nomac", + "notap", ], ) -- GitLab From a103552156432bcda7e29e5588e83c62d5154b88 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Thu, 19 Jul 2018 15:35:28 -0700 Subject: [PATCH 170/519] Disable unused import errors. --- .../contrib/tensorrt/test/tf_trt_integration_test_base.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py index 560dc256fa..301f7b44c4 100644 --- a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py +++ b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py @@ -24,7 +24,9 @@ import warnings import numpy as np import six +# pylint: disable=unused-import from tensorflow.contrib.tensorrt.python.ops import trt_engine_op +# pylint: enable=unused-import from tensorflow.contrib.tensorrt.python import trt_convert from tensorflow.core.protobuf import config_pb2 from tensorflow.core.protobuf import rewriter_config_pb2 -- GitLab From b7b86af2425f166c586fef80e6ae46991cdedde9 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Thu, 19 Jul 2018 15:39:25 -0700 Subject: [PATCH 171/519] Teach XlaTensorBuffer to not deallocate null pointers Zero sized XlaTensorBuffer can have null as the backing storage. De-allocating a null pointer produces an annoying warning from the BFCAllocator. PiperOrigin-RevId: 205314271 --- tensorflow/compiler/jit/xla_launch_util.h | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/jit/xla_launch_util.h b/tensorflow/compiler/jit/xla_launch_util.h index 90531174ff..1ea3fa4cf2 100644 --- a/tensorflow/compiler/jit/xla_launch_util.h +++ b/tensorflow/compiler/jit/xla_launch_util.h @@ -122,7 +122,11 @@ class XlaTensorBuffer : public TensorBuffer { data_ = const_cast(ptr); } - ~XlaTensorBuffer() override { allocator_->DeallocateRaw(data_); } + ~XlaTensorBuffer() override { + if (data_) { + allocator_->DeallocateRaw(data_); + } + } void* data() const override { return data_; } size_t size() const override { return expected_size_; } -- GitLab From 716d15118f62c17c29bbec4d006fd3055bb56812 Mon Sep 17 00:00:00 2001 From: Reed Wanderman-Milne Date: Thu, 19 Jul 2018 15:52:35 -0700 Subject: [PATCH 172/519] Update minimum bazel version to 0.13.0. I was using bazel 0.12.0 and was getting the error: file '@bazel_tools//tools/cpp:windows_cc_configure.bzl' does not contain symbol 'setup_vc_env_vars' PiperOrigin-RevId: 205316270 --- WORKSPACE | 2 +- configure.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/WORKSPACE b/WORKSPACE index fd7570a80a..e7cf23a159 100644 --- a/WORKSPACE +++ b/WORKSPACE @@ -18,7 +18,7 @@ closure_repositories() # files, in case the parsing of those build files depends on the bazel # version we require here. load("//tensorflow:version_check.bzl", "check_bazel_version_at_least") -check_bazel_version_at_least("0.10.0") +check_bazel_version_at_least("0.13.0") load("//tensorflow:workspace.bzl", "tf_workspace") diff --git a/configure.py b/configure.py index 60fe54b2f6..251bebc2e1 100644 --- a/configure.py +++ b/configure.py @@ -1451,7 +1451,7 @@ def main(): # environment variables. environ_cp = dict(os.environ) - check_bazel_version('0.10.0') + check_bazel_version('0.13.0') reset_tf_configure_bazelrc(args.workspace) cleanup_makefile() -- GitLab From 3647625e531e713ad9a7fb0f3c5b68863ae4e7b8 Mon Sep 17 00:00:00 2001 From: Guangda Lai Date: Thu, 19 Jul 2018 15:53:13 -0700 Subject: [PATCH 173/519] Implement something similar to std::align since it's not supported. PiperOrigin-RevId: 205316355 --- tensorflow/contrib/tensorrt/BUILD | 30 ++++++- .../contrib/tensorrt/convert/convert_nodes.h | 1 + .../tensorrt/resources/trt_allocator.cc | 49 +++++++++--- .../tensorrt/resources/trt_allocator.h | 14 +++- .../tensorrt/resources/trt_allocator_test.cc | 79 +++++++++++++++++++ 5 files changed, 160 insertions(+), 13 deletions(-) create mode 100644 tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index 70ce4a499c..a9378e9ad6 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -80,6 +80,7 @@ cc_library( copts = tf_copts(), visibility = ["//visibility:public"], deps = [ + ":trt_allocator", ":trt_logging", ":trt_plugins", ":trt_resources", @@ -195,17 +196,16 @@ tf_py_wrap_cc( tf_cuda_library( name = "trt_resources", srcs = [ - "resources/trt_allocator.cc", "resources/trt_int8_calibrator.cc", "resources/trt_resource_manager.cc", ], hdrs = [ - "resources/trt_allocator.h", "resources/trt_int8_calibrator.h", "resources/trt_resource_manager.h", "resources/trt_resources.h", ], deps = [ + ":trt_allocator", ":trt_logging", ":utils", "//tensorflow/core:framework_headers_lib", @@ -216,6 +216,31 @@ tf_cuda_library( ]), ) +tf_cuda_library( + name = "trt_allocator", + srcs = ["resources/trt_allocator.cc"], + hdrs = ["resources/trt_allocator.h"], + deps = [ + "//tensorflow/core:framework_headers_lib", + "//tensorflow/core:framework_lite", + "//tensorflow/core:lib_proto_parsing", + ] + if_tensorrt([ + "@local_config_tensorrt//:nv_infer", + ]), +) + +tf_cc_test( + name = "trt_allocator_test", + size = "small", + srcs = ["resources/trt_allocator_test.cc"], + tags = ["no_windows"], + deps = [ + ":trt_allocator", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + # Library for the node-level conversion portion of TensorRT operation creation tf_cuda_library( name = "trt_conversion", @@ -231,6 +256,7 @@ tf_cuda_library( ], deps = [ ":segment", + ":trt_allocator", ":trt_plugins", ":trt_logging", ":trt_resources", diff --git a/tensorflow/contrib/tensorrt/convert/convert_nodes.h b/tensorflow/contrib/tensorrt/convert/convert_nodes.h index 1a4c0e755d..81baf8e7c1 100644 --- a/tensorflow/contrib/tensorrt/convert/convert_nodes.h +++ b/tensorflow/contrib/tensorrt/convert/convert_nodes.h @@ -23,6 +23,7 @@ limitations under the License. #include #include "tensorflow/contrib/tensorrt/convert/utils.h" +#include "tensorflow/contrib/tensorrt/log/trt_logger.h" #include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" #include "tensorflow/contrib/tensorrt/resources/trt_int8_calibrator.h" #include "tensorflow/core/framework/graph.pb.h" diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc index 81d7330b49..d8f97bfbbc 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.cc +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.cc @@ -19,12 +19,42 @@ limitations under the License. #if GOOGLE_CUDA #if GOOGLE_TENSORRT +#include "cuda/include/cuda_runtime_api.h" +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +namespace tensorflow { +namespace tensorrt { + +// std::align is not supported, so this method mimic its behavior. +void* Align(size_t alignment, size_t size, void*& ptr, size_t& space) { + QCHECK_GT(alignment, 0) << "alignment must be greater than 0."; + QCHECK_EQ(0, alignment & (alignment - 1)) << "Alignment must be power of 2."; + QCHECK_GT(size, 0) << "size must be greater than 0."; + QCHECK(ptr) << "ptr must not be nullptr."; + QCHECK_GT(space, 0) << "space must be greater than 0."; + const uintptr_t ptr_val = reinterpret_cast(ptr); + QCHECK_GE(ptr_val + space, ptr_val) << "Provided space overflows."; + if (size > space) return nullptr; + const uintptr_t aligned_ptr_val = ((ptr_val + alignment - 1) & -alignment); + if (aligned_ptr_val > ptr_val + space - size) return nullptr; + ptr = reinterpret_cast(aligned_ptr_val); + const uintptr_t diff = aligned_ptr_val - ptr_val; + space -= diff; + return ptr; +} + +} // namespace tensorrt +} // namespace tensorflow + +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT #if NV_TENSORRT_MAJOR > 2 -#include "cuda/include/cuda_runtime_api.h" namespace tensorflow { namespace tensorrt { + void* TRTCudaAllocator::allocate(uint64_t size, uint64_t alignment, uint32_t flags) { assert((alignment & (alignment - 1)) == 0); // zero or a power of 2. @@ -44,17 +74,16 @@ void* TRTDeviceAllocator::allocate(uint64_t size, uint64_t alignment, assert((alignment & (alignment - 1)) == 0); // zero or a power of 2. size_t total_size = size + alignment; void* mem = allocator_->AllocateRaw(alignment, total_size); - if (!mem) { - return nullptr; - } + if (!mem) return nullptr; void* alloc_mem = mem; - CHECK(std::align(alignment, size, mem, total_size)); + QCHECK(Align(alignment, size, mem, total_size)); if (mem != alloc_mem) { - CHECK(mem_map_.insert({mem, alloc_mem}).second); + QCHECK(mem_map_.insert({mem, alloc_mem}).second); } - VLOG(2) << "Allocated " << size << " bytes with alignment " << alignment - << " @ " << mem; + VLOG(2) << "Allocated " << total_size << " bytes memory @" << alloc_mem + << "; aligned to " << size << " bytes @" << mem << " with alignment " + << alignment; return mem; } @@ -80,5 +109,5 @@ void TRTDeviceAllocator::free(void* memory) { } // namespace tensorflow #endif -#endif -#endif +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator.h b/tensorflow/contrib/tensorrt/resources/trt_allocator.h index b8825b108d..6f94492083 100644 --- a/tensorflow/contrib/tensorrt/resources/trt_allocator.h +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator.h @@ -16,13 +16,25 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_ #define TENSORFLOW_CONTRIB_TENSORRT_RESOURCES_TRT_ALLOCATOR_H_ -#include "tensorflow/contrib/tensorrt/log/trt_logger.h" +#include + #include "tensorflow/core/framework/allocator.h" #if GOOGLE_CUDA #if GOOGLE_TENSORRT #include "tensorrt/include/NvInfer.h" +#endif // GOOGLE_TENSORRT +#endif // GOOGLE_CUDA + +namespace tensorflow { +namespace tensorrt { +// std::align is not supported, so this function mimic its behavior. +void* Align(size_t alignment, size_t size, void*& ptr, size_t& space); +} // namespace tensorrt +} // namespace tensorflow +#if GOOGLE_CUDA +#if GOOGLE_TENSORRT #if NV_TENSORRT_MAJOR == 3 // Define interface here temporarily until TRT 4.0 is released namespace nvinfer1 { diff --git a/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc new file mode 100644 index 0000000000..f515ed03f2 --- /dev/null +++ b/tensorflow/contrib/tensorrt/resources/trt_allocator_test.cc @@ -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. +==============================================================================*/ + +#include "tensorflow/contrib/tensorrt/resources/trt_allocator.h" + +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace tensorrt { + +bool RunTest(const size_t alignment, const size_t size, + const intptr_t orig_ptr_val, const size_t orig_space) { + void* const orig_ptr = reinterpret_cast(orig_ptr_val); + void* ptr = orig_ptr; + size_t space = orig_space; + void* result = Align(alignment, size, ptr, space); + if (result == nullptr) { + EXPECT_EQ(orig_ptr, ptr); + EXPECT_EQ(orig_space, space); + return false; + } else { + EXPECT_EQ(result, ptr); + const intptr_t ptr_val = reinterpret_cast(ptr); + EXPECT_EQ(0, ptr_val % alignment); + EXPECT_GE(ptr_val, orig_ptr_val); + EXPECT_GE(space, size); + EXPECT_LE(space, orig_space); + EXPECT_EQ(ptr_val + space, orig_ptr_val + orig_space); + return true; + } +} + +TEST(TRTAllocatorTest, Align) { + for (const size_t space : + {1, 2, 3, 4, 7, 8, 9, 10, 16, 32, 511, 512, 513, 700, 12345}) { + for (size_t alignment = 1; alignment <= space * 4; alignment *= 2) { + for (const intptr_t ptr_val : + {1ul, alignment == 1 ? 1ul : alignment - 1, alignment, alignment + 1, + alignment + (alignment / 2)}) { + if (ptr_val % alignment == 0) { + for (const size_t size : + {1ul, space == 1 ? 1ul : space - 1, space, space + 1}) { + EXPECT_EQ(space >= size, RunTest(alignment, size, ptr_val, space)); + } + } else { + EXPECT_FALSE(RunTest(alignment, space, ptr_val, space)); + const size_t diff = alignment - ptr_val % alignment; + if (space > diff) { + EXPECT_TRUE( + RunTest(alignment, space - diff, ptr_val + diff, space - diff)); + for (const size_t size : + {1ul, space - diff > 1 ? space - diff - 1 : 1ul, space - diff, + space - diff + 1, space - 1}) { + EXPECT_EQ(space - diff >= size, + RunTest(alignment, size, ptr_val, space)); + } + } else { + EXPECT_FALSE(RunTest(alignment, 1, ptr_val, space)); + } + } + } + } + } +} + +} // namespace tensorrt +} // namespace tensorflow -- GitLab From 8f130ff5b021efb94946ed9deb1341890763fd3f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 15:58:10 -0700 Subject: [PATCH 174/519] Fix ResourceVariable placement during checkpointing to correctly colocate the copy of the variable on the same machine. Addresses Issue #20914. PiperOrigin-RevId: 205317119 --- tensorflow/python/training/saver.py | 7 ++++++- tensorflow/python/training/saver_test.py | 18 ++++++++++++++++++ 2 files changed, 24 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 1ee975fbe4..11510d9928 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -126,7 +126,12 @@ class BaseSaverBuilder(object): def f(): with ops.device(v.device): x = v.read_value() - with ops.device("/device:CPU:0"): + # To allow variables placed on non-CPU devices to be checkpointed, + # we copy them to CPU on the same machine first. + device_spec = pydev.DeviceSpec().parse_from_string(v.device) + device_spec.merge_from( + pydev.DeviceSpec().parse_from_string("/device:CPU:0")) + with ops.device(device_spec.to_string()): return array_ops.identity(x) return f diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py index ae9c244aaf..ecce8ae6bd 100644 --- a/tensorflow/python/training/saver_test.py +++ b/tensorflow/python/training/saver_test.py @@ -174,6 +174,24 @@ class SaverTest(test.TestCase): def testResourceBasic(self): self.basicSaveRestore(resource_variable_ops.ResourceVariable) + def testResourceColocation(self): + partitioner = partitioned_variables.fixed_size_partitioner(num_shards=2) + with ops_lib.device("/job:ps/device:GPU:0"): + v = variable_scope.get_variable("v0", + shape=[10, 2], + partitioner=partitioner, + use_resource=True) + saver_module.Saver({"v0": v}).build() + save_op = None + for op in ops_lib.get_default_graph().get_operations(): + if op.type == "SaveV2": + save_op = op + break + assert save_op is not None + for save_inp in save_op.inputs[3:]: + # Input to SaveV2 op is placed on CPU of the same device as the Variable. + self.assertEqual("/job:ps/device:CPU:0", save_inp.device) + def testResourceVariableReadOpsAddedDeterministically(self): graph_defs = [] num_graphs = 10 -- GitLab From 00affecfa57c7ad20e3438aadb3de5686bfce9d0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 16:48:04 -0700 Subject: [PATCH 175/519] Remove elem_type from tensorflow Pack export, tensorflow doesn't like graphs that use this attribute in Pack. PiperOrigin-RevId: 205324618 --- tensorflow/contrib/lite/toco/export_tensorflow.cc | 2 -- 1 file changed, 2 deletions(-) diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc index f9a6d31d60..b79bb300f0 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc @@ -1257,8 +1257,6 @@ void ConvertPackOperator(const Model& model, const PackOperator& src_op, for (const auto& input : src_op.inputs) { *pack_op->add_input() = input; } - (*pack_op->mutable_attr())["elem_type"].set_type( - GetTensorFlowDataType(model, src_op.outputs[0])); (*pack_op->mutable_attr())["axis"].set_i(src_op.axis); (*pack_op->mutable_attr())["N"].set_i(src_op.inputs.size()); (*pack_op->mutable_attr())["T"].set_type(GetTensorFlowDataType(src_op.dtype)); -- GitLab From ea06626358edc5fd3cbdae4839b3d51cd7b0dfa4 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Thu, 19 Jul 2018 17:10:08 -0700 Subject: [PATCH 176/519] Update comments in BFCAllocator. PiperOrigin-RevId: 205327986 --- .../core/common_runtime/bfc_allocator.cc | 4 --- .../core/common_runtime/bfc_allocator.h | 27 ++++++++++++++----- 2 files changed, 20 insertions(+), 11 deletions(-) diff --git a/tensorflow/core/common_runtime/bfc_allocator.cc b/tensorflow/core/common_runtime/bfc_allocator.cc index f8ca039d15..3bf0532491 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.cc +++ b/tensorflow/core/common_runtime/bfc_allocator.cc @@ -155,10 +155,6 @@ bool BFCAllocator::Extend(size_t alignment, size_t rounded_bytes) { region_manager_.set_handle(c->ptr, h); - // TODO(vrv): Try to merge this new region with an existing region, - // if the address space is contiguous, to avoid fragmentation - // across regions. - // Insert the chunk into the right bin. InsertFreeChunkIntoBin(h); diff --git a/tensorflow/core/common_runtime/bfc_allocator.h b/tensorflow/core/common_runtime/bfc_allocator.h index cd8ff6e5c0..580e61e2ea 100644 --- a/tensorflow/core/common_runtime/bfc_allocator.h +++ b/tensorflow/core/common_runtime/bfc_allocator.h @@ -88,11 +88,20 @@ class BFCAllocator : public VisitableAllocator { static const int kInvalidBinNum = -1; static const int kNumBins = 21; - // Chunks point to memory. Their prev/next pointers form a - // doubly-linked list of addresses sorted by base address that - // must be contiguous. Chunks contain information about whether - // they are in use or whether they are free, and contain a pointer - // to the bin they are in. + // A Chunk points to a piece of memory that's either entirely free or entirely + // in use by one user memory allocation. + // + // An AllocationRegion's memory is split up into one or more disjoint Chunks, + // which together cover the whole region without gaps. Chunks participate in + // a doubly-linked list, and the prev/next pointers point to the physically + // adjacent chunks. + // + // Since a chunk cannot be partially in use, we may need to split a free chunk + // in order to service a user allocation. We always merge adjacent free + // chunks. + // + // Chunks contain information about whether they are in use or whether they + // are free, and contain a pointer to the bin they are in. struct Chunk { size_t size = 0; // Full size of buffer. @@ -177,8 +186,12 @@ class BFCAllocator : public VisitableAllocator { static const size_t kMinAllocationBits = 8; static const size_t kMinAllocationSize = 1 << kMinAllocationBits; - // AllocationRegion maps pointers to ChunkHandles for a single - // contiguous memory region. + // BFCAllocator allocates memory into a collection of disjoint + // AllocationRegions. Each AllocationRegion corresponds to one call to + // SubAllocator::Alloc(). + // + // An AllocationRegion contains one or more Chunks, covering all of its + // memory. Its primary job is to map a pointers to ChunkHandles. // // This class is thread-compatible. class AllocationRegion { -- GitLab From 78121905a545ca8e91ab1dbc899b5509933331d7 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 17:22:25 -0700 Subject: [PATCH 177/519] Set correct TF_OVERRIDE_EIGEN_STRONG_INLINE value in build_tf_windows.sh PiperOrigin-RevId: 205329361 --- tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh | 4 +++- tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh | 4 +++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index dc7ea1dc57..42f58deb42 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -66,11 +66,13 @@ for ARG in "$@"; do fi done -if [[ "$release_build" != 1 ]]; then +if [[ "$release_build" == 1 ]]; then # Overriding eigen strong inline speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc # by 20 minutes. See https://github.com/tensorflow/tensorflow/issues/10521 # Because this hurts the performance of TF, we don't override it in release build. export TF_OVERRIDE_EIGEN_STRONG_INLINE=0 +else + export TF_OVERRIDE_EIGEN_STRONG_INLINE=1 fi # Enable short object file path to avoid long path issue on Windows. diff --git a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh index a4175a0e81..2a8c2d9167 100644 --- a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh @@ -66,11 +66,13 @@ for ARG in "$@"; do fi done -if [[ "$release_build" != 1 ]]; then +if [[ "$release_build" == 1 ]]; then # Overriding eigen strong inline speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc # by 20 minutes. See https://github.com/tensorflow/tensorflow/issues/10521 # Because this hurts the performance of TF, we don't override it in release build. export TF_OVERRIDE_EIGEN_STRONG_INLINE=0 +else + export TF_OVERRIDE_EIGEN_STRONG_INLINE=1 fi # Enable short object file path to avoid long path issue on Windows. -- GitLab From 95ec73a1c8b5174ec2221c2b5ecaf179c9deef48 Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Thu, 19 Jul 2018 17:28:52 -0700 Subject: [PATCH 178/519] Fix clang builds for NCCL error. PiperOrigin-RevId: 205330050 --- tensorflow/tools/ci_build/Dockerfile.gpu | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu b/tensorflow/tools/ci_build/Dockerfile.gpu index 7591ecc04e..6ab703d93d 100644 --- a/tensorflow/tools/ci_build/Dockerfile.gpu +++ b/tensorflow/tools/ci_build/Dockerfile.gpu @@ -14,6 +14,11 @@ 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 + +# Install NCCL +RUN apt-get update && apt-get install -y --no-install-recommends \ + libnccl2=2.2.13-1+cuda9.0 + RUN /install/install_pip_packages.sh RUN /install/install_bazel.sh RUN /install/install_golang.sh -- GitLab From 37bbf89920f013ef1d59f0eaef65431d4f4a4a28 Mon Sep 17 00:00:00 2001 From: Rohan Jain Date: Thu, 19 Jul 2018 17:37:22 -0700 Subject: [PATCH 179/519] Fixing bug where in Eager mode datasets not intended to be on the GPU end up being there. PiperOrigin-RevId: 205331171 --- .../contrib/eager/python/datasets_test.py | 14 ++++++++ tensorflow/python/data/ops/iterator_ops.py | 33 ++++++++++--------- 2 files changed, 31 insertions(+), 16 deletions(-) diff --git a/tensorflow/contrib/eager/python/datasets_test.py b/tensorflow/contrib/eager/python/datasets_test.py index 68bec9aee8..acc605247f 100644 --- a/tensorflow/contrib/eager/python/datasets_test.py +++ b/tensorflow/contrib/eager/python/datasets_test.py @@ -193,6 +193,20 @@ class IteratorTest(test.TestCase): x = math_ops.add(x, x) self.assertAllEqual([0., 2.], x.numpy()) + def testGpuTensor(self): + ds = Dataset.from_tensors([0., 1.]) + with ops.device(test.gpu_device_name()): + for x in ds: + y = math_ops.add(x, x) + self.assertAllEqual([0., 2.], y.numpy()) + + def testGpuDefinedDataset(self): + with ops.device(test.gpu_device_name()): + ds = Dataset.from_tensors([0., 1.]) + for x in ds: + y = math_ops.add(x, x) + self.assertAllEqual([0., 2.], y.numpy()) + def testTensorsExplicitPrefetchToDevice(self): ds = Dataset.from_tensor_slices([0., 1.]) ds = ds.apply(prefetching_ops.prefetch_to_device(test.gpu_device_name())) diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py index f0784ed3d0..3ef22cf981 100644 --- a/tensorflow/python/data/ops/iterator_ops.py +++ b/tensorflow/python/data/ops/iterator_ops.py @@ -500,22 +500,23 @@ class EagerIterator(object): "tf.data.Dataset.make_one_shot_iterator for graph construction". format(type(self))) self._device = context.context().device_name - ds_variant = dataset._as_variant_tensor() # pylint: disable=protected-access - self._output_classes = dataset.output_classes - self._output_types = dataset.output_types - self._output_shapes = dataset.output_shapes - self._flat_output_types = nest.flatten( - sparse.as_dense_types(self._output_types, self._output_classes)) - self._flat_output_shapes = nest.flatten( - sparse.as_dense_shapes(self._output_shapes, self._output_classes)) - with ops.colocate_with(ds_variant): - self._resource = gen_dataset_ops.anonymous_iterator( - output_types=self._flat_output_types, - output_shapes=self._flat_output_shapes) - gen_dataset_ops.make_iterator(ds_variant, self._resource) - # Delete the resource when this object is deleted - self._resource_deleter = resource_variable_ops.EagerResourceDeleter( - handle=self._resource, handle_device=self._device) + with ops.device("/cpu:0"): + ds_variant = dataset._as_variant_tensor() # pylint: disable=protected-access + self._output_classes = dataset.output_classes + self._output_types = dataset.output_types + self._output_shapes = dataset.output_shapes + self._flat_output_types = nest.flatten( + sparse.as_dense_types(self._output_types, self._output_classes)) + self._flat_output_shapes = nest.flatten( + sparse.as_dense_shapes(self._output_shapes, self._output_classes)) + with ops.colocate_with(ds_variant): + self._resource = gen_dataset_ops.anonymous_iterator( + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes) + gen_dataset_ops.make_iterator(ds_variant, self._resource) + # Delete the resource when this object is deleted + self._resource_deleter = resource_variable_ops.EagerResourceDeleter( + handle=self._resource, handle_device=self._device) def __iter__(self): return self -- GitLab From 77c7b1112210beb3f0752f206bfa519f22aaf5c6 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Thu, 19 Jul 2018 18:12:34 -0700 Subject: [PATCH 180/519] Fix AdamOptimizer usage in a defun Adds init_scope around Adam's non-slot variable lookup so it's always accessed in a consistent context. PiperOrigin-RevId: 205335191 --- tensorflow/python/eager/function_test.py | 18 ++++++++++++++++++ tensorflow/python/training/adam.py | 13 +++++++------ tensorflow/python/training/adam_test.py | 6 ++++++ 3 files changed, 31 insertions(+), 6 deletions(-) diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index cdd9fe1760..e6592b2e37 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -45,6 +45,7 @@ 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 +from tensorflow.python.training import adam from tensorflow.python.training import momentum from tensorflow.python.training import training_ops from tensorflow.python.util import compat @@ -1166,6 +1167,23 @@ class AutomaticControlDependenciesTest(test.TestCase): value = train() self.assertEqual(value.numpy(), -1.0) + # TODO(b/111663004): This should work when the outer context is graph + # building. + def testOptimizerNonSlotVarsInDefunNoError(self): + def loss(v): + return v**2 + + optimizer = adam.AdamOptimizer(learning_rate=1.0) + + @function.defun + def train(): + v = resource_variable_ops.ResourceVariable(1.0) + grad = backprop.implicit_grad(loss)(v) + optimizer.apply_gradients(grad) + return v.read_value() + + train() + def testOptimizerInDefunWithCapturedVariable(self): v = resource_variable_ops.ResourceVariable(1.0) def loss(): diff --git a/tensorflow/python/training/adam.py b/tensorflow/python/training/adam.py index b65c88e972..bcbe5907d6 100644 --- a/tensorflow/python/training/adam.py +++ b/tensorflow/python/training/adam.py @@ -109,12 +109,13 @@ class AdamOptimizer(optimizer.Optimizer): self._updated_lr = None def _get_beta_accumulators(self): - if context.executing_eagerly(): - graph = None - else: - graph = ops.get_default_graph() - return (self._get_non_slot_variable("beta1_power", graph=graph), - self._get_non_slot_variable("beta2_power", graph=graph)) + with ops.init_scope(): + if context.executing_eagerly(): + graph = None + else: + graph = ops.get_default_graph() + return (self._get_non_slot_variable("beta1_power", graph=graph), + self._get_non_slot_variable("beta2_power", graph=graph)) def _create_slots(self, var_list): # Create the beta1 and beta2 accumulators on the same device as the first diff --git a/tensorflow/python/training/adam_test.py b/tensorflow/python/training/adam_test.py index ccdc7e384d..8f84427654 100644 --- a/tensorflow/python/training/adam_test.py +++ b/tensorflow/python/training/adam_test.py @@ -315,6 +315,12 @@ class AdamOptimizerTest(test.TestCase): def testTwoSessions(self): optimizer = adam.AdamOptimizer() + + with context.eager_mode(): + var0 = variables.Variable(np.array([1.0, 2.0]), name="v0") + grads0 = constant_op.constant(np.array([0.1, 0.1])) + optimizer.apply_gradients([(grads0, var0)]) + g = ops.Graph() with g.as_default(): with session.Session(): -- GitLab From 0c11bcb5f3443ce870f31f5ba013ae8bc375ad2d Mon Sep 17 00:00:00 2001 From: Jacker Date: Fri, 20 Jul 2018 10:09:16 +0800 Subject: [PATCH 181/519] Update saver.py Fix device placement of save_op for ResourceVariable. --- tensorflow/python/training/saver.py | 11 ++++------- 1 file changed, 4 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 11510d9928..60885e9292 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -126,13 +126,10 @@ class BaseSaverBuilder(object): def f(): with ops.device(v.device): x = v.read_value() - # To allow variables placed on non-CPU devices to be checkpointed, - # we copy them to CPU on the same machine first. - device_spec = pydev.DeviceSpec().parse_from_string(v.device) - device_spec.merge_from( - pydev.DeviceSpec().parse_from_string("/device:CPU:0")) - with ops.device(device_spec.to_string()): - return array_ops.identity(x) + # To allow variables placed on non-CPU devices to be checkpointed, + # we copy them to CPU on the same machine first. + with ops.device("/device:CPU:0"): + return array_ops.identity(x) return f self.handle_op = var.handle -- GitLab From db308efbf4e95a7362fde90d35447091349b548e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 19 Jul 2018 19:45:33 -0700 Subject: [PATCH 182/519] Allow input_shape to be specified in TOCO python converter PiperOrigin-RevId: 205342464 --- tensorflow/contrib/lite/python/convert.py | 9 ++++++++- 1 file changed, 8 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/python/convert.py b/tensorflow/contrib/lite/python/convert.py index 0ea2630f71..ec49738fb5 100644 --- a/tensorflow/contrib/lite/python/convert.py +++ b/tensorflow/contrib/lite/python/convert.py @@ -115,6 +115,7 @@ def build_toco_convert_protos(input_tensors, inference_type=lite_constants.FLOAT, inference_input_type=None, input_format=lite_constants.TENSORFLOW_GRAPHDEF, + input_shapes=None, output_format=lite_constants.TFLITE, quantized_input_stats=None, default_ranges_stats=None, @@ -141,6 +142,8 @@ def build_toco_convert_protos(input_tensors, Must be `{FLOAT, QUANTIZED_UINT8}`. (default `inference_type`) input_format: Type of data to read Currently must be `{TENSORFLOW_GRAPHDEF}`. (default TENSORFLOW_GRAPHDEF) + input_shapes: Input array shape. It needs to be a list of the same length + as `input_tensors`, or None. (default None) output_format: Output file format. Currently must be `{TFLITE, GRAPHVIZ_DOT}`. (default TFLITE) quantized_input_stats: List of tuples of integers representing the mean and @@ -209,7 +212,11 @@ def build_toco_convert_protos(input_tensors, if inference_type == lite_constants.QUANTIZED_UINT8: input_array.mean_value, input_array.std_value = quantized_input_stats[idx] input_array.name = tensor_name(input_tensor) - input_array.shape.dims.extend(map(int, input_tensor.get_shape())) + if input_shapes is None: + shape = input_tensor.get_shape() + else: + shape = input_shapes[idx] + input_array.shape.dims.extend(map(int, shape)) for output_tensor in output_tensors: model.output_arrays.append(tensor_name(output_tensor)) -- GitLab From ee5271968e17b1bc0b852b0285dad4a38e1a97a0 Mon Sep 17 00:00:00 2001 From: Saurabh Saxena Date: Thu, 19 Jul 2018 23:16:08 -0700 Subject: [PATCH 183/519] Support Defuns and nested Defuns inside cond_v2 branches. Support nested cond_v2s. PiperOrigin-RevId: 205356562 --- tensorflow/core/kernels/functional_ops.cc | 1 + tensorflow/python/BUILD | 4 +- .../python/framework/function_def_to_graph.py | 32 +- .../framework/function_def_to_graph_test.py | 34 +- tensorflow/python/kernel_tests/BUILD | 3 +- .../python/kernel_tests/cond_v2_test.py | 372 +++++++++++++++++- tensorflow/python/ops/cond_v2_impl.py | 139 ++++--- 7 files changed, 515 insertions(+), 70 deletions(-) diff --git a/tensorflow/core/kernels/functional_ops.cc b/tensorflow/core/kernels/functional_ops.cc index 519c475332..cb285bf732 100644 --- a/tensorflow/core/kernels/functional_ops.cc +++ b/tensorflow/core/kernels/functional_ops.cc @@ -536,6 +536,7 @@ class FakeParamOp : public OpKernel { }; REGISTER_KERNEL_BUILDER(Name("FakeParam").Device(DEVICE_CPU), FakeParamOp); +REGISTER_KERNEL_BUILDER(Name("FakeParam").Device(DEVICE_GPU), FakeParamOp); } // namespace } // namespace tensorflow diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index c33a579ad2..9c7f3b7b25 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -745,8 +745,8 @@ py_library( srcs_version = "PY2AND3", deps = [ ":framework", + ":framework_ops", ":function", - ":op_def_registry", ":tensor_shape", ":versions", "//tensorflow/core:protos_all_py", @@ -762,8 +762,10 @@ py_test( deps = [ ":array_ops", ":client_testlib", + ":constant_op", ":dtypes", ":framework_ops", + ":function", ":function_def_to_graph", ":graph_to_function_def", ":math_ops", diff --git a/tensorflow/python/framework/function_def_to_graph.py b/tensorflow/python/framework/function_def_to_graph.py index 46c9c4c14a..1b09506662 100644 --- a/tensorflow/python/framework/function_def_to_graph.py +++ b/tensorflow/python/framework/function_def_to_graph.py @@ -25,7 +25,7 @@ from tensorflow.core.framework import types_pb2 from tensorflow.core.framework import versions_pb2 from tensorflow.python.framework import function from tensorflow.python.framework import importer -from tensorflow.python.framework import op_def_registry +from tensorflow.python.framework import ops from tensorflow.python.framework import versions from tensorflow.python.ops import cond_v2_impl @@ -114,6 +114,10 @@ def function_def_to_graph_def(fdef, input_shapes=None): producer=versions.GRAPH_DEF_VERSION, min_consumer=versions.GRAPH_DEF_VERSION_MIN_CONSUMER)) + # Copy *all* functions from outer graph to `graph_def` so that both direct + # and indirect references are safely handled. + ops.get_default_graph()._copy_functions_to_graph_def(graph_def, 0) # pylint: disable=protected-access + if input_shapes and len(input_shapes) != len(fdef.signature.input_arg): raise ValueError("Length of input_shapes must match the number of " + "input_args. len(input_shapes): {} len(input_arg): {}". @@ -142,24 +146,18 @@ def function_def_to_graph_def(fdef, input_shapes=None): nested_to_flat_tensor_name[arg_def.name] = "{}:0".format(arg_def.name) for node_def in fdef.node_def: - op_def = op_def_registry.get_registered_ops().get(node_def.op) - if not op_def: - # TODO(b/80470245): Support functions which refer other functions. - raise NotImplementedError( - "No op registered for {},".format(node_def.op) + - " it may be a function. function_def_to_graph_def " + - "currently does not support converting functions with " + - "references to other graph functions.") + op_def = ops.get_default_graph()._get_op_def(node_def.op) # pylint: disable=protected-access for attr in op_def.attr: - if attr.type in ("func", "list(func)"): - # TODO(b/80470245): Support functions which refer other functions. - raise NotImplementedError("Unsupported attr {} ".format(attr.name) + - " with type {}".format(attr.type) + - " in op {}. ".format(op_def.name) + - "function_def_to_graph_def currently does " + - "not support converting functions with " + - "references to other graph functions.") + if attr.type == "func": + fname = node_def.attr[attr.name].func.name + if not ops.get_default_graph()._is_function(fname): # pylint: disable=protected-access + raise ValueError("%s function not found." % fname) + elif attr.type == "list(func)": + for fn in node_def.attr[attr.name].list.func: + fname = fn.name + if not ops.get_default_graph()._is_function(fname): # pylint: disable=protected-access + raise ValueError("%s function not found." % fname) # Iterate over output_args in op_def to build the map. # Index of the output tensor in the flattened list of *all* output diff --git a/tensorflow/python/framework/function_def_to_graph_test.py b/tensorflow/python/framework/function_def_to_graph_test.py index 0f4e6ef54f..cd2a16ed5a 100644 --- a/tensorflow/python/framework/function_def_to_graph_test.py +++ b/tensorflow/python/framework/function_def_to_graph_test.py @@ -18,7 +18,9 @@ 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 function from tensorflow.python.framework import function_def_to_graph from tensorflow.python.framework import graph_to_function_def from tensorflow.python.framework import ops @@ -79,7 +81,6 @@ class FunctionDefToGraphTest(test.TestCase): g = function_def_to_graph.function_def_to_graph( fdef, input_shapes=[None, tensor_shape.matrix(5, 7)]) - print(g.as_graph_def()) self.assertIsNone(g.inputs[0].shape.dims) self.assertSequenceEqual(g.inputs[1].shape.dims, [5, 7]) self.assertSequenceEqual(g.outputs[0].shape.dims, [5, 7]) @@ -179,6 +180,37 @@ class FunctionDefToGraphDefTest(test.TestCase): self.assertEqual(g.node[0].attr["shape"].shape.unknown_rank, False) self.assertFalse("shape" in g.node[2].attr) + def testFunctionCallsFromFunction(self): + x = constant_op.constant(5.0) + y = constant_op.constant(10.0) + + @function.Defun() + def fn(): + + @function.Defun() + def inner_fn(): + return x + y + + return inner_fn() + + # Instantiate the function in this graph so that + # `function_def_to_graph` can find it. + fn() + + def fn2(): + return 2 * fn() + + fdef = function._DefinedFunction(fn2, [], []).definition + func_graph = function_def_to_graph.function_def_to_graph(fdef) + with func_graph.as_default(): + x_ph, y_ph = func_graph.inputs + with self.test_session(graph=func_graph) as sess: + self.assertEqual( + sess.run(func_graph.outputs[0], feed_dict={ + x_ph: 5.0, + y_ph: 10.0 + }), 30.0) + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index 838cf836f1..db2e7e2c2a 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -3095,7 +3095,7 @@ tf_py_test( tf_py_test( name = "cond_v2_test", - size = "small", + size = "medium", srcs = ["cond_v2_test.py"], additional_deps = [ "//tensorflow/python:array_ops", @@ -3110,4 +3110,5 @@ tf_py_test( "//tensorflow/python:training", ], grpc_enabled = True, + tags = ["no_gpu"], # TODO(b/111656070) ) diff --git a/tensorflow/python/kernel_tests/cond_v2_test.py b/tensorflow/python/kernel_tests/cond_v2_test.py index 759db5d5f4..97ce245fc8 100644 --- a/tensorflow/python/kernel_tests/cond_v2_test.py +++ b/tensorflow/python/kernel_tests/cond_v2_test.py @@ -22,6 +22,7 @@ from __future__ import print_function from tensorflow.core.protobuf import config_pb2 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.ops import array_ops from tensorflow.python.ops import cond_v2 @@ -35,10 +36,12 @@ from tensorflow.python.training import saver from tensorflow.python.util import compat -class NewCondTest(test.TestCase): +class CondV2Test(test.TestCase): - def _testCond(self, true_fn, false_fn, train_vals): - with self.test_session() as sess: + def _testCond(self, true_fn, false_fn, train_vals, feed_dict=None): + if not feed_dict: + feed_dict = {} + with self.test_session(graph=ops.get_default_graph()) as sess: pred = array_ops.placeholder(dtypes.bool, name="pred") expected = control_flow_ops.cond(pred, true_fn, false_fn, name="expected") @@ -47,13 +50,17 @@ class NewCondTest(test.TestCase): expected_grad = gradients_impl.gradients(expected, train_vals) actual_grad = gradients_impl.gradients(actual, train_vals) + sess_run_args = {pred: True} + sess_run_args.update(feed_dict) expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run( - (expected, actual, expected_grad, actual_grad), {pred: True}) + (expected, actual, expected_grad, actual_grad), sess_run_args) self.assertEqual(expected_val, actual_val) self.assertEqual(expected_grad_val, actual_grad_val) + sess_run_args = {pred: False} + sess_run_args.update(feed_dict) expected_val, actual_val, expected_grad_val, actual_grad_val = sess.run( - (expected, actual, expected_grad, actual_grad), {pred: False}) + (expected, actual, expected_grad, actual_grad), sess_run_args) self.assertEqual(expected_val, actual_val) self.assertEqual(expected_grad_val, actual_grad_val) @@ -131,6 +138,349 @@ class NewCondTest(test.TestCase): self.assertIn("foo_cond_1_true", ops.get_default_graph()._functions) self.assertIn("foo_cond_1_false", ops.get_default_graph()._functions) + def testDefunInCond(self): + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + + @function.Defun() + def fn(): + return x * y * 2.0 + + return fn() + + def false_fn(): + return 2.0 + + self._testCond(true_fn, false_fn, [x]) + self._testCond(true_fn, false_fn, [x, y]) + self._testCond(true_fn, false_fn, [y]) + + def testNestedDefunInCond(self): + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + return 2.0 + + def false_fn(): + + @function.Defun() + def fn(): + + @function.Defun() + def nested_fn(): + return x * y * 2.0 + + return nested_fn() + + return fn() + + self._testCond(true_fn, false_fn, [x]) + self._testCond(true_fn, false_fn, [x, y]) + self._testCond(true_fn, false_fn, [y]) + + def testDoubleNestedDefunInCond(self): + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + + @function.Defun() + def fn(): + + @function.Defun() + def nested_fn(): + + @function.Defun() + def nested_nested_fn(): + return x * y * 2.0 + + return nested_nested_fn() + + return nested_fn() + + return fn() + + def false_fn(): + return 2.0 + + self._testCond(true_fn, false_fn, [x]) + self._testCond(true_fn, false_fn, [x, y]) + self._testCond(true_fn, false_fn, [y]) + + def testNestedCond(self): + + def run_test(pred_value): + + def build_graph(): + pred = array_ops.placeholder(dtypes.bool, name="pred") + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + return 2.0 + + def false_fn(): + + def false_true_fn(): + return x * y * 2.0 + + def false_false_fn(): + return x * 5.0 + + return _cond(pred, false_true_fn, false_false_fn, "inside_false_fn") + + return x, y, pred, true_fn, false_fn + + with ops.Graph().as_default(): + x, y, pred, true_fn, false_fn = build_graph() + self._testCond(true_fn, false_fn, [x, y], {pred: pred_value}) + self._testCond(true_fn, false_fn, [x], {pred: pred_value}) + self._testCond(true_fn, false_fn, [y], {pred: pred_value}) + + run_test(True) + run_test(False) + + def testDoubleNestedCond(self): + + def run_test(pred1_value, pred2_value): + + def build_graph(): + pred1 = array_ops.placeholder(dtypes.bool, name="pred1") + pred2 = array_ops.placeholder(dtypes.bool, name="pred2") + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + return 2.0 + + def false_fn(): + + def false_true_fn(): + + def false_true_true_fn(): + return x * y * 2.0 + + def false_true_false_fn(): + return x * 10.0 + + return _cond( + pred1, + false_true_true_fn, + false_true_false_fn, + name="inside_false_true_fn") + + def false_false_fn(): + return x * 5.0 + + return _cond( + pred2, false_true_fn, false_false_fn, name="inside_false_fn") + + return x, y, pred1, pred2, true_fn, false_fn + + with ops.Graph().as_default(): + x, y, pred1, pred2, true_fn, false_fn = build_graph() + self._testCond(true_fn, false_fn, [x, y], { + pred1: pred1_value, + pred2: pred2_value + }) + x, y, pred1, pred2, true_fn, false_fn = build_graph() + self._testCond(true_fn, false_fn, [x], { + pred1: pred1_value, + pred2: pred2_value + }) + x, y, pred1, pred2, true_fn, false_fn = build_graph() + self._testCond(true_fn, false_fn, [y], { + pred1: pred1_value, + pred2: pred2_value + }) + + run_test(True, True) + run_test(True, False) + run_test(False, False) + run_test(False, True) + + def testGradientFromInsideDefun(self): + + def build_graph(): + pred_outer = array_ops.placeholder(dtypes.bool, name="pred_outer") + pred_inner = array_ops.placeholder(dtypes.bool, name="pred_inner") + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + return 2.0 + + def false_fn(): + + def inner_true_fn(): + return x * y * 2.0 + + def inner_false_fn(): + return x * 5.0 + + return cond_v2.cond_v2( + pred_inner, inner_true_fn, inner_false_fn, name="inner_cond") + + cond_outer = cond_v2.cond_v2( + pred_outer, true_fn, false_fn, name="outer_cond") + + # Compute grads inside a Defun. + @function.Defun() + def nesting_fn(): + return gradients_impl.gradients(cond_outer, [x, y]) + + grads = nesting_fn() + + return grads, pred_outer, pred_inner + + with ops.Graph().as_default(): + grads, pred_outer, pred_inner = build_graph() + with self.test_session(graph=ops.get_default_graph()) as sess: + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: True, + pred_inner: True + }), [0., 0.]) + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: True, + pred_inner: False + }), [0., 0.]) + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: False, + pred_inner: True + }), [4., 2.]) + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: False, + pred_inner: False + }), [5., 0.]) + + def testGradientFromInsideNestedDefun(self): + + def build_graph(): + pred_outer = array_ops.placeholder(dtypes.bool, name="pred_outer") + pred_inner = array_ops.placeholder(dtypes.bool, name="pred_inner") + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + def true_fn(): + return 2.0 + + def false_fn(): + + def inner_true_fn(): + return x * y * 2.0 + + def inner_false_fn(): + return x * 5.0 + + return cond_v2.cond_v2( + pred_inner, inner_true_fn, inner_false_fn, name="inner_cond") + + cond_outer = cond_v2.cond_v2( + pred_outer, true_fn, false_fn, name="outer_cond") + + # Compute grads inside a Defun. + @function.Defun() + def nesting_fn(): + + @function.Defun() + def inner_nesting_fn(): + return gradients_impl.gradients(cond_outer, [x, y]) + + return inner_nesting_fn() + + grads = nesting_fn() + + return grads, pred_outer, pred_inner + + with ops.Graph().as_default(): + grads, pred_outer, pred_inner = build_graph() + with self.test_session(graph=ops.get_default_graph()) as sess: + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: True, + pred_inner: True + }), [0., 0.]) + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: True, + pred_inner: False + }), [0., 0.]) + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: False, + pred_inner: True + }), [4., 2.]) + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: False, + pred_inner: False + }), [5., 0.]) + + def testBuildCondAndGradientInsideDefun(self): + + def build_graph(): + pred_outer = array_ops.placeholder(dtypes.bool, name="pred_outer") + pred_inner = array_ops.placeholder(dtypes.bool, name="pred_inner") + x = constant_op.constant(1.0, name="x") + y = constant_op.constant(2.0, name="y") + + # Build cond and its gradient inside a Defun. + @function.Defun() + def fn(): + + def true_fn(): + return 2.0 + + def false_fn(): + + def inner_true_fn(): + return x * y * 2.0 + + def inner_false_fn(): + return x * 5.0 + + return cond_v2.cond_v2( + pred_inner, inner_true_fn, inner_false_fn, name="inner_cond") + + cond_outer = cond_v2.cond_v2( + pred_outer, true_fn, false_fn, name="outer_cond") + return gradients_impl.gradients(cond_outer, [x, y]) + + grads = fn() + + return grads, pred_outer, pred_inner + + with ops.Graph().as_default(): + grads, pred_outer, pred_inner = build_graph() + with self.test_session(graph=ops.get_default_graph()) as sess: + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: True, + pred_inner: True + }), [0., 0.]) + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: True, + pred_inner: False + }), [0., 0.]) + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: False, + pred_inner: True + }), [4., 2.]) + self.assertSequenceEqual( + sess.run(grads, { + pred_outer: False, + pred_inner: False + }), [5., 0.]) + def testSecondDerivative(self): with self.test_session() as sess: pred = array_ops.placeholder(dtypes.bool, name="pred") @@ -532,5 +882,17 @@ class CondV2ColocationGroupAndDeviceTest(test.TestCase): self.assertTrue(len(run_metadata.partition_graphs) >= 2) +def _cond(pred, true_fn, false_fn, name): + if _is_old_cond(): + return control_flow_ops.cond(pred, true_fn, false_fn, name=name) + else: + return cond_v2.cond_v2(pred, true_fn, false_fn, name=name) + + +def _is_old_cond(): + return isinstance(ops.get_default_graph()._get_control_flow_context(), + control_flow_ops.CondContext) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/python/ops/cond_v2_impl.py b/tensorflow/python/ops/cond_v2_impl.py index d310f83dca..5cd0cb34de 100644 --- a/tensorflow/python/ops/cond_v2_impl.py +++ b/tensorflow/python/ops/cond_v2_impl.py @@ -135,6 +135,10 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): def _IfGrad(op, *grads): # pylint: disable=invalid-name """The gradient of an If op produced by cond_v2.""" true_graph, false_graph = _get_func_graphs(op) + # Note: op.graph != ops.get_default_graph() when we are computing the gradient + # of a nested cond. + assert true_graph._outer_graph == op.graph + assert false_graph._outer_graph == op.graph # Create grad functions that compute the gradient of the true/false forward # graphs. These functions will capture tensors from the forward pass @@ -147,15 +151,16 @@ def _IfGrad(op, *grads): # pylint: disable=invalid-name assert ([t.dtype for t in true_grad_graph.outputs] == [t.dtype for t in false_grad_graph.outputs]) - # Match up the captured grad function inputs with outputs of 'op' and other - # external tensors. - true_grad_inputs = _get_grad_inputs(op, true_graph, true_grad_graph) - false_grad_inputs = _get_grad_inputs(op, false_graph, false_grad_graph) + # Resolve references to forward graph tensors in grad graphs and ensure + # they are in-scope, i.e., belong to one of outer graphs of the grad graph. + true_grad_extra_inputs = _resolve_grad_inputs(true_graph, true_grad_graph) + false_grad_extra_inputs = _resolve_grad_inputs(false_graph, false_grad_graph) # Make the inputs to true_grad_graph and false_grad_graph match. Note that # this modifies true_grad_graph and false_grad_graph. grad_inputs = _make_inputs_match(true_grad_graph, false_grad_graph, - true_grad_inputs, false_grad_inputs) + true_grad_extra_inputs, + false_grad_extra_inputs) # Add all intermediate tensors as function outputs so they're available for # higher-order gradient computations. @@ -199,11 +204,20 @@ def _get_func_graphs(if_op): input_shapes = [t.shape for t in extra_inputs] func_name = if_op.get_attr(branch_name).name fdef = if_op.graph._get_function(func_name).definition - func_graph = _function_def_to_graph.function_def_to_graph( - fdef, input_shapes) + # `if_op.graph` may not be the same as `ops.get_default_graph()` e.g. + # in the case of nested if ops or when the gradient is being computed + # from inside a Defun. We build the `func_graph` with `if_op.graph` as its + # `outer_graph`. This resembles how the `_FuncGraph` was built in the + # forward pass. We need this so that we can resolve references to tensors + # in `func_graph` from its gradient graph in `_resolve_grad_inputs`. + with if_op.graph.as_default(): + func_graph = _function_def_to_graph.function_def_to_graph( + fdef, input_shapes) func_graph.extra_inputs = extra_inputs func_graph.extra_args = func_graph.inputs func_graph._captured = dict(zip(extra_inputs, func_graph.inputs)) + # Set the if op so that the gradient code can use it. + func_graph._if = if_op return func_graph return (_get_func_graph_for_branch("then_branch"), @@ -240,7 +254,7 @@ def _grad_fn(func_graph, grads): # Build the gradient graph. Note that this builds the gradient computation of # func_graph in the current graph, which requires capturing tensors from # func_graph. The captured func_graph tensors are resolved to external tensors - # in _get_grad_inputs. + # in _resolve_grad_inputs. result = _gradients_impl._GradientsHelper( ys, func_graph.inputs, grad_ys=grad_ys, src_graph=func_graph) @@ -261,43 +275,49 @@ def _create_grad_func(func_graph, grads, name): [], [], name) -def _get_grad_inputs(if_op, cond_graph, grad_graph): - """Returns the tensors we should pass to grad_graph. +def _resolve_grad_inputs(cond_graph, grad_graph): + """Returns the tensors to pass as `extra_inputs` to `grad_graph`. - This method handles tensors captured from cond_graph in grad_graph. It - converts these to suitable input tensors from the outer graph. + The `grad_graph` may have external references to + 1. Its outer graph containing the input gradients. These references are kept + as is. + 2. Tensors in the forward pass graph. These tensors may not be "live" + when the gradient is being computed. We replace such references by their + corresponding tensor in the least common ancestor graph of `grad_graph` and + `cond_graph`. Since we export intermediate tensors for all branch + functions, this is always possible. Args: - if_op: Operation. The forward-pass If op that uses cond_graph. cond_graph: function._FuncGraph. The forward-pass function. grad_graph: function._FuncGraph. The gradients function. Returns: A list of inputs tensors to be passed to grad_graph. """ - inputs = [] - - # Maps placeholders in cond_graph -> input tensor in outer graph. - forward_input_map = {v: k for k, v in cond_graph._captured.items()} + new_extra_inputs = [] for t in grad_graph.extra_inputs: - if t.graph == ops.get_default_graph(): - # t is in the outer graph (e.g. one of the input gradients). - inputs.append(t) - elif t in forward_input_map: - # t is an input placeholder in cond_graph. Get the corresponding input - # tensor in the outer graph. - assert t.graph == cond_graph - assert forward_input_map[t].graph == ops.get_default_graph() - inputs.append(forward_input_map[t]) - else: - # t is an intermediate value in cond_graph. Get the corresponding output - # of 'if_op' (note that all intermediate values are outputs). - assert t.graph == cond_graph - output_idx = cond_graph.outputs.index(t) - inputs.append(if_op.outputs[output_idx]) - - return inputs + if t.graph != grad_graph._outer_graph: + # `t` is a tensor in `cond_graph` or one of its ancestors. We bubble this + # tensor to the least common ancestor of the `cond_graph` and + # `grad_graph` so that it is "in-scope" for `grad_graph`. + # TODO(srbs): `_is_ancestor` calls may be expensive. Compute the least + # common ancestor once and re-use. + assert _is_ancestor(cond_graph, t.graph) + while not _is_ancestor(grad_graph, t.graph): + assert isinstance(t.graph, _function._FuncGraph) + if t in t.graph.extra_args: + # TODO(srbs): Consider building a map of extra_args -> extra_inputs. + # instead of searching for `t` twice. + t = t.graph.extra_inputs[t.graph.extra_args.index(t)] + else: + # Note: All intermediate tensors are output by the If op. + # TODO(srbs): .index() calls may be expensive. Optimize. + t = t.graph._if.outputs[t.graph.outputs.index(t)] + assert _is_ancestor(grad_graph, t.graph) + new_extra_inputs.append(t) + + return new_extra_inputs def _create_new_tf_function(func_graph): @@ -326,7 +346,8 @@ def _create_new_tf_function(func_graph): # a new TF_Function that we add to the graph. fdef = _function.function_def_from_tf_function(c_func) defined_func = _function._from_definition(fdef) - defined_func.add_to_graph(ops.get_default_graph()) + defined_func._sub_functions = func_graph._functions + defined_func.add_to_graph(func_graph._outer_graph) return func_graph.name @@ -389,7 +410,8 @@ def _pad_params(true_graph, false_graph, true_params, false_params): return new_true_params, new_false_inputs -def _make_inputs_match(true_graph, false_graph, true_inputs, false_inputs): +def _make_inputs_match(true_graph, false_graph, true_extra_inputs, + false_extra_inputs): """Modifies true_graph and false_graph so they have the same input signature. This method reorders and/or adds parameters to true_graph and false_graph so @@ -400,9 +422,9 @@ def _make_inputs_match(true_graph, false_graph, true_inputs, false_inputs): Args: true_graph: function._FuncGraph false_graph: function._FuncGraph - true_inputs: a list of Tensors in the outer graph. The inputs for + true_extra_inputs: a list of Tensors in the outer graph. The inputs for true_graph. - false_inputs: a list of Tensors in the outer graph. The inputs for + false_extra_inputs: a list of Tensors in the outer graph. The inputs for false_graph. Returns: @@ -411,12 +433,12 @@ def _make_inputs_match(true_graph, false_graph, true_inputs, false_inputs): false_inputs. """ shared_inputs, true_only_inputs, false_only_inputs = _separate_unique_inputs( - true_inputs, false_inputs) + true_extra_inputs, false_extra_inputs) new_inputs = shared_inputs + true_only_inputs + false_only_inputs - true_input_to_param = dict(zip(true_inputs, true_graph.inputs)) - false_input_to_param = dict(zip(false_inputs, false_graph.inputs)) + true_input_to_param = dict(zip(true_extra_inputs, true_graph.inputs)) + false_input_to_param = dict(zip(false_extra_inputs, false_graph.inputs)) true_graph.inputs = ( [true_input_to_param[t] for t in shared_inputs] + @@ -432,6 +454,9 @@ def _make_inputs_match(true_graph, false_graph, true_inputs, false_inputs): true_graph.extra_inputs = new_inputs false_graph.extra_inputs = new_inputs + true_graph.extra_args = true_graph.inputs + false_graph.extra_args = false_graph.inputs + true_graph._captured = dict(zip(new_inputs, true_graph.inputs)) false_graph._captured = dict(zip(new_inputs, false_graph.inputs)) @@ -454,14 +479,30 @@ def _create_dummy_params(func_graph, template_tensors): def _get_grad_fn_name(func_graph): - """Returns a unique name to use for the grad function of `func_graph`.""" + """Returns a unique name to use for the grad function of `func_graph`. + + Ensures this name is unique in the entire hierarchy. + + Args: + func_graph: The _FuncGraph. + + Returns: + A string, the name to use for the gradient function. + """ name = "%s_grad" % func_graph.name base_name = name counter = 1 - if ops.get_default_graph()._is_function(name): - name = "%s_%s" % (base_name, counter) - counter += 1 + has_conflict = True + while has_conflict: + curr_graph = func_graph._outer_graph + has_conflict = curr_graph._is_function(name) + while not has_conflict and isinstance(curr_graph, _function._FuncGraph): + curr_graph = curr_graph._outer_graph + has_conflict = curr_graph._is_function(name) + if has_conflict: + name = "%s_%s" % (base_name, counter) + counter += 1 return name @@ -477,3 +518,11 @@ def _check_same_outputs(true_graph, false_graph): "arguments, got:\n" " true_fn: %s\n" " false_fn: %s" % (true_output_types, false_output_types)) + + +def _is_ancestor(graph, maybe_ancestor): + if maybe_ancestor == graph: + return True + if isinstance(graph, _function._FuncGraph): + return _is_ancestor(graph._outer_graph, maybe_ancestor) + return False -- GitLab From 6533c5a8ade658568a82c3c7bb9d1368a641c0a3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 01:44:48 -0700 Subject: [PATCH 184/519] Describe what happens on case of duplicate indices on scatter_nd. PiperOrigin-RevId: 205367181 --- tensorflow/core/api_def/base_api/api_def_ScatterNd.pbtxt | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/api_def/base_api/api_def_ScatterNd.pbtxt b/tensorflow/core/api_def/base_api/api_def_ScatterNd.pbtxt index 58753a651a..ad1c527b01 100644 --- a/tensorflow/core/api_def/base_api/api_def_ScatterNd.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_ScatterNd.pbtxt @@ -32,8 +32,12 @@ slices within a tensor (initially zero for numeric, empty for string) of the given `shape` according to indices. This operator is the inverse of the @{tf.gather_nd} operator which extracts values or slices from a given tensor. +If `indices` contains duplicates, then their updates are accumulated (summed). + **WARNING**: The order in which updates are applied is nondeterministic, so the -output will be nondeterministic if `indices` contains duplicates. +output will be nondeterministic if `indices` contains duplicates -- because +of some numerical approximation issues, numbers summed in different order +may yield different results. `indices` is an integer tensor containing indices into a new tensor of shape `shape`. The last dimension of `indices` can be at most the rank of `shape`: -- GitLab From a641057c719aa95175a9ad3f9e26044f0c31416e Mon Sep 17 00:00:00 2001 From: Peter Hawkins Date: Fri, 20 Jul 2018 05:13:01 -0700 Subject: [PATCH 185/519] [TPU] Delete attr_scope that does nothing, now that the forward compatibility window has expired. PiperOrigin-RevId: 205383196 --- tensorflow/contrib/tpu/python/tpu/tpu.py | 25 ++++++++---------------- 1 file changed, 8 insertions(+), 17 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py index 7216626a58..06885bbc25 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu.py @@ -633,23 +633,14 @@ def split_compile_and_replicate(computation, with tpu_function.tpu_shard_context( num_replicas), ops.control_dependencies([metadata]): - # For backward compatibility reasons, we tag replicated inputs with the - # _tpu_replicated_input attribute. This does nothing and exists only for - # backward compatibility. - # TODO(phawkins): delete the attr_scope after 6/28/2018. - # pylint: disable=protected-access - with graph._attr_scope({ - "_tpu_replicated_input": attr_value_pb2.AttrValue(b=True) - }): - # Add identity ops so even unused inputs are "consumed" by the - # computation. This is to avoid orphaned TPUReplicatedInput nodes. - # TODO(phawkins): consider instead pruning unused TPUReplicatedInput - # and eliding trivial TPUReplicatedInput/TPUReplicatedOutput pairs. - computation_inputs = [ - array_ops.identity(x, name="replicated_input_{}".format(i)) - for i, x in enumerate(computation_inputs) - ] - # pylint: enable=protected-access + # Add identity ops so even unused inputs are "consumed" by the + # computation. This is to avoid orphaned TPUReplicatedInput nodes. + # TODO(phawkins): consider instead pruning unused TPUReplicatedInput + # and eliding trivial TPUReplicatedInput/TPUReplicatedOutput pairs. + computation_inputs = [ + array_ops.identity(x, name="replicated_input_{}".format(i)) + for i, x in enumerate(computation_inputs) + ] # If there is an infeed queue, adds the dequeued values to the # computation's inputs. -- GitLab From aede46743b0256ef382f23b0db0370fe0777116d Mon Sep 17 00:00:00 2001 From: Joe Yearsley Date: Fri, 20 Jul 2018 15:00:29 +0100 Subject: [PATCH 186/519] Update fold_old_batch_norms.cc Fixed my previous fix. --- tensorflow/tools/graph_transforms/fold_old_batch_norms.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc index f1d361e07d..156636ab82 100644 --- a/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc +++ b/tensorflow/tools/graph_transforms/fold_old_batch_norms.cc @@ -159,7 +159,7 @@ Status FuseScaleOffsetToConvWeights(const std::vector& scale_values, NodeDef bias_add_node; bias_add_node.set_op("BiasAdd"); bias_add_node.set_name(conv_output_name); - if (!conv_node.attr().count("data_format")) { + if (conv_node.attr().count("data_format") > 0) { CopyNodeAttr(conv_node, "data_format", "data_format", &bias_add_node); } CopyNodeAttr(conv_node, "T", "T", &bias_add_node); -- GitLab From 8d14663dbe9446ba50a36f64aaecfb5c06ea26d3 Mon Sep 17 00:00:00 2001 From: Jianwei Xie Date: Fri, 20 Jul 2018 08:52:47 -0700 Subject: [PATCH 187/519] Fixed keras_support related dependency in mnist example. PiperOrigin-RevId: 205403669 --- tensorflow/contrib/tpu/BUILD | 11 +++++++++++ 1 file changed, 11 insertions(+) diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD index ef6c752851..14e4e9cc2b 100644 --- a/tensorflow/contrib/tpu/BUILD +++ b/tensorflow/contrib/tpu/BUILD @@ -165,6 +165,17 @@ py_library( "python/tpu/keras_support.py", ], srcs_version = "PY2AND3", + visibility = [ + "//cloud/vmm/testing/tests/tpu:__subpackages__", + "//learning/brain:__subpackages__", + # TODO(b/111651964): Clean special visibility for keras_support. + # + # Note: If you are an end user, please do not add your project to this + # visibility. This feature is experimental, and will be made public + # when ready. + "//third_party/cloud_tpu/models/keras:__subpackages__", + "//tensorflow:__subpackages__", + ], deps = [ ":tpu_lib", ":tpu_py", -- GitLab From 2ff8c85dfca8afb2a4129e8fa86802bd5f25a1c6 Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Fri, 20 Jul 2018 09:28:37 -0700 Subject: [PATCH 188/519] [eager]: Correctly handle operation arguments of mixed types in the slow path. Consider the following: import tensorflow as tf tf.enable_eager_execution() x = tf.Variable(1.0) tf.Print(x, ["foo", x]) Prior to this commit, this snippet would fail with an error: ValueError: exceptions.TypeError: object of type 'ResourceVariable' has no len() raised from the call to ops.EagerTensor in convert_to_mixed_eager_tensors. With this commit, the tf.Print call works correctly. Note that convert_to_mixed_eager_tensors is only called in the slow path of operation execution (i.e., when TFE_Py_FastPathExecute fails). Which happens rarely (e.g., when mixing primitive string and EagerTensor/ResourceVariable arguments). PiperOrigin-RevId: 205408407 --- tensorflow/python/eager/core_test.py | 8 ++++++++ tensorflow/python/eager/execute.py | 6 +----- 2 files changed, 9 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/eager/core_test.py b/tensorflow/python/eager/core_test.py index 3fabe7060e..cc765725a4 100644 --- a/tensorflow/python/eager/core_test.py +++ b/tensorflow/python/eager/core_test.py @@ -610,6 +610,14 @@ class TFETest(test_util.TensorFlowTestCase): self.assertEquals(typ, dtypes.float32) self.assertIsInstance(t, ops.EagerTensor) + def testConvertMixedEagerTensorsWithVariables(self): + var = resource_variable_ops.ResourceVariable(1.0) + types, tensors = execute_lib.convert_to_mixed_eager_tensors( + ['foo', var], context.context()) + self.assertAllEqual([dtypes.string, dtypes.float32], types) + for t in tensors: + self.assertIsInstance(t, ops.EagerTensor) + class SendRecvTest(test_util.TensorFlowTestCase): diff --git a/tensorflow/python/eager/execute.py b/tensorflow/python/eager/execute.py index 2ff5b8d8f4..f9b8d2cb5d 100644 --- a/tensorflow/python/eager/execute.py +++ b/tensorflow/python/eager/execute.py @@ -198,11 +198,7 @@ def args_to_matching_eager(l, ctx, default_dtype=None): def convert_to_mixed_eager_tensors(values, ctx): - v = [ - t if isinstance(t, ops.EagerTensor) else ops.EagerTensor( - t, context=ctx._handle, device=ctx.device_name) # pylint: disable=protected-access - for t in values - ] + v = [ops.internal_convert_to_tensor(t, ctx=ctx) for t in values] types = [t._datatype_enum() for t in v] # pylint: disable=protected-access return types, v -- GitLab From 75ca1d8df21cd4c7904ceecb12c0cfc268da361f Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Fri, 20 Jul 2018 09:33:03 -0700 Subject: [PATCH 189/519] A notebook containing simple algorithms. PiperOrigin-RevId: 205408982 --- .../examples/notebooks/algorithms.ipynb | 1512 +++++++++++++++++ 1 file changed, 1512 insertions(+) create mode 100644 tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb diff --git a/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb b/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb new file mode 100644 index 0000000000..bf824e2760 --- /dev/null +++ b/tensorflow/contrib/autograph/examples/notebooks/algorithms.ipynb @@ -0,0 +1,1512 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "b9R-4ezU3NH0" + }, + "source": [ + "## AutoGraph: examples of simple algorithms\n", + "\n", + "This notebook shows how you can use AutoGraph to compile simple algorithms and run them in TensorFlow.\n", + "\n", + "It requires the nightly build of TensorFlow, which is installed below." + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "TuWj26KWz1fZ" + }, + "outputs": [], + "source": [ + "!pip install -U -q tf-nightly" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "3kudk1elq0Gh" + }, + "source": [ + "### Fibonacci numbers\n", + "\n", + "https://en.wikipedia.org/wiki/Fibonacci_number" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 197 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 7512, + "status": "ok", + "timestamp": 1532101577266, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "H7olFlMXqrHe", + "outputId": "472dbfe0-9449-4f93-e908-1a0785188a92" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0 : 1\n", + "1 : 2\n", + "2 : 3\n", + "3 : 5\n", + "4 : 8\n", + "5 : 13\n", + "6 : 21\n", + "7 : 34\n", + "8 : 55\n", + "9 : 89\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.contrib import autograph as ag\n", + "\n", + "\n", + "def fib(n):\n", + " f1 = 0\n", + " f2 = 1\n", + " for i in range(n):\n", + " tmp = f2\n", + " f2 = f2 + f1\n", + " f1 = tmp\n", + " print(i, ': ', f2)\n", + " return f2\n", + "\n", + "\n", + "with tf.Graph().as_default():\n", + " final_fib = ag.to_graph(fib)(tf.constant(10))\n", + " with tf.Session() as sess:\n", + " sess.run(final_fib)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "p8zZyj-tq4K3" + }, + "source": [ + "#### Generated code" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 541 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 103, + "status": "ok", + "timestamp": 1532101577412, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "UeWjK8rHq6Cj", + "outputId": "73ece895-12fb-489a-e52c-032945d7ed7a" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "from __future__ import print_function\n", + "import tensorflow as tf\n", + "\n", + "def tf__fib(n):\n", + " try:\n", + " with tf.name_scope('fib'):\n", + " f1 = 0\n", + " f2 = 1\n", + "\n", + " def extra_test(f1_1, f2_1):\n", + " with tf.name_scope('extra_test'):\n", + " return True\n", + "\n", + " def loop_body(i, f1_1, f2_1):\n", + " with tf.name_scope('loop_body'):\n", + " tmp = f2_1\n", + " f2_1 = f2_1 + f1_1\n", + " f1_1 = tmp\n", + " with ag__.utils.control_dependency_on_returns(ag__.utils.\n", + " dynamic_print(i, ': ', f2_1)):\n", + " f2, i_1 = ag__.utils.alias_tensors(f2_1, i)\n", + " return f1_1, f2\n", + " f1, f2 = ag__.for_stmt(ag__.utils.dynamic_builtin(range, n),\n", + " extra_test, loop_body, (f1, f2))\n", + " return f2\n", + " except:\n", + " ag__.rewrite_graph_construction_error(ag_source_map__)\n", + "\n" + ] + } + ], + "source": [ + "print(ag.to_code(fib))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "eIfVy6ZTrFEH" + }, + "source": [ + "### Fizz Buzz\n", + "\n", + "https://en.wikipedia.org/wiki/Fizz_buzz" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 125 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 233, + "status": "ok", + "timestamp": 1532101577681, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "33CAheYsrEQ7", + "outputId": "82a493ee-15b5-419d-8c9c-5f4159090a05" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Buzz\n", + "11\n", + "Fizz\n", + "13\n", + "14\n", + "FizzBuzz\n" + ] + } + ], + "source": [ + "import tensorflow as tf\n", + "from tensorflow.contrib import autograph as ag\n", + "\n", + "def fizzbuzz(i, n):\n", + " while i \u003c n:\n", + " msg = ''\n", + " if i % 3 == 0:\n", + " msg += 'Fizz'\n", + " if i % 5 == 0:\n", + " msg += 'Buzz'\n", + " if msg == '':\n", + " msg = tf.as_string(i)\n", + " print(msg)\n", + " i += 1\n", + " return i\n", + "\n", + "with tf.Graph().as_default():\n", + " final_i = ag.to_graph(fizzbuzz)(tf.constant(10), tf.constant(16))\n", + " with tf.Session() as sess:\n", + " sess.run(final_i)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "Lkq3DBGOv3fA" + }, + "source": [ + "#### Generated code" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 1081 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 289, + "status": "ok", + "timestamp": 1532101578003, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "bBhFIIaZrxvx", + "outputId": "d076a7ea-e643-4689-f90a-57f5d086dedc" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "from __future__ import print_function\n", + "import tensorflow as tf\n", + "\n", + "def tf__fizzbuzz(i, n):\n", + " try:\n", + " with tf.name_scope('fizzbuzz'):\n", + "\n", + " def loop_test(i_1):\n", + " with tf.name_scope('loop_test'):\n", + " return tf.less(i_1, n)\n", + "\n", + " def loop_body(i_1):\n", + " with tf.name_scope('loop_body'):\n", + " msg = ''\n", + "\n", + " def if_true():\n", + " with tf.name_scope('if_true'):\n", + " msg_1, = msg,\n", + " msg_1 += 'Fizz'\n", + " return msg_1,\n", + "\n", + " def if_false():\n", + " with tf.name_scope('if_false'):\n", + " return msg,\n", + " msg = ag__.utils.run_cond(tf.equal(i_1 % 3, 0), if_true, if_false)\n", + "\n", + " def if_true_1():\n", + " with tf.name_scope('if_true_1'):\n", + " msg_2, = msg,\n", + " msg_2 += 'Buzz'\n", + " return msg_2,\n", + "\n", + " def if_false_1():\n", + " with tf.name_scope('if_false_1'):\n", + " return msg,\n", + " msg = ag__.utils.run_cond(tf.equal(i_1 % 5, 0), if_true_1, if_false_1\n", + " )\n", + "\n", + " def if_true_2():\n", + " with tf.name_scope('if_true_2'):\n", + " msg_3, = msg,\n", + " msg_3 = tf.as_string(i_1)\n", + " return msg_3,\n", + "\n", + " def if_false_2():\n", + " with tf.name_scope('if_false_2'):\n", + " return msg,\n", + " msg = ag__.utils.run_cond(tf.equal(msg, ''), if_true_2, if_false_2)\n", + " with ag__.utils.control_dependency_on_returns(ag__.utils.\n", + " dynamic_print(msg)):\n", + " msg_4 = ag__.utils.alias_tensors(msg)\n", + " i_1 += 1\n", + " return i_1,\n", + " i = ag__.while_stmt(loop_test, loop_body, (i,), (tf, n, ag__, i))\n", + " return i\n", + " except:\n", + " ag__.rewrite_graph_construction_error(ag_source_map__)\n", + "\n" + ] + } + ], + "source": [ + "print(ag.to_code(fizzbuzz))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "BNRtprSvwJgk" + }, + "source": [ + "### Conway's Game of Life\n", + "\n", + "https://en.wikipedia.org/wiki/Conway%27s_Game_of_Life" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "r8_0ioEuAI-a" + }, + "source": [ + "#### Testing boilerplate" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "7moIlf8VABkl" + }, + "outputs": [], + "source": [ + "NUM_STEPS = 1" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "QlEvfIQPAYF5" + }, + "source": [ + "#### Game of Life for AutoGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "5pCK2qQSAAK4" + }, + "outputs": [], + "source": [ + "#@test {\"skip\": true} \n", + "NUM_STEPS = 100" + ] 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display\n", + "\n", + "\n", + "@ag.do_not_convert(ag.RunMode.PY_FUNC)\n", + "def render(boards):\n", + " fig = plt.figure()\n", + "\n", + " ims = []\n", + " for b in boards:\n", + " im = plt.imshow(b, interpolation='none')\n", + " im.axes.get_xaxis().set_visible(False)\n", + " im.axes.get_yaxis().set_visible(False)\n", + " ims.append([im])\n", + "\n", + " try:\n", + " ani = anim.ArtistAnimation(\n", + " fig, ims, interval=100, blit=True, repeat_delay=5000)\n", + " plt.close()\n", + "\n", + " display.display(display.HTML(ani.to_html5_video()))\n", + " except RuntimeError:\n", + " print('Coult not render animation:')\n", + " traceback.print_exc()\n", + "\n", + "\n", + "def gol_episode(board):\n", + " directions = tf.constant(\n", + " ((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1), (1, -1), (1, 0), (1, 1)))\n", + "\n", + " new_board = []\n", + " ag.set_element_type(new_board, tf.int32)\n", + "\n", + " for i in range(len(board)):\n", + " for j in range(len(board[i])):\n", + " num_neighbors = 0\n", + " for d in directions:\n", + " ni = i + d[0]\n", + " nj = j + d[1]\n", + " if ni \u003e= 0 and nj \u003e= 0 and ni \u003c len(board) and nj \u003c len(board[i]):\n", + " num_neighbors += board[ni][nj]\n", + " \n", + " new_cell = 0\n", + " if num_neighbors == 2:\n", + " new_cell = board[i][j]\n", + " elif num_neighbors == 3:\n", + " new_cell = 1\n", + " \n", + " new_board.append(new_cell)\n", + " final_board = ag.stack(new_board)\n", + " final_board = tf.reshape(final_board, board.shape)\n", + " return final_board\n", + " \n", + "\n", + "def gol(initial_board):\n", + " board = initial_board\n", + " boards = []\n", + " ag.set_element_type(boards, tf.int32)\n", + " # We are being explicit about tensor constants to ensure the loop\n", + " # is not unrolled in the graph. This may change in the future.\n", + " for i in range(tf.constant(NUM_STEPS)):\n", + " board = gol_episode(board)\n", + " boards.append(board)\n", + " boards = ag.stack(boards)\n", + " render(boards)\n", + " return tf.no_op()\n", + " \n", + "\n", + "with tf.Graph().as_default():\n", + " # Gosper glider gun\n", + " # Adapted from http://www.cplusplus.com/forum/lounge/75168/\n", + " _ = 0\n", + " initial_board = tf.constant((\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,1,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_,_,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,1,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_ ),\n", + " ( _,1,1,_,_,_,_,_,_,_,_,1,_,_,_,_,_,1,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,1,1,_,_,_,_,_,_,_,_,1,_,_,_,1,_,1,1,_,_,_,_,1,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,1,_,_,_,_,_,1,_,_,_,_,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,1,_,_,_,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,1,1,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ( _,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_,_ ),\n", + " ))\n", + " initial_board = tf.pad(initial_board, ((0, 20), (0, 10)))\n", + " \n", + " tf_gol = ag.to_graph(gol)\n", + " game_ops = tf_gol(initial_board)\n", + " with tf.Session() as sess:\n", + " sess.run(game_ops)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "7NgrSPCZxs3h" + }, + "source": [ + "#### Generated code" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 2323 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 753, + "status": "ok", + "timestamp": 1532101593840, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "hIGYeX0Cxs3i", + "outputId": "e0b62eb1-3e12-4e53-dc54-8a3fa56d823d" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "from __future__ import print_function\n", + "import tensorflow as tf\n", + "\n", + "def tf__gol_episode(board):\n", + " try:\n", + " with tf.name_scope('gol_episode'):\n", + " directions = tf.constant(((-1, -1), (-1, 0), (-1, 1), (0, -1), (0, 1),\n", + " (1, -1), (1, 0), (1, 1)))\n", + " new_board = ag__.new_list([])\n", + "\n", + " def extra_test_2(new_board_2):\n", + " with tf.name_scope('extra_test_2'):\n", + " return True\n", + "\n", + " def loop_body_2(i, new_board_2):\n", + " with tf.name_scope('loop_body_2'):\n", + "\n", + " def extra_test_1(new_board_1):\n", + " with tf.name_scope('extra_test_1'):\n", + " return True\n", + "\n", + " def loop_body_1(j, new_board_1):\n", + " with tf.name_scope('loop_body_1'):\n", + " num_neighbors = 0\n", + "\n", + " def extra_test(num_neighbors_2):\n", + " with tf.name_scope('extra_test'):\n", + " return True\n", + "\n", + " def loop_body(d, num_neighbors_2):\n", + " with tf.name_scope('loop_body'):\n", + " ni = i + ag__.get_item(d, (0), opts=ag__.GetItemOpts(\n", + " element_dtype=None))\n", + " nj = j + ag__.get_item(d, (1), opts=ag__.GetItemOpts(\n", + " element_dtype=None))\n", + "\n", + " def if_true():\n", + " with tf.name_scope('if_true'):\n", + " num_neighbors_1, = num_neighbors_2,\n", + " num_neighbors_1 += ag__.get_item(ag__.get_item(board,\n", + " (ni), opts=ag__.GetItemOpts(element_dtype=None)),\n", + " (nj), opts=ag__.GetItemOpts(element_dtype=None))\n", + " return num_neighbors_1,\n", + "\n", + " def if_false():\n", + " with tf.name_scope('if_false'):\n", + " return num_neighbors_2,\n", + " num_neighbors_2 = ag__.utils.run_cond(tf.logical_and(tf.\n", + " greater_equal(ni, 0), tf.logical_and(tf.greater_equal\n", + " (nj, 0), tf.logical_and(tf.less(ni, ag__.utils.\n", + " dynamic_builtin(len, board)), tf.less(nj, ag__.utils.\n", + " dynamic_builtin(len, ag__.get_item(board, (i), opts=\n", + " ag__.GetItemOpts(element_dtype=None))))))), if_true,\n", + " if_false)\n", + " return num_neighbors_2,\n", + " num_neighbors = ag__.for_stmt(directions, extra_test,\n", + " loop_body, (num_neighbors,))\n", + " new_cell = 0\n", + "\n", + " def if_true_2():\n", + " with tf.name_scope('if_true_2'):\n", + " new_cell_2, = new_cell,\n", + " new_cell_2 = ag__.get_item(ag__.get_item(board, (i), opts\n", + " =ag__.GetItemOpts(element_dtype=None)), (j), opts=\n", + " ag__.GetItemOpts(element_dtype=None))\n", + " return new_cell_2,\n", + "\n", + " def if_false_2():\n", + " with tf.name_scope('if_false_2'):\n", + " new_cell_3, = new_cell,\n", + "\n", + " def if_true_1():\n", + " with tf.name_scope('if_true_1'):\n", + " new_cell_1, = new_cell_3,\n", + " new_cell_1 = 1\n", + " return new_cell_1,\n", + "\n", + " def if_false_1():\n", + " with tf.name_scope('if_false_1'):\n", + " return new_cell_3,\n", + " new_cell_3 = ag__.utils.run_cond(tf.equal(num_neighbors, \n", + " 3), if_true_1, if_false_1)\n", + " return new_cell_3,\n", + " new_cell = ag__.utils.run_cond(tf.equal(num_neighbors, 2),\n", + " if_true_2, if_false_2)\n", + " new_board_1 = ag__.list_append(new_board_1, new_cell)\n", + " return new_board_1,\n", + " new_board_2 = ag__.for_stmt(ag__.utils.dynamic_builtin(range,\n", + " ag__.utils.dynamic_builtin(len, ag__.get_item(board, (i),\n", + " opts=ag__.GetItemOpts(element_dtype=None)))), extra_test_1,\n", + " loop_body_1, (new_board_2,))\n", + " return new_board_2,\n", + " new_board = ag__.for_stmt(ag__.utils.dynamic_builtin(range, ag__.\n", + " utils.dynamic_builtin(len, board)), extra_test_2, loop_body_2, (\n", + " new_board,))\n", + " final_board = ag__.list_stack(new_board, opts=ag__.ListStackOpts(\n", + " element_dtype=tf.int32, original_call=ag.stack))\n", + " final_board = tf.reshape(final_board, board.shape)\n", + " return final_board\n", + " except:\n", + " ag__.rewrite_graph_construction_error(ag_source_map__)\n", + "\n", + "def tf__gol(initial_board):\n", + " try:\n", + " with tf.name_scope('gol'):\n", + " board = initial_board\n", + " boards = ag__.new_list([])\n", + "\n", + " def extra_test(board_1, boards_1):\n", + " with tf.name_scope('extra_test'):\n", + " return True\n", + "\n", + " def loop_body(i, board_1, boards_1):\n", + " with tf.name_scope('loop_body'):\n", + " board_1 = tf__gol_episode(board_1)\n", + " boards_1 = ag__.list_append(boards_1, board_1)\n", + " return board_1, boards_1\n", + " board, boards = ag__.for_stmt(ag__.utils.dynamic_builtin(range, tf.\n", + " constant(NUM_STEPS)), extra_test, loop_body, (board, boards))\n", + " boards = ag__.list_stack(boards, opts=ag__.ListStackOpts(\n", + " element_dtype=tf.int32, original_call=ag.stack))\n", + " with ag__.utils.control_dependency_on_returns(render(boards)):\n", + " boards_2 = ag__.utils.alias_tensors(boards)\n", + " return tf.no_op()\n", + " except:\n", + " ag__.rewrite_graph_construction_error(ag_source_map__)\n", + "\n" + ] + } + ], + "source": [ + "print(ag.to_code(gol))" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [ + "p8zZyj-tq4K3", + "Lkq3DBGOv3fA", + "r8_0ioEuAI-a", + "7NgrSPCZxs3h" + ], + "default_view": {}, + "last_runtime": { + "build_target": "", + "kind": "local" + }, + "name": "Simple algorithms using AutoGraph", + "provenance": [ + { + "file_id": "19q8KdVF8Cb_fDd13i-WDOG_6n_QGNW5-", + "timestamp": 1528465909719 + } + ], + "version": "0.3.2", + "views": {} + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} -- GitLab From 7fc43521ee5dd12525afbc2ad766f562b24f0043 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Fri, 20 Jul 2018 10:03:44 -0700 Subject: [PATCH 190/519] Add CSS style sheet to preprocessed documentation and landing pages. Remove first button from rendered notebooks (already on page). Quarantine the home page a bit. PiperOrigin-RevId: 205413200 --- tensorflow/docs_src/tutorials/_index.yaml | 52 +---------------------- 1 file changed, 1 insertion(+), 51 deletions(-) diff --git a/tensorflow/docs_src/tutorials/_index.yaml b/tensorflow/docs_src/tutorials/_index.yaml index c74fe58089..9534114689 100644 --- a/tensorflow/docs_src/tutorials/_index.yaml +++ b/tensorflow/docs_src/tutorials/_index.yaml @@ -2,6 +2,7 @@ project_path: /_project.yaml book_path: /_book.yaml description: landing_page: + custom_css_path: /site-assets/css/style.css show_side_navs: True rows: - description: > @@ -14,57 +15,6 @@ landing_page:

items: - custom_html: > -

Learn and use ML

-- GitLab From c023f46956f8a867d0dc77f1ee742564a3622e68 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 10:08:23 -0700 Subject: [PATCH 191/519] Modify AllocatorRegistry to be an AllocatorFactoryRegistry in preparation for using NUMA node specific allocators. Also, add stub NUMA functions on the platform interface to platform/windows/port.cc. PiperOrigin-RevId: 205413998 --- tensorflow/contrib/gdr/gdr_memory_manager.cc | 32 +++-- tensorflow/contrib/verbs/rdma_mgr.cc | 30 ++--- .../core/common_runtime/threadpool_device.cc | 16 ++- tensorflow/core/framework/allocator.cc | 41 +++++- tensorflow/core/framework/allocator.h | 12 +- .../core/framework/allocator_registry.cc | 120 +++++++++++++----- .../core/framework/allocator_registry.h | 111 +++++++++++----- tensorflow/core/platform/windows/port.cc | 20 +++ 8 files changed, 269 insertions(+), 113 deletions(-) diff --git a/tensorflow/contrib/gdr/gdr_memory_manager.cc b/tensorflow/contrib/gdr/gdr_memory_manager.cc index 1435e19109..f3bbf6b4d7 100644 --- a/tensorflow/contrib/gdr/gdr_memory_manager.cc +++ b/tensorflow/contrib/gdr/gdr_memory_manager.cc @@ -33,10 +33,11 @@ limitations under the License. #include "tensorflow/core/common_runtime/bfc_allocator.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/dma_helper.h" +#include "tensorflow/core/common_runtime/pool_allocator.h" +#include "tensorflow/core/common_runtime/process_state.h" #if GOOGLE_CUDA #include "tensorflow/core/common_runtime/gpu/gpu_process_state.h" #include "tensorflow/core/common_runtime/gpu/gpu_util.h" -#include "tensorflow/core/common_runtime/process_state.h" #endif // GOOGLE_CUDA #include "tensorflow/core/framework/allocator_registry.h" #include "tensorflow/core/lib/core/status.h" @@ -182,28 +183,25 @@ class GdrMemoryManager : public RemoteMemoryManager { TF_DISALLOW_COPY_AND_ASSIGN(GdrMemoryManager); }; -// TODO(byronyi): remove this class duplicated from the one in -// common/runtime/gpu/pool_allocator.h when it is available in common_runtime -class BasicCPUAllocator : public SubAllocator { - public: - ~BasicCPUAllocator() override {} - - void* Alloc(size_t alignment, size_t num_bytes) override { - return port::AlignedMalloc(num_bytes, alignment); - } - void Free(void* ptr, size_t) override { port::AlignedFree(ptr); } -}; - // TODO(byronyi): remove this class and its registration when the default -// cpu_allocator() returns visitable allocator +// cpu_allocator() returns visitable allocator, or cpu_allocator() is no +// longer in use. class BFCRdmaAllocator : public BFCAllocator { public: BFCRdmaAllocator() - : BFCAllocator(new BasicCPUAllocator(), 1LL << 36, true, "cpu_rdma_bfc") { + : BFCAllocator(new BasicCPUAllocator(port::kNUMANoAffinity), 1LL << 36, + true, "cpu_rdma_bfc") {} +}; +class BFCRdmaAllocatorFactory : public AllocatorFactory { + public: + Allocator* CreateAllocator() override { return new BFCRdmaAllocator; } + + virtual SubAllocator* CreateSubAllocator(int numa_node) { + return new BasicCPUAllocator(numa_node); } }; -REGISTER_MEM_ALLOCATOR("BFCRdmaAllocator", 101, BFCRdmaAllocator); +REGISTER_MEM_ALLOCATOR("BFCRdmaAllocator", 101, BFCRdmaAllocatorFactory); GdrMemoryManager::GdrMemoryManager(const string& host, const string& port) : host_(host), @@ -276,8 +274,8 @@ Status GdrMemoryManager::Init() { Allocator* allocators[] = { #if GOOGLE_CUDA GPUProcessState::singleton()->GetCUDAHostAllocator(0), - ProcessState::singleton()->GetCPUAllocator(0), #endif // GOOGLE_CUDA + ProcessState::singleton()->GetCPUAllocator(0), cpu_allocator(), }; diff --git a/tensorflow/contrib/verbs/rdma_mgr.cc b/tensorflow/contrib/verbs/rdma_mgr.cc index 9cb3d1fbbf..3cb5e61fac 100644 --- a/tensorflow/contrib/verbs/rdma_mgr.cc +++ b/tensorflow/contrib/verbs/rdma_mgr.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/bfc_allocator.h" #include "tensorflow/core/common_runtime/gpu/gpu_process_state.h" #include "tensorflow/core/common_runtime/gpu/gpu_util.h" +#include "tensorflow/core/common_runtime/pool_allocator.h" #include "tensorflow/core/common_runtime/process_state.h" #include "tensorflow/core/distributed_runtime/rpc/grpc_worker_cache.h" #include "tensorflow/core/distributed_runtime/session_mgr.h" @@ -255,28 +256,25 @@ void MRDeleter(ibv_mr* mr) { } } -// TODO(byronyi): remove this class duplicated from the one in -// common/runtime/gpu/pool_allocator.h when it is available in common_runtime -class BasicCPUAllocator : public SubAllocator { - public: - ~BasicCPUAllocator() override {} - - void* Alloc(size_t alignment, size_t num_bytes) override { - return port::AlignedMalloc(num_bytes, alignment); - } - void Free(void* ptr, size_t) override { port::AlignedFree(ptr); } -}; - // TODO(byronyi): remove this class and its registration when the default -// cpu_allocator() returns visitable allocator +// cpu_allocator() returns visitable allocator, or cpu_allocator() is no +// longer in use. class BFCRdmaAllocator : public BFCAllocator { public: BFCRdmaAllocator() - : BFCAllocator(new BasicCPUAllocator(), 1LL << 36, true, "cpu_rdma_bfc") { + : BFCAllocator(new BasicCPUAllocator(port::kNUMANoAffinity), 1LL << 36, + true, "cpu_rdma_bfc") {} +}; +class BFCRdmaAllocatorFactory : public AllocatorFactory { + public: + Allocator* CreateAllocator() { return new BFCRdmaAllocator; } + + SubAllocator* CreateSubAllocator(int numa_node) { + return new BasicCPUAllocator(numa_node); } }; -REGISTER_MEM_ALLOCATOR("BFCRdmaAllocator", 101, BFCRdmaAllocator); +REGISTER_MEM_ALLOCATOR("BFCRdmaAllocator", 101, BFCRdmaAllocatorFactory); void RdmaMgr::InitAllocators() { RdmaMemoryMgr::Singleton().pd_ = rdma_adapter_->pd_; @@ -284,8 +282,8 @@ void RdmaMgr::InitAllocators() { Allocator* allocators[] = { #if GOOGLE_CUDA GPUProcessState::singleton()->GetCUDAHostAllocator(0), - ProcessState::singleton()->GetCPUAllocator(0), #endif // GOOGLE_CUDA + ProcessState::singleton()->GetCPUAllocator(0), cpu_allocator(), }; diff --git a/tensorflow/core/common_runtime/threadpool_device.cc b/tensorflow/core/common_runtime/threadpool_device.cc index 74a87215e1..7406ecf4f8 100644 --- a/tensorflow/core/common_runtime/threadpool_device.cc +++ b/tensorflow/core/common_runtime/threadpool_device.cc @@ -111,7 +111,21 @@ Status ThreadPoolDevice::MakeTensorFromProto( } #ifdef INTEL_MKL -REGISTER_MEM_ALLOCATOR("MklCPUAllocator", 200, MklCPUAllocator); +namespace { +class MklCPUAllocatorFactory : public AllocatorFactory { + public: + bool NumaEnabled() override { return false; } + + Allocator* CreateAllocator() override { return new MklCPUAllocator; } + + // Note: Ignores numa_node, for now. + virtual SubAllocator* CreateSubAllocator(int numa_node) { + return new MklSubAllocator; + } +}; + +REGISTER_MEM_ALLOCATOR("MklCPUAllocator", 200, MklCPUAllocatorFactory); +} // namespace #endif } // namespace tensorflow diff --git a/tensorflow/core/framework/allocator.cc b/tensorflow/core/framework/allocator.cc index 1c62d37955..888ed0c57b 100644 --- a/tensorflow/core/framework/allocator.cc +++ b/tensorflow/core/framework/allocator.cc @@ -91,6 +91,11 @@ void EnableCPUAllocatorFullStats(bool enable) { cpu_allocator_collect_full_stats = enable; } +namespace { +// A default Allocator for CPU devices. ProcessState::GetCPUAllocator() will +// return a different version that may perform better, but may also lack the +// optional stats triggered by the functions above. TODO(tucker): migrate all +// uses of cpu_allocator() except tests to use ProcessState instead. class CPUAllocator : public Allocator { public: CPUAllocator() @@ -170,14 +175,42 @@ class CPUAllocator : public Allocator { TF_DISALLOW_COPY_AND_ASSIGN(CPUAllocator); }; +class CPUAllocatorFactory : public AllocatorFactory { + public: + Allocator* CreateAllocator() override { return new CPUAllocator; } + + SubAllocator* CreateSubAllocator(int numa_node) override { + return new CPUSubAllocator(new CPUAllocator); + } + + private: + class CPUSubAllocator : public SubAllocator { + public: + explicit CPUSubAllocator(CPUAllocator* cpu_allocator) + : cpu_allocator_(cpu_allocator) {} + + void* Alloc(size_t alignment, size_t num_bytes) override { + return cpu_allocator_->AllocateRaw(alignment, num_bytes); + } + + void Free(void* ptr, size_t num_bytes) override { + cpu_allocator_->DeallocateRaw(ptr); + } + + private: + CPUAllocator* cpu_allocator_; + }; +}; + +REGISTER_MEM_ALLOCATOR("DefaultCPUAllocator", 100, CPUAllocatorFactory); +} // namespace + Allocator* cpu_allocator() { - static Allocator* cpu_alloc = AllocatorRegistry::Global()->GetAllocator(); + static Allocator* cpu_alloc = + AllocatorFactoryRegistry::singleton()->GetAllocator(); if (cpu_allocator_collect_full_stats && !cpu_alloc->TracksAllocationSizes()) { cpu_alloc = new TrackingAllocator(cpu_alloc, true); } return cpu_alloc; } - -REGISTER_MEM_ALLOCATOR("DefaultCPUAllocator", 100, CPUAllocator); - } // namespace tensorflow diff --git a/tensorflow/core/framework/allocator.h b/tensorflow/core/framework/allocator.h index 2bb4d32d57..774b1fe137 100644 --- a/tensorflow/core/framework/allocator.h +++ b/tensorflow/core/framework/allocator.h @@ -376,16 +376,18 @@ struct AllocatorAttributes { int32 scope_id = 0; }; -// Returns a trivial implementation of Allocator which uses the system -// default malloc. The returned allocator is a process singleton. +// Returns a trivial implementation of Allocator, which is a process singleton. +// Access through this function is only intended for use in tests and auxiliary +// processing. Performance sensitive uses should always obtain allocators from +// ProcessState. Allocator* cpu_allocator(); -// If 'enable' is true, the process-wide cpu allocator collects +// If 'enable' is true, the default CPU allocator implementation will collect // AllocatorStats. By default, it's disabled. void EnableCPUAllocatorStats(bool enable); -// If 'enable' is true, the process-wide cpu allocator collects full -// statistics. By default, it's disabled. +// If 'enable' is true, the default CPU allocator implementation will collect +// full statistics. By default, it's disabled. void EnableCPUAllocatorFullStats(bool enable); // Abstract interface of an object that does the underlying suballoc/free of diff --git a/tensorflow/core/framework/allocator_registry.cc b/tensorflow/core/framework/allocator_registry.cc index 486be39ae3..099c4bacc8 100644 --- a/tensorflow/core/framework/allocator_registry.cc +++ b/tensorflow/core/framework/allocator_registry.cc @@ -21,60 +21,110 @@ limitations under the License. namespace tensorflow { // static -AllocatorRegistry* AllocatorRegistry::Global() { - static AllocatorRegistry* global_allocator_registry = new AllocatorRegistry; - return global_allocator_registry; +AllocatorFactoryRegistry* AllocatorFactoryRegistry::singleton() { + static AllocatorFactoryRegistry* singleton = new AllocatorFactoryRegistry; + return singleton; } -Allocator* AllocatorRegistry::GetRegisteredAllocator(const string& name, - int priority) { - for (auto entry : allocators_) { +const AllocatorFactoryRegistry::FactoryEntry* +AllocatorFactoryRegistry::FindEntry(const string& name, int priority) const { + for (auto& entry : factories_) { if (!name.compare(entry.name) && priority == entry.priority) { - return entry.allocator; + return &entry; } } return nullptr; } -void AllocatorRegistry::Register(const string& name, int priority, - Allocator* allocator) { +void AllocatorFactoryRegistry::Register(const char* source_file, + int source_line, const string& name, + int priority, + AllocatorFactory* factory) { + mutex_lock l(mu_); + CHECK(!first_alloc_made_) << "Attempt to register an AllocatorFactory " + << "after call to GetAllocator()"; CHECK(!name.empty()) << "Need a valid name for Allocator"; CHECK_GE(priority, 0) << "Priority needs to be non-negative"; - Allocator* existing = GetRegisteredAllocator(name, priority); + const FactoryEntry* existing = FindEntry(name, priority); if (existing != nullptr) { - // A duplicate is if the registration name and priority match - // but the Allocator::Name()'s don't match. - CHECK_EQ(existing->Name(), allocator->Name()) - << "Allocator with name: [" << name << "], type [" << existing->Name() - << "], priority: [" << priority - << "] already registered. Choose a different name to register " - << "an allocator of type " << allocator->Name(); - - // The allocator names match, so we can just return. - // It should be safe to delete the allocator since the caller - // gives up ownership of it. - delete allocator; - return; + // Duplicate registration is a hard failure. + LOG(FATAL) << "New registration for AllocatorFactory with name=" << name + << " priority=" << priority << " at location " << source_file + << ":" << source_line + << " conflicts with previous registration at location " + << existing->source_file << ":" << existing->source_line; } - AllocatorRegistryEntry tmp_entry; - tmp_entry.name = name; - tmp_entry.priority = priority; - tmp_entry.allocator = allocator; + FactoryEntry entry; + entry.source_file = source_file; + entry.source_line = source_line; + entry.name = name; + entry.priority = priority; + entry.factory.reset(factory); + factories_.push_back(std::move(entry)); +} - allocators_.push_back(tmp_entry); - int high_pri = -1; - for (auto entry : allocators_) { - if (high_pri < entry.priority) { - m_curr_allocator_ = entry.allocator; - high_pri = entry.priority; +Allocator* AllocatorFactoryRegistry::GetAllocator() { + mutex_lock l(mu_); + first_alloc_made_ = true; + FactoryEntry* best_entry = nullptr; + for (auto& entry : factories_) { + if (best_entry == nullptr) { + best_entry = &entry; + } else if (entry.priority > best_entry->priority) { + best_entry = &entry; } } + if (best_entry) { + if (!best_entry->allocator) { + best_entry->allocator.reset(best_entry->factory->CreateAllocator()); + } + return best_entry->allocator.get(); + } else { + LOG(FATAL) << "No registered CPU AllocatorFactory"; + return nullptr; + } } -Allocator* AllocatorRegistry::GetAllocator() { - return CHECK_NOTNULL(m_curr_allocator_); +SubAllocator* AllocatorFactoryRegistry::GetSubAllocator(int numa_node) { + mutex_lock l(mu_); + first_alloc_made_ = true; + FactoryEntry* best_entry = nullptr; + for (auto& entry : factories_) { + if (best_entry == nullptr) { + best_entry = &entry; + } else if (best_entry->factory->NumaEnabled()) { + if (entry.factory->NumaEnabled() && + (entry.priority > best_entry->priority)) { + best_entry = &entry; + } + } else { + DCHECK(!best_entry->factory->NumaEnabled()); + if (entry.factory->NumaEnabled() || + (entry.priority > best_entry->priority)) { + best_entry = &entry; + } + } + } + if (best_entry) { + int index = 0; + if (numa_node != port::kNUMANoAffinity) { + CHECK_LE(numa_node, port::NUMANumNodes()); + index = 1 + numa_node; + } + if (best_entry->sub_allocators.size() < (index + 1)) { + best_entry->sub_allocators.resize(index + 1); + } + if (!best_entry->sub_allocators[index].get()) { + best_entry->sub_allocators[index].reset( + best_entry->factory->CreateSubAllocator(numa_node)); + } + return best_entry->sub_allocators[index].get(); + } else { + LOG(FATAL) << "No registered CPU AllocatorFactory"; + return nullptr; + } } } // namespace tensorflow diff --git a/tensorflow/core/framework/allocator_registry.h b/tensorflow/core/framework/allocator_registry.h index b26e79ac3b..24f282ce84 100644 --- a/tensorflow/core/framework/allocator_registry.h +++ b/tensorflow/core/framework/allocator_registry.h @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -// Classes to maintain a static registry of memory allocators +// Classes to maintain a static registry of memory allocator factories. #ifndef TENSORFLOW_CORE_FRAMEWORK_ALLOCATOR_REGISTRY_H_ #define TENSORFLOW_CORE_FRAMEWORK_ALLOCATOR_REGISTRY_H_ @@ -21,59 +21,100 @@ limitations under the License. #include #include "tensorflow/core/framework/allocator.h" +#include "tensorflow/core/platform/numa.h" namespace tensorflow { -// A global AllocatorRegistry is used to hold allocators for CPU backends -class AllocatorRegistry { +class AllocatorFactory { public: - // Add an allocator to the registry. Caller releases ownership of - // 'allocator'. - void Register(const string& name, int priority, Allocator* allocator); + virtual ~AllocatorFactory() {} - // Return allocator with highest priority - // If multiple allocators have the same high priority, return one of them + // Returns true if the factory will create a functionally different + // SubAllocator for different (legal) values of numa_node. + virtual bool NumaEnabled() { return false; } + + // Create an Allocator. + virtual Allocator* CreateAllocator() = 0; + + // Create a SubAllocator. If NumaEnabled() is true, then returned SubAllocator + // will allocate memory local to numa_node. If numa_node == kNUMANoAffinity + // then allocated memory is not specific to any NUMA node. + virtual SubAllocator* CreateSubAllocator(int numa_node) = 0; +}; + +// A singleton registry of AllocatorFactories. +// +// Allocators should be obtained through ProcessState or cpu_allocator() +// (deprecated), not directly through this interface. The purpose of this +// registry is to allow link-time discovery of multiple AllocatorFactories among +// which ProcessState will obtain the best fit at startup. +class AllocatorFactoryRegistry { + public: + AllocatorFactoryRegistry() {} + ~AllocatorFactoryRegistry() {} + + void Register(const char* source_file, int source_line, const string& name, + int priority, AllocatorFactory* factory); + + // Returns 'best fit' Allocator. Find the factory with the highest priority + // and return an allocator constructed by it. If multiple factories have + // been registered with the same priority, picks one by unspecified criteria. Allocator* GetAllocator(); - // Returns the global registry of allocators. - static AllocatorRegistry* Global(); + // Returns 'best fit' SubAllocator. First look for the highest priority + // factory that is NUMA-enabled. If none is registered, fall back to the + // highest priority non-NUMA-enabled factory. If NUMA-enabled, return a + // SubAllocator specific to numa_node, otherwise return a NUMA-insensitive + // SubAllocator. + SubAllocator* GetSubAllocator(int numa_node); + + // Returns the singleton value. + static AllocatorFactoryRegistry* singleton(); private: - typedef struct { + mutex mu_; + bool first_alloc_made_ = false; + struct FactoryEntry { + const char* source_file; + int source_line; string name; int priority; - Allocator* allocator; // not owned - } AllocatorRegistryEntry; - - // Returns the Allocator registered for 'name' and 'priority', - // or 'nullptr' if not found. - Allocator* GetRegisteredAllocator(const string& name, int priority); - - std::vector allocators_; - Allocator* m_curr_allocator_; // not owned + std::unique_ptr factory; + std::unique_ptr allocator; + // Index 0 corresponds to kNUMANoAffinity, other indices are (numa_node + + // 1). + std::vector> sub_allocators; + }; + std::vector factories_ GUARDED_BY(mu_); + + // Returns any FactoryEntry registered under 'name' and 'priority', + // or 'nullptr' if none found. + const FactoryEntry* FindEntry(const string& name, int priority) const + EXCLUSIVE_LOCKS_REQUIRED(mu_); + + TF_DISALLOW_COPY_AND_ASSIGN(AllocatorFactoryRegistry); }; -namespace allocator_registration { - -class AllocatorRegistration { +class AllocatorFactoryRegistration { public: - AllocatorRegistration(const string& name, int priority, - Allocator* allocator) { - AllocatorRegistry::Global()->Register(name, priority, allocator); + AllocatorFactoryRegistration(const char* file, int line, const string& name, + int priority, AllocatorFactory* factory) { + AllocatorFactoryRegistry::singleton()->Register(file, line, name, priority, + factory); } }; -} // namespace allocator_registration - -#define REGISTER_MEM_ALLOCATOR(name, priority, allocator) \ - REGISTER_MEM_ALLOCATOR_UNIQ_HELPER(__COUNTER__, name, priority, allocator) +#define REGISTER_MEM_ALLOCATOR(name, priority, factory) \ + REGISTER_MEM_ALLOCATOR_UNIQ_HELPER(__COUNTER__, __FILE__, __LINE__, name, \ + priority, factory) -#define REGISTER_MEM_ALLOCATOR_UNIQ_HELPER(ctr, name, priority, allocator) \ - REGISTER_MEM_ALLOCATOR_UNIQ(ctr, name, priority, allocator) +#define REGISTER_MEM_ALLOCATOR_UNIQ_HELPER(ctr, file, line, name, priority, \ + factory) \ + REGISTER_MEM_ALLOCATOR_UNIQ(ctr, file, line, name, priority, factory) -#define REGISTER_MEM_ALLOCATOR_UNIQ(ctr, name, priority, allocator) \ - static allocator_registration::AllocatorRegistration \ - register_allocator_##ctr(name, priority, new allocator) +#define REGISTER_MEM_ALLOCATOR_UNIQ(ctr, file, line, name, priority, factory) \ + static AllocatorFactoryRegistration allocator_factory_reg_##ctr( \ + file, line, name, priority, new factory) } // namespace tensorflow diff --git a/tensorflow/core/platform/windows/port.cc b/tensorflow/core/platform/windows/port.cc index f2aaf13bec..5375f56372 100644 --- a/tensorflow/core/platform/windows/port.cc +++ b/tensorflow/core/platform/windows/port.cc @@ -33,6 +33,7 @@ limitations under the License. #include "tensorflow/core/platform/init_main.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/mem.h" +#include "tensorflow/core/platform/numa.h" #include "tensorflow/core/platform/snappy.h" #include "tensorflow/core/platform/types.h" @@ -57,6 +58,17 @@ int NumSchedulableCPUs() { return system_info.dwNumberOfProcessors; } +bool NUMAEnabled() { + // Not yet implemented: coming soon. + return false; +} + +int NUMANumNodes() { return 1; } + +void NUMASetThreadNodeAffinity(int node) {} + +int NUMAGetThreadNodeAffinity() { return kNUMANoAffinity; } + void* AlignedMalloc(size_t size, int minimum_alignment) { #ifdef TENSORFLOW_USE_JEMALLOC void* ptr = NULL; @@ -108,6 +120,14 @@ void Free(void* ptr) { #endif } +void* NUMAMalloc(int node, size_t size, int minimum_alignment) { + return AlignedMalloc(size, minimum_alignment); +} + +void NUMAFree(void* ptr, size_t size) { Free(ptr); } + +int NUMAGetMemAffinity(const void* addr) { return kNUMANoAffinity; } + void MallocExtension_ReleaseToSystem(std::size_t num_bytes) { // No-op. } -- GitLab From 4921064dd535d84aa031f8116e583b151dd46e97 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 10:23:10 -0700 Subject: [PATCH 192/519] Update Keras TensorBoard callback to log metrics at the batch-level PiperOrigin-RevId: 205416192 --- tensorflow/python/keras/callbacks.py | 46 +++++++++++---- tensorflow/python/keras/callbacks_test.py | 68 +++++++++++++++++++++++ 2 files changed, 103 insertions(+), 11 deletions(-) diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py index 0857a3279f..d1b9dc27bd 100644 --- a/tensorflow/python/keras/callbacks.py +++ b/tensorflow/python/keras/callbacks.py @@ -740,6 +740,7 @@ class TensorBoard(Callback): self.write_images = write_images self.batch_size = batch_size self._current_batch = 0 + self._total_batches_seen = 0 # abstracted writer class to be able to stub for testing self._writer_class = tf_summary.FileWriter self.embeddings_freq = embeddings_freq @@ -883,6 +884,24 @@ class TensorBoard(Callback): self._epoch + self._current_val_batch / self._validation_batches) self._current_val_batch += 1 + def _write_custom_summaries(self, step, logs=None): + """Writes metrics out as custom scalar summaries. + + Arguments: + step: the global step to use for Tensorboard. + logs: dict. Keys are scalar summary names, values are + NumPy scalars. + + """ + logs = logs or {} + for name, value in logs.items(): + summary = tf_summary.Summary() + summary_value = summary.value.add() + summary_value.simple_value = value.item() + summary_value.tag = name + self.writer.add_summary(summary, step) + self.writer.flush() + def on_train_begin(self, logs=None): """Checks if histogram summaries can be run.""" @@ -899,6 +918,16 @@ class TensorBoard(Callback): raise ValueError( 'If printing histograms, validation data must have length > 0.') + def on_batch_end(self, batch, logs=None): + """Writes scalar summaries for metrics on every training batch.""" + # Don't output batch_size and batch number as Tensorboard summaries + logs = logs or {} + batch_logs = {('batch_' + k): v + for k, v in logs.items() + if k not in ['batch', 'size']} + self._write_custom_summaries(self._total_batches_seen, batch_logs) + self._total_batches_seen += 1 + def on_epoch_begin(self, epoch, logs=None): """Add histogram op to Model test_function callbacks, reset batch count.""" @@ -915,7 +944,12 @@ class TensorBoard(Callback): def on_epoch_end(self, epoch, logs=None): """Checks if summary ops should run next epoch, logs scalar summaries.""" - logs = logs or {} + # don't output batch_size and + # batch number as Tensorboard summaries + logs = {('epoch_' + k): v + for k, v in logs.items() + if k not in ['batch', 'size']} + self._write_custom_summaries(epoch, logs) # pop the histogram summary op after each epoch if self.histogram_freq: @@ -964,16 +998,6 @@ class TensorBoard(Callback): i += self.batch_size - for name, value in logs.items(): - if name in ['batch', 'size']: - continue - summary = tf_summary.Summary() - summary_value = summary.value.add() - summary_value.simple_value = value.item() - summary_value.tag = name - self.writer.add_summary(summary, epoch) - self.writer.flush() - def on_train_end(self, logs=None): self.writer.close() diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py index 45598cafd3..7d830078ce 100644 --- a/tensorflow/python/keras/callbacks_test.py +++ b/tensorflow/python/keras/callbacks_test.py @@ -1096,6 +1096,74 @@ class KerasCallbacksTest(test.TestCase): assert os.path.exists(temp_dir) + def test_Tensorboard_batch_logging(self): + + class FileWriterStub(object): + + def __init__(self, logdir, graph=None): + self.logdir = logdir + self.graph = graph + self.batches_logged = [] + self.summary_values = [] + self.summary_tags = [] + + def add_summary(self, summary, step): + self.summary_values.append(summary.value[0].simple_value) + self.summary_tags.append(summary.value[0].tag) + self.batches_logged.append(step) + + def flush(self): + pass + + def close(self): + pass + + logdir = 'fake_dir' + + # log every batch + tb_cbk = keras.callbacks.TensorBoard(logdir) + tb_cbk.writer = FileWriterStub(logdir) + + for batch in range(5): + tb_cbk.on_batch_end(batch, {'acc': np.float32(batch)}) + self.assertEqual(tb_cbk.writer.batches_logged, [0, 1, 2, 3, 4]) + self.assertEqual(tb_cbk.writer.summary_values, [0., 1., 2., 3., 4.]) + self.assertEqual(tb_cbk.writer.summary_tags, ['batch_acc'] * 5) + + def test_Tensorboard_epoch_and_batch_logging(self): + + class FileWriterStub(object): + + def __init__(self, logdir, graph=None): + self.logdir = logdir + self.graph = graph + + def add_summary(self, summary, step): + if 'batch_' in summary.value[0].tag: + self.batch_summary = (step, summary) + elif 'epoch_' in summary.value[0].tag: + self.epoch_summary = (step, summary) + + def flush(self): + pass + + def close(self): + pass + + logdir = 'fake_dir' + + tb_cbk = keras.callbacks.TensorBoard(logdir) + tb_cbk.writer = FileWriterStub(logdir) + + tb_cbk.on_batch_end(0, {'acc': np.float32(5.0)}) + tb_cbk.on_epoch_end(0, {'acc': np.float32(10.0)}) + batch_step, batch_summary = tb_cbk.writer.batch_summary + self.assertEqual(batch_step, 0) + self.assertEqual(batch_summary.value[0].simple_value, 5.0) + epoch_step, epoch_summary = tb_cbk.writer.epoch_summary + self.assertEqual(epoch_step, 0) + self.assertEqual(epoch_summary.value[0].simple_value, 10.0) + def test_RemoteMonitorWithJsonPayload(self): if requests is None: self.skipTest('`requests` required to run this test') -- GitLab From 3b6bceb87f91fc2d0e0c7d31d3583c39f2d3ca8d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 10:27:51 -0700 Subject: [PATCH 193/519] fixing some nits PiperOrigin-RevId: 205416917 --- .../examples/generative_examples/dcgan.ipynb | 20 ++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb index 232f9a8ef0..54cc4dc5da 100644 --- a/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb +++ b/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb @@ -27,9 +27,9 @@ "id": "ITZuApL56Mny" }, "source": [ - "This notebook demonstrates how to generate images of handwritten digits using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). To do this, we use Deep Convolutional Generative Adverserial Networks ([DCGAN](https://arxiv.org/pdf/1511.06434.pdf)).\n", + "This notebook demonstrates how to generate images of handwritten digits using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). To do so, we use Deep Convolutional Generative Adverserial Networks ([DCGAN](https://arxiv.org/pdf/1511.06434.pdf)).\n", "\n", - "On a colab GPU(Tesla K80), the model takes around 40 seconds per epoch to train.\n", + "This model takes about 40 seconds per epoch to train on a single Tesla K80 on Colab, as of July 2018.\n", "\n", "Below is the output generated after training the generator and discriminator models for 150 epochs.\n", "\n", @@ -80,6 +80,8 @@ }, "outputs": [], "source": [ + "from __future__ import absolute_import, division, print_function\n", + "\n", "# Import TensorFlow \u003e= 1.9 and enable eager execution\n", "import tensorflow as tf\n", "tf.enable_eager_execution()\n", @@ -202,12 +204,12 @@ "\n", "* **Generator** \n", " * It is responsible for **creating the convincing images good enough to fool the discriminator**.\n", - " * It consists of Conv2DTranspose(Upsampling) layers. We start with a fully connected layer and upsample the image 2 times so as to reach the desired image size(mnist image size) which is (28, 28, 1). \n", + " * It consists of Conv2DTranspose (Upsampling) layers. We start with a fully connected layer and upsample the image 2 times so as to reach the desired image size (mnist image size) which is (28, 28, 1). \n", " * We use **leaky relu** activation except for the **last layer** which uses **tanh** activation.\n", " \n", "* **Discriminator**\n", " * **The discriminator is responsible for classifying the fake images from the real images.**\n", - " * In other words, the discriminator is given generated images(from the generator) and the real MNIST images. The job of the discriminator is to classify these images into fake(generated) and real(MNIST images).\n", + " * In other words, the discriminator is given generated images (from the generator) and the real MNIST images. The job of the discriminator is to classify these images into fake (generated) and real (MNIST images).\n", " * **Basically the generator should be good enough to fool the discriminator that the generated images are real**." ] }, @@ -323,8 +325,8 @@ "\n", "* **Discriminator loss**\n", " * The discriminator loss function takes 2 inputs; **real images, generated images**\n", - " * real_loss is a sigmoid cross entropy loss of the **real images** and an **array of ones(since these are the real images)**\n", - " * generated_loss is a sigmoid cross entropy loss of the **generated images** and an **array of zeros(since these are the fake images)**\n", + " * real_loss is a sigmoid cross entropy loss of the **real images** and an **array of ones (since these are the real images)**\n", + " * generated_loss is a sigmoid cross entropy loss of the **generated images** and an **array of zeros (since these are the fake images)**\n", " * Then the total_loss is the sum of real_loss and the generated_loss\n", " \n", "* **Generator loss**\n", @@ -411,9 +413,9 @@ "\n", "* We start by iterating over the dataset\n", "* The generator is given **noise as an input** which when passed through the generator model will output a image looking like a handwritten digit\n", - "* The discriminator is given the **real MNIST images as well as the generated images(from the generator)**.\n", + "* The discriminator is given the **real MNIST images as well as the generated images (from the generator)**.\n", "* Next, we calculate the generator and the discriminator loss.\n", - "* Then, we calculate the gradients of loss with respect to both the generator and the discriminator variables(inputs) and apply those to the optimizer.\n", + "* Then, we calculate the gradients of loss with respect to both the generator and the discriminator variables (inputs) and apply those to the optimizer.\n", "\n", "## Generate Images\n", "\n", @@ -442,7 +444,7 @@ "noise_dim = 100\n", "num_examples_to_generate = 100\n", "\n", - "# keeping the random vector constant for generation(prediction) so\n", + "# keeping the random vector constant for generation (prediction) so\n", "# it will be easier to see the improvement of the gan.\n", "random_vector_for_generation = tf.random_normal([num_examples_to_generate,\n", " noise_dim])" -- GitLab From f965075fca32a41b544f8101a94641f6d322f1ec Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 10:31:03 -0700 Subject: [PATCH 194/519] Internal change. PiperOrigin-RevId: 205417414 --- tensorflow/contrib/lite/kernels/fully_connected.cc | 2 +- tensorflow/contrib/lite/kernels/lstm.cc | 4 ++-- tensorflow/contrib/lite/kernels/svdf.cc | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/fully_connected.cc b/tensorflow/contrib/lite/kernels/fully_connected.cc index 3b203dd480..d6e297a66a 100644 --- a/tensorflow/contrib/lite/kernels/fully_connected.cc +++ b/tensorflow/contrib/lite/kernels/fully_connected.cc @@ -71,7 +71,7 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) { // Instead, we allocate a new object to carry information from Prepare() to // Eval(). gemm_support::IncrementUsageCounter(context); - auto* op_data = new OpData; + auto* op_data = new OpData(); context->AddTensors(context, 1, &op_data->input_quantized_index); return op_data; } diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc index 4dfc891548..50487a8d59 100644 --- a/tensorflow/contrib/lite/kernels/lstm.cc +++ b/tensorflow/contrib/lite/kernels/lstm.cc @@ -97,7 +97,7 @@ constexpr int kCellStateTensor = 1; constexpr int kOutputTensor = 2; void* Init(TfLiteContext* context, const char* buffer, size_t length) { - auto* op_data = new OpData; + auto* op_data = new OpData(); op_data->kernel_type = kTfLiteLSTMFullKernel; context->AddTensors(context, /*tensors_to_add=*/7, &op_data->scratch_tensor_index); @@ -847,7 +847,7 @@ enum OutputTensor { }; void* Init(TfLiteContext* context, const char* buffer, size_t length) { - auto* op_data = new OpData; + auto* op_data = new OpData(); op_data->kernel_type = kTfLiteLSTMBasicKernel; // `scratch_tensor_index` is unused in this kernel. op_data->scratch_tensor_index = -1; diff --git a/tensorflow/contrib/lite/kernels/svdf.cc b/tensorflow/contrib/lite/kernels/svdf.cc index 22eebdd4ce..179c2dc266 100644 --- a/tensorflow/contrib/lite/kernels/svdf.cc +++ b/tensorflow/contrib/lite/kernels/svdf.cc @@ -105,7 +105,7 @@ constexpr int kStateTensor = 0; constexpr int kOutputTensor = 1; void* Init(TfLiteContext* context, const char* buffer, size_t length) { - auto* op_data = new OpData; + auto* op_data = new OpData(); op_data->float_weights_time_initialized = false; context->AddTensors(context, /*tensors_to_add=*/4, &op_data->scratch_tensor_index); -- GitLab From 8ed40cdd3ea7e9aea996339678efba2b2b04e1ad Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 10:55:31 -0700 Subject: [PATCH 195/519] Fix grammar in configure.py PiperOrigin-RevId: 205421605 --- configure.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/configure.py b/configure.py index 251bebc2e1..1df7bc736f 100644 --- a/configure.py +++ b/configure.py @@ -1422,7 +1422,7 @@ def set_windows_build_flags(environ_cp): environ_cp, 'TF_OVERRIDE_EIGEN_STRONG_INLINE', 'Eigen strong inline', True, ('Would you like to override eigen strong inline for some C++ ' - 'compilation to reduce the compiling time?'), + 'compilation to reduce the compilation time?'), 'Eigen strong inline overridden.', 'Not overriding eigen strong inline, ' 'some compilations could take more than 20 mins.'): -- GitLab From da929851e2b5446a5aaee29a869428037a72f2b7 Mon Sep 17 00:00:00 2001 From: Anjali Sridhar Date: Fri, 20 Jul 2018 11:11:42 -0700 Subject: [PATCH 196/519] Refactor properties and functions common to Mirrored and TowerLocal Variables. PiperOrigin-RevId: 205424692 --- .../contrib/distribute/python/values.py | 89 +++++++------------ 1 file changed, 33 insertions(+), 56 deletions(-) diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index 1761a43251..3162aebf5b 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -196,10 +196,43 @@ class DistributedVariable(DistributedDelegate): # to the container without introducing a reference cycle. for v in six.itervalues(index): v._distributed_container = weakref.ref(self) # pylint: disable=protected-access + # tf.keras keeps track of variables initialized using this attribute. When + # tf.keras gets the default session, it initializes all uninitialized vars. + # We need to make _keras_initialized a member of DistributedVariable because + # without this it will use `__getattr__` which will delegate to a component + # variable. + self._keras_initialized = False super(DistributedVariable, self).__init__(index) + def is_initialized(self, name=None): + """Identifies if all the component variables are initialized. + + Args: + name: Name of the final `logical_and` op. + + Returns: + The op that evaluates to True or False depending on if all the + component variables are initialized. + """ + # We have to cast the self._index.values() to a `list` because when we + # use `model_to_estimator` to run tf.keras models, self._index.values() is + # of type `dict_values` and not `list`. + values_list = list(self._index.values()) + result = values_list[0].is_initialized() + # We iterate through the list of values except the last one to allow us to + # name the final `logical_and` op the same name that is passed by the user + # to the `is_initialized` op. For distributed variables, the + # `is_initialized` op is a `logical_and` op. + for v in values_list[1:-1]: + result = math_ops.logical_and(result, v.is_initialized()) + result = math_ops.logical_and(result, values_list[-1].is_initialized(), + name=name) + return result + @property def initializer(self): + # return grouped ops of all the var initializations of component values of + # the mirrored variable return control_flow_ops.group([v.initializer for v in self._index.values()]) @property @@ -296,12 +329,6 @@ class MirroredVariable(DistributedVariable, Mirrored, for v in six.itervalues(index): v._mirrored_container = weakref.ref(self) # pylint: disable=protected-access self._primary_var = primary_var - # tf.keras keeps track of variables initialized using this attribute. When - # tf.keras gets the default session, it initializes all uninitialized vars. - # We need to make _keras_initialized a member of MirroredVariable because - # without this it will use `__getattr__` which will delegate to a component - # variable. - self._keras_initialized = False self._aggregation = aggregation super(MirroredVariable, self).__init__(index) @@ -357,28 +384,6 @@ class MirroredVariable(DistributedVariable, Mirrored, assign_fn = lambda var, *a, **kw: var.assign(*a, **kw) return self._assign_func(f=assign_fn, *args, **kwargs) - def is_initialized(self, name=None): - # We have to cast the self._index.values() to a `list` because when we - # use `model_to_estimator` to run tf.keras models, self._index.values() is - # of type `dict_values` and not `list`. - values_list = list(self._index.values()) - result = values_list[0].is_initialized() - # We iterate through the list of values except the last one to allow us to - # name the final `logical_and` op the same name that is passed by the user - # to the `is_initialized` op. For mirrored variables, the `is_initialized` - # op is a `logical_and` op. - for v in values_list[1:-1]: - result = math_ops.logical_and(result, v.is_initialized()) - result = math_ops.logical_and(result, values_list[-1].is_initialized(), - name=name) - return result - - @property - def initializer(self): - # return grouped ops of all the var initializations of component values of - # the mirrored variable - return control_flow_ops.group([v.initializer for v in self._index.values()]) - @property def aggregation(self): return self._aggregation @@ -466,12 +471,6 @@ class TowerLocalVariable(DistributedVariable, PerDevice, def __init__(self, index, primary_var, aggregation): self._primary_var = primary_var self._aggregation = aggregation - # tf.keras keeps track of variables initialized using this attribute. When - # tf.keras gets the default session, it initializes all uninitialized vars. - # We need to make _keras_initialized a member of TowerLocalVariable because - # without this it will use `__getattr__` which will delegate to a component - # variable. - self._keras_initialized = False super(TowerLocalVariable, self).__init__(index) def assign_sub(self, *args, **kwargs): @@ -486,28 +485,6 @@ class TowerLocalVariable(DistributedVariable, PerDevice, _assert_tower_context() return self.get().assign(*args, **kwargs) - def is_initialized(self, name=None): - # We have to cast the self._index.values() to a `list` because when we - # use `model_to_estimator` to run tf.keras models, self._index.values() is - # of type `dict_values` and not `list`. - values_list = list(self._index.values()) - result = values_list[0].is_initialized() - # We iterate through the list of values except the last one to allow us to - # name the final `logical_and` op the same name that is passed by the user - # to the `is_initialized` op. For tower local variables, the - # `is_initialized` op is a `logical_and` op. - for v in values_list[1:-1]: - result = math_ops.logical_and(result, v.is_initialized()) - result = math_ops.logical_and(result, values_list[-1].is_initialized(), - name=name) - return result - - @property - def initializer(self): - # return grouped ops of all the var initializations of component values of - # the tower local variable - return control_flow_ops.group([v.initializer for v in self._index.values()]) - @property def aggregation(self): return self._aggregation -- GitLab From b47e08b1580033ff63fd7b9a2661e30049afb43d Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Fri, 20 Jul 2018 11:21:44 -0700 Subject: [PATCH 197/519] Re-enable the deadness-analysis. The compile time problem issue has been fixed. PiperOrigin-RevId: 205426325 --- .../compiler/jit/mark_for_compilation_pass.cc | 15 +++++++++++++++ tensorflow/compiler/jit/xla_fusion_optimizer.cc | 12 ++++++++++++ 2 files changed, 27 insertions(+) diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc index 73db0d5952..a3949bc14b 100644 --- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc +++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc @@ -21,6 +21,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/jit/deadness_analysis.h" #include "tensorflow/compiler/jit/defs.h" #include "tensorflow/compiler/jit/graphcycles/graphcycles.h" #include "tensorflow/compiler/jit/legacy_flags/mark_for_compilation_pass_flags.h" @@ -463,6 +464,12 @@ Status MarkForCompilationPass::Run( VLOG(1) << "flags->tf_xla_fusion_only = " << flags->tf_xla_fusion_only; const FunctionLibraryDefinition* fld = options.flib_def; + std::unique_ptr deadness; + { + XLA_SCOPED_LOGGING_TIMER_LEVEL("DeadnessAnalysis", 1); + TF_RETURN_IF_ERROR(DeadnessAnalysis::Run(**options.graph, &deadness)); + } + auto is_compilable = [&](const Node* node, const DeviceType& device_type) { const XlaOpRegistry::DeviceRegistration* registration; if (!XlaOpRegistry::GetCompilationDevice(device_type.type(), @@ -490,6 +497,14 @@ Status MarkForCompilationPass::Run( status = fld->GetAttr(*node, kXlaCompileAttr, &compile); if (status.ok()) return compile; + // If inputs to `node` can have conflicting deadness (i.e. some are alive + // and some are dead) then don't compile it. XLA cannot represent the + // deadness semantics of these nodes correctly and auto-clustering these + // nodes can cause deadness to propagate to nodes that should be live. + if (node->IsMerge() || deadness->HasInputsWithMismatchingDeadness(*node)) { + return false; + } + // Check for fusable ops only if requested. if (global_jit_level > 0 && fusion_only && !IsXlaFusable(node->def())) { return false; diff --git a/tensorflow/compiler/jit/xla_fusion_optimizer.cc b/tensorflow/compiler/jit/xla_fusion_optimizer.cc index 74257b09a8..4b499b1613 100644 --- a/tensorflow/compiler/jit/xla_fusion_optimizer.cc +++ b/tensorflow/compiler/jit/xla_fusion_optimizer.cc @@ -20,6 +20,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/jit/deadness_analysis.h" #include "tensorflow/compiler/jit/defs.h" #include "tensorflow/compiler/jit/graphcycles/graphcycles.h" #include "tensorflow/compiler/jit/union_find.h" @@ -146,6 +147,9 @@ Status XlaFusionOptimizer::Optimize(grappler::Cluster* cluster, TF_RETURN_IF_ERROR( ImportGraphDef(options, item.graph, &graph, &shape_refiner)); + std::unique_ptr deadness; + TF_RETURN_IF_ERROR(DeadnessAnalysis::Run(graph, &deadness)); + // Collect nodes that can be fused via XLA, while ignoring those that // explicitly ask for XLA: (*) nodes that are marked to be compiled // explicitly. (*) nodes assigned to XLA device. @@ -185,6 +189,14 @@ Status XlaFusionOptimizer::Optimize(grappler::Cluster* cluster, continue; } + // If inputs to `node` can have conflicting deadness (i.e. some are alive + // and some are dead) then don't compile it. XLA cannot represent the + // deadness semantics of these nodes correctly and auto-clustering these + // nodes can cause deadness to propagate to nodes that should be live. + if (node->IsMerge() || deadness->HasInputsWithMismatchingDeadness(*node)) { + continue; + } + compilation_candidates.insert(node); } -- GitLab From 41b93403ac8148bd880c749165c40840ddb95b44 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 11:26:39 -0700 Subject: [PATCH 198/519] Create a config option to not link LGPL PiperOrigin-RevId: 205427089 --- tensorflow/BUILD | 8 ++++++++ tensorflow/tensorflow.bzl | 8 ++++++++ 2 files changed, 16 insertions(+) diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 518c2b0489..388ca3f293 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -24,6 +24,14 @@ load( "gen_api_init_files", # @unused ) +# Config setting used when building for products +# which requires restricted licenses to be avoided. +config_setting( + name = "no_lgpl_deps", + values = {"define": "__TENSORFLOW_NO_LGPL_DEPS__=1"}, + visibility = ["//visibility:public"], +) + # Config setting for determining if we are building for Android. config_setting( name = "android", diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 955b53f691..954940642b 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -137,6 +137,14 @@ def if_not_mobile(a): "//conditions:default": a, }) +# Config setting selector used when building for products +# which requires restricted licenses to be avoided. +def if_not_lgpl_restricted(a): + _ = (a,) + return select({ + "//conditions:default": [], + }) + def if_not_windows(a): return select({ clean_dep("//tensorflow:windows"): [], -- GitLab From 1d3440dea001d7466f6e9f40ddd3afdf94ed8dc4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 12:15:16 -0700 Subject: [PATCH 199/519] Add a method for getting in-memory profiles from ProfileContexts PiperOrigin-RevId: 205434648 --- tensorflow/python/profiler/profile_context.py | 33 ++++++++++++++++--- .../python/profiler/profile_context_test.py | 2 ++ 2 files changed, 31 insertions(+), 4 deletions(-) diff --git a/tensorflow/python/profiler/profile_context.py b/tensorflow/python/profiler/profile_context.py index 18eb66ef98..fa4260a712 100644 --- a/tensorflow/python/profiler/profile_context.py +++ b/tensorflow/python/profiler/profile_context.py @@ -88,16 +88,19 @@ def _profiled_run(self, to_profiles = self.profile_context._profile_candidates() for to_prof in to_profiles: cmd, opts, _ = to_prof + saved_views = self.profile_context._views.setdefault(cmd, {}) if self.profile_context._debug: sys.stderr.write('debug: profiling %s step: %d\n' % (cmd, step)) if cmd == 'graph': - self.profile_context.profiler.profile_graph(opts) + saved_views[step] = self.profile_context.profiler.profile_graph(opts) elif cmd == 'scope': - self.profile_context.profiler.profile_name_scope(opts) + saved_views[step] = self.profile_context.profiler.profile_name_scope( + opts) elif cmd == 'op': - self.profile_context.profiler.profile_operations(opts) + saved_views[step] = self.profile_context.profiler.profile_operations( + opts) elif cmd == 'code': - self.profile_context.profiler.profile_python(opts) + saved_views[step] = self.profile_context.profiler.profile_python(opts) else: raise ValueError('Unknown cmd: %s\n' % cmd) return ret @@ -185,8 +188,30 @@ class ProfileContext(object): self._traced_steps = 0 self._auto_profiles = [] self._profiler = None + self._views = {} self._lock = threading.Lock() + def get_profiles(self, cmd): + """Returns profiling results for each step at which `cmd` was run. + + Args: + cmd: string, profiling command used in an `add_auto_profiling` call. + + Returns: + dict[int: (MultiGraphNodeProto | GraphNodeProto)]. Keys are steps at which + the profiling command was run. Values are the outputs of profiling. + For "code" and "op" commands this will be a `MultiGraphNodeProto`, for + "scope" and "graph" commands this will be a `GraphNodeProto. + + Raises: + ValueError: if `cmd` was never run (either because no session.run call was + made or because there was no `add_auto_profiling` call with the specified + `cmd`. + """ + if cmd not in self._views: + raise ValueError('No autoprofiler for command: {}, was run'.format(cmd)) + return self._views[cmd] + def add_auto_profiling(self, cmd, options, profile_steps): """Traces and profiles at some session run steps. diff --git a/tensorflow/python/profiler/profile_context_test.py b/tensorflow/python/profiler/profile_context_test.py index a623beee23..107ad443c3 100644 --- a/tensorflow/python/profiler/profile_context_test.py +++ b/tensorflow/python/profiler/profile_context_test.py @@ -61,6 +61,8 @@ class ProfilerContextTest(test.TestCase): profile_str = f.read() gfile.Remove(outfile) + self.assertEqual(set([15, 50, 100]), set(pctx.get_profiles("op").keys())) + with lib.ProfilerFromFile( os.path.join(test.get_temp_dir(), "profile_100")) as profiler: profiler.profile_operations(options=opts) -- GitLab From dc9e568f7d4c01636747b13b9f7d12078aa52c24 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Fri, 20 Jul 2018 12:26:53 -0700 Subject: [PATCH 200/519] When in graph mode read will force a tape recording. This can bypass an incorrect recording which might be generated in the presence of loop contexts. PiperOrigin-RevId: 205436238 --- tensorflow/python/eager/backprop_test.py | 13 +++++++++++++ tensorflow/python/ops/resource_variable_ops.py | 10 ++++++++-- 2 files changed, 21 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py index bdda200ff6..95a3a8b629 100644 --- a/tensorflow/python/eager/backprop_test.py +++ b/tensorflow/python/eager/backprop_test.py @@ -96,6 +96,19 @@ class BackpropTest(test.TestCase): self.assertAllEqual(grads_and_vars[0][0], 1.0) self.assertAllEqual(id(grads_and_vars[0][1]), id(x)) + def testGradientInsideLoop(self): + with ops.Graph().as_default(): + v = resource_variable_ops.ResourceVariable(1.0) + + def body(_): + _ = v + 1.0 # This reads the variable inside the loop context + with backprop.GradientTape() as t: + result = v * 2 + self.assertTrue(t.gradient(result, v) is not None) + return 1.0 + + control_flow_ops.while_loop(lambda i: False, body, [1.0]) + def testWhereGradient(self): # Note: where is special because only some of its arguments are of # differentiable dtypes. diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py index 1f56ad25bf..db071e3974 100644 --- a/tensorflow/python/ops/resource_variable_ops.py +++ b/tensorflow/python/ops/resource_variable_ops.py @@ -742,8 +742,14 @@ class ResourceVariable(variables.RefVariable): def _read_variable_op(self): if self.trainable: tape.watch_variable(self) - return gen_resource_variable_ops.read_variable_op(self._handle, - self._dtype) + result = gen_resource_variable_ops.read_variable_op(self._handle, + self._dtype) + if not context.executing_eagerly(): + # Note that if a control flow context is active the input of the read op + # might not actually be the handle. This line bypasses it. + tape.record_operation( + "ReadVariableOp", [result], [self._handle], lambda x: [x]) + return result def read_value(self): """Constructs an op which reads the value of this variable. -- GitLab From 9f8256a61fcd44eeef7c0bf41c9bb4fddc505ae0 Mon Sep 17 00:00:00 2001 From: Bixia Zheng Date: Fri, 20 Jul 2018 12:28:30 -0700 Subject: [PATCH 201/519] [XLA] Increase the sample size for PrngTest.Uniformity256. The original test samples 256 numbers with a range of 256 and performs a ChiSquare test with 0.05 level of significance. Using the Philox RNG algorithm on GPU, this test produces error like this: Expected: (UniformChiSquared(256, 256)) < (293.248), actual: 300.086 vs 293.248 This change increases the sample size for the test to make it pass on the GPU. PiperOrigin-RevId: 205436438 --- tensorflow/compiler/xla/tests/prng_test.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/tests/prng_test.cc b/tensorflow/compiler/xla/tests/prng_test.cc index 5ebf8344d2..3f98099be6 100644 --- a/tensorflow/compiler/xla/tests/prng_test.cc +++ b/tensorflow/compiler/xla/tests/prng_test.cc @@ -177,7 +177,7 @@ XLA_TEST_F(PrngTest, Uniformity108) { EXPECT_LT(UniformChiSquared(108, 256), 132.144); } XLA_TEST_F(PrngTest, Uniformity256) { - EXPECT_LT(UniformChiSquared(256, 256), 293.248); + EXPECT_LT(UniformChiSquared(256, 512), 293.248); } XLA_TEST_F(PrngTest, MapUsingRng) { -- GitLab From 4efac3283749cb858b7c1bf859f1bf022268932d Mon Sep 17 00:00:00 2001 From: Sunitha Kambhampati Date: Fri, 20 Jul 2018 12:53:00 -0700 Subject: [PATCH 202/519] Remove dependency on pathlib and workaround it by using six.moves.urllib_parse --- tensorflow/contrib/summary/summary_ops_test.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/tensorflow/contrib/summary/summary_ops_test.py b/tensorflow/contrib/summary/summary_ops_test.py index 77b1c93ff2..4d1807130c 100644 --- a/tensorflow/contrib/summary/summary_ops_test.py +++ b/tensorflow/contrib/summary/summary_ops_test.py @@ -17,7 +17,6 @@ from __future__ import division from __future__ import print_function import os -import pathlib import tempfile import time @@ -280,7 +279,7 @@ class EagerDbTest(summary_test_util.SummaryDbTest): def testDbURIOpen(self): tmpdb_path = os.path.join(self.get_temp_dir(), 'tmpDbURITest.sqlite') - tmpdb_uri = pathlib.Path(tmpdb_path).as_uri() + tmpdb_uri = six.moves.urllib_parse.urljoin("file:", tmpdb_path) tmpdb_writer = summary_ops.create_db_writer( tmpdb_uri, "experimentA", -- GitLab From 770e5884629397a19b061eef2b925e0aec23dc14 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 13:16:28 -0700 Subject: [PATCH 203/519] Fix minor formatting problem. PiperOrigin-RevId: 205442775 --- tensorflow/contrib/lite/g3doc/apis.md | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/lite/g3doc/apis.md b/tensorflow/contrib/lite/g3doc/apis.md index a591a353dd..e94a2cc44e 100644 --- a/tensorflow/contrib/lite/g3doc/apis.md +++ b/tensorflow/contrib/lite/g3doc/apis.md @@ -53,6 +53,7 @@ typedef enum { ``` Failures can be easily verified with: + ```c++ if (status != kTfLiteOk) { // ... error handling here ... -- GitLab From 924a8f24a9b8b8a3b1a561123f3e4cf9ebe91708 Mon Sep 17 00:00:00 2001 From: Amit Patankar Date: Fri, 20 Jul 2018 13:19:12 -0700 Subject: [PATCH 204/519] Fix the version string in setup.py. The PR seemed to miss it. PiperOrigin-RevId: 205443195 --- tensorflow/tools/pip_package/setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index c630ca04b8..2e278aa60b 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -45,7 +45,7 @@ DOCLINES = __doc__.split('\n') # 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 = '1.9.0-rc0' +_VERSION = '1.9.0' REQUIRED_PACKAGES = [ 'absl-py >= 0.1.6', -- GitLab From 4638518bd3821fb887c59cb82326a77384ad4b69 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 13:23:31 -0700 Subject: [PATCH 205/519] Add leaf index modes as an argument, which specifies when output leaf indices. PiperOrigin-RevId: 205443722 --- .../python/training/functions/gbdt_batch.py | 18 +++++++++++++++--- 1 file changed, 15 insertions(+), 3 deletions(-) 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 1ee7f2395e..643d8d2498 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py @@ -287,7 +287,8 @@ class GradientBoostedDecisionTreeModel(object): loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS, feature_columns=None, use_core_columns=False, - output_leaf_index=False): + output_leaf_index=False, + output_leaf_index_modes=None): """Construct a new GradientBoostedDecisionTreeModel function. Args: @@ -307,6 +308,9 @@ class GradientBoostedDecisionTreeModel(object): used. output_leaf_index: A boolean variable indicating whether to output leaf index into predictions dictionary. + output_leaf_index_modes: A list of modes from (TRAIN, EVAL, INFER) which + dictates when leaf indices will be outputted. By default, leaf indices + are only outputted in INFER mode. Raises: ValueError: if inputs are not valid. @@ -404,7 +408,16 @@ class GradientBoostedDecisionTreeModel(object): self._learner_config.multi_class_strategy == learner_pb2.LearnerConfig.TREE_PER_CLASS and learner_config.num_classes == 2) + + if output_leaf_index_modes is None: + output_leaf_index_modes = [learn.ModeKeys.INFER] + elif not all( + mode in (learn.ModeKeys.TRAIN, learn.ModeKeys.EVAL, + learn.ModeKeys.INFER) for mode in output_leaf_index_modes): + raise ValueError("output_leaf_index_modes should only contain ModeKeys.") + self._output_leaf_index = output_leaf_index + self._output_leaf_index_modes = output_leaf_index_modes def _predict_and_return_dict(self, ensemble_handle, ensemble_stamp, mode): """Runs prediction and returns a dictionary of the prediction results. @@ -435,8 +448,7 @@ class GradientBoostedDecisionTreeModel(object): # the right stamp. with ops.control_dependencies(ensemble_stats): leaf_index = None - # Only used in infer (predict), not used in train and eval. - if self._output_leaf_index and mode == learn.ModeKeys.INFER: + if self._output_leaf_index and mode in self._output_leaf_index_modes: predictions, _, leaf_index = ( prediction_ops).gradient_trees_prediction_verbose( ensemble_handle, -- GitLab From 85d2a214fb93921b60383db17b8dbbf013034157 Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Fri, 20 Jul 2018 13:32:58 -0700 Subject: [PATCH 206/519] Support scalar inputs for reduce ops PiperOrigin-RevId: 205445091 --- .../internal/reference/reference_ops.h | 5 ++- .../contrib/lite/kernels/internal/types.h | 8 +++-- tensorflow/contrib/lite/kernels/reduce.cc | 4 +++ .../contrib/lite/kernels/reduce_test.cc | 35 +++++++++++++++++++ 4 files changed, 49 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 10e23f0b41..ef39be3f91 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -3517,7 +3517,6 @@ inline bool Reduce(const In* input_data, const int* input_dims, Out reducer(const Out current, const In in), Out* output_data) { // Reset input iterator. - TFLITE_DCHECK(input_num_dims > 0); for (int idx = 0; idx < input_num_dims; ++idx) { input_iter[idx] = 0; } @@ -3537,6 +3536,10 @@ inline bool ResolveAxis(const int num_dims, const int* axis, const int64_t num_axis, int* out_axis, int* out_num_axis) { *out_num_axis = 0; // Just in case. + // Short-circuit axis resolution for scalars; the axis will go unused. + if (num_dims == 0) { + return true; + } // o(n^2) is fine since out_num_axis should be really small, mostly <= 4 for (int64_t idx = 0; idx < num_axis; ++idx) { // Handle negative index. diff --git a/tensorflow/contrib/lite/kernels/internal/types.h b/tensorflow/contrib/lite/kernels/internal/types.h index fe113dfdd3..c44698b677 100644 --- a/tensorflow/contrib/lite/kernels/internal/types.h +++ b/tensorflow/contrib/lite/kernels/internal/types.h @@ -278,7 +278,9 @@ inline tflite::Dims<4> ToRuntimeDims(const tflite::RuntimeShape& array_shape) { // Gets next index to iterate through a multidimensional array. inline bool NextIndex(const int num_dims, const int* dims, int* current) { - TFLITE_DCHECK_GT(num_dims, 0); + if (num_dims == 0) { + return false; + } TFLITE_DCHECK(dims != nullptr); TFLITE_DCHECK(current != nullptr); int carry = 1; @@ -305,7 +307,9 @@ inline bool NextIndex(const int num_dims, const int* dims, int* current) { inline size_t ReducedOutputOffset(const int num_dims, const int* dims, const int* index, const int num_axis, const int* axis) { - TFLITE_DCHECK_GT(num_dims, 0); + if (num_dims == 0) { + return 0; + } TFLITE_DCHECK(dims != nullptr); TFLITE_DCHECK(index != nullptr); size_t offset = 0; diff --git a/tensorflow/contrib/lite/kernels/reduce.cc b/tensorflow/contrib/lite/kernels/reduce.cc index 52e4084ff8..e99f67c725 100644 --- a/tensorflow/contrib/lite/kernels/reduce.cc +++ b/tensorflow/contrib/lite/kernels/reduce.cc @@ -78,6 +78,10 @@ TfLiteStatus ResizeOutputTensor(TfLiteContext* context, OpContext* op_context) { size_t num_axis = NumElements(op_context->axis); const TfLiteIntArray* input_dims = op_context->input->dims; int input_num_dims = NumDimensions(op_context->input); + if (input_num_dims == 0) { + return context->ResizeTensor(context, op_context->output, + TfLiteIntArrayCreate(0)); + } const int* axis = GetTensorData(op_context->axis); if (op_context->params->keep_dims) { TfLiteIntArray* output_dims = TfLiteIntArrayCreate(input_num_dims); diff --git a/tensorflow/contrib/lite/kernels/reduce_test.cc b/tensorflow/contrib/lite/kernels/reduce_test.cc index 7d28931ecd..5d432d34ef 100644 --- a/tensorflow/contrib/lite/kernels/reduce_test.cc +++ b/tensorflow/contrib/lite/kernels/reduce_test.cc @@ -22,6 +22,7 @@ namespace tflite { namespace { using ::testing::ElementsAreArray; +using ::testing::IsEmpty; class BaseOpModel : public SingleOpModel { public: @@ -197,6 +198,16 @@ TEST(ConstFloatMeanOpTest, KeepDims) { ElementsAreArray(ArrayFloatNear({10.5, 12.5, 14.5}))); } +TEST(ConstFloatMeanOpTest, Scalar) { + std::vector data = {3.27}; + MeanOpConstModel m({TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, {}, + {0}, true); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), IsEmpty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({3.27}))); +} + TEST(DynamicFloatMeanOpTest, NotKeepDims) { std::vector 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, @@ -342,6 +353,16 @@ TEST(DynamicFloatSumOpTest, NotKeepDims) { ElementsAreArray(ArrayFloatNear({144, 156}))); } +TEST(ConstFloatSumOpTest, Scalar) { + std::vector data = {17.}; + SumOpConstModel m({TensorType_FLOAT32, {}}, {TensorType_FLOAT32, {}}, {}, {0}, + false); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), IsEmpty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({17.}))); +} + TEST(DynamicFloatSumOpTest, KeepDims) { std::vector 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, @@ -630,6 +651,20 @@ TEST(DynamicUint8MaxOpTest, KeepDims) { ArrayFloatNear({11.1294, 0.862745}, kQuantizedTolerance))); } +TEST(DynamicUint8MaxOpTest, Scalar) { + float kQuantizedTolerance = GetTolerance(-10.0, 12.0); + std::vector data = {11.14}; + MaxOpDynamicModel m({TensorType_UINT8, {}, -10.0, 12.0}, + {TensorType_UINT8, {}, -10.0, 12.0}, + {TensorType_INT32, {1}}, true); + std::vector axis = {0}; + m.QuantizeAndPopulate(m.Input(), data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), IsEmpty()); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({11.1294}, kQuantizedTolerance))); +} + } // namespace } // namespace tflite -- GitLab From 265292420de30f24805d28886d403dc42d3685b3 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Fri, 20 Jul 2018 13:44:39 -0700 Subject: [PATCH 207/519] Add estimator and TPU estimator training script. PiperOrigin-RevId: 205446896 --- .../eager/python/examples/revnet/BUILD | 36 ++ .../eager/python/examples/revnet/blocks.py | 374 ++++++++++++------ .../python/examples/revnet/cifar_input.py | 2 +- .../eager/python/examples/revnet/config.py | 16 +- .../eager/python/examples/revnet/main.py | 82 ++-- .../python/examples/revnet/main_estimator.py | 200 ++++++++++ .../examples/revnet/main_estimator_tpu.py | 328 +++++++++++++++ .../eager/python/examples/revnet/revnet.py | 110 ++---- .../python/examples/revnet/revnet_test.py | 25 +- 9 files changed, 905 insertions(+), 268 deletions(-) create mode 100644 tensorflow/contrib/eager/python/examples/revnet/main_estimator.py create mode 100644 tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py diff --git a/tensorflow/contrib/eager/python/examples/revnet/BUILD b/tensorflow/contrib/eager/python/examples/revnet/BUILD index 0c0e4c0eb9..3316dc1114 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/BUILD +++ b/tensorflow/contrib/eager/python/examples/revnet/BUILD @@ -113,3 +113,39 @@ py_binary( "//tensorflow:tensorflow_py", ], ) + +py_binary( + name = "main_estimator", + srcs = ["main_estimator.py"], + srcs_version = "PY2AND3", + deps = [ + ":cifar_input", + ":main", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "main_estimator_lib", + srcs = ["main_estimator.py"], + srcs_version = "PY2AND3", + deps = [ + ":cifar_input", + ":main", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "main_estimator_tpu_lib", + srcs = ["main_estimator_tpu.py"], + srcs_version = "PY2AND3", + deps = [ + ":cifar_input", + ":main", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py index 306096e9f8..639bb06a34 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -24,6 +24,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import functools +import operator + import tensorflow as tf from tensorflow.contrib.eager.python.examples.revnet import ops @@ -45,7 +48,7 @@ class RevBlock(tf.keras.Model): bottleneck=False, fused=True, dtype=tf.float32): - """Initialize RevBlock. + """Initialization. Args: n_res: number of residual blocks @@ -99,7 +102,6 @@ class RevBlock(tf.keras.Model): if i == 0: # First block usually contains downsampling that can't be reversed with tf.GradientTape() as tape: - x = tf.identity(x) tape.watch(x) y = block(x, training=training) @@ -121,16 +123,6 @@ class _Residual(tf.keras.Model): """Single residual block contained in a _RevBlock. Each `_Residual` object has two _ResidualInner objects, corresponding to the `F` and `G` functions in the paper. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC", - bottleneck: use bottleneck residual if True - fused: use fused batch normalization if True - dtype: float16, float32, or float64 """ def __init__(self, @@ -142,6 +134,18 @@ class _Residual(tf.keras.Model): bottleneck=False, fused=True, dtype=tf.float32): + """Initialization. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC", + bottleneck: use bottleneck residual if True + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + """ super(_Residual, self).__init__() self.filters = filters @@ -196,7 +200,6 @@ class _Residual(tf.keras.Model): dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=self.axis) with tf.GradientTape(persistent=True) as tape: - y = tf.identity(y) tape.watch(y) y1, y2 = tf.split(y, num_or_size_splits=2, axis=self.axis) z1 = y1 @@ -227,131 +230,252 @@ class _Residual(tf.keras.Model): return x, dx, grads, vars_ -def _BottleneckResidualInner(filters, - strides, - input_shape, - batch_norm_first=True, - data_format="channels_first", - fused=True, - dtype=tf.float32): +# Ideally, the following should be wrapped in `tf.keras.Sequential`, however +# there are subtle issues with its placeholder insertion policy and batch norm +class _BottleneckResidualInner(tf.keras.Model): """Single bottleneck residual inner function contained in _Resdual. Corresponds to the `F`/`G` functions in the paper. Suitable for training on ImageNet dataset. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC" - fused: use fused batch normalization if True - dtype: float16, float32, or float64 - - Returns: - A keras model """ - axis = 1 if data_format == "channels_first" else 3 - model = tf.keras.Sequential() - if batch_norm_first: - model.add( - tf.keras.layers.BatchNormalization( - axis=axis, input_shape=input_shape, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters // 4, - kernel_size=1, - strides=strides, - input_shape=input_shape, - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) - - model.add( - tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters // 4, - kernel_size=3, - strides=(1, 1), - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) - - model.add( - tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters, - kernel_size=1, - strides=(1, 1), - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) + def __init__(self, + filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True, + dtype=tf.float32): + """Initialization. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + """ + super(_BottleneckResidualInner, self).__init__() + axis = 1 if data_format == "channels_first" else 3 + if batch_norm_first: + self.batch_norm_0 = tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused, dtype=dtype) + + self.conv2d_1 = tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=1, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + self.batch_norm_1 = tf.keras.layers.BatchNormalization( + axis=axis, fused=fused, dtype=dtype) + + self.conv2d_2 = tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + + self.batch_norm_2 = tf.keras.layers.BatchNormalization( + axis=axis, fused=fused, dtype=dtype) + self.conv2d_3 = tf.keras.layers.Conv2D( + filters=filters, + kernel_size=1, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + + self.batch_norm_first = batch_norm_first + + def call(self, x, training=True): + net = x + if self.batch_norm_first: + net = self.batch_norm_0(net, training=training) + net = tf.nn.relu(net) + + net = self.conv2d_1(net) + net = self.batch_norm_1(net, training=training) + net = tf.nn.relu(net) + + net = self.conv2d_2(net) + net = self.batch_norm_2(net, training=training) + net = tf.nn.relu(net) - return model + net = self.conv2d_3(net) + return net -def _ResidualInner(filters, - strides, - input_shape, - batch_norm_first=True, - data_format="channels_first", - fused=True, - dtype=tf.float32): + +class _ResidualInner(tf.keras.Model): """Single residual inner function contained in _ResdualBlock. Corresponds to the `F`/`G` functions in the paper. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC" - fused: use fused batch normalization if True - dtype: float16, float32, or float64 - - Returns: - A keras model """ - axis = 1 if data_format == "channels_first" else 3 - model = tf.keras.Sequential() - if batch_norm_first: - model.add( - tf.keras.layers.BatchNormalization( - axis=axis, input_shape=input_shape, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters, - kernel_size=3, - strides=strides, - input_shape=input_shape, - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) - - model.add( - tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters, - kernel_size=3, - strides=(1, 1), - data_format=data_format, - use_bias=False, + def __init__(self, + filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True, + dtype=tf.float32): + """Initialization. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + """ + super(_ResidualInner, self).__init__() + axis = 1 if data_format == "channels_first" else 3 + if batch_norm_first: + self.batch_norm_0 = tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused, dtype=dtype) + self.conv2d_1 = tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + self.batch_norm_1 = tf.keras.layers.BatchNormalization( + axis=axis, fused=fused, dtype=dtype) + + self.conv2d_2 = tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + + self.batch_norm_first = batch_norm_first + + def call(self, x, training=True): + net = x + if self.batch_norm_first: + net = self.batch_norm_0(net, training=training) + net = tf.nn.relu(net) + + net = self.conv2d_1(net) + net = self.batch_norm_1(net, training=training) + + net = self.conv2d_2(net) + + return net + + +class InitBlock(tf.keras.Model): + """Initial block of RevNet.""" + + def __init__(self, config): + """Initialization. + + Args: + config: tf.contrib.training.HParams object; specifies hyperparameters + """ + super(InitBlock, self).__init__() + self.config = config + self.axis = 1 if self.config.data_format == "channels_first" else 3 + self.conv2d = tf.keras.layers.Conv2D( + filters=self.config.init_filters, + kernel_size=self.config.init_kernel, + strides=(self.config.init_stride, self.config.init_stride), + data_format=self.config.data_format, + use_bias=False, + padding="SAME", + input_shape=self.config.input_shape, + dtype=self.config.dtype) + self.batch_norm = tf.keras.layers.BatchNormalization( + axis=self.axis, fused=self.config.fused, dtype=self.config.dtype) + self.activation = tf.keras.layers.Activation("relu") + + if self.config.init_max_pool: + self.max_pool = tf.keras.layers.MaxPooling2D( + pool_size=(3, 3), + strides=(2, 2), padding="SAME", - dtype=dtype)) + data_format=self.config.data_format, + dtype=self.config.dtype) + + def call(self, x, training=True): + net = x + net = self.conv2d(net) + net = self.batch_norm(net, training=training) + net = self.activation(net) + + if self.config.init_max_pool: + net = self.max_pool(net) + + return net + - return model +class FinalBlock(tf.keras.Model): + """Final block of RevNet.""" + + def __init__(self, config): + """Initialization. + + Args: + config: tf.contrib.training.HParams object; specifies hyperparameters + + Raises: + ValueError: Unsupported data format + """ + super(FinalBlock, self).__init__() + self.config = config + self.axis = 1 if self.config.data_format == "channels_first" else 3 + + f = self.config.filters[-1] # Number of filters + r = functools.reduce(operator.mul, self.config.strides, 1) # Reduce ratio + r *= self.config.init_stride + if self.config.init_max_pool: + r *= 2 + + if self.config.data_format == "channels_first": + w, h = self.config.input_shape[1], self.config.input_shape[2] + input_shape = (f, w // r, h // r) + elif self.config.data_format == "channels_last": + w, h = self.config.input_shape[0], self.config.input_shape[1] + input_shape = (w // r, h // r, f) + else: + raise ValueError("Data format should be either `channels_first`" + " or `channels_last`") + self.batch_norm = tf.keras.layers.BatchNormalization( + axis=self.axis, + input_shape=input_shape, + fused=self.config.fused, + dtype=self.config.dtype) + self.activation = tf.keras.layers.Activation("relu") + self.global_avg_pool = tf.keras.layers.GlobalAveragePooling2D( + data_format=self.config.data_format, dtype=self.config.dtype) + self.dense = tf.keras.layers.Dense( + self.config.n_classes, dtype=self.config.dtype) + + def call(self, x, training=True): + net = x + net = self.batch_norm(net, training=training) + net = self.activation(net) + net = self.global_avg_pool(net) + net = self.dense(net) + + return net diff --git a/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py index b6d4c35bfd..e9672f13e1 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py +++ b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py @@ -111,6 +111,6 @@ def get_ds_from_tfrecords(data_dir, }[split] dataset = dataset.shuffle(size) - dataset = dataset.batch(batch_size) + dataset = dataset.batch(batch_size, drop_remainder=True) return dataset diff --git a/tensorflow/contrib/eager/python/examples/revnet/config.py b/tensorflow/contrib/eager/python/examples/revnet/config.py index 3d93fa955a..1532c7b67b 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/config.py +++ b/tensorflow/contrib/eager/python/examples/revnet/config.py @@ -27,17 +27,16 @@ from __future__ import division from __future__ import print_function import tensorflow as tf -tfe = tf.contrib.eager def get_hparams_cifar_38(): """RevNet-38 configurations for CIFAR-10/CIFAR-100.""" config = tf.contrib.training.HParams() + # Hyperparameters from the RevNet paper config.add_hparam("init_filters", 32) config.add_hparam("init_kernel", 3) config.add_hparam("init_stride", 1) - config.add_hparam("n_classes", 10) config.add_hparam("n_rev_blocks", 3) config.add_hparam("n_res", [3, 3, 3]) config.add_hparam("filters", [32, 64, 112]) @@ -46,7 +45,7 @@ def get_hparams_cifar_38(): config.add_hparam("bottleneck", False) config.add_hparam("fused", True) config.add_hparam("init_max_pool", False) - if tfe.num_gpus() > 0: + if tf.test.is_gpu_available() > 0: config.add_hparam("input_shape", (3, 32, 32)) config.add_hparam("data_format", "channels_first") else: @@ -71,6 +70,16 @@ def get_hparams_cifar_38(): config.add_hparam("iters_per_epoch", 50000 // config.batch_size) config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) + # Customized TPU hyperparameters due to differing batch size caused by + # TPU architecture specifics + # Suggested batch sizes to reduce overhead from excessive tensor padding + # https://cloud.google.com/tpu/docs/troubleshooting + config.add_hparam("tpu_batch_size", 128) + config.add_hparam("tpu_eval_batch_size", 1024) + config.add_hparam("tpu_iters_per_epoch", 50000 // config.tpu_batch_size) + config.add_hparam("tpu_epochs", + config.max_train_iter // config.tpu_iters_per_epoch) + return config @@ -101,7 +110,6 @@ def get_hparams_imagenet_56(): config.add_hparam("init_filters", 128) config.add_hparam("init_kernel", 7) config.add_hparam("init_stride", 2) - config.add_hparam("n_classes", 1000) config.add_hparam("n_rev_blocks", 4) config.add_hparam("n_res", [2, 2, 2, 2]) config.add_hparam("filters", [128, 256, 512, 832]) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main.py b/tensorflow/contrib/eager/python/examples/revnet/main.py index e2f43b03f9..1a4fd45c8b 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/main.py +++ b/tensorflow/contrib/eager/python/examples/revnet/main.py @@ -31,8 +31,11 @@ tfe = tf.contrib.eager def main(_): """Eager execution workflow with RevNet trained on CIFAR-10.""" - config = get_config() - ds_train, ds_train_one_shot, ds_validation, ds_test = get_datasets(config) + tf.enable_eager_execution() + + config = get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) + ds_train, ds_train_one_shot, ds_validation, ds_test = get_datasets( + data_dir=FLAGS.data_dir, config=config) model = revnet.RevNet(config=config) global_step = tf.train.get_or_create_global_step() # Ensure correct summary global_step.assign(1) @@ -52,23 +55,17 @@ def main(_): "with global_step: {}".format(latest_path, global_step.numpy())) sys.stdout.flush() - if FLAGS.manual_grad: - print("Using manual gradients.") - else: - print("Not using manual gradients.") - sys.stdout.flush() - for x, y in ds_train: train_one_iter(model, x, y, optimizer, global_step=global_step) if global_step.numpy() % config.log_every == 0: - it_train = ds_train_one_shot.make_one_shot_iterator() it_test = ds_test.make_one_shot_iterator() - acc_train, loss_train = evaluate(model, it_train) acc_test, loss_test = evaluate(model, it_test) if FLAGS.validate: + it_train = ds_train_one_shot.make_one_shot_iterator() it_validation = ds_validation.make_one_shot_iterator() + acc_train, loss_train = evaluate(model, it_train) acc_validation, loss_validation = evaluate(model, it_validation) print("Iter {}, " "training set accuracy {:.4f}, loss {:.4f}; " @@ -77,11 +74,8 @@ def main(_): global_step.numpy(), acc_train, loss_train, acc_validation, loss_validation, acc_test, loss_test)) else: - print("Iter {}, " - "training set accuracy {:.4f}, loss {:.4f}; " - "test accuracy {:.4f}, loss {:.4f}".format( - global_step.numpy(), acc_train, loss_train, acc_test, - loss_test)) + print("Iter {}, test accuracy {:.4f}, loss {:.4f}".format( + global_step.numpy(), acc_test, loss_test)) sys.stdout.flush() if FLAGS.train_dir: @@ -103,34 +97,38 @@ def main(_): sys.stdout.flush() -def get_config(): +def get_config(config_name="revnet-38", dataset="cifar-10"): """Return configuration.""" - print("Config: {}".format(FLAGS.config)) + print("Config: {}".format(config_name)) sys.stdout.flush() config = { "revnet-38": config_.get_hparams_cifar_38(), "revnet-110": config_.get_hparams_cifar_110(), "revnet-164": config_.get_hparams_cifar_164(), - }[FLAGS.config] + }[config_name] - if FLAGS.dataset == "cifar-100": - config.n_classes = 100 + if dataset == "cifar-10": + config.add_hparam("n_classes", 10) + config.add_hparam("dataset", "cifar-10") + else: + config.add_hparam("n_classes", 100) + config.add_hparam("dataset", "cifar-100") return config -def get_datasets(config): +def get_datasets(data_dir, config): """Return dataset.""" - if FLAGS.data_dir is None: + if data_dir is None: raise ValueError("No supplied data directory") - if not os.path.exists(FLAGS.data_dir): - raise ValueError("Data directory {} does not exist".format(FLAGS.data_dir)) - if FLAGS.dataset not in ["cifar-10", "cifar-100"]: - raise ValueError("Unknown dataset {}".format(FLAGS.dataset)) + if not os.path.exists(data_dir): + raise ValueError("Data directory {} does not exist".format(data_dir)) + if config.dataset not in ["cifar-10", "cifar-100"]: + raise ValueError("Unknown dataset {}".format(config.dataset)) - print("Training on {} dataset.".format(FLAGS.dataset)) + print("Training on {} dataset.".format(config.dataset)) sys.stdout.flush() - data_dir = os.path.join(FLAGS.data_dir, FLAGS.dataset) + data_dir = os.path.join(data_dir, config.dataset) if FLAGS.validate: # 40k Training set ds_train = cifar_input.get_ds_from_tfrecords( @@ -168,7 +166,7 @@ def get_datasets(config): prefetch=config.batch_size) ds_validation = None - # Always compute loss and accuracy on whole training and test set + # Always compute loss and accuracy on whole test set ds_train_one_shot = cifar_input.get_ds_from_tfrecords( data_dir=data_dir, split="train_all", @@ -196,19 +194,11 @@ def get_datasets(config): def train_one_iter(model, inputs, labels, optimizer, global_step=None): """Train for one iteration.""" - if FLAGS.manual_grad: - grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) - optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) - else: # For correctness validation - with tf.GradientTape() as tape: - logits, _ = model(inputs, training=True) - loss = model.compute_loss(logits=logits, labels=labels) - tf.logging.info("Logits are placed on device: {}".format(logits.device)) - grads = tape.gradient(loss, model.trainable_variables) - optimizer.apply_gradients( - zip(grads, model.trainable_variables), global_step=global_step) + grads, vars_, logits, loss = model.compute_gradients( + inputs, labels, training=True) + optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) - return loss.numpy() + return logits, loss def evaluate(model, iterator): @@ -241,16 +231,14 @@ if __name__ == "__main__": "validate", default=False, help="[Optional] Use the validation set or not for hyperparameter search") - flags.DEFINE_boolean( - "manual_grad", - default=False, - help="[Optional] Use manual gradient graph to save memory") flags.DEFINE_string( "dataset", default="cifar-10", help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") flags.DEFINE_string( - "config", default="revnet-38", help="[Optional] Architecture of network.") + "config", + default="revnet-38", + help="[Optional] Architecture of network. " + "Other options include `revnet-110` and `revnet-164`") FLAGS = flags.FLAGS - tf.enable_eager_execution() tf.app.run(main) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py b/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py new file mode 100644 index 0000000000..c875e8da6d --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py @@ -0,0 +1,200 @@ +# 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. +# ============================================================================== +"""Estimator workflow with RevNet train on CIFAR-10.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from absl import flags +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import cifar_input +from tensorflow.contrib.eager.python.examples.revnet import main as main_ +from tensorflow.contrib.eager.python.examples.revnet import revnet + + +def model_fn(features, labels, mode, params): + """Function specifying the model that is required by the `tf.estimator` API. + + Args: + features: Input images + labels: Labels of images + mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT' + params: A dictionary of extra parameter that might be passed + + Returns: + An instance of `tf.estimator.EstimatorSpec` + """ + + inputs = features + if isinstance(inputs, dict): + inputs = features["image"] + + config = params["config"] + model = revnet.RevNet(config=config) + + if mode == tf.estimator.ModeKeys.TRAIN: + global_step = tf.train.get_or_create_global_step() + learning_rate = tf.train.piecewise_constant( + global_step, config.lr_decay_steps, config.lr_list) + optimizer = tf.train.MomentumOptimizer( + learning_rate, momentum=config.momentum) + grads, vars_, logits, loss = model.compute_gradients( + inputs, labels, training=True) + train_op = optimizer.apply_gradients( + zip(grads, vars_), global_step=global_step) + + return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op) + else: + logits, _ = model(inputs, training=False) + predictions = tf.argmax(logits, axis=1) + probabilities = tf.nn.softmax(logits) + loss = model.compute_loss(labels=labels, logits=logits) + + if mode == tf.estimator.ModeKeys.EVAL: + return tf.estimator.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops={ + "accuracy": + tf.metrics.accuracy(labels=labels, predictions=predictions) + }) + + else: # mode == tf.estimator.ModeKeys.PREDICT + result = { + "classes": predictions, + "probabilities": probabilities, + } + + return tf.estimator.EstimatorSpec( + mode=mode, + predictions=predictions, + export_outputs={ + "classify": tf.estimator.export.PredictOutput(result) + }) + + +def get_input_fn(config, data_dir, split): + """Get the input function that is required by the `tf.estimator` API. + + Args: + config: Customized hyperparameters + data_dir: Directory where the data is stored + split: One of `train`, `validation`, `train_all`, and `test` + + Returns: + Input function required by the `tf.estimator` API + """ + + data_dir = os.path.join(data_dir, config.dataset) + # Fix split-dependent hyperparameters + if split == "train_all" or split == "train": + data_aug = True + batch_size = config.batch_size + epochs = config.epochs + shuffle = True + prefetch = config.batch_size + else: + data_aug = False + batch_size = config.eval_batch_size + epochs = 1 + shuffle = False + prefetch = config.eval_batch_size + + def input_fn(): + """Input function required by the `tf.estimator.Estimator` API.""" + return cifar_input.get_ds_from_tfrecords( + data_dir=data_dir, + split=split, + data_aug=data_aug, + batch_size=batch_size, + epochs=epochs, + shuffle=shuffle, + prefetch=prefetch, + data_format=config.data_format) + + return input_fn + + +def main(argv): + FLAGS = argv[0] # pylint:disable=invalid-name,redefined-outer-name + tf.logging.set_verbosity(tf.logging.INFO) + + # RevNet specific configuration + config = main_.get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) + + # Estimator specific configuration + run_config = tf.estimator.RunConfig( + model_dir=FLAGS.train_dir, # Directory for storing checkpoints + tf_random_seed=config.seed, + save_summary_steps=config.log_every, + save_checkpoints_steps=config.log_every, + session_config=None, # Using default + keep_checkpoint_max=100, + keep_checkpoint_every_n_hours=10000, # Using default + log_step_count_steps=config.log_every, + train_distribute=None # Default not use distribution strategy + ) + + # Construct estimator + revnet_estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.train_dir, + config=run_config, + params={"config": config}) + + # Construct input functions + train_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="train_all") + eval_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="test") + + # Train and evaluate estimator + revnet_estimator.train(input_fn=train_input_fn) + revnet_estimator.evaluate(input_fn=eval_input_fn) + + if FLAGS.export: + input_shape = (None,) + config.input_shape + inputs = tf.placeholder(tf.float32, shape=input_shape) + input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({ + "image": inputs + }) + revnet_estimator.export_savedmodel(FLAGS.train_dir, input_fn) + + +if __name__ == "__main__": + flags.DEFINE_string( + "data_dir", default=None, help="Directory to load tfrecords") + flags.DEFINE_string( + "train_dir", + default=None, + help="[Optional] Directory to store the training information") + flags.DEFINE_string( + "dataset", + default="cifar-10", + help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") + flags.DEFINE_boolean( + "export", + default=False, + help="[Optional] Export the model for serving if True") + flags.DEFINE_string( + "config", + default="revnet-38", + help="[Optional] Architecture of network. " + "Other options include `revnet-110` and `revnet-164`") + FLAGS = flags.FLAGS + tf.app.run(main=main, argv=[FLAGS]) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py b/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py new file mode 100644 index 0000000000..f1e1e530df --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py @@ -0,0 +1,328 @@ +# 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. +# ============================================================================== +"""Cloud TPU Estimator workflow with RevNet train on CIFAR-10.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import time + +from absl import flags +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import cifar_input +from tensorflow.contrib.eager.python.examples.revnet import main as main_ +from tensorflow.contrib.eager.python.examples.revnet import revnet +from tensorflow.contrib.training.python.training import evaluation +from tensorflow.python.estimator import estimator as estimator_ + + +def model_fn(features, labels, mode, params): + """Model function required by the `tf.contrib.tpu.TPUEstimator` API. + + Args: + features: Input images + labels: Labels of images + mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT' + params: A dictionary of extra parameter that might be passed + + Returns: + An instance of `tf.contrib.tpu.TPUEstimatorSpec` + """ + + inputs = features + if isinstance(inputs, dict): + inputs = features["image"] + + FLAGS = params["FLAGS"] # pylint:disable=invalid-name,redefined-outer-name + config = params["config"] + model = revnet.RevNet(config=config) + + if mode == tf.estimator.ModeKeys.TRAIN: + global_step = tf.train.get_or_create_global_step() + learning_rate = tf.train.piecewise_constant( + global_step, config.lr_decay_steps, config.lr_list) + optimizer = tf.train.MomentumOptimizer( + learning_rate, momentum=config.momentum) + + if FLAGS.use_tpu: + optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer) + + # Define gradients + grads, vars_, logits, loss = model.compute_gradients( + inputs, labels, training=True) + train_op = optimizer.apply_gradients( + zip(grads, vars_), global_step=global_step) + + names = [v.name for v in model.variables] + tf.logging.warn("{}".format(names)) + + return tf.contrib.tpu.TPUEstimatorSpec( + mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op) + + if mode == tf.estimator.ModeKeys.EVAL: + logits, _ = model(inputs, training=False) + loss = model.compute_loss(labels=labels, logits=logits) + + def metric_fn(labels, logits): + predictions = tf.argmax(logits, axis=1) + accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions) + return { + "accuracy": accuracy, + } + + return tf.contrib.tpu.TPUEstimatorSpec( + mode=mode, loss=loss, eval_metrics=(metric_fn, [labels, logits])) + + if mode == tf.estimator.ModeKeys.PREDICT: + logits, _ = model(inputs, training=False) + predictions = { + "classes": tf.argmax(logits, axis=1), + "probabilities": tf.nn.softmax(logits), + } + + return tf.contrib.tpu.TPUEstimatorSpec( + mode=mode, + predictions=predictions, + export_outputs={ + "classify": tf.estimator.export.PredictOutput(predictions) + }) + + +def get_input_fn(config, data_dir, split): + """Get the input function required by the `tf.contrib.tpu.TPUEstimator` API. + + Args: + config: Customized hyperparameters + data_dir: Directory where the data is stored + split: One of `train`, `validation`, `train_all`, and `test` + + Returns: + Input function required by the `tf.contrib.tpu.TPUEstimator` API + """ + + data_dir = os.path.join(data_dir, config.dataset) + # Fix split-dependent hyperparameters + if split == "train_all" or split == "train": + data_aug = True + epochs = config.tpu_epochs + shuffle = True + else: + data_aug = False + epochs = 1 + shuffle = False + + def input_fn(params): + """Input function required by the `tf.contrib.tpu.TPUEstimator` API.""" + batch_size = params["batch_size"] + return cifar_input.get_ds_from_tfrecords( + data_dir=data_dir, + split=split, + data_aug=data_aug, + batch_size=batch_size, # per-shard batch size + epochs=epochs, + shuffle=shuffle, + prefetch=batch_size, # per-shard batch size + data_format=config.data_format) + + return input_fn + + +def main(argv): + FLAGS = argv[0] # pylint:disable=invalid-name,redefined-outer-name + tf.logging.set_verbosity(tf.logging.INFO) + + # RevNet specific configuration + config = main_.get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) + + if FLAGS.use_tpu: + tf.logging.info("Using TPU.") + tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( + FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) + else: + tpu_cluster_resolver = None + + # TPU specific configuration + tpu_config = tf.contrib.tpu.TPUConfig( + # Recommended to be set as number of global steps for next checkpoint + iterations_per_loop=FLAGS.iterations_per_loop, + num_shards=FLAGS.num_shards) + + # Estimator specific configuration + run_config = tf.contrib.tpu.RunConfig( + cluster=tpu_cluster_resolver, + model_dir=FLAGS.model_dir, + session_config=tf.ConfigProto( + allow_soft_placement=True, log_device_placement=False), + tpu_config=tpu_config, + ) + + # Construct TPU Estimator + estimator = tf.contrib.tpu.TPUEstimator( + model_fn=model_fn, + use_tpu=FLAGS.use_tpu, + train_batch_size=config.tpu_batch_size, + eval_batch_size=config.tpu_eval_batch_size, + config=run_config, + params={ + "FLAGS": FLAGS, + "config": config, + }) + + # Construct input functions + train_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="train_all") + eval_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="test") + + # Disabling a range within an else block currently doesn't work + # due to https://github.com/PyCQA/pylint/issues/872 + # pylint: disable=protected-access + if FLAGS.mode == "eval": + # TPUEstimator.evaluate *requires* a steps argument. + # Note that the number of examples used during evaluation is + # --eval_steps * --batch_size. + # So if you change --batch_size then change --eval_steps too. + eval_steps = 10000 // config.tpu_eval_batch_size + + # Run evaluation when there's a new checkpoint + for ckpt in evaluation.checkpoints_iterator( + FLAGS.model_dir, timeout=FLAGS.eval_timeout): + tf.logging.info("Starting to evaluate.") + try: + start_timestamp = time.time() # This time will include compilation time + eval_results = estimator.evaluate( + input_fn=eval_input_fn, steps=eval_steps, checkpoint_path=ckpt) + elapsed_time = int(time.time() - start_timestamp) + tf.logging.info("Eval results: %s. Elapsed seconds: %d" % + (eval_results, elapsed_time)) + + # Terminate eval job when final checkpoint is reached + current_step = int(os.path.basename(ckpt).split("-")[1]) + if current_step >= config.max_train_iter: + tf.logging.info( + "Evaluation finished after training step %d" % current_step) + break + + except tf.errors.NotFoundError: + # Since the coordinator is on a different job than the TPU worker, + # sometimes the TPU worker does not finish initializing until long after + # the CPU job tells it to start evaluating. In this case, the checkpoint + # file could have been deleted already. + tf.logging.info( + "Checkpoint %s no longer exists, skipping checkpoint" % ckpt) + + else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval' + current_step = estimator_._load_global_step_from_checkpoint_dir( + FLAGS.model_dir) + tf.logging.info("Training for %d steps . Current" + " step %d." % (config.max_train_iter, current_step)) + + start_timestamp = time.time() # This time will include compilation time + if FLAGS.mode == "train": + estimator.train(input_fn=train_input_fn, max_steps=config.max_train_iter) + else: + eval_steps = 10000 // config.tpu_eval_batch_size + assert FLAGS.mode == "train_and_eval" + while current_step < config.max_train_iter: + # Train for up to steps_per_eval number of steps. + # At the end of training, a checkpoint will be written to --model_dir. + next_checkpoint = min(current_step + FLAGS.steps_per_eval, + config.max_train_iter) + estimator.train(input_fn=train_input_fn, max_steps=next_checkpoint) + current_step = next_checkpoint + + # Evaluate the model on the most recent model in --model_dir. + # Since evaluation happens in batches of --eval_batch_size, some images + # may be consistently excluded modulo the batch size. + tf.logging.info("Starting to evaluate.") + eval_results = estimator.evaluate( + input_fn=eval_input_fn, steps=eval_steps) + tf.logging.info("Eval results: %s" % eval_results) + + elapsed_time = int(time.time() - start_timestamp) + tf.logging.info("Finished training up to step %d. Elapsed seconds %d." % + (config.max_train_iter, elapsed_time)) + # pylint: enable=protected-access + + +if __name__ == "__main__": + # Cloud TPU Cluster Resolver flags + flags.DEFINE_string( + "tpu", + default=None, + help="The Cloud TPU to use for training. This should be either the name " + "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " + "url.") + flags.DEFINE_string( + "tpu_zone", + default=None, + help="[Optional] GCE zone where the Cloud TPU is located in. If not " + "specified, we will attempt to automatically detect the GCE project from " + "metadata.") + flags.DEFINE_string( + "gcp_project", + default=None, + help="[Optional] Project name for the Cloud TPU-enabled project. If not " + "specified, we will attempt to automatically detect the GCE project from " + "metadata.") + + # Model specific parameters + flags.DEFINE_string( + "data_dir", default=None, help="Directory to load tfrecords") + flags.DEFINE_string( + "model_dir", + default=None, + help="[Optional] Directory to store the model information") + flags.DEFINE_string( + "dataset", + default="cifar-10", + help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") + flags.DEFINE_string( + "config", + default="revnet-38", + help="[Optional] Architecture of network. " + "Other options include `revnet-110` and `revnet-164`") + flags.DEFINE_boolean( + "use_tpu", default=True, help="[Optional] Whether to use TPU") + flags.DEFINE_integer( + "num_shards", default=8, help="Number of shards (TPU chips).") + flags.DEFINE_integer( + "iterations_per_loop", + default=100, + help=( + "Number of steps to run on TPU before feeding metrics to the CPU." + " If the number of iterations in the loop would exceed the number of" + " train steps, the loop will exit before reaching" + " --iterations_per_loop. The larger this value is, the higher the" + " utilization on the TPU.")) + flags.DEFINE_string( + "mode", + default="train_and_eval", + help="[Optional] Mode to run: train, eval, train_and_eval") + flags.DEFINE_integer( + "eval_timeout", 60 * 60 * 24, + "Maximum seconds between checkpoints before evaluation terminates.") + flags.DEFINE_integer( + "steps_per_eval", + default=1000, + help=( + "Controls how often evaluation is performed. Since evaluation is" + " fairly expensive, it is advised to evaluate as infrequently as" + " possible (i.e. up to --train_steps, which evaluates the model only" + " after finishing the entire training regime).")) + FLAGS = flags.FLAGS + tf.app.run(main=main, argv=[FLAGS]) diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet.py b/tensorflow/contrib/eager/python/examples/revnet/revnet.py index af0d20fa72..a3c2f7dbec 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet.py @@ -24,9 +24,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import functools -import operator - import six import tensorflow as tf from tensorflow.contrib.eager.python.examples.revnet import blocks @@ -45,71 +42,9 @@ class RevNet(tf.keras.Model): self.axis = 1 if config.data_format == "channels_first" else 3 self.config = config - self._init_block = self._construct_init_block() + self._init_block = blocks.InitBlock(config=self.config) + self._final_block = blocks.FinalBlock(config=self.config) self._block_list = self._construct_intermediate_blocks() - self._final_block = self._construct_final_block() - - def _construct_init_block(self): - init_block = tf.keras.Sequential( - [ - tf.keras.layers.Conv2D( - filters=self.config.init_filters, - kernel_size=self.config.init_kernel, - strides=(self.config.init_stride, self.config.init_stride), - data_format=self.config.data_format, - use_bias=False, - padding="SAME", - input_shape=self.config.input_shape, - dtype=self.config.dtype), - tf.keras.layers.BatchNormalization( - axis=self.axis, - fused=self.config.fused, - dtype=self.config.dtype), - tf.keras.layers.Activation("relu"), - ], - name="init") - if self.config.init_max_pool: - init_block.add( - tf.keras.layers.MaxPooling2D( - pool_size=(3, 3), - strides=(2, 2), - padding="SAME", - data_format=self.config.data_format, - dtype=self.config.dtype)) - return init_block - - def _construct_final_block(self): - f = self.config.filters[-1] # Number of filters - r = functools.reduce(operator.mul, self.config.strides, 1) # Reduce ratio - r *= self.config.init_stride - if self.config.init_max_pool: - r *= 2 - - if self.config.data_format == "channels_first": - w, h = self.config.input_shape[1], self.config.input_shape[2] - input_shape = (f, w // r, h // r) - elif self.config.data_format == "channels_last": - w, h = self.config.input_shape[0], self.config.input_shape[1] - input_shape = (w // r, h // r, f) - else: - raise ValueError("Data format should be either `channels_first`" - " or `channels_last`") - - final_block = tf.keras.Sequential( - [ - tf.keras.layers.BatchNormalization( - axis=self.axis, - input_shape=input_shape, - fused=self.config.fused, - dtype=self.config.dtype), - tf.keras.layers.Activation("relu"), - tf.keras.layers.GlobalAveragePooling2D( - data_format=self.config.data_format, dtype=self.config.dtype), - tf.keras.layers.Dense( - self.config.n_classes, dtype=self.config.dtype) - ], - name="final") - return final_block def _construct_intermediate_blocks(self): # Precompute input shape after initial block @@ -206,13 +141,20 @@ class RevNet(tf.keras.Model): l2_reg: Apply l2 regularization Returns: - list of tuples each being (grad, var) for optimizer to use + A tuple with the first entry being a list of all gradients, the second + entry being a list of respective variables, the third being the logits, + and the forth being the loss """ - # Run forward pass to record hidden states; avoid updating running averages + # Run forward pass to record hidden states vars_and_vals = self.get_moving_stats() _, saved_hidden = self.call(inputs, training=training) - self.restore_moving_stats(vars_and_vals) + if tf.executing_eagerly(): + # Restore moving averages when executing eagerly to avoid updating twice + self.restore_moving_stats(vars_and_vals) + else: + # Fetch batch norm updates in graph mode + updates = self.get_updates_for(inputs) grads_all = [] vars_all = [] @@ -220,9 +162,8 @@ class RevNet(tf.keras.Model): # Manually backprop through last block x = saved_hidden[-1] with tf.GradientTape() as tape: - x = tf.identity(x) tape.watch(x) - # Running stats updated below + # Running stats updated here logits = self._final_block(x, training=training) loss = self.compute_loss(logits, labels) @@ -236,6 +177,7 @@ class RevNet(tf.keras.Model): for block in reversed(self._block_list): y = saved_hidden.pop() x = saved_hidden[-1] + # Running stats updated here dy, grads, vars_ = block.backward_grads_and_vars( x, y, dy, training=training) grads_all += grads @@ -247,8 +189,7 @@ class RevNet(tf.keras.Model): assert not saved_hidden # Cleared after backprop with tf.GradientTape() as tape: - x = tf.identity(x) - # Running stats updated below + # Running stats updated here y = self._init_block(x, training=training) grads_all += tape.gradient( @@ -259,7 +200,13 @@ class RevNet(tf.keras.Model): if l2_reg: grads_all = self._apply_weight_decay(grads_all, vars_all) - return grads_all, vars_all, loss + if not tf.executing_eagerly(): + # Force updates to be executed before gradient computation in graph mode + # This does nothing when the function is wrapped in defun + with tf.control_dependencies(updates): + grads_all[0] = tf.identity(grads_all[0]) + + return grads_all, vars_all, logits, loss def _apply_weight_decay(self, grads, vars_): """Update gradients to reflect weight decay.""" @@ -284,8 +231,10 @@ class RevNet(tf.keras.Model): n = v.name return n.endswith("moving_mean:0") or n.endswith("moving_variance:0") - for v in filter(_is_moving_var, self.variables): - vars_and_vals[v] = v.read_value() + device = "/gpu:0" if tf.test.is_gpu_available() else "/cpu:0" + with tf.device(device): + for v in filter(_is_moving_var, self.variables): + vars_and_vals[v] = v.read_value() return vars_and_vals @@ -297,5 +246,8 @@ class RevNet(tf.keras.Model): Args: vars_and_vals: The dictionary mapping variables to their previous values. """ - for var_, val in six.iteritems(vars_and_vals): - var_.assign(val) + device = "/gpu:0" if tf.test.is_gpu_available() else "/cpu:0" + with tf.device(device): + for var_, val in six.iteritems(vars_and_vals): + # `assign` causes a copy to GPU (if variable is already on GPU) + var_.assign(val) diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py index b0d0a5486d..26b0847523 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py @@ -31,10 +31,11 @@ tfe = tf.contrib.eager def train_one_iter(model, inputs, labels, optimizer, global_step=None): """Train for one iteration.""" - grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) + grads, vars_, logits, loss = model.compute_gradients( + inputs, labels, training=True) optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) - return loss + return logits, loss class RevNetTest(tf.test.TestCase): @@ -42,6 +43,8 @@ class RevNetTest(tf.test.TestCase): def setUp(self): super(RevNetTest, self).setUp() config = config_.get_hparams_cifar_38() + config.add_hparam("n_classes", 10) + config.add_hparam("dataset", "cifar-10") # Reconstruction could cause numerical error, use double precision for tests config.dtype = tf.float64 config.fused = False # Fused batch norm does not support tf.float64 @@ -94,7 +97,7 @@ class RevNetTest(tf.test.TestCase): def test_compute_gradients(self): """Test `compute_gradients` function.""" self.model(self.x, training=False) # Initialize model - grads, vars_, loss = self.model.compute_gradients( + grads, vars_, logits, loss = self.model.compute_gradients( inputs=self.x, labels=self.t, training=True, l2_reg=True) self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) @@ -119,7 +122,7 @@ class RevNetTest(tf.test.TestCase): def test_compute_gradients_defun(self): """Test `compute_gradients` function with defun.""" compute_gradients = tfe.defun(self.model.compute_gradients) - grads, vars_, _ = compute_gradients(self.x, self.t, training=True) + grads, vars_, _, _ = compute_gradients(self.x, self.t, training=True) self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) self.assertEqual(len(grads), len(vars_)) @@ -131,6 +134,9 @@ class RevNetTest(tf.test.TestCase): """Test model training in graph mode.""" with tf.Graph().as_default(): config = config_.get_hparams_cifar_38() + config.add_hparam("n_classes", 10) + config.add_hparam("dataset", "cifar-10") + x = tf.random_normal( shape=(self.config.batch_size,) + self.config.input_shape) t = tf.random_uniform( @@ -140,15 +146,10 @@ class RevNetTest(tf.test.TestCase): dtype=tf.int32) global_step = tf.Variable(0., trainable=False) model = revnet.RevNet(config=config) - model(x) - updates = model.get_updates_for(x) - - x_ = tf.identity(x) - grads_all, vars_all, _ = model.compute_gradients(x_, t, training=True) + grads_all, vars_all, _, _ = model.compute_gradients(x, t, training=True) optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) - with tf.control_dependencies(updates): - train_op = optimizer.apply_gradients( - zip(grads_all, vars_all), global_step=global_step) + train_op = optimizer.apply_gradients( + zip(grads_all, vars_all), global_step=global_step) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) -- GitLab From e542062aa1613dc01b82b6378675563160fe0abf Mon Sep 17 00:00:00 2001 From: Nick Desaulniers Date: Fri, 20 Jul 2018 13:50:37 -0700 Subject: [PATCH 208/519] Start implementation of Iota HLO. PiperOrigin-RevId: 205447892 --- .../compiler/xla/client/lib/numeric_test.cc | 3 + .../xla/client/xla_client/xla_builder.cc | 7 +++ .../xla/client/xla_client/xla_builder.h | 3 + tensorflow/compiler/xla/literal.cc | 4 +- .../compiler/xla/service/cpu/ir_emitter.cc | 5 ++ .../compiler/xla/service/cpu/ir_emitter.h | 1 + .../compiler/xla/service/dfs_hlo_visitor.h | 1 + .../service/dfs_hlo_visitor_with_default.h | 3 + .../compiler/xla/service/gpu/ir_emitter.cc | 5 ++ .../compiler/xla/service/gpu/ir_emitter.h | 1 + .../compiler/xla/service/hlo_cost_analysis.cc | 4 ++ .../compiler/xla/service/hlo_cost_analysis.h | 1 + .../xla/service/hlo_evaluator_typed_visitor.h | 24 ++++++++ .../compiler/xla/service/hlo_graph_dumper.cc | 1 + .../compiler/xla/service/hlo_instruction.cc | 10 +++ .../compiler/xla/service/hlo_instruction.h | 3 + tensorflow/compiler/xla/service/hlo_opcode.h | 1 + tensorflow/compiler/xla/service/hlo_parser.cc | 8 +++ .../compiler/xla/service/hlo_parser_test.cc | 11 ++++ .../compiler/xla/service/hlo_verifier.cc | 6 ++ .../compiler/xla/service/hlo_verifier.h | 1 + .../xla/service/instruction_fusion.cc | 1 + tensorflow/compiler/xla/tests/BUILD | 20 ++++++ tensorflow/compiler/xla/tests/iota_test.cc | 61 +++++++++++++++++++ .../performance/xla/operation_semantics.md | 13 ++++ 25 files changed, 197 insertions(+), 1 deletion(-) create mode 100644 tensorflow/compiler/xla/tests/iota_test.cc diff --git a/tensorflow/compiler/xla/client/lib/numeric_test.cc b/tensorflow/compiler/xla/client/lib/numeric_test.cc index bfea3f539d..113d359197 100644 --- a/tensorflow/compiler/xla/client/lib/numeric_test.cc +++ b/tensorflow/compiler/xla/client/lib/numeric_test.cc @@ -30,6 +30,9 @@ class NumericTest : public ClientLibraryTestBase { void TestMatrixDiagonal(); }; +// TODO(b/64798317): Delete this test case once xla::IotaGen is converted to +// xla::Iota. This test is already implemented for xla::IotaGen in +// xla/tests/iota_test.cc. XLA_TEST_F(NumericTest, Iota) { XlaBuilder builder(TestName()); Iota(&builder, S32, 10); diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc index ced26fc2ed..a9a4b3bc5d 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc @@ -2873,4 +2873,11 @@ XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, grad_output, epsilon, feature_index); } +XlaOp IotaGen(XlaBuilder* builder, PrimitiveType type, int64 size) { + HloInstructionProto instr; + *instr.mutable_shape() = ShapeUtil::MakeShape(type, {size}); + return builder->ReportErrorOrReturn( + builder->AddInstruction(std::move(instr), HloOpcode::kIota)); +} + } // namespace xla diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.h b/tensorflow/compiler/xla/client/xla_client/xla_builder.h index 445c1e0d77..3c016ebe8f 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.h +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.h @@ -1253,6 +1253,9 @@ class XlaBuilder { friend XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, tensorflow::gtl::ArraySlice broadcast_dimensions); friend XlaOp IsFinite(const XlaOp& operand); + // TODO(b/64798317): Finish CPU & GPU implementation, then replace xla::Iota + // in xla/client/lib/numeric.h with this (renamed to xla::Iota). + friend XlaOp IotaGen(XlaBuilder* builder, PrimitiveType type, int64 size); friend XlaOp ConvertElementType(const XlaOp& operand, PrimitiveType new_element_type); friend XlaOp BitcastConvertType(const XlaOp& operand, diff --git a/tensorflow/compiler/xla/literal.cc b/tensorflow/compiler/xla/literal.cc index 5db124b5a2..0545deb096 100644 --- a/tensorflow/compiler/xla/literal.cc +++ b/tensorflow/compiler/xla/literal.cc @@ -1775,7 +1775,9 @@ void LiteralBase::Piece::WriteToProto(LiteralProto* proto) const { // Nothing to do but assign the shape which is done above. return; default: - LOG(FATAL) << "Unhandled primitive type " << subshape().element_type(); + // TODO(b/111551621): Support serializing more PrimitiveTypes. + LOG(FATAL) << "Unhandled primitive type " + << PrimitiveType_Name(subshape().element_type()); } } diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 05f431642c..aeab5d8957 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -2546,6 +2546,11 @@ Status IrEmitter::HandleAfterAll(HloInstruction* gen_token) { return Status::OK(); } +Status IrEmitter::HandleIota(HloInstruction* iota) { + // TODO(b/64798317): implement iota on CPU. + return Unimplemented("Iota is not implemented on CPU."); +} + Status IrEmitter::FinishVisit(HloInstruction* root) { // When this method is called, we should have already emitted an IR value for // the root (return) op. The IR value holds the address of the buffer holding diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h index 419f19c24d..2840c14303 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h @@ -148,6 +148,7 @@ class IrEmitter : public DfsHloVisitorWithDefault { Status HandleConcatenate(HloInstruction* concatenate) override; Status HandleConditional(HloInstruction* conditional) override; Status HandleAfterAll(HloInstruction* gen_token) override; + Status HandleIota(HloInstruction* iota) override; Status FinishVisit(HloInstruction* root) override; Status Preprocess(HloInstruction* hlo) override; diff --git a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h index 51f16bdc94..097fa23027 100644 --- a/tensorflow/compiler/xla/service/dfs_hlo_visitor.h +++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor.h @@ -212,6 +212,7 @@ class DfsHloVisitorBase { virtual Status HandleReverse(HloInstructionPtr hlo) = 0; virtual Status HandleSort(HloInstructionPtr hlo) = 0; virtual Status HandleConstant(HloInstructionPtr hlo) = 0; + virtual Status HandleIota(HloInstructionPtr hlo) = 0; virtual Status HandleGetTupleElement(HloInstructionPtr hlo) = 0; virtual Status HandleReduce(HloInstructionPtr hlo) = 0; virtual Status HandleBitcast(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 0686ca74af..f4316e0fb7 100644 --- a/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h +++ b/tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h @@ -115,6 +115,9 @@ class DfsHloVisitorWithDefaultBase Status HandleConstant(HloInstructionPtr constant) override { return DefaultAction(constant); } + Status HandleIota(HloInstructionPtr iota) override { + return DefaultAction(iota); + } Status HandleGetTupleElement(HloInstructionPtr get_tuple_element) override { return DefaultAction(get_tuple_element); } diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index 449a18e710..d7e8be1cf8 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -784,6 +784,11 @@ Status IrEmitter::HandleBatchNormGrad(HloInstruction*) { "to a cudnn CustomCall using CudnnBatchNormRewriter."); } +Status IrEmitter::HandleIota(HloInstruction*) { + // TODO(b/64798317): implement iota on GPU. + return Unimplemented("Iota is not implemented on GPU."); +} + StatusOr IrEmitter::ComputeNestedElement( const HloComputation& computation, tensorflow::gtl::ArraySlice parameter_elements) { diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.h b/tensorflow/compiler/xla/service/gpu/ir_emitter.h index 77e48d729c..da03ef831b 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.h @@ -96,6 +96,7 @@ class IrEmitter : public DfsHloVisitorWithDefault { Status HandleBatchNormInference(HloInstruction* batch_norm) override; Status HandleBatchNormTraining(HloInstruction* batch_norm) override; Status HandleBatchNormGrad(HloInstruction* batch_norm) override; + Status HandleIota(HloInstruction* iota) override; Status FinishVisit(HloInstruction* root) override { return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc index c49cf7f5db..1f672502f7 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.cc @@ -155,6 +155,10 @@ Status HloCostAnalysis::HandleConstant(const HloInstruction*) { return Status::OK(); } +Status HloCostAnalysis::HandleIota(const HloInstruction*) { + return Status::OK(); +} + Status HloCostAnalysis::HandleGetTupleElement(const HloInstruction*) { // GetTupleElement forwards a pointer and does not touch each element in the // output. diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis.h b/tensorflow/compiler/xla/service/hlo_cost_analysis.h index 0181138a6d..82d650dc7b 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis.h +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis.h @@ -52,6 +52,7 @@ class HloCostAnalysis : public ConstDfsHloVisitor { Status HandleElementwiseUnary(const HloInstruction* hlo) override; Status HandleElementwiseBinary(const HloInstruction* hlo) override; Status HandleConstant(const HloInstruction* constant) override; + Status HandleIota(const HloInstruction* iota) override; Status HandleGetTupleElement( const HloInstruction* get_tuple_element) override; Status HandleSelect(const HloInstruction* hlo) override; diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h index c0a8ea8bcb..f5e477e115 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h @@ -1997,6 +1997,30 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { return HandleReducePrecision(reduce_precision); } + template ::value || + std::is_same::value || + std::is_same::value>::type* = nullptr> + Status HandleIota(HloInstruction* iota) { + auto result = MakeUnique(iota->shape()); + auto data = result->data(); + std::iota(data.begin(), data.end(), 0); + parent_->evaluated_[iota] = std::move(result); + return Status::OK(); + } + template ::value || + std::is_same::value || + std::is_same::value)>::type* = nullptr> + Status HandleIota(HloInstruction* iota) { + return InvalidArgument("Unsupported type for iota"); + } + Status HandleIota(HloInstruction* iota) override { + return HandleIota(iota); + } + private: // Creates a vector of multipliers which can be used to create a linear index // into shape. diff --git a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc index 57cf34d7de..fd5085bed2 100644 --- a/tensorflow/compiler/xla/service/hlo_graph_dumper.cc +++ b/tensorflow/compiler/xla/service/hlo_graph_dumper.cc @@ -948,6 +948,7 @@ ColorScheme HloDotDumper::GetInstructionColor(const HloInstruction* instr) { case HloOpcode::kGe: case HloOpcode::kGt: case HloOpcode::kImag: + case HloOpcode::kIota: case HloOpcode::kIsFinite: case HloOpcode::kLe: case HloOpcode::kLog: diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index ae30d2ad8d..7685c822f4 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -463,6 +463,11 @@ StatusOr> HloInstruction::CreateFromProto( return MakeUnique(std::move(literal)); } +/* static */ std::unique_ptr HloInstruction::CreateIota( + const Shape& shape) { + return WrapUnique(new HloInstruction(HloOpcode::kIota, shape)); +} + /* static */ std::unique_ptr HloInstruction::CreateGetTupleElement(const Shape& shape, HloInstruction* operand, int64 index) { @@ -1119,6 +1124,7 @@ std::unique_ptr HloInstruction::CloneWithNewOperands( case HloOpcode::kDynamicSlice: case HloOpcode::kSort: case HloOpcode::kGather: + case HloOpcode::kIota: clone = CloneWithNewOperandsImpl(shape, new_operands, context); break; // Unary ops. @@ -1556,6 +1562,7 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kMap: case HloOpcode::kSlice: case HloOpcode::kConstant: + case HloOpcode::kIota: case HloOpcode::kTrace: case HloOpcode::kFusion: case HloOpcode::kRng: @@ -1576,6 +1583,7 @@ bool HloInstruction::IdenticalSlowPath( LOG(FATAL) << "Base class impl called for opcode with subclass: " << opcode(); } + return false; } void HloInstruction::RemoveUser(HloInstruction* user) { @@ -2300,6 +2308,8 @@ Status HloInstruction::Visit(DfsHloVisitorBase* visitor) { return visitor->HandleDomain(this); case HloOpcode::kAfterAll: return visitor->HandleAfterAll(this); + case HloOpcode::kIota: + return visitor->HandleIota(this); // These opcodes are not handled here. case HloOpcode::kTrace: diff --git a/tensorflow/compiler/xla/service/hlo_instruction.h b/tensorflow/compiler/xla/service/hlo_instruction.h index c6faa69a78..30bff286c2 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.h +++ b/tensorflow/compiler/xla/service/hlo_instruction.h @@ -346,6 +346,9 @@ class HloInstruction { static std::unique_ptr CreateConstant( std::unique_ptr literal); + // Creates an Iota instruction. + static std::unique_ptr CreateIota(const Shape& shape); + // Creates a get tuple element instruction. static std::unique_ptr CreateGetTupleElement( const Shape& shape, HloInstruction* operand, int64 index); diff --git a/tensorflow/compiler/xla/service/hlo_opcode.h b/tensorflow/compiler/xla/service/hlo_opcode.h index 39e12c4815..59e9a5a94a 100644 --- a/tensorflow/compiler/xla/service/hlo_opcode.h +++ b/tensorflow/compiler/xla/service/hlo_opcode.h @@ -87,6 +87,7 @@ namespace xla { V(kHostCompute, "host-compute") \ V(kImag, "imag") \ V(kInfeed, "infeed") \ + V(kIota, "iota") \ V(kIsFinite, "is-finite") \ V(kLe, "less-than-or-equal-to", kHloOpcodeIsComparison) \ V(kLog, "log") \ diff --git a/tensorflow/compiler/xla/service/hlo_parser.cc b/tensorflow/compiler/xla/service/hlo_parser.cc index 496eca0739..e8eaf54949 100644 --- a/tensorflow/compiler/xla/service/hlo_parser.cc +++ b/tensorflow/compiler/xla/service/hlo_parser.cc @@ -492,6 +492,14 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, HloInstruction::CreateConstant(std::move(literal))); break; } + case HloOpcode::kIota: { + if (!ParseOperands(&operands, /*expected_size=*/0) || + !ParseAttributes(attrs)) { + return false; + } + instruction = builder->AddInstruction(HloInstruction::CreateIota(shape)); + break; + } // Unary ops. case HloOpcode::kAbs: case HloOpcode::kRoundNearestAfz: diff --git a/tensorflow/compiler/xla/service/hlo_parser_test.cc b/tensorflow/compiler/xla/service/hlo_parser_test.cc index 6ba34cf22a..1f0572c576 100644 --- a/tensorflow/compiler/xla/service/hlo_parser_test.cc +++ b/tensorflow/compiler/xla/service/hlo_parser_test.cc @@ -1004,6 +1004,17 @@ ENTRY CrossReplicaSumWithSubgroups { ROOT cross-replica-sum = f32[128,32]{0,1} cross-replica-sum(input), replica_group_ids={0,0,1,1}, barrier="abc", to_apply=add } +)" +}, +// Iota +{ +"Iota", +R"(HloModule iota + +ENTRY Iota { + ROOT iota = f32[100]{0} iota() +} + )" } }); diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc index 6a32093b6e..c80c1e0e7d 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier.cc @@ -210,6 +210,12 @@ Status ShapeVerifier::HandleConstant(HloInstruction* constant) { return CheckShape(constant, constant->literal().shape()); } +Status ShapeVerifier::HandleIota(HloInstruction* iota) { + return ShapeUtil::Rank(iota->shape()) == 1 + ? Status::OK() + : InternalError("Iota only supports arrays of rank 1."); +} + Status ShapeVerifier::HandleGetTupleElement(HloInstruction* get_tuple_element) { return CheckShape(get_tuple_element, ShapeInference::InferGetTupleElementShape( diff --git a/tensorflow/compiler/xla/service/hlo_verifier.h b/tensorflow/compiler/xla/service/hlo_verifier.h index 810c66cf02..79f7aa9f4c 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.h +++ b/tensorflow/compiler/xla/service/hlo_verifier.h @@ -37,6 +37,7 @@ class ShapeVerifier : public DfsHloVisitor { Status HandleSelect(HloInstruction* select) override; Status HandleTupleSelect(HloInstruction* tuple_select) override; Status HandleConcatenate(HloInstruction* concatenate) override; + Status HandleIota(HloInstruction* iota) override; Status HandleConvert(HloInstruction* convert) override; Status HandleBitcastConvert(HloInstruction* convert) override; Status HandleCopy(HloInstruction* copy) override; diff --git a/tensorflow/compiler/xla/service/instruction_fusion.cc b/tensorflow/compiler/xla/service/instruction_fusion.cc index da91262130..af07370135 100644 --- a/tensorflow/compiler/xla/service/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/instruction_fusion.cc @@ -73,6 +73,7 @@ bool IsAlwaysDuplicable(const HloInstruction& instruction) { case HloOpcode::kGt: case HloOpcode::kImag: case HloOpcode::kInfeed: + case HloOpcode::kIota: case HloOpcode::kIsFinite: case HloOpcode::kLe: case HloOpcode::kLt: diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 6a75aa6794..e840067056 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -2060,3 +2060,23 @@ xla_test( "//tensorflow/core:test", ], ) + +xla_test( + name = "iota_test", + srcs = ["iota_test.cc"], + blacklisted_backends = [ + "cpu", + "gpu", + ], + tags = [ + "enable_for_xla_interpreter", + ], + deps = [ + ":client_library_test_base", + ":literal_test_util", + ":xla_internal_test_main", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/core:lib", + "//tensorflow/core:test", + ], +) diff --git a/tensorflow/compiler/xla/tests/iota_test.cc b/tensorflow/compiler/xla/tests/iota_test.cc new file mode 100644 index 0000000000..f950aa1e8f --- /dev/null +++ b/tensorflow/compiler/xla/tests/iota_test.cc @@ -0,0 +1,61 @@ +/* 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 +#include + +#include "tensorflow/compiler/xla/tests/client_library_test_base.h" +#include "tensorflow/core/lib/core/errors.h" + +namespace xla { +namespace { + +class IotaTest : public ClientLibraryTestBase { + public: + explicit IotaTest(se::Platform* platform = nullptr) + : ClientLibraryTestBase(platform) {} + template + std::vector GetExpected(const int64 num_elements) { + std::vector result(num_elements); + std::iota(result.begin(), result.end(), 0); + return result; + } +}; + +TEST_F(IotaTest, SimpleR1) { + for (int num_elements = 1; num_elements < 10000001; num_elements *= 10) { + { + XlaBuilder builder(TestName() + "_f32"); + IotaGen(&builder, F32, num_elements); + ComputeAndCompareR1(&builder, GetExpected(num_elements), {}, + ErrorSpec{0.0001}); + } + { + XlaBuilder builder(TestName() + "_u32"); + IotaGen(&builder, U32, num_elements); + ComputeAndCompareR1(&builder, GetExpected(num_elements), + {}); + } + { + XlaBuilder builder(TestName() + "_s32"); + IotaGen(&builder, S32, num_elements); + ComputeAndCompareR1(&builder, GetExpected(num_elements), + {}); + } + } +} + +} // namespace +} // namespace xla diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md index 68c427a316..d6fa8ab5f9 100644 --- a/tensorflow/docs_src/performance/xla/operation_semantics.md +++ b/tensorflow/docs_src/performance/xla/operation_semantics.md @@ -1293,6 +1293,19 @@ Infeed of the device. > which case the compiler will provide information about how the Infeed > operations are serialized in the compiled program. +## Iota + + `Iota()` + +Builds a constant literal on device rather than a potentially large host +transfer. Creates a rank 1 tensor of values starting at zero and incrementing +by one. + +Arguments | Type | Semantics +------------------ | --------------- | --------------------------- +`type` | `PrimitiveType` | type U +`size` | `int64` | The number of elements in the tensor. + ## Map See also -- GitLab From 8257891f378027a1a7c0403ba6ba0aeb313496a0 Mon Sep 17 00:00:00 2001 From: Katherine Wu Date: Fri, 20 Jul 2018 13:59:59 -0700 Subject: [PATCH 209/519] Add estimator in contrib that loads its model function from a SavedModel. PiperOrigin-RevId: 205449314 --- tensorflow/contrib/estimator/BUILD | 41 ++ tensorflow/contrib/estimator/__init__.py | 5 + .../python/estimator/saved_model_estimator.py | 445 ++++++++++++++++++ .../estimator/saved_model_estimator_test.py | 369 +++++++++++++++ tensorflow/python/estimator/estimator.py | 62 ++- tensorflow/python/framework/importer.py | 2 +- tensorflow/python/framework/meta_graph.py | 68 ++- tensorflow/python/saved_model/loader_impl.py | 13 +- tensorflow/python/saved_model/loader_test.py | 19 +- tensorflow/python/training/saver.py | 28 +- 10 files changed, 1017 insertions(+), 35 deletions(-) create mode 100644 tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py create mode 100644 tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD index 1aa3df8d8d..349f48f7f7 100644 --- a/tensorflow/contrib/estimator/BUILD +++ b/tensorflow/contrib/estimator/BUILD @@ -28,6 +28,7 @@ py_library( ":multi_head", ":replicate_model_fn", ":rnn", + ":saved_model_estimator", "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -465,3 +466,43 @@ py_test( "@absl_py//absl/testing:parameterized", ], ) + +py_library( + name = "saved_model_estimator", + srcs = ["python/estimator/saved_model_estimator.py"], + deps = [ + ":export", + "//tensorflow/python:framework_ops", + "//tensorflow/python:platform", + "//tensorflow/python:training", + "//tensorflow/python/estimator", + "//tensorflow/python/estimator:export", + "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/saved_model", + ], +) + +py_test( + name = "saved_model_estimator_test", + size = "medium", + srcs = ["python/estimator/saved_model_estimator_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":export", + ":saved_model_estimator", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:framework_ops", + "//tensorflow/python:metrics", + "//tensorflow/python:platform", + "//tensorflow/python:state_ops", + "//tensorflow/python:training", + "//tensorflow/python:variables", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/estimator", + "//tensorflow/python/estimator:export_export", + "//tensorflow/python/estimator:export_output", + "//tensorflow/python/estimator:model_fn", + ], +) diff --git a/tensorflow/contrib/estimator/__init__.py b/tensorflow/contrib/estimator/__init__.py index 09fcfd66a1..e1453ae1d0 100644 --- a/tensorflow/contrib/estimator/__init__.py +++ b/tensorflow/contrib/estimator/__init__.py @@ -33,6 +33,8 @@ from tensorflow.contrib.estimator.python.estimator.logit_fns import * from tensorflow.contrib.estimator.python.estimator.multi_head import * from tensorflow.contrib.estimator.python.estimator.replicate_model_fn import * from tensorflow.contrib.estimator.python.estimator.rnn import * +from tensorflow.contrib.estimator.python.estimator.saved_model_estimator import * +from tensorflow.python.estimator.export.export import * from tensorflow.python.util.all_util import remove_undocumented # pylint: enable=unused-import,line-too-long,wildcard-import @@ -70,6 +72,9 @@ _allowed_symbols = [ 'stop_if_higher_hook', 'stop_if_no_increase_hook', 'stop_if_no_decrease_hook', + 'build_raw_supervised_input_receiver_fn', + 'build_supervised_input_receiver_fn_from_input_fn', + 'SavedModelEstimator' ] remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py new file mode 100644 index 0000000000..22188fe663 --- /dev/null +++ b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py @@ -0,0 +1,445 @@ +# 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. +# ============================================================================== +"""Class that creates an Estimator from a SavedModel.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import six + +from tensorflow.python.estimator import estimator as estimator_lib +from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator.export import export as export_lib +from tensorflow.python.estimator.export import export_output +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import loader_impl +from tensorflow.python.saved_model import signature_constants +from tensorflow.python.training import checkpoint_utils +from tensorflow.python.training import monitored_session +from tensorflow.python.training import training_util + + +class SavedModelEstimator(estimator_lib.Estimator): + """Create an Estimator from a SavedModel. + + Only SavedModels exported with + `tf.contrib.estimator.export_all_saved_models()` or + `tf.estimator.Estimator.export_savedmodel()` are supported for this class. + + Example with `tf.estimator.DNNClassifier`: + + **Step 1: Create and train DNNClassifier.** + ```python + feature1 = tf.feature_column.embedding_column( + tf.feature_column.categorical_column_with_vocabulary_list( + key='feature1', vocabulary_list=('green', 'yellow')), dimension=1) + feature2 = tf.feature_column.numeric_column(key='feature2', default_value=0.0) + + classifier = tf.estimator.DNNClassifier( + hidden_units=[4,2], feature_columns=[feature1, feature2]) + + def input_fn(): + features = {'feature1': tf.constant(['green', 'green', 'yellow']), + 'feature2': tf.constant([3.5, 4.2, 6.1])} + label = tf.constant([1., 0., 0.]) + return tf.data.Dataset.from_tensors((features, label)).repeat() + + classifier.train(input_fn=input_fn, steps=10) + ``` + + **Step 2: Export classifier.** + First, build functions that specify the expected inputs. + ```python + # During train and evaluation, both the features and labels should be defined. + supervised_input_receiver_fn = ( + tf.contrib.estimator.build_raw_supervised_input_receiver_fn( + {'feature1': tf.placeholder(dtype=tf.string, shape=[None]), + 'feature2': tf.placeholder(dtype=tf.float32, shape=[None])}, + tf.placeholder(dtype=tf.float32, shape=[None]))) + + # During predict mode, expect to receive a `tf.Example` proto, so a parsing + # function is used. + serving_input_receiver_fn = ( + tf.estimator.export.build_parsing_serving_input_receiver_fn( + tf.feature_column.make_parse_example_spec([feature1, feature2]))) + ``` + + Next, export the model as a SavedModel. A timestamped directory will be + created (for example `/tmp/export_all/1234567890`). + ```python + # Option 1: Save all modes (train, eval, predict) + export_dir = tf.contrib.estimator.export_all_saved_models( + classifier, '/tmp/export_all', + {tf.estimator.ModeKeys.TRAIN: supervised_input_receiver_fn, + tf.estimator.ModeKeys.EVAL: supervised_input_receiver_fn, + tf.estimator.ModeKeys.PREDICT: serving_input_receiver_fn}) + + # Option 2: Only export predict mode + export_dir = classifier.export_savedmodel( + '/tmp/export_predict', serving_input_receiver_fn) + ``` + + **Step 3: Create a SavedModelEstimator from the exported SavedModel.** + ```python + est = tf.contrib.estimator.SavedModelEstimator(export_dir) + + # If all modes were exported, you can immediately evaluate and predict, or + # continue training. Otherwise only predict is available. + eval_results = est.evaluate(input_fn=input_fn, steps=1) + print(eval_results) + + est.train(input_fn=input_fn, steps=20) + + def predict_input_fn(): + example = example_pb2.Example() + example.features.feature['feature1'].bytes_list.value.extend(['yellow']) + example.features.feature['feature2'].float_list.value.extend([1.]) + return {'inputs':tf.constant([example.SerializeToString()])} + + predictions = est.predict(predict_input_fn) + print(next(predictions)) + ``` + """ + + def __init__(self, saved_model_dir, model_dir=None): + """Initialize a SavedModelEstimator. + + The SavedModelEstimator loads its model function and variable values from + the graphs defined in the SavedModel. There is no option to pass in + `RunConfig` or `params` arguments, because the model function graph is + defined statically in the SavedModel. + + Args: + saved_model_dir: Directory containing SavedModel protobuf and subfolders. + model_dir: Directory to save new checkpoints during training. + + Raises: + NotImplementedError: If a DistributionStrategy is defined in the config. + Unless the SavedModelEstimator is subclassed, this shouldn't happen. + """ + checkpoint = estimator_lib._get_saved_model_ckpt(saved_model_dir) # pylint: disable=protected-access + vars_to_warm_start = [name for name, _ in + checkpoint_utils.list_variables(checkpoint)] + warm_start_settings = estimator_lib.WarmStartSettings( + ckpt_to_initialize_from=checkpoint, + vars_to_warm_start=vars_to_warm_start) + + super(SavedModelEstimator, self).__init__( + model_fn=self._model_fn_from_saved_model, model_dir=model_dir, + warm_start_from=warm_start_settings) + if self._distribution is not None: + raise NotImplementedError( + 'SavedModelEstimator currently does not support ' + 'DistributionStrategy.') + self.saved_model_dir = saved_model_dir + self.saved_model_loader = loader_impl.SavedModelLoader(saved_model_dir) + self._available_modes = self._extract_available_modes() + + def _extract_available_modes(self): + """Return list of modes found in SavedModel.""" + available_modes = [] + logging.info('Checking available modes for SavedModelEstimator.') + for mode in [model_fn_lib.ModeKeys.TRAIN, model_fn_lib.ModeKeys.EVAL, + model_fn_lib.ModeKeys.PREDICT]: + try: + self._get_meta_graph_def_for_mode(mode) + except RuntimeError: + logging.warning('%s mode not found in SavedModel.' % mode) + continue + + if self._get_signature_def_for_mode(mode) is not None: + available_modes.append(mode) + + logging.info('Available modes for Estimator: %s' % available_modes) + return available_modes + + def _validate_mode(self, mode): + """Make sure that mode can be run using the SavedModel.""" + if mode not in self._available_modes: + raise RuntimeError('%s mode is not available in the SavedModel. Use ' + 'saved_model_cli to check that the Metagraph for this ' + 'mode has been exported.' % mode) + + def _get_meta_graph_def_for_mode(self, mode): + tags = model_fn_lib.EXPORT_TAG_MAP[mode] + return self.saved_model_loader.get_meta_graph_def_from_tags(tags) + + def _get_signature_def_for_mode(self, mode): + meta_graph_def = self._get_meta_graph_def_for_mode(mode) + sig_def_key = (signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY + if mode == model_fn_lib.ModeKeys.PREDICT else mode) + if sig_def_key not in meta_graph_def.signature_def: + logging.warning('Metagraph for mode %s was found, but SignatureDef with' + ' key \"%s\" is missing.' % (mode, sig_def_key)) + return None + return meta_graph_def.signature_def[sig_def_key] + + def _create_and_assert_global_step(self, graph): + # Do nothing here. The global step variable will be created/loaded from the + # SavedModel. If a global step variable were created here, the result + # will be two duplicate global step variables, causing issues during + # the warm-start phase. + # Due to the global variable being created in the model function, this may + # cause issues when running DistributionStrategy. Thus, DistributionStrategy + # is not yet supported with SavedModelEstimator. + pass + + def _model_fn_from_saved_model(self, features, labels, mode): + """Load a SavedModel graph and return an EstimatorSpec.""" + # TODO(kathywu): Model function loads placeholders from the graph. Calling + # export_all_saved_models creates another placeholder for the inputs, on top + # of the original placeholders. There should be a way to avoid this. + self._validate_mode(mode) + + g = ops.get_default_graph() + if training_util.get_global_step(g) is not None: + raise RuntimeError( + 'Graph must not contain a global step tensor before the SavedModel is' + ' loaded. Please make sure that the input function does not create a ' + 'global step.') + + # Extract SignatureDef for information about the input and output tensors. + signature_def = self._get_signature_def_for_mode(mode) + + # Generate input map for replacing the inputs in the SavedModel graph with + # the provided features and labels. + input_map = _generate_input_map(signature_def, features, labels) + + # Create a list of the names of output tensors. When the graph is loaded, + # names of the output tensors may be remapped. This ensures that the correct + # tensors are returned in the EstimatorSpec. + output_tensor_names = [ + value.name for value in six.itervalues(signature_def.outputs)] + + # Load the graph. `output_tensors` contains output `Tensors` in the same + # same order as the `output_tensor_names` list. + tags = model_fn_lib.EXPORT_TAG_MAP[mode] + _, output_tensors = self.saved_model_loader.load_graph( + g, tags, input_map=input_map, return_elements=output_tensor_names) + + # Create a scaffold from the MetaGraphDef that contains ops to initialize + # the graph. This should mirror the steps from _add_meta_graph_for_mode(), + # which creates a MetaGraphDef from the EstimatorSpec's scaffold. + scaffold = monitored_session.Scaffold( + local_init_op=loader_impl._get_legacy_init_op_tensor( # pylint: disable=protected-access + self._get_meta_graph_def_for_mode(mode))) + + # Ensure that a global step tensor has been created. + global_step_tensor = training_util.get_global_step(g) + training_util.assert_global_step(global_step_tensor) + + # Extract values to return in the EstimatorSpec. + output_map = dict(zip(output_tensor_names, output_tensors)) + outputs = {key: output_map[value.name] + for key, value in six.iteritems(signature_def.outputs)} + + loss, predictions, metrics = _validate_and_extract_outputs( + mode, outputs, signature_def.method_name) + + train_op = ops.get_collection(constants.TRAIN_OP_KEY) + if len(train_op) > 1: + raise RuntimeError('Multiple ops found in the train_op collection.') + train_op = None if not train_op else train_op[0] + + _clear_saved_model_collections() + return model_fn_lib.EstimatorSpec( + scaffold=scaffold, + mode=mode, + loss=loss, + train_op=train_op, + predictions=predictions, + eval_metric_ops=metrics) + + +def _clear_saved_model_collections(): + """Clear collections that are expected empty when exporting a SavedModel. + + The SavedModel builder uses these collections to track ops necessary to + restore the graph state. These collections are expected to be empty before + MetaGraphs are added to the builder. + """ + del ops.get_collection_ref(constants.ASSETS_KEY)[:] + del ops.get_collection_ref(constants.LEGACY_INIT_OP_KEY)[:] + del ops.get_collection_ref(constants.MAIN_OP_KEY)[:] + del ops.get_collection_ref(constants.TRAIN_OP_KEY)[:] + + +def _generate_input_map(signature_def, features, labels): + """Return dict mapping an input tensor name to a feature or label tensor. + + Args: + signature_def: SignatureDef loaded from SavedModel + features: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or + `SparseTensor`, specifying the features to be passed to the model. + labels: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or + `SparseTensor`, specifying the labels to be passed to the model. May be + `None`. + + Returns: + dict mapping string names of inputs to features or labels tensors + + Raises: + ValueError: if SignatureDef inputs are not completely mapped by the input + features and labels. + """ + # pylint: disable=protected-access + if not isinstance(features, dict): + features = {export_lib._SINGLE_FEATURE_DEFAULT_NAME: features} + if labels is not None and not isinstance(labels, dict): + labels = {export_lib._SINGLE_LABEL_DEFAULT_NAME: labels} + # pylint: enable=protected-access + + inputs = signature_def.inputs + input_map = {} + for key, tensor_info in six.iteritems(inputs): + input_name = tensor_info.name + if ':' in input_name: + input_name = input_name[:input_name.find(':')] + + # When tensors are used as control inputs for operations, their names are + # prepended with a '^' character in the GraphDef. To handle possible control + # flow edge cases, control input names must be included in the input map. + control_dependency_name = '^' + input_name + + if key in features: + _check_same_dtype_and_shape(features[key], tensor_info, key) + input_map[input_name] = input_map[control_dependency_name] = features[key] + elif labels is not None and key in labels: + _check_same_dtype_and_shape(labels[key], tensor_info, key) + input_map[input_name] = input_map[control_dependency_name] = labels[key] + else: + raise ValueError( + 'Key \"%s\" not found in features or labels passed in to the model ' + 'function. All required keys: %s' % (key, inputs.keys())) + + return input_map + + +def _check_same_dtype_and_shape(tensor, tensor_info, name): + """Validate that tensor has the same properties as the TensorInfo proto. + + Args: + tensor: a `Tensor` object. + tensor_info: a `TensorInfo` proto. + name: Name of the input (to identify Tensor if an error is raised). + + Raises: + ValueError: If the tensor shape or dtype don't match the TensorInfo + """ + dtype_error = (tensor.dtype != dtypes.DType(tensor_info.dtype)) + shape_error = not tensor.shape.is_compatible_with(tensor_info.tensor_shape) + + if dtype_error or shape_error: + msg = 'Tensor shape and/or dtype validation failed for input %s:' % name + if dtype_error: + msg += ('\n\tExpected dtype: %s, Got: %s' + % (dtypes.DType(tensor_info.dtype), tensor.dtype)) + if shape_error: + msg += ('\n\tExpected shape: %s, Got: %s' + % (tensor_shape.TensorShape(tensor_info.tensor_shape), + tensor.shape)) + + raise ValueError(msg) + + +def _extract_eval_metrics(output_dict): + """Return a eval metric dict extracted from the output_dict. + + Eval metrics consist of a value tensor and an update op. Both must be in the + passed-in tensor dictionary for an eval metric to be added to the returned + dictionary. + + Args: + output_dict: a dict that maps strings to tensors. + + Returns: + dict mapping strings to (value, update_op) tuples. + """ + # pylint: disable=protected-access + metric_ops = {} + separator_char = export_output._SupervisedOutput._SEPARATOR_CHAR + + for key, tensor in six.iteritems(output_dict): + split_key = key.split(separator_char) + + # The metric name may contain the separator character, so recreate its name. + metric_name = separator_char.join(split_key[:-1]) + + if split_key[0] == export_output._SupervisedOutput.METRICS_NAME: + # If the key ends with the value suffix, and there is a corresponding + # key ending with the update_op suffix, then add tensors to metrics dict. + if split_key[-1] == export_output._SupervisedOutput.METRIC_VALUE_SUFFIX: + update_op = ''.join( + [metric_name, separator_char, + export_output._SupervisedOutput.METRIC_UPDATE_SUFFIX]) + if update_op in output_dict: + update_op_tensor = output_dict[update_op] + metric_ops[metric_name] = (tensor, update_op_tensor) + + # pylint: enable=protected-access + return metric_ops + + +def _validate_and_extract_outputs(mode, output_dict, method_name): + """Extract values from SignatureDef output dictionary. + + Args: + mode: One of the modes enumerated in `tf.estimator.ModeKeys`. + output_dict: dict of string SignatureDef keys to `Tensor`. + method_name: Method name of the SignatureDef as a string. + + Returns: + Tuple of ( + loss: `Tensor` object, + predictions: dictionary mapping string keys to `Tensor` objects, + metrics: dictionary mapping string keys to a tuple of two `Tensor` objects + ) + + Raises: + RuntimeError: raised if SignatureDef has an invalid method name for the mode + """ + # pylint: disable=protected-access + loss, predictions, metrics = None, None, None + + if mode == model_fn_lib.ModeKeys.PREDICT: + predictions = output_dict + else: + # Validate that the SignatureDef's method name matches the expected name for + # the given mode. + expected_method_name = signature_constants.SUPERVISED_TRAIN_METHOD_NAME + if mode == model_fn_lib.ModeKeys.EVAL: + expected_method_name = signature_constants.SUPERVISED_EVAL_METHOD_NAME + if method_name != expected_method_name: + raise RuntimeError( + 'Invalid SignatureDef method name for mode %s.\n\tExpected: %s\n\t' + 'Got: %s\nPlease ensure that the SavedModel was exported with ' + '`tf.contrib.estimator.export_all_saved_models()`.' % + (mode, expected_method_name, method_name)) + + # Extract loss, metrics and predictions from the output dict. + loss = output_dict[export_output._SupervisedOutput.LOSS_NAME] + metrics = _extract_eval_metrics(output_dict) + predictions = { + key: value for key, value in six.iteritems(output_dict) + if key.split(export_output._SupervisedOutput._SEPARATOR_CHAR)[0] == ( + export_output._SupervisedOutput.PREDICTIONS_NAME)} + + # pylint: enable=protected-access + return loss, predictions, metrics diff --git a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py new file mode 100644 index 0000000000..718da1367c --- /dev/null +++ b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py @@ -0,0 +1,369 @@ +# 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 SavedModelEstimator.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import shutil +import tempfile + +from tensorflow.contrib.estimator.python.estimator import export as contrib_export +from tensorflow.contrib.estimator.python.estimator import saved_model_estimator +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.estimator import estimator +from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator.export import export +from tensorflow.python.estimator.export import export_output +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 control_flow_ops +from tensorflow.python.ops import metrics as metrics_lib +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import monitored_session +from tensorflow.python.training import training + + +def dummy_input_fn(): + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([[1], [-2]], dtype=dtypes.int64)}, + constant_op.constant([[4], [-3]], dtype=dtypes.float32))).repeat() + + +def dummy_input_fn_features_only(): + return dataset_ops.Dataset.from_tensors( + {'x': constant_op.constant([[5], [6]], dtype=dtypes.int64)}).repeat() + + +def dummy_supervised_receiver_fn(): + feature_spec = { + 'x': array_ops.placeholder( + dtype=dtypes.int64, shape=(2, 1), name='feature_x'), + } + label_spec = array_ops.placeholder( + dtype=dtypes.float32, shape=[2, 1], name='truth') + return export.build_raw_supervised_input_receiver_fn( + feature_spec, label_spec) + + +def dummy_serving_receiver_fn(): + feature_spec = {'x': array_ops.placeholder( + dtype=dtypes.int64, shape=(2, 1), name='feature_x'),} + return export.build_raw_serving_input_receiver_fn(feature_spec) + + +def model_fn_diff_modes(features, labels, mode): + _, _ = features, labels + v = variables.Variable(21, name='some_var') + train_op = None + loss = constant_op.constant(104) + if mode == model_fn_lib.ModeKeys.TRAIN: + loss = constant_op.constant(105) + predictions = constant_op.constant([501]) + train_op = control_flow_ops.group( + state_ops.assign_add(training.get_global_step(), 1), + state_ops.assign_add(v, 3)) + elif mode == model_fn_lib.ModeKeys.EVAL: + loss = constant_op.constant(106) + predictions = constant_op.constant([502]) + else: + loss = constant_op.constant(107) + predictions = constant_op.constant([503]) + return model_fn_lib.EstimatorSpec( + mode, + loss=loss, + train_op=train_op, + eval_metric_ops={ + 'abs_err': metrics_lib.mean_absolute_error( + constant_op.constant(0), predictions)}, + predictions=predictions) + + +class SavedModelEstimatorTest(test.TestCase): + + def setUp(self): + self.tmpdirs = [] + + def tearDown(self): + for tmpdir in self.tmpdirs: + # gfile.DeleteRecursively fails in the windows cmake test, so use shutil. + shutil.rmtree(tmpdir, ignore_errors=True) + self.tmpdirs = [] + + def _get_tmp_dir(self): + tmpdir = tempfile.mkdtemp() + self.tmpdirs.append(tmpdir) + return tmpdir + + def _export_estimator(self, train=True, evaluate=True, predict=True, + model_fn=model_fn_diff_modes): + est = estimator.Estimator(model_fn, self._get_tmp_dir()) + est.train(input_fn=dummy_input_fn, steps=10) + + input_receiver_fn_map = {} + if train: + input_receiver_fn_map[model_fn_lib.ModeKeys.TRAIN] = ( + dummy_supervised_receiver_fn()) + if evaluate: + input_receiver_fn_map[model_fn_lib.ModeKeys.EVAL] = ( + dummy_supervised_receiver_fn()) + if predict: + input_receiver_fn_map[model_fn_lib.ModeKeys.PREDICT] = ( + dummy_serving_receiver_fn()) + + export_base_path = self._get_tmp_dir() + export_dir = contrib_export.export_all_saved_models( + est, export_base_path, input_receiver_fn_map) + return export_dir + + def test_load_all_modes(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + sme.train(input_fn=dummy_input_fn, steps=1) + sme.train(input_fn=dummy_input_fn, steps=2) + self.assertEqual(13, sme.get_variable_value('global_step')) + self.assertEqual(60, sme.get_variable_value('some_var')) + + eval_results = sme.evaluate(dummy_input_fn, steps=5) + + self.assertEqual(13, eval_results['global_step']) + self.assertEqual(106, eval_results['loss']) + self.assertEqual(502, eval_results['metrics/abs_err']) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_load_all_modes_no_train(self): + """Ensure that all functions can be used without requiring a ckpt.""" + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + eval_results = sme.evaluate(dummy_input_fn, steps=5) + self.assertEqual(10, eval_results['global_step']) + self.assertEqual(106, eval_results['loss']) + self.assertEqual(502, eval_results['metrics/abs_err']) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_partial_exported_estimator(self): + sme1 = saved_model_estimator.SavedModelEstimator( + self._export_estimator(train=False, predict=False), self._get_tmp_dir()) + sme1.evaluate(dummy_input_fn, steps=5) + with self.assertRaisesRegexp(RuntimeError, 'train mode is not available'): + sme1.train(input_fn=dummy_input_fn, steps=1) + with self.assertRaisesRegexp(RuntimeError, 'infer mode is not available'): + next(sme1.predict(dummy_input_fn_features_only)) + + sme2 = saved_model_estimator.SavedModelEstimator( + self._export_estimator(evaluate=False), self._get_tmp_dir()) + sme2.train(input_fn=dummy_input_fn, steps=1) + next(sme2.predict(dummy_input_fn_features_only)) + with self.assertRaisesRegexp(RuntimeError, 'eval mode is not available'): + sme2.evaluate(dummy_input_fn, steps=5) + + def test_with_incorrect_input(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + + def bad_shape_input_fn(): + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([1, 2], dtype=dtypes.int64)}, + constant_op.constant([1, 2], dtype=dtypes.float32))) + + with self.assertRaisesRegexp(ValueError, 'Expected shape'): + sme.train(bad_shape_input_fn, steps=1) + + def bad_dtype_input_fn(): + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([[1], [1]], dtype=dtypes.int32)}, + constant_op.constant([[1], [1]], dtype=dtypes.int64))) + + with self.assertRaisesRegexp(ValueError, 'Expected dtype'): + sme.train(bad_dtype_input_fn, steps=1) + + def test_input_fn_with_global_step(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + + def bad_input_fn(): + training.get_or_create_global_step() + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([[1], [1]], dtype=dtypes.int64)}, + constant_op.constant([[1], [1]], dtype=dtypes.float32))) + + with self.assertRaisesRegexp(RuntimeError, + 'Graph must not contain a global step tensor'): + sme.train(bad_input_fn, steps=1) + + def test_re_export_saved_model_serving_only(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + sme.train(dummy_input_fn, steps=3) + self.assertEqual(13, sme.get_variable_value('global_step')) + self.assertEqual(60, sme.get_variable_value('some_var')) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + # Export SavedModel, and test that the variable and prediction values are + # the same. + sme_export_dir = sme.export_savedmodel( + self._get_tmp_dir(), dummy_serving_receiver_fn()) + + sme2 = saved_model_estimator.SavedModelEstimator( + sme_export_dir, self._get_tmp_dir()) + self.assertEqual(60, sme.get_variable_value('some_var')) + self.assertEqual(13, sme.get_variable_value('global_step')) + + predictions = next(sme2.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_re_export_saved_model(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + self.assertDictEqual( + {'loss': 106, 'metrics/abs_err': 502, 'global_step': 10}, + sme.evaluate(dummy_input_fn, steps=1)) + + sme.train(dummy_input_fn, steps=3) + self.assertDictEqual( + {'loss': 106, 'metrics/abs_err': 502, 'global_step': 13}, + sme.evaluate(dummy_input_fn, steps=1)) + self.assertEqual(60, sme.get_variable_value('some_var')) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + # Export SavedModel for all modes + input_receiver_fn_map = { + model_fn_lib.ModeKeys.TRAIN: dummy_supervised_receiver_fn(), + model_fn_lib.ModeKeys.EVAL: dummy_supervised_receiver_fn(), + model_fn_lib.ModeKeys.PREDICT: dummy_serving_receiver_fn()} + sme_export_dir = contrib_export.export_all_saved_models( + sme, self._get_tmp_dir(), input_receiver_fn_map) + + sme2 = saved_model_estimator.SavedModelEstimator( + sme_export_dir, self._get_tmp_dir()) + self.assertDictEqual( + {'loss': 106, 'metrics/abs_err': 502, 'global_step': 13}, + sme.evaluate(dummy_input_fn, steps=1)) + self.assertEqual(60, sme.get_variable_value('some_var')) + + sme.train(dummy_input_fn, steps=7) + self.assertEqual(20, sme.get_variable_value('global_step')) + + predictions = next(sme2.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_load_saved_model_from_serving_only(self): + def model_fn(features, labels, mode): + _, _ = features, labels + return model_fn_lib.EstimatorSpec( + mode, + loss=constant_op.constant([103]), + train_op=state_ops.assign_add(training.get_global_step(), 1), + predictions=constant_op.constant([502]), + export_outputs={'test': export_output.ClassificationOutput( + constant_op.constant([[32.]]))}) + + est = estimator.Estimator(model_fn, self._get_tmp_dir()) + est.train(input_fn=dummy_input_fn, steps=10) + + def serving_input_receiver_fn(): + return export.ServingInputReceiver( + {'test-features': constant_op.constant([[1], [1]])}, + array_ops.placeholder(dtype=dtypes.string)) + + export_dir = est.export_savedmodel( + self._get_tmp_dir(), serving_input_receiver_fn) + + sme = saved_model_estimator.SavedModelEstimator( + export_dir, self._get_tmp_dir()) + + def input_fn(): + return {'inputs': constant_op.constant('someinputstr')} + + prediction = next(sme.predict(input_fn)) + self.assertDictEqual({'scores': 32}, prediction) + + def test_with_local_init_op(self): + def model_fn(features, labels, mode): + _, _ = features, labels + v = variables.Variable(21, name='some_var') + scaffold = monitored_session.Scaffold( + local_init_op=state_ops.assign_add(v, -3).op + ) + return model_fn_lib.EstimatorSpec( + mode, + scaffold=scaffold, + train_op=state_ops.assign_add(training.get_global_step(), 1), + loss=array_ops.identity(v)) + export_dir = self._export_estimator(predict=False, model_fn=model_fn) + sme = saved_model_estimator.SavedModelEstimator( + export_dir, self._get_tmp_dir()) + + eval_results1 = sme.evaluate(dummy_input_fn, steps=2) + self.assertEqual(15, eval_results1['loss']) + + sme.train(dummy_input_fn, steps=1) + self.assertEqual(15, sme.get_variable_value('some_var')) + + eval_results2 = sme.evaluate(dummy_input_fn, steps=5) + self.assertEqual(12, eval_results2['loss']) + + def test_with_working_input_fn(self): + def model_fn(features, labels, mode): + loss = None + if labels is not None: + loss = labels[0][0] + labels[1][0] + return model_fn_lib.EstimatorSpec( + mode, + loss=loss, + train_op=state_ops.assign_add(training.get_global_step(), 1), + predictions={'features_0': array_ops.identity([features['x'][0][0]]), + 'features_1': array_ops.identity([features['x'][1][0]])}) + + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(model_fn=model_fn), self._get_tmp_dir()) + eval_results = sme.evaluate(dummy_input_fn, steps=1) + self.assertEqual(1, eval_results['loss']) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'features_0': 5, 'features_1': 6}, predictions) + + def test_control_dependency(self): + # Control dependencies are saved with "^" appended to the start of the input + # name. The input map must include control dependencies as well. + def model_fn(features, labels, mode): + _ = labels + with ops.control_dependencies([features['x']]): + loss = features['x'][1][0] + return model_fn_lib.EstimatorSpec( + mode, + loss=loss, + train_op=state_ops.assign_add(training.get_global_step(), 1)) + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(train=False, predict=False, model_fn=model_fn), + self._get_tmp_dir()) + sme.evaluate(dummy_input_fn, steps=1) # Should run without error + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 2fd6f6fab9..148fcf61fa 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -568,13 +568,14 @@ class Estimator(object): def _assert_members_are_not_overridden(self): """Asserts members of `Estimator` are not overridden.""" allowed_overrides = set([ - '_call_input_fn', '_create_global_step', + '_call_input_fn', '_call_model_fn', '_convert_train_steps_to_hooks', '_convert_eval_steps_to_hooks', + '_create_global_step', '_create_and_assert_global_step', '_tf_api_names', '_tf_api_names_v1', '_estimator_api_names', '_estimator_api_names_v1', '_estimator_api_constants', '_estimator_api_constants_v1', '_validate_features_in_predict_input', - '_call_model_fn', '_add_meta_graph_for_mode' + '_add_meta_graph_for_mode' ]) estimator_members = set([m for m in Estimator.__dict__.keys() if not m.startswith('__')]) @@ -901,9 +902,10 @@ class Estimator(object): with tf_session.Session(config=self._session_config) as session: - local_init_op = ( - estimator_spec.scaffold.local_init_op or - monitored_session.Scaffold.default_local_init_op()) + if estimator_spec.scaffold.local_init_op is not None: + local_init_op = estimator_spec.scaffold.local_init_op + else: + local_init_op = monitored_session.Scaffold.default_local_init_op() # This saver will be used both for restoring variables now, # and in saving out the metagraph below. This ensures that any @@ -1154,14 +1156,15 @@ class Estimator(object): worker_hooks = [] with ops.Graph().as_default() as g, g.device(self._device_fn): random_seed.set_random_seed(self._config.tf_random_seed) - global_step_tensor = self._create_and_assert_global_step(g) - training_util._get_or_create_global_step_read() # pylint: disable=protected-access + self._create_and_assert_global_step(g) features, labels, input_hooks = ( self._get_features_and_labels_from_input_fn( input_fn, model_fn_lib.ModeKeys.TRAIN)) worker_hooks.extend(input_hooks) estimator_spec = self._call_model_fn( features, labels, model_fn_lib.ModeKeys.TRAIN, self.config) + global_step_tensor = training_util.get_global_step(g) + training_util._get_or_create_global_step_read() # pylint: disable=protected-access return self._train_with_estimator_spec(estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners) @@ -1364,10 +1367,8 @@ class Estimator(object): def _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners): """Train a model with the given Estimator Spec.""" - if self._warm_start_settings: - logging.info('Warm-starting with WarmStartSettings: %s' % - (self._warm_start_settings,)) - warm_starting_util.warm_start(*self._warm_start_settings) + self._maybe_warm_start(self.latest_checkpoint()) + # Check if the user created a loss summary, and add one if they didn't. # We assume here that the summary is called 'loss'. If it is not, we will # make another one with the name 'loss' to ensure it shows up in the right @@ -1448,13 +1449,13 @@ class Estimator(object): def _evaluate_build_graph(self, input_fn, hooks=None, checkpoint_path=None): """Builds the graph and related hooks to run evaluation.""" random_seed.set_random_seed(self._config.tf_random_seed) - global_step_tensor = self._create_and_assert_global_step( - ops.get_default_graph()) + self._create_and_assert_global_step(ops.get_default_graph()) features, labels, input_hooks = ( self._get_features_and_labels_from_input_fn(input_fn, model_fn_lib.ModeKeys.EVAL)) estimator_spec = self._call_model_fn( features, labels, model_fn_lib.ModeKeys.EVAL, self.config) + global_step_tensor = training_util.get_global_step(ops.get_default_graph()) # Call to warm_start has to be after model_fn is called. self._maybe_warm_start(checkpoint_path) @@ -1480,7 +1481,21 @@ class Estimator(object): all_hooks.extend(hooks) all_hooks.extend(list(estimator_spec.evaluation_hooks or [])) - return estimator_spec.scaffold, update_op, eval_dict, all_hooks + # New local variables have been added, so update the estimator spec's + # local init op if it was defined. + scaffold = estimator_spec.scaffold + if estimator_spec.scaffold and estimator_spec.scaffold.local_init_op: + # Ensure that eval step has been created before updating local init op. + evaluation._get_or_create_eval_step() # pylint: disable=protected-access + + scaffold = monitored_session.Scaffold( + local_init_op=control_flow_ops.group( + estimator_spec.scaffold.local_init_op, + monitored_session.Scaffold.default_local_init_op()), + copy_from_scaffold=scaffold + ) + + return scaffold, update_op, eval_dict, all_hooks def _evaluate_run(self, checkpoint_path, scaffold, update_op, eval_dict, all_hooks, output_dir): @@ -1911,6 +1926,19 @@ class WarmStartSettings( ) +def _get_saved_model_ckpt(saved_model_dir): + """Return path to variables checkpoint in a SavedModel directory.""" + if not gfile.Exists( + os.path.join(compat.as_bytes(saved_model_dir), + compat.as_bytes('variables/variables.index'))): + raise ValueError('Directory provided has an invalid SavedModel format: %s' + % saved_model_dir) + return os.path.join( + compat.as_bytes(saved_model_dir), + compat.as_bytes('{}/{}'.format(constants.VARIABLES_DIRECTORY, + constants.VARIABLES_FILENAME))) + + def _get_default_warm_start_settings(warm_start_from): """Returns default WarmStartSettings. @@ -1934,10 +1962,8 @@ def _get_default_warm_start_settings(warm_start_from): if gfile.Exists(os.path.join(compat.as_bytes(warm_start_from), compat.as_bytes('variables/variables.index'))): logging.info('Warm-starting from a SavedModel') - return WarmStartSettings(ckpt_to_initialize_from=os.path.join( - compat.as_bytes(warm_start_from), - compat.as_bytes('{}/{}'.format(constants.VARIABLES_DIRECTORY, - constants.VARIABLES_FILENAME)))) + return WarmStartSettings( + ckpt_to_initialize_from=_get_saved_model_ckpt(warm_start_from)) return WarmStartSettings(ckpt_to_initialize_from=warm_start_from) elif isinstance(warm_start_from, WarmStartSettings): return warm_start_from diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py index 699d2b70d1..687bfebd43 100644 --- a/tensorflow/python/framework/importer.py +++ b/tensorflow/python/framework/importer.py @@ -205,7 +205,7 @@ def _PopulateTFImportGraphDefOptions(options, prefix, input_map, for input_src, input_dst in input_map.items(): input_src = compat.as_str(input_src) if input_src.startswith('^'): - src_name = compat.as_bytes(input_src[1:]) + src_name = compat.as_str(input_src[1:]) dst_op = input_dst._as_tf_output().oper # pylint: disable=protected-access c_api.TF_ImportGraphDefOptionsRemapControlDependency( options, src_name, dst_op) diff --git a/tensorflow/python/framework/meta_graph.py b/tensorflow/python/framework/meta_graph.py index 923e76fc9c..33631282bd 100644 --- a/tensorflow/python/framework/meta_graph.py +++ b/tensorflow/python/framework/meta_graph.py @@ -696,6 +696,67 @@ def import_scoped_meta_graph(meta_graph_or_file, Raises: ValueError: If the graph_def contains unbound inputs. """ + return import_scoped_meta_graph_with_return_elements( + meta_graph_or_file, clear_devices, graph, import_scope, input_map, + unbound_inputs_col_name, restore_collections_predicate)[0] + + +def import_scoped_meta_graph_with_return_elements( + meta_graph_or_file, + clear_devices=False, + graph=None, + import_scope=None, + input_map=None, + unbound_inputs_col_name="unbound_inputs", + restore_collections_predicate=(lambda key: True), + return_elements=None): + """Imports graph from `MetaGraphDef` and returns vars and return elements. + + This function takes a `MetaGraphDef` protocol buffer as input. If + the argument is a file containing a `MetaGraphDef` protocol buffer , + it constructs a protocol buffer from the file content. The function + then adds all the nodes from the `graph_def` field to the + current graph, recreates the desired collections, and returns a dictionary of + all the Variables imported into the name scope. + + In combination with `export_scoped_meta_graph()`, this function can be used to + + * Serialize a graph along with other Python objects such as `QueueRunner`, + `Variable` into a `MetaGraphDef`. + + * Restart training from a saved graph and checkpoints. + + * Run inference from a saved graph and checkpoints. + + Args: + meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including + the path) containing a `MetaGraphDef`. + clear_devices: Boolean which controls whether to clear device information + from graph_def. Default false. + graph: The `Graph` to import into. If `None`, use the default graph. + import_scope: Optional `string`. Name scope into which to import the + subgraph. If `None`, the graph is imported to the root name scope. + input_map: A dictionary mapping input names (as strings) in `graph_def` to + `Tensor` objects. The values of the named input tensors in the imported + graph will be re-mapped to the respective `Tensor` values. + unbound_inputs_col_name: Collection name for looking up unbound inputs. + restore_collections_predicate: a predicate on collection names. A collection + named c (i.e whose key is c) will be restored iff + 1) `restore_collections_predicate(c)` is True, and + 2) `c != unbound_inputs_col_name`. + return_elements: A list of strings containing operation names in the + `MetaGraphDef` that will be returned as `Operation` objects; and/or + tensor names in `MetaGraphDef` that will be returned as `Tensor` objects. + + Returns: + A tuple of ( + dictionary of all the `Variables` imported into the name scope, + list of `Operation` or `Tensor` objects from the `return_elements` list). + + Raises: + ValueError: If the graph_def contains unbound inputs. + + """ if context.executing_eagerly(): raise ValueError("Exporting/importing meta graphs is not supported when " "eager execution is enabled.") @@ -737,11 +798,12 @@ def import_scoped_meta_graph(meta_graph_or_file, scope_to_prepend_to_names = graph.unique_name( import_scope or "", mark_as_used=False) - importer.import_graph_def( + imported_return_elements = importer.import_graph_def( input_graph_def, name=(import_scope or scope_to_prepend_to_names), input_map=input_map, - producer_op_list=producer_op_list) + producer_op_list=producer_op_list, + return_elements=return_elements) # Restores all the other collections. variable_objects = {} @@ -806,7 +868,7 @@ def import_scoped_meta_graph(meta_graph_or_file, for v in variables: var_list[ops.strip_name_scope(v.name, scope_to_prepend_to_names)] = v - return var_list + return var_list, imported_return_elements def export_scoped_meta_graph(filename=None, diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index e5f649fdab..685a913f9c 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -284,12 +284,15 @@ class SavedModelLoader(object): **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph. Returns: - Saver defined by the MetaGraph, which can be used to restore the variable - values. + A tuple of + * Saver defined by the MetaGraph, which can be used to restore the + variable values. + * List of `Operation`/`Tensor` objects returned from + `tf.import_graph_def` (may be `None`). """ meta_graph_def = self.get_meta_graph_def_from_tags(tags) with graph.as_default(): - return tf_saver.import_meta_graph( + return tf_saver._import_meta_graph_with_return_elements( # pylint: disable=protected-access meta_graph_def, import_scope=import_scope, **saver_kwargs) def restore_variables(self, sess, saver, import_scope=None): @@ -361,8 +364,8 @@ class SavedModelLoader(object): `MetagraphDef` proto of the graph that was loaded. """ with sess.graph.as_default(): - saver = self.load_graph(sess.graph, tags, import_scope, - **saver_kwargs) + saver, _ = self.load_graph(sess.graph, tags, import_scope, + **saver_kwargs) self.restore_variables(sess, saver, import_scope) self.run_init_ops(sess, tags, import_scope) return self.get_meta_graph_def_from_tags(tags) diff --git a/tensorflow/python/saved_model/loader_test.py b/tensorflow/python/saved_model/loader_test.py index ce18859f6b..9a0b276a4b 100644 --- a/tensorflow/python/saved_model/loader_test.py +++ b/tensorflow/python/saved_model/loader_test.py @@ -111,7 +111,8 @@ class SavedModelLoaderTest(test.TestCase): def test_load_with_import_scope(self): loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) with self.test_session(graph=ops.Graph()) as sess: - saver = loader.load_graph(sess.graph, ["foo_graph"], import_scope="baz") + saver, _ = loader.load_graph( + sess.graph, ["foo_graph"], import_scope="baz") # The default saver should not work when the import scope is set. with self.assertRaises(errors.NotFoundError): @@ -149,7 +150,7 @@ class SavedModelLoaderTest(test.TestCase): def test_run_init_op(self): loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) graph = ops.Graph() - saver = loader.load_graph(graph, ["foo_graph"]) + saver, _ = loader.load_graph(graph, ["foo_graph"]) with self.test_session(graph=graph) as sess: loader.restore_variables(sess, saver) self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) @@ -203,7 +204,7 @@ class SavedModelLoaderTest(test.TestCase): loader = loader_impl.SavedModelLoader(path) with self.test_session(graph=ops.Graph()) as sess: - saver = loader.load_graph(sess.graph, ["foo_graph"]) + saver, _ = loader.load_graph(sess.graph, ["foo_graph"]) self.assertFalse(variables._all_saveable_objects()) self.assertIsNotNone(saver) @@ -212,6 +213,18 @@ class SavedModelLoaderTest(test.TestCase): self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) + def test_load_saved_model_graph_with_return_elements(self): + """Ensure that the correct elements are returned.""" + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + graph = ops.Graph() + _, ret = loader.load_graph(graph, ["foo_graph"], + return_elements=["y:0", "x:0"]) + + self.assertEqual(graph.get_tensor_by_name("y:0"), ret[0]) + self.assertEqual(graph.get_tensor_by_name("x:0"), ret[1]) + + with self.assertRaisesRegexp(ValueError, "not found in graph"): + loader.load_graph(graph, ["foo_graph"], return_elements=["z:0"]) if __name__ == "__main__": test.main() diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 11510d9928..3a06a52812 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -1928,6 +1928,14 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False, execution is enabled. @end_compatibility """ # pylint: disable=g-doc-exception + return _import_meta_graph_with_return_elements( + meta_graph_or_file, clear_devices, import_scope, **kwargs)[0] + + +def _import_meta_graph_with_return_elements( + meta_graph_or_file, clear_devices=False, import_scope=None, + return_elements=None, **kwargs): + """Import MetaGraph, and return both a saver and returned elements.""" if context.executing_eagerly(): raise RuntimeError("Exporting/importing meta graphs is not supported when " "eager execution is enabled. No graph exists when eager " @@ -1937,12 +1945,22 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False, else: meta_graph_def = meta_graph_or_file - imported_vars = meta_graph.import_scoped_meta_graph( - meta_graph_def, - clear_devices=clear_devices, - import_scope=import_scope, - **kwargs) + imported_vars, imported_return_elements = ( + meta_graph.import_scoped_meta_graph_with_return_elements( + meta_graph_def, + clear_devices=clear_devices, + import_scope=import_scope, + return_elements=return_elements, + **kwargs)) + + saver = _create_saver_from_imported_meta_graph( + meta_graph_def, import_scope, imported_vars) + return saver, imported_return_elements + +def _create_saver_from_imported_meta_graph( + meta_graph_def, import_scope, imported_vars): + """Return a saver for restoring variable values to an imported MetaGraph.""" if meta_graph_def.HasField("saver_def"): # Infer the scope that is prepended by `import_scoped_meta_graph`. scope = import_scope -- GitLab From c92f6011009392bfea14d5673b67e724018286b7 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Fri, 20 Jul 2018 14:03:16 -0700 Subject: [PATCH 210/519] Add lookup table size ops to checkpointing whitelist. Reading the size doesn't actually alter the state of the lookup table, so it's safe to checkpoint. PiperOrigin-RevId: 205449858 --- tensorflow/core/ops/lookup_ops.cc | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/core/ops/lookup_ops.cc b/tensorflow/core/ops/lookup_ops.cc index 444aa8b954..2059741da9 100644 --- a/tensorflow/core/ops/lookup_ops.cc +++ b/tensorflow/core/ops/lookup_ops.cc @@ -140,11 +140,13 @@ REGISTER_OP("LookupTableSize") .Input("table_handle: Ref(string)") .Output("size: int64") .SetShapeFn(TwoElementVectorInputsAndScalarOutputs); +WHITELIST_STATEFUL_OP_FOR_DATASET_FUNCTIONS("LookupTableSize"); REGISTER_OP("LookupTableSizeV2") .Input("table_handle: resource") .Output("size: int64") .SetShapeFn(ScalarAndTwoElementVectorInputsAndScalarOutputs); +WHITELIST_STATEFUL_OP_FOR_DATASET_FUNCTIONS("LookupTableSizeV2"); REGISTER_OP("LookupTableExport") .Input("table_handle: Ref(string)") -- GitLab From 2c1f2847c2afbc6f23ef7040d49c71ffaa8b669c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 14:14:34 -0700 Subject: [PATCH 211/519] Place infeed_enqueue and infeed_enqueue_tuple ops on the host device rather than the accelerator device in TPUEstimator. This removes the potential for extra accidental transfers of tensors to and from device. PiperOrigin-RevId: 205451735 --- .../contrib/tpu/python/tpu/keras_support.py | 32 +++++++------ .../contrib/tpu/python/tpu/tpu_context.py | 46 +++++++++++-------- .../contrib/tpu/python/tpu/tpu_estimator.py | 8 ++-- 3 files changed, 51 insertions(+), 35 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/keras_support.py b/tensorflow/contrib/tpu/python/tpu/keras_support.py index 8292c920fc..81798ee423 100644 --- a/tensorflow/contrib/tpu/python/tpu/keras_support.py +++ b/tensorflow/contrib/tpu/python/tpu/keras_support.py @@ -441,21 +441,23 @@ class TPUNumpyInfeedManager(TPUInfeedManager): shard_infeed_tensors = [] for shard_id in range(self._strategy.num_towers): - with ops.device('/device:TPU:%d' % shard_id): + with ops.device('/device:CPU:0'): infeed_tensors = [] - for spec in input_specs: - # Construct placeholders for each of the inputs. - infeed_tensors.append( - array_ops.placeholder( - dtype=spec.dtype, - shape=spec.shape, - name='infeed-enqueue-%s-%d' % (spec.name, shard_id))) + with ops.device('/device:TPU:%d' % shard_id): + for spec in input_specs: + # Construct placeholders for each of the inputs. + infeed_tensors.append( + array_ops.placeholder( + dtype=spec.dtype, + shape=spec.shape, + name='infeed-enqueue-%s-%d' % (spec.name, shard_id))) shard_infeed_tensors.append(infeed_tensors) infeed_op.append( tpu_ops.infeed_enqueue_tuple( infeed_tensors, [spec.shape for spec in input_specs], - name='infeed-enqueue-%s-%d' % (execution_mode, shard_id))) + name='infeed-enqueue-%s-%d' % (execution_mode, shard_id), + device_ordinal=shard_id)) return SizedInfeed(infeed_ops=infeed_op, sharded_infeed_tensors=shard_infeed_tensors) @@ -584,12 +586,13 @@ class TPUDatasetInfeedManager(TPUInfeedManager): assert len(shard_infeed_tensors) == self._strategy.num_towers infeed_ops = [] for shard_id in range(self._strategy.num_towers): - with ops.device('/device:TPU:%d' % shard_id): + with ops.device('/device:CPU:0'): infeed_ops.append( tpu_ops.infeed_enqueue_tuple( shard_infeed_tensors[shard_id], [spec.shape for spec in input_specs], - name='infeed-enqueue-%s-%d' % (execution_mode, shard_id))) + name='infeed-enqueue-%s-%d' % (execution_mode, shard_id), + device_ordinal=shard_id)) return SizedInfeed(infeed_ops=infeed_ops, sharded_infeed_tensors=shard_infeed_tensors) @@ -740,12 +743,13 @@ class TPUFunction(object): # Build output ops. outfeed_op = [] for shard_id in range(self._strategy.num_towers): - with ops.device('/device:TPU:%d' % shard_id): + with ops.device('/device:CPU:0'): outfeed_op.extend( tpu_ops.outfeed_dequeue_tuple( dtypes=[spec.dtype for spec in self._outfeed_spec], shapes=[spec.shape for spec in self._outfeed_spec], - name='outfeed-dequeue-%s-%d' % (self.execution_mode, shard_id))) + name='outfeed-dequeue-%s-%d' % (self.execution_mode, shard_id), + device_ordinal=shard_id)) return TPUModelOp( compile_op, @@ -1126,7 +1130,7 @@ Output shape: %(output_shape)s 'layer': layer, 'input_shape': layer.input_shape, 'output_shape': layer.output_shape - }) + }) @experimental diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_context.py b/tensorflow/contrib/tpu/python/tpu/tpu_context.py index 750e677263..2cb68f74a0 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_context.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_context.py @@ -146,24 +146,7 @@ class TPUContext(object): # Note that: For the non-model parallelism, the mapping could be # a random permutation. The order should not matter in most cases # as far as model is replicated to all cores in the system. - - # If the precise replica_id to device mapping is required, please - # set the num_cores_per_replica to 1 in TPUConfig to enable the - # model parallelism. - if self._internal_ctx.model_parallelism_enabled: - return RuntimeError( - 'device_for_replica is not yet implemented for model parallelism. ' - 'b/79689078.') - - master = self._internal_ctx.master_job - job_device = '' if master is None else ('/job:%s' % master) - - num_of_replicas_per_host = self._internal_ctx.num_of_replicas_per_host - host_id = replica_id / num_of_replicas_per_host - ordinal_id = replica_id % num_of_replicas_per_host - - host_device = '%s/task:%d/device:CPU:0' % (job_device, host_id) - return (host_device, ordinal_id) + return self._internal_ctx.device_for_replica(replica_id) class _InternalTPUContext(object): @@ -634,6 +617,33 @@ class _InternalTPUContext(object): # Record the state "validated" into lazy dictionary. self._lazy_validation_dict[mode] = True + def device_for_replica(self, replica_id): + """Returns the tuple of (CPU device and device ordinal) for replica. + + This should be used for full replicate for non-model-parallelism. + + Args: + replica_id: Int, the replica index. + + Returns: + A tuple of device spec for CPU device and int device ordinal. + """ + master = self.master_job + + if self.model_parallelism_enabled: + return (self.device_assignment.host_device( + replica=replica_id, job=master), + self.device_assignment.tpu_ordinal(replica=replica_id)) + + job_device = '' if master is None else ('/job:%s' % master) + + num_of_replicas_per_host = self.num_of_replicas_per_host + host_id = replica_id / num_of_replicas_per_host + ordinal_id = replica_id % num_of_replicas_per_host + + host_device = '%s/task:%d/device:CPU:0' % (job_device, host_id) + return (host_device, ordinal_id) + class _OneCoreTPUContext(_InternalTPUContext): """Special _InternalTPUContext for one core usage.""" diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 73dfefd19c..8ae0a31b6a 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -1660,11 +1660,13 @@ class _OutfeedHostCall(object): # Outfeed ops execute on each replica's first logical core. Note: we must # constraint it such that we have at most one outfeed dequeue and enqueue # per replica. - tpu_device_placement_fn = self._ctx.tpu_device_placement_function for i in xrange(self._ctx.num_replicas): - with ops.device(tpu_device_placement_fn(i)): + host_device, ordinal_id = self._ctx.device_for_replica(i) + with ops.device(host_device): outfeed_tensors = tpu_ops.outfeed_dequeue_tuple( - dtypes=tensor_dtypes, shapes=tensor_shapes) + dtypes=tensor_dtypes, + shapes=tensor_shapes, + device_ordinal=ordinal_id) for j, item in enumerate(outfeed_tensors): dequeue_ops[j].append(item) -- GitLab From 67869d1266f17b4502391a13eac9180bda5bce0b Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Fri, 20 Jul 2018 14:33:29 -0700 Subject: [PATCH 212/519] [XLA] s/ir_builder/b/ Brevity. PiperOrigin-RevId: 205454869 --- .../xla/service/cpu/dot_op_emitter.cc | 265 ++--- .../compiler/xla/service/cpu/dot_op_emitter.h | 7 +- .../xla/service/cpu/elemental_ir_emitter.cc | 15 +- .../xla/service/cpu/elemental_ir_emitter.h | 2 +- .../compiler/xla/service/cpu/ir_emitter.cc | 791 ++++++------- .../compiler/xla/service/cpu/ir_emitter.h | 17 +- .../compiler/xla/service/cpu/ir_function.cc | 71 +- .../compiler/xla/service/cpu/ir_function.h | 11 +- .../xla/service/cpu/llvm_ir_runtime.cc | 53 +- .../xla/service/cpu/parallel_loop_emitter.cc | 9 +- .../xla/service/cpu/parallel_loop_emitter.h | 2 +- .../xla/service/cpu/tests/cpu_noalias_test.cc | 8 +- .../xla/service/cpu/vector_support_library.cc | 163 ++- .../xla/service/cpu/vector_support_library.h | 36 +- .../xla/service/elemental_ir_emitter.cc | 1013 ++++++++--------- .../xla/service/elemental_ir_emitter.h | 14 +- .../xla/service/gpu/elemental_ir_emitter.cc | 90 +- .../xla/service/gpu/elemental_ir_emitter.h | 2 +- .../xla/service/gpu/hlo_to_ir_bindings.cc | 20 +- .../xla/service/gpu/hlo_to_ir_bindings.h | 9 +- .../compiler/xla/service/gpu/ir_emitter.cc | 240 ++-- .../compiler/xla/service/gpu/ir_emitter.h | 2 +- .../xla/service/gpu/ir_emitter_nested.cc | 16 +- .../xla/service/gpu/ir_emitter_unnested.cc | 560 +++++---- .../xla/service/gpu/parallel_loop_emitter.cc | 60 +- .../xla/service/gpu/parallel_loop_emitter.h | 6 +- .../llvm_ir/dynamic_update_slice_util.cc | 66 +- .../llvm_ir/dynamic_update_slice_util.h | 7 +- .../xla/service/llvm_ir/fused_ir_emitter.cc | 30 +- .../xla/service/llvm_ir/fused_ir_emitter.h | 4 +- .../compiler/xla/service/llvm_ir/ir_array.cc | 54 +- .../compiler/xla/service/llvm_ir/ir_array.h | 22 +- .../service/llvm_ir/kernel_support_library.cc | 45 +- .../service/llvm_ir/kernel_support_library.h | 56 +- .../xla/service/llvm_ir/kernel_tiling.cc | 18 +- .../xla/service/llvm_ir/kernel_tiling.h | 2 +- .../compiler/xla/service/llvm_ir/llvm_loop.cc | 71 +- .../compiler/xla/service/llvm_ir/llvm_loop.h | 21 +- .../compiler/xla/service/llvm_ir/llvm_util.cc | 150 ++- .../compiler/xla/service/llvm_ir/llvm_util.h | 28 +- .../xla/service/llvm_ir/loop_emitter.cc | 36 +- .../xla/service/llvm_ir/loop_emitter.h | 11 +- .../compiler/xla/service/llvm_ir/sort_util.cc | 82 +- .../compiler/xla/service/llvm_ir/sort_util.h | 3 +- .../compiler/xla/service/llvm_ir/tuple_ops.cc | 49 +- .../compiler/xla/service/llvm_ir/tuple_ops.h | 7 +- 46 files changed, 1974 insertions(+), 2270 deletions(-) diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc index 58228180ca..1fdeceb860 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc @@ -49,15 +49,15 @@ class MemoryTile { // `tile_size_along_major_dim` vectors from the matrix `matrix`, starting at // `major_dim_offset` in the major dimension. The tile size along the minor // dimension is the vector size, and that is implicitly determined by `vsl`. - MemoryTile(VectorSupportLibrary* vsl, llvm::IRBuilder<>* ir_builder, + MemoryTile(VectorSupportLibrary* vsl, llvm::IRBuilder<>* b, llvm::Value* matrix, int64 matrix_size_along_minor_dim, llvm::Value* major_dim_offset, int64 tile_size_along_major_dim) - : vsl_(vsl), ir_builder_(ir_builder) { + : vsl_(vsl), b_(b) { pointers_.reserve(tile_size_along_major_dim); for (int64 i = 0; i < tile_size_along_major_dim; i++) { - llvm::Value* total_offset = ir_builder->CreateMul( - ir_builder->getInt64(matrix_size_along_minor_dim), - ir_builder->CreateAdd(ir_builder->getInt64(i), major_dim_offset)); + llvm::Value* total_offset = + b->CreateMul(b->getInt64(matrix_size_along_minor_dim), + b->CreateAdd(b->getInt64(i), major_dim_offset)); pointers_.push_back(vsl_->ComputeOffsetPointer(matrix, total_offset)); } } @@ -101,8 +101,7 @@ class MemoryTile { for (int64 i = 0; i < pointers_.size(); i++) { for (int64 j = 0; j < tile_size_along_middle_dim; j++) { result[i].push_back(vsl_->LoadBroadcast( - pointers_[i], ir_builder_->CreateAdd(minor_dim_offset, - ir_builder_->getInt64(j)))); + pointers_[i], b_->CreateAdd(minor_dim_offset, b_->getInt64(j)))); } } return result; @@ -110,7 +109,7 @@ class MemoryTile { private: VectorSupportLibrary* vsl_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; std::vector pointers_; }; @@ -249,16 +248,15 @@ class ColumnMajorMatrixVectorProductEmitter ColumnMajorMatrixVectorProductEmitter(const Config& config, llvm::Value* lhs, llvm::Value* rhs, llvm::Value* addend, llvm::Value* result, - llvm::IRBuilder<>* ir_builder) + llvm::IRBuilder<>* b) : config_(config), lhs_(lhs), rhs_(rhs), addend_(addend), result_(result), - ir_builder_(ir_builder), - ksl_(ir_builder_), - vsl_(config.scalar_type(), /*vector_size=*/config.tile_rows(), - ir_builder_, "") { + b_(b), + ksl_(b_), + vsl_(config.scalar_type(), /*vector_size=*/config.tile_rows(), b_, "") { CHECK(tile_rows() > 0 && IsPowerOfTwo(static_cast(tile_rows()))); CHECK(!has_addend() || addend != nullptr); } @@ -272,7 +270,7 @@ class ColumnMajorMatrixVectorProductEmitter bool is_first_column); MemoryTile GetLhsMemoryTile(llvm::Value* column_start, int64 column_count) { - return MemoryTile(&vsl_, ir_builder_, /*matrix=*/lhs_, + return MemoryTile(&vsl_, b_, /*matrix=*/lhs_, /*matrix_size_along_minor_dim=*/m(), /*major_dim_offset=*/column_start, /*tile_size_along_major_dim=*/column_count); @@ -302,7 +300,7 @@ class ColumnMajorMatrixVectorProductEmitter llvm::Value* rhs_; llvm::Value* addend_; llvm::Value* result_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; KernelSupportLibrary ksl_; VectorSupportLibrary vsl_; }; @@ -331,7 +329,7 @@ void ColumnMajorMatrixVectorProductEmitter::Emit() { }); if (column_remainder != 0) { - EmitOuterLoopBody(ir_builder_->getInt64(column_limit), column_remainder, + EmitOuterLoopBody(b_->getInt64(column_limit), column_remainder, column_limit == 0); } } @@ -364,7 +362,7 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( return; } - llvm::Value* columns_llvm = ir_builder_->getInt64(columns); + llvm::Value* columns_llvm = b_->getInt64(columns); // for (col = current_tile_col; col < (columns + current_tile_col); col++) // for (row = row_start, row < m_; row++) { @@ -375,12 +373,11 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( ksl_.ForReturnVoid( "dot.inner.epilg.outer", /*start=*/current_tile_col, - /*end=*/ir_builder_->CreateAdd(columns_llvm, current_tile_col), + /*end=*/b_->CreateAdd(columns_llvm, current_tile_col), /*step=*/1, /*peel_first_iteration=*/false, [&](llvm::Value* col, llvm::Value* is_first_scalar_col) { llvm::Value* rhs_element = vsl_.LoadScalar(rhs_, col); - llvm::Value* total_offset = - ir_builder_->CreateMul(col, ir_builder_->getInt64(m())); + llvm::Value* total_offset = b_->CreateMul(col, b_->getInt64(m())); llvm::Value* lhs_base_pointer = vsl_.ComputeOffsetPointer(lhs_, total_offset); ksl_.ForReturnVoid( @@ -388,9 +385,8 @@ void ColumnMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( /*step=*/1, [&](llvm::Value* scalar_row) { llvm::Value* product = vsl_.Mul( vsl_.LoadScalar(lhs_base_pointer, scalar_row), rhs_element); - llvm::Value* setting_result_first_time = ir_builder_->CreateAnd( - is_first_scalar_col, - ir_builder_->getInt1(is_first_tiled_column)); + llvm::Value* setting_result_first_time = b_->CreateAnd( + is_first_scalar_col, b_->getInt1(is_first_tiled_column)); ksl_.IfReturnVoid( setting_result_first_time, /*true_block_generator=*/ @@ -478,16 +474,15 @@ class RowMajorMatrixVectorProductEmitter RowMajorMatrixVectorProductEmitter(const Config& config, llvm::Value* lhs, llvm::Value* rhs, llvm::Value* addend, - llvm::Value* result, - llvm::IRBuilder<>* ir_builder) + llvm::Value* result, llvm::IRBuilder<>* b) : config_(config), lhs_(lhs), rhs_(rhs), addend_(addend), result_(result), - ir_builder_(ir_builder), - ksl_(ir_builder_), - vsl_(scalar_type(), /*vector_size=*/tile_cols(), ir_builder_, "") { + b_(b), + ksl_(b_), + vsl_(scalar_type(), /*vector_size=*/tile_cols(), b_, "") { CHECK(tile_cols() > 0 && IsPowerOfTwo(static_cast(tile_cols()))); CHECK(!has_addend() || addend != nullptr); } @@ -498,7 +493,7 @@ class RowMajorMatrixVectorProductEmitter private: MemoryTile GetLhsMemoryTile(llvm::Value* row_start, int64 row_count) { - return MemoryTile(&vsl_, ir_builder_, /*matrix=*/lhs_, + return MemoryTile(&vsl_, b_, /*matrix=*/lhs_, /*matrix_size_along_minor_dim=*/k(), /*major_dim_offset=*/row_start, /*tile_size_along_major_dim=*/row_count); @@ -517,7 +512,7 @@ class RowMajorMatrixVectorProductEmitter llvm::Value* rhs_; llvm::Value* addend_; llvm::Value* result_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; KernelSupportLibrary ksl_; VectorSupportLibrary vsl_; }; @@ -559,7 +554,7 @@ void RowMajorMatrixVectorProductEmitter::EmitOuterLoopBody(llvm::Value* row, for (int i = 0; i < row_count; i++) { llvm::Value* result_value = vsl_.Add(horizontal_sums[i], scalar_accumulators[i].Get()); - llvm::Value* offset = ir_builder_->CreateAdd(ir_builder_->getInt64(i), row); + llvm::Value* offset = b_->CreateAdd(b_->getInt64(i), row); if (addend_ && row_count != vsl_.vector_size()) { result_value = vsl_.Add(vsl_.LoadScalar(addend_, offset), result_value); } @@ -578,7 +573,7 @@ void RowMajorMatrixVectorProductEmitter::Emit() { [&](llvm::Value* row) { EmitOuterLoopBody(row, tile_rows()); }); if (row_remainder != 0) { - EmitOuterLoopBody(ir_builder_->getInt64(row_limit), row_remainder); + EmitOuterLoopBody(b_->getInt64(row_limit), row_remainder); } } @@ -609,9 +604,8 @@ void RowMajorMatrixVectorProductEmitter::EmitInnerLoopEpilogue( } for (int r = 0; r < rows; r++) { - llvm::Value* total_offset = ir_builder_->CreateMul( - ir_builder_->CreateAdd(ir_builder_->getInt64(r), current_tile_row), - ir_builder_->getInt64(k())); + llvm::Value* total_offset = b_->CreateMul( + b_->CreateAdd(b_->getInt64(r), current_tile_row), b_->getInt64(k())); llvm::Value* lhs_base_pointer = vsl_.ComputeOffsetPointer(lhs_, total_offset); ksl_.ForReturnVoid( @@ -722,13 +716,13 @@ class MatrixMatrixBlockPanelEmitter { // `lhs` with `rhs` and stores the result in `result`. explicit MatrixMatrixBlockPanelEmitter(Config config, llvm::Value* lhs, llvm::Value* rhs, llvm::Value* result, - llvm::IRBuilder<>* ir_builder) + llvm::IRBuilder<>* b) : lhs_(lhs), rhs_(rhs), result_(result), config_(config), - ir_builder_(ir_builder), - ksl_(ir_builder_) { + b_(b), + ksl_(b_) { CHECK(max_vectorization_width() > 0 && IsPowerOfTwo(static_cast(max_vectorization_width()))); CHECK_GT(max_vector_count(), 0); @@ -761,7 +755,7 @@ class MatrixMatrixBlockPanelEmitter { int64 tile_size_m, llvm::Value* m_start, llvm::Value* m_end); - llvm::Value* GetInt64(int64 value) { return ir_builder_->getInt64(value); } + llvm::Value* GetInt64(int64 value) { return b_->getInt64(value); } Config config() const { return config_; } Dimensions dims() const { return config().dims(); } @@ -782,7 +776,7 @@ class MatrixMatrixBlockPanelEmitter { llvm::Value* result_; Config config_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; KernelSupportLibrary ksl_; }; @@ -804,8 +798,8 @@ void MatrixMatrixBlockPanelEmitter::HandleResiduesOnN() { current_vectorization_width >= min_vectorization_width()) { int64 n_end = dims().n() - (dims().n() % current_vectorization_width); if (n_start != n_end) { - VectorSupportLibrary vsl(scalar_type(), current_vectorization_width, - ir_builder_, "gebp"); + VectorSupportLibrary vsl(scalar_type(), current_vectorization_width, b_, + "gebp"); HandleResiduesOnK(&vsl, GetInt64(n_start), GetInt64(n_end)); n_start = n_end; } @@ -819,10 +813,9 @@ void MatrixMatrixBlockPanelEmitter::HandleResiduesOnN() { } if (n_start != dims().n()) { - VectorSupportLibrary vsl(scalar_type(), 1, ir_builder_, "gebp"); + VectorSupportLibrary vsl(scalar_type(), 1, b_, "gebp"); ksl_.ForReturnVoid("epi.n", n_start, dims().n(), 1, [&](llvm::Value* n_i) { - llvm::Value* n_i_next = - ir_builder_->CreateAdd(n_i, ir_builder_->getInt64(1)); + llvm::Value* n_i_next = b_->CreateAdd(n_i, b_->getInt64(1)); HandleResiduesOnK(&vsl, n_i, n_i_next); }); } @@ -935,11 +928,11 @@ void MatrixMatrixBlockPanelEmitter::EmitTiledGemm( ksl_.ForReturnVoid( "dot.m", m_start, m_end, tile_size_m, [&](llvm::Value* m_i) { MemoryTile result_memory_tile( - vsl, ir_builder_, /*matrix=*/result_, + vsl, b_, /*matrix=*/result_, /*matrix_size_along_minor_dim=*/dims().n(), /*major_dim_offset=*/m_i, /*tile_size_along_major_dim=*/tile_size_m); - MemoryTile lhs_memory_tile(vsl, ir_builder_, /*matrix=*/lhs_, + MemoryTile lhs_memory_tile(vsl, b_, /*matrix=*/lhs_, /*matrix_size_along_minor_dim=*/dims().k(), /*major_dim_offset=*/m_i, /*tile_size_along_major_dim=*/tile_size_m); @@ -949,8 +942,8 @@ void MatrixMatrixBlockPanelEmitter::EmitTiledGemm( result_memory_tile.LoadTile(n_i)); ksl_.ForReturnVoid( "dot.k", k_start, k_end, tile_size_k, [&](llvm::Value* k_i) { - MemoryTile rhs_memory_tile(vsl, ir_builder_, rhs_, - dims().n(), k_i, tile_size_k); + MemoryTile rhs_memory_tile(vsl, b_, rhs_, dims().n(), k_i, + tile_size_k); std::vector> lhs_tile = lhs_memory_tile.LoadBroadcastTile(k_i, tile_size_k); std::vector rhs_tile = @@ -980,7 +973,7 @@ DotOpEmitter::DotOpEmitter(const HloInstruction& dot, const llvm_ir::IrArray& rhs_array, const llvm_ir::IrArray* addend_array, llvm::Value* executable_run_options_value, - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, const HloModuleConfig& hlo_module_config, const TargetMachineFeatures& target_machine_features) : dot_(dot), @@ -989,7 +982,7 @@ DotOpEmitter::DotOpEmitter(const HloInstruction& dot, rhs_array_(rhs_array), addend_array_(addend_array), executable_run_options_value_(executable_run_options_value), - ir_builder_(ir_builder), + b_(b), hlo_module_config_(hlo_module_config), target_machine_features_(target_machine_features) {} @@ -997,15 +990,14 @@ DotOpEmitter::DotOpEmitter(const HloInstruction& dot, const HloInstruction& dot, const llvm_ir::IrArray& target_array, const llvm_ir::IrArray& lhs_array, const llvm_ir::IrArray& rhs_array, const llvm_ir::IrArray* addend_array, - llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder, + llvm::Value* executable_run_options_value, llvm::IRBuilder<>* b, const HloModuleConfig& hlo_module_config, const TargetMachineFeatures& target_machine_features) { PrimitiveType type = target_array.GetShape().element_type(); TF_RET_CHECK(F16 == type || F32 == type || F64 == type || C64 == type); DotOpEmitter dot_emitter(dot, target_array, lhs_array, rhs_array, - addend_array, executable_run_options_value, - ir_builder, hlo_module_config, - target_machine_features); + addend_array, executable_run_options_value, b, + hlo_module_config, target_machine_features); return dot_emitter.Emit(); } @@ -1050,13 +1042,13 @@ bool DotOpEmitter::EmitExperimentalGebpDotIfEnabled( } int64 size_bytes = m * n * ShapeUtil::ByteSizeOfPrimitiveType(primitive_type); - ir_builder_->CreateMemSet( - target, ir_builder_->getInt8(0), size_bytes, + b_->CreateMemSet( + target, b_->getInt8(0), size_bytes, target_machine_features_.minimum_alignment_for_allocation(size_bytes)); int64 max_target_vector_width = target_machine_features_.vector_register_num_elements( - *ir_builder_->GetInsertBlock()->getParent(), primitive_type); + *b_->GetInsertBlock()->getParent(), primitive_type); int64 tile_size_m, tile_size_k, tile_size_n_in_vector_width; std::tie(tile_size_m, tile_size_k, tile_size_n_in_vector_width) = @@ -1080,12 +1072,12 @@ bool DotOpEmitter::EmitExperimentalGebpDotIfEnabled( KernelSupportLibrary::EmitAndCallOutlinedKernel( /*enable_fast_math=*/enable_fast_math, - /*optimize_for_size=*/optimize_for_size, ir_builder_, - config.GetCacheKey(), lhs, rhs, target, + /*optimize_for_size=*/optimize_for_size, b_, config.GetCacheKey(), lhs, + rhs, target, [this, config](llvm::Value* lhs, llvm::Value* rhs, llvm::Value* target) { - MatrixMatrixBlockPanelEmitter gebp_emitter( - config, /*lhs=*/lhs, /*rhs=*/rhs, - /*result=*/target, ir_builder_); + MatrixMatrixBlockPanelEmitter gebp_emitter(config, /*lhs=*/lhs, + /*rhs=*/rhs, + /*result=*/target, b_); gebp_emitter.Emit(); }); @@ -1163,7 +1155,7 @@ bool DotOpEmitter::EmitLlvmIrDotIfProfitable() { const int target_vector_register_element_size = target_machine_features_.vector_register_num_elements( - *ir_builder_->GetInsertBlock()->getParent(), primitive_type); + *b_->GetInsertBlock()->getParent(), primitive_type); // We may not always know the vector register size for the target we're // compiling against, in which case target_vector_register_element_size is 0. @@ -1184,13 +1176,13 @@ bool DotOpEmitter::EmitLlvmIrDotIfProfitable() { KernelSupportLibrary::EmitAndCallOutlinedKernel( /*enable_fast_math=*/enable_fast_math, - /*optimize_for_size=*/optimize_for_size, ir_builder_, - config.GetCacheKey(), lhs_op, rhs_op, + /*optimize_for_size=*/optimize_for_size, b_, config.GetCacheKey(), + lhs_op, rhs_op, addend_array_ ? addend_array_->GetBasePointer() : nullptr, result_op, [this, config](llvm::Value* lhs_op, llvm::Value* rhs_op, llvm::Value* addend_op, llvm::Value* result_op) { ColumnMajorMatrixVectorProductEmitter emitter( - config, lhs_op, rhs_op, addend_op, result_op, ir_builder_); + config, lhs_op, rhs_op, addend_op, result_op, b_); emitter.Emit(); }); } else { @@ -1203,13 +1195,13 @@ bool DotOpEmitter::EmitLlvmIrDotIfProfitable() { KernelSupportLibrary::EmitAndCallOutlinedKernel( /*enable_fast_math=*/enable_fast_math, - /*optimize_for_size=*/optimize_for_size, ir_builder_, - config.GetCacheKey(), lhs_op, rhs_op, + /*optimize_for_size=*/optimize_for_size, b_, config.GetCacheKey(), + lhs_op, rhs_op, addend_array_ ? addend_array_->GetBasePointer() : nullptr, result_op, [this, config](llvm::Value* lhs_op, llvm::Value* rhs_op, llvm::Value* addend_op, llvm::Value* result_op) { - RowMajorMatrixVectorProductEmitter emitter( - config, lhs_op, rhs_op, addend_op, result_op, ir_builder_); + RowMajorMatrixVectorProductEmitter emitter(config, lhs_op, rhs_op, + addend_op, result_op, b_); emitter.Emit(); }); } @@ -1285,7 +1277,7 @@ Status DotOpEmitter::Emit() { // Create loop nests which loop through the LHS operand dimensions and the RHS // operand dimensions. The reduction dimension of the LHS and RHS are handled // in a separate innermost loop which performs the sum of products. - llvm_ir::ForLoopNest loop_nest(llvm_ir::IrName(&dot_), ir_builder_); + llvm_ir::ForLoopNest loop_nest(llvm_ir::IrName(&dot_), b_); llvm_ir::IrArray::Index lhs_index = EmitOperandArrayLoopNest( &loop_nest, lhs_array_, lhs_reduction_dimension, "lhs"); llvm_ir::IrArray::Index rhs_index = EmitOperandArrayLoopNest( @@ -1319,62 +1311,55 @@ Status DotOpEmitter::Emit() { // Function entry basic block. // - Emit alloca for accumulator llvm::Function* func = reduction_loop->GetPreheaderBasicBlock()->getParent(); - SetToFirstInsertPoint(&func->getEntryBlock(), ir_builder_); + SetToFirstInsertPoint(&func->getEntryBlock(), b_); llvm::Type* accum_type = target_array_.GetElementLlvmType(); - llvm::Value* accum_address = ir_builder_->CreateAlloca( - accum_type, /*ArraySize=*/nullptr, "accum_address"); + llvm::Value* accum_address = + b_->CreateAlloca(accum_type, /*ArraySize=*/nullptr, "accum_address"); // Preheader basic block of reduction loop: // - Initialize accumulator to zero. llvm::BasicBlock* preheader_bb = reduction_loop->GetPreheaderBasicBlock(); - ir_builder_->SetInsertPoint(preheader_bb->getTerminator()); + b_->SetInsertPoint(preheader_bb->getTerminator()); - ir_builder_->CreateStore(llvm::Constant::getNullValue(accum_type), - accum_address); + b_->CreateStore(llvm::Constant::getNullValue(accum_type), accum_address); // Body basic block of reduction loop: // - Load elements from lhs and rhs array. // - Multiply lhs-element and rhs-element. // - Load accumulator and add to product. // - Store sum back into accumulator. - SetToFirstInsertPoint(reduction_loop->GetBodyBasicBlock(), ir_builder_); + SetToFirstInsertPoint(reduction_loop->GetBodyBasicBlock(), b_); - llvm::Value* lhs_element = - lhs_array_.EmitReadArrayElement(lhs_index, ir_builder_); - llvm::Value* rhs_element = - rhs_array_.EmitReadArrayElement(rhs_index, ir_builder_); + llvm::Value* lhs_element = lhs_array_.EmitReadArrayElement(lhs_index, b_); + llvm::Value* rhs_element = rhs_array_.EmitReadArrayElement(rhs_index, b_); - llvm::Value* accum = ir_builder_->CreateLoad(accum_address); + llvm::Value* accum = b_->CreateLoad(accum_address); llvm::Value* updated_accum; 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* 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}); + auto real = [&](llvm::Value* x) { return b_->CreateExtractValue(x, {0}); }; + auto imag = [&](llvm::Value* x) { return b_->CreateExtractValue(x, {1}); }; + llvm::Value* product_real = + b_->CreateFSub(b_->CreateFMul(real(lhs_element), real(rhs_element)), + b_->CreateFMul(imag(lhs_element), imag(rhs_element))); + llvm::Value* product_imag = + b_->CreateFAdd(b_->CreateFMul(real(lhs_element), imag(rhs_element)), + b_->CreateFMul(imag(lhs_element), real(rhs_element))); + updated_accum = b_->CreateInsertValue( + accum, b_->CreateFAdd(real(accum), product_real), {0}); + updated_accum = b_->CreateInsertValue( + updated_accum, b_->CreateFAdd(imag(accum), product_imag), {1}); } else { - llvm::Value* product = ir_builder_->CreateFMul(lhs_element, rhs_element); - updated_accum = ir_builder_->CreateFAdd(accum, product); + llvm::Value* product = b_->CreateFMul(lhs_element, rhs_element); + updated_accum = b_->CreateFAdd(accum, product); } - ir_builder_->CreateStore(updated_accum, accum_address); + b_->CreateStore(updated_accum, accum_address); // Exit basic block of reduction loop. // - Load accumulator value (the result). // - Store into output array. - SetToFirstInsertPoint(reduction_loop->GetExitBasicBlock(), ir_builder_); + SetToFirstInsertPoint(reduction_loop->GetExitBasicBlock(), b_); - llvm::Value* result = ir_builder_->CreateLoad(accum_address); + llvm::Value* result = b_->CreateLoad(accum_address); // Create index into target address. The target index is the concatenation of // the rhs and lhs indexes with the reduction dimensions removed. The terms @@ -1392,11 +1377,11 @@ Status DotOpEmitter::Emit() { } } - target_array_.EmitWriteArrayElement(target_index, result, ir_builder_); + target_array_.EmitWriteArrayElement(target_index, result, b_); // Set the IR builder insert point to the exit basic block of the outer most // loop. - ir_builder_->SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); + b_->SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); return Status::OK(); } @@ -1405,31 +1390,30 @@ Status DotOpEmitter::EmitScalarDot() { // A scalar dot is just a scalar multiply. llvm::Value* result; // Use the same index_type for all tensor accesses in the same kernel. - llvm::Type* index_type = ir_builder_->getInt64Ty(); + llvm::Type* index_type = b_->getInt64Ty(); llvm_ir::IrArray::Index element_index(index_type); llvm::Value* lhs_value = - lhs_array_.EmitReadArrayElement(/*index=*/element_index, ir_builder_); + lhs_array_.EmitReadArrayElement(/*index=*/element_index, b_); llvm::Value* rhs_value = - rhs_array_.EmitReadArrayElement(/*index=*/element_index, ir_builder_); + rhs_array_.EmitReadArrayElement(/*index=*/element_index, b_); if (ShapeUtil::ElementIsComplex(lhs_array_.GetShape())) { -#define REAL(x) ir_builder_->CreateExtractValue(x, {0}) -#define IMAG(x) ir_builder_->CreateExtractValue(x, {1}) - llvm::Value* real = ir_builder_->CreateFSub( - ir_builder_->CreateFMul(REAL(lhs_value), REAL(rhs_value)), - ir_builder_->CreateFMul(IMAG(lhs_value), IMAG(rhs_value))); - llvm::Value* imag = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(REAL(lhs_value), IMAG(rhs_value)), - ir_builder_->CreateFMul(IMAG(lhs_value), REAL(rhs_value))); +#define REAL(x) b_->CreateExtractValue(x, {0}) +#define IMAG(x) b_->CreateExtractValue(x, {1}) + llvm::Value* real = + b_->CreateFSub(b_->CreateFMul(REAL(lhs_value), REAL(rhs_value)), + b_->CreateFMul(IMAG(lhs_value), IMAG(rhs_value))); + llvm::Value* imag = + b_->CreateFAdd(b_->CreateFMul(REAL(lhs_value), IMAG(rhs_value)), + b_->CreateFMul(IMAG(lhs_value), REAL(rhs_value))); #undef IMAG #undef REAL result = llvm::ConstantAggregateZero::get(lhs_array_.GetElementLlvmType()); - result = ir_builder_->CreateInsertValue(result, real, {0}); - result = ir_builder_->CreateInsertValue(result, imag, {1}); + result = b_->CreateInsertValue(result, real, {0}); + result = b_->CreateInsertValue(result, imag, {1}); } else { - result = ir_builder_->CreateFMul(lhs_value, rhs_value); + result = b_->CreateFMul(lhs_value, rhs_value); } - target_array_.EmitWriteArrayElement(/*index=*/element_index, result, - ir_builder_); + target_array_.EmitWriteArrayElement(/*index=*/element_index, result, b_); return Status::OK(); } @@ -1452,7 +1436,7 @@ Status DotOpEmitter::EmitCallToRuntime() { fn_name = multi_threaded ? runtime::kEigenMatMulF16SymbolName : runtime::kEigenSingleThreadedMatMulF16SymbolName; - float_type = ir_builder_->getHalfTy(); + float_type = b_->getHalfTy(); break; case F32: fn_name = multi_threaded @@ -1461,7 +1445,7 @@ Status DotOpEmitter::EmitCallToRuntime() { : (use_mkl_dnn ? runtime::kMKLSingleThreadedMatMulF32SymbolName : runtime::kEigenSingleThreadedMatMulF32SymbolName); - float_type = ir_builder_->getFloatTy(); + float_type = b_->getFloatTy(); break; case F64: fn_name = multi_threaded @@ -1470,7 +1454,7 @@ Status DotOpEmitter::EmitCallToRuntime() { : (use_mkl_dnn ? runtime::kMKLSingleThreadedMatMulF64SymbolName : runtime::kEigenSingleThreadedMatMulF64SymbolName); - float_type = ir_builder_->getDoubleTy(); + float_type = b_->getDoubleTy(); break; default: return Unimplemented("Invalid type %s for dot operation", @@ -1478,16 +1462,16 @@ Status DotOpEmitter::EmitCallToRuntime() { } llvm::Type* float_ptr_type = float_type->getPointerTo(); - llvm::Type* int64_type = ir_builder_->getInt64Ty(); - llvm::Type* int32_type = ir_builder_->getInt32Ty(); - llvm::Type* int8_ptr_type = ir_builder_->getInt8Ty()->getPointerTo(); + llvm::Type* int64_type = b_->getInt64Ty(); + llvm::Type* int32_type = b_->getInt32Ty(); + llvm::Type* int8_ptr_type = b_->getInt8Ty()->getPointerTo(); llvm::FunctionType* matmul_type = llvm::FunctionType::get( - ir_builder_->getVoidTy(), + b_->getVoidTy(), {int8_ptr_type, float_ptr_type, float_ptr_type, float_ptr_type, int64_type, int64_type, int64_type, int32_type, int32_type}, /*isVarArg=*/false); - llvm::Function* function = ir_builder_->GetInsertBlock()->getParent(); + llvm::Function* function = b_->GetInsertBlock()->getParent(); llvm::Module* module = function->getParent(); llvm::Function* matmul_func = llvm::cast( @@ -1522,18 +1506,15 @@ Status DotOpEmitter::EmitCallToRuntime() { std::swap(transpose_lhs, transpose_rhs); } - ir_builder_->CreateCall( + b_->CreateCall( matmul_func, - {ir_builder_->CreateBitCast(executable_run_options_value_, int8_ptr_type), - ir_builder_->CreateBitCast(target_array_.GetBasePointer(), - float_ptr_type), - ir_builder_->CreateBitCast(lhs->GetBasePointer(), float_ptr_type), - ir_builder_->CreateBitCast(rhs->GetBasePointer(), float_ptr_type), - ir_builder_->getInt64(mat_mult_dims.m), - ir_builder_->getInt64(mat_mult_dims.n), - ir_builder_->getInt64(mat_mult_dims.k), - ir_builder_->getInt32(transpose_lhs), - ir_builder_->getInt32(transpose_rhs)}); + {b_->CreateBitCast(executable_run_options_value_, int8_ptr_type), + b_->CreateBitCast(target_array_.GetBasePointer(), float_ptr_type), + b_->CreateBitCast(lhs->GetBasePointer(), float_ptr_type), + b_->CreateBitCast(rhs->GetBasePointer(), float_ptr_type), + b_->getInt64(mat_mult_dims.m), b_->getInt64(mat_mult_dims.n), + b_->getInt64(mat_mult_dims.k), b_->getInt32(transpose_lhs), + b_->getInt32(transpose_rhs)}); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h index ed2a18976a..c2eeb0a1f9 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h @@ -61,7 +61,7 @@ class DotOpEmitter { const HloInstruction& dot, const llvm_ir::IrArray& target_array, const llvm_ir::IrArray& lhs_array, const llvm_ir::IrArray& rhs_array, const llvm_ir::IrArray* addend_array, - llvm::Value* executable_run_options_value, llvm::IRBuilder<>* ir_builder, + llvm::Value* executable_run_options_value, llvm::IRBuilder<>* b, const HloModuleConfig& hlo_module_config, const TargetMachineFeatures& target_machine_features); @@ -70,8 +70,7 @@ class DotOpEmitter { const llvm_ir::IrArray& lhs_array, const llvm_ir::IrArray& rhs_array, const llvm_ir::IrArray* addend_array, - llvm::Value* executable_run_options_value, - llvm::IRBuilder<>* ir_builder, + llvm::Value* executable_run_options_value, llvm::IRBuilder<>* b, const HloModuleConfig& hlo_module_config, const TargetMachineFeatures& target_machine_features); @@ -171,7 +170,7 @@ class DotOpEmitter { const llvm_ir::IrArray& rhs_array_; const llvm_ir::IrArray* addend_array_; llvm::Value* executable_run_options_value_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; const HloModuleConfig& hlo_module_config_; const TargetMachineFeatures& target_machine_features_; }; diff --git a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc index e97113dfa0..cf955a8add 100644 --- a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.cc @@ -38,8 +38,7 @@ StatusOr CpuElementalIrEmitter::EmitFloatUnaryOp( switch (element_type) { case F16: cast_result_to_fp16 = true; - operand_value = ir_builder_->CreateFPCast(operand_value, - ir_builder_->getFloatTy()); + operand_value = b_->CreateFPCast(operand_value, b_->getFloatTy()); TF_FALLTHROUGH_INTENDED; case F32: function_name = "tanhf"; @@ -59,9 +58,9 @@ StatusOr CpuElementalIrEmitter::EmitFloatUnaryOp( function->setDoesNotThrow(); function->setDoesNotAccessMemory(); // Create an instruction to call the function. - llvm::Value* result = ir_builder_->CreateCall(function, operand_value); + llvm::Value* result = b_->CreateCall(function, operand_value); if (cast_result_to_fp16) { - result = ir_builder_->CreateFPCast(result, ir_builder_->getHalfTy()); + result = b_->CreateFPCast(result, b_->getHalfTy()); } return result; } @@ -77,8 +76,8 @@ StatusOr CpuElementalIrEmitter::EmitAtan2( switch (prim_type) { case F16: cast_result_to_fp16 = true; - lhs = ir_builder_->CreateFPCast(lhs, ir_builder_->getFloatTy()); - rhs = ir_builder_->CreateFPCast(rhs, ir_builder_->getFloatTy()); + lhs = b_->CreateFPCast(lhs, b_->getFloatTy()); + rhs = b_->CreateFPCast(rhs, b_->getFloatTy()); TF_FALLTHROUGH_INTENDED; case F32: function_name = "atan2f"; @@ -98,9 +97,9 @@ StatusOr CpuElementalIrEmitter::EmitAtan2( function->setDoesNotThrow(); function->setDoesNotAccessMemory(); // Create an instruction to call the function. - llvm::Value* result = ir_builder_->CreateCall(function, {lhs, rhs}); + llvm::Value* result = b_->CreateCall(function, {lhs, rhs}); if (cast_result_to_fp16) { - result = ir_builder_->CreateFPCast(result, ir_builder_->getHalfTy()); + result = b_->CreateFPCast(result, b_->getHalfTy()); } return result; } diff --git a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h index 4446dfd282..9598a886ab 100644 --- a/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/elemental_ir_emitter.h @@ -31,7 +31,7 @@ class CpuElementalIrEmitter : public ElementalIrEmitter { public: CpuElementalIrEmitter(const HloModuleConfig& module_config, IrEmitter* ir_emitter, llvm::Module* module) - : ElementalIrEmitter(module_config, module, ir_emitter->ir_builder()), + : ElementalIrEmitter(module_config, module, ir_emitter->b()), ir_emitter_(ir_emitter) {} llvm_ir::ElementGenerator MakeElementGenerator( diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index aeab5d8957..d4ac35a604 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -89,14 +89,14 @@ IrEmitter::IrEmitter( : assignment_(assignment), module_(llvm_module), arch_type_(llvm::Triple(llvm_module->getTargetTriple()).getArch()), - ir_builder_(llvm_module->getContext()), + b_(llvm_module->getContext()), instruction_to_profile_idx_(std::move(instruction_to_profile_idx)), computation_to_profile_idx_(std::move(computation_to_profile_idx)), alias_analysis_(hlo_module, assignment, &llvm_module->getContext()), hlo_module_config_(hlo_module.config()), is_top_level_computation_(false), target_machine_features_(*target_machine_features) { - ir_builder_.setFastMathFlags(llvm_ir::GetFastMathFlags( + b_.setFastMathFlags(llvm_ir::GetFastMathFlags( /*fast_math_enabled=*/hlo_module_config_.debug_options() .xla_enable_fast_math())); } @@ -146,7 +146,7 @@ void IrEmitter::InitializeIrFunction(const string& function_name) { new IrFunction(function_name, linkage, options::OptimizeForSizeRequested(hlo_module_config_), hlo_module_config_.debug_options().xla_enable_fast_math(), - module_, &ir_builder_, num_dynamic_loop_bounds_)); + module_, &b_, num_dynamic_loop_bounds_)); } IrEmitter::~IrEmitter() {} @@ -154,9 +154,9 @@ IrEmitter::~IrEmitter() {} Status IrEmitter::HandleBitcast(HloInstruction* bitcast) { VLOG(2) << "HandleBitcast: " << bitcast->ToString(); emitted_value_[bitcast] = - ir_builder_.CreateBitCast(GetEmittedValueFor(bitcast->operand(0)), - IrShapeType(bitcast->shape())->getPointerTo(), - AsStringRef(IrName(bitcast))); + b_.CreateBitCast(GetEmittedValueFor(bitcast->operand(0)), + IrShapeType(bitcast->shape())->getPointerTo(), + AsStringRef(IrName(bitcast))); return Status::OK(); } @@ -273,7 +273,7 @@ Status IrEmitter::HandleGetTupleElement(HloInstruction* get_tuple_element) { const Shape& shape = get_tuple_element->shape(); emitted_value_[get_tuple_element] = llvm_ir::EmitGetTupleElement( shape, get_tuple_element->tuple_index(), MinimumAlignmentForShape(shape), - GetEmittedValueFor(operand), &ir_builder_, module_); + GetEmittedValueFor(operand), &b_, module_); return Status::OK(); } @@ -293,7 +293,7 @@ Status IrEmitter::HandleTupleSelect(HloInstruction* tuple_select) { TF_RETURN_IF_ERROR(EmitTargetAddressForOp(tuple_select)); llvm_ir::EmitTupleSelect(GetIrArrayFor(tuple_select), GetIrArrayFor(pred), GetEmittedValueFor(on_true), - GetEmittedValueFor(on_false), &ir_builder_, module_); + GetEmittedValueFor(on_false), &b_, module_); return Status::OK(); } @@ -316,8 +316,8 @@ Status IrEmitter::HandleInfeed(HloInstruction* instruction) { assignment_.GetUniqueSlice(infeed, {1})); llvm::Value* token_address = EmitTempBufferPointer( token_slice, ShapeUtil::GetTupleElementShape(infeed->shape(), 1)); - llvm_ir::EmitTuple(GetIrArrayFor(infeed), {data_address, token_address}, - &ir_builder_, module_); + llvm_ir::EmitTuple(GetIrArrayFor(infeed), {data_address, token_address}, &b_, + module_); if (ShapeUtil::IsTuple(data_shape)) { TF_RET_CHECK(!ShapeUtil::IsNestedTuple(data_shape)); @@ -348,7 +348,7 @@ Status IrEmitter::HandleInfeed(HloInstruction* instruction) { } llvm_ir::EmitTuple(llvm_ir::IrArray(data_address, data_shape), - tuple_element_addresses, &ir_builder_, module_); + tuple_element_addresses, &b_, module_); } else { TF_RETURN_IF_ERROR( EmitXfeedTransfer(XfeedKind::kInfeed, data_shape, data_address)); @@ -369,14 +369,14 @@ Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, const Shape& shape, int32 length_32 = static_cast(length); int32 shape_length; - TF_ASSIGN_OR_RETURN(llvm::Value * shape_ptr, - llvm_ir::EncodeSelfDescribingShapeConstant( - shape, &shape_length, &ir_builder_)); + TF_ASSIGN_OR_RETURN( + llvm::Value * shape_ptr, + llvm_ir::EncodeSelfDescribingShapeConstant(shape, &shape_length, &b_)); // The signature of the acquire infeed buffer function is: // // (void*)(int32 length); - llvm::Type* int32_type = ir_builder_.getInt32Ty(); + llvm::Type* int32_type = b_.getInt32Ty(); llvm::Type* i8_ptr_type = llvm::Type::getInt8PtrTy(module_->getContext()); llvm::FunctionType* acquire_type = llvm::FunctionType::get( i8_ptr_type, {int32_type, i8_ptr_type, int32_type}, @@ -396,8 +396,7 @@ Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, const Shape& shape, // // (void)(int32 length, void* buffer); llvm::FunctionType* release_type = llvm::FunctionType::get( - ir_builder_.getVoidTy(), - {int32_type, i8_ptr_type, i8_ptr_type, int32_type}, + b_.getVoidTy(), {int32_type, i8_ptr_type, i8_ptr_type, int32_type}, /*isVarArg=*/false); llvm::Function* release_func; @@ -414,25 +413,22 @@ Status IrEmitter::EmitXfeedTransfer(XfeedKind kind, const Shape& shape, // of size exactly 'length_32', and the runtime is responsible for // check-failing the process if there is a mismatch, versus passing us back a // buffer that we might overrun. - llvm::Value* acquired_pointer = ir_builder_.CreateCall( - acquire_func, {ir_builder_.getInt32(length_32), shape_ptr, - ir_builder_.getInt32(shape_length)}); + llvm::Value* acquired_pointer = b_.CreateCall( + acquire_func, + {b_.getInt32(length_32), shape_ptr, b_.getInt32(shape_length)}); if (kind == XfeedKind::kInfeed) { // Copy to the program buffer address from the acquired buffer. - ir_builder_.CreateMemCpy(program_buffer_address, /*DstAlign=*/1, - acquired_pointer, - /*SrcAlign=*/1, length_32); + b_.CreateMemCpy(program_buffer_address, /*DstAlign=*/1, acquired_pointer, + /*SrcAlign=*/1, length_32); } else { // Outfeed -- copy from the in-program address to the acquired buffer. - ir_builder_.CreateMemCpy(acquired_pointer, /*DstAlign=*/1, - program_buffer_address, - /*SrcAlign=*/1, length_32); + b_.CreateMemCpy(acquired_pointer, /*DstAlign=*/1, program_buffer_address, + /*SrcAlign=*/1, length_32); } - ir_builder_.CreateCall(release_func, - {ir_builder_.getInt32(length_32), acquired_pointer, - shape_ptr, ir_builder_.getInt32(shape_length)}); + b_.CreateCall(release_func, {b_.getInt32(length_32), acquired_pointer, + shape_ptr, b_.getInt32(shape_length)}); return Status::OK(); } @@ -453,7 +449,7 @@ Status IrEmitter::HandleOutfeed(HloInstruction* outfeed) { ShapeUtil::GetTupleElementShape(operand_shape, i); llvm::Value* tuple_element = llvm_ir::EmitGetTupleElement( tuple_element_shape, i, MinimumAlignmentForShape(tuple_element_shape), - value, &ir_builder_, module_); + value, &b_, module_); TF_RETURN_IF_ERROR(EmitXfeedTransfer(XfeedKind::kOutfeed, tuple_element_shape, tuple_element)); } @@ -472,7 +468,7 @@ Status IrEmitter::HandleTuple(HloInstruction* tuple) { for (auto operand : tuple->operands()) { base_ptrs.push_back(GetEmittedValueFor(operand)); } - llvm_ir::EmitTuple(GetIrArrayFor(tuple), base_ptrs, &ir_builder_, module_); + llvm_ir::EmitTuple(GetIrArrayFor(tuple), base_ptrs, &b_, module_); return Status::OK(); } @@ -483,8 +479,7 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForMap( std::vector parameter_addresses; for (const HloInstruction* operand : map->operands()) { const llvm_ir::IrArray& array = GetIrArrayFor(operand); - parameter_addresses.push_back( - array.EmitArrayElementAddress(index, &ir_builder_)); + parameter_addresses.push_back(array.EmitArrayElementAddress(index, &b_)); } return EmitElementFunctionCall(mapped_ir_function, map->shape(), parameter_addresses, "map_function"); @@ -510,13 +505,12 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForReduceWindow( PrimitiveType operand_element_type = operand->shape().element_type(); llvm::Value* accumulator_address = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(operand_element_type, module_), - "reduce_window_accumulator_address", &ir_builder_, + "reduce_window_accumulator_address", &b_, MinimumAlignmentForPrimitiveType(operand_element_type)); - ir_builder_.CreateStore( - ir_builder_.CreateLoad(GetEmittedValueFor(reduce_window->operand(1))), - accumulator_address); + b_.CreateStore(b_.CreateLoad(GetEmittedValueFor(reduce_window->operand(1))), + accumulator_address); - llvm_ir::ForLoopNest loops(IrName(reduce_window, "inner"), &ir_builder_); + llvm_ir::ForLoopNest loops(IrName(reduce_window, "inner"), &b_); std::vector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); @@ -525,48 +519,47 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForReduceWindow( ShapeUtil::MakeShape(operand_element_type, window_size), "window"); CHECK_EQ(window_index.size(), index.size()); - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); - llvm_ir::IrArray::Index input_index(ir_builder_.getInt64Ty(), index.size()); + llvm_ir::IrArray::Index input_index(b_.getInt64Ty(), index.size()); llvm::Value* in_bounds_condition = nullptr; for (size_t i = 0; i < index.size(); ++i) { - llvm::Value* strided_index = ir_builder_.CreateNSWMul( - index[i], ir_builder_.getInt64(window.dimensions(i).stride())); - input_index[i] = ir_builder_.CreateNSWSub( - ir_builder_.CreateNSWAdd(strided_index, window_index[i]), - ir_builder_.getInt64(window.dimensions(i).padding_low())); + llvm::Value* strided_index = + b_.CreateNSWMul(index[i], b_.getInt64(window.dimensions(i).stride())); + input_index[i] = + b_.CreateNSWSub(b_.CreateNSWAdd(strided_index, window_index[i]), + b_.getInt64(window.dimensions(i).padding_low())); // We need to check if 0 <= input_index[i] < bound, as otherwise we are in // the padding so that we can skip the computation. That is equivalent to // input_index[i] < bound as an *unsigned* comparison, since a negative // value will wrap to a large positive value. - llvm::Value* index_condition = ir_builder_.CreateICmpULT( + llvm::Value* index_condition = b_.CreateICmpULT( input_index[i], - ir_builder_.getInt64(ShapeUtil::GetDimension(operand->shape(), i))); + b_.getInt64(ShapeUtil::GetDimension(operand->shape(), i))); if (in_bounds_condition == nullptr) { in_bounds_condition = index_condition; } else { - in_bounds_condition = - ir_builder_.CreateAnd(in_bounds_condition, index_condition); + in_bounds_condition = b_.CreateAnd(in_bounds_condition, index_condition); } } CHECK(in_bounds_condition != nullptr); llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &ir_builder_); - SetToFirstInsertPoint(if_data.true_block, &ir_builder_); + llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &b_); + SetToFirstInsertPoint(if_data.true_block, &b_); // We are not in the padding, so carry out the computation. llvm_ir::IrArray input_array(GetIrArrayFor(operand)); llvm::Value* input_value_address = - input_array.EmitArrayElementAddress(input_index, &ir_builder_); + input_array.EmitArrayElementAddress(input_index, &b_); llvm::Value* result = EmitElementFunctionCall( reducer_function, reduce_window->shape(), {accumulator_address, input_value_address}, "reducer_function"); - ir_builder_.CreateStore(result, accumulator_address); + b_.CreateStore(result, accumulator_address); - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - return ir_builder_.CreateLoad(accumulator_address); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); + return b_.CreateLoad(accumulator_address); } Status IrEmitter::HandleReduceWindow(HloInstruction* reduce_window) { @@ -649,141 +642,127 @@ Status IrEmitter::HandleSelectAndScatter(HloInstruction* select_and_scatter) { select_and_scatter, /*desc=*/IrName(select_and_scatter, "init"), [this, init_value](const llvm_ir::IrArray::Index& target_index) { llvm::Value* init_value_addr = GetEmittedValueFor(init_value); - return ir_builder_.CreateLoad(init_value_addr); + return b_.CreateLoad(init_value_addr); })); // Create a loop to iterate over the source array to scatter to the output. - llvm_ir::ForLoopNest source_loops(IrName(select_and_scatter), &ir_builder_); + llvm_ir::ForLoopNest source_loops(IrName(select_and_scatter), &b_); const llvm_ir::IrArray::Index source_index = source_loops.AddLoopsForShape(source->shape(), "source"); - SetToFirstInsertPoint(source_loops.GetInnerLoopBodyBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(source_loops.GetInnerLoopBodyBasicBlock(), &b_); // Allocate space to keep the currently selected value, its index, and // the boolean initialized_flag, which is initially set to false. llvm::Value* selected_value_address = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(operand_element_type, module_), - "selected_value_address", &ir_builder_, + "selected_value_address", &b_, MinimumAlignmentForPrimitiveType(operand_element_type)); llvm::Value* selected_index_address = llvm_ir::EmitAllocaAtFunctionEntryWithCount( - ir_builder_.getInt64Ty(), ir_builder_.getInt32(rank), - "selected_index_address", &ir_builder_); + b_.getInt64Ty(), b_.getInt32(rank), "selected_index_address", &b_); llvm::Value* initialized_flag_address = llvm_ir::EmitAllocaAtFunctionEntry( - ir_builder_.getInt1Ty(), "initialized_flag_address", &ir_builder_); - ir_builder_.CreateStore(ir_builder_.getInt1(false), initialized_flag_address); + b_.getInt1Ty(), "initialized_flag_address", &b_); + b_.CreateStore(b_.getInt1(false), initialized_flag_address); // Create the inner loop to iterate over the window. - llvm_ir::ForLoopNest window_loops(IrName(select_and_scatter, "window"), - &ir_builder_); + llvm_ir::ForLoopNest window_loops(IrName(select_and_scatter, "window"), &b_); std::vector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); } const llvm_ir::IrArray::Index window_index = window_loops.AddLoopsForShape( ShapeUtil::MakeShape(operand_element_type, window_size), "window"); - SetToFirstInsertPoint(window_loops.GetInnerLoopBodyBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(window_loops.GetInnerLoopBodyBasicBlock(), &b_); // Compute the operand index to visit and evaluate the condition whether the // operand index is within the bounds. The unsigned comparison includes // checking whether the operand index >= 0. - llvm_ir::IrArray::Index operand_index(ir_builder_.getInt64Ty(), - source_index.size()); - llvm::Value* in_bounds_condition = ir_builder_.getTrue(); + llvm_ir::IrArray::Index operand_index(b_.getInt64Ty(), source_index.size()); + llvm::Value* in_bounds_condition = b_.getTrue(); for (int64 i = 0; i < rank; ++i) { - llvm::Value* strided_index = ir_builder_.CreateNSWMul( - source_index[i], ir_builder_.getInt64(window.dimensions(i).stride())); - operand_index[i] = ir_builder_.CreateNSWSub( - ir_builder_.CreateNSWAdd(strided_index, window_index[i]), - ir_builder_.getInt64(window.dimensions(i).padding_low())); - llvm::Value* index_condition = ir_builder_.CreateICmpULT( + llvm::Value* strided_index = b_.CreateNSWMul( + source_index[i], b_.getInt64(window.dimensions(i).stride())); + operand_index[i] = + b_.CreateNSWSub(b_.CreateNSWAdd(strided_index, window_index[i]), + b_.getInt64(window.dimensions(i).padding_low())); + llvm::Value* index_condition = b_.CreateICmpULT( operand_index[i], - ir_builder_.getInt64(ShapeUtil::GetDimension(operand->shape(), i))); - in_bounds_condition = - ir_builder_.CreateAnd(in_bounds_condition, index_condition); + b_.getInt64(ShapeUtil::GetDimension(operand->shape(), i))); + in_bounds_condition = b_.CreateAnd(in_bounds_condition, index_condition); } CHECK(in_bounds_condition != nullptr); // Only need to do something if the operand index is within the bounds. First // check if the initialized_flag is set. llvm_ir::LlvmIfData if_in_bounds = - llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &ir_builder_); - SetToFirstInsertPoint(if_in_bounds.true_block, &ir_builder_); - llvm_ir::LlvmIfData if_initialized = - llvm_ir::EmitIfThenElse(ir_builder_.CreateLoad(initialized_flag_address), - "initialized", &ir_builder_); + llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &b_); + SetToFirstInsertPoint(if_in_bounds.true_block, &b_); + llvm_ir::LlvmIfData if_initialized = llvm_ir::EmitIfThenElse( + b_.CreateLoad(initialized_flag_address), "initialized", &b_); // If the initialized_flag is false, initialize the selected value and index // with the currently visiting operand. - SetToFirstInsertPoint(if_initialized.false_block, &ir_builder_); + SetToFirstInsertPoint(if_initialized.false_block, &b_); const auto save_operand_index = [&](const llvm_ir::IrArray::Index& operand_index) { for (int64 i = 0; i < rank; ++i) { llvm::Value* selected_index_address_slot = - ir_builder_.CreateInBoundsGEP(selected_index_address, - {ir_builder_.getInt32(i)}); - ir_builder_.CreateStore(operand_index[i], - selected_index_address_slot); + b_.CreateInBoundsGEP(selected_index_address, {b_.getInt32(i)}); + b_.CreateStore(operand_index[i], selected_index_address_slot); } }; llvm_ir::IrArray operand_array(GetIrArrayFor(operand)); llvm::Value* operand_data = - operand_array.EmitReadArrayElement(operand_index, &ir_builder_); - ir_builder_.CreateStore(operand_data, selected_value_address); + operand_array.EmitReadArrayElement(operand_index, &b_); + b_.CreateStore(operand_data, selected_value_address); save_operand_index(operand_index); - ir_builder_.CreateStore(ir_builder_.getInt1(true), initialized_flag_address); + b_.CreateStore(b_.getInt1(true), initialized_flag_address); // If the initialized_flag is true, call the `select` function to potentially // update the selected value and index with the currently visiting operand. - SetToFirstInsertPoint(if_initialized.true_block, &ir_builder_); + SetToFirstInsertPoint(if_initialized.true_block, &b_); const Shape output_shape = ShapeUtil::MakeShape(PRED, {}); llvm::Value* operand_address = - operand_array.EmitArrayElementAddress(operand_index, &ir_builder_); + operand_array.EmitArrayElementAddress(operand_index, &b_); llvm::Value* result = EmitElementFunctionCall( select_function, output_shape, {selected_value_address, operand_address}, "select_function"); // If the 'select' function returns false, update the selected value and the // index to the currently visiting operand. - llvm::Value* cond = ir_builder_.CreateICmpNE( + llvm::Value* cond = b_.CreateICmpNE( result, llvm::ConstantInt::get(llvm_ir::PrimitiveTypeToIrType(PRED, module_), 0), "boolean_predicate"); llvm_ir::LlvmIfData if_select_lhs = - llvm_ir::EmitIfThenElse(cond, "if-select-lhs", &ir_builder_); - SetToFirstInsertPoint(if_select_lhs.false_block, &ir_builder_); - ir_builder_.CreateStore(ir_builder_.CreateLoad(operand_address), - selected_value_address); + llvm_ir::EmitIfThenElse(cond, "if-select-lhs", &b_); + SetToFirstInsertPoint(if_select_lhs.false_block, &b_); + b_.CreateStore(b_.CreateLoad(operand_address), selected_value_address); save_operand_index(operand_index); // After iterating over the window elements, scatter the source element to // the selected index of the output. The value we store at the output // location is computed by calling the `scatter` function with the source // value and the current output value. - SetToFirstInsertPoint(window_loops.GetOuterLoopExitBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(window_loops.GetOuterLoopExitBasicBlock(), &b_); llvm_ir::IrArray::Index selected_index(source_index.GetType()); for (int64 i = 0; i < rank; ++i) { - llvm::Value* selected_index_address_slot = ir_builder_.CreateInBoundsGEP( - selected_index_address, {ir_builder_.getInt32(i)}); - selected_index.push_back( - ir_builder_.CreateLoad(selected_index_address_slot)); + llvm::Value* selected_index_address_slot = + b_.CreateInBoundsGEP(selected_index_address, {b_.getInt32(i)}); + selected_index.push_back(b_.CreateLoad(selected_index_address_slot)); } llvm_ir::IrArray source_array(GetIrArrayFor(source)); llvm::Value* source_value_address = - source_array.EmitArrayElementAddress(source_index, &ir_builder_); + source_array.EmitArrayElementAddress(source_index, &b_); llvm_ir::IrArray output_array(GetIrArrayFor(select_and_scatter)); llvm::Value* output_value_address = - output_array.EmitArrayElementAddress(selected_index, &ir_builder_); + output_array.EmitArrayElementAddress(selected_index, &b_); llvm::Value* scatter_value = EmitElementFunctionCall( scatter_function, source->shape(), {output_value_address, source_value_address}, "scatter_function"); - output_array.EmitWriteArrayElement(selected_index, scatter_value, - &ir_builder_); + output_array.EmitWriteArrayElement(selected_index, scatter_value, &b_); - SetToFirstInsertPoint(source_loops.GetOuterLoopExitBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(source_loops.GetOuterLoopExitBasicBlock(), &b_); return Status::OK(); } @@ -822,7 +801,7 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { // Dot operation is complicated so we delegate to a helper class. return DotOpEmitter::EmitDotOperation( *dot, target_array, lhs_array, rhs_array, /*addend_array=*/nullptr, - GetExecutableRunOptionsArgument(), &ir_builder_, hlo_module_config_, + GetExecutableRunOptionsArgument(), &b_, hlo_module_config_, target_machine_features_); } @@ -849,12 +828,12 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForConvolution( llvm::Type* lhs_llvm_type = llvm_ir::PrimitiveTypeToIrType(lhs_element_type, module_); llvm::Value* sum_address = llvm_ir::EmitAllocaAtFunctionEntry( - lhs_llvm_type, "convolution_sum_address", &ir_builder_, + lhs_llvm_type, "convolution_sum_address", &b_, MinimumAlignmentForPrimitiveType(lhs_element_type)); llvm::Value* constant_zero = llvm::Constant::getNullValue(lhs_llvm_type); - ir_builder_.CreateStore(constant_zero, sum_address); + b_.CreateStore(constant_zero, sum_address); - llvm_ir::ForLoopNest loops(IrName(convolution, "inner"), &ir_builder_); + llvm_ir::ForLoopNest loops(IrName(convolution, "inner"), &b_); std::vector kernel_spatial(num_spatial_dims); for (int i = 0; i < num_spatial_dims; ++i) { kernel_spatial[i] = @@ -870,7 +849,7 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForConvolution( "iz") ->GetIndVarValue(); - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); // Calculate the spatial index in the input array, taking striding, dilation // and padding into account. An index in the padding will be out of the bounds @@ -878,13 +857,12 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForConvolution( const auto calculate_input_index = [this](llvm::Value* output_index, llvm::Value* kernel_index, const WindowDimension& window_dim) { - llvm::Value* strided_index = ir_builder_.CreateNSWMul( - output_index, ir_builder_.getInt64(window_dim.stride())); - llvm::Value* dilated_kernel_index = ir_builder_.CreateNSWMul( - kernel_index, ir_builder_.getInt64(window_dim.window_dilation())); - return ir_builder_.CreateNSWSub( - ir_builder_.CreateNSWAdd(strided_index, dilated_kernel_index), - ir_builder_.getInt64(window_dim.padding_low())); + llvm::Value* strided_index = + b_.CreateNSWMul(output_index, b_.getInt64(window_dim.stride())); + llvm::Value* dilated_kernel_index = b_.CreateNSWMul( + kernel_index, b_.getInt64(window_dim.window_dilation())); + return b_.CreateNSWSub(b_.CreateNSWAdd(strided_index, dilated_kernel_index), + b_.getInt64(window_dim.padding_low())); }; std::vector input_spatial(num_spatial_dims); for (int i = 0; i < num_spatial_dims; ++i) { @@ -901,30 +879,27 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForConvolution( // Also need to check that the input coordinates are not in one of the // holes created by base dilation. const auto not_in_hole = [&](llvm::Value* input_index, int64 base_dilation) { - llvm::Value* remainder = ir_builder_.CreateSRem( - input_index, ir_builder_.getInt64(base_dilation)); - return ir_builder_.CreateICmpEQ(remainder, ir_builder_.getInt64(0)); + llvm::Value* remainder = + b_.CreateSRem(input_index, b_.getInt64(base_dilation)); + return b_.CreateICmpEQ(remainder, b_.getInt64(0)); }; - llvm::Value* in_bounds_condition = ir_builder_.getInt1(true); + llvm::Value* in_bounds_condition = b_.getInt1(true); for (int i = 0; i < num_spatial_dims; ++i) { - llvm::ConstantInt* input_bound = - ir_builder_.getInt64(window_util::DilatedBound( - lhs->shape().dimensions(dnums.input_spatial_dimensions(i)), - window.dimensions(i).base_dilation())); - llvm::Value* dim_in_bound = - ir_builder_.CreateICmpULT(input_spatial[i], input_bound); + llvm::ConstantInt* input_bound = b_.getInt64(window_util::DilatedBound( + lhs->shape().dimensions(dnums.input_spatial_dimensions(i)), + window.dimensions(i).base_dilation())); + llvm::Value* dim_in_bound = b_.CreateICmpULT(input_spatial[i], input_bound); llvm::Value* dim_not_in_hole = not_in_hole(input_spatial[i], window.dimensions(i).base_dilation()); - llvm::Value* dim_ok = ir_builder_.CreateAnd(dim_in_bound, dim_not_in_hole); - in_bounds_condition = ir_builder_.CreateAnd(in_bounds_condition, dim_ok); + llvm::Value* dim_ok = b_.CreateAnd(dim_in_bound, dim_not_in_hole); + in_bounds_condition = b_.CreateAnd(in_bounds_condition, dim_ok); } // Now we need to map the dilated base coordinates back to the actual // data indices on the lhs. const auto undilate = [&](llvm::Value* input_index, int64 base_dilation) { - return ir_builder_.CreateSDiv(input_index, - ir_builder_.getInt64(base_dilation)); + return b_.CreateSDiv(input_index, b_.getInt64(base_dilation)); }; for (int i = 0; i < num_spatial_dims; ++i) { input_spatial[i] = @@ -932,12 +907,12 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForConvolution( } llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &ir_builder_); - SetToFirstInsertPoint(if_data.true_block, &ir_builder_); + llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &b_); + SetToFirstInsertPoint(if_data.true_block, &b_); // We are not in the padding, so carry out the computation. int num_dims = num_spatial_dims + 2; - llvm_ir::IrArray::Index input_index(ir_builder_.getInt64Ty(), num_dims); + llvm_ir::IrArray::Index input_index(b_.getInt64Ty(), num_dims); for (int i = 0; i < num_spatial_dims; ++i) { input_index[dnums.input_spatial_dimensions(i)] = input_spatial[i]; } @@ -945,13 +920,12 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForConvolution( input_index[dnums.input_batch_dimension()] = batch; llvm_ir::IrArray kernel_array(GetIrArrayFor(rhs)); - llvm_ir::IrArray::Index kernel_index(ir_builder_.getInt64Ty(), num_dims); + llvm_ir::IrArray::Index kernel_index(b_.getInt64Ty(), num_dims); for (int i = 0; i < num_spatial_dims; ++i) { kernel_index[dnums.kernel_spatial_dimensions(i)] = window.dimensions(i).window_reversal() - ? ir_builder_.CreateNSWSub( - ir_builder_.getInt64(window.dimensions(i).size() - 1), - kernel_spatial[i]) + ? b_.CreateNSWSub(b_.getInt64(window.dimensions(i).size() - 1), + kernel_spatial[i]) : kernel_spatial[i]; } @@ -959,15 +933,14 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForConvolution( kernel_index[dnums.kernel_output_feature_dimension()] = output_feature; llvm_ir::IrArray input_array(GetIrArrayFor(lhs)); - llvm::Value* product = ir_builder_.CreateFMul( - input_array.EmitReadArrayElement(input_index, &ir_builder_), - kernel_array.EmitReadArrayElement(kernel_index, &ir_builder_)); - llvm::Value* sum = - ir_builder_.CreateFAdd(ir_builder_.CreateLoad(sum_address), product); - ir_builder_.CreateStore(sum, sum_address); + llvm::Value* product = + b_.CreateFMul(input_array.EmitReadArrayElement(input_index, &b_), + kernel_array.EmitReadArrayElement(kernel_index, &b_)); + llvm::Value* sum = b_.CreateFAdd(b_.CreateLoad(sum_address), product); + b_.CreateStore(sum, sum_address); - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - return ir_builder_.CreateLoad(sum_address); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); + return b_.CreateLoad(sum_address); } Status IrEmitter::HandleConvolution(HloInstruction* convolution) { @@ -1056,12 +1029,12 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { PrimitiveType primitive_type = lhs->shape().element_type(); llvm::Type* ir_ptr_type = primitive_type == F16 - ? ir_builder_.getHalfTy()->getPointerTo() - : ir_builder_.getFloatTy()->getPointerTo(); - llvm::Type* int64_type = ir_builder_.getInt64Ty(); - llvm::Type* int8_ptr_type = ir_builder_.getInt8Ty()->getPointerTo(); + ? b_.getHalfTy()->getPointerTo() + : b_.getFloatTy()->getPointerTo(); + llvm::Type* int64_type = b_.getInt64Ty(); + llvm::Type* int8_ptr_type = b_.getInt8Ty()->getPointerTo(); llvm::FunctionType* conv_type = llvm::FunctionType::get( - ir_builder_.getVoidTy(), + b_.getVoidTy(), {int8_ptr_type, ir_ptr_type, ir_ptr_type, ir_ptr_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, int64_type, @@ -1093,34 +1066,34 @@ Status IrEmitter::HandleConvolution(HloInstruction* convolution) { conv_func->setCallingConv(llvm::CallingConv::C); conv_func->setDoesNotThrow(); conv_func->setOnlyAccessesArgMemory(); - ir_builder_.CreateCall( - conv_func, { - GetExecutableRunOptionsArgument(), - ir_builder_.CreateBitCast( - GetEmittedValueFor(convolution), ir_ptr_type), - ir_builder_.CreateBitCast(lhs_address, ir_ptr_type), - ir_builder_.CreateBitCast(rhs_address, ir_ptr_type), - ir_builder_.getInt64(input_batch), - ir_builder_.getInt64(input_rows), - ir_builder_.getInt64(input_cols), - ir_builder_.getInt64(input_channels), - ir_builder_.getInt64(kernel_rows), - ir_builder_.getInt64(kernel_cols), - ir_builder_.getInt64(kernel_channels), - ir_builder_.getInt64(kernel_filters), - ir_builder_.getInt64(output_rows), - ir_builder_.getInt64(output_cols), - ir_builder_.getInt64(row_stride), - ir_builder_.getInt64(col_stride), - ir_builder_.getInt64(padding_top), - ir_builder_.getInt64(padding_bottom), - ir_builder_.getInt64(padding_left), - ir_builder_.getInt64(padding_right), - ir_builder_.getInt64(lhs_row_dilation), - ir_builder_.getInt64(lhs_col_dilation), - ir_builder_.getInt64(rhs_row_dilation), - ir_builder_.getInt64(rhs_col_dilation), - }); + b_.CreateCall( + conv_func, + { + GetExecutableRunOptionsArgument(), + b_.CreateBitCast(GetEmittedValueFor(convolution), ir_ptr_type), + b_.CreateBitCast(lhs_address, ir_ptr_type), + b_.CreateBitCast(rhs_address, ir_ptr_type), + b_.getInt64(input_batch), + b_.getInt64(input_rows), + b_.getInt64(input_cols), + b_.getInt64(input_channels), + b_.getInt64(kernel_rows), + b_.getInt64(kernel_cols), + b_.getInt64(kernel_channels), + b_.getInt64(kernel_filters), + b_.getInt64(output_rows), + b_.getInt64(output_cols), + b_.getInt64(row_stride), + b_.getInt64(col_stride), + b_.getInt64(padding_top), + b_.getInt64(padding_bottom), + b_.getInt64(padding_left), + b_.getInt64(padding_right), + b_.getInt64(lhs_row_dilation), + b_.getInt64(lhs_col_dilation), + b_.getInt64(rhs_row_dilation), + b_.getInt64(rhs_col_dilation), + }); return Status::OK(); } @@ -1159,11 +1132,11 @@ Status IrEmitter::HandleFft(HloInstruction* fft) { } // Args have been computed, make the call. - llvm::Type* int8_ptr_type = ir_builder_.getInt8Ty()->getPointerTo(); - llvm::Type* int32_type = ir_builder_.getInt32Ty(); - llvm::Type* int64_type = ir_builder_.getInt64Ty(); + llvm::Type* int8_ptr_type = b_.getInt8Ty()->getPointerTo(); + llvm::Type* int32_type = b_.getInt32Ty(); + llvm::Type* int64_type = b_.getInt64Ty(); llvm::FunctionType* fft_type = llvm::FunctionType::get( - ir_builder_.getVoidTy(), + b_.getVoidTy(), {int8_ptr_type, int8_ptr_type, int8_ptr_type, int32_type, int32_type, int64_type, int64_type, int64_type, int64_type}, /*isVarArg=*/false); @@ -1180,16 +1153,15 @@ Status IrEmitter::HandleFft(HloInstruction* fft) { fft_func->setDoesNotThrow(); fft_func->setOnlyAccessesInaccessibleMemOrArgMem(); const int fft_rank = fft_length.size(); - ir_builder_.CreateCall( + b_.CreateCall( fft_func, {GetExecutableRunOptionsArgument(), - ir_builder_.CreateBitCast(GetEmittedValueFor(fft), int8_ptr_type), - ir_builder_.CreateBitCast(operand_address, int8_ptr_type), - ir_builder_.getInt32(fft->fft_type()), ir_builder_.getInt32(fft_rank), - ir_builder_.getInt64(input_batch), - ir_builder_.getInt64(fft_rank > 0 ? fft_length[0] : 0), - ir_builder_.getInt64(fft_rank > 1 ? fft_length[1] : 0), - ir_builder_.getInt64(fft_rank > 2 ? fft_length[2] : 0)}); + b_.CreateBitCast(GetEmittedValueFor(fft), int8_ptr_type), + b_.CreateBitCast(operand_address, int8_ptr_type), + b_.getInt32(fft->fft_type()), b_.getInt32(fft_rank), + b_.getInt64(input_batch), b_.getInt64(fft_rank > 0 ? fft_length[0] : 0), + b_.getInt64(fft_rank > 1 ? fft_length[1] : 0), + b_.getInt64(fft_rank > 2 ? fft_length[2] : 0)}); return Status::OK(); } @@ -1228,11 +1200,10 @@ Status IrEmitter::HandleCrossReplicaSum(HloInstruction* crs) { operand_ptrs.push_back(EmitTempBufferPointer(out_slice, operand_shape)); // TODO(b/63762267): Be more aggressive about specifying alignment. - ir_builder_.CreateMemCpy(operand_ptrs.back(), /*DstAlign=*/1, in_ptr, - /*SrcAlign=*/1, - ShapeUtil::ByteSizeOf(operand_shape)); + b_.CreateMemCpy(operand_ptrs.back(), /*DstAlign=*/1, in_ptr, + /*SrcAlign=*/1, ShapeUtil::ByteSizeOf(operand_shape)); } - llvm_ir::EmitTuple(GetIrArrayFor(crs), operand_ptrs, &ir_builder_, module_); + llvm_ir::EmitTuple(GetIrArrayFor(crs), operand_ptrs, &b_, module_); return Status::OK(); } @@ -1278,9 +1249,8 @@ Status IrEmitter::HandleParameter(HloInstruction* parameter) { // example, float for an XLA F32 element type). llvm::Value* params = compute_function_->parameters_arg(); llvm::Value* param_address_offset = - llvm_ir::EmitBufferIndexingGEP(params, param_number, &ir_builder_); - llvm::LoadInst* param_address_untyped = - ir_builder_.CreateLoad(param_address_offset); + llvm_ir::EmitBufferIndexingGEP(params, param_number, &b_); + llvm::LoadInst* param_address_untyped = b_.CreateLoad(param_address_offset); param_address_untyped->setName(AsStringRef(IrName(parameter, "untyped"))); if (is_top_level_computation_ && hlo_module_config_.debug_options() @@ -1295,7 +1265,7 @@ Status IrEmitter::HandleParameter(HloInstruction* parameter) { llvm::MDNode::get(param_address_untyped->getContext(), /*MDs=*/{})); } - llvm::Value* param_address_typed = ir_builder_.CreateBitCast( + llvm::Value* param_address_typed = b_.CreateBitCast( param_address_untyped, IrShapeType(param_shape)->getPointerTo()); emitted_value_[parameter] = param_address_typed; @@ -1403,62 +1373,61 @@ IrEmitter::ReductionGenerator IrEmitter::MatchReductionGenerator( return nullptr; case HloOpcode::kAdd: - return [root_is_integral](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, + return [root_is_integral](llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { - return root_is_integral ? ir_builder->CreateAdd(lhs, rhs) - : ir_builder->CreateFAdd(lhs, rhs); + return root_is_integral ? b->CreateAdd(lhs, rhs) + : b->CreateFAdd(lhs, rhs); }; case HloOpcode::kMultiply: - return [root_is_integral](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, + return [root_is_integral](llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { - return root_is_integral ? ir_builder->CreateMul(lhs, rhs) - : ir_builder->CreateFMul(lhs, rhs); + return root_is_integral ? b->CreateMul(lhs, rhs) + : b->CreateFMul(lhs, rhs); }; case HloOpcode::kAnd: - return [](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, - llvm::Value* rhs) { return ir_builder->CreateAnd(lhs, rhs); }; + return [](llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { + return b->CreateAnd(lhs, rhs); + }; case HloOpcode::kOr: - return [](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, - llvm::Value* rhs) { return ir_builder->CreateOr(lhs, rhs); }; + return [](llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { + return b->CreateOr(lhs, rhs); + }; case HloOpcode::kXor: - return [](llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, - llvm::Value* rhs) { return ir_builder->CreateXor(lhs, rhs); }; + return [](llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { + return b->CreateXor(lhs, rhs); + }; case HloOpcode::kMaximum: return [root_is_floating_point, root_is_signed]( - llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, - llvm::Value* rhs) { + llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { if (root_is_floating_point) { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::maxnum, - {lhs, rhs}, {lhs->getType()}, - ir_builder); + {lhs, rhs}, {lhs->getType()}, b); } - return ir_builder->CreateSelect( - ir_builder->CreateICmp(root_is_signed ? llvm::ICmpInst::ICMP_SGE - : llvm::ICmpInst::ICMP_UGE, - lhs, rhs), + return b->CreateSelect( + b->CreateICmp(root_is_signed ? llvm::ICmpInst::ICMP_SGE + : llvm::ICmpInst::ICMP_UGE, + lhs, rhs), lhs, rhs); }; case HloOpcode::kMinimum: return [root_is_floating_point, root_is_signed]( - llvm::IRBuilder<>* ir_builder, llvm::Value* lhs, - llvm::Value* rhs) { + llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs) { if (root_is_floating_point) { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::minnum, - {lhs, rhs}, {lhs->getType()}, - ir_builder); + {lhs, rhs}, {lhs->getType()}, b); } - return ir_builder->CreateSelect( - ir_builder->CreateICmp(root_is_signed ? llvm::ICmpInst::ICMP_SLE - : llvm::ICmpInst::ICMP_ULE, - lhs, rhs), + return b->CreateSelect( + b->CreateICmp(root_is_signed ? llvm::ICmpInst::ICMP_SLE + : llvm::ICmpInst::ICMP_ULE, + lhs, rhs), lhs, rhs); }; } @@ -1527,34 +1496,31 @@ IrEmitter::EmitInnerLoopForVectorizedReduction( accumulator.reserve(accumulator_type.size()); for (auto accumulator_shard_type : accumulator_type) { accumulator.push_back(llvm_ir::EmitAllocaAtFunctionEntry( - accumulator_shard_type, "accumulator", &ir_builder_, 0)); + accumulator_shard_type, "accumulator", &b_, 0)); } - llvm::Value* init_value_ssa = - ir_builder_.CreateLoad(GetEmittedValueFor(init_value)); + llvm::Value* init_value_ssa = b_.CreateLoad(GetEmittedValueFor(init_value)); for (llvm::Value* accumulator_shard : accumulator) { llvm::Value* initial_value; auto shard_type = accumulator_shard->getType()->getPointerElementType(); if (auto vector_type = llvm::dyn_cast(shard_type)) { - initial_value = ir_builder_.CreateVectorSplat( - vector_type->getNumElements(), init_value_ssa); + initial_value = + b_.CreateVectorSplat(vector_type->getNumElements(), init_value_ssa); } else { initial_value = init_value_ssa; } - ir_builder_.CreateAlignedStore(initial_value, accumulator_shard, - element_alignment); + b_.CreateAlignedStore(initial_value, accumulator_shard, element_alignment); } llvm_ir::ForLoopNest reduction_loop_nest(IrName(arg, "vectorized_inner"), - &ir_builder_); + &b_); llvm_ir::IrArray::Index reduced_dims_index = reduction_loop_nest.AddLoopsForShapeOnDimensions(arg->shape(), dimensions, "reduction_dim"); - SetToFirstInsertPoint(reduction_loop_nest.GetInnerLoopBodyBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(reduction_loop_nest.GetInnerLoopBodyBasicBlock(), &b_); llvm_ir::IrArray arg_array(GetIrArrayFor(arg)); llvm_ir::IrArray::Index input_index = reduced_dims_index; @@ -1567,38 +1533,34 @@ IrEmitter::EmitInnerLoopForVectorizedReduction( } CHECK(output_index.end() == it); - llvm::Value* input_address = ir_builder_.CreateBitCast( - arg_array.EmitArrayElementAddress(input_index, &ir_builder_), - ir_builder_.getInt8PtrTy()); + llvm::Value* input_address = b_.CreateBitCast( + arg_array.EmitArrayElementAddress(input_index, &b_), b_.getInt8PtrTy()); for (int i = 0; i < accumulator.size(); i++) { auto input_address_typed = - ir_builder_.CreateBitCast(input_address, accumulator[i]->getType()); + b_.CreateBitCast(input_address, accumulator[i]->getType()); auto current_accumulator_value = - ir_builder_.CreateAlignedLoad(accumulator[i], element_alignment); - auto addend = - ir_builder_.CreateAlignedLoad(input_address_typed, element_alignment); + b_.CreateAlignedLoad(accumulator[i], element_alignment); + auto addend = b_.CreateAlignedLoad(input_address_typed, element_alignment); arg_array.AnnotateLoadStoreInstructionWithMetadata(addend); auto reduced_result = - reduction_generator(&ir_builder_, current_accumulator_value, addend); - ir_builder_.CreateAlignedStore(reduced_result, accumulator[i], - element_alignment); + reduction_generator(&b_, current_accumulator_value, addend); + b_.CreateAlignedStore(reduced_result, accumulator[i], element_alignment); if (i != (accumulator.size() - 1)) { - input_address = ir_builder_.CreateConstInBoundsGEP1_32( - reduced_result->getType(), input_address_typed, 1); + input_address = b_.CreateConstInBoundsGEP1_32(reduced_result->getType(), + input_address_typed, 1); } } - SetToFirstInsertPoint(reduction_loop_nest.GetOuterLoopExitBasicBlock(), - &ir_builder_); + SetToFirstInsertPoint(reduction_loop_nest.GetOuterLoopExitBasicBlock(), &b_); ShardedVector result_ssa; result_ssa.reserve(accumulator.size()); for (auto accumulator_shard : accumulator) { result_ssa.push_back( - ir_builder_.CreateAlignedLoad(accumulator_shard, element_alignment)); + b_.CreateAlignedLoad(accumulator_shard, element_alignment)); } return result_ssa; } @@ -1607,17 +1569,17 @@ void IrEmitter::EmitShardedVectorStore( llvm::Value* store_address, const std::vector& value_to_store, const int alignment, const llvm_ir::IrArray& containing_array) { for (int i = 0; i < value_to_store.size(); i++) { - auto store_address_typed = ir_builder_.CreateBitCast( + auto store_address_typed = b_.CreateBitCast( store_address, llvm::PointerType::getUnqual(value_to_store[i]->getType())); - auto store_instruction = ir_builder_.CreateAlignedStore( + auto store_instruction = b_.CreateAlignedStore( value_to_store[i], store_address_typed, alignment); containing_array.AnnotateLoadStoreInstructionWithMetadata( store_instruction); if (i != (value_to_store.size() - 1)) { - store_address = ir_builder_.CreateConstInBoundsGEP1_32( + store_address = b_.CreateConstInBoundsGEP1_32( value_to_store[i]->getType(), store_address_typed, 1); } } @@ -1683,8 +1645,8 @@ StatusOr IrEmitter::EmitVectorizedReduce( // } // } - llvm_ir::ForLoopNest loop_nest(IrName(reduce), &ir_builder_); - llvm_ir::IrArray::Index array_index(ir_builder_.getInt64Ty(), + llvm_ir::ForLoopNest loop_nest(IrName(reduce), &b_); + llvm_ir::IrArray::Index array_index(b_.getInt64Ty(), reduce->shape().dimensions_size()); for (int i = LayoutUtil::MinorToMajor(reduce->shape()).size() - 1; i > 0; --i) { @@ -1703,7 +1665,7 @@ StatusOr IrEmitter::EmitVectorizedReduce( if (llvm::BasicBlock* innermost_body_bb = loop_nest.GetInnerLoopBodyBasicBlock()) { - SetToFirstInsertPoint(innermost_body_bb, &ir_builder_); + SetToFirstInsertPoint(innermost_body_bb, &b_); } auto outermost_loop_exit_block = loop_nest.GetOuterLoopExitBasicBlock(); @@ -1717,7 +1679,7 @@ StatusOr IrEmitter::EmitVectorizedReduce( tensorflow::strings::Printf("dim.%lld", innermost_dimension)); array_index[innermost_dimension] = loop->GetIndVarValue(); - SetToFirstInsertPoint(loop->GetBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loop->GetBodyBasicBlock(), &b_); ShardedVectorType vector_type = CreateShardedVectorType( reduce->shape().element_type(), vectorization_factor); @@ -1728,16 +1690,16 @@ StatusOr IrEmitter::EmitVectorizedReduce( llvm_ir::IrArray target_array = GetIrArrayFor(reduce); llvm::Value* output_address = - target_array.EmitArrayElementAddress(array_index, &ir_builder_); + target_array.EmitArrayElementAddress(array_index, &b_); EmitShardedVectorStore(output_address, accumulator, element_alignment, target_array); if (auto exit_terminator = loop->GetExitBasicBlock()->getTerminator()) { CHECK_GT(LayoutUtil::MinorToMajor(reduce->shape()).size(), 1); - ir_builder_.SetInsertPoint(exit_terminator); + b_.SetInsertPoint(exit_terminator); } else { CHECK_EQ(LayoutUtil::MinorToMajor(reduce->shape()).size(), 1); - ir_builder_.SetInsertPoint(loop->GetExitBasicBlock()); + b_.SetInsertPoint(loop->GetExitBasicBlock()); } } @@ -1747,8 +1709,8 @@ StatusOr IrEmitter::EmitVectorizedReduce( if (innermost_dimension_size % vectorization_factor) { // TODO(b/63775531): Consider using a scalar loop here to save on code size. array_index[innermost_dimension] = - ir_builder_.getInt64(innermost_dimension_size - - (innermost_dimension_size % vectorization_factor)); + b_.getInt64(innermost_dimension_size - + (innermost_dimension_size % vectorization_factor)); ShardedVectorType vector_type = CreateShardedVectorType( reduce->shape().element_type(), @@ -1760,13 +1722,13 @@ StatusOr IrEmitter::EmitVectorizedReduce( llvm_ir::IrArray target_array = GetIrArrayFor(reduce); llvm::Value* output_address = - target_array.EmitArrayElementAddress(array_index, &ir_builder_); + target_array.EmitArrayElementAddress(array_index, &b_); EmitShardedVectorStore(output_address, accumulator, element_alignment, target_array); } if (outermost_loop_exit_block) { - ir_builder_.SetInsertPoint(outermost_loop_exit_block); + b_.SetInsertPoint(outermost_loop_exit_block); } return true; @@ -1785,22 +1747,22 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForReduce( PrimitiveType accumulator_type = reduce->shape().element_type(); llvm::AllocaInst* accumulator_addr = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(accumulator_type, module_), "accumulator", - &ir_builder_, MinimumAlignmentForPrimitiveType(accumulator_type)); + &b_, MinimumAlignmentForPrimitiveType(accumulator_type)); llvm::Value* init_value_addr = GetEmittedValueFor(init_value); - llvm::Value* load_init_value = ir_builder_.CreateLoad(init_value_addr); - ir_builder_.CreateStore(load_init_value, accumulator_addr); + llvm::Value* load_init_value = b_.CreateLoad(init_value_addr); + b_.CreateStore(load_init_value, accumulator_addr); // The enclosing loops go over all the target elements. Now we have to compute // the actual target element. For this, we build a new loop nest to iterate // over all the reduction dimensions in the argument. // AddLoopsForShapeOnDimensions will return an Index where induction Value*s // are placed for each dimension in dimensions, and all the rest are nullptrs. - llvm_ir::ForLoopNest loops(IrName(reduce, "inner"), &ir_builder_); + llvm_ir::ForLoopNest loops(IrName(reduce, "inner"), &b_); const llvm_ir::IrArray::Index reduced_dims_index = loops.AddLoopsForShapeOnDimensions(arg->shape(), dimensions, "reduction_dim"); - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); // Build a full index for the input argument, using reduced_dims_index as the // base. In reduced_dims_index only the reduction dimensions are filled in. We @@ -1820,14 +1782,14 @@ StatusOr IrEmitter::EmitTargetElementLoopBodyForReduce( // Apply the reduction function to the loaded value. llvm::Value* input_address = - arg_array.EmitArrayElementAddress(input_index, &ir_builder_); + arg_array.EmitArrayElementAddress(input_index, &b_); llvm::Value* result = EmitElementFunctionCall( reducer_function, reduce->shape(), {accumulator_addr, input_address}, "reduce_function"); - ir_builder_.CreateStore(result, accumulator_addr); + b_.CreateStore(result, accumulator_addr); - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - return ir_builder_.CreateLoad(accumulator_addr); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); + return b_.CreateLoad(accumulator_addr); } Status IrEmitter::HandleReduce(HloInstruction* reduce) { @@ -1957,7 +1919,7 @@ Status IrEmitter::HandleSlice(HloInstruction* slice) { llvm_ir::IrArray target_array = GetIrArrayFor(slice); const int64 num_outer_loops = outer_dims.size(); - llvm_ir::ForLoopNest loops(IrName(slice), &ir_builder_); + llvm_ir::ForLoopNest loops(IrName(slice), &b_); llvm_ir::IrArray::Index target_index = loops.AddLoopsForShapeOnDimensions(slice->shape(), outer_dims, "slice"); @@ -1966,21 +1928,21 @@ Status IrEmitter::HandleSlice(HloInstruction* slice) { // for the rest of the dimensions the copy writes to the full dimension. std::replace(target_index.begin(), target_index.end(), static_cast(nullptr), - static_cast(ir_builder_.getInt64(0))); + static_cast(b_.getInt64(0))); if (num_outer_loops > 0) { - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); } llvm_ir::IrArray source_array = GetIrArrayFor(operand); const llvm_ir::IrArray::Index source_index = target_index.SourceIndexOfSlice( /*shape=*/slice->shape(), /*starts=*/slice->slice_starts(), - /*strides=*/slice->slice_strides(), /*builder=*/&ir_builder_); + /*strides=*/slice->slice_strides(), /*builder=*/&b_); - llvm::Value* memcpy_dest = target_array.EmitArrayElementAddress( - target_index, &ir_builder_, "slice.dest"); - llvm::Value* memcpy_source = source_array.EmitArrayElementAddress( - source_index, &ir_builder_, "slice.source"); + llvm::Value* memcpy_dest = + target_array.EmitArrayElementAddress(target_index, &b_, "slice.dest"); + llvm::Value* memcpy_source = + source_array.EmitArrayElementAddress(source_index, &b_, "slice.source"); const int64 memcpy_elements = primitive_elements_per_logical_element * memcpy_logical_elements; @@ -1997,7 +1959,7 @@ Status IrEmitter::HandleSlice(HloInstruction* slice) { } if (num_outer_loops > 0) { - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); } return Status::OK(); @@ -2023,7 +1985,7 @@ Status IrEmitter::HandleDynamicUpdateSlice( auto operands = GetIrArraysForOperandsOf(dynamic_update_slice); return llvm_ir::EmitDynamicUpdateSliceInPlace( operands, GetIrArrayFor(dynamic_update_slice), - IrName(dynamic_update_slice, "in_place"), &ir_builder_); + IrName(dynamic_update_slice, "in_place"), &b_); } return DefaultAction(dynamic_update_slice); } @@ -2057,43 +2019,41 @@ Status IrEmitter::HandlePad(HloInstruction* pad) { [this, pad](const llvm_ir::IrArray::Index& target_index) { const HloInstruction* padding_value = pad->operand(1); llvm::Value* padding_value_addr = GetEmittedValueFor(padding_value); - return ir_builder_.CreateLoad(padding_value_addr); + return b_.CreateLoad(padding_value_addr); })); // Create a loop to iterate over the operand elements and update the output // locations where the operand elements should be stored. - llvm_ir::ForLoopNest loops(IrName(pad, "assign"), &ir_builder_); + llvm_ir::ForLoopNest loops(IrName(pad, "assign"), &b_); const HloInstruction* operand = pad->operand(0); const llvm_ir::IrArray::Index operand_index = loops.AddLoopsForShape(operand->shape(), "operand"); - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); // Load an element from the operand. llvm_ir::IrArray operand_array(GetIrArrayFor(operand)); llvm::Value* operand_data = - operand_array.EmitReadArrayElement(operand_index, &ir_builder_); + operand_array.EmitReadArrayElement(operand_index, &b_); // Compute the output index the operand element should be assigned to. // output_index := edge_padding_low + operand_index * (interior_padding + 1) const PaddingConfig& padding_config = pad->padding_config(); llvm_ir::IrArray::Index output_index(operand_index.GetType()); for (size_t i = 0; i < operand_index.size(); ++i) { - llvm::Value* offset = ir_builder_.CreateMul( + llvm::Value* offset = b_.CreateMul( operand_index[i], - ir_builder_.getInt64(padding_config.dimensions(i).interior_padding() + - 1)); - llvm::Value* index = ir_builder_.CreateAdd( - offset, - ir_builder_.getInt64(padding_config.dimensions(i).edge_padding_low())); + b_.getInt64(padding_config.dimensions(i).interior_padding() + 1)); + llvm::Value* index = b_.CreateAdd( + offset, b_.getInt64(padding_config.dimensions(i).edge_padding_low())); output_index.push_back(index); } // Store the operand element to the computed output location. llvm_ir::IrArray output_array(GetIrArrayFor(pad)); - output_array.EmitWriteArrayElement(output_index, operand_data, &ir_builder_); + output_array.EmitWriteArrayElement(output_index, operand_data, &b_); - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); return Status::OK(); } @@ -2115,8 +2075,7 @@ Status IrEmitter::HandleFusion(HloInstruction* fusion) { // Delegate to common implementation of fused in-place dynamic-update-slice. auto operands = GetIrArraysForOperandsOf(fusion); return llvm_ir::EmitFusedDynamicUpdateSliceInPlace( - fusion, operands, GetIrArrayFor(fusion), &elemental_emitter, - &ir_builder_); + fusion, operands, GetIrArrayFor(fusion), &elemental_emitter, &b_); } else if (fusion->fusion_kind() == HloInstruction::FusionKind::kLoop) { VLOG(3) << "HandleFusion kLoop"; CpuElementalIrEmitter elemental_emitter(hlo_module_config_, this, module_); @@ -2151,7 +2110,7 @@ Status IrEmitter::HandleFusion(HloInstruction* fusion) { TF_RETURN_IF_ERROR(DotOpEmitter::EmitDotOperation( *dot, target_array, lhs_array, rhs_array, &addend_array, - GetExecutableRunOptionsArgument(), &ir_builder_, hlo_module_config_, + GetExecutableRunOptionsArgument(), &b_, hlo_module_config_, target_machine_features_)); return Status::OK(); } else { @@ -2174,7 +2133,7 @@ Status IrEmitter::HandleCall(HloInstruction* call) { // ParallelTaskAssignment assigned partitions, emit call to // ParallelForkJoin. std::vector call_args = GetArrayFunctionCallArguments( - parameter_addresses, &ir_builder_, computation->name(), + parameter_addresses, &b_, computation->name(), /*return_value_buffer=*/emitted_value_[call], /*exec_run_options_arg=*/GetExecutableRunOptionsArgument(), /*temp_buffers_arg=*/GetTempBuffersArgument(), @@ -2182,8 +2141,8 @@ Status IrEmitter::HandleCall(HloInstruction* call) { HloInstruction* root = computation->root_instruction(); TF_RETURN_IF_ERROR(EmitCallToParallelForkJoin( - call_args, root->shape(), root->outer_dimension_partitions(), - &ir_builder_, call_ir_function, computation->name())); + call_args, root->shape(), root->outer_dimension_partitions(), &b_, + call_ir_function, computation->name())); } else { EmitArrayFunctionCallInto(call_ir_function, parameter_addresses, emitted_value_[call], computation->name()); @@ -2195,33 +2154,31 @@ Status IrEmitter::HandleCall(HloInstruction* call) { Status IrEmitter::HandleCustomCall(HloInstruction* custom_call) { gtl::ArraySlice operands(custom_call->operands()); tensorflow::StringPiece custom_call_target(custom_call->custom_call_target()); - llvm::Type* i8_ptr_type = ir_builder_.getInt8PtrTy(); + llvm::Type* i8_ptr_type = b_.getInt8PtrTy(); llvm::AllocaInst* operands_alloca = llvm_ir::EmitAllocaAtFunctionEntryWithCount( - i8_ptr_type, ir_builder_.getInt32(operands.size()), - "cc_operands_alloca", &ir_builder_); + i8_ptr_type, b_.getInt32(operands.size()), "cc_operands_alloca", &b_); for (size_t i = 0; i < operands.size(); ++i) { const HloInstruction* operand = operands[i]; llvm::Value* operand_as_i8ptr = - ir_builder_.CreatePointerCast(GetEmittedValueFor(operand), i8_ptr_type); - llvm::Value* slot_in_operands_alloca = ir_builder_.CreateInBoundsGEP( - operands_alloca, {ir_builder_.getInt64(i)}); - ir_builder_.CreateStore(operand_as_i8ptr, slot_in_operands_alloca); + b_.CreatePointerCast(GetEmittedValueFor(operand), i8_ptr_type); + llvm::Value* slot_in_operands_alloca = + b_.CreateInBoundsGEP(operands_alloca, {b_.getInt64(i)}); + b_.CreateStore(operand_as_i8ptr, slot_in_operands_alloca); } auto* custom_call_ir_function = llvm::cast(module_->getOrInsertFunction( AsStringRef(custom_call_target), llvm::FunctionType::get( - /*Result=*/ir_builder_.getVoidTy(), + /*Result=*/b_.getVoidTy(), /*Params=*/{i8_ptr_type, operands_alloca->getType()}, /*isVarArg=*/false))); TF_RETURN_IF_ERROR(EmitTargetAddressForOp(custom_call)); - auto* output_address_arg = ir_builder_.CreatePointerCast( - GetEmittedValueFor(custom_call), i8_ptr_type); + auto* output_address_arg = + b_.CreatePointerCast(GetEmittedValueFor(custom_call), i8_ptr_type); - ir_builder_.CreateCall(custom_call_ir_function, - {output_address_arg, operands_alloca}); + b_.CreateCall(custom_call_ir_function, {output_address_arg, operands_alloca}); return Status::OK(); } @@ -2286,8 +2243,8 @@ Status IrEmitter::HandleWhile(HloInstruction* xla_while) { llvm::BasicBlock* header_bb = llvm::BasicBlock::Create( module_->getContext(), AsStringRef(IrName(xla_while, "header")), compute_function_->function()); - ir_builder_.CreateBr(header_bb); - ir_builder_.SetInsertPoint(header_bb); + b_.CreateBr(header_bb); + b_.SetInsertPoint(header_bb); // Calls the condition function to determine whether to proceed with the // body. It must return a bool, so use the scalar call form. @@ -2295,7 +2252,7 @@ Status IrEmitter::HandleWhile(HloInstruction* xla_while) { llvm::Value* while_condition = EmitElementFunctionCall( condition_ir_function, condition->root_instruction()->shape(), {while_result}, IrName(xla_while, "cond")); - llvm::Value* while_predicate = ir_builder_.CreateICmpNE( + llvm::Value* while_predicate = b_.CreateICmpNE( while_condition, llvm::ConstantInt::get(llvm_ir::PrimitiveTypeToIrType(PRED, module_), 0)); @@ -2305,20 +2262,20 @@ Status IrEmitter::HandleWhile(HloInstruction* xla_while) { compute_function_->function()); llvm::BasicBlock* exit_bb = llvm::BasicBlock::Create( module_->getContext(), AsStringRef(IrName(xla_while, "exit"))); - ir_builder_.CreateCondBr(while_predicate, body_bb, exit_bb); + b_.CreateCondBr(while_predicate, body_bb, exit_bb); // Calls the body function from the body block. - ir_builder_.SetInsertPoint(body_bb); + b_.SetInsertPoint(body_bb); // Calls the body function. EmitArrayFunctionCallInto(body_ir_function, {while_result}, while_result, IrName(xla_while, "body")); // Finishes with a branch back to the header. - ir_builder_.CreateBr(header_bb); + b_.CreateBr(header_bb); // Adds the exit block to the function and sets the insert point there. compute_function_->function()->getBasicBlockList().push_back(exit_bb); - ir_builder_.SetInsertPoint(exit_bb); + b_.SetInsertPoint(exit_bb); return Status::OK(); } @@ -2360,21 +2317,21 @@ StatusOr IrEmitter::EmitFastConcatenate( std::vector outer_dims(std::next(concat_dim_layout_itr), output_min2maj.end()); - llvm::Type* i8_ptr_type = ir_builder_.getInt8PtrTy(); - llvm::Type* i8_type = ir_builder_.getInt8Ty(); + llvm::Type* i8_ptr_type = b_.getInt8PtrTy(); + llvm::Type* i8_type = b_.getInt8Ty(); TF_RETURN_IF_ERROR(EmitTargetAddressForOp(concatenate)); llvm_ir::IrArray target_array = GetIrArrayFor(concatenate); - llvm_ir::ForLoopNest loops(IrName(concatenate), &ir_builder_); + llvm_ir::ForLoopNest loops(IrName(concatenate), &b_); llvm_ir::IrArray::Index outer_dims_index = loops.AddLoopsForShapeOnDimensions(output_shape, outer_dims, "concat"); std::replace(outer_dims_index.begin(), outer_dims_index.end(), static_cast(nullptr), - static_cast(ir_builder_.getInt64(0))); + static_cast(b_.getInt64(0))); if (!outer_dims.empty()) { - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); } PrimitiveType primitive_type = output_shape.element_type(); @@ -2383,10 +2340,10 @@ StatusOr IrEmitter::EmitFastConcatenate( // Contiguous subregions from each operand to the concatenate contribute to a // contiguous subregion in the target buffer starting at target_region_begin. - llvm::Value* target_region_begin = ir_builder_.CreateBitCast( - target_array.EmitArrayElementAddress(outer_dims_index, &ir_builder_, - "target_region"), - i8_ptr_type); + llvm::Value* target_region_begin = + b_.CreateBitCast(target_array.EmitArrayElementAddress( + outer_dims_index, &b_, "target_region"), + i8_ptr_type); int64 byte_offset_into_target_region = 0; int64 inner_dims_product = @@ -2400,14 +2357,13 @@ StatusOr IrEmitter::EmitFastConcatenate( for (HloInstruction* operand : operands) { const Shape& input_shape = operand->shape(); llvm_ir::IrArray source_array = GetIrArrayFor(operand); - llvm::Value* copy_source_address = ir_builder_.CreateBitCast( - source_array.EmitArrayElementAddress(outer_dims_index, &ir_builder_, - "src_addr"), + llvm::Value* copy_source_address = b_.CreateBitCast( + source_array.EmitArrayElementAddress(outer_dims_index, &b_, "src_addr"), i8_ptr_type); - llvm::Value* copy_target_address = ir_builder_.CreateGEP( - i8_type, target_region_begin, - ir_builder_.getInt64(byte_offset_into_target_region)); + llvm::Value* copy_target_address = + b_.CreateGEP(i8_type, target_region_begin, + b_.getInt64(byte_offset_into_target_region)); EmitTransferElements( copy_target_address, copy_source_address, @@ -2420,7 +2376,7 @@ StatusOr IrEmitter::EmitFastConcatenate( } if (!outer_dims.empty()) { - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); } return true; @@ -2439,16 +2395,15 @@ void IrEmitter::EmitTransferElements(llvm::Value* target, llvm::Value* source, llvm_ir::PrimitiveTypeToIrType(primitive_type, module_)); if (element_count == 1) { - auto* load_instruction = ir_builder_.CreateAlignedLoad( - ir_builder_.CreateBitCast(source, primitive_ptr_type), - element_alignment); + auto* load_instruction = b_.CreateAlignedLoad( + b_.CreateBitCast(source, primitive_ptr_type), element_alignment); source_array.AnnotateLoadStoreInstructionWithMetadata(load_instruction); - auto* store_instruction = ir_builder_.CreateAlignedStore( - load_instruction, ir_builder_.CreateBitCast(target, primitive_ptr_type), + auto* store_instruction = b_.CreateAlignedStore( + load_instruction, b_.CreateBitCast(target, primitive_ptr_type), element_alignment); target_array.AnnotateLoadStoreInstructionWithMetadata(store_instruction); } else { - auto* memcpy_instruction = ir_builder_.CreateMemCpy( + auto* memcpy_instruction = b_.CreateMemCpy( target, /*DstAlign=*/element_alignment, source, /*SrcAlign=*/element_alignment, element_count * primitive_type_size); @@ -2518,24 +2473,24 @@ Status IrEmitter::HandleConditional(HloInstruction* conditional) { // cond_result = true_computation(true_operand) // else // cond_result = false_computation(false_operand) - llvm::LoadInst* pred_value = ir_builder_.CreateLoad( + llvm::LoadInst* pred_value = b_.CreateLoad( GetIrArrayFor(pred).GetBasePointer(), "load_predicate_value"); - llvm::Value* pred_cond = ir_builder_.CreateICmpNE( + llvm::Value* pred_cond = b_.CreateICmpNE( pred_value, llvm::ConstantInt::get(llvm_ir::PrimitiveTypeToIrType(PRED, module_), 0), "boolean_predicate"); llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(pred_cond, "conditional", &ir_builder_); + llvm_ir::EmitIfThenElse(pred_cond, "conditional", &b_); - SetToFirstInsertPoint(if_data.true_block, &ir_builder_); + SetToFirstInsertPoint(if_data.true_block, &b_); EmitArrayFunctionCallInto(true_function, {GetEmittedValueFor(true_arg)}, conditional_result, IrName(conditional, "_true")); - SetToFirstInsertPoint(if_data.false_block, &ir_builder_); + SetToFirstInsertPoint(if_data.false_block, &b_); EmitArrayFunctionCallInto(false_function, {GetEmittedValueFor(false_arg)}, conditional_result, IrName(conditional, "_false")); - SetToFirstInsertPoint(if_data.after_block, &ir_builder_); + SetToFirstInsertPoint(if_data.after_block, &b_); return Status::OK(); } @@ -2568,7 +2523,7 @@ Status IrEmitter::FinishVisit(HloInstruction* root) { auto record_complete_computation = [&](llvm::Value* prof_counter) { if (prof_counter) { - profiling_state_.RecordCompleteComputation(&ir_builder_, prof_counter); + profiling_state_.RecordCompleteComputation(&b_, prof_counter); } }; @@ -2590,54 +2545,51 @@ llvm::Value* IrEmitter::GetProfileCounterCommon( int64 prof_counter_idx = it->second; string counter_name = IrName("prof_counter", hlo.name()); - return ir_builder_.CreateGEP(GetProfileCountersArgument(), - ir_builder_.getInt64(prof_counter_idx), - AsStringRef(counter_name)); + return b_.CreateGEP(GetProfileCountersArgument(), + b_.getInt64(prof_counter_idx), AsStringRef(counter_name)); } -void IrEmitter::ProfilingState::UpdateProfileCounter( - llvm::IRBuilder<>* ir_builder, llvm::Value* prof_counter, - llvm::Value* cycle_end, llvm::Value* cycle_start) { - auto* cycle_diff = ir_builder->CreateSub(cycle_end, cycle_start); +void IrEmitter::ProfilingState::UpdateProfileCounter(llvm::IRBuilder<>* b, + llvm::Value* prof_counter, + llvm::Value* cycle_end, + llvm::Value* cycle_start) { + auto* cycle_diff = b->CreateSub(cycle_end, cycle_start); llvm::LoadInst* old_cycle_count = - ir_builder->CreateLoad(prof_counter, "old_cycle_count"); + b->CreateLoad(prof_counter, "old_cycle_count"); auto* new_cycle_count = - ir_builder->CreateAdd(cycle_diff, old_cycle_count, "new_cycle_count"); - ir_builder->CreateStore(new_cycle_count, prof_counter); + b->CreateAdd(cycle_diff, old_cycle_count, "new_cycle_count"); + b->CreateStore(new_cycle_count, prof_counter); } -llvm::Value* IrEmitter::ProfilingState::ReadCycleCounter( - llvm::IRBuilder<>* ir_builder) { - llvm::Module* module = ir_builder->GetInsertBlock()->getModule(); +llvm::Value* IrEmitter::ProfilingState::ReadCycleCounter(llvm::IRBuilder<>* b) { + llvm::Module* module = b->GetInsertBlock()->getModule(); if (use_rdtscp_) { llvm::Function* func_llvm_readcyclecounter = llvm::Intrinsic::getDeclaration(module, llvm::Intrinsic::readcyclecounter); - return ir_builder->CreateCall(func_llvm_readcyclecounter); + return b->CreateCall(func_llvm_readcyclecounter); } llvm::Function* func_llvm_x86_rdtscp = llvm::Intrinsic::getDeclaration(module, llvm::Intrinsic::x86_rdtscp); if (!aux_i8ptr_) { - llvm::AllocaInst* rdtscp_aux = llvm_ir::EmitAllocaAtFunctionEntry( - ir_builder->getInt32Ty(), "rdtscp_aux", ir_builder); - aux_i8ptr_ = - ir_builder->CreateBitCast(rdtscp_aux, ir_builder->getInt8PtrTy()); + llvm::AllocaInst* rdtscp_aux = + llvm_ir::EmitAllocaAtFunctionEntry(b->getInt32Ty(), "rdtscp_aux", b); + aux_i8ptr_ = b->CreateBitCast(rdtscp_aux, b->getInt8PtrTy()); } - llvm::ConstantInt* alloca_size = ir_builder->getInt64(4); + llvm::ConstantInt* alloca_size = b->getInt64(4); llvm::Function* func_llvm_lifetime_start = llvm::Intrinsic::getDeclaration(module, llvm::Intrinsic::lifetime_start); - ir_builder->CreateCall(func_llvm_lifetime_start, {alloca_size, aux_i8ptr_}); - llvm::Value* rdtscp_call = - ir_builder->CreateCall(func_llvm_x86_rdtscp, aux_i8ptr_); + b->CreateCall(func_llvm_lifetime_start, {alloca_size, aux_i8ptr_}); + llvm::Value* rdtscp_call = b->CreateCall(func_llvm_x86_rdtscp, aux_i8ptr_); llvm::Function* func_llvm_lifetime_end = llvm::Intrinsic::getDeclaration(module, llvm::Intrinsic::lifetime_end); - ir_builder->CreateCall(func_llvm_lifetime_end, {alloca_size, aux_i8ptr_}); + b->CreateCall(func_llvm_lifetime_end, {alloca_size, aux_i8ptr_}); return rdtscp_call; } -void IrEmitter::ProfilingState::RecordCycleStart(llvm::IRBuilder<>* ir_builder, +void IrEmitter::ProfilingState::RecordCycleStart(llvm::IRBuilder<>* b, HloInstruction* hlo) { - auto* cycle_start = ReadCycleCounter(ir_builder); + auto* cycle_start = ReadCycleCounter(b); cycle_start->setName(AsStringRef(IrName(hlo, "cycle_start"))); cycle_starts_[hlo] = cycle_start; if (first_read_cycle_start_ == nullptr) { @@ -2645,20 +2597,20 @@ void IrEmitter::ProfilingState::RecordCycleStart(llvm::IRBuilder<>* ir_builder, } } -void IrEmitter::ProfilingState::RecordCycleDelta(llvm::IRBuilder<>* ir_builder, +void IrEmitter::ProfilingState::RecordCycleDelta(llvm::IRBuilder<>* b, HloInstruction* hlo, llvm::Value* prof_counter) { - auto* cycle_end = ReadCycleCounter(ir_builder); + auto* cycle_end = ReadCycleCounter(b); cycle_end->setName(AsStringRef(IrName(hlo, "cycle_end"))); auto* cycle_start = cycle_starts_[hlo]; - UpdateProfileCounter(ir_builder, prof_counter, cycle_end, cycle_start); + UpdateProfileCounter(b, prof_counter, cycle_end, cycle_start); last_read_cycle_end_ = cycle_end; } void IrEmitter::ProfilingState::RecordCompleteComputation( - llvm::IRBuilder<>* ir_builder, llvm::Value* prof_counter) { + llvm::IRBuilder<>* b, llvm::Value* prof_counter) { if (last_read_cycle_end_ && first_read_cycle_start_) { - UpdateProfileCounter(ir_builder, prof_counter, last_read_cycle_end_, + UpdateProfileCounter(b, prof_counter, last_read_cycle_end_, first_read_cycle_start_); } } @@ -2666,14 +2618,14 @@ void IrEmitter::ProfilingState::RecordCompleteComputation( Status IrEmitter::Preprocess(HloInstruction* hlo) { VLOG(3) << "Visiting: " << hlo->ToString(); if (instruction_to_profile_idx_.count(hlo)) { - profiling_state_.RecordCycleStart(&ir_builder_, hlo); + profiling_state_.RecordCycleStart(&b_, hlo); } return Status::OK(); } Status IrEmitter::Postprocess(HloInstruction* hlo) { if (auto* prof_counter = GetProfileCounterFor(*hlo)) { - profiling_state_.RecordCycleDelta(&ir_builder_, hlo, prof_counter); + profiling_state_.RecordCycleDelta(&b_, hlo, prof_counter); } return Status::OK(); } @@ -2732,22 +2684,20 @@ llvm::Value* IrEmitter::EmitTempBufferPointer( CHECK_EQ(1, assigned_buffers.size()); const Shape& shape = assigned_buffers.begin()->first->shape(); - llvm::AllocaInst*& tempbuf_address = thread_local_buffers_[{ - ir_builder_.GetInsertBlock()->getParent(), slice}]; + llvm::AllocaInst*& tempbuf_address = + thread_local_buffers_[{b_.GetInsertBlock()->getParent(), slice}]; if (tempbuf_address == nullptr) { tempbuf_address = llvm_ir::EmitAllocaAtFunctionEntry( IrShapeType(shape), - tensorflow::strings::StrCat("thread_local", slice.ToString()), - &ir_builder_, MinimumAlignmentForShape(target_shape)); + tensorflow::strings::StrCat("thread_local", slice.ToString()), &b_, + MinimumAlignmentForShape(target_shape)); } - return ir_builder_.CreateBitCast(tempbuf_address, - element_type->getPointerTo()); + return b_.CreateBitCast(tempbuf_address, element_type->getPointerTo()); } llvm::Value* tempbuf_address_ptr = llvm_ir::EmitBufferIndexingGEP( - GetTempBuffersArgument(), slice.index(), &ir_builder_); - llvm::LoadInst* tempbuf_address_base = - ir_builder_.CreateLoad(tempbuf_address_ptr); + GetTempBuffersArgument(), slice.index(), &b_); + llvm::LoadInst* tempbuf_address_base = b_.CreateLoad(tempbuf_address_ptr); if (is_top_level_computation_ && hlo_module_config_.debug_options() .xla_llvm_enable_invariant_load_metadata()) { @@ -2766,11 +2716,11 @@ llvm::Value* IrEmitter::EmitTempBufferPointer( llvm::Value* tempbuf_address_untyped = tempbuf_address_base; if (slice.offset() > 0) { // Adjust the address to account for the slice offset. - tempbuf_address_untyped = ir_builder_.CreateInBoundsGEP( - tempbuf_address_base, ir_builder_.getInt64(slice.offset())); + tempbuf_address_untyped = + b_.CreateInBoundsGEP(tempbuf_address_base, b_.getInt64(slice.offset())); } - return ir_builder_.CreateBitCast(tempbuf_address_untyped, - element_type->getPointerTo()); + return b_.CreateBitCast(tempbuf_address_untyped, + element_type->getPointerTo()); } // Emits a function call returning a single array element. Allocates space @@ -2781,7 +2731,7 @@ llvm::Value* IrEmitter::EmitElementFunctionCall( tensorflow::StringPiece name) { llvm::Value* return_value_buffer = EmitArrayFunctionCall( function, return_shape, 1, parameter_addresses, name); - return ir_builder_.CreateLoad( + return b_.CreateLoad( return_value_buffer, AsStringRef(tensorflow::strings::StrCat(name, "_return_value"))); } @@ -2799,9 +2749,9 @@ llvm::Value* IrEmitter::EmitElementFunctionCall( void IrEmitter::EmitArrayFunctionCallInto( llvm::Function* function, gtl::ArraySlice parameter_addresses, llvm::Value* return_value_buffer, tensorflow::StringPiece name) { - ir_builder_.CreateCall( - function, GetArrayFunctionCallArguments( - parameter_addresses, &ir_builder_, name, + b_.CreateCall(function, + GetArrayFunctionCallArguments( + parameter_addresses, &b_, name, /*return_value_buffer=*/return_value_buffer, /*exec_run_options_arg=*/GetExecutableRunOptionsArgument(), /*temp_buffers_arg=*/GetTempBuffersArgument(), @@ -2813,13 +2763,13 @@ llvm::Value* IrEmitter::EmitArrayFunctionCall( gtl::ArraySlice parameter_addresses, tensorflow::StringPiece name) { llvm::Value* elements = - llvm::ConstantInt::get(ir_builder_.getInt64Ty(), element_count); + llvm::ConstantInt::get(b_.getInt64Ty(), element_count); PrimitiveType return_type = return_shape.element_type(); llvm::Value* return_value_buffer = llvm_ir::EmitAllocaAtFunctionEntryWithCount( llvm_ir::PrimitiveTypeToIrType(return_type, module_), elements, - tensorflow::strings::StrCat(name, "_return_value_address"), - &ir_builder_, MinimumAlignmentForPrimitiveType(return_type)); + tensorflow::strings::StrCat(name, "_return_value_address"), &b_, + MinimumAlignmentForPrimitiveType(return_type)); EmitArrayFunctionCallInto(function, parameter_addresses, return_value_buffer, name); return return_value_buffer; @@ -2841,8 +2791,7 @@ Status IrEmitter::EmitTargetAddressForOp(const HloInstruction* op) { attr_builder.addDereferenceableAttr(ByteSizeOf(target_shape)); retval->addAttrs(attr_builder); } - addr = ir_builder_.CreateBitCast(retval, - IrShapeType(target_shape)->getPointerTo()); + addr = b_.CreateBitCast(retval, IrShapeType(target_shape)->getPointerTo()); } else { // For other nodes, we need the temporary buffer allocated for this node to // write the result into. @@ -2884,14 +2833,14 @@ Status IrEmitter::EmitTargetElementLoop( llvm_ir::IrArray(op_target_address, element_shape)); } TF_RETURN_IF_ERROR( - llvm_ir::LoopEmitter(element_generator, output_arrays, &ir_builder_) + llvm_ir::LoopEmitter(element_generator, output_arrays, &b_) .EmitLoop(IrName(target_op))); std::vector tuple_operand_ptrs; for (int64 i = 0; i < output_arrays.size(); ++i) { tuple_operand_ptrs.push_back(output_arrays[i].GetBasePointer()); } - llvm_ir::EmitTuple(target_array, tuple_operand_ptrs, &ir_builder_, module_); + llvm_ir::EmitTuple(target_array, tuple_operand_ptrs, &b_, module_); } else { if (ShouldEmitParallelLoopFor(*target_op)) { @@ -2900,11 +2849,11 @@ Status IrEmitter::EmitTargetElementLoop( compute_function_->GetDynamicLoopBounds(); // Emit parallel loop with dynamic loop bounds for most-major dimensions. TF_RETURN_IF_ERROR(ParallelLoopEmitter(element_generator, target_array, - &dynamic_loop_bounds, &ir_builder_) + &dynamic_loop_bounds, &b_) .EmitLoop(IrName(target_op))); } else { TF_RETURN_IF_ERROR( - llvm_ir::LoopEmitter(element_generator, target_array, &ir_builder_) + llvm_ir::LoopEmitter(element_generator, target_array, &b_) .EmitLoop(IrName(target_op))); } } @@ -2917,8 +2866,8 @@ Status IrEmitter::EmitMemcpy(const HloInstruction& source, llvm::Value* destination_value = GetEmittedValueFor(&destination); int64 source_size = ByteSizeOf(source.shape()); // TODO(b/63762267): Be more aggressive about specifying alignment. - ir_builder_.CreateMemCpy(destination_value, /*DstAlign=*/1, source_value, - /*SrcAlign=*/1, source_size); + b_.CreateMemCpy(destination_value, /*DstAlign=*/1, source_value, + /*SrcAlign=*/1, source_size); return Status::OK(); } @@ -2946,7 +2895,7 @@ Status IrEmitter::DefaultAction(HloInstruction* hlo) { ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator; for (const HloInstruction* operand : hlo->operands()) { operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) { - return GetIrArrayFor(operand).EmitReadArrayElement(index, &ir_builder_); + return GetIrArrayFor(operand).EmitReadArrayElement(index, &b_); }; } CpuElementalIrEmitter elemental_emitter(hlo_module_config_, this, module_); @@ -2961,8 +2910,8 @@ StatusOr IrEmitter::EmitScalarCall( std::vector argument_addrs; for (auto argument : arguments) { llvm::Value* argument_addr = llvm_ir::EmitAllocaAtFunctionEntry( - argument->getType(), "arg_addr", &ir_builder_); - ir_builder_.CreateStore(argument, argument_addr); + argument->getType(), "arg_addr", &b_); + b_.CreateStore(argument, argument_addr); argument_addrs.push_back(argument_addr); } return EmitElementFunctionCall(llvm_function, diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h index 2840c14303..4e928ffadc 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h @@ -98,7 +98,7 @@ class IrEmitter : public DfsHloVisitorWithDefault { bool is_top_level_computation, std::vector* instruction_order); - llvm::IRBuilder<>* ir_builder() { return &ir_builder_; } + llvm::IRBuilder<>* b() { return &b_; } // Emits a call to `computation` with scalar arguments `arguments`. StatusOr EmitScalarCall( @@ -416,7 +416,7 @@ class IrEmitter : public DfsHloVisitorWithDefault { // creates the encapsulated llvm::Function s.t. it is added to the llvm // module's function list). std::unique_ptr compute_function_; - llvm::IRBuilder<> ir_builder_; + llvm::IRBuilder<> b_; // Maps HLO instructions to their index into the profile counter array. const std::unordered_map @@ -452,23 +452,22 @@ class IrEmitter : public DfsHloVisitorWithDefault { : use_rdtscp_(use_rdtscp), prof_counters_(prof_counters) {} // Record the cycle counter before an HLO executes. - void RecordCycleStart(llvm::IRBuilder<>* ir_builder, HloInstruction* hlo); + void RecordCycleStart(llvm::IRBuilder<>* b, HloInstruction* hlo); // Record the number of cycles it took for an HLO to execute. - void RecordCycleDelta(llvm::IRBuilder<>* ir_builder, HloInstruction* hlo, + void RecordCycleDelta(llvm::IRBuilder<>* b, HloInstruction* hlo, llvm::Value* prof_counter); // Record the number of cycles it took for the entire computation to // execute. - void RecordCompleteComputation(llvm::IRBuilder<>* ir_builder, + void RecordCompleteComputation(llvm::IRBuilder<>* b, llvm::Value* prof_counter); // Convenience function to generate a call to an intrinsic which reads the // CPU cycle counter. - llvm::Value* ReadCycleCounter(llvm::IRBuilder<>* ir_builder); + llvm::Value* ReadCycleCounter(llvm::IRBuilder<>* b); // Store the cycle counter delta to the per-HLO profile counter. - void UpdateProfileCounter(llvm::IRBuilder<>* ir_builder, - llvm::Value* prof_counter, llvm::Value* cycle_end, - llvm::Value* cycle_start); + void UpdateProfileCounter(llvm::IRBuilder<>* b, llvm::Value* prof_counter, + llvm::Value* cycle_end, llvm::Value* cycle_start); private: // Should we use the x86-specific rdtscp or the generic readcyclecounter diff --git a/tensorflow/compiler/xla/service/cpu/ir_function.cc b/tensorflow/compiler/xla/service/cpu/ir_function.cc index 2d6f2f3818..6aff838462 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_function.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_function.cc @@ -49,11 +49,10 @@ IrFunction::IrFunction(const string& function_name, llvm::Function::LinkageTypes linkage, const bool optimize_for_size_requested, const bool enable_fast_math, llvm::Module* llvm_module, - llvm::IRBuilder<>* ir_builder, - int64 num_dynamic_loop_bounds) - : ir_builder_(ir_builder), + llvm::IRBuilder<>* b, int64 num_dynamic_loop_bounds) + : b_(b), llvm_module_(llvm_module), - caller_insert_point_guard_(*ir_builder), + caller_insert_point_guard_(*b), num_dynamic_loop_bounds_(num_dynamic_loop_bounds) { Initialize(function_name, linkage, optimize_for_size_requested, enable_fast_math); @@ -61,7 +60,7 @@ IrFunction::IrFunction(const string& function_name, IrFunction::~IrFunction() { // Emit function return value. - ir_builder_->CreateRetVoid(); + b_->CreateRetVoid(); } DynamicLoopBounds IrFunction::GetDynamicLoopBounds() { @@ -174,7 +173,7 @@ void IrFunction::Initialize(const string& function_name, function_->addAttribute(argument.getArgNo() + 1, llvm::Attribute::NoAlias); } - ir_builder_->SetInsertPoint(llvm::BasicBlock::Create( + b_->SetInsertPoint(llvm::BasicBlock::Create( /*Context=*/llvm_module_->getContext(), /*Name=*/"entry", /*Parent=*/function_)); @@ -184,9 +183,8 @@ llvm::Value* IrFunction::GetDynamicLoopBound(const int64 offset) { CHECK_GT(num_dynamic_loop_bounds_, 0); CHECK_LT(offset, num_dynamic_loop_bounds_ * 2); string name = tensorflow::strings::StrCat("dynamic_loop_bound_", offset); - return ir_builder_->CreateLoad( - ir_builder_->CreateGEP(CHECK_NOTNULL(dynamic_loop_bounds_arg_), - ir_builder_->getInt64(offset), AsStringRef(name))); + return b_->CreateLoad(b_->CreateGEP(CHECK_NOTNULL(dynamic_loop_bounds_arg_), + b_->getInt64(offset), AsStringRef(name))); } // Emits code to allocate an array of parameter address pointers, and store @@ -195,27 +193,25 @@ llvm::Value* IrFunction::GetDynamicLoopBound(const int64 offset) { // address buffer). std::vector GetArrayFunctionCallArguments( tensorflow::gtl::ArraySlice parameter_addresses, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece name, + llvm::IRBuilder<>* b, tensorflow::StringPiece name, llvm::Value* return_value_buffer, llvm::Value* exec_run_options_arg, llvm::Value* temp_buffers_arg, llvm::Value* profile_counters_arg) { llvm::Value* parameter_addresses_buffer = llvm_ir::EmitAllocaAtFunctionEntryWithCount( - ir_builder->getInt8PtrTy(), - ir_builder->getInt32(parameter_addresses.size()), - tensorflow::strings::StrCat(name, "_parameter_addresses"), - ir_builder); + b->getInt8PtrTy(), b->getInt32(parameter_addresses.size()), + tensorflow::strings::StrCat(name, "_parameter_addresses"), b); for (size_t i = 0; i < parameter_addresses.size(); ++i) { - llvm::Value* parameter_as_i8ptr = ir_builder->CreateBitCast( - parameter_addresses[i], ir_builder->getInt8PtrTy(), - AsStringRef(tensorflow::strings::StrCat(name, "_parameter_", i, - "_address_as_i8ptr"))); - 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_addresses); + llvm::Value* parameter_as_i8ptr = + b->CreateBitCast(parameter_addresses[i], b->getInt8PtrTy(), + AsStringRef(tensorflow::strings::StrCat( + name, "_parameter_", i, "_address_as_i8ptr"))); + llvm::Value* slot_in_param_addresses = + b->CreateInBoundsGEP(parameter_addresses_buffer, {b->getInt64(i)}); + b->CreateStore(parameter_as_i8ptr, slot_in_param_addresses); } const auto to_int8_ptr = [=](llvm::Value* ptr) { - return ir_builder->CreatePointerCast(ptr, ir_builder->getInt8PtrTy()); + return b->CreatePointerCast(ptr, b->getInt8PtrTy()); }; std::vector arguments{ to_int8_ptr(return_value_buffer), to_int8_ptr(exec_run_options_arg), @@ -230,22 +226,21 @@ std::vector GetArrayFunctionCallArguments( // calls to 'parallel_function' (and joins threads before returning). Status EmitCallToParallelForkJoin( const std::vector& arguments, const Shape& shape, - const std::vector& dimension_partition_counts, - llvm::IRBuilder<>* ir_builder, llvm::Function* parallel_function, - const string& name) { - llvm::Module* module = ir_builder->GetInsertBlock()->getModule(); + const std::vector& dimension_partition_counts, llvm::IRBuilder<>* b, + llvm::Function* parallel_function, const string& name) { + llvm::Module* module = b->GetInsertBlock()->getModule(); // Build ParallelForkJoin function type. std::vector compute_function_params = GetComputeFunctionParams(module, /*num_dynamic_loop_bounds=*/0); // Number of parallel compute functions. - compute_function_params.push_back(ir_builder->getInt32Ty()); + compute_function_params.push_back(b->getInt32Ty()); // Array of partitions. There is an array element for each // partition x partition_dim x 2 (for dimension start and limit). compute_function_params.push_back( llvm::Type::getInt64PtrTy(module->getContext())); // Number of partitioned most-major dimensions in 'shape'. - compute_function_params.push_back(ir_builder->getInt32Ty()); + compute_function_params.push_back(b->getInt32Ty()); // Function pointer for compute function to be dispatched in parallel. compute_function_params.push_back( llvm::Type::getInt8PtrTy(module->getContext())); @@ -268,7 +263,7 @@ Status EmitCallToParallelForkJoin( ShapePartitionIterator partition_iterator(shape, dimension_partition_counts); const int64 num_partitions = partition_iterator.GetTotalPartitionCount(); // Add argument specifying the number of parallel partitions. - fork_join_arguments.push_back(ir_builder->getInt32(num_partitions)); + fork_join_arguments.push_back(b->getInt32(num_partitions)); // The number of partitioned most-major dimensions in 'shape'. const int32 num_partitioned_dims = dimension_partition_counts.size(); @@ -293,15 +288,15 @@ Status EmitCallToParallelForkJoin( const std::pair& dim_partition = dim_partitions[j]; const int32 index = partition_index + j * dim_partition_size; // Store partition [dim_start, dim_limit) intervals for each dimension. - partitions[index] = ir_builder->getInt64(dim_partition.first); + partitions[index] = b->getInt64(dim_partition.first); partitions[index + 1] = - ir_builder->getInt64(dim_partition.first + dim_partition.second); + b->getInt64(dim_partition.first + dim_partition.second); } } // Create global variable out of dimension partitions in 'partitions'. llvm::ArrayType* partitions_array_type = - llvm::ArrayType::get(ir_builder->getInt64Ty(), partition_array_size); + llvm::ArrayType::get(b->getInt64Ty(), partition_array_size); llvm::Constant* partitions_array = llvm::ConstantArray::get(partitions_array_type, partitions); llvm::GlobalVariable* global_partitions_array = new llvm::GlobalVariable( @@ -315,16 +310,16 @@ Status EmitCallToParallelForkJoin( tensorflow::strings::StrCat(name, "_parallel_dimension_partitions"))); // Add argument specifying parallel dimension partitions. - fork_join_arguments.push_back(ir_builder->CreateBitCast( - global_partitions_array, - llvm::Type::getInt64PtrTy(module->getContext()))); + fork_join_arguments.push_back( + b->CreateBitCast(global_partitions_array, + llvm::Type::getInt64PtrTy(module->getContext()))); // Add argument specifying the number of partitioned most-major dimensions. - fork_join_arguments.push_back(ir_builder->getInt32(num_partitioned_dims)); + fork_join_arguments.push_back(b->getInt32(num_partitioned_dims)); // Add argument for parallel compute function pointer. fork_join_arguments.push_back( - ir_builder->CreateBitCast(parallel_function, ir_builder->getInt8PtrTy())); + b->CreateBitCast(parallel_function, b->getInt8PtrTy())); // Emit call to parallel fork/join. - ir_builder->CreateCall(fork_join_func, fork_join_arguments); + b->CreateCall(fork_join_func, fork_join_arguments); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/cpu/ir_function.h b/tensorflow/compiler/xla/service/cpu/ir_function.h index 2e55181eed..a41cbb64cd 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_function.h +++ b/tensorflow/compiler/xla/service/cpu/ir_function.h @@ -54,7 +54,7 @@ class IrFunction { IrFunction(const string& function_name, llvm::Function::LinkageTypes linkage, const bool optimize_for_size_requested, const bool enable_fast_math, llvm::Module* llvm_module, - llvm::IRBuilder<>* ir_builder, int64 num_dynamic_loop_bounds); + llvm::IRBuilder<>* b, int64 num_dynamic_loop_bounds); ~IrFunction(); // Emit ir to read and return the set of ir values representing the dynamic @@ -97,7 +97,7 @@ class IrFunction { // 'offset' from the "dynamic_loop_bounds" argument of this function. llvm::Value* GetDynamicLoopBound(int64 offset); - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm::Module* llvm_module_; llvm::IRBuilder<>::InsertPointGuard caller_insert_point_guard_; @@ -116,7 +116,7 @@ class IrFunction { // Returns an array of compute function call argument ir values. std::vector GetArrayFunctionCallArguments( tensorflow::gtl::ArraySlice parameter_addresses, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece name, + llvm::IRBuilder<>* b, tensorflow::StringPiece name, llvm::Value* return_value_buffer, llvm::Value* exec_run_options_arg, llvm::Value* temp_buffers_arg, llvm::Value* profile_counters_arg); @@ -124,9 +124,8 @@ std::vector GetArrayFunctionCallArguments( // calls to 'parallel_function' (and joins threads before returning). Status EmitCallToParallelForkJoin( const std::vector& arguments, const Shape& shape, - const std::vector& dimension_partition_counts, - llvm::IRBuilder<>* ir_builder, llvm::Function* parallel_function, - const string& name); + const std::vector& dimension_partition_counts, llvm::IRBuilder<>* b, + llvm::Function* parallel_function, const string& name); } // namespace cpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc b/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc index 2e5cc96098..ec0498e04e 100644 --- a/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc @@ -52,12 +52,12 @@ llvm::Function* EmitVectorF32TanhIfNeeded(llvm::Module* module, llvm::BasicBlock* vector_tanh_body = llvm::BasicBlock::Create(*context, "body", vector_tanh_function); - llvm::IRBuilder<> ir_builder(vector_tanh_body); + llvm::IRBuilder<> b(vector_tanh_body); llvm::FastMathFlags fast_math_flags; fast_math_flags.setFast(); - ir_builder.setFastMathFlags(fast_math_flags); + b.setFastMathFlags(fast_math_flags); - VectorSupportLibrary vsl(F32, vector_width, &ir_builder, "tanh_f32"); + VectorSupportLibrary vsl(F32, vector_width, &b, "tanh_f32"); llvm::Value* input = &*vector_tanh_function->arg_begin(); CHECK_EQ(input->getType(), vsl.vector_type()); @@ -91,7 +91,7 @@ llvm::Function* EmitVectorF32TanhIfNeeded(llvm::Module* module, } llvm::Value* result = vsl.Div(numerator, denominator); - ir_builder.CreateRet(result); + b.CreateRet(result); DCHECK(!llvm::verifyFunction(*vector_tanh_function)); return vector_tanh_function; @@ -113,12 +113,12 @@ llvm::Function* EmitVectorF32ExpIfNeeded(llvm::Module* module, llvm::BasicBlock* vector_exp_body = llvm::BasicBlock::Create(*context, "body", vector_exp_function); - llvm::IRBuilder<> ir_builder(vector_exp_body); + llvm::IRBuilder<> b(vector_exp_body); llvm::FastMathFlags fast_math_flags; fast_math_flags.setFast(); - ir_builder.setFastMathFlags(fast_math_flags); + b.setFastMathFlags(fast_math_flags); - VectorSupportLibrary vsl(F32, vector_width, &ir_builder, "exp_f32"); + VectorSupportLibrary vsl(F32, vector_width, &b, "exp_f32"); // This implements the same polynomial approximation as implemented in Eigen3. @@ -160,21 +160,21 @@ llvm::Function* EmitVectorF32ExpIfNeeded(llvm::Module* module, // VectorSupportLibrary (intentionally) can't juggle more than one type at a // time so drop down to IRBuilder for this bit. llvm::Value* vector_constant_0x7f = - ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(0x7f)); + b.CreateVectorSplat(vector_width, b.getInt32(0x7f)); llvm::Value* vector_constant_23 = - ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(23)); + b.CreateVectorSplat(vector_width, b.getInt32(23)); llvm::Type* i32_vector_type = - llvm::VectorType::get(ir_builder.getInt32Ty(), vector_width); + llvm::VectorType::get(b.getInt32Ty(), vector_width); // fx is clamped so we don't have to worry about it being out of range for // i32. - llvm::Value* emm0 = ir_builder.CreateFPToSI(fx, i32_vector_type); - emm0 = ir_builder.CreateAdd(emm0, vector_constant_0x7f); - emm0 = ir_builder.CreateShl(emm0, vector_constant_23); - llvm::Value* emm0_f32 = ir_builder.CreateBitCast(emm0, vsl.vector_type()); + llvm::Value* emm0 = b.CreateFPToSI(fx, i32_vector_type); + emm0 = b.CreateAdd(emm0, vector_constant_0x7f); + emm0 = b.CreateShl(emm0, vector_constant_23); + llvm::Value* emm0_f32 = b.CreateBitCast(emm0, vsl.vector_type()); llvm::Value* result = vsl.Max(vsl.Mul(y, emm0_f32), input); - ir_builder.CreateRet(result); + b.CreateRet(result); DCHECK(!llvm::verifyFunction(*vector_exp_function)); return vector_exp_function; @@ -196,13 +196,13 @@ llvm::Function* EmitVectorF32LogIfNeeded(llvm::Module* module, llvm::BasicBlock* vector_log_body = llvm::BasicBlock::Create(*context, "body", vector_log_function); - llvm::IRBuilder<> ir_builder(vector_log_body); + llvm::IRBuilder<> b(vector_log_body); llvm::FastMathFlags fast_math_flags; fast_math_flags.setFast(); - ir_builder.setFastMathFlags(fast_math_flags); + b.setFastMathFlags(fast_math_flags); llvm::Value* input = &*vector_log_function->arg_begin(); - VectorSupportLibrary vsl(F32, vector_width, &ir_builder, "log_f32"); + VectorSupportLibrary vsl(F32, vector_width, &b, "log_f32"); const llvm::APFloat half = GetIeeeF32(0.5); const llvm::APFloat one = GetIeeeF32(1.0); @@ -238,22 +238,21 @@ llvm::Function* EmitVectorF32LogIfNeeded(llvm::Module* module, // VectorSupportLibrary (intentionally) can't juggle more than one type at a // time so drop down to IRBuilder for this bit. llvm::Value* vector_constant_0x7f = - ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(0x7f)); + b.CreateVectorSplat(vector_width, b.getInt32(0x7f)); llvm::Value* vector_constant_23 = - ir_builder.CreateVectorSplat(vector_width, ir_builder.getInt32(23)); + b.CreateVectorSplat(vector_width, b.getInt32(23)); llvm::Type* i32_vector_type = - llvm::VectorType::get(ir_builder.getInt32Ty(), vector_width); + llvm::VectorType::get(b.getInt32Ty(), vector_width); - llvm::Value* emm0 = ir_builder.CreateLShr( - ir_builder.CreateBitCast(input, i32_vector_type), vector_constant_23); + llvm::Value* emm0 = + b.CreateLShr(b.CreateBitCast(input, i32_vector_type), vector_constant_23); // Keep only the fractional part. input = vsl.FloatAnd(input, inv_mant_mask); input = vsl.FloatOr(input, half); - emm0 = ir_builder.CreateSub(emm0, vector_constant_0x7f); - llvm::Value* e = - vsl.Add(one, ir_builder.CreateSIToFP(emm0, vsl.vector_type())); + emm0 = b.CreateSub(emm0, vector_constant_0x7f); + llvm::Value* e = vsl.Add(one, b.CreateSIToFP(emm0, vsl.vector_type())); // part2: // if( x < SQRTHF ) { @@ -294,7 +293,7 @@ llvm::Function* EmitVectorF32LogIfNeeded(llvm::Module* module, llvm::Value* or_rhs = vsl.FloatAnd(iszero_mask, minus_inf); llvm::Value* result = vsl.FloatOr(or_lhs, or_rhs); - ir_builder.CreateRet(result); + b.CreateRet(result); DCHECK(!llvm::verifyFunction(*vector_log_function)); return vector_log_function; diff --git a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc index 59ae5acd8b..8560e4296a 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.cc @@ -25,8 +25,8 @@ namespace cpu { ParallelLoopEmitter::ParallelLoopEmitter( const llvm_ir::ElementGenerator& target_element_generator, const llvm_ir::IrArray& target_array, - const DynamicLoopBounds* dynamic_loop_bounds, llvm::IRBuilder<>* ir_builder) - : LoopEmitter(target_element_generator, target_array, ir_builder), + const DynamicLoopBounds* dynamic_loop_bounds, llvm::IRBuilder<>* b) + : LoopEmitter(target_element_generator, target_array, b), dynamic_loop_bounds_(dynamic_loop_bounds) {} std::vector @@ -37,7 +37,7 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( CHECK(!ShapeUtil::IsTuple(shape_)); CHECK(!ShapeUtil::IsScalar(shape_)); - llvm_ir::ForLoopNest loop_nest(loop_name, ir_builder_); + llvm_ir::ForLoopNest loop_nest(loop_name, b_); const int64 num_dims = shape_.dimensions_size(); llvm_ir::IrArray::Index array_index(index_type, num_dims); @@ -65,8 +65,7 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( } } // Point IR builder at inner loop BB. - llvm_ir::SetToFirstInsertPoint(loop_nest.GetInnerLoopBodyBasicBlock(), - ir_builder_); + llvm_ir::SetToFirstInsertPoint(loop_nest.GetInnerLoopBodyBasicBlock(), b_); // Set exit_bb_ to the exit block of the loop nest. exit_bb_ = loop_nest.GetOuterLoopExitBasicBlock(); diff --git a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h index 25e182a26d..076c683ca5 100644 --- a/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/parallel_loop_emitter.h @@ -54,7 +54,7 @@ class ParallelLoopEmitter : public llvm_ir::LoopEmitter { ParallelLoopEmitter(const llvm_ir::ElementGenerator& target_element_generator, const llvm_ir::IrArray& target_array, const DynamicLoopBounds* dynamic_loop_bounds, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); ParallelLoopEmitter(const ParallelLoopEmitter&) = delete; ParallelLoopEmitter& operator=(const ParallelLoopEmitter&) = delete; diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc index ccb61740f6..01daed4bcd 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_noalias_test.cc @@ -78,7 +78,7 @@ TEST_F(CpuNoAliasTest, Concat) { llvm::Function* func = llvm::cast( ir_module.getOrInsertFunction("test_fn", llvm::Type::getVoidTy(context))); llvm::BasicBlock* bb = llvm::BasicBlock::Create(context, "body", func); - llvm::IRBuilder<> ir_builder(bb); + llvm::IRBuilder<> b(bb); auto* zero = llvm::ConstantInt::get(llvm::Type::getInt32Ty(context), 0); llvm_ir::IrArray::Index zero2D({zero, zero}); @@ -90,7 +90,7 @@ TEST_F(CpuNoAliasTest, Concat) { ir_module.getOrInsertGlobal("param_x", array2d_type); llvm_ir::IrArray param_x_array(param_x_val, param_shape); aa.AddAliasingInformationToIrArray(*param_x, ¶m_x_array); - param_x_array.EmitReadArrayElement(zero2D, &ir_builder) + param_x_array.EmitReadArrayElement(zero2D, &b) ->setName("read_param_x_array"); } @@ -100,7 +100,7 @@ TEST_F(CpuNoAliasTest, Concat) { auto shape = ShapeUtil::MakeShape(F32, {2, 4}); llvm_ir::IrArray concat1_array(concat1_val, shape); aa.AddAliasingInformationToIrArray(*concat1, &concat1_array); - concat1_array.EmitReadArrayElement(zero2D, &ir_builder) + concat1_array.EmitReadArrayElement(zero2D, &b) ->setName("read_concat1_array"); } @@ -110,7 +110,7 @@ TEST_F(CpuNoAliasTest, Concat) { auto shape = ShapeUtil::MakeShape(F32, {2, 6}); llvm_ir::IrArray concat2_array(concat2_val, shape); aa.AddAliasingInformationToIrArray(*concat2, &concat2_array); - concat2_array.EmitReadArrayElement(zero2D, &ir_builder) + concat2_array.EmitReadArrayElement(zero2D, &b) ->setName("read_concat2_array"); } diff --git a/tensorflow/compiler/xla/service/cpu/vector_support_library.cc b/tensorflow/compiler/xla/service/cpu/vector_support_library.cc index c444d15185..3274be8d9d 100644 --- a/tensorflow/compiler/xla/service/cpu/vector_support_library.cc +++ b/tensorflow/compiler/xla/service/cpu/vector_support_library.cc @@ -23,14 +23,14 @@ namespace xla { namespace cpu { VectorSupportLibrary::VectorSupportLibrary(PrimitiveType primitive_type, int64 vector_size, - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, std::string name) : vector_size_(vector_size), primitive_type_(primitive_type), - ir_builder_(ir_builder), + b_(b), name_(std::move(name)) { scalar_type_ = llvm_ir::PrimitiveTypeToIrType( - primitive_type, ir_builder_->GetInsertBlock()->getModule()); + primitive_type, b_->GetInsertBlock()->getModule()); scalar_pointer_type_ = llvm::PointerType::getUnqual(scalar_type_); vector_type_ = llvm::VectorType::get(scalar_type_, vector_size); vector_pointer_type_ = llvm::PointerType::getUnqual(vector_type_); @@ -63,9 +63,9 @@ llvm::Value* VectorSupportLibrary::Mul(llvm::Value* lhs, llvm::Value* rhs) { llvm::Value* VectorSupportLibrary::MulInternal(llvm::Value* lhs, llvm::Value* rhs) { if (scalar_type_->isFloatingPointTy()) { - return ir_builder()->CreateFMul(lhs, rhs, name()); + return b()->CreateFMul(lhs, rhs, name()); } else { - return ir_builder()->CreateMul(lhs, rhs, name()); + return b()->CreateMul(lhs, rhs, name()); } } @@ -76,13 +76,13 @@ llvm::Value* VectorSupportLibrary::Add(llvm::Value* lhs, llvm::Value* rhs) { llvm::Value* VectorSupportLibrary::Sub(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); - return ir_builder()->CreateFSub(lhs, rhs); + return b()->CreateFSub(lhs, rhs); } llvm::Value* VectorSupportLibrary::Max(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); if (scalar_type_->isFloatingPointTy()) { - return llvm_ir::EmitFloatMax(lhs, rhs, ir_builder_); + return llvm_ir::EmitFloatMax(lhs, rhs, b_); } else { LOG(FATAL) << "Max for integers is unimplemented"; } @@ -91,13 +91,13 @@ llvm::Value* VectorSupportLibrary::Max(llvm::Value* lhs, llvm::Value* rhs) { llvm::Value* VectorSupportLibrary::Floor(llvm::Value* a) { AssertCorrectTypes({a}); return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::floor, {a}, - {a->getType()}, ir_builder()); + {a->getType()}, b()); } llvm::Value* VectorSupportLibrary::Div(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); if (scalar_type_->isFloatingPointTy()) { - return ir_builder()->CreateFDiv(lhs, rhs, name()); + return b()->CreateFDiv(lhs, rhs, name()); } else { LOG(FATAL) << "Division for integers is unimplemented"; } @@ -111,42 +111,41 @@ llvm::Value* VectorSupportLibrary::Clamp(llvm::Value* a, CHECK(low.compare(high) == llvm::APFloat::cmpLessThan); CHECK(scalar_type_->isFloatingPointTy()); return llvm_ir::EmitFloatMin( - llvm_ir::EmitFloatMax(a, GetConstantFloat(type, low), ir_builder_), - GetConstantFloat(type, high), ir_builder_); + llvm_ir::EmitFloatMax(a, GetConstantFloat(type, low), b_), + GetConstantFloat(type, high), b_); } llvm::Value* VectorSupportLibrary::FCmpEQMask(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); - return I1ToFloat(ir_builder()->CreateFCmpOEQ(lhs, rhs, name())); + return I1ToFloat(b()->CreateFCmpOEQ(lhs, rhs, name())); } llvm::Value* VectorSupportLibrary::FCmpOLTMask(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); - return I1ToFloat(ir_builder()->CreateFCmpOLT(lhs, rhs, name())); + return I1ToFloat(b()->CreateFCmpOLT(lhs, rhs, name())); } llvm::Value* VectorSupportLibrary::FCmpULEMask(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); - return I1ToFloat(ir_builder()->CreateFCmpULE(lhs, rhs, name())); + return I1ToFloat(b()->CreateFCmpULE(lhs, rhs, name())); } llvm::Value* VectorSupportLibrary::I1ToFloat(llvm::Value* i1) { bool is_vector = llvm::isa(i1->getType()); llvm::Type* integer_type = IntegerTypeForFloatSize(is_vector); - return ir_builder()->CreateBitCast( - ir_builder()->CreateSExt(i1, integer_type, name()), - is_vector ? vector_type() : scalar_type(), name()); + return b()->CreateBitCast(b()->CreateSExt(i1, integer_type, name()), + is_vector ? vector_type() : scalar_type(), name()); } llvm::Type* VectorSupportLibrary::IntegerTypeForFloatSize(bool vector) { CHECK(scalar_type()->isFloatingPointTy()); const llvm::DataLayout& data_layout = - ir_builder()->GetInsertBlock()->getModule()->getDataLayout(); + b()->GetInsertBlock()->getModule()->getDataLayout(); int64 float_size_bits = data_layout.getTypeSizeInBits(scalar_type()); - llvm::Type* scalar_int_type = ir_builder()->getIntNTy(float_size_bits); + llvm::Type* scalar_int_type = b()->getIntNTy(float_size_bits); if (vector) { return llvm::VectorType::get(scalar_int_type, vector_size()); } else { @@ -156,7 +155,7 @@ llvm::Type* VectorSupportLibrary::IntegerTypeForFloatSize(bool vector) { llvm::Value* VectorSupportLibrary::BroadcastScalar(llvm::Value* x) { CHECK_EQ(x->getType(), scalar_type()); - return ir_builder()->CreateVectorSplat(vector_size(), x, name()); + return b()->CreateVectorSplat(vector_size(), x, name()); } llvm::Value* VectorSupportLibrary::FloatAnd(llvm::Value* lhs, @@ -164,10 +163,9 @@ llvm::Value* VectorSupportLibrary::FloatAnd(llvm::Value* lhs, AssertCorrectTypes({lhs, rhs}); llvm::Type* int_type = IntegerTypeForFloatSize(lhs->getType() == vector_type()); - return ir_builder()->CreateBitCast( - ir_builder()->CreateAnd( - ir_builder()->CreateBitCast(lhs, int_type, name()), - ir_builder()->CreateBitCast(rhs, int_type, name()), name()), + return b()->CreateBitCast( + b()->CreateAnd(b()->CreateBitCast(lhs, int_type, name()), + b()->CreateBitCast(rhs, int_type, name()), name()), vector_type()); } @@ -175,9 +173,8 @@ llvm::Value* VectorSupportLibrary::FloatNot(llvm::Value* lhs) { AssertCorrectTypes({lhs}); llvm::Type* int_type = IntegerTypeForFloatSize(lhs->getType() == vector_type()); - return ir_builder()->CreateBitCast( - ir_builder()->CreateNot( - ir_builder()->CreateBitCast(lhs, int_type, name()), name()), + return b()->CreateBitCast( + b()->CreateNot(b()->CreateBitCast(lhs, int_type, name()), name()), vector_type()); } @@ -185,47 +182,43 @@ llvm::Value* VectorSupportLibrary::FloatOr(llvm::Value* lhs, llvm::Value* rhs) { AssertCorrectTypes({lhs, rhs}); llvm::Type* int_type = IntegerTypeForFloatSize(lhs->getType() == vector_type()); - return ir_builder()->CreateBitCast( - ir_builder()->CreateOr(ir_builder()->CreateBitCast(lhs, int_type, name()), - ir_builder()->CreateBitCast(rhs, int_type, name()), - name()), + return b()->CreateBitCast( + b()->CreateOr(b()->CreateBitCast(lhs, int_type, name()), + b()->CreateBitCast(rhs, int_type, name()), name()), vector_type(), name()); } llvm::Value* VectorSupportLibrary::AddInternal(llvm::Value* lhs, llvm::Value* rhs) { if (scalar_type_->isFloatingPointTy()) { - return ir_builder()->CreateFAdd(lhs, rhs, name()); + return b()->CreateFAdd(lhs, rhs, name()); } else { - return ir_builder()->CreateAdd(lhs, rhs, name()); + return b()->CreateAdd(lhs, rhs, name()); } } llvm::Value* VectorSupportLibrary::ComputeOffsetPointer( llvm::Value* base_pointer, llvm::Value* offset_elements) { if (base_pointer->getType() != scalar_pointer_type()) { - base_pointer = ir_builder()->CreateBitCast(base_pointer, - scalar_pointer_type(), name()); + base_pointer = + b()->CreateBitCast(base_pointer, scalar_pointer_type(), name()); } - return ir_builder()->CreateInBoundsGEP(base_pointer, {offset_elements}, - name()); + return b()->CreateInBoundsGEP(base_pointer, {offset_elements}, name()); } llvm::Value* VectorSupportLibrary::LoadVector(llvm::Value* pointer) { if (pointer->getType() != vector_pointer_type()) { - pointer = - ir_builder()->CreateBitCast(pointer, vector_pointer_type(), name()); + pointer = b()->CreateBitCast(pointer, vector_pointer_type(), name()); } - return ir_builder()->CreateAlignedLoad( + return b()->CreateAlignedLoad( pointer, ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_), name()); } llvm::Value* VectorSupportLibrary::LoadScalar(llvm::Value* pointer) { if (pointer->getType() != scalar_pointer_type()) { - pointer = - ir_builder()->CreateBitCast(pointer, scalar_pointer_type(), name()); + pointer = b()->CreateBitCast(pointer, scalar_pointer_type(), name()); } - return ir_builder()->CreateAlignedLoad( + return b()->CreateAlignedLoad( pointer, ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_), name()); } @@ -233,30 +226,28 @@ void VectorSupportLibrary::StoreVector(llvm::Value* value, llvm::Value* pointer) { AssertCorrectTypes({value}); if (pointer->getType() != vector_pointer_type()) { - pointer = ir_builder()->CreateBitCast(pointer, vector_pointer_type()); + pointer = b()->CreateBitCast(pointer, vector_pointer_type()); } - ir_builder()->CreateAlignedStore( - value, pointer, ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_)); + b()->CreateAlignedStore(value, pointer, + ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_)); } void VectorSupportLibrary::StoreScalar(llvm::Value* value, llvm::Value* pointer) { AssertCorrectTypes({value}); if (pointer->getType() != scalar_pointer_type()) { - pointer = - ir_builder()->CreateBitCast(pointer, scalar_pointer_type(), name()); + pointer = b()->CreateBitCast(pointer, scalar_pointer_type(), name()); } - ir_builder()->CreateAlignedStore( - value, pointer, ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_)); + b()->CreateAlignedStore(value, pointer, + ShapeUtil::ByteSizeOfPrimitiveType(primitive_type_)); } llvm::Value* VectorSupportLibrary::LoadBroadcast(llvm::Value* pointer) { if (pointer->getType() != scalar_pointer_type()) { - pointer = - ir_builder()->CreateBitCast(pointer, scalar_pointer_type(), name()); + pointer = b()->CreateBitCast(pointer, scalar_pointer_type(), name()); } - return ir_builder()->CreateVectorSplat( - vector_size(), ir_builder()->CreateLoad(pointer), name()); + return b()->CreateVectorSplat(vector_size(), b()->CreateLoad(pointer), + name()); } llvm::Value* VectorSupportLibrary::AddReduce(llvm::Value* vector) { @@ -267,20 +258,19 @@ llvm::Value* VectorSupportLibrary::AddReduce(llvm::Value* vector) { for (unsigned j = 0; j < vector_size(); ++j) { if (j < (i / 2)) { - mask[j] = ir_builder()->getInt32(i / 2 + j); + mask[j] = b()->getInt32(i / 2 + j); } else { - mask[j] = llvm::UndefValue::get(ir_builder()->getInt32Ty()); + mask[j] = llvm::UndefValue::get(b()->getInt32Ty()); } } - llvm::Value* half_remaining_lanes = ir_builder()->CreateShuffleVector( - vector, llvm::UndefValue::get(vector_type()), - llvm::ConstantVector::get(mask), ""); + llvm::Value* half_remaining_lanes = + b()->CreateShuffleVector(vector, llvm::UndefValue::get(vector_type()), + llvm::ConstantVector::get(mask), ""); vector = Add(vector, half_remaining_lanes); } - return ir_builder()->CreateExtractElement(vector, ir_builder()->getInt32(0), - name()); + return b()->CreateExtractElement(vector, b()->getInt32(0), name()); } llvm::Value* VectorSupportLibrary::AvxStyleHorizontalAdd(llvm::Value* lhs, @@ -307,19 +297,19 @@ llvm::Value* VectorSupportLibrary::AvxStyleHorizontalAdd(llvm::Value* lhs, // vector, which are the lanes 2 and 3 in the rhs vector. for (int i = 0; i < vector_size(); i += 2) { int increment = i < vector_size() / 2 ? 0 : (vector_size() / 2); - mask_a.push_back(ir_builder()->getInt32(increment + i)); - mask_b.push_back(ir_builder()->getInt32(increment + i + 1)); + mask_a.push_back(b()->getInt32(increment + i)); + mask_b.push_back(b()->getInt32(increment + i + 1)); } for (int i = 0; i < vector_size(); i += 2) { int increment = i < vector_size() / 2 ? (vector_size() / 2) : vector_size(); - mask_a.push_back(ir_builder()->getInt32(increment + i)); - mask_b.push_back(ir_builder()->getInt32(increment + i + 1)); + mask_a.push_back(b()->getInt32(increment + i)); + mask_b.push_back(b()->getInt32(increment + i + 1)); } - llvm::Value* shuffle_0 = ir_builder()->CreateShuffleVector( - lhs, rhs, llvm::ConstantVector::get(mask_a)); - llvm::Value* shuffle_1 = ir_builder()->CreateShuffleVector( - lhs, rhs, llvm::ConstantVector::get(mask_b)); + llvm::Value* shuffle_0 = + b()->CreateShuffleVector(lhs, rhs, llvm::ConstantVector::get(mask_a)); + llvm::Value* shuffle_1 = + b()->CreateShuffleVector(lhs, rhs, llvm::ConstantVector::get(mask_b)); return Add(shuffle_0, shuffle_1); } @@ -327,23 +317,21 @@ llvm::Value* VectorSupportLibrary::AvxStyleHorizontalAdd(llvm::Value* lhs, llvm::Value* VectorSupportLibrary::ExtractLowHalf(llvm::Value* vector) { llvm::SmallVector mask; for (int i = 0; i < vector_size() / 2; i++) { - mask.push_back(ir_builder()->getInt32(i)); + mask.push_back(b()->getInt32(i)); } - return ir_builder()->CreateShuffleVector(vector, - llvm::UndefValue::get(vector_type()), - llvm::ConstantVector::get(mask)); + return b()->CreateShuffleVector(vector, llvm::UndefValue::get(vector_type()), + llvm::ConstantVector::get(mask)); } llvm::Value* VectorSupportLibrary::ExtractHighHalf(llvm::Value* vector) { llvm::SmallVector mask; for (int i = 0; i < vector_size() / 2; i++) { - mask.push_back(ir_builder()->getInt32(i + vector_size() / 2)); + mask.push_back(b()->getInt32(i + vector_size() / 2)); } - return ir_builder()->CreateShuffleVector(vector, - llvm::UndefValue::get(vector_type()), - llvm::ConstantVector::get(mask)); + return b()->CreateShuffleVector(vector, llvm::UndefValue::get(vector_type()), + llvm::ConstantVector::get(mask)); } std::vector VectorSupportLibrary::ComputeHorizontalSums( @@ -360,8 +348,8 @@ std::vector VectorSupportLibrary::ComputeHorizontalSums( [this](llvm::Value* vector) { return AddReduce(vector); }); if (init_values) { for (int64 i = 0, e = result.size(); i < e; i++) { - result[i] = Add(result[i], ir_builder()->CreateExtractElement( - init_values, ir_builder()->getInt32(i))); + result[i] = Add(result[i], + b()->CreateExtractElement(init_values, b()->getInt32(i))); } } return result; @@ -398,9 +386,9 @@ VectorSupportLibrary::ComputeAvxOptimizedHorizontalSums( std::vector results; for (int i = 0; i < lane_width; i++) { - llvm::Value* scalar_result = ir_builder()->CreateExtractElement( - i < (lane_width / 2) ? low : high, - ir_builder()->getInt32(i % (lane_width / 2)), name()); + llvm::Value* scalar_result = + b()->CreateExtractElement(i < (lane_width / 2) ? low : high, + b()->getInt32(i % (lane_width / 2)), name()); results.push_back(scalar_result); } @@ -415,17 +403,14 @@ llvm::Value* VectorSupportLibrary::GetZeroScalar() { return llvm::Constant::getNullValue(scalar_type()); } -LlvmVariable::LlvmVariable(llvm::Type* type, llvm::IRBuilder<>* ir_builder) - : ir_builder_(ir_builder) { - alloca_ = llvm_ir::EmitAllocaAtFunctionEntry(type, "", ir_builder_); +LlvmVariable::LlvmVariable(llvm::Type* type, llvm::IRBuilder<>* b) : b_(b) { + alloca_ = llvm_ir::EmitAllocaAtFunctionEntry(type, "", b_); } -llvm::Value* LlvmVariable::Get() const { - return ir_builder_->CreateLoad(alloca_); -} +llvm::Value* LlvmVariable::Get() const { return b_->CreateLoad(alloca_); } void LlvmVariable::Set(llvm::Value* new_value) { - ir_builder_->CreateStore(new_value, alloca_); + b_->CreateStore(new_value, alloca_); } TileVariable::TileVariable(VectorSupportLibrary* vector_support, diff --git a/tensorflow/compiler/xla/service/cpu/vector_support_library.h b/tensorflow/compiler/xla/service/cpu/vector_support_library.h index 49c2a4e2f4..c728f6df0a 100644 --- a/tensorflow/compiler/xla/service/cpu/vector_support_library.h +++ b/tensorflow/compiler/xla/service/cpu/vector_support_library.h @@ -46,11 +46,11 @@ class VectorSupportLibrary { // instance (i.e. LoadVector will load a vector of type <`vector_size` x // `primitive_type`>). VectorSupportLibrary(PrimitiveType primitive_type, int64 vector_size, - llvm::IRBuilder<>* ir_builder, std::string name); + llvm::IRBuilder<>* b, std::string name); llvm::Value* Mul(llvm::Value* lhs, llvm::Value* rhs); llvm::Value* Mul(int64 lhs, llvm::Value* rhs) { - return Mul(ir_builder()->getInt64(lhs), rhs); + return Mul(b()->getInt64(lhs), rhs); } llvm::Value* Mul(const llvm::APFloat& lhs, llvm::Value* rhs) { return Mul(GetConstantFloat(rhs->getType(), lhs), rhs); @@ -63,7 +63,7 @@ class VectorSupportLibrary { llvm::Value* Add(llvm::Value* lhs, llvm::Value* rhs); llvm::Value* Add(int64 lhs, llvm::Value* rhs) { - return Add(ir_builder()->getInt64(lhs), rhs); + return Add(b()->getInt64(lhs), rhs); } llvm::Value* Add(const llvm::APFloat& lhs, llvm::Value* rhs) { return Add(GetConstantFloat(rhs->getType(), lhs), rhs); @@ -147,13 +147,11 @@ class VectorSupportLibrary { llvm::Value* ComputeOffsetPointer(llvm::Value* base_pointer, llvm::Value* offset_elements, int64 scale) { return ComputeOffsetPointer( - base_pointer, - ir_builder_->CreateMul(ir_builder_->getInt64(scale), offset_elements)); + base_pointer, b_->CreateMul(b_->getInt64(scale), offset_elements)); } llvm::Value* ComputeOffsetPointer(llvm::Value* base_pointer, int64 offset_elements) { - return ComputeOffsetPointer(base_pointer, - ir_builder()->getInt64(offset_elements)); + return ComputeOffsetPointer(base_pointer, b()->getInt64(offset_elements)); } llvm::Value* LoadVector(llvm::Value* pointer); @@ -164,7 +162,7 @@ class VectorSupportLibrary { } llvm::Value* LoadVector(llvm::Value* base_pointer, int64 offset_elements) { - return LoadVector(base_pointer, ir_builder()->getInt64(offset_elements)); + return LoadVector(base_pointer, b()->getInt64(offset_elements)); } llvm::Value* LoadScalar(llvm::Value* pointer); @@ -175,7 +173,7 @@ class VectorSupportLibrary { } llvm::Value* LoadScalar(llvm::Value* base_pointer, int64 offset_elements) { - return LoadScalar(base_pointer, ir_builder()->getInt64(offset_elements)); + return LoadScalar(base_pointer, b()->getInt64(offset_elements)); } void StoreVector(llvm::Value* value, llvm::Value* pointer); @@ -187,7 +185,7 @@ class VectorSupportLibrary { void StoreVector(llvm::Value* value, llvm::Value* base_pointer, int64 offset_elements) { - StoreVector(value, base_pointer, ir_builder()->getInt64(offset_elements)); + StoreVector(value, base_pointer, b()->getInt64(offset_elements)); } void StoreScalar(llvm::Value* value, llvm::Value* pointer); @@ -198,7 +196,7 @@ class VectorSupportLibrary { void StoreScalar(llvm::Value* value, llvm::Value* base_pointer, int64 offset_elements) { - StoreScalar(base_pointer, ir_builder()->getInt64(offset_elements)); + StoreScalar(base_pointer, b()->getInt64(offset_elements)); } llvm::Value* LoadBroadcast(llvm::Value* pointer); @@ -207,7 +205,7 @@ class VectorSupportLibrary { return LoadBroadcast(ComputeOffsetPointer(base_pointer, offset_elements)); } llvm::Value* LoadBroadcast(llvm::Value* base_pointer, int64 offset_elements) { - return LoadBroadcast(base_pointer, ir_builder()->getInt64(offset_elements)); + return LoadBroadcast(base_pointer, b()->getInt64(offset_elements)); } // Compute the horizontal sum of each vector in `vectors`. The i'th element @@ -220,7 +218,7 @@ class VectorSupportLibrary { llvm::Value* GetZeroVector(); llvm::Value* GetZeroScalar(); - llvm::IRBuilder<>* ir_builder() const { return ir_builder_; } + llvm::IRBuilder<>* b() const { return b_; } int64 vector_size() const { return vector_size_; } llvm::Type* vector_type() const { return vector_type_; } llvm::Type* vector_pointer_type() const { return vector_pointer_type_; } @@ -277,7 +275,7 @@ class VectorSupportLibrary { int64 vector_size_; PrimitiveType primitive_type_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm::Type* vector_type_; llvm::Type* vector_pointer_type_; llvm::Type* scalar_type_; @@ -289,22 +287,21 @@ class VectorSupportLibrary { // can later convert to a SSA value. class LlvmVariable { public: - LlvmVariable(llvm::Type*, llvm::IRBuilder<>* ir_builder); + LlvmVariable(llvm::Type*, llvm::IRBuilder<>* b); llvm::Value* Get() const; void Set(llvm::Value* new_value); private: llvm::AllocaInst* alloca_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; }; class VectorVariable : public LlvmVariable { public: VectorVariable(VectorSupportLibrary* vector_support, llvm::Value* initial_value) - : LlvmVariable(vector_support->vector_type(), - vector_support->ir_builder()) { + : LlvmVariable(vector_support->vector_type(), vector_support->b()) { Set(initial_value); } }; @@ -313,8 +310,7 @@ class ScalarVariable : public LlvmVariable { public: ScalarVariable(VectorSupportLibrary* vector_support, llvm::Value* initial_value) - : LlvmVariable(vector_support->scalar_type(), - vector_support->ir_builder()) { + : LlvmVariable(vector_support->scalar_type(), vector_support->b()) { Set(initial_value); } }; diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index c51632597a..1eedd85363 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -61,13 +61,13 @@ int64 GlobalRandomValue() { llvm::Value* EmitReducePrecisionFloat(llvm::Value* x, int64 exponent_bits, int64 mantissa_bits, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { // Integer and float types for casting and constant generation. llvm::Type* float_type = x->getType(); - llvm::IntegerType* int_type = ir_builder->getInt32Ty(); + llvm::IntegerType* int_type = b->getInt32Ty(); // Cast the input value to an integer for bitwise manipulation. - llvm::Value* x_as_int = ir_builder->CreateBitCast(x, int_type); + llvm::Value* x_as_int = b->CreateBitCast(x, int_type); if (mantissa_bits < 23) { // Last remaining mantissa bit. @@ -77,22 +77,22 @@ llvm::Value* EmitReducePrecisionFloat(llvm::Value* x, int64 exponent_bits, // equal to a base value of 0111... plus one bit if the last remaining // mantissa bit is 1. const uint32_t base_rounding_bias = (last_mantissa_bit_mask >> 1) - 1; - llvm::Value* x_last_mantissa_bit = ir_builder->CreateLShr( - ir_builder->CreateAnd( - x_as_int, llvm::ConstantInt::get(int_type, last_mantissa_bit_mask)), + llvm::Value* x_last_mantissa_bit = b->CreateLShr( + b->CreateAnd(x_as_int, + llvm::ConstantInt::get(int_type, last_mantissa_bit_mask)), (23 - mantissa_bits)); - llvm::Value* x_rounding_bias = ir_builder->CreateAdd( - x_last_mantissa_bit, - llvm::ConstantInt::get(int_type, base_rounding_bias)); + llvm::Value* x_rounding_bias = + b->CreateAdd(x_last_mantissa_bit, + llvm::ConstantInt::get(int_type, base_rounding_bias)); // Add rounding bias, and mask out truncated bits. Note that the case // where adding the rounding bias overflows into the exponent bits is // correct; the non-masked mantissa bits will all be zero, and the // exponent will be incremented by one. const uint32_t truncation_mask = ~(last_mantissa_bit_mask - 1); - x_as_int = ir_builder->CreateAdd(x_as_int, x_rounding_bias); - x_as_int = ir_builder->CreateAnd( - x_as_int, llvm::ConstantInt::get(int_type, truncation_mask)); + x_as_int = b->CreateAdd(x_as_int, x_rounding_bias); + x_as_int = b->CreateAnd(x_as_int, + llvm::ConstantInt::get(int_type, truncation_mask)); } if (exponent_bits < 8) { @@ -120,29 +120,29 @@ llvm::Value* EmitReducePrecisionFloat(llvm::Value* x, int64 exponent_bits, f32_exponent_bias - reduced_exponent_bias; // Do we overflow or underflow? - llvm::Value* x_exponent = ir_builder->CreateAnd( + llvm::Value* x_exponent = b->CreateAnd( x_as_int, llvm::ConstantInt::get(int_type, f32_exp_bits_mask)); - llvm::Value* x_overflows = ir_builder->CreateICmpUGT( + llvm::Value* x_overflows = b->CreateICmpUGT( x_exponent, llvm::ConstantInt::get(int_type, reduced_max_exponent << 23)); - llvm::Value* x_underflows = ir_builder->CreateICmpULE( + llvm::Value* x_underflows = b->CreateICmpULE( x_exponent, llvm::ConstantInt::get(int_type, reduced_min_exponent << 23)); // Compute appropriately-signed values of zero and infinity. - llvm::Value* x_signed_zero = ir_builder->CreateAnd( + llvm::Value* x_signed_zero = b->CreateAnd( x_as_int, llvm::ConstantInt::get(int_type, f32_sign_bit_mask)); - llvm::Value* x_signed_inf = ir_builder->CreateOr( + llvm::Value* x_signed_inf = b->CreateOr( x_signed_zero, llvm::ConstantInt::get(int_type, f32_exp_bits_mask)); // Force to zero or infinity if overflow or underflow. (Note that this // truncates all denormal values to zero, rather than rounding them.) - x_as_int = ir_builder->CreateSelect(x_overflows, x_signed_inf, x_as_int); - x_as_int = ir_builder->CreateSelect(x_underflows, x_signed_zero, x_as_int); + x_as_int = b->CreateSelect(x_overflows, x_signed_inf, x_as_int); + x_as_int = b->CreateSelect(x_underflows, x_signed_zero, x_as_int); } // Cast the result back to a floating-point type. - llvm::Value* result = ir_builder->CreateBitCast(x_as_int, float_type); + llvm::Value* result = b->CreateBitCast(x_as_int, float_type); // Correct result for NaN inputs. // @@ -154,53 +154,49 @@ llvm::Value* EmitReducePrecisionFloat(llvm::Value* x, int64 exponent_bits, // // If the fast-math flags are set to assume no NaNs, the comparison is likely // to be optimized away, so there's no point in even emitting it. - if (!ir_builder->getFastMathFlags().noNaNs()) { - llvm::Value* x_is_nan = ir_builder->CreateFCmpUNO(x, x); + if (!b->getFastMathFlags().noNaNs()) { + llvm::Value* x_is_nan = b->CreateFCmpUNO(x, x); if (mantissa_bits > 0) { - result = ir_builder->CreateSelect(x_is_nan, x, result); + result = b->CreateSelect(x_is_nan, x, result); } else { - result = ir_builder->CreateSelect( + result = b->CreateSelect( x_is_nan, llvm::ConstantFP::getInfinity(float_type), result); } } return result; } -llvm::Value* EmitF32ToBF16(llvm::Value* f32_value, - llvm::IRBuilder<>* ir_builder) { +llvm::Value* EmitF32ToBF16(llvm::Value* f32_value, llvm::IRBuilder<>* b) { auto reduced_precision = EmitReducePrecisionFloat( f32_value, /*exponent_bits=*/primitive_util::kBFloat16ExponentBits, - /*mantissa_bits=*/primitive_util::kBFloat16MantissaBits, ir_builder); - auto as_int32 = - ir_builder->CreateBitCast(reduced_precision, ir_builder->getInt32Ty()); - auto shifted = ir_builder->CreateLShr(as_int32, 16); - auto truncated = ir_builder->CreateTrunc(shifted, ir_builder->getInt16Ty()); - return ir_builder->CreateBitCast(truncated, ir_builder->getInt16Ty()); + /*mantissa_bits=*/primitive_util::kBFloat16MantissaBits, b); + auto as_int32 = b->CreateBitCast(reduced_precision, b->getInt32Ty()); + auto shifted = b->CreateLShr(as_int32, 16); + auto truncated = b->CreateTrunc(shifted, b->getInt16Ty()); + return b->CreateBitCast(truncated, b->getInt16Ty()); } -llvm::Value* EmitBF16ToF32(llvm::Value* bf16_value, - llvm::IRBuilder<>* ir_builder) { - auto as_int16 = - ir_builder->CreateBitCast(bf16_value, ir_builder->getInt16Ty()); - auto as_int32 = ir_builder->CreateZExt(as_int16, ir_builder->getInt32Ty()); - auto shifted = ir_builder->CreateShl(as_int32, 16); - return ir_builder->CreateBitCast(shifted, ir_builder->getFloatTy()); +llvm::Value* EmitBF16ToF32(llvm::Value* bf16_value, llvm::IRBuilder<>* b) { + auto as_int16 = b->CreateBitCast(bf16_value, b->getInt16Ty()); + auto as_int32 = b->CreateZExt(as_int16, b->getInt32Ty()); + auto shifted = b->CreateShl(as_int32, 16); + return b->CreateBitCast(shifted, b->getFloatTy()); } llvm::Value* EmitIntegralToFloating(llvm::Value* integer_value, PrimitiveType from_type, PrimitiveType to_type, llvm::Module* module, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { if (primitive_util::IsSignedIntegralType(from_type)) { - return ir_builder->CreateSIToFP( - integer_value, llvm_ir::PrimitiveTypeToIrType(to_type, module)); + return b->CreateSIToFP(integer_value, + llvm_ir::PrimitiveTypeToIrType(to_type, module)); } else { CHECK(primitive_util::IsUnsignedIntegralType(from_type) || from_type == PRED); - return ir_builder->CreateUIToFP( - integer_value, llvm_ir::PrimitiveTypeToIrType(to_type, module)); + return b->CreateUIToFP(integer_value, + llvm_ir::PrimitiveTypeToIrType(to_type, module)); } } @@ -231,34 +227,31 @@ StatusOr ElementalIrEmitter::EmitIntegerUnaryOp( return operand_value; } if (primitive_util::IsIntegralType(to_type)) { - return ir_builder_->CreateIntCast( + return b_->CreateIntCast( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_), primitive_util::IsSignedIntegralType(from_type)); } if (primitive_util::IsFloatingPointType(to_type)) { if (to_type == BF16) { - return EmitF32ToBF16( - EmitIntegralToFloating(operand_value, from_type, F32, module_, - ir_builder_), - ir_builder_); + return EmitF32ToBF16(EmitIntegralToFloating(operand_value, from_type, + F32, module_, b_), + b_); } return EmitIntegralToFloating(operand_value, from_type, to_type, - module_, ir_builder_); + module_, b_); } if (primitive_util::IsComplexType(to_type)) { auto to_ir_component_type = llvm_ir::PrimitiveTypeToIrType( primitive_util::ComplexComponentType(to_type), module_); if (primitive_util::IsSignedIntegralType(from_type)) { return EmitComposeComplex( - op, - ir_builder_->CreateSIToFP(operand_value, to_ir_component_type), + op, b_->CreateSIToFP(operand_value, to_ir_component_type), nullptr); } if (primitive_util::IsUnsignedIntegralType(from_type) || from_type == PRED) { return EmitComposeComplex( - op, - ir_builder_->CreateUIToFP(operand_value, to_ir_component_type), + op, b_->CreateUIToFP(operand_value, to_ir_component_type), nullptr); } } @@ -275,7 +268,7 @@ StatusOr ElementalIrEmitter::EmitIntegerUnaryOp( } if (primitive_util::BitWidth(from_type) == primitive_util::BitWidth(to_type)) { - return ir_builder_->CreateBitCast( + return b_->CreateBitCast( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); } return InvalidArgument( @@ -293,18 +286,18 @@ StatusOr ElementalIrEmitter::EmitIntegerUnaryOp( auto type = llvm_ir::PrimitiveTypeToIrType(op->shape().element_type(), module_); auto zero = llvm::ConstantInt::get(type, 0); - auto cmp = ir_builder_->CreateICmpSGE(operand_value, zero); - return ir_builder_->CreateSelect(cmp, operand_value, - ir_builder_->CreateNeg(operand_value)); + auto cmp = b_->CreateICmpSGE(operand_value, zero); + return b_->CreateSelect(cmp, operand_value, + b_->CreateNeg(operand_value)); } else { return operand_value; } } case HloOpcode::kClz: { - auto is_zero_undef = ir_builder_->getFalse(); - return llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::ctlz, {operand_value, is_zero_undef}, - {operand_value->getType()}, ir_builder_); + auto is_zero_undef = b_->getFalse(); + return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::ctlz, + {operand_value, is_zero_undef}, + {operand_value->getType()}, b_); } case HloOpcode::kSign: { bool is_signed = @@ -312,31 +305,28 @@ StatusOr ElementalIrEmitter::EmitIntegerUnaryOp( auto type = llvm_ir::PrimitiveTypeToIrType(op->shape().element_type(), module_); auto zero = llvm::ConstantInt::get(type, 0); - auto cmp = ir_builder_->CreateICmpEQ(operand_value, zero); + auto cmp = b_->CreateICmpEQ(operand_value, zero); if (is_signed) { - auto ashr = ir_builder_->CreateAShr(operand_value, - type->getIntegerBitWidth() - 1); - return ir_builder_->CreateSelect(cmp, zero, - ir_builder_->CreateOr(ashr, 1)); + auto ashr = + b_->CreateAShr(operand_value, type->getIntegerBitWidth() - 1); + return b_->CreateSelect(cmp, zero, b_->CreateOr(ashr, 1)); } else { - return ir_builder_->CreateSelect(cmp, zero, - llvm::ConstantInt::get(type, 1)); + return b_->CreateSelect(cmp, zero, llvm::ConstantInt::get(type, 1)); } } case HloOpcode::kNegate: - return ir_builder_->CreateNeg(operand_value); + return b_->CreateNeg(operand_value); case HloOpcode::kNot: { auto type = op->shape().element_type(); if (type == PRED) { // It is not sufficient to just call CreateNot() here because a PRED // is represented as an i8 and the truth value is stored only in the // bottom bit. - return ir_builder_->CreateZExt( - ir_builder_->CreateNot(ir_builder_->CreateTrunc( - operand_value, ir_builder_->getInt1Ty())), + return b_->CreateZExt( + b_->CreateNot(b_->CreateTrunc(operand_value, b_->getInt1Ty())), llvm_ir::PrimitiveTypeToIrType(PRED, module_)); } else if (primitive_util::IsIntegralType(type)) { - return ir_builder_->CreateNot(operand_value); + return b_->CreateNot(operand_value); } return Unimplemented("unary op Not is not defined for type '%d'", type); } @@ -364,32 +354,31 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( } return EmitComposeComplex( op, - ir_builder_->CreateFPCast( - operand_value, - llvm_ir::PrimitiveTypeToIrType(to_component_type, module_)), + b_->CreateFPCast(operand_value, llvm_ir::PrimitiveTypeToIrType( + to_component_type, module_)), nullptr); } if (from_type == BF16) { TF_RET_CHECK(to_type != BF16); - operand_value = EmitBF16ToF32(operand_value, ir_builder_); + operand_value = EmitBF16ToF32(operand_value, b_); from_type = F32; if (from_type == to_type) { return operand_value; } } if (from_type == F32 && to_type == BF16) { - return EmitF32ToBF16(operand_value, ir_builder_); + return EmitF32ToBF16(operand_value, b_); } if (primitive_util::IsFloatingPointType(to_type)) { - return ir_builder_->CreateFPCast( + return b_->CreateFPCast( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); } if (primitive_util::IsSignedIntegralType(to_type)) { - return ir_builder_->CreateFPToSI( + return b_->CreateFPToSI( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); } if (primitive_util::IsUnsignedIntegralType(to_type)) { - return ir_builder_->CreateFPToUI( + return b_->CreateFPToUI( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); } return Unimplemented("unhandled conversion operation: %s => %s", @@ -405,7 +394,7 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( } if (primitive_util::BitWidth(from_type) == primitive_util::BitWidth(to_type)) { - return ir_builder_->CreateBitCast( + return b_->CreateBitCast( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); } return InvalidArgument( @@ -429,45 +418,45 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( case HloOpcode::kSin: return EmitSin(op->shape().element_type(), operand_value); case HloOpcode::kFloor: - return llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::floor, {operand_value}, {operand_value->getType()}, - ir_builder_); + return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::floor, + {operand_value}, + {operand_value->getType()}, b_); case HloOpcode::kCeil: - return llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::ceil, {operand_value}, {operand_value->getType()}, - ir_builder_); + return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::ceil, + {operand_value}, + {operand_value->getType()}, b_); case HloOpcode::kAbs: - return llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::fabs, {operand_value}, {operand_value->getType()}, - ir_builder_); + return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, + {operand_value}, + {operand_value->getType()}, b_); case HloOpcode::kRoundNearestAfz: - return llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::round, {operand_value}, {operand_value->getType()}, - ir_builder_); + return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::round, + {operand_value}, + {operand_value->getType()}, b_); case HloOpcode::kSign: { // TODO(b/32151903): Ensure consistent sign behavior for -0.0. auto type = operand_value->getType(); auto zero = llvm::ConstantFP::get(type, 0.0); - auto oeq = ir_builder_->CreateFCmpOEQ(operand_value, zero); - auto olt = ir_builder_->CreateFCmpOLT(operand_value, zero); - return ir_builder_->CreateSelect( + auto oeq = b_->CreateFCmpOEQ(operand_value, zero); + auto olt = b_->CreateFCmpOLT(operand_value, zero); + return b_->CreateSelect( oeq, zero, - ir_builder_->CreateSelect(olt, llvm::ConstantFP::get(type, -1.0), - llvm::ConstantFP::get(type, 1.0))); + b_->CreateSelect(olt, llvm::ConstantFP::get(type, -1.0), + llvm::ConstantFP::get(type, 1.0))); } case HloOpcode::kIsFinite: { // abs(x) o!= inf, this works because the comparison returns false if // either operand is NaN. auto type = operand_value->getType(); auto abs_value = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::fabs, {operand_value}, {type}, ir_builder_); + llvm::Intrinsic::fabs, {operand_value}, {type}, b_); auto infinity = llvm::ConstantFP::getInfinity(type); - auto not_infinite = ir_builder_->CreateFCmpONE(abs_value, infinity); - return ir_builder_->CreateZExt( - not_infinite, llvm_ir::PrimitiveTypeToIrType(PRED, module_)); + auto not_infinite = b_->CreateFCmpONE(abs_value, infinity); + return b_->CreateZExt(not_infinite, + llvm_ir::PrimitiveTypeToIrType(PRED, module_)); } case HloOpcode::kNegate: - return ir_builder_->CreateFNeg(operand_value); + return b_->CreateFNeg(operand_value); case HloOpcode::kReal: return operand_value; case HloOpcode::kImag: @@ -491,13 +480,12 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( auto a = EmitExtractReal(operand_value); auto b = EmitExtractImag(operand_value); llvm::Type* llvm_ty = a->getType(); - auto sum_sq = ir_builder_->CreateFAdd(ir_builder_->CreateFMul(a, a), - ir_builder_->CreateFMul(b, b)); + auto sum_sq = b_->CreateFAdd(b_->CreateFMul(a, a), b_->CreateFMul(b, b)); TF_ASSIGN_OR_RETURN(auto log_sum_sq, EmitLog(component_type, sum_sq)); TF_ASSIGN_OR_RETURN(auto angle, EmitAtan2(component_type, b, a)); auto one_half = llvm::ConstantFP::get(llvm_ty, 0.5); - return EmitComposeComplex( - op, ir_builder_->CreateFMul(one_half, log_sum_sq), angle); + return EmitComposeComplex(op, b_->CreateFMul(one_half, log_sum_sq), + angle); } case HloOpcode::kLog1p: { // log1p(a+bi) = .5*log((a+1)^2+b^2) + i*atan2(b, a + 1) @@ -505,15 +493,14 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( auto b = EmitExtractImag(operand_value); llvm::Type* llvm_ty = a->getType(); auto one = llvm::ConstantFP::get(llvm_ty, 1.0); - auto a_plus_one = ir_builder_->CreateFAdd(a, one); - auto sum_sq = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(a_plus_one, a_plus_one), - ir_builder_->CreateFMul(b, b)); + auto a_plus_one = b_->CreateFAdd(a, one); + auto sum_sq = b_->CreateFAdd(b_->CreateFMul(a_plus_one, a_plus_one), + b_->CreateFMul(b, b)); TF_ASSIGN_OR_RETURN(auto log_sum_sq, EmitLog(component_type, sum_sq)); TF_ASSIGN_OR_RETURN(auto angle, EmitAtan2(component_type, b, a_plus_one)); auto one_half = llvm::ConstantFP::get(llvm_ty, 0.5); - return EmitComposeComplex( - op, ir_builder_->CreateFMul(one_half, log_sum_sq), angle); + return EmitComposeComplex(op, b_->CreateFMul(one_half, log_sum_sq), + angle); } case HloOpcode::kConvert: { PrimitiveType from_type = op->operand(0)->shape().element_type(); @@ -527,12 +514,11 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( primitive_util::ComplexComponentType(to_type); auto to_ir_component_type = llvm_ir::PrimitiveTypeToIrType(to_component_type, module_); - return EmitComposeComplex( - op, - ir_builder_->CreateFPCast(EmitExtractReal(operand_value), - to_ir_component_type), - ir_builder_->CreateFPCast(EmitExtractImag(operand_value), - to_ir_component_type)); + return EmitComposeComplex(op, + b_->CreateFPCast(EmitExtractReal(operand_value), + to_ir_component_type), + b_->CreateFPCast(EmitExtractImag(operand_value), + to_ir_component_type)); } case HloOpcode::kExp: { // e^(a+bi) = e^a*(cos(b)+sin(b)i) @@ -542,8 +528,8 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( auto cos_b, EmitCos(component_type, EmitExtractImag(operand_value))); TF_ASSIGN_OR_RETURN( auto sin_b, EmitSin(component_type, EmitExtractImag(operand_value))); - return EmitComposeComplex(op, ir_builder_->CreateFMul(exp_a, cos_b), - ir_builder_->CreateFMul(exp_a, sin_b)); + return EmitComposeComplex(op, b_->CreateFMul(exp_a, cos_b), + b_->CreateFMul(exp_a, sin_b)); } case HloOpcode::kExpm1: { // e^(a+bi)-1 = (e^a*cos(b)-1)+e^a*sin(b)i @@ -554,9 +540,8 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( TF_ASSIGN_OR_RETURN( auto sin_b, EmitSin(component_type, EmitExtractImag(operand_value))); auto one = llvm::ConstantFP::get(exp_a->getType(), 1.0); - auto real_result = - ir_builder_->CreateFSub(ir_builder_->CreateFMul(exp_a, cos_b), one); - auto imag_result = ir_builder_->CreateFMul(exp_a, sin_b); + auto real_result = b_->CreateFSub(b_->CreateFMul(exp_a, cos_b), one); + auto imag_result = b_->CreateFMul(exp_a, sin_b); return EmitComposeComplex(op, real_result, imag_result); } case HloOpcode::kCos: { @@ -571,18 +556,14 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( auto b = EmitExtractImag(operand_value); auto type = a->getType(); TF_ASSIGN_OR_RETURN(auto exp_b, EmitExp(component_type, b)); - auto half_exp_b = - ir_builder_->CreateFMul(llvm::ConstantFP::get(type, 0.5), exp_b); + auto half_exp_b = b_->CreateFMul(llvm::ConstantFP::get(type, 0.5), exp_b); auto half_exp_neg_b = - ir_builder_->CreateFDiv(llvm::ConstantFP::get(type, 0.5), exp_b); + b_->CreateFDiv(llvm::ConstantFP::get(type, 0.5), exp_b); TF_ASSIGN_OR_RETURN(auto cos_a, EmitCos(component_type, a)); TF_ASSIGN_OR_RETURN(auto sin_a, EmitSin(component_type, a)); return EmitComposeComplex( - op, - ir_builder_->CreateFMul( - cos_a, ir_builder_->CreateFAdd(half_exp_neg_b, half_exp_b)), - ir_builder_->CreateFMul( - sin_a, ir_builder_->CreateFSub(half_exp_neg_b, half_exp_b))); + op, b_->CreateFMul(cos_a, b_->CreateFAdd(half_exp_neg_b, half_exp_b)), + b_->CreateFMul(sin_a, b_->CreateFSub(half_exp_neg_b, half_exp_b))); } case HloOpcode::kSin: { // sin(z) = .5i(e^(-iz) - e^(iz)) @@ -598,18 +579,14 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( auto b = EmitExtractImag(operand_value); auto type = a->getType(); TF_ASSIGN_OR_RETURN(auto exp_b, EmitExp(component_type, b)); - auto half_exp_b = - ir_builder_->CreateFMul(llvm::ConstantFP::get(type, 0.5), exp_b); + auto half_exp_b = b_->CreateFMul(llvm::ConstantFP::get(type, 0.5), exp_b); auto half_exp_neg_b = - ir_builder_->CreateFDiv(llvm::ConstantFP::get(type, 0.5), exp_b); + b_->CreateFDiv(llvm::ConstantFP::get(type, 0.5), exp_b); TF_ASSIGN_OR_RETURN(auto cos_a, EmitCos(component_type, a)); TF_ASSIGN_OR_RETURN(auto sin_a, EmitSin(component_type, a)); return EmitComposeComplex( - op, - ir_builder_->CreateFMul( - sin_a, ir_builder_->CreateFAdd(half_exp_b, half_exp_neg_b)), - ir_builder_->CreateFMul( - cos_a, ir_builder_->CreateFSub(half_exp_b, half_exp_neg_b))); + op, b_->CreateFMul(sin_a, b_->CreateFAdd(half_exp_b, half_exp_neg_b)), + b_->CreateFMul(cos_a, b_->CreateFSub(half_exp_b, half_exp_neg_b))); } case HloOpcode::kTanh: { /* @@ -637,64 +614,61 @@ StatusOr ElementalIrEmitter::EmitComplexUnaryOp( TF_ASSIGN_OR_RETURN(auto exp_a, EmitExp(component_type, a)); TF_ASSIGN_OR_RETURN(auto cos_b, EmitCos(component_type, b)); TF_ASSIGN_OR_RETURN(auto sin_b, EmitSin(component_type, b)); - auto exp_neg_a = ir_builder_->CreateFDiv( - llvm::ConstantFP::get(exp_a->getType(), 1), exp_a); - auto exp_2a_minus_exp_neg_2a = ir_builder_->CreateFSub( - ir_builder_->CreateFMul(exp_a, exp_a), - ir_builder_->CreateFMul(exp_neg_a, exp_neg_a)); - auto cos_b_sq = ir_builder_->CreateFMul(cos_b, cos_b); - auto sin_b_sq = ir_builder_->CreateFMul(sin_b, sin_b); - auto real_num = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(cos_b_sq, exp_2a_minus_exp_neg_2a), - ir_builder_->CreateFMul(sin_b_sq, exp_2a_minus_exp_neg_2a)); - auto cos_b_sin_b = ir_builder_->CreateFMul(cos_b, sin_b); - auto exp_a_plus_exp_neg_a = ir_builder_->CreateFAdd(exp_a, exp_neg_a); + auto exp_neg_a = + b_->CreateFDiv(llvm::ConstantFP::get(exp_a->getType(), 1), exp_a); + auto exp_2a_minus_exp_neg_2a = b_->CreateFSub( + b_->CreateFMul(exp_a, exp_a), b_->CreateFMul(exp_neg_a, exp_neg_a)); + auto cos_b_sq = b_->CreateFMul(cos_b, cos_b); + auto sin_b_sq = b_->CreateFMul(sin_b, sin_b); + auto real_num = + b_->CreateFAdd(b_->CreateFMul(cos_b_sq, exp_2a_minus_exp_neg_2a), + b_->CreateFMul(sin_b_sq, exp_2a_minus_exp_neg_2a)); + auto cos_b_sin_b = b_->CreateFMul(cos_b, sin_b); + auto exp_a_plus_exp_neg_a = b_->CreateFAdd(exp_a, exp_neg_a); auto exp_a_plus_exp_neg_a_sq = - ir_builder_->CreateFMul(exp_a_plus_exp_neg_a, exp_a_plus_exp_neg_a); - auto exp_a_minus_exp_neg_a = ir_builder_->CreateFSub(exp_a, exp_neg_a); + b_->CreateFMul(exp_a_plus_exp_neg_a, exp_a_plus_exp_neg_a); + auto exp_a_minus_exp_neg_a = b_->CreateFSub(exp_a, exp_neg_a); auto exp_a_minus_exp_neg_a_sq = - ir_builder_->CreateFMul(exp_a_minus_exp_neg_a, exp_a_minus_exp_neg_a); - auto imag_num = ir_builder_->CreateFMul( - cos_b_sin_b, ir_builder_->CreateFSub(exp_a_plus_exp_neg_a_sq, - exp_a_minus_exp_neg_a_sq)); - auto denom = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(cos_b_sq, exp_a_plus_exp_neg_a_sq), - ir_builder_->CreateFMul(sin_b_sq, exp_a_minus_exp_neg_a_sq)); - return EmitComposeComplex(op, ir_builder_->CreateFDiv(real_num, denom), - ir_builder_->CreateFDiv(imag_num, denom)); + b_->CreateFMul(exp_a_minus_exp_neg_a, exp_a_minus_exp_neg_a); + auto imag_num = b_->CreateFMul( + cos_b_sin_b, + b_->CreateFSub(exp_a_plus_exp_neg_a_sq, exp_a_minus_exp_neg_a_sq)); + auto denom = + b_->CreateFAdd(b_->CreateFMul(cos_b_sq, exp_a_plus_exp_neg_a_sq), + b_->CreateFMul(sin_b_sq, exp_a_minus_exp_neg_a_sq)); + return EmitComposeComplex(op, b_->CreateFDiv(real_num, denom), + b_->CreateFDiv(imag_num, denom)); } case HloOpcode::kAbs: { - auto sum_sq = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(operand_value), - EmitExtractReal(operand_value)), - ir_builder_->CreateFMul(EmitExtractImag(operand_value), - EmitExtractImag(operand_value))); + auto sum_sq = + b_->CreateFAdd(b_->CreateFMul(EmitExtractReal(operand_value), + EmitExtractReal(operand_value)), + b_->CreateFMul(EmitExtractImag(operand_value), + EmitExtractImag(operand_value))); return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::sqrt, {sum_sq}, - {sum_sq->getType()}, ir_builder_); + {sum_sq->getType()}, b_); } case HloOpcode::kSign: { // Sign(c) = c / |c| - auto sum_sq = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(operand_value), - EmitExtractReal(operand_value)), - ir_builder_->CreateFMul(EmitExtractImag(operand_value), - EmitExtractImag(operand_value))); + auto sum_sq = + b_->CreateFAdd(b_->CreateFMul(EmitExtractReal(operand_value), + EmitExtractReal(operand_value)), + b_->CreateFMul(EmitExtractImag(operand_value), + EmitExtractImag(operand_value))); auto cplx_abs = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::sqrt, {sum_sq}, {sum_sq->getType()}, ir_builder_); + llvm::Intrinsic::sqrt, {sum_sq}, {sum_sq->getType()}, b_); auto type = cplx_abs->getType(); auto zero = llvm::ConstantFP::get(type, 0.0); - auto oeq = ir_builder_->CreateFCmpOEQ(cplx_abs, zero); - return ir_builder_->CreateSelect( + auto oeq = b_->CreateFCmpOEQ(cplx_abs, zero); + return b_->CreateSelect( oeq, EmitComposeComplex(op, zero, zero), EmitComposeComplex( - op, - ir_builder_->CreateFDiv(EmitExtractReal(operand_value), cplx_abs), - ir_builder_->CreateFDiv(EmitExtractImag(operand_value), - cplx_abs))); + op, b_->CreateFDiv(EmitExtractReal(operand_value), cplx_abs), + b_->CreateFDiv(EmitExtractImag(operand_value), cplx_abs))); } case HloOpcode::kNegate: - return EmitComposeComplex( - op, ir_builder_->CreateFNeg(EmitExtractReal(operand_value)), - ir_builder_->CreateFNeg(EmitExtractImag(operand_value))); + return EmitComposeComplex(op, + b_->CreateFNeg(EmitExtractReal(operand_value)), + b_->CreateFNeg(EmitExtractImag(operand_value))); case HloOpcode::kReal: return EmitExtractReal(operand_value); case HloOpcode::kImag: @@ -728,15 +702,15 @@ StatusOr ElementalIrEmitter::EmitFloatBinaryOp( case HloOpcode::kComplex: return EmitComposeComplex(op, lhs_value, rhs_value); case HloOpcode::kAdd: - return ir_builder_->CreateFAdd(lhs_value, rhs_value); + return b_->CreateFAdd(lhs_value, rhs_value); case HloOpcode::kSubtract: - return ir_builder_->CreateFSub(lhs_value, rhs_value); + return b_->CreateFSub(lhs_value, rhs_value); case HloOpcode::kMultiply: - return ir_builder_->CreateFMul(lhs_value, rhs_value); + return b_->CreateFMul(lhs_value, rhs_value); case HloOpcode::kDivide: - return ir_builder_->CreateFDiv(lhs_value, rhs_value); + return b_->CreateFDiv(lhs_value, rhs_value); case HloOpcode::kRemainder: - return ir_builder_->CreateFRem(lhs_value, rhs_value); + return b_->CreateFRem(lhs_value, rhs_value); // LLVM comparisons can be "unordered" (U) or "ordered" (O) -- ordered // comparisons always return false when one of the operands is NaN, whereas // unordered comparisons return true. @@ -746,22 +720,22 @@ StatusOr ElementalIrEmitter::EmitFloatBinaryOp( // matches C++'s semantics. case HloOpcode::kEq: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OEQ, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kNe: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_UNE, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kLt: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OLT, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kGt: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OGT, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kLe: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OLE, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kGe: return llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OGE, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kMaximum: return EmitFloatMax(lhs_value, rhs_value); @@ -782,64 +756,56 @@ StatusOr ElementalIrEmitter::EmitComplexBinaryOp( llvm::Value* rhs_value) const { switch (op->opcode()) { case HloOpcode::kAdd: - return EmitComposeComplex( - op, - ir_builder_->CreateFAdd(EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFAdd(EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value))); + return EmitComposeComplex(op, + b_->CreateFAdd(EmitExtractReal(lhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFAdd(EmitExtractImag(lhs_value), + EmitExtractImag(rhs_value))); case HloOpcode::kSubtract: - return EmitComposeComplex( - op, - ir_builder_->CreateFSub(EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFSub(EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value))); + return EmitComposeComplex(op, + b_->CreateFSub(EmitExtractReal(lhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFSub(EmitExtractImag(lhs_value), + EmitExtractImag(rhs_value))); case HloOpcode::kMultiply: return EmitComposeComplex( op, - ir_builder_->CreateFSub( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value))), - ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractImag(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractReal(rhs_value)))); + b_->CreateFSub(b_->CreateFMul(EmitExtractReal(lhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFMul(EmitExtractImag(lhs_value), + EmitExtractImag(rhs_value))), + b_->CreateFAdd(b_->CreateFMul(EmitExtractReal(lhs_value), + EmitExtractImag(rhs_value)), + b_->CreateFMul(EmitExtractImag(lhs_value), + EmitExtractReal(rhs_value)))); case HloOpcode::kDivide: { // (a+bi) / (c+di) = ((a+bi)(c-di)) / ((c+di)(c-di)) // = ((ac + bd) + (bc - ad)i) / (c^2 + d^2) - auto rhs_sum_sq = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(rhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(rhs_value), - EmitExtractImag(rhs_value))); + auto rhs_sum_sq = + b_->CreateFAdd(b_->CreateFMul(EmitExtractReal(rhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFMul(EmitExtractImag(rhs_value), + EmitExtractImag(rhs_value))); auto type = rhs_sum_sq->getType(); auto zero = llvm::ConstantFP::get(type, 0.0); - auto oeq = ir_builder_->CreateFCmpOEQ(rhs_sum_sq, zero); - auto real_inf_or_nan = - ir_builder_->CreateFDiv(EmitExtractReal(lhs_value), zero); - auto imag_inf_or_nan = - ir_builder_->CreateFDiv(EmitExtractImag(lhs_value), zero); - return ir_builder_->CreateSelect( + auto oeq = b_->CreateFCmpOEQ(rhs_sum_sq, zero); + auto real_inf_or_nan = b_->CreateFDiv(EmitExtractReal(lhs_value), zero); + auto imag_inf_or_nan = b_->CreateFDiv(EmitExtractImag(lhs_value), zero); + return b_->CreateSelect( oeq, EmitComposeComplex(op, real_inf_or_nan, imag_inf_or_nan), EmitComposeComplex( op, - ir_builder_->CreateFDiv( - ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value))), + b_->CreateFDiv( + b_->CreateFAdd(b_->CreateFMul(EmitExtractReal(lhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFMul(EmitExtractImag(lhs_value), + EmitExtractImag(rhs_value))), rhs_sum_sq), - ir_builder_->CreateFDiv( - ir_builder_->CreateFSub( - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractImag(rhs_value))), + b_->CreateFDiv( + b_->CreateFSub(b_->CreateFMul(EmitExtractImag(lhs_value), + EmitExtractReal(rhs_value)), + b_->CreateFMul(EmitExtractReal(lhs_value), + EmitExtractImag(rhs_value))), rhs_sum_sq))); } // LLVM comparisons can be "unordered" (U) or "ordered" (O) -- ordered @@ -850,21 +816,21 @@ StatusOr ElementalIrEmitter::EmitComplexBinaryOp( // unordered comparison. This makes x != y equivalent to !(x == y), and // matches C++'s semantics. case HloOpcode::kEq: - return ir_builder_->CreateAnd( + return b_->CreateAnd( llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OEQ, EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value), ir_builder_), + EmitExtractReal(rhs_value), b_), llvm_ir::EmitComparison(llvm::CmpInst::FCMP_OEQ, EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value), ir_builder_)); + EmitExtractImag(rhs_value), b_)); case HloOpcode::kNe: - return ir_builder_->CreateOr( + return b_->CreateOr( llvm_ir::EmitComparison(llvm::CmpInst::FCMP_UNE, EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value), ir_builder_), + EmitExtractReal(rhs_value), b_), llvm_ir::EmitComparison(llvm::CmpInst::FCMP_UNE, EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value), ir_builder_)); + EmitExtractImag(rhs_value), b_)); case HloOpcode::kPower: { // (a+bi)^(c+di) = @@ -876,29 +842,26 @@ StatusOr ElementalIrEmitter::EmitComplexBinaryOp( auto b = EmitExtractImag(lhs_value); auto c = EmitExtractReal(rhs_value); auto d = EmitExtractImag(rhs_value); - auto aa_p_bb = ir_builder_->CreateFAdd(ir_builder_->CreateFMul(a, a), - ir_builder_->CreateFMul(b, b)); + auto aa_p_bb = b_->CreateFAdd(b_->CreateFMul(a, a), b_->CreateFMul(b, b)); auto one_half = llvm::ConstantFP::get(a->getType(), 0.5); - auto half_c = ir_builder_->CreateFMul(one_half, c); + auto half_c = b_->CreateFMul(one_half, c); TF_ASSIGN_OR_RETURN(auto aa_p_bb_to_half_c, EmitPow(component_type, aa_p_bb, half_c)); - auto neg_d = ir_builder_->CreateFNeg(d); + auto neg_d = b_->CreateFNeg(d); TF_ASSIGN_OR_RETURN(auto arg_lhs, EmitAtan2(component_type, b, a)); - auto neg_d_arg_lhs = ir_builder_->CreateFMul(neg_d, arg_lhs); + auto neg_d_arg_lhs = b_->CreateFMul(neg_d, arg_lhs); TF_ASSIGN_OR_RETURN(auto e_to_neg_d_arg_lhs, EmitExp(component_type, neg_d_arg_lhs)); - auto coeff = - ir_builder_->CreateFMul(aa_p_bb_to_half_c, e_to_neg_d_arg_lhs); + auto coeff = b_->CreateFMul(aa_p_bb_to_half_c, e_to_neg_d_arg_lhs); TF_ASSIGN_OR_RETURN(auto ln_aa_p_bb, EmitLog(component_type, aa_p_bb)); - auto half_d = ir_builder_->CreateFMul(one_half, d); - auto q = - ir_builder_->CreateFAdd(ir_builder_->CreateFMul(c, arg_lhs), - ir_builder_->CreateFMul(half_d, ln_aa_p_bb)); + auto half_d = b_->CreateFMul(one_half, d); + auto q = b_->CreateFAdd(b_->CreateFMul(c, arg_lhs), + b_->CreateFMul(half_d, ln_aa_p_bb)); TF_ASSIGN_OR_RETURN(auto cos_q, EmitCos(component_type, q)); TF_ASSIGN_OR_RETURN(auto sin_q, EmitSin(component_type, q)); - return EmitComposeComplex(op, ir_builder_->CreateFMul(coeff, cos_q), - ir_builder_->CreateFMul(coeff, sin_q)); + return EmitComposeComplex(op, b_->CreateFMul(coeff, cos_q), + b_->CreateFMul(coeff, sin_q)); } default: return Unimplemented("binary complex op '%s'", @@ -908,12 +871,12 @@ StatusOr ElementalIrEmitter::EmitComplexBinaryOp( llvm::Value* ElementalIrEmitter::EmitFloatMax(llvm::Value* lhs_value, llvm::Value* rhs_value) const { - return llvm_ir::EmitFloatMax(lhs_value, rhs_value, ir_builder_); + return llvm_ir::EmitFloatMax(lhs_value, rhs_value, b_); } llvm::Value* ElementalIrEmitter::EmitFloatMin(llvm::Value* lhs_value, llvm::Value* rhs_value) const { - return llvm_ir::EmitFloatMin(lhs_value, rhs_value, ir_builder_); + return llvm_ir::EmitFloatMin(lhs_value, rhs_value, b_); } StatusOr ElementalIrEmitter::EmitErfInv(PrimitiveType prim_type, @@ -925,15 +888,14 @@ StatusOr ElementalIrEmitter::EmitErfInv(PrimitiveType prim_type, "type F32."); } auto getFloat = [&](const float f) { - return llvm::ConstantFP::get(ir_builder_->getFloatTy(), f); + return llvm::ConstantFP::get(b_->getFloatTy(), f); }; auto multiply_add = [&](tensorflow::gtl::ArraySlice coefficients, llvm::Value* w) { llvm::Value* p = getFloat(coefficients.front()); coefficients.pop_front(); for (float coefficient : coefficients) { - p = ir_builder_->CreateFAdd(ir_builder_->CreateFMul(p, w), - getFloat(coefficient)); + p = b_->CreateFAdd(b_->CreateFMul(p, w), getFloat(coefficient)); } return p; }; @@ -951,50 +913,48 @@ StatusOr ElementalIrEmitter::EmitErfInv(PrimitiveType prim_type, // } // return p*x llvm::Function* logf_fn = llvm::Intrinsic::getDeclaration( - module_, llvm::Intrinsic::log, {ir_builder_->getFloatTy()}); + module_, llvm::Intrinsic::log, {b_->getFloatTy()}); - llvm::Value* w = ir_builder_->CreateFNeg(ir_builder_->CreateCall( - logf_fn, - {ir_builder_->CreateFMul(ir_builder_->CreateFSub(getFloat(1.0f), x), - ir_builder_->CreateFAdd(getFloat(1.0f), x))})); + llvm::Value* w = b_->CreateFNeg(b_->CreateCall( + logf_fn, {b_->CreateFMul(b_->CreateFSub(getFloat(1.0f), x), + b_->CreateFAdd(getFloat(1.0f), x))})); - llvm::Value* p_addr = llvm_ir::EmitAllocaAtFunctionEntry( - ir_builder_->getFloatTy(), "p.addr", ir_builder_); + llvm::Value* p_addr = + llvm_ir::EmitAllocaAtFunctionEntry(b_->getFloatTy(), "p.addr", b_); - llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(ir_builder_->CreateFCmpOLT(w, getFloat(5.0f)), - "w_less_than_five", ir_builder_); + llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( + b_->CreateFCmpOLT(w, getFloat(5.0f)), "w_less_than_five", b_); // Handle true BB. - SetToFirstInsertPoint(if_data.true_block, ir_builder_); + SetToFirstInsertPoint(if_data.true_block, b_); { - llvm::Value* lw = ir_builder_->CreateFSub(w, getFloat(2.5f)); + llvm::Value* lw = b_->CreateFSub(w, getFloat(2.5f)); tensorflow::gtl::ArraySlice lq{ 2.81022636e-08f, 3.43273939e-07f, -3.5233877e-06f, -4.39150654e-06f, 0.00021858087f, -0.00125372503f, -0.00417768164f, 0.246640727f, 1.50140941f}; llvm::Value* p = multiply_add(lq, lw); - ir_builder_->CreateStore(p, p_addr); + b_->CreateStore(p, p_addr); } // Handle false BB. - SetToFirstInsertPoint(if_data.false_block, ir_builder_); + SetToFirstInsertPoint(if_data.false_block, b_); { llvm::Function* sqrtf_fn = llvm::Intrinsic::getDeclaration( - module_, llvm::Intrinsic::sqrt, {ir_builder_->getFloatTy()}); + module_, llvm::Intrinsic::sqrt, {b_->getFloatTy()}); - llvm::Value* gw = ir_builder_->CreateFSub( - ir_builder_->CreateCall(sqrtf_fn, {w}), getFloat(3.0f)); + llvm::Value* gw = + b_->CreateFSub(b_->CreateCall(sqrtf_fn, {w}), getFloat(3.0f)); tensorflow::gtl::ArraySlice gq{ -0.000200214257f, 0.000100950558f, 0.00134934322f, -0.00367342844f, 0.00573950773f, -0.0076224613f, 0.00943887047f, 1.00167406f, 2.83297682f}; llvm::Value* p = multiply_add(gq, gw); - ir_builder_->CreateStore(p, p_addr); + b_->CreateStore(p, p_addr); } - SetToFirstInsertPoint(if_data.after_block, ir_builder_); - llvm::Value* p = ir_builder_->CreateLoad(p_addr); - return ir_builder_->CreateFMul(p, x); + SetToFirstInsertPoint(if_data.after_block, b_); + llvm::Value* p = b_->CreateLoad(p_addr); + return b_->CreateFMul(p, x); } StatusOr ElementalIrEmitter::EmitErfcInv( @@ -1002,13 +962,13 @@ StatusOr ElementalIrEmitter::EmitErfcInv( // Compute erfcinv(value) by calculating erfinv(1.0 - value). auto type = llvm_ir::PrimitiveTypeToIrType(prim_type, module_); auto one = llvm::ConstantFP::get(type, 1.0); - return EmitErfInv(prim_type, ir_builder_->CreateFSub(one, value)); + return EmitErfInv(prim_type, b_->CreateFSub(one, value)); } StatusOr ElementalIrEmitter::EmitLog(PrimitiveType prim_type, llvm::Value* value) const { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::log, {value}, - {value->getType()}, ir_builder_); + {value->getType()}, b_); } StatusOr ElementalIrEmitter::EmitLog1p(PrimitiveType prim_type, @@ -1020,35 +980,34 @@ StatusOr ElementalIrEmitter::EmitLog1p(PrimitiveType prim_type, // When x is large, the naive evaluation of ln(x + 1) is more // accurate than the Taylor series. TF_ASSIGN_OR_RETURN(auto for_large_x, - EmitLog(prim_type, ir_builder_->CreateFAdd(x, one))); + EmitLog(prim_type, b_->CreateFAdd(x, one))); // The Taylor series for ln(x+1) is x - x^2/2 - x^3/3 + …. - auto for_small_x = ir_builder_->CreateFMul( - ir_builder_->CreateFAdd(ir_builder_->CreateFMul(negative_half, x), one), - x); + auto for_small_x = + b_->CreateFMul(b_->CreateFAdd(b_->CreateFMul(negative_half, x), one), x); const auto kAntilogarithmIsSmallThreshold = 1e-4; - auto abs_x = llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, {value}, - {type}, ir_builder_); - auto x_is_small = ir_builder_->CreateFCmpOLT( + auto abs_x = + llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, {value}, {type}, b_); + auto x_is_small = b_->CreateFCmpOLT( abs_x, llvm::ConstantFP::get(type, kAntilogarithmIsSmallThreshold)); - return ir_builder_->CreateSelect(x_is_small, for_small_x, for_large_x); + return b_->CreateSelect(x_is_small, for_small_x, for_large_x); } StatusOr ElementalIrEmitter::EmitSin(PrimitiveType prim_type, llvm::Value* value) const { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::sin, {value}, - {value->getType()}, ir_builder_); + {value->getType()}, b_); } StatusOr ElementalIrEmitter::EmitCos(PrimitiveType prim_type, llvm::Value* value) const { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::cos, {value}, - {value->getType()}, ir_builder_); + {value->getType()}, b_); } StatusOr ElementalIrEmitter::EmitExp(PrimitiveType prim_type, llvm::Value* value) const { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::exp, {value}, - {value->getType()}, ir_builder_); + {value->getType()}, b_); } StatusOr ElementalIrEmitter::EmitExpm1(PrimitiveType prim_type, @@ -1060,25 +1019,25 @@ StatusOr ElementalIrEmitter::EmitExpm1(PrimitiveType prim_type, // When the exponent is large, the naive evaluation of e^(x) - 1 is more // accurate than the Taylor series. TF_ASSIGN_OR_RETURN(auto exp_x, EmitExp(prim_type, value)); - auto for_large_x = ir_builder_->CreateFSub(exp_x, one); + auto for_large_x = b_->CreateFSub(exp_x, one); // The Taylor series for exp(x) is 1 + x + x^2/2 + x^3/6 + …. // We want exp(x)-1 which is x + x^2/2 + x^3/6 + …. - auto x_squared = ir_builder_->CreateFAdd(x, x); - auto x_squared_over_two = ir_builder_->CreateFMul(x_squared, half); - auto for_small_x = ir_builder_->CreateFAdd(x, x_squared_over_two); + auto x_squared = b_->CreateFAdd(x, x); + auto x_squared_over_two = b_->CreateFMul(x_squared, half); + auto for_small_x = b_->CreateFAdd(x, x_squared_over_two); const auto kExponentIsSmallThreshold = 1e-5; - auto abs_x = llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, {value}, - {type}, ir_builder_); - auto x_is_small = ir_builder_->CreateFCmpOLT( + auto abs_x = + llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::fabs, {value}, {type}, b_); + auto x_is_small = b_->CreateFCmpOLT( abs_x, llvm::ConstantFP::get(type, kExponentIsSmallThreshold)); - return ir_builder_->CreateSelect(x_is_small, for_small_x, for_large_x); + return b_->CreateSelect(x_is_small, for_small_x, for_large_x); } StatusOr ElementalIrEmitter::EmitPow(PrimitiveType prim_type, llvm::Value* lhs, llvm::Value* rhs) const { return llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::pow, {lhs, rhs}, - {lhs->getType()}, ir_builder_); + {lhs->getType()}, b_); } StatusOr ElementalIrEmitter::EmitAtan2(PrimitiveType prim_type, @@ -1093,11 +1052,10 @@ StatusOr ElementalIrEmitter::EmitReducePrecision( return Unimplemented("reduce-precision only implemented for F32"); } return EmitReducePrecisionFloat(x, /*exponent_bits=*/hlo->exponent_bits(), - /*mantissa_bits=*/hlo->mantissa_bits(), - ir_builder_); + /*mantissa_bits=*/hlo->mantissa_bits(), b_); } -static llvm::Value* SaturateShiftIfNecessary(llvm::IRBuilder<>* ir_builder, +static llvm::Value* SaturateShiftIfNecessary(llvm::IRBuilder<>* b, llvm::Value* lhs, llvm::Value* rhs, llvm::Value* shift_result, bool saturate_to_sign_bit) { @@ -1110,15 +1068,14 @@ static llvm::Value* SaturateShiftIfNecessary(llvm::IRBuilder<>* ir_builder, 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); + saturated_value = + b->CreateSelect(b->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); + b->CreateICmpULT(rhs, integer_bitsize_constant, "shft.chk"); + return b->CreateSelect(shift_amt_in_range, shift_result, saturated_value); } StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( @@ -1127,49 +1084,49 @@ StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( switch (op->opcode()) { // TODO(jingyue): add the "nsw" attribute for signed types. case HloOpcode::kAdd: - return ir_builder_->CreateAdd(lhs_value, rhs_value); + return b_->CreateAdd(lhs_value, rhs_value); case HloOpcode::kSubtract: - return ir_builder_->CreateSub(lhs_value, rhs_value); + return b_->CreateSub(lhs_value, rhs_value); case HloOpcode::kMultiply: - return ir_builder_->CreateMul(lhs_value, rhs_value); + return b_->CreateMul(lhs_value, rhs_value); case HloOpcode::kDivide: - return is_signed ? ir_builder_->CreateSDiv(lhs_value, rhs_value) - : ir_builder_->CreateUDiv(lhs_value, rhs_value); + return is_signed ? b_->CreateSDiv(lhs_value, rhs_value) + : b_->CreateUDiv(lhs_value, rhs_value); case HloOpcode::kRemainder: - return is_signed ? ir_builder_->CreateSRem(lhs_value, rhs_value) - : ir_builder_->CreateURem(lhs_value, rhs_value); + return is_signed ? b_->CreateSRem(lhs_value, rhs_value) + : b_->CreateURem(lhs_value, rhs_value); case HloOpcode::kEq: return llvm_ir::EmitComparison(llvm::CmpInst::ICMP_EQ, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kNe: return llvm_ir::EmitComparison(llvm::CmpInst::ICMP_NE, lhs_value, - rhs_value, ir_builder_); + rhs_value, b_); case HloOpcode::kLt: return llvm_ir::EmitComparison( is_signed ? llvm::CmpInst::ICMP_SLT : llvm::CmpInst::ICMP_ULT, - lhs_value, rhs_value, ir_builder_); + lhs_value, rhs_value, b_); case HloOpcode::kGt: return llvm_ir::EmitComparison( is_signed ? llvm::CmpInst::ICMP_SGT : llvm::CmpInst::ICMP_UGT, - lhs_value, rhs_value, ir_builder_); + lhs_value, rhs_value, b_); case HloOpcode::kLe: return llvm_ir::EmitComparison( is_signed ? llvm::CmpInst::ICMP_SLE : llvm::CmpInst::ICMP_ULE, - lhs_value, rhs_value, ir_builder_); + lhs_value, rhs_value, b_); case HloOpcode::kGe: return llvm_ir::EmitComparison( is_signed ? llvm::CmpInst::ICMP_SGE : llvm::CmpInst::ICMP_UGE, - lhs_value, rhs_value, ir_builder_); + lhs_value, rhs_value, b_); case HloOpcode::kMinimum: return EmitIntegralMin(lhs_value, rhs_value, is_signed); case HloOpcode::kMaximum: return EmitIntegralMax(lhs_value, rhs_value, is_signed); case HloOpcode::kAnd: - return ir_builder_->CreateAnd(lhs_value, rhs_value); + return b_->CreateAnd(lhs_value, rhs_value); case HloOpcode::kOr: - return ir_builder_->CreateOr(lhs_value, rhs_value); + return b_->CreateOr(lhs_value, rhs_value); case HloOpcode::kXor: - return ir_builder_->CreateXor(lhs_value, rhs_value); + return b_->CreateXor(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 @@ -1177,20 +1134,17 @@ StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( // UB. We replace the poison value with a constant to avoid this deferred // UB. case HloOpcode::kShiftRightArithmetic: - return SaturateShiftIfNecessary( - ir_builder_, lhs_value, rhs_value, - ir_builder_->CreateAShr(lhs_value, rhs_value), - /*saturate_to_sign_bit=*/true); + return SaturateShiftIfNecessary(b_, lhs_value, rhs_value, + b_->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); + return SaturateShiftIfNecessary(b_, lhs_value, rhs_value, + b_->CreateShl(lhs_value, rhs_value), + /*saturate_to_sign_bit=*/false); case HloOpcode::kShiftRightLogical: - return SaturateShiftIfNecessary( - ir_builder_, lhs_value, rhs_value, - ir_builder_->CreateLShr(lhs_value, rhs_value), - /*saturate_to_sign_bit=*/false); + return SaturateShiftIfNecessary(b_, lhs_value, rhs_value, + b_->CreateLShr(lhs_value, rhs_value), + /*saturate_to_sign_bit=*/false); default: return Unimplemented("binary integer op '%s'", HloOpcodeString(op->opcode()).c_str()); @@ -1200,21 +1154,19 @@ StatusOr ElementalIrEmitter::EmitIntegerBinaryOp( llvm::Value* ElementalIrEmitter::EmitIntegralMax(llvm::Value* lhs_value, llvm::Value* rhs_value, bool is_signed) const { - return ir_builder_->CreateSelect( - ir_builder_->CreateICmp( - is_signed ? llvm::ICmpInst::ICMP_SGE : llvm::ICmpInst::ICMP_UGE, - lhs_value, rhs_value), - lhs_value, rhs_value); + return b_->CreateSelect(b_->CreateICmp(is_signed ? llvm::ICmpInst::ICMP_SGE + : llvm::ICmpInst::ICMP_UGE, + lhs_value, rhs_value), + lhs_value, rhs_value); } llvm::Value* ElementalIrEmitter::EmitIntegralMin(llvm::Value* lhs_value, llvm::Value* rhs_value, bool is_signed) const { - return ir_builder_->CreateSelect( - ir_builder_->CreateICmp( - is_signed ? llvm::ICmpInst::ICMP_SLE : llvm::ICmpInst::ICMP_ULE, - lhs_value, rhs_value), - lhs_value, rhs_value); + return b_->CreateSelect(b_->CreateICmp(is_signed ? llvm::ICmpInst::ICMP_SLE + : llvm::ICmpInst::ICMP_ULE, + lhs_value, rhs_value), + lhs_value, rhs_value); } llvm_ir::IrArray::Index ElementalIrEmitter::ElementwiseSourceIndex( @@ -1267,10 +1219,10 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeRngElementGenerator( // Same values as PCG library // https://github.com/imneme/pcg-c/blob/master/include/pcg_variants.h - llvm::Value* multiplier = ir_builder_->getInt( - llvm::APInt(128, {0x4385DF649FCCF645, 0x2360ED051FC65DA4})); - llvm::Value* increment = ir_builder_->getInt( - llvm::APInt(128, {0x14057B7EF767814F, 0x5851F42D4C957F2D})); + llvm::Value* multiplier = + b_->getInt(llvm::APInt(128, {0x4385DF649FCCF645, 0x2360ED051FC65DA4})); + llvm::Value* increment = + b_->getInt(llvm::APInt(128, {0x14057B7EF767814F, 0x5851F42D4C957F2D})); auto random_value_from_hlo = [hlo]() { const HloModule* module = @@ -1291,10 +1243,10 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeRngElementGenerator( // values. llvm::GlobalVariable* state_ptr0 = new llvm::GlobalVariable( /*M=*/*module_, - /*Ty=*/ir_builder_->getInt64Ty(), + /*Ty=*/b_->getInt64Ty(), /*isConstant=*/false, /*Linkage=*/llvm::GlobalValue::PrivateLinkage, - /*Initializer=*/ir_builder_->getInt64(random_value_from_hlo()), + /*Initializer=*/b_->getInt64(random_value_from_hlo()), /*Name=*/"state_ptr0"); // When the module config seed is 0, the expected result of a prng is a random @@ -1305,17 +1257,16 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeRngElementGenerator( : GlobalRandomValue(); llvm::GlobalVariable* state_ptr1 = new llvm::GlobalVariable( /*M=*/*module_, - /*Ty=*/ir_builder_->getInt64Ty(), + /*Ty=*/b_->getInt64Ty(), /*isConstant=*/false, /*Linkage=*/llvm::GlobalValue::PrivateLinkage, - /*Initializer=*/ir_builder_->getInt64(graph_seed), + /*Initializer=*/b_->getInt64(graph_seed), /*Name=*/"state_ptr1"); // We want each thread to use its own stream, so we modify the increment per // thread. We want the increment to remain odd, so we shift the thread id left // 1 and add it to the increment. - increment = ir_builder_->CreateAdd(increment, - ir_builder_->CreateShl(EmitThreadId(), 1)); + increment = b_->CreateAdd(increment, b_->CreateShl(EmitThreadId(), 1)); // PCG-XSL-RR algorithm // http://www.pcg-random.org/pdf/toms-oneill-pcg-family-v1.02.pdf @@ -1323,38 +1274,29 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeRngElementGenerator( // return uint64_t(state ^ (state >> 64))) >>> (state >> 122) // where ">>>" is bitwise rotation auto get_next_i64 = [=]() { - llvm::Value* state0 = ir_builder_->CreateZExtOrTrunc( - ir_builder_->CreateLoad(state_ptr0, "state0"), - ir_builder_->getInt128Ty()); - llvm::Value* state1 = ir_builder_->CreateShl( - ir_builder_->CreateZExtOrTrunc( - ir_builder_->CreateLoad(state_ptr1, "state1"), - ir_builder_->getInt128Ty()), + llvm::Value* state0 = b_->CreateZExtOrTrunc( + b_->CreateLoad(state_ptr0, "state0"), b_->getInt128Ty()); + llvm::Value* state1 = b_->CreateShl( + b_->CreateZExtOrTrunc(b_->CreateLoad(state_ptr1, "state1"), + b_->getInt128Ty()), 64); - llvm::Value* state = ir_builder_->CreateOr(state0, state1); - llvm::Value* updated = ir_builder_->CreateAdd( - ir_builder_->CreateMul(state, multiplier), increment); - ir_builder_->CreateStore( - ir_builder_->CreateTrunc(updated, ir_builder_->getInt64Ty()), - state_ptr0); - ir_builder_->CreateStore( - ir_builder_->CreateTrunc(ir_builder_->CreateLShr(updated, 64), - ir_builder_->getInt64Ty()), + llvm::Value* state = b_->CreateOr(state0, state1); + llvm::Value* updated = + b_->CreateAdd(b_->CreateMul(state, multiplier), increment); + b_->CreateStore(b_->CreateTrunc(updated, b_->getInt64Ty()), state_ptr0); + b_->CreateStore( + b_->CreateTrunc(b_->CreateLShr(updated, 64), b_->getInt64Ty()), state_ptr1); return llvm_ir::CreateRor( - ir_builder_->CreateTrunc( - ir_builder_->CreateXor(state, ir_builder_->CreateLShr(state, 64)), - ir_builder_->getInt64Ty()), - ir_builder_->CreateTrunc(ir_builder_->CreateLShr(state, 122), - ir_builder_->getInt64Ty()), - ir_builder_); + b_->CreateTrunc(b_->CreateXor(state, b_->CreateLShr(state, 64)), + b_->getInt64Ty()), + b_->CreateTrunc(b_->CreateLShr(state, 122), b_->getInt64Ty()), b_); }; auto get_next_uniform_float = [=]() { - return ir_builder_->CreateFDiv( - ir_builder_->CreateUIToFP(get_next_i64(), param_ir_type), - llvm::ConstantFP::get(param_ir_type, 0x1p64)); + return b_->CreateFDiv(b_->CreateUIToFP(get_next_i64(), param_ir_type), + llvm::ConstantFP::get(param_ir_type, 0x1p64)); }; return [=](const llvm_ir::IrArray::Index& index) -> StatusOr { @@ -1365,52 +1307,50 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeRngElementGenerator( TF_ASSIGN_OR_RETURN(llvm::Value * q, operand_to_generator.at(hlo->operand(1))(index)); if (primitive_util::IsFloatingPointType(param_prim_type)) { - return ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(ir_builder_->CreateFSub(q, p), - get_next_uniform_float()), + return b_->CreateFAdd( + b_->CreateFMul(b_->CreateFSub(q, p), get_next_uniform_float()), p); } else { - auto r = ir_builder_->CreateSub(q, p); + auto r = b_->CreateSub(q, p); auto leading_zeros = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::ctlz, {r, ir_builder_->getInt1(true)}, - {param_ir_type}, ir_builder_); - auto in_block = ir_builder_->GetInsertBlock(); + llvm::Intrinsic::ctlz, {r, b_->getInt1(true)}, {param_ir_type}, + b_); + auto in_block = b_->GetInsertBlock(); // A terminator should be present iff we're emitting code // into the middle (as opposed to the end) of a basic block. - CHECK_EQ(ir_builder_->GetInsertPoint() == in_block->end(), + CHECK_EQ(b_->GetInsertPoint() == in_block->end(), in_block->getTerminator() == nullptr); llvm::BasicBlock* body_block; llvm::BasicBlock* out_block; - if (ir_builder_->GetInsertPoint() == in_block->end()) { - body_block = llvm_ir::CreateBasicBlock( - nullptr, IrName(hlo, "rng_body"), ir_builder_); - out_block = llvm_ir::CreateBasicBlock( - nullptr, IrName(hlo, "rng_out"), ir_builder_); + if (b_->GetInsertPoint() == in_block->end()) { + body_block = + llvm_ir::CreateBasicBlock(nullptr, IrName(hlo, "rng_body"), b_); + out_block = + llvm_ir::CreateBasicBlock(nullptr, IrName(hlo, "rng_out"), b_); llvm::BranchInst::Create(body_block, in_block); } else { - body_block = in_block->splitBasicBlock( - ir_builder_->GetInsertPoint(), "rng_body"); - out_block = body_block->splitBasicBlock( - ir_builder_->GetInsertPoint(), "rng_out"); + body_block = + in_block->splitBasicBlock(b_->GetInsertPoint(), "rng_body"); + out_block = + body_block->splitBasicBlock(b_->GetInsertPoint(), "rng_out"); body_block->getTerminator()->eraseFromParent(); } - SetToFirstInsertPoint(body_block, ir_builder_); - auto random = ir_builder_->CreateAnd( - ir_builder_->CreateZExtOrTrunc(get_next_i64(), param_ir_type), - ir_builder_->CreateLShr(llvm::ConstantInt::get(param_ir_type, ~0), - leading_zeros)); + SetToFirstInsertPoint(body_block, b_); + auto random = b_->CreateAnd( + b_->CreateZExtOrTrunc(get_next_i64(), param_ir_type), + b_->CreateLShr(llvm::ConstantInt::get(param_ir_type, ~0), + leading_zeros)); llvm::BranchInst::Create(out_block, body_block, - ir_builder_->CreateICmpULT(random, r), - body_block); - SetToFirstInsertPoint(out_block, ir_builder_); - return ir_builder_->CreateAdd( - p, ir_builder_->CreateSelect( - ir_builder_->CreateICmpEQ(p, q), - llvm::ConstantInt::get(param_ir_type, 0), random)); + b_->CreateICmpULT(random, r), body_block); + SetToFirstInsertPoint(out_block, b_); + return b_->CreateAdd( + p, b_->CreateSelect(b_->CreateICmpEQ(p, q), + llvm::ConstantInt::get(param_ir_type, 0), + random)); } } case RNG_NORMAL: { @@ -1420,11 +1360,11 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeRngElementGenerator( operand_to_generator.at(hlo->operand(1))(index)); TF_ASSIGN_OR_RETURN( llvm::Value * r, - EmitErfcInv(param_prim_type, - ir_builder_->CreateFMul( - llvm::ConstantFP::get(param_ir_type, 2.0), - get_next_uniform_float()))); - return ir_builder_->CreateFAdd(ir_builder_->CreateFMul(r, s), m); + EmitErfcInv( + param_prim_type, + b_->CreateFMul(llvm::ConstantFP::get(param_ir_type, 2.0), + get_next_uniform_float()))); + return b_->CreateFAdd(b_->CreateFMul(r, s), m); } default: return InvalidArgument( @@ -1447,9 +1387,8 @@ StatusOr ElementalIrEmitter::EmitElementalSelect( TF_ASSIGN_OR_RETURN(llvm::Value * on_false_value, operand_to_generator.at(hlo->operand(2))( ElementwiseSourceIndex(index, *hlo, 2))); - return ir_builder_->CreateSelect( - ir_builder_->CreateTrunc(pred_value, ir_builder_->getInt1Ty()), - on_true_value, on_false_value); + return b_->CreateSelect(b_->CreateTrunc(pred_value, b_->getInt1Ty()), + on_true_value, on_false_value); } StatusOr ElementalIrEmitter::EmitElementalClamp( @@ -1485,64 +1424,62 @@ StatusOr ElementalIrEmitter::EmitElementalConcatenate( const int64 concat_dim = hlo->dimensions(0); auto source_index = target_index; - llvm::BasicBlock* init_block = ir_builder_->GetInsertBlock(); + llvm::BasicBlock* init_block = b_->GetInsertBlock(); // A terminator should be present iff we're emitting code // into the middle (as opposed to the end) of a basic block. - CHECK_EQ(ir_builder_->GetInsertPoint() == init_block->end(), + CHECK_EQ(b_->GetInsertPoint() == init_block->end(), init_block->getTerminator() == nullptr); llvm::BasicBlock* exit_block; - if (ir_builder_->GetInsertPoint() == init_block->end()) { + if (b_->GetInsertPoint() == init_block->end()) { exit_block = llvm_ir::CreateBasicBlock( - /*insert_before=*/nullptr, IrName(hlo, "merge"), ir_builder_); + /*insert_before=*/nullptr, IrName(hlo, "merge"), b_); } else { - exit_block = init_block->splitBasicBlock(ir_builder_->GetInsertPoint(), + exit_block = init_block->splitBasicBlock(b_->GetInsertPoint(), AsStringRef(IrName(hlo, "merge"))); init_block->getTerminator()->eraseFromParent(); } - llvm_ir::SetToFirstInsertPoint(exit_block, ir_builder_); - llvm::PHINode* output = ir_builder_->CreatePHI( + llvm_ir::SetToFirstInsertPoint(exit_block, b_); + llvm::PHINode* output = b_->CreatePHI( llvm_ir::PrimitiveTypeToIrType(hlo->shape().element_type(), module_), hlo->operands().size()); - auto prior_insert_point = ir_builder_->GetInsertPoint(); + auto prior_insert_point = b_->GetInsertPoint(); - ir_builder_->SetInsertPoint(init_block); + b_->SetInsertPoint(init_block); for (int64 operand_idx = 0; operand_idx < hlo->operand_count(); ++operand_idx) { const HloInstruction* operand = hlo->operand(operand_idx); auto true_block = llvm_ir::CreateBasicBlock( - exit_block, StrCat("concat_index_from_operand", operand_idx), - ir_builder_); + exit_block, StrCat("concat_index_from_operand", operand_idx), b_); auto false_block = llvm_ir::CreateBasicBlock( - exit_block, StrCat("concat_index_not_from_operand", operand_idx), - ir_builder_); + exit_block, StrCat("concat_index_not_from_operand", operand_idx), b_); auto concat_dim_size = llvm::ConstantInt::get(source_index[concat_dim]->getType(), operand->shape().dimensions(concat_dim)); - ir_builder_->CreateCondBr( - ir_builder_->CreateICmpULT(source_index[concat_dim], concat_dim_size), + b_->CreateCondBr( + b_->CreateICmpULT(source_index[concat_dim], concat_dim_size), true_block, false_block); // Create the terminator of the true block before calling operand // generators, because they require non-degenerate basic blocks. - ir_builder_->SetInsertPoint( + b_->SetInsertPoint( llvm::BranchInst::Create(exit_block, /*InsertAtEnd=*/true_block)); TF_ASSIGN_OR_RETURN(llvm::Value * value, operand_to_generator.at(operand)(source_index)); - output->addIncoming(value, ir_builder_->GetInsertBlock()); + output->addIncoming(value, b_->GetInsertBlock()); // Subtract the size of the concat dimension of the current operand // from the source index. - ir_builder_->SetInsertPoint(false_block); + b_->SetInsertPoint(false_block); source_index[concat_dim] = - ir_builder_->CreateSub(source_index[concat_dim], concat_dim_size); + b_->CreateSub(source_index[concat_dim], concat_dim_size); } - ir_builder_->CreateUnreachable(); - ir_builder_->SetInsertPoint(exit_block, prior_insert_point); + b_->CreateUnreachable(); + b_->SetInsertPoint(exit_block, prior_insert_point); return output; } @@ -1570,8 +1507,7 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( // TODO(b/74360564): This is implementation defined behavior, but is // currently respected by all implementations. Change this if we ever decide // to officially document different behavior. - start_index_value = - ir_builder_->CreateSExtOrTrunc(start_index_value, index_type); + start_index_value = b_->CreateSExtOrTrunc(start_index_value, index_type); int64 largest_valid_start_index = input_hlo->shape().dimensions(i) - hlo->shape().dimensions(i); CHECK_GE(largest_valid_start_index, 0); @@ -1591,7 +1527,7 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( for (int64 i = 0; i < rank; ++i) { // Emit IR which computes: // input_index = start_index + offset_index - input_index[i] = ir_builder_->CreateAdd(slice_start_index[i], index[i]); + input_index[i] = b_->CreateAdd(slice_start_index[i], index[i]); } return operand_to_generator.at(input_hlo)(input_index); } @@ -1649,7 +1585,7 @@ StatusOr ElementalIrEmitter::EmitElementalGather( auto add_to_operand_index = [&](llvm::Value* index_component, int64 dim) { llvm::Value* gather_dim_component_extended = - ir_builder_->CreateSExtOrTrunc(index_component, index_type); + b_->CreateSExtOrTrunc(index_component, index_type); int64 operand_dim = dim_numbers.gather_dims_to_operand_dims(dim); int64 output_dim = operand_to_output_dim[operand_dim]; // If 'output_dim' is -1, it means 'operand_dim' is an elided window dim. @@ -1673,7 +1609,7 @@ StatusOr ElementalIrEmitter::EmitElementalGather( gather_dim_component_extended, is_signed), is_signed); - operand_index[operand_dim] = ir_builder_->CreateAdd( + operand_index[operand_dim] = b_->CreateAdd( operand_index[operand_dim], gather_dim_component_extended_inbound); }; @@ -1708,7 +1644,7 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( llvm_ir::IrArray::Index slice_limit_index(index.GetType(), rank); // Slice intersection gathers (ANDs) conditions on all ranks for which // 'input' is set to 'update' - llvm::Value* slice_intersection = ir_builder_->getTrue(); + llvm::Value* slice_intersection = b_->getTrue(); for (int64 i = 0; i < rank; ++i) { llvm::Type* index_type = index[0]->getType(); @@ -1725,8 +1661,7 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( // TODO(b/74360564): This is implementation defined behavior, but is // currently respected by all implementations. Change this if we ever decide // to officially document different behavior. - start_index_value = - ir_builder_->CreateSExtOrTrunc(start_index_value, index_type); + start_index_value = b_->CreateSExtOrTrunc(start_index_value, index_type); llvm::Value* update_dim_size = index_typed_const(update_hlo->shape().dimensions(i)); int64 largest_valid_start_index = @@ -1742,16 +1677,13 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( start_index_value->setName( AsStringRef(IrName(hlo, StrCat("start_idx", i)))); slice_start_index[i] = start_index_value; - slice_limit_index[i] = - ir_builder_->CreateAdd(slice_start_index[i], update_dim_size); + slice_limit_index[i] = b_->CreateAdd(slice_start_index[i], update_dim_size); - slice_intersection = ir_builder_->CreateAnd( - slice_intersection, - ir_builder_->CreateICmpSGE(index[i], slice_start_index[i]), + slice_intersection = b_->CreateAnd( + slice_intersection, b_->CreateICmpSGE(index[i], slice_start_index[i]), "slice_intersection"); - slice_intersection = ir_builder_->CreateAnd( - slice_intersection, - ir_builder_->CreateICmpSLT(index[i], slice_limit_index[i]), + slice_intersection = b_->CreateAnd( + slice_intersection, b_->CreateICmpSLT(index[i], slice_limit_index[i]), "slice_intersection"); } @@ -1760,29 +1692,29 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( // else -> return data from 'input'. llvm::Value* ret_value_addr = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(hlo->shape().element_type(), module_), - "ret_value_addr", ir_builder_); - llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - slice_intersection, "slice_intersection", ir_builder_); + "ret_value_addr", b_); + llvm_ir::LlvmIfData if_data = + llvm_ir::EmitIfThenElse(slice_intersection, "slice_intersection", b_); // Handle true BB (return data from 'update') - SetToFirstInsertPoint(if_data.true_block, ir_builder_); + SetToFirstInsertPoint(if_data.true_block, b_); // Compute update index for intersection case. llvm_ir::IrArray::Index update_index(index.GetType(), rank); for (int64 i = 0; i < rank; ++i) { - update_index[i] = ir_builder_->CreateSub(index[i], slice_start_index[i]); + update_index[i] = b_->CreateSub(index[i], slice_start_index[i]); } TF_ASSIGN_OR_RETURN(llvm::Value * true_value, operand_to_generator.at(update_hlo)(update_index)); - ir_builder_->CreateStore(true_value, ret_value_addr); + b_->CreateStore(true_value, ret_value_addr); // Handle false BB (return data from 'input') - SetToFirstInsertPoint(if_data.false_block, ir_builder_); + SetToFirstInsertPoint(if_data.false_block, b_); TF_ASSIGN_OR_RETURN(llvm::Value * false_value, operand_to_generator.at(input_hlo)(index)); - ir_builder_->CreateStore(false_value, ret_value_addr); + b_->CreateStore(false_value, ret_value_addr); - SetToFirstInsertPoint(if_data.after_block, ir_builder_); - return ir_builder_->CreateLoad(ret_value_addr); + SetToFirstInsertPoint(if_data.after_block, b_); + return b_->CreateLoad(ret_value_addr); } StatusOr ElementalIrEmitter::EmitElementalPad( @@ -1790,29 +1722,29 @@ StatusOr ElementalIrEmitter::EmitElementalPad( const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, const llvm_ir::IrArray::Index& padded_index) const { auto index = padded_index; - llvm::Value* in_bounds = ir_builder_->getTrue(); + llvm::Value* in_bounds = b_->getTrue(); for (size_t i = 0; i < index.size(); ++i) { auto index_typed_const = [=](int64 n) { return llvm::ConstantInt::get(index[i]->getType(), n); }; const auto& pad_dim = hlo->padding_config().dimensions(i); - index[i] = ir_builder_->CreateSub( - index[i], index_typed_const(pad_dim.edge_padding_low())); - in_bounds = ir_builder_->CreateAnd( - in_bounds, ir_builder_->CreateICmpSGE(index[i], index_typed_const(0)), - "in_bounds"); - in_bounds = ir_builder_->CreateAnd( + index[i] = + b_->CreateSub(index[i], index_typed_const(pad_dim.edge_padding_low())); + in_bounds = b_->CreateAnd(in_bounds, + b_->CreateICmpSGE(index[i], index_typed_const(0)), + "in_bounds"); + in_bounds = b_->CreateAnd( in_bounds, - ir_builder_->CreateICmpEQ( + b_->CreateICmpEQ( index_typed_const(0), - ir_builder_->CreateURem( - index[i], index_typed_const(pad_dim.interior_padding() + 1))), + b_->CreateURem(index[i], + index_typed_const(pad_dim.interior_padding() + 1))), "in_bounds"); - index[i] = ir_builder_->CreateSDiv( + index[i] = b_->CreateSDiv( index[i], index_typed_const(pad_dim.interior_padding() + 1)); - in_bounds = ir_builder_->CreateAnd( + in_bounds = b_->CreateAnd( in_bounds, - ir_builder_->CreateICmpSLT( + b_->CreateICmpSLT( index[i], index_typed_const(hlo->operand(0)->shape().dimensions(i))), "in_bounds"); @@ -1825,26 +1757,26 @@ StatusOr ElementalIrEmitter::EmitElementalPad( // } llvm::Value* ret_value_addr = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(hlo->shape().element_type(), module_), - "pad_result_addr", ir_builder_); + "pad_result_addr", b_); llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", ir_builder_); - SetToFirstInsertPoint(if_data.true_block, ir_builder_); + llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", b_); + SetToFirstInsertPoint(if_data.true_block, b_); TF_ASSIGN_OR_RETURN(llvm::Value * operand_value, operand_to_generator.at(hlo->operand(0))(index)); - ir_builder_->CreateStore(operand_value, ret_value_addr); + b_->CreateStore(operand_value, ret_value_addr); - SetToFirstInsertPoint(if_data.false_block, ir_builder_); + SetToFirstInsertPoint(if_data.false_block, b_); TF_ASSIGN_OR_RETURN(llvm::Value * padding_value, operand_to_generator.at(hlo->operand(1))( IrArray::Index(index.GetType()))); - ir_builder_->CreateStore(padding_value, ret_value_addr); + b_->CreateStore(padding_value, ret_value_addr); - SetToFirstInsertPoint(if_data.after_block, ir_builder_); + SetToFirstInsertPoint(if_data.after_block, b_); // Don't create phi(operand_value, padding_value) here, because invoking // operand_to_generator may create new basic blocks, making the parent // of operand_value or padding_value no longer a predecessor of // if_data.after_block. - return ir_builder_->CreateLoad(ret_value_addr); + return b_->CreateLoad(ret_value_addr); } StatusOr ElementalIrEmitter::EmitElementalDot( @@ -1868,21 +1800,20 @@ StatusOr ElementalIrEmitter::EmitElementalDot( return llvm::ConstantInt::get(index_type, c); }; - std::unique_ptr inner_loop = - llvm_ir::ForLoop::EmitForLoop(IrName(hlo, "inner"), index_typed_const(0), - index_typed_const(contracted_dim_size), - index_typed_const(1), ir_builder_); + std::unique_ptr inner_loop = llvm_ir::ForLoop::EmitForLoop( + IrName(hlo, "inner"), index_typed_const(0), + index_typed_const(contracted_dim_size), index_typed_const(1), b_); - SetToFirstInsertPoint(inner_loop->GetPreheaderBasicBlock(), ir_builder_); + SetToFirstInsertPoint(inner_loop->GetPreheaderBasicBlock(), b_); PrimitiveType primitive_type = hlo->shape().element_type(); llvm::Type* primitive_type_llvm = llvm_ir::PrimitiveTypeToIrType(primitive_type, module_); - llvm::Value* accumulator_alloca = llvm_ir::EmitAllocaAtFunctionEntry( - primitive_type_llvm, "dot_acc", ir_builder_); - ir_builder_->CreateStore(llvm::Constant::getNullValue(primitive_type_llvm), - accumulator_alloca); + llvm::Value* accumulator_alloca = + llvm_ir::EmitAllocaAtFunctionEntry(primitive_type_llvm, "dot_acc", b_); + b_->CreateStore(llvm::Constant::getNullValue(primitive_type_llvm), + accumulator_alloca); - SetToFirstInsertPoint(inner_loop->GetBodyBasicBlock(), ir_builder_); + SetToFirstInsertPoint(inner_loop->GetBodyBasicBlock(), b_); // This is the inner reduction loop for a dot operation that produces // one element in the output. If the operands to the dot operation have @@ -1902,43 +1833,36 @@ StatusOr ElementalIrEmitter::EmitElementalDot( } rhs_index.InsertAt(rhs_contracting_dim, inner_loop->GetIndVarValue()); - llvm::Value* current_accumulator = - ir_builder_->CreateLoad(accumulator_alloca); + llvm::Value* current_accumulator = b_->CreateLoad(accumulator_alloca); TF_ASSIGN_OR_RETURN(llvm::Value * lhs_value, lhs_generator(lhs_index)); TF_ASSIGN_OR_RETURN(llvm::Value * rhs_value, rhs_generator(rhs_index)); llvm::Value* next_accumulator; if (primitive_util::IsComplexType(primitive_type)) { - llvm::Value* product_real = ir_builder_->CreateFSub( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractReal(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractImag(rhs_value))); - llvm::Value* product_imag = ir_builder_->CreateFAdd( - ir_builder_->CreateFMul(EmitExtractReal(lhs_value), - EmitExtractImag(rhs_value)), - ir_builder_->CreateFMul(EmitExtractImag(lhs_value), - EmitExtractReal(rhs_value))); - next_accumulator = ir_builder_->CreateInsertValue( + llvm::Value* product_real = b_->CreateFSub( + b_->CreateFMul(EmitExtractReal(lhs_value), EmitExtractReal(rhs_value)), + b_->CreateFMul(EmitExtractImag(lhs_value), EmitExtractImag(rhs_value))); + llvm::Value* product_imag = b_->CreateFAdd( + b_->CreateFMul(EmitExtractReal(lhs_value), EmitExtractImag(rhs_value)), + b_->CreateFMul(EmitExtractImag(lhs_value), EmitExtractReal(rhs_value))); + next_accumulator = b_->CreateInsertValue( current_accumulator, - ir_builder_->CreateFAdd(EmitExtractReal(current_accumulator), - product_real), + b_->CreateFAdd(EmitExtractReal(current_accumulator), product_real), {0}); - next_accumulator = ir_builder_->CreateInsertValue( + next_accumulator = b_->CreateInsertValue( next_accumulator, - ir_builder_->CreateFAdd(EmitExtractImag(current_accumulator), - product_imag), + b_->CreateFAdd(EmitExtractImag(current_accumulator), product_imag), {1}); } else if (primitive_util::IsFloatingPointType(primitive_type)) { - next_accumulator = ir_builder_->CreateFAdd( - current_accumulator, ir_builder_->CreateFMul(lhs_value, rhs_value)); + next_accumulator = b_->CreateFAdd(current_accumulator, + b_->CreateFMul(lhs_value, rhs_value)); } else { - next_accumulator = ir_builder_->CreateAdd( - current_accumulator, ir_builder_->CreateMul(lhs_value, rhs_value)); + next_accumulator = + b_->CreateAdd(current_accumulator, b_->CreateMul(lhs_value, rhs_value)); } - ir_builder_->CreateStore(next_accumulator, accumulator_alloca); + b_->CreateStore(next_accumulator, accumulator_alloca); - SetToFirstInsertPoint(inner_loop->GetExitBasicBlock(), ir_builder_); - return ir_builder_->CreateLoad(accumulator_alloca); + SetToFirstInsertPoint(inner_loop->GetExitBasicBlock(), b_); + return b_->CreateLoad(accumulator_alloca); } llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( @@ -2038,7 +1962,7 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( const HloInstruction* operand = hlo->operand(0); auto source_index = target_index; for (int64 dim : hlo->dimensions()) { - source_index[dim] = ir_builder_->CreateSub( + source_index[dim] = b_->CreateSub( llvm::ConstantInt::get(target_index[dim]->getType(), hlo->shape().dimensions(dim) - 1), target_index[dim]); @@ -2051,16 +1975,16 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( const HloInstruction* operand = hlo->operand(0); // The `dimensions` member of the broadcast instruction maps from // input dimensions to output dimensions. - return operand_to_generator.at( - operand)(target_index.SourceIndexOfBroadcast( - hlo->shape(), operand->shape(), hlo->dimensions(), ir_builder_)); + return operand_to_generator.at(operand)( + target_index.SourceIndexOfBroadcast(hlo->shape(), operand->shape(), + hlo->dimensions(), b_)); }; case HloOpcode::kSlice: return [this, hlo, &operand_to_generator]( const IrArray::Index& index) -> StatusOr { IrArray::Index sliced_index = index.SourceIndexOfSlice( /*shape=*/hlo->shape(), /*starts=*/hlo->slice_starts(), - /*strides=*/hlo->slice_strides(), /*builder=*/ir_builder_); + /*strides=*/hlo->slice_strides(), /*builder=*/b_); return operand_to_generator.at(hlo->operand(0))(sliced_index); }; case HloOpcode::kDynamicSlice: @@ -2085,24 +2009,23 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( ShapeUtil::ElementsIn(hlo->operand(0)->shape())); return [this, hlo, &operand_to_generator](const IrArray::Index& index) { const HloInstruction* operand = hlo->operand(0); - return operand_to_generator.at(operand)(index.SourceIndexOfBitcast( - hlo->shape(), operand->shape(), ir_builder_)); + return operand_to_generator.at(operand)( + index.SourceIndexOfBitcast(hlo->shape(), operand->shape(), b_)); }; case HloOpcode::kReshape: CHECK_EQ(ShapeUtil::ElementsIn(hlo->shape()), ShapeUtil::ElementsIn(hlo->operand(0)->shape())); return [this, hlo, &operand_to_generator](const IrArray::Index& index) { const HloInstruction* operand = hlo->operand(0); - return operand_to_generator.at(operand)(index.SourceIndexOfReshape( - hlo->shape(), operand->shape(), ir_builder_)); + return operand_to_generator.at(operand)( + index.SourceIndexOfReshape(hlo->shape(), operand->shape(), b_)); }; case HloOpcode::kTranspose: return [this, hlo, &operand_to_generator](const IrArray::Index& target_index) { return operand_to_generator.at(hlo->operand(0))( target_index.SourceIndexOfTranspose( - hlo->shape(), hlo->operand(0)->shape(), hlo->dimensions(), - ir_builder_)); + hlo->shape(), hlo->operand(0)->shape(), hlo->dimensions(), b_)); }; case HloOpcode::kRng: return MakeRngElementGenerator(hlo, operand_to_generator); @@ -2127,11 +2050,11 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( } llvm::Value* ElementalIrEmitter::EmitExtractReal(llvm::Value* value) const { - return ir_builder_->CreateExtractValue(value, {0}); + return b_->CreateExtractValue(value, {0}); } llvm::Value* ElementalIrEmitter::EmitExtractImag(llvm::Value* value) const { - return ir_builder_->CreateExtractValue(value, {1}); + return b_->CreateExtractValue(value, {1}); } llvm::Value* ElementalIrEmitter::EmitComposeComplex(const HloInstruction* op, @@ -2139,10 +2062,10 @@ llvm::Value* ElementalIrEmitter::EmitComposeComplex(const HloInstruction* op, llvm::Value* imag) const { auto cplx_type = llvm_ir::PrimitiveTypeToIrType(op->shape().element_type(), module_); - auto complex = ir_builder_->CreateInsertValue( + auto complex = b_->CreateInsertValue( llvm::ConstantAggregateZero::get(cplx_type), real, {0}); if (imag != nullptr) { - complex = ir_builder_->CreateInsertValue(complex, imag, {1}); + complex = b_->CreateInsertValue(complex, imag, {1}); } return complex; } diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/elemental_ir_emitter.h index d199473374..deba6bea0a 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.h @@ -34,10 +34,8 @@ class ElementalIrEmitter { std::unordered_map; ElementalIrEmitter(const HloModuleConfig& hlo_module_config, - llvm::Module* module, llvm::IRBuilder<>* ir_builder) - : ir_builder_(ir_builder), - module_(module), - hlo_module_config_(hlo_module_config) {} + llvm::Module* module, llvm::IRBuilder<>* b) + : b_(b), module_(module), hlo_module_config_(hlo_module_config) {} virtual ~ElementalIrEmitter() = default; @@ -54,7 +52,7 @@ class ElementalIrEmitter { const HloInstruction* hlo, const HloToElementGeneratorMap& operand_to_generator) const; - llvm::IRBuilder<>* ir_builder() const { return ir_builder_; } + llvm::IRBuilder<>* b() const { return b_; } llvm::Module* module() const { return module_; } protected: @@ -144,9 +142,7 @@ class ElementalIrEmitter { int64 operand_no) const; // Identifier of the thread unique among all threads on the device - virtual llvm::Value* EmitThreadId() const { - return ir_builder_->getIntN(128, 0); - } + virtual llvm::Value* EmitThreadId() const { return b_->getIntN(128, 0); } StatusOr EmitElementalSelect( const HloInstruction* hlo, @@ -188,7 +184,7 @@ class ElementalIrEmitter { const HloToElementGeneratorMap& operand_to_generator, const llvm_ir::IrArray::Index& dot_result_index) const; - llvm::IRBuilder<>* const ir_builder_; + llvm::IRBuilder<>* const b_; llvm::Module* module_; diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc index e594cec2f8..b97a627d9b 100644 --- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc @@ -67,8 +67,8 @@ bool IsFPLiteralWithValue(const HloInstruction* operand, float value) { GpuElementalIrEmitter::GpuElementalIrEmitter( const HloModuleConfig& hlo_module_config, llvm::Module* module, - llvm::IRBuilder<>* ir_builder, NestedComputer compute_nested) - : ElementalIrEmitter(hlo_module_config, module, ir_builder), + llvm::IRBuilder<>* b, NestedComputer compute_nested) + : ElementalIrEmitter(hlo_module_config, module, b), hlo_module_config_(hlo_module_config), compute_nested_(std::move(compute_nested)) {} @@ -92,8 +92,8 @@ StatusOr GpuElementalIrEmitter::EmitLibdeviceMathCall( cast_result_to_fp16 = true; for (int64 i = 0; i < operands.size(); ++i) { if (input_types[i] == F16) { - converted_operands[i] = ir_builder_->CreateFPCast( - converted_operands[i], ir_builder_->getFloatTy()); + converted_operands[i] = + b_->CreateFPCast(converted_operands[i], b_->getFloatTy()); converted_input_types[i] = F32; } } @@ -112,7 +112,7 @@ StatusOr GpuElementalIrEmitter::EmitLibdeviceMathCall( converted_input_types, output_type) .ValueOrDie(); if (cast_result_to_fp16) { - result = ir_builder_->CreateFPCast(result, ir_builder_->getHalfTy()); + result = b_->CreateFPCast(result, b_->getHalfTy()); } return result; } @@ -215,7 +215,7 @@ StatusOr GpuElementalIrEmitter::EmitPowerOp( // LLVM's NVPTX backend knows how to transform 1/sqrt(A) into the NVPTX // rsqrt.approx instruction. TF_ASSIGN_OR_RETURN(auto* sqrt, make_sqrt()); - return ir_builder_->CreateFDiv(llvm::ConstantFP::get(llvm_ty, 1), sqrt); + return b_->CreateFDiv(llvm::ConstantFP::get(llvm_ty, 1), sqrt); } VLOG(10) << "emitting pow as regular call to pow(): " << op->ToString(); @@ -302,32 +302,31 @@ llvm::Value* GpuElementalIrEmitter::EmitDeviceFunctionCall( // Declares the callee if it is not declared already. llvm::Function* callee = llvm::cast( - ir_builder_->GetInsertBlock()->getModule()->getOrInsertFunction( + b_->GetInsertBlock()->getModule()->getOrInsertFunction( llvm_ir::AsStringRef(callee_name), callee_type)); for (auto attribute : attributes) { callee->addFnAttr(attribute); } - return ir_builder_->CreateCall(callee, llvm_ir::AsArrayRef(operands)); + return b_->CreateCall(callee, llvm_ir::AsArrayRef(operands)); } llvm::Value* GpuElementalIrEmitter::EmitThreadId() const { - llvm::Value* block_id = ir_builder_->CreateIntCast( + llvm::Value* block_id = b_->CreateIntCast( llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_read_ptx_sreg_ctaid_x, - {}, {}, ir_builder_), - ir_builder_->getIntNTy(128), /*isSigned=*/true, "block.id"); - llvm::Value* thread_id_in_block = ir_builder_->CreateIntCast( + {}, {}, b_), + b_->getIntNTy(128), /*isSigned=*/true, "block.id"); + llvm::Value* thread_id_in_block = b_->CreateIntCast( llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_read_ptx_sreg_tid_x, - {}, {}, ir_builder_), - ir_builder_->getIntNTy(128), /*isSigned=*/true, "thread.id"); - llvm::Value* threads_per_block = ir_builder_->CreateIntCast( + {}, {}, b_), + b_->getIntNTy(128), /*isSigned=*/true, "thread.id"); + llvm::Value* threads_per_block = b_->CreateIntCast( llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_read_ptx_sreg_ntid_x, - {}, {}, ir_builder_), - ir_builder_->getIntNTy(128), /*isSigned=*/true, "threads_per_block"); - return ir_builder_->CreateNSWAdd( - ir_builder_->CreateNSWMul(block_id, threads_per_block), - thread_id_in_block); + {}, {}, b_), + b_->getIntNTy(128), /*isSigned=*/true, "threads_per_block"); + return b_->CreateNSWAdd(b_->CreateNSWMul(block_id, threads_per_block), + thread_id_in_block); } llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( @@ -373,12 +372,12 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( PrimitiveType operand_element_type = operand->shape().element_type(); llvm::Value* accum_ptr = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(operand_element_type, module_), - "reduce_window_accum_ptr", ir_builder_); + "reduce_window_accum_ptr", b_); { TF_ASSIGN_OR_RETURN(llvm::Value * init_value, operand_to_generator.at(hlo->operand(1))( IrArray::Index(index.GetType()))); - ir_builder_->CreateStore(init_value, accum_ptr); + b_->CreateStore(init_value, accum_ptr); } llvm::Type* index_type = index.GetType(); @@ -386,7 +385,7 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( return index.GetConstantWithIndexType(c); }; - llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_, index_type); + llvm_ir::ForLoopNest loops(IrName(hlo), b_, index_type); std::vector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); @@ -395,15 +394,15 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( ShapeUtil::MakeShape(operand_element_type, window_size), "window"); CHECK_EQ(window_index.size(), index.size()); - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), b_); IrArray::Index input_index(index_type, index.size()); - llvm::Value* in_bounds = ir_builder_->getInt1(true); + llvm::Value* in_bounds = b_->getInt1(true); for (size_t i = 0; i < index.size(); ++i) { - llvm::Value* stridden_index = ir_builder_->CreateNSWMul( + llvm::Value* stridden_index = b_->CreateNSWMul( index[i], index_typed_const(window.dimensions(i).stride())); - input_index[i] = ir_builder_->CreateNSWSub( - ir_builder_->CreateNSWAdd(stridden_index, window_index[i]), + input_index[i] = b_->CreateNSWSub( + b_->CreateNSWAdd(stridden_index, window_index[i]), index_typed_const(window.dimensions(i).padding_low())); // We must check whether 0 ≤ input_index[i] < bound, as otherwise @@ -411,16 +410,16 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( // comparison is equivalent to the unsigned comparison // input_index[i] < bound, as a negative value wraps to a large // positive value. - in_bounds = ir_builder_->CreateAnd( + in_bounds = b_->CreateAnd( in_bounds, - ir_builder_->CreateICmpULT( + b_->CreateICmpULT( input_index[i], index_typed_const(operand->shape().dimensions(i)))); } llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", ir_builder_); - SetToFirstInsertPoint(if_data.true_block, ir_builder_); + llvm_ir::EmitIfThenElse(in_bounds, "in_bounds", b_); + SetToFirstInsertPoint(if_data.true_block, b_); // We are not in pad, so do the computation. TF_ASSIGN_OR_RETURN(llvm::Value * input_value, @@ -428,26 +427,26 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( TF_ASSIGN_OR_RETURN( llvm::Value * accum_value, compute_nested_(*hlo->to_apply(), - {ir_builder_->CreateLoad(accum_ptr), input_value})); - ir_builder_->CreateStore(accum_value, accum_ptr); + {b_->CreateLoad(accum_ptr), input_value})); + b_->CreateStore(accum_value, accum_ptr); - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), ir_builder_); - return ir_builder_->CreateLoad(accum_ptr); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), b_); + return b_->CreateLoad(accum_ptr); }; case HloOpcode::kReduce: return [=, &operand_to_generator]( const IrArray::Index& output_index) -> StatusOr { const HloInstruction* operand = hlo->operand(0); llvm::Value* accum_ptr = - ir_builder()->CreateAlloca(llvm_ir::PrimitiveTypeToIrType( + b()->CreateAlloca(llvm_ir::PrimitiveTypeToIrType( hlo->shape().element_type(), module_)); llvm::Type* index_type = output_index.GetType(); TF_ASSIGN_OR_RETURN(llvm::Value * init_value, operand_to_generator.at(hlo->operand(1))( IrArray::Index(index_type))); - ir_builder()->CreateStore(init_value, accum_ptr); + b()->CreateStore(init_value, accum_ptr); - llvm_ir::ForLoopNest loops(IrName(hlo), ir_builder_, index_type); + llvm_ir::ForLoopNest loops(IrName(hlo), b_, index_type); IrArray::Index input_index = loops.AddLoopsForShapeOnDimensions( operand->shape(), hlo->dimensions(), "reduction_dim"); if (!ShapeUtil::IsScalar(hlo->shape())) { @@ -462,18 +461,17 @@ llvm_ir::ElementGenerator GpuElementalIrEmitter::MakeElementGenerator( CHECK_EQ(output_index.size(), j); } - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), ir_builder()); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), b()); TF_ASSIGN_OR_RETURN( llvm::Value * input_value, operand_to_generator.at(hlo->operand(0))(input_index)); TF_ASSIGN_OR_RETURN( llvm::Value * accum_value, - compute_nested_( - *hlo->to_apply(), - {ir_builder()->CreateLoad(accum_ptr), input_value})); - ir_builder()->CreateStore(accum_value, accum_ptr); - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), ir_builder()); - return ir_builder()->CreateLoad(accum_ptr); + compute_nested_(*hlo->to_apply(), + {b()->CreateLoad(accum_ptr), input_value})); + b()->CreateStore(accum_value, accum_ptr); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), b()); + return b()->CreateLoad(accum_ptr); }; default: return ElementalIrEmitter::MakeElementGenerator(hlo, diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h index 91f4d960aa..e3eacef133 100644 --- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.h @@ -43,7 +43,7 @@ class GpuElementalIrEmitter : public ElementalIrEmitter { const HloComputation&, tensorflow::gtl::ArraySlice)>; GpuElementalIrEmitter(const HloModuleConfig& hlo_module_config, - llvm::Module* module, llvm::IRBuilder<>* ir_builder, + llvm::Module* module, llvm::IRBuilder<>* b, NestedComputer compute_nested); llvm_ir::ElementGenerator MakeElementGenerator( diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc index 6f2a7e1850..1b6315ec03 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc @@ -39,7 +39,7 @@ void HloToIrBindings::EmitBasePointersForHlos( // I/O HLOs are bound to the arguments of the current IR function. I.e., // // void IrFunction(io_0, io_1, ..., io_{m-1}, temp_buffer_base) { - llvm::Function* function = ir_builder_->GetInsertBlock()->getParent(); + llvm::Function* function = b_->GetInsertBlock()->getParent(); CHECK_EQ(io_hlos.size() + 1, function->arg_size()); // An HLO can have duplicated operands. This data structure remembers which @@ -79,8 +79,8 @@ void HloToIrBindings::EmitBasePointersForHlos( const int64 offset = slice.offset(); CHECK_NE(nullptr, temp_buffer_base_); // Emit IR for GetTupleElement instruction and bind to emitted value. - llvm::Value* base_ptr = ir_builder_->CreateInBoundsGEP( - temp_buffer_base_, ir_builder_->getInt64(offset)); + llvm::Value* base_ptr = + b_->CreateInBoundsGEP(temp_buffer_base_, b_->getInt64(offset)); BindHloToIrValue(*non_io_hlo, EmitGetTupleElement(non_io_hlo, base_ptr)); } @@ -108,15 +108,14 @@ void HloToIrBindings::EmitBasePointersForHlos( if (slice.allocation()->is_thread_local()) { llvm::Type* pointee_type = llvm_ir::ShapeToIrType(non_io_hlo->shape(), module_); - BindHloToIrValue(*non_io_hlo, - ir_builder_->CreateAlloca(pointee_type), index); + BindHloToIrValue(*non_io_hlo, b_->CreateAlloca(pointee_type), + index); } else { const int64 offset = slice.offset(); CHECK_NE(nullptr, temp_buffer_base_); BindHloToIrValue( *non_io_hlo, - ir_builder_->CreateInBoundsGEP(temp_buffer_base_, - ir_builder_->getInt64(offset)), + b_->CreateInBoundsGEP(temp_buffer_base_, b_->getInt64(offset)), index); } }); @@ -129,11 +128,11 @@ llvm::Value* HloToIrBindings::EmitGetTupleElement(const HloInstruction* gte, if (gte->operand(0)->opcode() != HloOpcode::kGetTupleElement) { return llvm_ir::EmitGetTupleElement( gte->shape(), gte->tuple_index(), /*alignment=*/1, - GetTypedIrValue(*gte->operand(0), {}, base_ptr), ir_builder_, module_); + GetTypedIrValue(*gte->operand(0), {}, base_ptr), b_, module_); } return llvm_ir::EmitGetTupleElement( gte->shape(), gte->tuple_index(), /*alignment=*/1, - EmitGetTupleElement(gte->operand(0), base_ptr), ir_builder_, module_); + EmitGetTupleElement(gte->operand(0), base_ptr), b_, module_); } llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo, @@ -148,8 +147,7 @@ llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo, typed_ir_value = llvm::ConstantExpr::getPointerBitCastOrAddrSpaceCast( llvm::cast(ir_value), dest_type); } else { - typed_ir_value = - ir_builder_->CreateBitCast(ir_value, pointee_type->getPointerTo()); + typed_ir_value = b_->CreateBitCast(ir_value, pointee_type->getPointerTo()); } ir_value->setName(llvm_ir::AsStringRef(llvm_ir::IrName(&hlo, "raw"))); typed_ir_value->setName(llvm_ir::AsStringRef(llvm_ir::IrName(&hlo, "typed"))); diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h index a86e6e78c6..eee40b0e91 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.h @@ -36,14 +36,13 @@ class HloToIrBindings { public: HloToIrBindings(const HloModule& module, const BufferAssignment* buffer_assignment, - llvm::IRBuilder<>* ir_builder, llvm::Module* llvm_module, + llvm::IRBuilder<>* b, llvm::Module* llvm_module, bool is_nested) : buffer_assignment_(buffer_assignment), is_nested_(is_nested), - ir_builder_(ir_builder), + b_(b), module_(llvm_module), - alias_analysis_(module, *buffer_assignment_, - &ir_builder_->getContext()) {} + alias_analysis_(module, *buffer_assignment_, &b_->getContext()) {} void EmitBasePointersForHlos( tensorflow::gtl::ArraySlice io_hlos, @@ -104,7 +103,7 @@ class HloToIrBindings { const bool is_nested_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm::Module* module_; // Stores the underlying llvm::IrArray for each HloInstruction. diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index d7e8be1cf8..76180cf486 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -58,12 +58,12 @@ IrEmitter::IrEmitter(const HloModuleConfig& hlo_module_config, IrEmitterContext* ir_emitter_context, bool is_nested) : ir_emitter_context_(ir_emitter_context), module_(ir_emitter_context->llvm_module()), - ir_builder_(module_->getContext()), + b_(module_->getContext()), bindings_(ir_emitter_context->hlo_module(), - &ir_emitter_context->buffer_assignment(), &ir_builder_, module_, + &ir_emitter_context->buffer_assignment(), &b_, module_, is_nested), hlo_module_config_(hlo_module_config) { - ir_builder_.setFastMathFlags(llvm_ir::GetFastMathFlags( + b_.setFastMathFlags(llvm_ir::GetFastMathFlags( /*fast_math_enabled=*/hlo_module_config.debug_options() .xla_enable_fast_math())); } @@ -72,12 +72,11 @@ Status IrEmitter::DefaultAction(HloInstruction* hlo) { ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator; for (const HloInstruction* operand : hlo->operands()) { operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) { - return GetIrArray(*operand, *hlo) - .EmitReadArrayElement(index, &ir_builder_); + return GetIrArray(*operand, *hlo).EmitReadArrayElement(index, &b_); }; } return EmitTargetElementLoop( - *hlo, GpuElementalIrEmitter(hlo_module_config_, module_, &ir_builder_, + *hlo, GpuElementalIrEmitter(hlo_module_config_, module_, &b_, GetNestedComputer()) .MakeElementGenerator(hlo, operand_to_generator)); } @@ -120,7 +119,7 @@ Status IrEmitter::HandleGetTupleElement(HloInstruction* get_tuple_element) { get_tuple_element->shape(), get_tuple_element->tuple_index(), // TODO(b/26344050): tighten the alignment here // based on the real element type. - /*alignment=*/1, GetBasePointer(*operand), &ir_builder_, module_)); + /*alignment=*/1, GetBasePointer(*operand), &b_, module_)); return Status::OK(); } @@ -132,7 +131,7 @@ Status IrEmitter::HandleSort(HloInstruction* sort) { } int dimension_to_sort = sort->dimensions(0); return llvm_ir::EmitSortInPlace(dimension_to_sort, GetIrArray(*sort, *sort), - IrName(sort), &ir_builder_); + IrName(sort), &b_); } Status IrEmitter::HandleSend(HloInstruction*) { @@ -156,8 +155,7 @@ Status IrEmitter::HandleTuple(HloInstruction* tuple) { for (const HloInstruction* operand : tuple->operands()) { base_ptrs.push_back(GetBasePointer(*operand)); } - llvm_ir::EmitTuple(GetIrArray(*tuple, *tuple), base_ptrs, &ir_builder_, - module_); + llvm_ir::EmitTuple(GetIrArray(*tuple, *tuple), base_ptrs, &b_, module_); return Status::OK(); } @@ -178,7 +176,7 @@ Status IrEmitter::EmitCallToNestedComputation( std::vector arguments(operands.begin(), operands.end()); arguments.push_back(output); arguments.push_back(bindings_.GetTempBufferBase()); - ir_builder_.CreateCall(emitted_function, arguments); + b_.CreateCall(emitted_function, arguments); return Status::OK(); } @@ -200,21 +198,20 @@ bool IrEmitter::MaybeEmitDirectAtomicOperation( computation.root_instruction()->shape().element_type(); bool is_atomic_integral = element_type == S32 || element_type == U32 || element_type == S64 || element_type == U64; - llvm::Value* source = ir_builder_.CreateLoad(source_address, "source"); + llvm::Value* source = b_.CreateLoad(source_address, "source"); if (root_opcode == HloOpcode::kAdd) { // NVPTX supports atomicAdd on F32 and integer types. if (element_type == F32) { // F32 + F32 llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_atomic_load_add_f32, {output_address, source}, - {output_address->getType()}, &ir_builder_); + {output_address->getType()}, &b_); return true; } if (is_atomic_integral) { // integral + integral - ir_builder_.CreateAtomicRMW(llvm::AtomicRMWInst::Add, output_address, - source, - llvm::AtomicOrdering::SequentiallyConsistent); + b_.CreateAtomicRMW(llvm::AtomicRMWInst::Add, output_address, source, + llvm::AtomicOrdering::SequentiallyConsistent); return true; } } @@ -225,8 +222,8 @@ bool IrEmitter::MaybeEmitDirectAtomicOperation( auto opcode = primitive_util::IsSignedIntegralType(element_type) ? llvm::AtomicRMWInst::Max : llvm::AtomicRMWInst::UMax; - ir_builder_.CreateAtomicRMW(opcode, output_address, source, - llvm::AtomicOrdering::SequentiallyConsistent); + b_.CreateAtomicRMW(opcode, output_address, source, + llvm::AtomicOrdering::SequentiallyConsistent); return true; } @@ -235,8 +232,8 @@ bool IrEmitter::MaybeEmitDirectAtomicOperation( auto opcode = primitive_util::IsSignedIntegralType(element_type) ? llvm::AtomicRMWInst::Min : llvm::AtomicRMWInst::UMin; - ir_builder_.CreateAtomicRMW(opcode, output_address, source, - llvm::AtomicOrdering::SequentiallyConsistent); + b_.CreateAtomicRMW(opcode, output_address, source, + llvm::AtomicOrdering::SequentiallyConsistent); return true; } @@ -308,20 +305,20 @@ Status IrEmitter::EmitAtomicOperationUsingCAS(const HloComputation& computation, llvm::Type* element_address_type = element_type->getPointerTo(); int atomic_size = (element_size < 32) ? 32 : element_size; - llvm::Type* atomic_type = ir_builder_.getIntNTy(atomic_size); + llvm::Type* atomic_type = b_.getIntNTy(atomic_size); llvm::Type* atomic_address_type = atomic_type->getPointerTo(output_address_type->getPointerAddressSpace()); // cas_old_output_address and cas_new_output_address point to the scratch // memory where we store the old and new values for the repeated atomicCAS // operations. - llvm::Value* cas_old_output_address = ir_builder_.CreateAlloca( + llvm::Value* cas_old_output_address = b_.CreateAlloca( atomic_type, /*ArraySize=*/nullptr, "cas_old_output_address"); - llvm::Value* cas_new_output_address = ir_builder_.CreateAlloca( + llvm::Value* cas_new_output_address = b_.CreateAlloca( atomic_type, /*ArraySize=*/nullptr, "cas_new_output_address"); // Emit preparation code to the preheader. - llvm::BasicBlock* loop_preheader_bb = ir_builder_.GetInsertBlock(); + llvm::BasicBlock* loop_preheader_bb = b_.GetInsertBlock(); llvm::Value* atomic_memory_address; // binop_output_address points to the scratch memory that stores the @@ -332,77 +329,71 @@ Status IrEmitter::EmitAtomicOperationUsingCAS(const HloComputation& computation, CHECK_EQ((element_size % sizeof(char)), 0); llvm::Type* address_int_type = module_->getDataLayout().getIntPtrType(output_address_type); - atomic_memory_address = - ir_builder_.CreatePtrToInt(output_address, address_int_type); + atomic_memory_address = b_.CreatePtrToInt(output_address, address_int_type); llvm::Value* mask = llvm::ConstantInt::get(address_int_type, 3); - llvm::Value* offset = ir_builder_.CreateAnd(atomic_memory_address, mask); + llvm::Value* offset = b_.CreateAnd(atomic_memory_address, mask); mask = llvm::ConstantInt::get(address_int_type, -4); - atomic_memory_address = ir_builder_.CreateAnd(atomic_memory_address, mask); + atomic_memory_address = b_.CreateAnd(atomic_memory_address, mask); atomic_memory_address = - ir_builder_.CreateIntToPtr(atomic_memory_address, atomic_address_type); - binop_output_address = ir_builder_.CreateAdd( - ir_builder_.CreatePtrToInt(cas_new_output_address, address_int_type), - offset); + b_.CreateIntToPtr(atomic_memory_address, atomic_address_type); + binop_output_address = b_.CreateAdd( + b_.CreatePtrToInt(cas_new_output_address, address_int_type), offset); binop_output_address = - ir_builder_.CreateIntToPtr(binop_output_address, element_address_type); + b_.CreateIntToPtr(binop_output_address, element_address_type); } else { atomic_memory_address = - ir_builder_.CreateBitCast(output_address, atomic_address_type); + b_.CreateBitCast(output_address, atomic_address_type); binop_output_address = - ir_builder_.CreateBitCast(cas_new_output_address, element_address_type); + b_.CreateBitCast(cas_new_output_address, element_address_type); } // Use the value from the memory that atomicCAS operates on to initialize // cas_old_output. llvm::Value* cas_old_output = - ir_builder_.CreateLoad(atomic_memory_address, "cas_old_output"); - ir_builder_.CreateStore(cas_old_output, cas_old_output_address); + b_.CreateLoad(atomic_memory_address, "cas_old_output"); + b_.CreateStore(cas_old_output, cas_old_output_address); llvm::BasicBlock* loop_exit_bb = loop_preheader_bb->splitBasicBlock( - ir_builder_.GetInsertPoint(), "atomic_op_loop_exit"); - llvm::BasicBlock* loop_body_bb = - llvm::BasicBlock::Create(ir_builder_.getContext(), "atomic_op_loop_body", - ir_builder_.GetInsertBlock()->getParent()); - ir_builder_.SetInsertPoint(loop_body_bb); + b_.GetInsertPoint(), "atomic_op_loop_exit"); + llvm::BasicBlock* loop_body_bb = llvm::BasicBlock::Create( + b_.getContext(), "atomic_op_loop_body", b_.GetInsertBlock()->getParent()); + b_.SetInsertPoint(loop_body_bb); // Change preheader's successor from loop_exit_bb to loop_body_bb. loop_preheader_bb->getTerminator()->setSuccessor(0, loop_body_bb); // Emit the body of the loop that repeatedly invokes atomicCAS. // // Use cas_old_output to initialize cas_new_output. - cas_old_output = - ir_builder_.CreateLoad(cas_old_output_address, "cas_old_output"); - ir_builder_.CreateStore(cas_old_output, cas_new_output_address); + cas_old_output = b_.CreateLoad(cas_old_output_address, "cas_old_output"); + b_.CreateStore(cas_old_output, cas_new_output_address); // Emits code to calculate new_output = operation(old_output, source); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( computation, {binop_output_address, source_address}, binop_output_address)); llvm::Value* cas_new_output = - ir_builder_.CreateLoad(cas_new_output_address, "cas_new_output"); + b_.CreateLoad(cas_new_output_address, "cas_new_output"); // Emit code to perform the atomicCAS operation // (cas_old_output, success) = atomicCAS(memory_address, cas_old_output, // cas_new_output); - llvm::Value* ret_value = ir_builder_.CreateAtomicCmpXchg( + llvm::Value* ret_value = b_.CreateAtomicCmpXchg( atomic_memory_address, cas_old_output, cas_new_output, llvm::AtomicOrdering::SequentiallyConsistent, llvm::AtomicOrdering::SequentiallyConsistent); // Extract the memory value returned from atomicCAS and store it as // cas_old_output. - ir_builder_.CreateStore( - ir_builder_.CreateExtractValue(ret_value, 0, "cas_old_output"), - cas_old_output_address); + b_.CreateStore(b_.CreateExtractValue(ret_value, 0, "cas_old_output"), + cas_old_output_address); // Extract the success bit returned from atomicCAS and generate a // conditional branch on the success bit. - ir_builder_.CreateCondBr( - ir_builder_.CreateExtractValue(ret_value, 1, "success"), loop_exit_bb, - loop_body_bb); + b_.CreateCondBr(b_.CreateExtractValue(ret_value, 1, "success"), loop_exit_bb, + loop_body_bb); // Set the insertion point to the exit basic block so that the caller of // this method can continue emitting code to the right place. - SetToFirstInsertPoint(loop_exit_bb, &ir_builder_); + SetToFirstInsertPoint(loop_exit_bb, &b_); return Status::OK(); } @@ -445,32 +436,32 @@ Status IrEmitter::HandleTupleSelect(HloInstruction* tuple_select) { llvm_ir::EmitTupleSelect(GetIrArray(*tuple_select, *tuple_select), GetIrArray(*pred, *tuple_select), GetBasePointer(*on_true), GetBasePointer(*on_false), - &ir_builder_, module_); + &b_, module_); return Status::OK(); } 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 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); +llvm::Value* Real(llvm::Value* x, llvm::IRBuilder<>* b) { + return b->CreateExtractValue(x, {0}); +} + +llvm::Value* Imag(llvm::Value* x, llvm::IRBuilder<>* b) { + return b->CreateExtractValue(x, {1}); +} + +std::pair MultiplyComplex(llvm::Value* lhs_value, + llvm::Value* rhs_value, + llvm::IRBuilder<>* b) { + llvm::Value* lhs_real = Real(lhs_value, b); + llvm::Value* lhs_imag = Imag(lhs_value, b); + llvm::Value* rhs_real = Real(rhs_value, b); + llvm::Value* rhs_imag = Imag(rhs_value, b); + llvm::Value* real_result1 = b->CreateFMul(lhs_real, rhs_real); + llvm::Value* real_result2 = b->CreateFMul(lhs_imag, rhs_imag); + llvm::Value* real_result = b->CreateFSub(real_result1, real_result2); + llvm::Value* imag_result1 = b->CreateFMul(lhs_real, rhs_imag); + llvm::Value* imag_result2 = b->CreateFMul(lhs_imag, rhs_real); + llvm::Value* imag_result = b->CreateFAdd(imag_result1, imag_result2); return {real_result, imag_result}; } } // namespace @@ -486,25 +477,24 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { const Shape& rhs_shape = rhs_instruction->shape(); // TODO(b/110211620): Convert to use i32 index_type when it is possible. - llvm::Type* index_type = ir_builder_.getInt64Ty(); + llvm::Type* index_type = b_.getInt64Ty(); llvm_ir::IrArray::Index element_index(index_type); if (ShapeUtil::IsScalar(lhs_shape) && ShapeUtil::IsScalar(rhs_shape)) { // If the operands are scalar, don't emit any loops. llvm::Value* lhs_value = - lhs_array.EmitReadArrayElement(/*index=*/element_index, &ir_builder_); + lhs_array.EmitReadArrayElement(/*index=*/element_index, &b_); llvm::Value* rhs_value = - rhs_array.EmitReadArrayElement(/*index=*/element_index, &ir_builder_); + rhs_array.EmitReadArrayElement(/*index=*/element_index, &b_); llvm::Value* result; if (ShapeUtil::ElementIsComplex(lhs_shape)) { - auto value = MultiplyComplex(lhs_value, rhs_value, &ir_builder_); + auto value = MultiplyComplex(lhs_value, rhs_value, &b_); result = llvm::ConstantAggregateZero::get(lhs_array.GetElementLlvmType()); - result = ir_builder_.CreateInsertValue(result, value.first, {0}); - result = ir_builder_.CreateInsertValue(result, value.second, {1}); + result = b_.CreateInsertValue(result, value.first, {0}); + result = b_.CreateInsertValue(result, value.second, {1}); } else { - result = ir_builder_.CreateFMul(lhs_value, rhs_value); + result = b_.CreateFMul(lhs_value, rhs_value); } - target_array.EmitWriteArrayElement(/*index=*/element_index, result, - &ir_builder_); + target_array.EmitWriteArrayElement(/*index=*/element_index, result, &b_); return Status::OK(); } @@ -531,7 +521,7 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { // Create loop nests which loop through the LHS operand dimensions and the RHS // operand dimensions. The reduction dimension of the LHS and RHS are handled // in a separate innermost loop which performs the sum of products. - llvm_ir::ForLoopNest loop_nest(IrName(dot), &ir_builder_); + llvm_ir::ForLoopNest loop_nest(IrName(dot), &b_); llvm_ir::IrArray::Index lhs_index = loop_nest.EmitOperandArrayLoopNest( lhs_array, /*dimension_to_skip=*/lhs_reduction_dimension, "lhs"); llvm_ir::IrArray::Index rhs_index = loop_nest.EmitOperandArrayLoopNest( @@ -555,7 +545,7 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { llvm::Value* accum_address = llvm_ir::EmitAllocaAtFunctionEntry( accum_type, // The pointee type of the alloca instruction. "accum_address", // The name of the alloca instruction. - &ir_builder_); + &b_); // Initialize the accumulator in the preheader to zero. new llvm::StoreInst( @@ -569,27 +559,25 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { // updated_accum = accum + lhs_element * rhs_element // *accum_address = updated_accum TF_RET_CHECK(!reduction_loop->GetBodyBasicBlock()->empty()); - ir_builder_.SetInsertPoint( + b_.SetInsertPoint( &*reduction_loop->GetBodyBasicBlock()->getFirstInsertionPt()); - llvm::Value* lhs_element = - lhs_array.EmitReadArrayElement(lhs_index, &ir_builder_); - llvm::Value* rhs_element = - rhs_array.EmitReadArrayElement(rhs_index, &ir_builder_); - llvm::Value* accum = ir_builder_.CreateLoad(accum_address); + llvm::Value* lhs_element = lhs_array.EmitReadArrayElement(lhs_index, &b_); + llvm::Value* rhs_element = rhs_array.EmitReadArrayElement(rhs_index, &b_); + llvm::Value* accum = b_.CreateLoad(accum_address); llvm::Value* updated_accum; if (ShapeUtil::ElementIsComplex(lhs_shape)) { - 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}); + auto value = MultiplyComplex(lhs_element, rhs_element, &b_); + llvm::Value* accum_real = Real(accum, &b_); + llvm::Value* real_sum = b_.CreateFAdd(accum_real, value.first); + updated_accum = b_.CreateInsertValue(accum, real_sum, {0}); + llvm::Value* accum_imag = Imag(accum, &b_); + llvm::Value* imag_sum = b_.CreateFAdd(accum_imag, value.second); + updated_accum = b_.CreateInsertValue(updated_accum, imag_sum, {1}); } else { - llvm::Value* product = ir_builder_.CreateFMul(lhs_element, rhs_element); - updated_accum = ir_builder_.CreateFAdd(accum, product); + llvm::Value* product = b_.CreateFMul(lhs_element, rhs_element); + updated_accum = b_.CreateFAdd(accum, product); } - ir_builder_.CreateStore(updated_accum, accum_address); + b_.CreateStore(updated_accum, accum_address); // After the reduction loop exits, store the accumulator into the target // address. The index into the target address is the concatenation of the rhs @@ -606,16 +594,15 @@ Status IrEmitter::HandleDot(HloInstruction* dot) { target_index.push_back(rhs_index[dimension]); } } - SetToFirstInsertPoint(reduction_loop->GetExitBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(reduction_loop->GetExitBasicBlock(), &b_); target_array.EmitWriteArrayElement( target_index, - ir_builder_.CreateLoad( - accum_address), // The value written to the target array. - &ir_builder_); + b_.CreateLoad(accum_address), // The value written to the target array. + &b_); // Set the IR builder insert point to the exit basic block of the outer most // loop. This ensures later instructions are inserted after this loop nest. - ir_builder_.SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); + b_.SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); return Status::OK(); } @@ -657,11 +644,10 @@ Status IrEmitter::HandleReduce(HloInstruction* reduce) { [=](const llvm_ir::IrArray::Index& index) -> StatusOr { // Initialize an accumulator with init_value. llvm::AllocaInst* accumulator_addr = - ir_builder_.CreateAlloca(llvm_ir::PrimitiveTypeToIrType( + b_.CreateAlloca(llvm_ir::PrimitiveTypeToIrType( reduce->shape().element_type(), module_)); - ir_builder_.CreateStore( - ir_builder_.CreateLoad(GetBasePointer(*init_value)), - accumulator_addr); + b_.CreateStore(b_.CreateLoad(GetBasePointer(*init_value)), + accumulator_addr); // The enclosing loops go over all the target elements. Now we have to // compute the actual target element. For this, we build a new loop nest @@ -669,12 +655,12 @@ Status IrEmitter::HandleReduce(HloInstruction* reduce) { // AddLoopsForShapeOnDimensions will return an Index where induction // Value*s are placed for each dimension in dimensions, and all the rest // are nullptrs. - llvm_ir::ForLoopNest loops(IrName(reduce, "inner"), &ir_builder_); + llvm_ir::ForLoopNest loops(IrName(reduce, "inner"), &b_); const llvm_ir::IrArray::Index reduced_dims_index = loops.AddLoopsForShapeOnDimensions(arg->shape(), dimensions, "reduction_dim"); - SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &ir_builder_); + SetToFirstInsertPoint(loops.GetInnerLoopBodyBasicBlock(), &b_); // Build a full index for the input argument, using reduced_dims_index // as the base. In reduced_dims_index only the reduction dimensions are @@ -693,13 +679,12 @@ Status IrEmitter::HandleReduce(HloInstruction* reduce) { // Apply the reduction function to the loaded value. llvm::Value* input_address = - GetIrArray(*arg, *reduce) - .EmitArrayElementAddress(input_index, &ir_builder_); + GetIrArray(*arg, *reduce).EmitArrayElementAddress(input_index, &b_); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *function, {accumulator_addr, input_address}, accumulator_addr)); - SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &ir_builder_); - return ir_builder_.CreateLoad(accumulator_addr); + SetToFirstInsertPoint(loops.GetOuterLoopExitBasicBlock(), &b_); + return b_.CreateLoad(accumulator_addr); }); } @@ -712,8 +697,8 @@ Status IrEmitter::HandleFusion(HloInstruction* fusion) { for (HloInstruction* operand : fusion->operands()) { parameter_arrays.push_back(GetIrArray(*operand, *fusion)); } - GpuElementalIrEmitter elemental_emitter(hlo_module_config_, module_, - &ir_builder_, GetNestedComputer()); + GpuElementalIrEmitter elemental_emitter(hlo_module_config_, module_, &b_, + GetNestedComputer()); FusedIrEmitter fused_emitter(parameter_arrays, &elemental_emitter); TF_RETURN_IF_ERROR(fusion->fused_expression_root()->Accept(&fused_emitter)); @@ -747,17 +732,16 @@ Status IrEmitter::HandleRng(HloInstruction* random) { ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator; for (const HloInstruction* operand : random->operands()) { operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) { - return GetIrArray(*operand, *random) - .EmitReadArrayElement(index, &ir_builder_); + return GetIrArray(*operand, *random).EmitReadArrayElement(index, &b_); }; } // Emits a single-threaded loop because the loop body generated by the element // generator for Rng can't be parallelized (b/32333178). return llvm_ir::LoopEmitter( - GpuElementalIrEmitter(hlo_module_config_, module_, &ir_builder_, + GpuElementalIrEmitter(hlo_module_config_, module_, &b_, GetNestedComputer()) .MakeElementGenerator(random, operand_to_generator), - GetIrArray(*random, *random), &ir_builder_) + GetIrArray(*random, *random), &b_) .EmitLoop(IrName(random)); } @@ -795,16 +779,16 @@ StatusOr IrEmitter::ComputeNestedElement( llvm::Value* return_buffer = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType( computation.root_instruction()->shape().element_type(), module_), - "return_buffer", &ir_builder_); + "return_buffer", &b_); std::vector parameter_buffers; for (llvm::Value* parameter_element : parameter_elements) { parameter_buffers.push_back(llvm_ir::EmitAllocaAtFunctionEntry( - parameter_element->getType(), "parameter_buffer", &ir_builder_)); - ir_builder_.CreateStore(parameter_element, parameter_buffers.back()); + parameter_element->getType(), "parameter_buffer", &b_)); + b_.CreateStore(parameter_element, parameter_buffers.back()); } TF_RETURN_IF_ERROR(EmitCallToNestedComputation(computation, parameter_buffers, return_buffer)); - return ir_builder_.CreateLoad(return_buffer); + return b_.CreateLoad(return_buffer); } } // namespace gpu diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.h b/tensorflow/compiler/xla/service/gpu/ir_emitter.h index da03ef831b..172d4a4e29 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.h @@ -163,7 +163,7 @@ class IrEmitter : public DfsHloVisitorWithDefault { // The following fields track the IR emission state. According to LLVM memory // management rules, their memory is owned by the module. - llvm::IRBuilder<> ir_builder_; + llvm::IRBuilder<> b_; // Mapping from HLO to its underlying LLVM value. HloToIrBindings bindings_; diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc index c9574c87a3..5c827e5f9c 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_nested.cc @@ -70,10 +70,10 @@ llvm::Function* IrEmitterNested::EmitBasePointersForNestedComputation( argument_dereferenceable_bytes.push_back(root_size); } // The base pointer of the memory block for all pre-allocated temp buffers. - argument_types.push_back(ir_builder_.getInt8PtrTy()); + argument_types.push_back(b_.getInt8PtrTy()); llvm::FunctionType* function_type = - llvm::FunctionType::get(ir_builder_.getVoidTy(), argument_types, false); + llvm::FunctionType::get(b_.getVoidTy(), argument_types, false); llvm::Function* function = llvm::Function::Create( function_type, // The function type. llvm::GlobalValue::InternalLinkage, // The linkage type. @@ -96,8 +96,7 @@ llvm::Function* IrEmitterNested::EmitBasePointersForNestedComputation( llvm::BasicBlock::Create(function->getContext(), "entry", function); // Emit a "return void" at entry_bb's end, and sets the insert point before // that return instruction. - ir_builder_.SetInsertPoint( - llvm::ReturnInst::Create(function->getContext(), entry_bb)); + b_.SetInsertPoint(llvm::ReturnInst::Create(function->getContext(), entry_bb)); std::vector non_io_hlos; for (const auto* hlo : nested_computation.instructions()) { @@ -127,20 +126,17 @@ Status IrEmitterNested::EmitTargetElementLoop( target_arrays.push_back(GetIrArray(hlo, hlo, {i})); } TF_RETURN_IF_ERROR( - llvm_ir::LoopEmitter(element_generator, target_arrays, &ir_builder_) - .EmitLoop()); + llvm_ir::LoopEmitter(element_generator, target_arrays, &b_).EmitLoop()); std::vector tuple_operand_ptrs; tuple_operand_ptrs.reserve(num_elems); for (const llvm_ir::IrArray& array : target_arrays) { tuple_operand_ptrs.push_back(array.GetBasePointer()); } - llvm_ir::EmitTuple(GetIrArray(hlo, hlo), tuple_operand_ptrs, &ir_builder_, - module_); + llvm_ir::EmitTuple(GetIrArray(hlo, hlo), tuple_operand_ptrs, &b_, module_); return Status::OK(); } - return llvm_ir::LoopEmitter(element_generator, GetIrArray(hlo, hlo), - &ir_builder_) + return llvm_ir::LoopEmitter(element_generator, GetIrArray(hlo, hlo), &b_) .EmitLoop(); } diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 1caf10a6c1..7100c9a08a 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -213,7 +213,7 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype( llvm::LLVMContext& context = module->getContext(); llvm::FunctionType* kernel_type = llvm::FunctionType::get( /*Result=*/llvm::Type::getVoidTy(context), - std::vector(args.size(), ir_builder_.getInt8PtrTy()), + std::vector(args.size(), b_.getInt8PtrTy()), /*isVarArg=*/false); llvm::Function* kernel = llvm::Function::Create(kernel_type, llvm::GlobalValue::ExternalLinkage, @@ -249,7 +249,7 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype( nvvm_annotations_node->addOperand(llvm::MDNode::get( context, {llvm::ConstantAsMetadata::get(kernel), llvm::MDString::get(context, "kernel"), - llvm::ConstantAsMetadata::get(ir_builder_.getInt32(1))})); + llvm::ConstantAsMetadata::get(b_.getInt32(1))})); // Update the insert point to the entry basic block. llvm::BasicBlock* entry_bb = @@ -257,7 +257,7 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype( // Emit a "return void" at entry_bb's end, and set the insert point before // that return instruction. - ir_builder_.SetInsertPoint(llvm::ReturnInst::Create(context, entry_bb)); + b_.SetInsertPoint(llvm::ReturnInst::Create(context, entry_bb)); return kernel; } @@ -295,7 +295,7 @@ int ComputeMaxUnrollFactor(const HloInstruction* hlo) { // range of i32. // Otherwise, the return type is i64. llvm::Type* GetIndexTypeForKernel(const HloInstruction* hlo, int64 launch_size, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { // Find the unnested hlo instructon for which the kernel is generated for. const HloInstruction* unnested_hlo = hlo; const HloComputation* computation = hlo->parent(); @@ -316,7 +316,7 @@ llvm::Type* GetIndexTypeForKernel(const HloInstruction* hlo, int64 launch_size, return in_range; }; - llvm::Type* i64_ty = ir_builder->getInt64Ty(); + llvm::Type* i64_ty = b->getInt64Ty(); // Check launch dimension if (!IsInt32(launch_size)) { return i64_ty; @@ -345,7 +345,7 @@ llvm::Type* GetIndexTypeForKernel(const HloInstruction* hlo, int64 launch_size, } } - return ir_builder->getInt32Ty(); + return b->getInt32Ty(); } } // namespace @@ -600,8 +600,8 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { parameter_arrays.push_back(GetIrArray(*operand, *fusion)); } GpuElementalIrEmitter elemental_emitter( - hlo_module_config_, ir_emitter_context_->llvm_module(), - &ir_builder_, GetNestedComputer()); + hlo_module_config_, ir_emitter_context_->llvm_module(), &b_, + GetNestedComputer()); FusedIrEmitter fused_emitter(parameter_arrays, &elemental_emitter); TF_RETURN_IF_ERROR(root->Accept(&fused_emitter)); @@ -674,7 +674,7 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { } GpuElementalIrEmitter elemental_emitter(hlo_module_config_, ir_emitter_context_->llvm_module(), - &ir_builder_, GetNestedComputer()); + &b_, GetNestedComputer()); // Shape of the dynamic-update-slice's "update" operand. Shape update_shape = root->operand(1)->shape(); @@ -692,7 +692,7 @@ Status IrEmitterUnnested::HandleFusion(HloInstruction* fusion) { return llvm_ir::EmitParallelFusedDynamicUpdateSliceInPlace( fusion, operand_arrays, output_array, &elemental_emitter, - launch_dimensions, &ir_builder_); + launch_dimensions, &b_); } if (ImplementedAsGemm(*fusion)) { @@ -740,11 +740,11 @@ Status IrEmitterUnnested::EmitExtraOutputsForReduce( const HloInstruction* output = reduce->parent()->FusionInstruction(); llvm::Value* extra_output_address = GetIrArray(*output, *output, extra_output_gens[i].second) - .EmitArrayElementAddress(index, &ir_builder_, + .EmitArrayElementAddress(index, &b_, "extra_output_element_address"); TF_ASSIGN_OR_RETURN(llvm::Value* const extra_output_ir_value, extra_output_gens[i].first(index)); - ir_builder_.CreateStore(extra_output_ir_value, extra_output_address); + b_.CreateStore(extra_output_ir_value, extra_output_address); } return Status::OK(); } @@ -774,8 +774,8 @@ Status IrEmitterUnnested::EmitReductionToScalar( LaunchDimensions launch_dimensions = CalculateLaunchDimensions( tiled_input_shape, ir_emitter_context_->device_description()); - llvm::Type* index_ty = GetIndexTypeForKernel( - reduce, launch_dimensions.launch_bound(), &ir_builder_); + llvm::Type* index_ty = + GetIndexTypeForKernel(reduce, launch_dimensions.launch_bound(), &b_); auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { return llvm::ConstantInt::get(index_ty, c); @@ -825,52 +825,51 @@ Status IrEmitterUnnested::EmitReductionToScalar( llvm_ir::PrimitiveTypeToIrType(input_shape.element_type(), module_); std::vector partial_reduction_result_addresses; for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result_address = ir_builder_.CreateAlloca( - element_ir_type, /*ArraySize=*/nullptr, - "partial_reduction_result." + llvm::Twine(i)); + llvm::Value* partial_reduction_result_address = + b_.CreateAlloca(element_ir_type, /*ArraySize=*/nullptr, + "partial_reduction_result." + llvm::Twine(i)); TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, init_value_gens[i](IrArray::Index(index_ty))); - ir_builder_.CreateStore(init_ir_value, partial_reduction_result_address); + b_.CreateStore(init_ir_value, partial_reduction_result_address); partial_reduction_result_addresses.push_back( partial_reduction_result_address); } llvm::Value* x_in_tiles = tile_index[0]; - x_in_tiles = ir_builder_.CreateZExtOrTrunc(x_in_tiles, index_ty); + x_in_tiles = b_.CreateZExtOrTrunc(x_in_tiles, index_ty); // Emit an inner for-loop that reduces the elements in the tile. auto emit_tile_element_loop = [=](bool tile_in_bounds) -> Status { std::unique_ptr tile_element_loop = - llvm_ir::ForLoop::EmitForLoop("element_id_in_tile", - index_typed_constant(0), - index_typed_constant(kTileSize), - index_typed_constant(1), &ir_builder_); + llvm_ir::ForLoop::EmitForLoop( + "element_id_in_tile", index_typed_constant(0), + index_typed_constant(kTileSize), index_typed_constant(1), &b_); // Emit the body of the partial reduction loop. llvm_ir::SetToFirstInsertPoint(tile_element_loop->GetBodyBasicBlock(), - &ir_builder_); - llvm::Value* x = ir_builder_.CreateNSWAdd( - ir_builder_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileSize)), + &b_); + llvm::Value* x = b_.CreateNSWAdd( + b_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileSize)), tile_element_loop->GetIndVarValue()); // Unless we know the tile is entirely in bounds, we have to emit a // x-in-bounds check before reading from the input. if (!tile_in_bounds) { llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpULT(x, index_typed_constant(num_elems)), - "x_in_bounds", &ir_builder_); + b_.CreateICmpULT(x, index_typed_constant(num_elems)), "x_in_bounds", + &b_); // Emit code that reads the input element and accumulates it to // the partial reduction result. - llvm_ir::SetToFirstInsertPoint(if_data.true_block, &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_data.true_block, &b_); } IrArray::Index input_index( - /*linear=*/x, input_shape, &ir_builder_); - llvm::Value* input_address = ir_builder_.CreateAlloca(element_ir_type); + /*linear=*/x, input_shape, &b_); + llvm::Value* input_address = b_.CreateAlloca(element_ir_type); for (int i = 0; i != num_reduces; ++i) { TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, input_gens[i](input_index)); - ir_builder_.CreateStore(input_ir_value, input_address); + b_.CreateStore(input_ir_value, input_address); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *reducers[i], {partial_reduction_result_addresses[i], input_address}, @@ -881,52 +880,48 @@ Status IrEmitterUnnested::EmitReductionToScalar( // x_end = kTileSize + x_in_tiles * kTileSize, i.e., the location that's // immediately beyond the tile. - llvm::Value* x_end = ir_builder_.CreateNSWAdd( + llvm::Value* x_end = b_.CreateNSWAdd( index_typed_constant(kTileSize), - ir_builder_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileSize))); + b_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileSize))); // The tile is entirely in bound if all_threads_in_bounds or // x_end <= num_elems. - llvm::Value* tile_in_bounds = ir_builder_.CreateOr( - ir_builder_.CreateICmpULE(x_end, index_typed_constant(num_elems)), - ir_builder_.getInt1(all_threads_in_bounds)); + llvm::Value* tile_in_bounds = + b_.CreateOr(b_.CreateICmpULE(x_end, index_typed_constant(num_elems)), + b_.getInt1(all_threads_in_bounds)); llvm_ir::LlvmIfData if_tile_in_bounds_data = - llvm_ir::EmitIfThenElse(tile_in_bounds, "tile_in_bounds", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.true_block, - &ir_builder_); + llvm_ir::EmitIfThenElse(tile_in_bounds, "tile_in_bounds", &b_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.true_block, &b_); TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_bounds=*/true)); - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.false_block, - &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.false_block, &b_); TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_bounds=*/false)); // After the if-then-else statement on tile_in_bounds, emit calls to // shfl_down that accumulate the partial reduction results of all threads // from the warp. - llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.after_block, - &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_bounds_data.after_block, &b_); int bit_width = llvm_ir::GetSizeInBits(element_ir_type); // bitcast cannot be applied to aggregate types (even packed ones), so we // instead bitcast addresses of load/store to intN* of the same bit-width. llvm::Type* shuffle_ir_type = element_ir_type->isStructTy() - ? ir_builder_.getIntNTy(bit_width) + ? b_.getIntNTy(bit_width) : element_ir_type; for (int shuffle_distance = kWarpSize / 2; shuffle_distance >= 1; shuffle_distance /= 2) { - llvm::Value* result_from_other_lane = ir_builder_.CreateAlloca( - element_ir_type, nullptr, "result_from_other_lane"); + llvm::Value* result_from_other_lane = + b_.CreateAlloca(element_ir_type, nullptr, "result_from_other_lane"); for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result = ir_builder_.CreateLoad( - ir_builder_.CreateBitCast(partial_reduction_result_addresses[i], - shuffle_ir_type->getPointerTo()), + llvm::Value* partial_reduction_result = b_.CreateLoad( + b_.CreateBitCast(partial_reduction_result_addresses[i], + shuffle_ir_type->getPointerTo()), "partial_reduction_result"); CHECK_EQ(launch_dimensions.threads_per_block() % kWarpSize, 0) << "Requires block size a multiple of the warp size, otherwise we " "will read undefined elements."; - ir_builder_.CreateStore( + b_.CreateStore( EmitFullWarpShuffleDown(partial_reduction_result, - ir_builder_.getInt32(shuffle_distance), - &ir_builder_), - ir_builder_.CreateBitCast(result_from_other_lane, - shuffle_ir_type->getPointerTo())); + b_.getInt32(shuffle_distance), &b_), + b_.CreateBitCast(result_from_other_lane, + shuffle_ir_type->getPointerTo())); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *reducers[i], {partial_reduction_result_addresses[i], result_from_other_lane}, @@ -940,24 +935,23 @@ Status IrEmitterUnnested::EmitReductionToScalar( // Emit an atomic operation that accumulates the partial reduction result of // lane 0 (which holds the partially accumulated result for its warp) to the // output element. - llvm::Value* lane_id = ir_builder_.CreateURem( - x_in_tiles, index_typed_constant(kWarpSize), "lane_id"); + llvm::Value* lane_id = + b_.CreateURem(x_in_tiles, index_typed_constant(kWarpSize), "lane_id"); llvm_ir::LlvmIfData if_lane_id_is_zero_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpEQ(lane_id, index_typed_constant(0)), - "lane_id_is_zero", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, - &ir_builder_); + b_.CreateICmpEQ(lane_id, index_typed_constant(0)), "lane_id_is_zero", + &b_); + llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, &b_); for (int i = 0; i != num_reduces; ++i) { llvm::Value* output_address = GetIrArray(*output, *output, reduce_output_shapes[i]) .EmitArrayElementAddress( IrArray::Index( - /*linear=*/ir_builder_.getInt64(0), + /*linear=*/b_.getInt64(0), ShapeUtil::GetSubshape(output->shape(), reduce_output_shapes[i]), - &ir_builder_), - &ir_builder_, "output_element_address"); + &b_), + &b_, "output_element_address"); TF_RETURN_IF_ERROR(EmitAtomicOperationForNestedComputation( *reducers[i], output_address, partial_reduction_result_addresses[i])); } @@ -971,7 +965,7 @@ Status IrEmitterUnnested::EmitReductionToScalar( static_cast(LastThunk())->thunks().back().get(), ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, - launch_dimensions, &ir_builder_) + launch_dimensions, &b_) .EmitLoop(IrName(reduce), index_ty); } @@ -1015,7 +1009,7 @@ Status IrEmitterUnnested::EmitColumnReduction( tiled_input_shape, ir_emitter_context_->device_description()); // TODO(b/110211620): Convert to use i32 index_type when it is possible. - llvm::Type* index_ty = ir_builder_.getInt64Ty(); + llvm::Type* index_ty = b_.getInt64Ty(); auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { return llvm::ConstantInt::get(index_ty, c); @@ -1065,14 +1059,12 @@ Status IrEmitterUnnested::EmitColumnReduction( for (int i = 0; i != num_reduces; ++i) { for (int x_offset = 0; x_offset < kTileWidth; ++x_offset) { llvm::Value* partial_reduction_result_address = - ir_builder_.CreateAlloca( - element_ir_type, /*ArraySize=*/nullptr, - "partial_reduction_result." + - llvm::Twine(i * kTileWidth + x_offset)); + b_.CreateAlloca(element_ir_type, /*ArraySize=*/nullptr, + "partial_reduction_result." + + llvm::Twine(i * kTileWidth + x_offset)); TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, init_value_gens[i](IrArray::Index(index_ty))); - ir_builder_.CreateStore(init_ir_value, - partial_reduction_result_address); + b_.CreateStore(init_ir_value, partial_reduction_result_address); partial_reduction_result_addresses.push_back( partial_reduction_result_address); } @@ -1083,50 +1075,47 @@ Status IrEmitterUnnested::EmitColumnReduction( llvm::Value* y_in_tiles = tile_index[0]; llvm::Value* x_in_tiles = tile_index[1]; - y_in_tiles = ir_builder_.CreateZExtOrTrunc(y_in_tiles, index_ty); - x_in_tiles = ir_builder_.CreateZExtOrTrunc(x_in_tiles, index_ty); + y_in_tiles = b_.CreateZExtOrTrunc(y_in_tiles, index_ty); + x_in_tiles = b_.CreateZExtOrTrunc(x_in_tiles, index_ty); auto emit_tile_element_loop = [=](bool tile_in_y_bounds, bool tile_in_x_bounds) -> Status { std::unique_ptr tile_element_loop = - llvm_ir::ForLoop::EmitForLoop("element_id_in_tile", - index_typed_constant(0), - index_typed_constant(kTileHeight), - index_typed_constant(1), &ir_builder_); + llvm_ir::ForLoop::EmitForLoop( + "element_id_in_tile", index_typed_constant(0), + index_typed_constant(kTileHeight), index_typed_constant(1), &b_); // Emit the body of the partial reduction loop. llvm_ir::SetToFirstInsertPoint(tile_element_loop->GetBodyBasicBlock(), - &ir_builder_); - llvm::Value* y = ir_builder_.CreateNSWAdd( - ir_builder_.CreateNSWMul(y_in_tiles, - index_typed_constant(kTileHeight)), + &b_); + llvm::Value* y = b_.CreateNSWAdd( + b_.CreateNSWMul(y_in_tiles, index_typed_constant(kTileHeight)), tile_element_loop->GetIndVarValue()); // Unless we know that y is in bounds, we have to emit a check before // reading from the input. if (!tile_in_y_bounds) { llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpULT(y, index_typed_constant(height)), - "y_in_bounds", &ir_builder_); + b_.CreateICmpULT(y, index_typed_constant(height)), "y_in_bounds", + &b_); // Emit code that reads the input element and accumulates it to // the partial reduction result. - llvm_ir::SetToFirstInsertPoint(if_data.true_block, &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_data.true_block, &b_); } for (int x_offset = 0; x_offset < kTileWidth; ++x_offset) { - llvm::Value* x = ir_builder_.CreateNSWAdd( - ir_builder_.CreateNSWMul(x_in_tiles, - index_typed_constant(kTileWidth)), + llvm::Value* x = b_.CreateNSWAdd( + b_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileWidth)), index_typed_constant(x_offset)); // Unless we know that x is in bounds, we have to emit a check before // reading from the input. if (!tile_in_x_bounds) { llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpULT(x, index_typed_constant(width)), - "x_in_bounds", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_data.true_block, &ir_builder_); + b_.CreateICmpULT(x, index_typed_constant(width)), "x_in_bounds", + &b_); + llvm_ir::SetToFirstInsertPoint(if_data.true_block, &b_); } - llvm::Value* input_address = ir_builder_.CreateAlloca(element_ir_type); + llvm::Value* input_address = b_.CreateAlloca(element_ir_type); // {y,x} is an index to input_matrix_shape [height,width]. We need to // convert that to an index to input_shape (the shape of the operand of // "reduce"). This conversion is composed of a transposition from @@ -1143,18 +1132,17 @@ Status IrEmitterUnnested::EmitColumnReduction( ShapeUtil::MakeShapeWithDescendingLayout(input_shape.element_type(), {height, width}); const IrArray::Index input_matrix_index({y, x}, input_matrix_shape, - &ir_builder_); + &b_); const IrArray::Index input_index = input_matrix_index .SourceIndexOfReshape(input_matrix_shape, - normalized_input_shape, &ir_builder_) + normalized_input_shape, &b_) .SourceIndexOfTranspose(normalized_input_shape, input_shape, - transpose_dimension_mapping, - &ir_builder_); + transpose_dimension_mapping, &b_); for (int i = 0; i != num_reduces; ++i) { TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, input_gens[i](input_index)); - ir_builder_.CreateStore(input_ir_value, input_address); + b_.CreateStore(input_ir_value, input_address); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *reducers[i], {partial_reduction_result_addresses[i * kTileWidth + x_offset], @@ -1169,64 +1157,55 @@ Status IrEmitterUnnested::EmitColumnReduction( // y_end = kTileHeight + y_in_tiles * kTileHeight, i.e., the y location // that's immediately beyond the tile. - llvm::Value* y_end = ir_builder_.CreateNSWAdd( + llvm::Value* y_end = b_.CreateNSWAdd( index_typed_constant(kTileHeight), - ir_builder_.CreateNSWMul(y_in_tiles, - index_typed_constant(kTileHeight))); + b_.CreateNSWMul(y_in_tiles, index_typed_constant(kTileHeight))); // x_end = kTileWidth + x_in_tiles * kTileWidth, i.e., the x location // that's immediately beyond the tile. - llvm::Value* x_end = ir_builder_.CreateNSWAdd( + llvm::Value* x_end = b_.CreateNSWAdd( index_typed_constant(kTileWidth), - ir_builder_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileWidth))); - llvm::Value* tile_in_y_bounds = ir_builder_.CreateOr( - ir_builder_.CreateICmpULE(y_end, index_typed_constant(height)), - ir_builder_.getInt1(height % kTileHeight == 0)); - llvm::Value* tile_in_x_bounds = ir_builder_.CreateOr( - ir_builder_.CreateICmpULE(x_end, index_typed_constant(width)), - ir_builder_.getInt1(width % kTileWidth == 0)); + b_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileWidth))); + llvm::Value* tile_in_y_bounds = + b_.CreateOr(b_.CreateICmpULE(y_end, index_typed_constant(height)), + b_.getInt1(height % kTileHeight == 0)); + llvm::Value* tile_in_x_bounds = + b_.CreateOr(b_.CreateICmpULE(x_end, index_typed_constant(width)), + b_.getInt1(width % kTileWidth == 0)); // The tile is in y bounds if "height" is a multiple of kTileHeight or // y_end <= height. - llvm_ir::LlvmIfData if_tile_in_y_bounds_data = llvm_ir::EmitIfThenElse( - tile_in_y_bounds, "tile_in_y_bounds", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.true_block, - &ir_builder_); + llvm_ir::LlvmIfData if_tile_in_y_bounds_data = + llvm_ir::EmitIfThenElse(tile_in_y_bounds, "tile_in_y_bounds", &b_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.true_block, &b_); // The tile is in x bounds if "width" is a multiple of kTileWidth or // x_end <= width. - llvm_ir::LlvmIfData if_tile_in_x_bounds_data = llvm_ir::EmitIfThenElse( - tile_in_x_bounds, "tile_in_x_bounds", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.true_block, - &ir_builder_); + llvm_ir::LlvmIfData if_tile_in_x_bounds_data = + llvm_ir::EmitIfThenElse(tile_in_x_bounds, "tile_in_x_bounds", &b_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.true_block, &b_); TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/true, /*tile_in_x_bounds=*/true)); - llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.false_block, - &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.false_block, &b_); TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/true, /*tile_in_x_bounds=*/false)); - llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.false_block, - &ir_builder_); - if_tile_in_x_bounds_data = llvm_ir::EmitIfThenElse( - tile_in_x_bounds, "tile_in_x_bounds", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.true_block, - &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.false_block, &b_); + if_tile_in_x_bounds_data = + llvm_ir::EmitIfThenElse(tile_in_x_bounds, "tile_in_x_bounds", &b_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.true_block, &b_); TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/false, /*tile_in_x_bounds=*/true)); - llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.false_block, - &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_x_bounds_data.false_block, &b_); TF_RETURN_IF_ERROR(emit_tile_element_loop(/*tile_in_y_bounds=*/false, /*tile_in_x_bounds=*/false)); // After the nested if-then-else statement on tile_in_y_bounds and // tile_in_x_bounds, emit atomic operations to accumulate the partial // reduction result to the output element. - llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.after_block, - &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_tile_in_y_bounds_data.after_block, &b_); const HloInstruction* output = reduce->IsFused() ? reduce->parent()->FusionInstruction() : reduce; for (int i = 0; i != num_reduces; ++i) { for (int x_offset = 0; x_offset < kTileWidth; ++x_offset) { - llvm::Value* x = ir_builder_.CreateNSWAdd( - ir_builder_.CreateNSWMul(x_in_tiles, - index_typed_constant(kTileWidth)), + llvm::Value* x = b_.CreateNSWAdd( + b_.CreateNSWMul(x_in_tiles, index_typed_constant(kTileWidth)), index_typed_constant(x_offset)); llvm::Value* output_address = GetIrArray(*output, *output, reduce_output_shapes[i]) @@ -1235,8 +1214,8 @@ Status IrEmitterUnnested::EmitColumnReduction( x, ShapeUtil::GetSubshape(output->shape(), reduce_output_shapes[i]), - &ir_builder_), - &ir_builder_, "output_element_address"); + &b_), + &b_, "output_element_address"); TF_RETURN_IF_ERROR(EmitAtomicOperationForNestedComputation( *reducers[i], output_address, partial_reduction_result_addresses[i * kTileWidth + x_offset])); @@ -1252,7 +1231,7 @@ Status IrEmitterUnnested::EmitColumnReduction( static_cast(LastThunk())->thunks().back().get(), ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, - launch_dimensions, &ir_builder_) + launch_dimensions, &b_) .EmitLoop(IrName(reduce), index_ty); } @@ -1402,8 +1381,8 @@ Status IrEmitterUnnested::EmitRowReduction( {depth / z_tile_size, height, width_in_tiles}, {2, 1, 0}); LaunchDimensions launch_dimensions = CalculateLaunchDimensions( tiled_input_shape, ir_emitter_context_->device_description()); - llvm::Type* index_ty = GetIndexTypeForKernel( - reduce, launch_dimensions.launch_bound(), &ir_builder_); + llvm::Type* index_ty = + GetIndexTypeForKernel(reduce, launch_dimensions.launch_bound(), &b_); auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { return llvm::ConstantInt::get(index_ty, c); @@ -1415,12 +1394,12 @@ Status IrEmitterUnnested::EmitRowReduction( input_shape.element_type(), ir_emitter_context_->llvm_module()); std::vector partial_reduction_result_addresses; for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result_address = ir_builder_.CreateAlloca( - element_ir_type, /*ArraySize=*/nullptr, - "partial_reduction_result." + llvm::Twine(i)); + llvm::Value* partial_reduction_result_address = + b_.CreateAlloca(element_ir_type, /*ArraySize=*/nullptr, + "partial_reduction_result." + llvm::Twine(i)); TF_ASSIGN_OR_RETURN(llvm::Value* const init_ir_value, init_value_gens[i](IrArray::Index(index_ty))); - ir_builder_.CreateStore(init_ir_value, partial_reduction_result_address); + b_.CreateStore(init_ir_value, partial_reduction_result_address); partial_reduction_result_addresses.push_back( partial_reduction_result_address); } @@ -1429,25 +1408,25 @@ Status IrEmitterUnnested::EmitRowReduction( llvm::Value* y = tile_index[1]; llvm::Value* x_tile = tile_index[2]; - x_tile = ir_builder_.CreateZExtOrTrunc(x_tile, index_ty); + x_tile = b_.CreateZExtOrTrunc(x_tile, index_ty); - llvm::Value* warp_id = ir_builder_.CreateUDiv( - x_tile, index_typed_constant(kWarpSize), "warp_id"); - llvm::Value* lane_id = ir_builder_.CreateURem( - x_tile, index_typed_constant(kWarpSize), "lane_id"); + llvm::Value* warp_id = + b_.CreateUDiv(x_tile, index_typed_constant(kWarpSize), "warp_id"); + llvm::Value* lane_id = + b_.CreateURem(x_tile, index_typed_constant(kWarpSize), "lane_id"); // The x-location of the last element in this z-x-tile. // last_x = lane_id + warpSize * (x_tile_size - 1 + warp_id * x_tile_size); - llvm::Value* last_x = ir_builder_.CreateNSWAdd( - lane_id, ir_builder_.CreateNSWMul( - index_typed_constant(kWarpSize), - ir_builder_.CreateNSWAdd( - index_typed_constant(x_tile_size - 1), - ir_builder_.CreateNSWMul( - warp_id, index_typed_constant(x_tile_size))))); + llvm::Value* last_x = b_.CreateNSWAdd( + lane_id, + b_.CreateNSWMul( + index_typed_constant(kWarpSize), + b_.CreateNSWAdd( + index_typed_constant(x_tile_size - 1), + b_.CreateNSWMul(warp_id, index_typed_constant(x_tile_size))))); KernelSupportLibrary ksl( - &ir_builder_, + &b_, /*unroll_mode=*/xla::llvm_ir::UnrollMode::kFullyUnroll, /*prevent_vectorization=*/false); @@ -1456,9 +1435,9 @@ Status IrEmitterUnnested::EmitRowReduction( auto emit_z_x_tile_element_loop = [&](bool x_tile_in_bounds, int64 x_tile_loop_bound) -> Status { auto emit_z_tile_element_loop = [&](llvm::Value* z_indvar) -> Status { - llvm::Value* z = ir_builder_.CreateNSWAdd( - z_indvar, ir_builder_.CreateNSWMul( - index_typed_constant(z_tile_size), z_tile)); + llvm::Value* z = b_.CreateNSWAdd( + z_indvar, + b_.CreateNSWMul(index_typed_constant(z_tile_size), z_tile)); TF_RETURN_IF_ERROR(ksl.For( "x_tile", /*start=*/index_typed_constant(0), @@ -1466,12 +1445,12 @@ Status IrEmitterUnnested::EmitRowReduction( /*step=*/1, [&](llvm::Value* x_indvar) -> Status { // x = lane_id + // warpSize * (element_id_in_x_tile + warp_id * x_tile_size); - llvm::Value* x = ir_builder_.CreateNSWAdd( + llvm::Value* x = b_.CreateNSWAdd( lane_id, - ir_builder_.CreateNSWMul( + b_.CreateNSWMul( index_typed_constant(kWarpSize), - ir_builder_.CreateNSWAdd( - x_indvar, ir_builder_.CreateNSWMul( + b_.CreateNSWAdd( + x_indvar, b_.CreateNSWMul( warp_id, llvm::ConstantInt::get( index_ty, x_tile_size))))); @@ -1479,18 +1458,17 @@ Status IrEmitterUnnested::EmitRowReduction( // emit a x-in-bounds check before reading from the input. if (!x_tile_in_bounds) { llvm_ir::LlvmIfData if_x_in_bounds_data = - llvm_ir::EmitIfThenElse(ir_builder_.CreateICmpULT( - x, index_typed_constant(width)), - "x_in_bounds", &ir_builder_); - // Points ir_builder_ to the then-block. + llvm_ir::EmitIfThenElse( + b_.CreateICmpULT(x, index_typed_constant(width)), + "x_in_bounds", &b_); + // Points b_ to the then-block. llvm_ir::SetToFirstInsertPoint(if_x_in_bounds_data.true_block, - &ir_builder_); + &b_); } // Emit code that reads the input element and accumulates it // to the partial reduction result. - llvm::Value* input_address = - ir_builder_.CreateAlloca(element_ir_type); + llvm::Value* input_address = b_.CreateAlloca(element_ir_type); { // {z,y,x} is an index to input_3d_tensor_shape // [depth,height,width]. We need to convert that to an index @@ -1509,20 +1487,19 @@ Status IrEmitterUnnested::EmitRowReduction( ShapeUtil::MakeShapeWithDescendingLayout( input_shape.element_type(), {depth, height, width}); const IrArray::Index input_3d_tensor_index( - {z, y, x}, input_3d_tensor_shape, &ir_builder_); + {z, y, x}, input_3d_tensor_shape, &b_); const IrArray::Index input_index = input_3d_tensor_index .SourceIndexOfReshape(input_3d_tensor_shape, - normalized_input_shape, - &ir_builder_) + normalized_input_shape, &b_) .SourceIndexOfTranspose( normalized_input_shape, input_shape, - transpose_dimension_mapping, &ir_builder_); + transpose_dimension_mapping, &b_); for (int i = 0; i != num_reduces; ++i) { TF_ASSIGN_OR_RETURN(llvm::Value* const input_ir_value, input_gens[i](input_index)); - ir_builder_.CreateStore(input_ir_value, input_address); + b_.CreateStore(input_ir_value, input_address); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *reducers[i], {partial_reduction_result_addresses[i], input_address}, @@ -1541,9 +1518,9 @@ Status IrEmitterUnnested::EmitRowReduction( /*step=*/1, emit_z_tile_element_loop); }; - llvm::Value* tile_in_bounds = ir_builder_.CreateOr( - ir_builder_.getInt1(width % (x_tile_size * kWarpSize) == 0), - ir_builder_.CreateICmpULT(last_x, index_typed_constant(width))); + llvm::Value* tile_in_bounds = + b_.CreateOr(b_.getInt1(width % (x_tile_size * kWarpSize) == 0), + b_.CreateICmpULT(last_x, index_typed_constant(width))); TF_RETURN_IF_ERROR( ksl.If(tile_in_bounds, @@ -1566,26 +1543,25 @@ Status IrEmitterUnnested::EmitRowReduction( // bitcast cannot be applied to aggregate types (even packed ones), so we // instead bitcast addresses of load/store to intN* of the same bit-width. llvm::Type* shuffle_ir_type = element_ir_type->isStructTy() - ? ir_builder_.getIntNTy(bit_width) + ? b_.getIntNTy(bit_width) : element_ir_type; for (int shuffle_distance = 16; shuffle_distance >= 1; shuffle_distance /= 2) { - llvm::Value* result_from_other_lane = ir_builder_.CreateAlloca( - element_ir_type, nullptr, "result_from_other_lane"); + llvm::Value* result_from_other_lane = + b_.CreateAlloca(element_ir_type, nullptr, "result_from_other_lane"); for (int i = 0; i != num_reduces; ++i) { - llvm::Value* partial_reduction_result = ir_builder_.CreateLoad( - ir_builder_.CreateBitCast(partial_reduction_result_addresses[i], - shuffle_ir_type->getPointerTo()), + llvm::Value* partial_reduction_result = b_.CreateLoad( + b_.CreateBitCast(partial_reduction_result_addresses[i], + shuffle_ir_type->getPointerTo()), "partial_reduction_result"); CHECK_EQ(launch_dimensions.threads_per_block() % kWarpSize, 0) << "Requires block size a multiple of the warp size, otherwise we " "will read undefined elements."; - ir_builder_.CreateStore( + b_.CreateStore( EmitFullWarpShuffleDown(partial_reduction_result, - ir_builder_.getInt32(shuffle_distance), - &ir_builder_), - ir_builder_.CreateBitCast(result_from_other_lane, - shuffle_ir_type->getPointerTo())); + b_.getInt32(shuffle_distance), &b_), + b_.CreateBitCast(result_from_other_lane, + shuffle_ir_type->getPointerTo())); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *reducers[i], {partial_reduction_result_addresses[i], result_from_other_lane}, @@ -1600,10 +1576,9 @@ Status IrEmitterUnnested::EmitRowReduction( // lane 0 (which holds the partially accumulated result for its warp) to the // output element. llvm_ir::LlvmIfData if_lane_id_is_zero_data = llvm_ir::EmitIfThenElse( - ir_builder_.CreateICmpEQ(lane_id, index_typed_constant(0)), - "lane_id_is_zero", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, - &ir_builder_); + b_.CreateICmpEQ(lane_id, index_typed_constant(0)), "lane_id_is_zero", + &b_); + llvm_ir::SetToFirstInsertPoint(if_lane_id_is_zero_data.true_block, &b_); for (int i = 0; i != num_reduces; ++i) { llvm::Value* output_address = GetIrArray(*output, *output, reduce_output_shapes[i]) @@ -1611,8 +1586,8 @@ Status IrEmitterUnnested::EmitRowReduction( IrArray::Index(y, ShapeUtil::GetSubshape( output->shape(), reduce_output_shapes[i]), - &ir_builder_), - &ir_builder_, "output_element_address"); + &b_), + &b_, "output_element_address"); // We don't need to emit atomic operations if there is only one tile of // results. 'depth' is the z dimension, 'width' is the x dimension. if (z_tile_size >= depth && x_tile_size >= width) { @@ -1636,7 +1611,7 @@ Status IrEmitterUnnested::EmitRowReduction( static_cast(LastThunk())->thunks().back().get(), ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, tiled_input_shape, - launch_dimensions, &ir_builder_) + launch_dimensions, &b_) .EmitLoop(IrName(reduce), index_ty); } @@ -1762,12 +1737,11 @@ Status IrEmitterUnnested::HandleReduce(HloInstruction* reduce) { return EmitReductionToVector( reduce, input->shape(), {[&](const IrArray::Index& index) { - return GetIrArray(*input, *reduce) - .EmitReadArrayElement(index, &ir_builder_); + return GetIrArray(*input, *reduce).EmitReadArrayElement(index, &b_); }}, {[&](const IrArray::Index& index) { return GetIrArray(*init_value, *reduce) - .EmitReadArrayElement(index, &ir_builder_); + .EmitReadArrayElement(index, &b_); }}, dimensions_to_reduce, {reducer}, {{}}, {}); } @@ -1842,7 +1816,7 @@ Status IrEmitterUnnested::HandleSelectAndScatter( LaunchDimensions launch_dimensions = CalculateLaunchDimensions( source->shape(), ir_emitter_context_->device_description()); llvm::Type* index_type = GetIndexTypeForKernel( - select_and_scatter, launch_dimensions.launch_bound(), &ir_builder_); + select_and_scatter, launch_dimensions.launch_bound(), &b_); auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { return llvm::ConstantInt::get(index_type, c); }; @@ -1873,19 +1847,18 @@ Status IrEmitterUnnested::HandleSelectAndScatter( llvm::Value* selected_value_address = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(operand_element_type, ir_emitter_context_->llvm_module()), - "selected_value_address", &ir_builder_); + "selected_value_address", &b_); llvm::Value* selected_index_address = llvm_ir::EmitAllocaAtFunctionEntryWithCount( index_type, index_typed_constant(rank), "selected_index_address", - &ir_builder_); + &b_); llvm::Value* initialized_flag_address = llvm_ir::EmitAllocaAtFunctionEntry( - ir_builder_.getInt1Ty(), "initialized_flag_address", &ir_builder_); - ir_builder_.CreateStore(ir_builder_.getInt1(false), - initialized_flag_address); + b_.getInt1Ty(), "initialized_flag_address", &b_); + b_.CreateStore(b_.getInt1(false), initialized_flag_address); // Create the inner loop to iterate over the window. - llvm_ir::ForLoopNest window_loops(IrName(select_and_scatter, "inner"), - &ir_builder_, index_type); + llvm_ir::ForLoopNest window_loops(IrName(select_and_scatter, "inner"), &b_, + index_type); std::vector window_size; for (const auto& dim : window.dimensions()) { window_size.push_back(dim.size()); @@ -1894,84 +1867,79 @@ Status IrEmitterUnnested::HandleSelectAndScatter( const IrArray::Index window_index = window_loops.AddLoopsForShape( ShapeUtil::MakeShape(operand_element_type, window_size), "window"); llvm_ir::SetToFirstInsertPoint(window_loops.GetInnerLoopBodyBasicBlock(), - &ir_builder_); + &b_); // Compute the operand index to visit and evaluate the condition whether the // operand index is within the bounds. The unsigned comparison includes // checking whether the operand index >= 0. IrArray::Index operand_index(index_type, source_index.size()); - llvm::Value* in_bounds_condition = ir_builder_.getInt1(true); + llvm::Value* in_bounds_condition = b_.getInt1(true); for (int64 i = 0; i < rank; ++i) { - llvm::Value* strided_index = ir_builder_.CreateNSWMul( + llvm::Value* strided_index = b_.CreateNSWMul( source_index[i], index_typed_constant(window.dimensions(i).stride())); - operand_index[i] = ir_builder_.CreateNSWSub( - ir_builder_.CreateNSWAdd(strided_index, window_index[i]), + operand_index[i] = b_.CreateNSWSub( + b_.CreateNSWAdd(strided_index, window_index[i]), index_typed_constant(window.dimensions(i).padding_low())); - llvm::Value* index_condition = ir_builder_.CreateICmpULT( + llvm::Value* index_condition = b_.CreateICmpULT( operand_index[i], index_typed_constant(ShapeUtil::GetDimension(operand->shape(), i))); - in_bounds_condition = - ir_builder_.CreateAnd(in_bounds_condition, index_condition); + in_bounds_condition = b_.CreateAnd(in_bounds_condition, index_condition); } CHECK(in_bounds_condition != nullptr); // Only need to do something if the operand index is within the bounds. // First check if the initialized_flag is set. llvm_ir::LlvmIfData if_in_bounds = - llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_in_bounds.true_block, &ir_builder_); + llvm_ir::EmitIfThenElse(in_bounds_condition, "in-bounds", &b_); + llvm_ir::SetToFirstInsertPoint(if_in_bounds.true_block, &b_); llvm_ir::LlvmIfData if_initialized = llvm_ir::EmitIfThenElse( - ir_builder_.CreateLoad(initialized_flag_address), "initialized", - &ir_builder_); + b_.CreateLoad(initialized_flag_address), "initialized", &b_); // If the initialized_flag is false, initialize the selected value and index // with the currently visiting operand. - llvm_ir::SetToFirstInsertPoint(if_initialized.false_block, &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_initialized.false_block, &b_); const auto save_operand_index = [&](const IrArray::Index& operand_index) { for (int64 i = 0; i < rank; ++i) { llvm::Value* selected_index_address_slot = - ir_builder_.CreateInBoundsGEP(selected_index_address, - {ir_builder_.getInt32(i)}); - ir_builder_.CreateStore(operand_index[i], selected_index_address_slot); + b_.CreateInBoundsGEP(selected_index_address, {b_.getInt32(i)}); + b_.CreateStore(operand_index[i], selected_index_address_slot); } }; IrArray operand_array = GetIrArray(*operand, *select_and_scatter); llvm::Value* operand_data = - operand_array.EmitReadArrayElement(operand_index, &ir_builder_); - ir_builder_.CreateStore(operand_data, selected_value_address); + operand_array.EmitReadArrayElement(operand_index, &b_); + b_.CreateStore(operand_data, selected_value_address); save_operand_index(operand_index); - ir_builder_.CreateStore(ir_builder_.getInt1(true), - initialized_flag_address); + b_.CreateStore(b_.getInt1(true), initialized_flag_address); // If the initialized_flag is true, call the `select` function to // potentially update the selected value and index with the currently // visiting operand. - llvm_ir::SetToFirstInsertPoint(if_initialized.true_block, &ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_initialized.true_block, &b_); const Shape output_shape = ShapeUtil::MakeShape(PRED, {}); llvm::Value* operand_address = - operand_array.EmitArrayElementAddress(operand_index, &ir_builder_); + operand_array.EmitArrayElementAddress(operand_index, &b_); llvm::Value* select_return_buffer = llvm_ir::EmitAllocaAtFunctionEntry( llvm_ir::PrimitiveTypeToIrType(PRED, ir_emitter_context_->llvm_module()), - "select_return_buffer", &ir_builder_); + "select_return_buffer", &b_); TF_RETURN_IF_ERROR(EmitCallToNestedComputation( *select_and_scatter->select(), {selected_value_address, operand_address}, select_return_buffer)); - llvm::Value* result = ir_builder_.CreateLoad(select_return_buffer); + llvm::Value* result = b_.CreateLoad(select_return_buffer); // If the 'select' function returns false, update the selected value and the // index to the currently visiting operand. - llvm::Value* cond = ir_builder_.CreateICmpNE( + llvm::Value* cond = b_.CreateICmpNE( result, llvm::ConstantInt::get(llvm_ir::PrimitiveTypeToIrType( PRED, ir_emitter_context_->llvm_module()), 0), "boolean_predicate"); llvm_ir::LlvmIfData if_select_lhs = - llvm_ir::EmitIfThenElse(cond, "if-select-lhs", &ir_builder_); - llvm_ir::SetToFirstInsertPoint(if_select_lhs.false_block, &ir_builder_); - ir_builder_.CreateStore(ir_builder_.CreateLoad(operand_address), - selected_value_address); + llvm_ir::EmitIfThenElse(cond, "if-select-lhs", &b_); + llvm_ir::SetToFirstInsertPoint(if_select_lhs.false_block, &b_); + b_.CreateStore(b_.CreateLoad(operand_address), selected_value_address); save_operand_index(operand_index); // After iterating over the window elements, scatter the source element to @@ -1979,20 +1947,19 @@ Status IrEmitterUnnested::HandleSelectAndScatter( // location is computed by calling the `scatter` function with the source // value and the current output value. llvm_ir::SetToFirstInsertPoint(window_loops.GetOuterLoopExitBasicBlock(), - &ir_builder_); + &b_); IrArray::Index selected_index(operand_index.GetType()); for (int64 i = 0; i < rank; ++i) { - llvm::Value* selected_index_address_slot = ir_builder_.CreateInBoundsGEP( - selected_index_address, {ir_builder_.getInt32(i)}); - selected_index.push_back( - ir_builder_.CreateLoad(selected_index_address_slot)); + llvm::Value* selected_index_address_slot = + b_.CreateInBoundsGEP(selected_index_address, {b_.getInt32(i)}); + selected_index.push_back(b_.CreateLoad(selected_index_address_slot)); } llvm::Value* source_value_address = GetIrArray(*source, *select_and_scatter) - .EmitArrayElementAddress(source_index, &ir_builder_); + .EmitArrayElementAddress(source_index, &b_); llvm::Value* output_value_address = GetIrArray(*select_and_scatter, *select_and_scatter) - .EmitArrayElementAddress(selected_index, &ir_builder_); + .EmitArrayElementAddress(selected_index, &b_); return EmitAtomicOperationForNestedComputation( *select_and_scatter->scatter(), output_value_address, source_value_address); @@ -2007,7 +1974,7 @@ Status IrEmitterUnnested::HandleSelectAndScatter( static_cast(LastThunk())->thunks().back().get(), ir_emitter_context_->llvm_module()); return ParallelLoopEmitter(loop_body_emitter, source->shape(), - launch_dimensions, &ir_builder_) + launch_dimensions, &b_) .EmitLoop(IrName(select_and_scatter), index_type); } @@ -2326,18 +2293,16 @@ std::unique_ptr IrEmitterUnnested::BuildKernelThunk( << " is found in slice " << slice.ToString() << " at GTE index " << gte_index.ToString(); - llvm::Value* loc = - ir_builder_.CreateInBoundsGEP(kernel_args.at(slice.allocation()), - {ir_builder_.getInt64(slice.offset())}); + llvm::Value* loc = b_.CreateInBoundsGEP(kernel_args.at(slice.allocation()), + {b_.getInt64(slice.offset())}); // If gte_index is nonempty, we have to dereference `loc` to get to the // value we're ultimately interested in. llvm::Type* int8_double_pointer = - llvm::PointerType::get(ir_builder_.getInt8PtrTy(), /*AddressSpace=*/0); + llvm::PointerType::get(b_.getInt8PtrTy(), /*AddressSpace=*/0); for (int64 idx : gte_index) { - loc = ir_builder_.CreateBitCast(loc, int8_double_pointer); - loc = ir_builder_.CreateLoad( - ir_builder_.CreateInBoundsGEP(loc, {ir_builder_.getInt64(idx)})); + loc = b_.CreateBitCast(loc, int8_double_pointer); + loc = b_.CreateLoad(b_.CreateInBoundsGEP(loc, {b_.getInt64(idx)})); } bindings_.BindHloToIrValue(*instr, loc, index); @@ -2349,7 +2314,7 @@ std::unique_ptr IrEmitterUnnested::BuildKernelThunk( bindings_.SetTempBufferBase(kernel_args.at(*temp_buffer)); } else { bindings_.SetTempBufferBase( - llvm::ConstantPointerNull::get(ir_builder_.getInt8PtrTy())); + llvm::ConstantPointerNull::get(b_.getInt8PtrTy())); } return MakeUnique(buffers, llvm_ir::AsString(kernel->getName()), @@ -2596,10 +2561,9 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( TF_RETURN_IF_ERROR(ParallelLoopEmitter( [=](const IrArray::Index& index) { return GetIrArray(*init_value, *hlo) - .EmitReadArrayElement(index, &ir_builder_); + .EmitReadArrayElement(index, &b_); }, - GetIrArray(*hlo, *hlo, index), launch_dimensions, - &ir_builder_) + GetIrArray(*hlo, *hlo, index), launch_dimensions, &b_) .EmitLoop(IrName(hlo))); // Clean up state left behind by emitting the loop above. (This is normally @@ -2783,10 +2747,10 @@ Status IrEmitterUnnested::EmitTargetElementLoopInThunk( ir_emitter_context_->llvm_module()); if (!hlo.IsMultiOutputFusion()) { return ParallelLoopEmitter(element_generator, GetIrArray(hlo, hlo), - launch_dimensions, &ir_builder_, unroll_factor) - .EmitLoop(IrName(&hlo), - GetIndexTypeForKernel(&hlo, launch_dimensions.launch_bound(), - &ir_builder_)); + launch_dimensions, &b_, unroll_factor) + .EmitLoop( + IrName(&hlo), + GetIndexTypeForKernel(&hlo, launch_dimensions.launch_bound(), &b_)); } // For multioutput fusion, we need to emit each operand and the root. @@ -2796,18 +2760,17 @@ Status IrEmitterUnnested::EmitTargetElementLoopInThunk( } TF_RETURN_IF_ERROR( ParallelLoopEmitter(element_generator, output_arrays, launch_dimensions, - &ir_builder_, unroll_factor) + &b_, unroll_factor) .EmitLoop(IrName(&hlo), GetIndexTypeForKernel( - &hlo, launch_dimensions.launch_bound(), &ir_builder_))); + &hlo, launch_dimensions.launch_bound(), &b_))); std::vector tuple_operand_ptrs; for (int64 i = 0; i < output_arrays.size(); ++i) { tuple_operand_ptrs.push_back(output_arrays[i].GetBasePointer()); } - ir_builder_.SetInsertPoint(ir_builder_.GetInsertBlock()->getTerminator()); - llvm_ir::EmitTuple(GetIrArray(hlo, hlo), tuple_operand_ptrs, &ir_builder_, - module_); + b_.SetInsertPoint(b_.GetInsertBlock()->getTerminator()); + llvm_ir::EmitTuple(GetIrArray(hlo, hlo), tuple_operand_ptrs, &b_, module_); return Status::OK(); } @@ -2858,14 +2821,14 @@ int IrEmitterUnnested::ConstructOutputReducedShapeAndCastOutputIrArrayToShape( output_reduced_shapes->push_back(ShapeUtil::MakeShapeWithDescendingLayout( ShapeUtil::GetSubshape(hlo.shape(), {i}).element_type(), reduced_output_dims)); - output_in_reduced_shape_arrays->push_back(output_arrays[i].CastToShape( - (*output_reduced_shapes)[i], &ir_builder_)); + output_in_reduced_shape_arrays->push_back( + output_arrays[i].CastToShape((*output_reduced_shapes)[i], &b_)); } } else { output_reduced_shapes->push_back(ShapeUtil::MakeShapeWithDescendingLayout( hlo.shape().element_type(), reduced_output_dims)); - output_in_reduced_shape_arrays->push_back(output_arrays[0].CastToShape( - (*output_reduced_shapes)[0], &ir_builder_)); + output_in_reduced_shape_arrays->push_back( + output_arrays[0].CastToShape((*output_reduced_shapes)[0], &b_)); } return num_outputs; } @@ -2889,8 +2852,8 @@ int IrEmitterUnnested::ConstructInputReducedShapeAndCastInputIrArrayToShape( param_reduced_shapes->push_back(ShapeUtil::MakeShapeWithDescendingLayout( param->shape().element_type(), Permute({0, 2, 1}, reduced_output_dims))); - param_in_reduced_shape_arrays->push_back(param_arrays[id].CastToShape( - (*param_reduced_shapes)[id], &ir_builder_)); + param_in_reduced_shape_arrays->push_back( + param_arrays[id].CastToShape((*param_reduced_shapes)[id], &b_)); } return num_params; } @@ -3039,7 +3002,7 @@ LaunchDimensions IrEmitterUnnested::EmitHlo021Tile( kTileSize); const int kNVPTXSharedMemoryAddrSpace = 3; auto* tile_base_ptr = new llvm::GlobalVariable( - *ir_builder_.GetInsertBlock()->getParent()->getParent(), tile_type, + *b_.GetInsertBlock()->getParent()->getParent(), tile_type, /*isConstant=*/false, llvm::GlobalValue::PrivateLinkage, llvm::UndefValue::get(tile_type), llvm_ir::AsStringRef(IrName(hlo, StrCat("tile", id))), nullptr, @@ -3063,8 +3026,8 @@ LaunchDimensions IrEmitterUnnested::EmitHlo021Tile( c_accumulate(output_dims_in_tiles, 1, std::multiplies()); LaunchDimensions launch_dimensions(num_tiles, kThreadsPerTile); - llvm::Type* index_ty = GetIndexTypeForKernel( - hlo, launch_dimensions.launch_bound(), &ir_builder_); + llvm::Type* index_ty = + GetIndexTypeForKernel(hlo, launch_dimensions.launch_bound(), &b_); auto index_typed_constant = [&](uint64 c) -> llvm::Constant* { return llvm::ConstantInt::get(index_ty, c); }; @@ -3092,23 +3055,23 @@ LaunchDimensions IrEmitterUnnested::EmitHlo021Tile( llvm::Value* x; llvm::Value* y; std::tie(y, x) = CalculateYXCoordinateWithinTile( - &ir_builder_, index_typed_constant(kTileSize), kThreadsPerTile); + &b_, index_typed_constant(kTileSize), kThreadsPerTile); // Calculate the index for the current output tile from block_id. const IrArray::Index output_tile_index( - GetBlockIdx(&ir_builder_, index_ty, num_tiles), + GetBlockIdx(&b_, index_ty, num_tiles), ShapeUtil::MakeShapeWithDescendingLayout(PRED /*arbitrary*/, output_dims_in_tiles), - &ir_builder_); + &b_); // Output tile origin is the index for the first element of the current output // tile. const IrArray::Index output_tile_origin = [&] { IrArray::Index index = output_tile_index; for (int i = 1; i < 3; ++i) { - index[i] = ir_builder_.CreateMul(output_tile_index[i], - index_typed_constant(kTileSize), - "tile_origin." + std::to_string(i)); + index[i] = + b_.CreateMul(output_tile_index[i], index_typed_constant(kTileSize), + "tile_origin." + std::to_string(i)); } return index; }(); @@ -3121,16 +3084,15 @@ LaunchDimensions IrEmitterUnnested::EmitHlo021Tile( std::vector output_tile_bounds(3); for (int i = 1; i < 3; ++i) { // Only last row or column may not have full size. - output_tile_bounds[i] = ir_builder_.CreateSelect( - ir_builder_.CreateICmpEQ( - output_tile_index[i], - index_typed_constant(output_dims_in_tiles[i] - 1)), + output_tile_bounds[i] = b_.CreateSelect( + b_.CreateICmpEQ(output_tile_index[i], + index_typed_constant(output_dims_in_tiles[i] - 1)), index_typed_constant(reduced_output_dims[i] - (output_dims_in_tiles[i] - 1) * kTileSize), index_typed_constant(kTileSize), "kTileSize"); } - KernelSupportLibrary ksl(&ir_builder_, llvm_ir::UnrollMode::kDefaultUnroll); + KernelSupportLibrary ksl(&b_, llvm_ir::UnrollMode::kDefaultUnroll); // Curry a few parameters to EmitTiledElementalCodeWithBoundsCheck. auto emit_tiled_elemental_code_with_bounds_check = @@ -3139,13 +3101,13 @@ LaunchDimensions IrEmitterUnnested::EmitHlo021Tile( const std::function& emit_elem_function) { EmitTiledElementalCodeWithBoundsCheck( - kTileSize, kNumRows, index, loop_name, &ksl, &ir_builder_, y, x, - tile_width, tile_height, emit_elem_function); + kTileSize, kNumRows, index, loop_name, &ksl, &b_, y, x, tile_width, + tile_height, emit_elem_function); }; // Adds `addend` to the given `dim` of `index`. auto offset_dim = [&](IrArray::Index index, llvm::Value* addend, int64 dim) { - index[dim] = ir_builder_.CreateAdd(index[dim], addend); + index[dim] = b_.CreateAdd(index[dim], addend); return index; }; const IrArray::Index input_index = @@ -3161,19 +3123,17 @@ LaunchDimensions IrEmitterUnnested::EmitHlo021Tile( llvm::Value* shmem_buffer = param_shmem_buffers[id]; // TODO(jlebar): Add AA metadata to this store. Tile buffers are // global variables, so LLVM can't infer much about it. - ir_builder_.CreateStore( - input_in_logical_shape.EmitReadArrayElement(index, &ir_builder_, + b_.CreateStore( + input_in_logical_shape.EmitReadArrayElement(index, &b_, "input_element"), - ir_builder_.CreateGEP(shmem_buffer, - {index_typed_constant(0), y_loc, x})); + b_.CreateGEP(shmem_buffer, {index_typed_constant(0), y_loc, x})); } }); // Wait for all threads to reach this point, lest we copy a value from tile to // output before the other thread copies it from input to tile. // This is `__syncthreads` in CUDA. - llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_barrier0, {}, {}, - &ir_builder_); + llvm_ir::EmitCallToIntrinsic(llvm::Intrinsic::nvvm_barrier0, {}, {}, &b_); llvm_ir::TiledParameterInfo tiled_param_info(param_shmem_buffers, y, x); @@ -3187,27 +3147,26 @@ LaunchDimensions IrEmitterUnnested::EmitHlo021Tile( output_index, "output", output_tile_bounds[2], output_tile_bounds[1], [&](const IrArray::Index& index, llvm::Value* y_loc) { // TODO(jlebar): Add AA metadata to this load. - llvm::Instruction* load_from_shmem_buffer = ir_builder_.CreateLoad( - ir_builder_.CreateGEP(param_shmem_buffers[0], - {ir_builder_.getInt64(0), x, y_loc}), + llvm::Instruction* load_from_shmem_buffer = b_.CreateLoad( + b_.CreateGEP(param_shmem_buffers[0], {b_.getInt64(0), x, y_loc}), "output_element"); output_in_reduced_shape_arrays[0].EmitWriteArrayElement( - index, load_from_shmem_buffer, &ir_builder_); + index, load_from_shmem_buffer, &b_); }); } else { CHECK_EQ(hlo->opcode(), HloOpcode::kFusion); emit_tiled_elemental_code_with_bounds_check( output_index, "output", output_tile_bounds[2], output_tile_bounds[1], [&](const IrArray::Index& index, llvm::Value* y_loc) { - GpuElementalIrEmitter elem_emitter(hlo_module_config_, module_, - &ir_builder_, GetNestedComputer()); + GpuElementalIrEmitter elem_emitter(hlo_module_config_, module_, &b_, + GetNestedComputer()); FusedIrEmitter fused_emitter(param_arrays, &elem_emitter); tiled_param_info.set_y(y_loc); fused_emitter.SetTiledParameterInfo(&tiled_param_info); TF_CHECK_OK(hlo->fused_expression_root()->Accept(&fused_emitter)); IrArray::Index untiled_index = llvm_ir::GetUnreducedOutputIndex( index, output_reduced_shapes[0], output_arrays[0].GetShape(), - &ir_builder_); + &b_); const llvm_ir::ElementGenerator& output_generator = fused_emitter.GetRootGenerator(); llvm::Value* output_value = @@ -3218,12 +3177,11 @@ LaunchDimensions IrEmitterUnnested::EmitHlo021Tile( output_in_reduced_shape_arrays.size()); for (int64 i = 0; i < output_in_reduced_shape_arrays.size(); ++i) { output_in_reduced_shape_arrays[i].EmitWriteArrayElement( - index, ir_builder_.CreateExtractValue(output_value, i), - &ir_builder_); + index, b_.CreateExtractValue(output_value, i), &b_); } } else { output_in_reduced_shape_arrays[0].EmitWriteArrayElement( - index, output_value, &ir_builder_); + index, output_value, &b_); } }); } @@ -3234,7 +3192,7 @@ LaunchDimensions IrEmitterUnnested::EmitHlo021Tile( for (int64 i = 0; i < output_arrays.size(); ++i) { tuple_operand_ptrs.push_back(output_arrays[i].GetBasePointer()); } - llvm_ir::EmitTuple(GetIrArray(*hlo, *hlo), tuple_operand_ptrs, &ir_builder_, + llvm_ir::EmitTuple(GetIrArray(*hlo, *hlo), tuple_operand_ptrs, &b_, module_); } diff --git a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc index cd833ec7bd..3838fee674 100644 --- a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.cc @@ -32,27 +32,27 @@ namespace gpu { ParallelLoopEmitter::ParallelLoopEmitter( BodyEmitter body_emitter, const Shape& shape, - const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* ir_builder, + const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b, int unroll_factor) - : LoopEmitter(body_emitter, shape, ir_builder), + : LoopEmitter(body_emitter, shape, b), launch_dimensions_(launch_dimensions), unroll_factor_(unroll_factor) {} ParallelLoopEmitter::ParallelLoopEmitter( const llvm_ir::ElementGenerator& target_element_generator, tensorflow::gtl::ArraySlice target_arrays, - const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* ir_builder, + const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b, int unroll_factor) - : LoopEmitter(target_element_generator, target_arrays, ir_builder), + : LoopEmitter(target_element_generator, target_arrays, b), launch_dimensions_(launch_dimensions), unroll_factor_(unroll_factor) {} ParallelLoopEmitter::ParallelLoopEmitter( const llvm_ir::ElementGenerator& target_element_generator, const llvm_ir::IrArray& target_array, - const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* ir_builder, + const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b, int unroll_factor) - : LoopEmitter(target_element_generator, target_array, ir_builder), + : LoopEmitter(target_element_generator, target_array, b), launch_dimensions_(launch_dimensions), unroll_factor_(unroll_factor) {} @@ -74,29 +74,27 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( CHECK_NE(index_type, nullptr); std::vector array_indices; llvm::Value* block_id = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::nvvm_read_ptx_sreg_ctaid_x, {}, {}, ir_builder_); + llvm::Intrinsic::nvvm_read_ptx_sreg_ctaid_x, {}, {}, b_); llvm_ir::AddRangeMetadata(0, launch_dimensions_.block_count(), static_cast(block_id)); - block_id = ir_builder_->CreateZExtOrTrunc(block_id, index_type, "block_id"); + block_id = b_->CreateZExtOrTrunc(block_id, index_type, "block_id"); // Per the PTX documentation: // "It is guaranteed that [...] 0 <= %tid.x < %ntid.x" // // %ntid.x is currently specified as 1024. llvm::Value* thread_id = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::nvvm_read_ptx_sreg_tid_x, {}, {}, ir_builder_); + llvm::Intrinsic::nvvm_read_ptx_sreg_tid_x, {}, {}, b_); llvm_ir::AddRangeMetadata(0, launch_dimensions_.threads_per_block(), static_cast(thread_id)); - thread_id = - ir_builder_->CreateZExtOrTrunc(thread_id, index_type, "thread_id"); - - llvm::Value* linear_index_base = ir_builder_->CreateAdd( - ir_builder_->CreateMul( - block_id, - llvm::ConstantInt::get(index_type, - launch_dimensions_.threads_per_block()), - "", - /*HasNUW=*/true, /*HasNSW=*/true), + thread_id = b_->CreateZExtOrTrunc(thread_id, index_type, "thread_id"); + + llvm::Value* linear_index_base = b_->CreateAdd( + b_->CreateMul(block_id, + llvm::ConstantInt::get( + index_type, launch_dimensions_.threads_per_block()), + "", + /*HasNUW=*/true, /*HasNSW=*/true), thread_id, "linear_index", /*HasNUW=*/true, /*HasNSW=*/true); // Add an @llvm.assume(linear_index < threads_per_block * num_blocks). @@ -109,41 +107,41 @@ ParallelLoopEmitter::EmitIndexAndSetExitBasicBlock( // conditions in the same basic block as their operands. llvm_ir::EmitCallToIntrinsic( llvm::Intrinsic::assume, - {ir_builder_->CreateICmpULT( + {b_->CreateICmpULT( linear_index_base, llvm::ConstantInt::get(index_type, launch_dimensions_.threads_per_block() * launch_dimensions_.block_count()), "linear_index_in_range")}, - {}, ir_builder_); + {}, b_); if (unroll_factor_ > 1) { - linear_index_base = ir_builder_->CreateMul( + linear_index_base = b_->CreateMul( linear_index_base, llvm::ConstantInt::get(index_type, unroll_factor_), "linear_index_base", /*HasNUW=*/true, /*HasNSW=*/true); } - array_indices.emplace_back(linear_index_base, shape_, ir_builder_); + array_indices.emplace_back(linear_index_base, shape_, b_); for (int i = 1; i < unroll_factor_; ++i) { - llvm::Value* linear_index = ir_builder_->CreateAdd( - linear_index_base, llvm::ConstantInt::get(index_type, i), - "linear_index", - /*HasNUW=*/true, /*HasNSW=*/true); - array_indices.emplace_back(linear_index, shape_, ir_builder_); + llvm::Value* linear_index = + b_->CreateAdd(linear_index_base, llvm::ConstantInt::get(index_type, i), + "linear_index", + /*HasNUW=*/true, /*HasNSW=*/true); + array_indices.emplace_back(linear_index, shape_, b_); } auto if_in_bounds = llvm_ir::EmitIfThenElse( - ir_builder_->CreateICmpULT( + b_->CreateICmpULT( linear_index_base, llvm::ConstantInt::get(index_type, ShapeUtil::ElementsIn(shape_))), - llvm_ir::IrName(loop_name, "in_bounds"), ir_builder_, false); + llvm_ir::IrName(loop_name, "in_bounds"), b_, false); // Set exit_bb_ to the exit block of the if structure. exit_bb_ = if_in_bounds.after_block; CHECK_NE(nullptr, exit_bb_); // Set IR builder insertion point to the body of the if structure. - llvm_ir::SetToFirstInsertPoint(if_in_bounds.true_block, ir_builder_); + llvm_ir::SetToFirstInsertPoint(if_in_bounds.true_block, b_); return array_indices; } diff --git a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h index 302e1bf1bc..b82a23419d 100644 --- a/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h @@ -34,13 +34,13 @@ class ParallelLoopEmitter : public llvm_ir::LoopEmitter { // The meanings of other parameters are the same as LoopEmitter. ParallelLoopEmitter(BodyEmitter body_emitter, const Shape& shape, const LaunchDimensions& launch_dimensions, - llvm::IRBuilder<>* ir_builder, int unroll_factor = 1); + llvm::IRBuilder<>* b, int unroll_factor = 1); // Constructs a ParallelLoopEmitter from an element generator that generates // each element of the given target array. ParallelLoopEmitter(const llvm_ir::ElementGenerator& target_element_generator, const llvm_ir::IrArray& target_array, const LaunchDimensions& launch_dimensions, - llvm::IRBuilder<>* ir_builder, int unroll_factor = 1); + llvm::IRBuilder<>* b, int unroll_factor = 1); // Constructs a loop emitter for a loop that generates on element of each of N // arrays on each iteration. @@ -50,7 +50,7 @@ class ParallelLoopEmitter : public llvm_ir::LoopEmitter { ParallelLoopEmitter( const llvm_ir::ElementGenerator& target_element_generator, tensorflow::gtl::ArraySlice target_arrays, - const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* ir_builder, + const LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b, int unroll_factor = 1); ParallelLoopEmitter(const ParallelLoopEmitter&) = delete; diff --git a/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.cc b/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.cc index 7048fcfdc9..1bd73fc793 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.cc @@ -40,14 +40,14 @@ static Status EmitDynamicUpdateSliceInPlaceImpl( const Shape& update_shape, const ElementGenerator& start_indices_generator, bool is_signed, ElementGenerator update_array_generator, const IrArray& output_array, const gpu::LaunchDimensions* launch_dimensions, - tensorflow::StringPiece name, llvm::IRBuilder<>* ir_builder) { + tensorflow::StringPiece name, llvm::IRBuilder<>* b) { const Shape& output_shape = output_array.GetShape(); // Read start indices from start_indices_generator. const int64 rank = ShapeUtil::Rank(output_shape); - IrArray::Index start_index(ir_builder->getInt64Ty(), rank); + IrArray::Index start_index(b->getInt64Ty(), rank); for (int64 i = 0; i < rank; ++i) { - IrArray::Index dim_index({ir_builder->getInt64(i)}); + IrArray::Index dim_index({b->getInt64(i)}); TF_ASSIGN_OR_RETURN(start_index[i], start_indices_generator(dim_index)); llvm::Value* output_dim_size = llvm::ConstantInt::get( start_index[i]->getType(), output_shape.dimensions(i)); @@ -60,20 +60,19 @@ static Status EmitDynamicUpdateSliceInPlaceImpl( // TODO(b/74360564): This is implementation defined behavior, but is // currently respected by all implementations. Change this if we ever decide // to officially document different behavior. - llvm::Value* max_bound = - ir_builder->CreateSub(output_dim_size, update_dim_size); + llvm::Value* max_bound = b->CreateSub(output_dim_size, update_dim_size); llvm::Value* zero = llvm::ConstantInt::get(start_index[i]->getType(), 0); - start_index[i] = ir_builder->CreateSelect( - ir_builder->CreateICmp( - is_signed ? llvm::ICmpInst::ICMP_SGE : llvm::ICmpInst::ICMP_UGE, - zero, start_index[i]), - zero, start_index[i]); - - start_index[i] = ir_builder->CreateSelect( - ir_builder->CreateICmp( - is_signed ? llvm::ICmpInst::ICMP_SLE : llvm::ICmpInst::ICMP_ULE, - max_bound, start_index[i]), - max_bound, start_index[i]); + start_index[i] = + b->CreateSelect(b->CreateICmp(is_signed ? llvm::ICmpInst::ICMP_SGE + : llvm::ICmpInst::ICMP_UGE, + zero, start_index[i]), + zero, start_index[i]); + + start_index[i] = + b->CreateSelect(b->CreateICmp(is_signed ? llvm::ICmpInst::ICMP_SLE + : llvm::ICmpInst::ICMP_ULE, + max_bound, start_index[i]), + max_bound, start_index[i]); } auto loop_body_emitter = [&](const IrArray::Index& update_index) -> Status { @@ -84,31 +83,30 @@ static Status EmitDynamicUpdateSliceInPlaceImpl( // IrArray::Index output_index(start_index.GetType(), rank); for (int64 i = 0; i < rank; ++i) { - llvm::Value* start_index0 = ir_builder->CreateSExtOrBitCast( - start_index[i], update_index[i]->getType()); - output_index[i] = ir_builder->CreateAdd(start_index0, update_index[i]); + llvm::Value* start_index0 = + b->CreateSExtOrBitCast(start_index[i], update_index[i]->getType()); + output_index[i] = b->CreateAdd(start_index0, update_index[i]); } // Do output[output_index] = update[update_index]. TF_ASSIGN_OR_RETURN(llvm::Value * update_data, update_array_generator(update_index)); - output_array.EmitWriteArrayElement(output_index, update_data, ir_builder); + output_array.EmitWriteArrayElement(output_index, update_data, b); return Status::OK(); }; if (launch_dimensions != nullptr) { return gpu::ParallelLoopEmitter(loop_body_emitter, update_shape, - *launch_dimensions, ir_builder) + *launch_dimensions, b) .EmitLoop(name); } - return LoopEmitter(loop_body_emitter, update_shape, ir_builder) - .EmitLoop(name); + return LoopEmitter(loop_body_emitter, update_shape, b).EmitLoop(name); } Status EmitDynamicUpdateSliceInPlace( tensorflow::gtl::ArraySlice operand_arrays, const IrArray& output_array, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { VLOG(2) << "EmitDynamicUpdateSliceInPlace for " << name; // No need to use operand_arrays[0], the input array of the @@ -119,16 +117,16 @@ Status EmitDynamicUpdateSliceInPlace( Shape update_shape = update_array.GetShape(); ElementGenerator start_indices_generator = [&](const IrArray::Index& index) { - return start_indices_array.EmitReadArrayElement(index, ir_builder); + return start_indices_array.EmitReadArrayElement(index, b); }; ElementGenerator update_array_generator = [&](const IrArray::Index& index) { - return update_array.EmitReadArrayElement(index, ir_builder); + return update_array.EmitReadArrayElement(index, b); }; bool is_signed = ShapeUtil::ElementIsSigned(start_indices_array.GetShape()); return EmitDynamicUpdateSliceInPlaceImpl( update_shape, start_indices_generator, is_signed, update_array_generator, - output_array, /*launch_dimensions=*/nullptr, name, ir_builder); + output_array, /*launch_dimensions=*/nullptr, name, b); } // Shared implementation for EmitFusedDynamicUpdateSliceInPlace and @@ -139,8 +137,7 @@ static Status EmitFusedDynamicUpdateSliceInPlaceImpl( HloInstruction* fusion, tensorflow::gtl::ArraySlice fusion_operand_arrays, const IrArray& fusion_output_array, ElementalIrEmitter* elemental_emitter, - const gpu::LaunchDimensions* launch_dimensions, - llvm::IRBuilder<>* ir_builder) { + const gpu::LaunchDimensions* launch_dimensions, llvm::IRBuilder<>* b) { CHECK_EQ(fusion->opcode(), HloOpcode::kFusion); VLOG(2) << "EmitFusedDynamicUpdateSliceInPlace for " << fusion->ToShortString(); @@ -177,28 +174,27 @@ static Status EmitFusedDynamicUpdateSliceInPlaceImpl( bool is_signed = ShapeUtil::ElementIsSigned(start_indices->shape()); return EmitDynamicUpdateSliceInPlaceImpl( update_shape, start_indices_generator, is_signed, update_array_generator, - fusion_output_array, launch_dimensions, IrName(fusion), ir_builder); + fusion_output_array, launch_dimensions, IrName(fusion), b); } Status EmitFusedDynamicUpdateSliceInPlace( HloInstruction* fusion, tensorflow::gtl::ArraySlice fusion_operand_arrays, const IrArray& fusion_output_array, ElementalIrEmitter* elemental_emitter, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { return EmitFusedDynamicUpdateSliceInPlaceImpl( fusion, fusion_operand_arrays, fusion_output_array, elemental_emitter, - /*launch_dimensions=*/nullptr, ir_builder); + /*launch_dimensions=*/nullptr, b); } Status EmitParallelFusedDynamicUpdateSliceInPlace( HloInstruction* fusion, tensorflow::gtl::ArraySlice fusion_operand_arrays, const IrArray& fusion_output_array, ElementalIrEmitter* elemental_emitter, - const gpu::LaunchDimensions& launch_dimensions, - llvm::IRBuilder<>* ir_builder) { + const gpu::LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b) { return EmitFusedDynamicUpdateSliceInPlaceImpl( fusion, fusion_operand_arrays, fusion_output_array, elemental_emitter, - &launch_dimensions, ir_builder); + &launch_dimensions, b); } } // namespace llvm_ir diff --git a/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h b/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h index 7f73fb6b29..3502577d23 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h +++ b/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h @@ -66,7 +66,7 @@ inline bool CanEmitFusedDynamicUpdateSliceInPlace( Status EmitDynamicUpdateSliceInPlace( tensorflow::gtl::ArraySlice operand_arrays, const IrArray& output_array, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Given a loop-fusion node whose root is a dynamic-update-slice op whose // array-to-be-updated and output share the same buffer slice, emits @@ -76,7 +76,7 @@ Status EmitFusedDynamicUpdateSliceInPlace( HloInstruction* fusion, tensorflow::gtl::ArraySlice fusion_operand_arrays, const IrArray& fusion_output_array, ElementalIrEmitter* elemental_emitter, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Same as EmitFusedDynamicUpdateSliceInPlace, except emits a parallel loop with // the given launch dimensions. @@ -84,8 +84,7 @@ Status EmitParallelFusedDynamicUpdateSliceInPlace( HloInstruction* fusion, tensorflow::gtl::ArraySlice fusion_operand_arrays, const IrArray& fusion_output_array, ElementalIrEmitter* elemental_emitter, - const gpu::LaunchDimensions& launch_dimensions, - llvm::IRBuilder<>* ir_builder); + const gpu::LaunchDimensions& launch_dimensions, llvm::IRBuilder<>* b); } // namespace llvm_ir } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc index b12ce97e28..72ede377e1 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.cc @@ -52,7 +52,7 @@ Status FusedIrEmitter::DefaultAction(HloInstruction* hlo) { // that would be regenerated without caching. But this might increase the // JIT compilation time. if (generated_value_bb == nullptr || - generated_value_bb == ir_builder_->GetInsertBlock()) { + generated_value_bb == b_->GetInsertBlock()) { VLOG(3) << "The cached generated value is reused."; return generated_value; } @@ -60,8 +60,7 @@ Status FusedIrEmitter::DefaultAction(HloInstruction* hlo) { "a different BB (" << llvm_ir::AsString(generated_value_bb->getName()) << ") from the current insertion block (" - << llvm_ir::AsString(ir_builder_->GetInsertBlock()->getName()) - << ")."; + << llvm_ir::AsString(b_->GetInsertBlock()->getName()) << ")."; } TF_ASSIGN_OR_RETURN( @@ -77,14 +76,14 @@ Status FusedIrEmitter::HandleConstant(HloInstruction* constant) { llvm::Constant* initializer = llvm_ir::ConvertLiteralToIrConstant(literal, module_); llvm::GlobalVariable* global = new llvm::GlobalVariable( - *ir_builder_->GetInsertBlock()->getModule(), initializer->getType(), + *b_->GetInsertBlock()->getModule(), initializer->getType(), /*isConstant=*/true, llvm::GlobalValue::ExternalLinkage, initializer, /*Name=*/""); llvm::Constant* shape_constant = llvm::ConstantExpr::getBitCast( global, llvm_ir::ShapeToIrType(literal.shape(), module_)->getPointerTo()); generators_[constant] = [=](const IrArray::Index& index) { return IrArray(shape_constant, constant->shape()) - .EmitReadArrayElement(index, ir_builder_); + .EmitReadArrayElement(index, b_); }; return Status::OK(); @@ -104,7 +103,7 @@ Status FusedIrEmitter::HandleGetTupleElement( // Emit code to lookup tuple element pointer, and store it in 'gte_values_'. llvm::Value* tuple_element_ptr = llvm_ir::EmitGetTupleElement( get_tuple_element->shape(), get_tuple_element->tuple_index(), - /*alignment=*/1, it->second, ir_builder_, module_); + /*alignment=*/1, it->second, b_, module_); gte_values_.insert(std::make_pair(get_tuple_element, tuple_element_ptr)); // Emit code to read base tuple element array (if non-tuple shaped). if (!ShapeUtil::IsTuple(get_tuple_element->shape())) { @@ -112,7 +111,7 @@ Status FusedIrEmitter::HandleGetTupleElement( [=](const IrArray::Index& index) -> StatusOr { // TODO(b/34080002) Add aliasing information to tuple element IrArray. return IrArray(tuple_element_ptr, get_tuple_element->shape()) - .EmitReadArrayElement(index, ir_builder_); + .EmitReadArrayElement(index, b_); }; } return Status::OK(); @@ -129,16 +128,15 @@ Status FusedIrEmitter::HandleParameter(HloInstruction* parameter) { // want the AA info to be present before address spaces are inferred // (which is pretty late in the pipeline), so even if we had // address-space-based AA in LLVM, it wouldn't help us much here. - return ir_builder_->CreateLoad( - ir_builder_->CreateGEP( - param_tile_buffer, - {index.GetConstantWithIndexType(0), tiled_parameter_info_->x(), - tiled_parameter_info_->y()}), + return b_->CreateLoad( + b_->CreateGEP(param_tile_buffer, {index.GetConstantWithIndexType(0), + tiled_parameter_info_->x(), + tiled_parameter_info_->y()}), "tiled_buffer"); } } return parameter_arrays_[parameter->parameter_number()] - .EmitReadArrayElement(index, ir_builder_); + .EmitReadArrayElement(index, b_); }; // Store ir value for fusion operand associated with fusion parameter to be // accessed by subsequent fused GetTupleElement instructions. @@ -157,11 +155,11 @@ Status FusedIrEmitter::HandleTuple(HloInstruction* tuple) { } generators_[tuple] = [=](const IrArray::Index& index) -> StatusOr { - llvm::Value* ret = llvm::UndefValue::get(llvm::StructType::get( - ir_builder_->getContext(), operand_elemental_ir_types)); + llvm::Value* ret = llvm::UndefValue::get( + llvm::StructType::get(b_->getContext(), operand_elemental_ir_types)); for (size_t i = 0; i < ShapeUtil::TupleElementCount(tuple->shape()); ++i) { TF_ASSIGN_OR_RETURN(llvm::Value * val_i, generators_[operands[i]](index)); - ret = ir_builder_->CreateInsertValue(ret, val_i, i); + ret = b_->CreateInsertValue(ret, val_i, i); } return ret; }; diff --git a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h index a6ceec7b23..30471480c4 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h +++ b/tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h @@ -59,7 +59,7 @@ class FusedIrEmitter : public DfsHloVisitorWithDefault { : parameter_arrays_(parameter_arrays), tiled_parameter_info_(nullptr), elemental_emitter_(elemental_emitter), - ir_builder_(elemental_emitter->ir_builder()), + b_(elemental_emitter->b()), module_(elemental_emitter->module()) {} Status DefaultAction(HloInstruction* hlo) override; @@ -103,7 +103,7 @@ class FusedIrEmitter : public DfsHloVisitorWithDefault { const HloInstruction* fused_root_ = nullptr; // Borrowed - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm::Module* module_; // Map from instruction pointers to functions to generate elements of their diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc index dcf9838d80..7a9170f379 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc @@ -31,7 +31,7 @@ namespace llvm_ir { void IrArray::Index::Delinearize(std::vector* multidim, llvm::Value* linear, const Shape& shape, - llvm::IRBuilder<>* ir_builder) const { + llvm::IRBuilder<>* b) const { int64 divisor = 1; const Layout& layout = shape.layout(); for (int64 i = 0; i < layout.minor_to_major_size(); ++i) { @@ -48,10 +48,9 @@ void IrArray::Index::Delinearize(std::vector* multidim, // useful because cuda-memcheck can't help us much in XLA: Most of our // memory lives in one big allocation, so cuda-memcheck can't detect // out-of-bounds accesses. - auto* quot = - ir_builder->CreateUDiv(linear, GetConstantWithIndexType(divisor)); + auto* quot = b->CreateUDiv(linear, GetConstantWithIndexType(divisor)); if (i < layout.minor_to_major_size() - 1) { - (*multidim)[dimension] = ir_builder->CreateURem( + (*multidim)[dimension] = b->CreateURem( quot, GetConstantWithIndexType(size_of_current_dimension)); } else { (*multidim)[dimension] = quot; @@ -61,7 +60,7 @@ void IrArray::Index::Delinearize(std::vector* multidim, } IrArray::Index::Index(llvm::Value* linear, const Shape& shape, - llvm::IRBuilder<>* ir_builder) + llvm::IRBuilder<>* b) : multidim_(ShapeUtil::Rank(shape)), linear_(linear), layout_(shape.layout()), @@ -71,7 +70,7 @@ IrArray::Index::Index(llvm::Value* linear, const Shape& shape, CHECK(LayoutUtil::HasLayout(shape)) << "Shape " << ShapeUtil::HumanStringWithLayout(shape) << " should have a layout."; - Delinearize(&multidim_, linear, shape, ir_builder); + Delinearize(&multidim_, linear, shape, b); } IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, @@ -94,7 +93,7 @@ IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, } IrArray::Index::Index(tensorflow::gtl::ArraySlice multidim, - const Shape& shape, llvm::IRBuilder<>* ir_builder) + const Shape& shape, llvm::IRBuilder<>* b) : multidim_(multidim.begin(), multidim.end()), layout_(shape.layout()), dims_(shape.dimensions().begin(), shape.dimensions().end()) { @@ -343,7 +342,7 @@ llvm::Value* IrArray::Index::Linearize( } llvm::Value* IrArray::EmitArrayElementAddress( - const IrArray::Index& index, llvm::IRBuilder<>* ir_builder, + const IrArray::Index& index, llvm::IRBuilder<>* b, tensorflow::StringPiece name) const { if (ShapeUtil::IsScalar(*shape_)) { // Special handling of scalars: a scalar pretends to have the same value for @@ -354,12 +353,11 @@ llvm::Value* IrArray::EmitArrayElementAddress( CHECK_EQ(index.size(), ShapeUtil::Rank(*shape_)); if (index.LinearValidOnShape(*shape_)) { - llvm::Module* module = - ir_builder->GetInsertBlock()->getParent()->getParent(); - return ir_builder->CreateInBoundsGEP( - ir_builder->CreateBitCast( - base_ptr_, PrimitiveTypeToIrType(shape_->element_type(), module) - ->getPointerTo()), + llvm::Module* module = b->GetInsertBlock()->getParent()->getParent(); + return b->CreateInBoundsGEP( + b->CreateBitCast(base_ptr_, + PrimitiveTypeToIrType(shape_->element_type(), module) + ->getPointerTo()), {index.linear()}, llvm_ir::AsStringRef(name)); } @@ -385,8 +383,8 @@ llvm::Value* IrArray::EmitArrayElementAddress( int64 dimension = LayoutUtil::Major(shape_->layout(), i); gep_indices.push_back(actual_index[dimension]); } - return ir_builder->CreateInBoundsGEP(base_ptr_, gep_indices, - llvm_ir::AsStringRef(name)); + return b->CreateInBoundsGEP(base_ptr_, gep_indices, + llvm_ir::AsStringRef(name)); } void IrArray::AnnotateLoadStoreInstructionWithMetadata( @@ -402,29 +400,27 @@ void IrArray::AnnotateLoadStoreInstructionWithMetadata( } llvm::Value* IrArray::EmitReadArrayElement(const Index& index, - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, tensorflow::StringPiece name) const { - llvm::Value* element_address = - EmitArrayElementAddress(index, ir_builder, name); - llvm::LoadInst* load = ir_builder->CreateLoad(element_address); + llvm::Value* element_address = EmitArrayElementAddress(index, b, name); + llvm::LoadInst* load = b->CreateLoad(element_address); AnnotateLoadStoreInstructionWithMetadata(load); return load; } void IrArray::EmitWriteArrayElement(const Index& index, llvm::Value* value, - llvm::IRBuilder<>* ir_builder) const { - llvm::Value* element_address = EmitArrayElementAddress(index, ir_builder); - llvm::StoreInst* store = ir_builder->CreateStore(value, element_address); + llvm::IRBuilder<>* b) const { + llvm::Value* element_address = EmitArrayElementAddress(index, b); + llvm::StoreInst* store = b->CreateStore(value, element_address); AnnotateLoadStoreInstructionWithMetadata(store); } IrArray IrArray::CastToShape(const Shape& new_shape, - llvm::IRBuilder<>* ir_builder) const { - llvm::Module* module = ir_builder->GetInsertBlock()->getParent()->getParent(); + llvm::IRBuilder<>* b) const { + llvm::Module* module = b->GetInsertBlock()->getParent()->getParent(); llvm::Type* new_ir_type = llvm_ir::ShapeToIrType(new_shape, module); IrArray new_irarray( - ir_builder->CreatePointerCast(base_ptr_, new_ir_type->getPointerTo()), - new_shape); + b->CreatePointerCast(base_ptr_, new_ir_type->getPointerTo()), new_shape); new_irarray.metadata_ = metadata_; return new_irarray; } @@ -432,9 +428,9 @@ IrArray IrArray::CastToShape(const Shape& new_shape, /* static */ IrArray::Index IrArray::BumpIndex(const Index& index, int64 which_dimension, int64 addend, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { Index new_index = index; - new_index[which_dimension] = ir_builder->CreateAdd( + new_index[which_dimension] = b->CreateAdd( index[which_dimension], llvm::ConstantInt::get(index[which_dimension]->getType(), addend), "", /*HasNUW=*/true, diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h index 0777c49923..28ca793e3e 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.h +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.h @@ -87,20 +87,19 @@ class IrArray { } // Constructs an index from linear index "linear" and computes the - // multi-dimensional index from "linear" and "shape". "ir_builder" is the IR + // multi-dimensional index from "linear" and "shape". "b" is the IR // builder to emit the index of each dimension in the multi-dimensional // index. // // Precondition: "shape" has a layout. - Index(llvm::Value* linear, const Shape& shape, - llvm::IRBuilder<>* ir_builder); + Index(llvm::Value* linear, const Shape& shape, llvm::IRBuilder<>* b); // Constructs an index from the given multi-dimensional index and the shape // that it indexes into. // // Precondition: "shape" has a layout. Index(tensorflow::gtl::ArraySlice multidim, - const Shape& shape, llvm::IRBuilder<>* ir_builder); + const Shape& shape, llvm::IRBuilder<>* b); // Constructs an index from both a multi-dimensional index and a linear // index. "shape" has the same meaning as that in the constructor that takes @@ -191,7 +190,7 @@ class IrArray { } void Delinearize(std::vector* multidim, llvm::Value* linear, - const Shape& shape, llvm::IRBuilder<>* ir_builder) const; + const Shape& shape, llvm::IRBuilder<>* b) const; std::vector multidim_; @@ -240,8 +239,7 @@ class IrArray { // // The optional name is useful for debugging when looking at // the emitted LLVM IR. - llvm::Value* EmitArrayElementAddress(const Index& index, - llvm::IRBuilder<>* ir_builder, + llvm::Value* EmitArrayElementAddress(const Index& index, llvm::IRBuilder<>* b, tensorflow::StringPiece name = "") const; // Attach metadata this IrArray instance knows about to "instruction". @@ -255,18 +253,16 @@ class IrArray { // // The optional name is useful for debugging when looking at // the emitted LLVM IR. - llvm::Value* EmitReadArrayElement(const Index& index, - llvm::IRBuilder<>* ir_builder, + llvm::Value* EmitReadArrayElement(const Index& index, llvm::IRBuilder<>* b, tensorflow::StringPiece name = "") const; // Emit IR to write the given value to the array element at the given index. void EmitWriteArrayElement(const Index& index, llvm::Value* value, - llvm::IRBuilder<>* ir_builder) const; + llvm::IRBuilder<>* b) const; // Returns a new IrArray whose shape is "new_shape" and base pointer is a // bitcast of the base pointer of "this" IrArray. - IrArray CastToShape(const Shape& new_shape, - llvm::IRBuilder<>* ir_builder) const; + IrArray CastToShape(const Shape& new_shape, llvm::IRBuilder<>* b) const; void AddAliasScopeMetadata(llvm::MDNode* alias_scope) { CHECK_NE(alias_scope, nullptr); @@ -312,7 +308,7 @@ class IrArray { // Bumps the "which_dimension" value within the provided index by the provided // addend. static Index BumpIndex(const Index& index, int64 which_dimension, - int64 addend, llvm::IRBuilder<>* ir_builder); + int64 addend, llvm::IRBuilder<>* b); private: // Add the specified LLVM IR metadata to loads/stores associated with this diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc index 98d0ceb3e2..b79567369a 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.cc @@ -22,9 +22,9 @@ Status KernelSupportLibrary::For( tensorflow::StringPiece name, llvm::Value* start, llvm::Value* end, llvm::Value* step, const std::function& for_body_generator) { - return If(ir_builder_->CreateICmpSLT(start, end), [&]() -> Status { + return If(b_->CreateICmpSLT(start, end), [&]() -> Status { TF_RETURN_IF_ERROR(for_body_generator(start, /*is_first_iteration=*/true)); - return For(name, ir_builder_->CreateAdd(start, step), end, step, + return For(name, b_->CreateAdd(start, step), end, step, [&](llvm::Value* iv) { return for_body_generator(iv, false); }); }); } @@ -37,20 +37,20 @@ Status KernelSupportLibrary::For( if (peel_first_iteration) { return For(name, start, end, step, true, [&](llvm::Value* indvar, bool is_first_iteration) -> Status { - return for_body_generator( - indvar, ir_builder_->getInt1(is_first_iteration)); + return for_body_generator(indvar, + b_->getInt1(is_first_iteration)); }); } else { std::unique_ptr loop = llvm_ir::ForLoop::EmitForLoop( - name, start, end, step, ir_builder_, + name, start, end, step, b_, /*unroll_mode=*/unroll_mode_, /*prevent_vectorization=*/prevent_vectorization_); - ir_builder_->SetInsertPoint(&loop->GetBodyBasicBlock()->back()); + b_->SetInsertPoint(&loop->GetBodyBasicBlock()->back()); TF_RETURN_IF_ERROR( for_body_generator(loop->GetIndVarValue(), - /*is_first_iteration=*/ir_builder_->CreateICmpEQ( + /*is_first_iteration=*/b_->CreateICmpEQ( loop->GetIndVarValue(), start))); - llvm_ir::SetToLastInsertPoint(loop->GetExitBasicBlock(), ir_builder_); + llvm_ir::SetToLastInsertPoint(loop->GetExitBasicBlock(), b_); return Status::OK(); } } @@ -59,23 +59,22 @@ Status KernelSupportLibrary::If( tensorflow::StringPiece name, llvm::Value* condition, const std::function& true_block_generator, const std::function& false_block_generator) { - llvm_ir::LlvmIfData if_data = - llvm_ir::EmitIfThenElse(condition, name, ir_builder_); - ir_builder_->SetInsertPoint(&if_data.true_block->back()); + llvm_ir::LlvmIfData if_data = llvm_ir::EmitIfThenElse(condition, name, b_); + b_->SetInsertPoint(&if_data.true_block->back()); TF_RETURN_IF_ERROR(true_block_generator()); - ir_builder_->SetInsertPoint(&if_data.false_block->back()); + b_->SetInsertPoint(&if_data.false_block->back()); TF_RETURN_IF_ERROR(false_block_generator()); - llvm_ir::SetToLastInsertPoint(if_data.after_block, ir_builder_); + llvm_ir::SetToLastInsertPoint(if_data.after_block, b_); return Status::OK(); } void KernelSupportLibrary::EmitAndCallOutlinedKernel( - bool enable_fast_math, bool optimize_for_size, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece kernel_name, + bool enable_fast_math, bool optimize_for_size, llvm::IRBuilder<>* b, + tensorflow::StringPiece kernel_name, KernelSupportLibrary::ArgumentVector arguments, const std::function& kernel_body_generator) { - llvm::Module* module = ir_builder->GetInsertBlock()->getModule(); + llvm::Module* module = b->GetInsertBlock()->getModule(); llvm::Function* function = module->getFunction(llvm_ir::AsStringRef(kernel_name)); @@ -98,22 +97,22 @@ void KernelSupportLibrary::EmitAndCallOutlinedKernel( std::back_inserter(arg_types), [](llvm::Value* arg) { return arg->getType(); }); - auto* function_type = llvm::FunctionType::get( - ir_builder->getVoidTy(), arg_types, /*isVarArg=*/false); + auto* function_type = + llvm::FunctionType::get(b->getVoidTy(), arg_types, /*isVarArg=*/false); function = llvm_ir::CreateFunction( function_type, llvm::GlobalValue::InternalLinkage, /*enable_fast_math=*/enable_fast_math, /*optimize_for_size=*/optimize_for_size, kernel_name, module); - llvm::IRBuilder<>::InsertPointGuard guard(*ir_builder); + llvm::IRBuilder<>::InsertPointGuard guard(*b); auto* entry_bb = - llvm::BasicBlock::Create(ir_builder->getContext(), "entry", function); - auto* return_inst = llvm::ReturnInst::Create(ir_builder->getContext(), + llvm::BasicBlock::Create(b->getContext(), "entry", function); + auto* return_inst = llvm::ReturnInst::Create(b->getContext(), /*retVal=*/nullptr, entry_bb); // Set the insert point to before return_inst. - ir_builder->SetInsertPoint(return_inst); + b->SetInsertPoint(return_inst); std::vector arg_values; /* @@ -133,7 +132,7 @@ void KernelSupportLibrary::EmitAndCallOutlinedKernel( VLOG(3) << "Re-using kernel for " << kernel_name; } - ir_builder->CreateCall(function, llvm_ir::AsArrayRef(sanitized_args)); + b->CreateCall(function, llvm_ir::AsArrayRef(sanitized_args)); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h index 9d770cc4c3..b00f903d56 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h @@ -30,14 +30,14 @@ namespace xla { // flow more readable. class KernelSupportLibrary { public: - // `ir_builder` is the llvm::IRBuilder instance used to generate LLVM IR. + // `b` is the llvm::IRBuilder instance used to generate LLVM IR. // `unroll_mode` specifies the desired LLVM unrolling behavior for every loop // generated by this instance of KernelSupportLibrary. explicit KernelSupportLibrary( - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, llvm_ir::UnrollMode unroll_mode = llvm_ir::UnrollMode::kNoUnroll, bool prevent_vectorization = true) - : ir_builder_(ir_builder), + : b_(b), unroll_mode_(unroll_mode), prevent_vectorization_(prevent_vectorization) {} @@ -71,18 +71,18 @@ class KernelSupportLibrary { const std::function& for_body_generator) { - return For(name, /*start=*/ir_builder_->getInt64(start), - /*end=*/ir_builder_->getInt64(end), - /*step=*/ir_builder_->getInt64(step), for_body_generator); + return For(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } void ForReturnVoid( tensorflow::StringPiece name, int64 start, int64 end, int64 step, const std::function& for_body_generator) { - ForReturnVoid(name, /*start=*/ir_builder_->getInt64(start), - /*end=*/ir_builder_->getInt64(end), - /*step=*/ir_builder_->getInt64(step), for_body_generator); + ForReturnVoid(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } // Generates the following control flow structure if `peel_first_iteration` is @@ -184,17 +184,17 @@ class KernelSupportLibrary { Status For( tensorflow::StringPiece name, int64 start, int64 end, int64 step, const std::function& for_body_generator) { - return For(name, /*start=*/ir_builder_->getInt64(start), - /*end=*/ir_builder_->getInt64(end), - /*step=*/ir_builder_->getInt64(step), for_body_generator); + return For(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } void ForReturnVoid( tensorflow::StringPiece name, int64 start, int64 end, int64 step, const std::function& for_body_generator) { - ForReturnVoid(name, /*start=*/ir_builder_->getInt64(start), - /*end=*/ir_builder_->getInt64(end), - /*step=*/ir_builder_->getInt64(step), for_body_generator); + ForReturnVoid(name, /*start=*/b_->getInt64(start), + /*end=*/b_->getInt64(end), + /*step=*/b_->getInt64(step), for_body_generator); } // Generates the following control flow structure: @@ -258,41 +258,39 @@ class KernelSupportLibrary { // in a nullptr llvm::Value* in its position to `kernel_body_generator`. // Currently we only support at most one nullptr value in `arguments`. static void EmitAndCallOutlinedKernel( - bool enable_fast_math, bool optimize_for_size, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece kernel_name, - ArgumentVector arguments, + bool enable_fast_math, bool optimize_for_size, llvm::IRBuilder<>* b, + tensorflow::StringPiece kernel_name, ArgumentVector arguments, const std::function& kernel_body_generator); // Thin wrappers around the more general EmitAndCallOutlinedKernel above. static void EmitAndCallOutlinedKernel( - bool enable_fast_math, bool optimize_for_size, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece kernel_name, - llvm::Value* arg0, llvm::Value* arg1, llvm::Value* arg2, + bool enable_fast_math, bool optimize_for_size, llvm::IRBuilder<>* b, + tensorflow::StringPiece kernel_name, llvm::Value* arg0, llvm::Value* arg1, + llvm::Value* arg2, const std::function& kernel_body_generator) { EmitAndCallOutlinedKernel( - enable_fast_math, optimize_for_size, ir_builder, kernel_name, - {arg0, arg1, arg2}, [&](ArgumentVector args) { + enable_fast_math, optimize_for_size, b, kernel_name, {arg0, arg1, arg2}, + [&](ArgumentVector args) { kernel_body_generator(args[0], args[1], args[2]); }); } static void EmitAndCallOutlinedKernel( - bool enable_fast_math, bool optimize_for_size, - llvm::IRBuilder<>* ir_builder, tensorflow::StringPiece kernel_name, - llvm::Value* arg0, llvm::Value* arg1, llvm::Value* arg2, - llvm::Value* arg3, + bool enable_fast_math, bool optimize_for_size, llvm::IRBuilder<>* b, + tensorflow::StringPiece kernel_name, llvm::Value* arg0, llvm::Value* arg1, + llvm::Value* arg2, llvm::Value* arg3, const std::function& kernel_body_generator) { EmitAndCallOutlinedKernel( - enable_fast_math, optimize_for_size, ir_builder, kernel_name, + enable_fast_math, optimize_for_size, b, kernel_name, {arg0, arg1, arg2, arg3}, [&](ArgumentVector args) { kernel_body_generator(args[0], args[1], args[2], args[3]); }); } private: - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm_ir::UnrollMode unroll_mode_; bool prevent_vectorization_; }; diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.cc b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.cc index 533b75cdae..35b3941272 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.cc @@ -94,24 +94,24 @@ tensorflow::gtl::optional > FindTranspose021( IrArray::Index GetUnreducedOutputIndex( const IrArray::Index& reduced_output_index, const Shape& reduced_output_shape, const Shape& unreduced_output_shape, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { auto bounds = reduced_output_shape.dimensions(); auto minor_to_major = reduced_output_shape.layout().minor_to_major(); llvm::Value* linear_index = reduced_output_index.GetConstantWithIndexType(0); int64 multiplier = 1; for (int i = 0; i < reduced_output_index.size(); ++i) { int64 dim = minor_to_major[i]; - llvm::Value* addend = ir_builder->CreateMul( - reduced_output_index[dim], - reduced_output_index.GetConstantWithIndexType(multiplier), - "linearizing", - /*HasNUW=*/true, /*HasNSW=*/true); - linear_index = ir_builder->CreateAdd(linear_index, addend, "", - /*HasNUW=*/true, /*HasNSW=*/true); + llvm::Value* addend = + b->CreateMul(reduced_output_index[dim], + reduced_output_index.GetConstantWithIndexType(multiplier), + "linearizing", + /*HasNUW=*/true, /*HasNSW=*/true); + linear_index = b->CreateAdd(linear_index, addend, "", + /*HasNUW=*/true, /*HasNSW=*/true); multiplier *= bounds[dim]; } - return IrArray::Index(linear_index, unreduced_output_shape, ir_builder); + return IrArray::Index(linear_index, unreduced_output_shape, b); } } // namespace llvm_ir diff --git a/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h index 6f1268fffb..ccb9b8ba3e 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h +++ b/tensorflow/compiler/xla/service/llvm_ir/kernel_tiling.h @@ -44,7 +44,7 @@ tensorflow::gtl::optional > FindTranspose021(const Shape& a, IrArray::Index GetUnreducedOutputIndex( const IrArray::Index& reduced_output_index, const Shape& reduced_output_shape, const Shape& unreduced_output_shape, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // A class to represent information for tiled parameters to support IR emission // for 021 transpose. diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc index 1227534779..ba7f94834c 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.cc @@ -47,27 +47,27 @@ ForLoop::ForLoop(tensorflow::StringPiece prefix, tensorflow::StringPiece suffix, /* static */ std::unique_ptr ForLoop::EmitForLoop( tensorflow::StringPiece prefix, llvm::Value* start_index, - llvm::Value* end_index, llvm::Value* step, llvm::IRBuilder<>* ir_builder, + llvm::Value* end_index, llvm::Value* step, llvm::IRBuilder<>* b, UnrollMode unroll_mode, bool prevent_vectorization) { std::unique_ptr loop(new ForLoop(prefix, /*suffix=*/"", start_index, end_index, step, unroll_mode, prevent_vectorization)); - loop->Emit(ir_builder); + loop->Emit(b); return loop; } -void ForLoop::Emit(llvm::IRBuilder<>* ir_builder) { +void ForLoop::Emit(llvm::IRBuilder<>* b) { // The preheader block is the block the builder is currently emitting // code into. - preheader_bb_ = ir_builder->GetInsertBlock(); + preheader_bb_ = b->GetInsertBlock(); - llvm::BasicBlock::iterator insert_point = ir_builder->GetInsertPoint(); + llvm::BasicBlock::iterator insert_point = b->GetInsertPoint(); if (insert_point == preheader_bb_->end()) { // We're emitting the loop at the end of a basic block. Verify there is no // terminator (eg, branch) in the basic block. CHECK_EQ(nullptr, preheader_bb_->getTerminator()); - exit_bb_ = CreateLoopBB("loop_exit", ir_builder); + exit_bb_ = CreateLoopBB("loop_exit", b); } else { // We're emitting the loop into the middle of a basic block. splitBasicBlock // requires that this basic block be well-formed (have a terminator). @@ -86,51 +86,50 @@ void ForLoop::Emit(llvm::IRBuilder<>* ir_builder) { insert_before_bb_ = exit_bb_; // Create remaining basic block which form the inside of the loop. - header_bb_ = CreateLoopBB("loop_header", ir_builder); - body_bb_ = CreateLoopBB("loop_body", ir_builder); + header_bb_ = CreateLoopBB("loop_header", b); + body_bb_ = CreateLoopBB("loop_body", b); // Function entry basic block. // Emit alloca for the induction variable. We do this at the entry to the // basic block to ensure the alloc only executes once per function (we could // be emitting a nested loop). llvm::Function* func = preheader_bb_->getParent(); - ir_builder->SetInsertPoint(&func->getEntryBlock(), - func->getEntryBlock().getFirstInsertionPt()); + b->SetInsertPoint(&func->getEntryBlock(), + func->getEntryBlock().getFirstInsertionPt()); llvm::Value* indvar_address = - ir_builder->CreateAlloca(start_index_->getType(), nullptr, - AsStringRef(GetQualifiedName("invar_address"))); + b->CreateAlloca(start_index_->getType(), nullptr, + AsStringRef(GetQualifiedName("invar_address"))); // Preheader basic block. // Initialize induction variable starting index. Create branch to the header. - ir_builder->SetInsertPoint(preheader_bb_); - ir_builder->CreateStore(start_index_, indvar_address); + b->SetInsertPoint(preheader_bb_); + b->CreateStore(start_index_, indvar_address); // The preheader should not have a branch yet. CHECK_EQ(preheader_bb_->getTerminator(), nullptr); - ir_builder->CreateBr(header_bb_); + b->CreateBr(header_bb_); // Header basic block. // Emit the loop conditional branch. Load and compare indvar with ending // index and jump to loop exit if equal. Jump to body otherwise. - ir_builder->SetInsertPoint(header_bb_); - indvar_ = ir_builder->CreateLoad(indvar_address, - AsStringRef(GetQualifiedName("indvar"))); - llvm::Value* exit_cond = ir_builder->CreateICmpUGE(indvar_, end_index_); - ir_builder->CreateCondBr(/*Cond=*/exit_cond, - /*True=*/exit_bb_, /*False=*/body_bb_); + b->SetInsertPoint(header_bb_); + indvar_ = + b->CreateLoad(indvar_address, AsStringRef(GetQualifiedName("indvar"))); + llvm::Value* exit_cond = b->CreateICmpUGE(indvar_, end_index_); + b->CreateCondBr(/*Cond=*/exit_cond, + /*True=*/exit_bb_, /*False=*/body_bb_); // Body basic block. // Increment indvar, store indvar, and jump to header. - ir_builder->SetInsertPoint(body_bb_); + b->SetInsertPoint(body_bb_); llvm::Value* step = step_; llvm::Value* indvar = indvar_; - llvm::Value* indvar_inc = - ir_builder->CreateAdd(indvar, step, "invar.inc", - /*HasNUW=*/true, /*HasNSW=*/true); - ir_builder->CreateStore(indvar_inc, indvar_address); - llvm::BranchInst* back_branch = ir_builder->CreateBr(header_bb_); + llvm::Value* indvar_inc = b->CreateAdd(indvar, step, "invar.inc", + /*HasNUW=*/true, /*HasNSW=*/true); + b->CreateStore(indvar_inc, indvar_address); + llvm::BranchInst* back_branch = b->CreateBr(header_bb_); - std::vector loop_metadata = GetLoopMetadata(ir_builder); + std::vector loop_metadata = GetLoopMetadata(b); if (!loop_metadata.empty()) { llvm::LLVMContext* ctx = &start_index_->getContext(); auto temp_node = llvm::MDNode::getTemporary(*ctx, llvm::None); @@ -141,11 +140,10 @@ void ForLoop::Emit(llvm::IRBuilder<>* ir_builder) { } // Re-point the IR builder to the loop exit block. - ir_builder->SetInsertPoint(exit_bb_); + b->SetInsertPoint(exit_bb_); } -std::vector ForLoop::GetLoopMetadata( - llvm::IRBuilder<>* ir_builder) { +std::vector ForLoop::GetLoopMetadata(llvm::IRBuilder<>* b) { const char* const kLlvmLoopUnrollDisableMDName = "llvm.loop.unroll.disable"; const char* const kLlvmLoopUnrollFullMDName = "llvm.loop.unroll.full"; const char* const kLlvmLoopVectorizeMDName = "llvm.loop.vectorize.enable"; @@ -160,7 +158,7 @@ std::vector ForLoop::GetLoopMetadata( if (prevent_vectorization_) { result.push_back(llvm::MDNode::get( *ctx, {llvm::MDString::get(*ctx, kLlvmLoopVectorizeMDName), - llvm::ConstantAsMetadata::get(ir_builder->getFalse())})); + llvm::ConstantAsMetadata::get(b->getFalse())})); } if (unroll_mode_ == xla::llvm_ir::UnrollMode::kFullyUnroll) { @@ -175,9 +173,8 @@ string ForLoop::GetQualifiedName(tensorflow::StringPiece name) { } llvm::BasicBlock* ForLoop::CreateLoopBB(tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder) { - return CreateBasicBlock(insert_before_bb_, GetQualifiedName(name), - ir_builder); + llvm::IRBuilder<>* b) { + return CreateBasicBlock(insert_before_bb_, GetQualifiedName(name), b); } std::unique_ptr ForLoopNest::AddLoop(tensorflow::StringPiece suffix, @@ -197,12 +194,12 @@ std::unique_ptr ForLoopNest::AddLoop(tensorflow::StringPiece suffix, bool prevent_vectorization) { if (inner_loop_body_bb_ != nullptr) { // Create this loop inside the previous one. - ir_builder_->SetInsertPoint(&*inner_loop_body_bb_->getFirstInsertionPt()); + b_->SetInsertPoint(&*inner_loop_body_bb_->getFirstInsertionPt()); } std::unique_ptr loop(new ForLoop( /*prefix=*/name_, suffix, start_index, end_index, stride, unroll_mode, prevent_vectorization)); - loop->Emit(ir_builder_); + loop->Emit(b_); if (outer_loop_preheader_bb_ == nullptr) { outer_loop_preheader_bb_ = loop->GetPreheaderBasicBlock(); diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h index b3266022db..a4fed5c8dc 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h @@ -79,7 +79,7 @@ class ForLoop { // loop. static std::unique_ptr EmitForLoop( tensorflow::StringPiece prefix, llvm::Value* start_index, - llvm::Value* end_index, llvm::Value* step, llvm::IRBuilder<>* ir_builder, + llvm::Value* end_index, llvm::Value* step, llvm::IRBuilder<>* b, UnrollMode unroll_mode = llvm_ir::UnrollMode::kDefaultUnroll, bool prevent_vectorization = false); @@ -138,10 +138,10 @@ class ForLoop { UnrollMode unroll_mode, bool prevent_vectorization); // Emit the loop at the insert point of the builder. - void Emit(llvm::IRBuilder<>* ir_builder); + void Emit(llvm::IRBuilder<>* b); llvm::BasicBlock* CreateLoopBB(tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Creates a name for an LLVM construct, appending prefix_ and suffix_, if // they are set. @@ -149,7 +149,7 @@ class ForLoop { // Return a list of metadata nodes that should be associated with the // llvm::Loop for this `ForLoop`. - std::vector GetLoopMetadata(llvm::IRBuilder<>* ir_builder); + std::vector GetLoopMetadata(llvm::IRBuilder<>* b); string prefix_; string suffix_; @@ -177,19 +177,18 @@ class ForLoop { // A simple class for constructing nested for-loops. class ForLoopNest { public: - explicit ForLoopNest(llvm::IRBuilder<>* ir_builder, - llvm::Type* index_ty = nullptr) - : ForLoopNest(/*name=*/"", ir_builder) { + explicit ForLoopNest(llvm::IRBuilder<>* b, llvm::Type* index_ty = nullptr) + : ForLoopNest(/*name=*/"", b) { SetIndexType(index_ty); } - ForLoopNest(tensorflow::StringPiece name, llvm::IRBuilder<>* ir_builder, + ForLoopNest(tensorflow::StringPiece name, llvm::IRBuilder<>* b, llvm::Type* index_ty = nullptr) : name_(std::string(name)), outer_loop_preheader_bb_(nullptr), outer_loop_exit_bb_(nullptr), inner_loop_body_bb_(nullptr), - ir_builder_(ir_builder) { + b_(b) { SetIndexType(index_ty); } @@ -270,7 +269,7 @@ class ForLoopNest { private: void SetIndexType(llvm::Type* index_ty) { - index_type_ = index_ty == nullptr ? ir_builder_->getInt64Ty() : index_ty; + index_type_ = index_ty == nullptr ? b_->getInt64Ty() : index_ty; } llvm::Constant* GetConstantWithIndexType(int64 c) const { @@ -289,7 +288,7 @@ class ForLoopNest { // has been added yet. llvm::BasicBlock* inner_loop_body_bb_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; llvm::Type* index_type_; diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc index 6c55361b44..e4f65bd427 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc @@ -48,8 +48,8 @@ namespace { // Note, this function is only useful in an insertion context; in a global // (e.g. constants) context it will CHECK fail. -llvm::Module* ModuleFromIRBuilder(llvm::IRBuilder<>* ir_builder) { - auto block = CHECK_NOTNULL(ir_builder->GetInsertBlock()); +llvm::Module* ModuleFromIRBuilder(llvm::IRBuilder<>* b) { + auto block = CHECK_NOTNULL(b->GetInsertBlock()); auto fn = CHECK_NOTNULL(block->getParent()); auto module = CHECK_NOTNULL(fn->getParent()); return module; @@ -87,41 +87,41 @@ llvm::Value* EmitCallToIntrinsic( llvm::Intrinsic::ID intrinsic_id, tensorflow::gtl::ArraySlice operands, tensorflow::gtl::ArraySlice overloaded_types, - llvm::IRBuilder<>* ir_builder) { - llvm::Module* module = ModuleFromIRBuilder(ir_builder); + llvm::IRBuilder<>* b) { + llvm::Module* module = ModuleFromIRBuilder(b); llvm::Function* intrinsic = llvm::Intrinsic::getDeclaration( module, intrinsic_id, AsArrayRef(overloaded_types)); - return ir_builder->CreateCall(intrinsic, AsArrayRef(operands)); + return b->CreateCall(intrinsic, AsArrayRef(operands)); } llvm::Value* EmitFloatMax(llvm::Value* lhs_value, llvm::Value* rhs_value, - llvm::IRBuilder<>* ir_builder) { - if (ir_builder->getFastMathFlags().noNaNs()) { - auto cmp = ir_builder->CreateFCmpUGE(lhs_value, rhs_value); - return ir_builder->CreateSelect(cmp, lhs_value, rhs_value); + llvm::IRBuilder<>* b) { + if (b->getFastMathFlags().noNaNs()) { + auto cmp = b->CreateFCmpUGE(lhs_value, rhs_value); + return b->CreateSelect(cmp, lhs_value, rhs_value); } else { - 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); + auto cmp_ge = b->CreateFCmpOGE(lhs_value, rhs_value); + auto lhs_is_nan = b->CreateFCmpUNE(lhs_value, lhs_value); + auto sel_lhs = b->CreateOr(cmp_ge, lhs_is_nan); + return b->CreateSelect(sel_lhs, lhs_value, rhs_value); } } llvm::Value* EmitFloatMin(llvm::Value* lhs_value, llvm::Value* rhs_value, - llvm::IRBuilder<>* ir_builder) { - if (ir_builder->getFastMathFlags().noNaNs()) { - auto cmp = ir_builder->CreateFCmpULE(lhs_value, rhs_value); - return ir_builder->CreateSelect(cmp, lhs_value, rhs_value); + llvm::IRBuilder<>* b) { + if (b->getFastMathFlags().noNaNs()) { + auto cmp = b->CreateFCmpULE(lhs_value, rhs_value); + return b->CreateSelect(cmp, lhs_value, rhs_value); } else { - 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); + auto cmp_le = b->CreateFCmpOLE(lhs_value, rhs_value); + auto lhs_is_nan = b->CreateFCmpUNE(lhs_value, lhs_value); + auto sel_lhs = b->CreateOr(cmp_le, lhs_is_nan); + return b->CreateSelect(sel_lhs, lhs_value, rhs_value); } } llvm::Value* EmitBufferIndexingGEP(llvm::Value* array, llvm::Value* index, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { llvm::Type* array_type = array->getType(); CHECK(array_type->isPointerTy()); llvm::PointerType* array_type_as_pointer = @@ -131,16 +131,16 @@ llvm::Value* EmitBufferIndexingGEP(llvm::Value* array, llvm::Value* index, << " array=" << llvm_ir::DumpToString(*array) << " index=" << llvm_ir::DumpToString(*index); - return ir_builder->CreateInBoundsGEP( + return b->CreateInBoundsGEP( array_type_as_pointer->getElementType(), array, llvm::isa(array) - ? llvm::ArrayRef({ir_builder->getInt64(0), index}) + ? llvm::ArrayRef({b->getInt64(0), index}) : index); } llvm::Value* EmitBufferIndexingGEP(llvm::Value* array, int64 index, - llvm::IRBuilder<>* ir_builder) { - return EmitBufferIndexingGEP(array, ir_builder->getInt64(index), ir_builder); + llvm::IRBuilder<>* b) { + return EmitBufferIndexingGEP(array, b->getInt64(index), b); } llvm::Type* PrimitiveTypeToIrType(PrimitiveType element_type, @@ -232,14 +232,15 @@ llvm::Type* ShapeToIrType(const Shape& shape, llvm::Module* module) { return result_type; } -StatusOr EncodeSelfDescribingShapeConstant( - const Shape& shape, int32* shape_size, llvm::IRBuilder<>* ir_builder) { +StatusOr EncodeSelfDescribingShapeConstant(const Shape& shape, + int32* shape_size, + llvm::IRBuilder<>* b) { string encoded_shape = shape.SerializeAsString(); if (encoded_shape.size() > std::numeric_limits::max()) { return InternalError("Encoded shape size exceeded int32 size limit."); } *shape_size = static_cast(encoded_shape.size()); - return ir_builder->CreateGlobalStringPtr(llvm_ir::AsStringRef(encoded_shape)); + return b->CreateGlobalStringPtr(llvm_ir::AsStringRef(encoded_shape)); } StatusOr DecodeSelfDescribingShapeConstant(const void* shape_ptr, @@ -262,59 +263,57 @@ llvm::Constant* ConvertLiteralToIrConstant(const Literal& literal, llvm::AllocaInst* EmitAllocaAtFunctionEntry(llvm::Type* type, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, int alignment) { - return EmitAllocaAtFunctionEntryWithCount(type, nullptr, name, ir_builder, - alignment); + return EmitAllocaAtFunctionEntryWithCount(type, nullptr, name, b, alignment); } llvm::AllocaInst* EmitAllocaAtFunctionEntryWithCount( llvm::Type* type, llvm::Value* element_count, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, int alignment) { - llvm::IRBuilder<>::InsertPoint insert_point = ir_builder->saveIP(); - llvm::Function* function = ir_builder->GetInsertBlock()->getParent(); - ir_builder->SetInsertPoint(&function->getEntryBlock(), - function->getEntryBlock().getFirstInsertionPt()); + llvm::IRBuilder<>* b, int alignment) { + llvm::IRBuilder<>::InsertPoint insert_point = b->saveIP(); + llvm::Function* function = b->GetInsertBlock()->getParent(); + b->SetInsertPoint(&function->getEntryBlock(), + function->getEntryBlock().getFirstInsertionPt()); llvm::AllocaInst* alloca = - ir_builder->CreateAlloca(type, element_count, AsStringRef(name)); + b->CreateAlloca(type, element_count, AsStringRef(name)); if (alignment != 0) { alloca->setAlignment(alignment); } - ir_builder->restoreIP(insert_point); + b->restoreIP(insert_point); return alloca; } llvm::BasicBlock* CreateBasicBlock(llvm::BasicBlock* insert_before, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { return llvm::BasicBlock::Create( - /*Context=*/ir_builder->getContext(), + /*Context=*/b->getContext(), /*Name=*/AsStringRef(name), - /*Parent=*/ir_builder->GetInsertBlock()->getParent(), + /*Parent=*/b->GetInsertBlock()->getParent(), /*InsertBefore*/ insert_before); } LlvmIfData EmitIfThenElse(llvm::Value* condition, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, bool emit_else) { + llvm::IRBuilder<>* b, bool emit_else) { llvm_ir::LlvmIfData if_data; - if_data.if_block = ir_builder->GetInsertBlock(); - if_data.true_block = CreateBasicBlock( - nullptr, tensorflow::strings::StrCat(name, "-true"), ir_builder); + if_data.if_block = b->GetInsertBlock(); + if_data.true_block = + CreateBasicBlock(nullptr, tensorflow::strings::StrCat(name, "-true"), b); if_data.false_block = - emit_else ? CreateBasicBlock(nullptr, - tensorflow::strings::StrCat(name, "-false"), - ir_builder) + emit_else ? CreateBasicBlock( + nullptr, tensorflow::strings::StrCat(name, "-false"), b) : nullptr; // Add a terminator to the if block, if necessary. if (if_data.if_block->getTerminator() == nullptr) { - ir_builder->SetInsertPoint(if_data.if_block); + b->SetInsertPoint(if_data.if_block); if_data.after_block = CreateBasicBlock( - nullptr, tensorflow::strings::StrCat(name, "-after"), ir_builder); - ir_builder->CreateBr(if_data.after_block); + nullptr, tensorflow::strings::StrCat(name, "-after"), b); + b->CreateBr(if_data.after_block); } else { if_data.after_block = if_data.if_block->splitBasicBlock( - ir_builder->GetInsertPoint(), + b->GetInsertPoint(), AsStringRef(tensorflow::strings::StrCat(name, "-after"))); } @@ -322,39 +321,37 @@ LlvmIfData EmitIfThenElse(llvm::Value* condition, tensorflow::StringPiece name, // we're going to replace it with a conditional branch. if_data.if_block->getTerminator()->eraseFromParent(); - ir_builder->SetInsertPoint(if_data.if_block); - ir_builder->CreateCondBr( - condition, if_data.true_block, - emit_else ? if_data.false_block : if_data.after_block); + b->SetInsertPoint(if_data.if_block); + b->CreateCondBr(condition, if_data.true_block, + emit_else ? if_data.false_block : if_data.after_block); - ir_builder->SetInsertPoint(if_data.true_block); - ir_builder->CreateBr(if_data.after_block); + b->SetInsertPoint(if_data.true_block); + b->CreateBr(if_data.after_block); if (emit_else) { - ir_builder->SetInsertPoint(if_data.false_block); - ir_builder->CreateBr(if_data.after_block); + b->SetInsertPoint(if_data.false_block); + b->CreateBr(if_data.after_block); } - ir_builder->SetInsertPoint(if_data.after_block, - if_data.after_block->getFirstInsertionPt()); + b->SetInsertPoint(if_data.after_block, + if_data.after_block->getFirstInsertionPt()); return if_data; } llvm::Value* EmitComparison(llvm::CmpInst::Predicate predicate, llvm::Value* lhs_value, llvm::Value* rhs_value, - llvm::IRBuilder<>* ir_builder) { + llvm::IRBuilder<>* b) { llvm::Value* comparison_result; if (lhs_value->getType()->isIntegerTy()) { - comparison_result = ir_builder->CreateICmp(predicate, lhs_value, rhs_value); + comparison_result = b->CreateICmp(predicate, lhs_value, rhs_value); } else { - comparison_result = ir_builder->CreateFCmp(predicate, lhs_value, rhs_value); + comparison_result = b->CreateFCmp(predicate, lhs_value, rhs_value); } // comparison_result is i1, but the NVPTX codegen incorrectly lowers i1 // arrays. So we extend it to i8 so that it's addressable. - return ir_builder->CreateZExt( - comparison_result, - llvm_ir::PrimitiveTypeToIrType(PRED, ModuleFromIRBuilder(ir_builder))); + return b->CreateZExt(comparison_result, llvm_ir::PrimitiveTypeToIrType( + PRED, ModuleFromIRBuilder(b))); } // Internal helper that is called from emitted code to log an int64 value with a @@ -363,17 +360,14 @@ static void LogS64(const char* tag, int64 value) { LOG(INFO) << tag << " (int64): " << value; } -void EmitLogging(const char* tag, llvm::Value* value, - llvm::IRBuilder<>* ir_builder) { +void EmitLogging(const char* tag, llvm::Value* value, llvm::IRBuilder<>* b) { llvm::FunctionType* log_function_type = llvm::FunctionType::get( - ir_builder->getVoidTy(), - {ir_builder->getInt64Ty(), ir_builder->getInt64Ty()}, /*isVarArg=*/false); - ir_builder->CreateCall( + b->getVoidTy(), {b->getInt64Ty(), b->getInt64Ty()}, /*isVarArg=*/false); + b->CreateCall( log_function_type, - ir_builder->CreateIntToPtr( - ir_builder->getInt64(tensorflow::bit_cast(&LogS64)), - log_function_type->getPointerTo()), - {ir_builder->getInt64(tensorflow::bit_cast(tag)), value}); + b->CreateIntToPtr(b->getInt64(tensorflow::bit_cast(&LogS64)), + log_function_type->getPointerTo()), + {b->getInt64(tensorflow::bit_cast(tag)), value}); } void SetAlignmentMetadataForLoad(llvm::LoadInst* load, uint64_t alignment) { diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h index 9c51861eac..d8746ffe01 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h @@ -105,26 +105,26 @@ llvm::Value* EmitCallToIntrinsic( llvm::Intrinsic::ID intrinsic_id, tensorflow::gtl::ArraySlice operands, tensorflow::gtl::ArraySlice overloaded_types, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Emit float max. Emit maxnum intrinsic is fast math is disabled, or // fcmp+select otherwise llvm::Value* EmitFloatMax(llvm::Value* lhs_value, llvm::Value* rhs_value, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Emit float min. Emit minnum intrinsic is fast math is disabled, or // fcmp+select otherwise llvm::Value* EmitFloatMin(llvm::Value* lhs_value, llvm::Value* rhs_value, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Convenience methods for emitting a GEP instruction that indexes into a buffer // (1-dimensional array), equivalent to array[index]. The type is automatically // determined from the element type of the array. The int64 index overload // wraps the index in a i64 llvm::Value. llvm::Value* EmitBufferIndexingGEP(llvm::Value* array, llvm::Value* index, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); llvm::Value* EmitBufferIndexingGEP(llvm::Value* array, int64 index, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Returns the LLVM type which represents the given XLA primitive type. llvm::Type* PrimitiveTypeToIrType(PrimitiveType element_type, @@ -139,8 +139,9 @@ llvm::Type* ShapeToIrType(const Shape& shape, llvm::Module* module); // Returns a value that represents a pointer to a global string constant that // encodes the shape as a serialized protobuf. -StatusOr EncodeSelfDescribingShapeConstant( - const Shape& shape, int32* shape_size, llvm::IRBuilder<>* ir_builder); +StatusOr EncodeSelfDescribingShapeConstant(const Shape& shape, + int32* shape_size, + llvm::IRBuilder<>* b); // Inverses the encoding of a Shape protobuf into an LLVM global variable. // @@ -164,21 +165,21 @@ llvm::Constant* ConvertLiteralToIrConstant(const Literal& literal, // through a loop. llvm::AllocaInst* EmitAllocaAtFunctionEntry(llvm::Type* type, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, + llvm::IRBuilder<>* b, int alignment = 0); // As EmitAllocaAtFunctionEntry, but allocates element_count entries // instead of a single element. llvm::AllocaInst* EmitAllocaAtFunctionEntryWithCount( llvm::Type* type, llvm::Value* element_count, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, int alignment = 0); + llvm::IRBuilder<>* b, int alignment = 0); // Creates a basic block with the same context and function as for the // builder. Inserts at the end of the function if insert_before is // null. llvm::BasicBlock* CreateBasicBlock(llvm::BasicBlock* insert_before, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Struct with data on a conditional branch in a diamond shape created // via EmitIfThenElse. @@ -210,13 +211,13 @@ struct LlvmIfData { // block with a terminator. If you need to use this for a // non-terminated block, just make the function able to do that too. LlvmIfData EmitIfThenElse(llvm::Value* condition, tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder, bool emit_else = true); + llvm::IRBuilder<>* b, bool emit_else = true); // Emits a compare operation between "lhs" and "rhs" with the given predicate, // and then converts the result to i8 so that it is addressable. llvm::Value* EmitComparison(llvm::CmpInst::Predicate predicate, llvm::Value* lhs, llvm::Value* rhs, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Emits a call that logs the given value with the given tag as a prefix. // The provided tag and value are passed to a runtime logging call that is @@ -228,8 +229,7 @@ llvm::Value* EmitComparison(llvm::CmpInst::Predicate predicate, // Precondition: value must be an int64. // Precondition: tag must be a stable pointer for the lifetime of the generated // program (the constant pointer is burned in to the program). -void EmitLogging(const char* tag, llvm::Value* value, - llvm::IRBuilder<>* ir_builder); +void EmitLogging(const char* tag, llvm::Value* value, llvm::IRBuilder<>* b); // Adds alignment metadata to a load instruction using the given alignment. // The alignment refers to the result of the load, not the load itself. diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc index e8b0605b9d..36f5fa1952 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.cc @@ -33,26 +33,24 @@ namespace xla { namespace llvm_ir { LoopEmitter::LoopEmitter(const BodyEmitter& body_emitter, const Shape& shape, - llvm::IRBuilder<>* ir_builder) - : body_emitter_(body_emitter), shape_(shape), ir_builder_(ir_builder) {} + llvm::IRBuilder<>* b) + : body_emitter_(body_emitter), shape_(shape), b_(b) {} LoopEmitter::LoopEmitter(const ElementGenerator& target_element_generator, - const IrArray& target_array, - llvm::IRBuilder<>* ir_builder) + const IrArray& target_array, llvm::IRBuilder<>* b) : body_emitter_([=](const llvm_ir::IrArray::Index array_index) -> Status { // Convert target_element_generator to a BodyEmitter. TF_ASSIGN_OR_RETURN(llvm::Value * target_element, target_element_generator(array_index)); - target_array.EmitWriteArrayElement(array_index, target_element, - ir_builder); + target_array.EmitWriteArrayElement(array_index, target_element, b); return Status::OK(); }), shape_(target_array.GetShape()), - ir_builder_(ir_builder) {} + b_(b) {} static LoopEmitter::BodyEmitter MakeBodyEmitterForMultiOutputFusion( const ElementGenerator& target_element_generator, - const std::vector& target_arrays, llvm::IRBuilder<>* ir_builder) { + const std::vector& target_arrays, llvm::IRBuilder<>* b) { return [=](const llvm_ir::IrArray::Index array_index) { TF_ASSIGN_OR_RETURN(llvm::Value * target_element, target_element_generator(array_index)); @@ -64,8 +62,7 @@ static LoopEmitter::BodyEmitter MakeBodyEmitterForMultiOutputFusion( for (int64 i = 0; i < target_arrays.size(); ++i) { target_arrays[i].EmitWriteArrayElement( - array_index, ir_builder->CreateExtractValue(target_element, i), - ir_builder); + array_index, b->CreateExtractValue(target_element, i), b); } return Status::OK(); }; @@ -73,13 +70,12 @@ static LoopEmitter::BodyEmitter MakeBodyEmitterForMultiOutputFusion( LoopEmitter::LoopEmitter(const ElementGenerator& target_element_generator, tensorflow::gtl::ArraySlice target_arrays, - llvm::IRBuilder<>* ir_builder) + llvm::IRBuilder<>* b) : body_emitter_(MakeBodyEmitterForMultiOutputFusion( target_element_generator, - std::vector(target_arrays.begin(), target_arrays.end()), - ir_builder)), + std::vector(target_arrays.begin(), target_arrays.end()), b)), shape_(target_arrays[0].GetShape()), - ir_builder_(ir_builder) { + b_(b) { // Sanity check: In multi-output fusion, all shapes produced must have the // same dimensions. for (const IrArray& array : target_arrays) { @@ -102,7 +98,7 @@ std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( // Loops are added from outermost to innermost order with the ForLoopNest // class so emit loops in order from most-major dimension down to most-minor // dimension (of the target shape). - ForLoopNest loop_nest(loop_name, ir_builder_); + ForLoopNest loop_nest(loop_name, b_); IrArray::Index array_index(index_type, shape_.dimensions_size()); for (int i = 0; i < LayoutUtil::MinorToMajor(shape_).size(); ++i) { int64 dimension = LayoutUtil::Major(shape_.layout(), i); @@ -116,8 +112,8 @@ std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( // Set IR builder insertion point to the loop body basic block of the // innermost loop. llvm::BasicBlock* innermost_body_bb = loop_nest.GetInnerLoopBodyBasicBlock(); - ir_builder_->SetInsertPoint(innermost_body_bb, - innermost_body_bb->getFirstInsertionPt()); + b_->SetInsertPoint(innermost_body_bb, + innermost_body_bb->getFirstInsertionPt()); // Set exit_bb_ to the exit block of the loop nest. exit_bb_ = loop_nest.GetOuterLoopExitBasicBlock(); @@ -129,7 +125,7 @@ std::vector LoopEmitter::EmitIndexAndSetExitBasicBlock( Status LoopEmitter::EmitLoop(tensorflow::StringPiece loop_name, llvm::Type* index_type) { if (index_type == nullptr) { - index_type = ir_builder_->getInt64Ty(); + index_type = b_->getInt64Ty(); } for (const IrArray::Index& array_index : @@ -137,10 +133,10 @@ Status LoopEmitter::EmitLoop(tensorflow::StringPiece loop_name, TF_RETURN_IF_ERROR(body_emitter_(array_index)); } - // Set the insertion point of ir_builder_ to the loop exit, so that + // Set the insertion point of b_ to the loop exit, so that // code emitted for later instructions will be correctly placed. if (exit_bb_ != nullptr) { - ir_builder_->SetInsertPoint(exit_bb_); + b_->SetInsertPoint(exit_bb_); } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h index 6be1c2fba2..c4f5c82086 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h +++ b/tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h @@ -41,11 +41,11 @@ class LoopEmitter { using BodyEmitter = std::function; LoopEmitter(const BodyEmitter& body_emitter, const Shape& shape, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); // Constructs a LoopEmitter from an element generator that generates each // element of the given target array. LoopEmitter(const ElementGenerator& target_element_generator, - const IrArray& target_array, llvm::IRBuilder<>* ir_builder); + const IrArray& target_array, llvm::IRBuilder<>* b); // Constructs a LoopEmitter that emits one element into each of N separate // arrays on each iteration of the loop. @@ -54,7 +54,7 @@ class LoopEmitter { // produce an LLVM struct with N elements. LoopEmitter(const ElementGenerator& target_element_generator, tensorflow::gtl::ArraySlice target_arrays, - llvm::IRBuilder<>* ir_builder); + llvm::IRBuilder<>* b); LoopEmitter(const LoopEmitter&) = delete; LoopEmitter& operator=(const LoopEmitter&) = delete; @@ -65,8 +65,7 @@ class LoopEmitter { // specifies the element, will return multiple indices if the loop is // unrolled. std::vector EmitIndexAndSetExitBasicBlock() { - return EmitIndexAndSetExitBasicBlock(/*loop_name=*/"", - ir_builder_->getInt64Ty()); + return EmitIndexAndSetExitBasicBlock(/*loop_name=*/"", b_->getInt64Ty()); } virtual std::vector EmitIndexAndSetExitBasicBlock( @@ -87,7 +86,7 @@ class LoopEmitter { // scalar, no loops are emitted and exit_bb_ is nullptr in that case. llvm::BasicBlock* exit_bb_; - llvm::IRBuilder<>* ir_builder_; + llvm::IRBuilder<>* b_; }; } // namespace llvm_ir diff --git a/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc b/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc index 16a9a5aaeb..585364458a 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc @@ -38,46 +38,42 @@ namespace { void EmitCompareLoop(int64 dimension_to_sort, const llvm_ir::IrArray::Index& keys_index, const llvm_ir::IrArray::Index& compare_keys_index, - const llvm_ir::IrArray& keys_array, - llvm::IRBuilder<>* ir_builder) { + const llvm_ir::IrArray& keys_array, llvm::IRBuilder<>* b) { // TODO(b/26783907): parallelize this loop. // if (is_smaller_index && // compare_keys[dimension_to_sort] < dimension_to_sort_bound) - llvm::Value* is_smaller_index = ir_builder->CreateICmpSLT( + llvm::Value* is_smaller_index = b->CreateICmpSLT( keys_index[dimension_to_sort], compare_keys_index[dimension_to_sort]); int64 dimension_to_sort_bound = keys_array.GetShape().dimensions(dimension_to_sort); auto if_data = llvm_ir::EmitIfThenElse( - ir_builder->CreateAnd( - is_smaller_index, - ir_builder->CreateICmpSLT( - compare_keys_index[dimension_to_sort], - keys_index.GetConstantWithIndexType(dimension_to_sort_bound))), - "smaller_comparison_index", ir_builder, /*emit_else=*/false); - SetToFirstInsertPoint(if_data.true_block, ir_builder); - auto key1 = keys_array.EmitReadArrayElement(keys_index, ir_builder); - auto key2 = keys_array.EmitReadArrayElement(compare_keys_index, ir_builder); + b->CreateAnd(is_smaller_index, + b->CreateICmpSLT(compare_keys_index[dimension_to_sort], + keys_index.GetConstantWithIndexType( + dimension_to_sort_bound))), + "smaller_comparison_index", b, /*emit_else=*/false); + SetToFirstInsertPoint(if_data.true_block, b); + auto key1 = keys_array.EmitReadArrayElement(keys_index, b); + auto key2 = keys_array.EmitReadArrayElement(compare_keys_index, b); auto key_type = keys_array.GetShape().element_type(); auto comparison = primitive_util::IsFloatingPointType(key_type) // TODO(b/26783907): Figure out how to handle NaNs. - ? ir_builder->CreateFCmp(llvm::FCmpInst::FCMP_ULT, key1, key2) - : ir_builder->CreateICmp( - primitive_util::IsSignedIntegralType(key_type) - ? llvm::ICmpInst::ICMP_SLT - : llvm::ICmpInst::ICMP_ULT, - key1, key2); - auto min_key = ir_builder->CreateSelect(comparison, key1, key2); - auto max_key = ir_builder->CreateSelect(comparison, key2, key1); - keys_array.EmitWriteArrayElement(keys_index, min_key, ir_builder); - keys_array.EmitWriteArrayElement(compare_keys_index, max_key, ir_builder); + ? b->CreateFCmp(llvm::FCmpInst::FCMP_ULT, key1, key2) + : b->CreateICmp(primitive_util::IsSignedIntegralType(key_type) + ? llvm::ICmpInst::ICMP_SLT + : llvm::ICmpInst::ICMP_ULT, + key1, key2); + auto min_key = b->CreateSelect(comparison, key1, key2); + auto max_key = b->CreateSelect(comparison, key2, key1); + keys_array.EmitWriteArrayElement(keys_index, min_key, b); + keys_array.EmitWriteArrayElement(compare_keys_index, max_key, b); } } // namespace Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, - tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder) { + tensorflow::StringPiece name, llvm::IRBuilder<>* b) { const Shape& keys_shape = keys_array.GetShape(); // TODO(b/26783907): This case can probably be avoided with the Algebraic @@ -89,7 +85,7 @@ Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, // Create loop nests which loop through the operand dimensions. The sort // dimension is handled in three separate innermost loops which perform the // sorting. - ForLoopNest loop_nest(name, ir_builder); + ForLoopNest loop_nest(name, b); IrArray::Index keys_index = loop_nest.EmitOperandArrayLoopNest(keys_array, dimension_to_sort, "keys"); @@ -149,12 +145,11 @@ Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, // This follows the algorithm described on Wikipedia: // https://en.wikipedia.org/wiki/Bitonic_sorter - SetToFirstInsertPoint(stages_loop->GetBodyBasicBlock(), ir_builder); + SetToFirstInsertPoint(stages_loop->GetBodyBasicBlock(), b); // The first xor mask of a stage is 2^(stage + 1) - 1. - auto first_xor_mask = ir_builder->CreateSub( - ir_builder->CreateShl( - keys_index.GetConstantWithIndexType(1), - ir_builder->CreateAdd(stages_loop->GetIndVarValue(), + auto first_xor_mask = b->CreateSub( + b->CreateShl(keys_index.GetConstantWithIndexType(1), + b->CreateAdd(stages_loop->GetIndVarValue(), keys_index.GetConstantWithIndexType(1))), keys_index.GetConstantWithIndexType(1)); std::unique_ptr first_compare_loop = ForLoop::EmitForLoop( @@ -163,36 +158,35 @@ Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, /*end_index=*/ keys_index.GetConstantWithIndexType(dimension_to_sort_bound), /*step=*/keys_index.GetConstantWithIndexType(1), - /*ir_builder=*/ir_builder); + /*b=*/b); - SetToFirstInsertPoint(first_compare_loop->GetBodyBasicBlock(), ir_builder); + SetToFirstInsertPoint(first_compare_loop->GetBodyBasicBlock(), b); // 'first_compare_loop' iterates through the 'dimension_to_sort'. keys_index[dimension_to_sort] = first_compare_loop->GetIndVarValue(); - compare_keys_index[dimension_to_sort] = ir_builder->CreateXor( - first_compare_loop->GetIndVarValue(), first_xor_mask); + compare_keys_index[dimension_to_sort] = + b->CreateXor(first_compare_loop->GetIndVarValue(), first_xor_mask); EmitCompareLoop(dimension_to_sort, keys_index, compare_keys_index, keys_array, - ir_builder); + b); - SetToFirstInsertPoint(compare_loop->GetPreheaderBasicBlock(), ir_builder); + SetToFirstInsertPoint(compare_loop->GetPreheaderBasicBlock(), b); // The later masks of a stage are 2^(stage - (mask_loop_ind_var + 1)). - auto later_xor_mask = ir_builder->CreateShl( + auto later_xor_mask = b->CreateShl( keys_index.GetConstantWithIndexType(1), - ir_builder->CreateSub( - stages_loop->GetIndVarValue(), - ir_builder->CreateAdd(mask_loop->GetIndVarValue(), + b->CreateSub(stages_loop->GetIndVarValue(), + b->CreateAdd(mask_loop->GetIndVarValue(), keys_index.GetConstantWithIndexType(1)))); - SetToFirstInsertPoint(compare_loop->GetBodyBasicBlock(), ir_builder); + SetToFirstInsertPoint(compare_loop->GetBodyBasicBlock(), b); // 'compare_loop' iterates through the 'dimension_to_sort'. keys_index[dimension_to_sort] = compare_loop->GetIndVarValue(); compare_keys_index[dimension_to_sort] = - ir_builder->CreateXor(compare_loop->GetIndVarValue(), later_xor_mask); + b->CreateXor(compare_loop->GetIndVarValue(), later_xor_mask); EmitCompareLoop(dimension_to_sort, keys_index, compare_keys_index, keys_array, - ir_builder); + b); // Set the IR builder insert point to the exit basic block of the outer most // loop. This ensures later instructions are inserted after this loop nest. - ir_builder->SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); + b->SetInsertPoint(loop_nest.GetOuterLoopExitBasicBlock()); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/llvm_ir/sort_util.h b/tensorflow/compiler/xla/service/llvm_ir/sort_util.h index fc45bfab12..d0f185e70b 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/sort_util.h +++ b/tensorflow/compiler/xla/service/llvm_ir/sort_util.h @@ -26,8 +26,7 @@ namespace llvm_ir { // Emits llvm IR to sort the 'dimension_to_sort' dimension of 'keys_array' into // ascending order. Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, - tensorflow::StringPiece name, - llvm::IRBuilder<>* ir_builder); + tensorflow::StringPiece name, llvm::IRBuilder<>* b); } // namespace llvm_ir } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.cc b/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.cc index 5fc08aab91..11ed6ee59f 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.cc @@ -31,12 +31,12 @@ namespace llvm_ir { void EmitTupleSelect(const IrArray& select, const IrArray& pred, llvm::Value* on_true, llvm::Value* on_false, - llvm::IRBuilder<>* ir_builder, llvm::Module* module) { + llvm::IRBuilder<>* b, llvm::Module* module) { CHECK(ShapeUtil::IsScalar(pred.GetShape())); llvm::LoadInst* pred_value = - ir_builder->CreateLoad(pred.GetBasePointer(), "load_predicate_value"); - llvm::Value* pred_cond = ir_builder->CreateICmpNE( + b->CreateLoad(pred.GetBasePointer(), "load_predicate_value"); + llvm::Value* pred_cond = b->CreateICmpNE( pred_value, llvm::ConstantInt::get(PrimitiveTypeToIrType(PRED, module), 0), "boolean_predicate"); @@ -46,47 +46,42 @@ void EmitTupleSelect(const IrArray& select, const IrArray& pred, VLOG(2) << " pred_cond: " << DumpToString(*pred_cond); for (int i = 0; i < ShapeUtil::TupleElementCount(select.GetShape()); ++i) { - llvm::Value* const element_index[] = {ir_builder->getInt64(0), - ir_builder->getInt64(i)}; + llvm::Value* const element_index[] = {b->getInt64(0), b->getInt64(i)}; llvm::Value* on_true_element_address = - ir_builder->CreateInBoundsGEP(on_true, element_index); - llvm::Value* on_true_element = ir_builder->CreateLoad( + b->CreateInBoundsGEP(on_true, element_index); + llvm::Value* on_true_element = b->CreateLoad( on_true_element_address, "on_true_element_" + llvm::Twine(i)); llvm::Value* on_false_element_address = - ir_builder->CreateInBoundsGEP(on_false, element_index); - llvm::Value* on_false_element = ir_builder->CreateLoad( + b->CreateInBoundsGEP(on_false, element_index); + llvm::Value* on_false_element = b->CreateLoad( on_false_element_address, "on_false_element_" + llvm::Twine(i)); llvm::Value* output_element_address = - ir_builder->CreateInBoundsGEP(select.GetBasePointer(), element_index); - ir_builder->CreateStore( - ir_builder->CreateSelect(pred_cond, on_true_element, on_false_element, - "select_output_element_" + llvm::Twine(i)), - output_element_address); + b->CreateInBoundsGEP(select.GetBasePointer(), element_index); + b->CreateStore(b->CreateSelect(pred_cond, on_true_element, on_false_element, + "select_output_element_" + llvm::Twine(i)), + output_element_address); } } void EmitTuple(const IrArray& tuple, tensorflow::gtl::ArraySlice operands, - llvm::IRBuilder<>* ir_builder, llvm::Module* module) { + llvm::IRBuilder<>* b, llvm::Module* module) { for (size_t i = 0; i < operands.size(); ++i) { - auto* store = ir_builder->CreateStore( - ir_builder->CreatePointerCast(operands[i], - PrimitiveTypeToIrType(TUPLE, module)), - ir_builder->CreateInBoundsGEP( - tuple.GetBasePointer(), - {ir_builder->getInt64(0), ir_builder->getInt64(i)})); + auto* store = b->CreateStore( + b->CreatePointerCast(operands[i], PrimitiveTypeToIrType(TUPLE, module)), + b->CreateInBoundsGEP(tuple.GetBasePointer(), + {b->getInt64(0), b->getInt64(i)})); tuple.AnnotateLoadStoreInstructionWithMetadata(store); } } llvm::Value* EmitGetTupleElement(const Shape& target_shape, int64 index, int alignment, llvm::Value* operand, - llvm::IRBuilder<>* ir_builder, - llvm::Module* module) { - llvm::Value* element_ptr = ir_builder->CreateInBoundsGEP( - operand, {ir_builder->getInt64(0), ir_builder->getInt64(index)}); - llvm::LoadInst* src_buffer = ir_builder->CreateLoad(element_ptr); + llvm::IRBuilder<>* b, llvm::Module* module) { + llvm::Value* element_ptr = + b->CreateInBoundsGEP(operand, {b->getInt64(0), b->getInt64(index)}); + llvm::LoadInst* src_buffer = b->CreateLoad(element_ptr); // Mark the loaded pointer as dereferenceable if we know its shape. if (!ShapeUtil::IsOpaque(target_shape)) { @@ -98,7 +93,7 @@ llvm::Value* EmitGetTupleElement(const Shape& target_shape, int64 index, llvm::Type* element_type = ShapeToIrType(target_shape, module); llvm::Value* ret_val = - ir_builder->CreateBitCast(src_buffer, element_type->getPointerTo()); + b->CreateBitCast(src_buffer, element_type->getPointerTo()); return ret_val; } diff --git a/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h b/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h index 352d34ebf8..cf6bf5d0b1 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h +++ b/tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h @@ -61,13 +61,13 @@ namespace llvm_ir { // output[i] = pred ? tuple_on_true[i] : tuple_on_false[i] void EmitTupleSelect(const IrArray& select, const IrArray& pred, llvm::Value* on_true, llvm::Value* on_false, - llvm::IRBuilder<>* ir_builder, llvm::Module* module); + llvm::IRBuilder<>* b, llvm::Module* module); // A tuple is an array of pointers, one for each operand. Each pointer points to // the output buffer of its corresponding operand. void EmitTuple(const IrArray& tuple, tensorflow::gtl::ArraySlice operands, - llvm::IRBuilder<>* ir_builder, llvm::Module* module); + llvm::IRBuilder<>* b, llvm::Module* module); // A tuple is an array of pointers, one for each operand. Each pointer points to // the output buffer of its corresponding operand. A GetTupleElement instruction @@ -75,8 +75,7 @@ void EmitTuple(const IrArray& tuple, // Returns an llvm value representing a pointer to the tuple element buffer. llvm::Value* EmitGetTupleElement(const Shape& target_shape, int64 index, int alignment, llvm::Value* operand, - llvm::IRBuilder<>* ir_builder, - llvm::Module* module); + llvm::IRBuilder<>* b, llvm::Module* module); } // namespace llvm_ir } // namespace xla -- GitLab From 7e8a83543b7eb36647894453129f15eeec60b3ba Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Fri, 20 Jul 2018 14:45:30 -0700 Subject: [PATCH 213/519] [XLA] Make it illegal to call XlaOp::builder() if the op is uninitialized. It's very common to do foo.builder()->bar(). Without this precondition, if foo.builder() is null, the call to bar will segfault at some point possibly deep in the callstack when we finally dereference `this`. The precondition lets us avoid this tricky-to-debug problem. PiperOrigin-RevId: 205456769 --- .../compiler/xla/client/xla_client/xla_builder.h | 11 ++++++++++- 1 file changed, 10 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.h b/tensorflow/compiler/xla/client/xla_client/xla_builder.h index 3c016ebe8f..8359d936b7 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.h +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.h @@ -54,7 +54,16 @@ class XlaOp { } ~XlaOp() = default; - XlaBuilder* builder() const { return builder_; } + // Precondition: !IsUninitialized(). + // + // It's very common to do foo.builder()->bar(). Without this precondition, if + // foo.builder() is null, the call to bar will segfault at some point possibly + // deep in the callstack when we finally dereference `this`. The precondition + // lets us avoid this tricky-to-debug problem. + XlaBuilder* builder() const { + CHECK(builder_ != nullptr); + return builder_; + } // Returns true if the XlaOp represents valid, non-erroneous value. bool valid() const { return handle_ >= 0; } -- GitLab From 1711a9a08ce29029c66924f880fa1e619aed10aa Mon Sep 17 00:00:00 2001 From: RJ Ryan Date: Fri, 20 Jul 2018 14:47:04 -0700 Subject: [PATCH 214/519] Remove float64 math in linear_to_mel_weight_matrix. This was causing portability problems for platforms that do not support float64. Callers who want higher precision can simply pass tf.float64 as the dtype. PiperOrigin-RevId: 205457007 --- .../python/kernel_tests/mel_ops_test.py | 13 ++++++---- .../contrib/signal/python/ops/mel_ops.py | 24 ++++++++----------- 2 files changed, 18 insertions(+), 19 deletions(-) diff --git a/tensorflow/contrib/signal/python/kernel_tests/mel_ops_test.py b/tensorflow/contrib/signal/python/kernel_tests/mel_ops_test.py index 345eb6cfaa..f4348e80ea 100644 --- a/tensorflow/contrib/signal/python/kernel_tests/mel_ops_test.py +++ b/tensorflow/contrib/signal/python/kernel_tests/mel_ops_test.py @@ -53,7 +53,8 @@ def spectrogram_to_mel_matrix(num_mel_bins=20, num_spectrogram_bins=129, audio_sample_rate=8000, lower_edge_hertz=125.0, - upper_edge_hertz=3800.0): + upper_edge_hertz=3800.0, + unused_dtype=None): """Return a matrix that can post-multiply spectrogram rows to make mel. Copied from @@ -132,9 +133,9 @@ class LinearToMelTest(test.TestCase): # lower_edge_hertz, upper_edge_hertz) to test. configs = [ # Defaults. - (20, 129, 8000.0, 125.0, 3800.0), + (20, 129, 8000.0, 125.0, 3800.0, dtypes.float64), # Settings used by Tacotron (https://arxiv.org/abs/1703.10135). - (80, 1025, 24000.0, 80.0, 12000.0) + (80, 1025, 24000.0, 80.0, 12000.0, dtypes.float64) ] with self.test_session(use_gpu=True): for config in configs: @@ -143,7 +144,8 @@ class LinearToMelTest(test.TestCase): self.assertAllClose(mel_matrix_np, mel_matrix.eval(), atol=3e-6) def test_dtypes(self): - for dtype in (dtypes.float16, dtypes.float32, dtypes.float64): + # LinSpace is not supported for tf.float16. + for dtype in (dtypes.bfloat16, dtypes.float32, dtypes.float64): self.assertEqual(dtype, mel_ops.linear_to_mel_weight_matrix(dtype=dtype).dtype) @@ -167,7 +169,8 @@ class LinearToMelTest(test.TestCase): def test_constant_folding(self): """Mel functions should be constant foldable.""" - for dtype in (dtypes.float16, dtypes.float32, dtypes.float64): + # TODO(rjryan): tf.bloat16 cannot be constant folded by Grappler. + for dtype in (dtypes.float32, dtypes.float64): g = ops.Graph() with g.as_default(): mel_matrix = mel_ops.linear_to_mel_weight_matrix(dtype=dtype) diff --git a/tensorflow/contrib/signal/python/ops/mel_ops.py b/tensorflow/contrib/signal/python/ops/mel_ops.py index 1e84006116..062d84aea1 100644 --- a/tensorflow/contrib/signal/python/ops/mel_ops.py +++ b/tensorflow/contrib/signal/python/ops/mel_ops.py @@ -151,22 +151,21 @@ def linear_to_mel_weight_matrix(num_mel_bins=20, _validate_arguments(num_mel_bins, sample_rate, lower_edge_hertz, upper_edge_hertz, dtype) - # To preserve accuracy, we compute the matrix at float64 precision and then - # cast to `dtype` at the end. This function can be constant folded by graph - # optimization since there are no Tensor inputs. + # This function can be constant folded by graph optimization since there are + # no Tensor inputs. sample_rate = ops.convert_to_tensor( - sample_rate, dtypes.float64, name='sample_rate') + sample_rate, dtype, name='sample_rate') lower_edge_hertz = ops.convert_to_tensor( - lower_edge_hertz, dtypes.float64, name='lower_edge_hertz') + lower_edge_hertz, dtype, name='lower_edge_hertz') upper_edge_hertz = ops.convert_to_tensor( - upper_edge_hertz, dtypes.float64, name='upper_edge_hertz') - zero_float64 = ops.convert_to_tensor(0.0, dtypes.float64) + upper_edge_hertz, dtype, name='upper_edge_hertz') + zero = ops.convert_to_tensor(0.0, dtype) # HTK excludes the spectrogram DC bin. bands_to_zero = 1 nyquist_hertz = sample_rate / 2.0 linear_frequencies = math_ops.linspace( - zero_float64, nyquist_hertz, num_spectrogram_bins)[bands_to_zero:] + zero, nyquist_hertz, num_spectrogram_bins)[bands_to_zero:] spectrogram_bins_mel = array_ops.expand_dims( _hertz_to_mel(linear_frequencies), 1) @@ -193,11 +192,8 @@ def linear_to_mel_weight_matrix(num_mel_bins=20, # Intersect the line segments with each other and zero. mel_weights_matrix = math_ops.maximum( - zero_float64, math_ops.minimum(lower_slopes, upper_slopes)) + zero, math_ops.minimum(lower_slopes, upper_slopes)) # Re-add the zeroed lower bins we sliced out above. - mel_weights_matrix = array_ops.pad( - mel_weights_matrix, [[bands_to_zero, 0], [0, 0]]) - - # Cast to the desired type. - return math_ops.cast(mel_weights_matrix, dtype, name=name) + return array_ops.pad( + mel_weights_matrix, [[bands_to_zero, 0], [0, 0]], name=name) -- GitLab From 248980e6422f97aa44d6bbac942389f2e9de75ad Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Fri, 20 Jul 2018 14:56:08 -0700 Subject: [PATCH 215/519] Fix a bug in stats updation and add a test. PiperOrigin-RevId: 205458337 --- .../lite/profiling/profile_summarizer.cc | 19 +---- tensorflow/core/BUILD | 10 +++ tensorflow/core/util/stat_summarizer.cc | 22 ++---- tensorflow/core/util/stats_calculator.cc | 21 ++++- tensorflow/core/util/stats_calculator.h | 5 +- tensorflow/core/util/stats_calculator_test.cc | 76 +++++++++++++++++++ 6 files changed, 117 insertions(+), 36 deletions(-) create mode 100644 tensorflow/core/util/stats_calculator_test.cc diff --git a/tensorflow/contrib/lite/profiling/profile_summarizer.cc b/tensorflow/contrib/lite/profiling/profile_summarizer.cc index 36e87b666a..720bd717b9 100644 --- a/tensorflow/contrib/lite/profiling/profile_summarizer.cc +++ b/tensorflow/contrib/lite/profiling/profile_summarizer.cc @@ -23,8 +23,6 @@ namespace tflite { namespace profiling { namespace { -using Detail = tensorflow::StatsCalculator::Detail; - struct OperatorDetails { std::string name; std::vector inputs; @@ -125,28 +123,17 @@ void ProfileSummarizer::ProcessProfiles( int64_t base_start_us = events[0]->begin_timestamp_us; int node_num = 0; int64_t curr_total_us = 0; - std::map details; for (auto event : events) { auto op_details = GetOperatorDetails(interpreter, event->event_metadata); auto node_name = ToString(op_details.outputs); - auto result = details.emplace(node_name, Detail()); - Detail* detail = &(result.first->second); - detail->start_us.UpdateStat(event->begin_timestamp_us - base_start_us); + int64_t start_us = event->begin_timestamp_us - base_start_us; int64_t node_exec_time = event->end_timestamp_us - event->begin_timestamp_us; - detail->rel_end_us.UpdateStat(node_exec_time); + stats_calculator_->AddNodeStats(node_name, op_details.name, node_num, + start_us, node_exec_time, 0 /*memory */); curr_total_us += node_exec_time; ++node_num; - - if (result.second) { - detail->name = node_name; - detail->type = op_details.name; - detail->run_order = node_num; - detail->times_called = 0; - } - ++detail->times_called; } - stats_calculator_->UpdateDetails(details); stats_calculator_->UpdateRunTotalUs(curr_total_us); } } // namespace profiling diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index fc12027291..870bde7bc8 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -883,6 +883,16 @@ cc_library( copts = tf_copts(), ) +tf_cc_test( + name = "stats_calculator_test", + srcs = ["util/stats_calculator_test.cc"], + deps = [ + ":stats_calculator_portable", + ":test", + ":test_main", + ], +) + cc_library( name = "overflow", hdrs = ["util/overflow.h"], diff --git a/tensorflow/core/util/stat_summarizer.cc b/tensorflow/core/util/stat_summarizer.cc index a5c1fda102..2117042034 100644 --- a/tensorflow/core/util/stat_summarizer.cc +++ b/tensorflow/core/util/stat_summarizer.cc @@ -133,7 +133,6 @@ void StatSummarizer::ProcessStepStats(const StepStats& step_stats) { int64 first_node_start_us = step_stats.dev_stats(0).node_stats(0).all_start_micros(); - std::map details; int node_num = 0; for (const auto& ds : step_stats.dev_stats()) { @@ -177,22 +176,15 @@ void StatSummarizer::ProcessStepStats(const StepStats& step_stats) { ++node_num; const int64 curr_time = ns.all_end_rel_micros(); curr_total_us += curr_time; - auto result = details.emplace(name, Detail()); auto output_result = outputs_.emplace(name, std::vector()); std::vector* outputs = &(output_result.first->second); - Detail* detail = &(result.first->second); - detail->start_us.UpdateStat(ns.all_start_micros() - first_node_start_us); - detail->rel_end_us.UpdateStat(curr_time); + int64_t start_us = (ns.all_start_micros() - first_node_start_us); + int64_t rel_end_us = curr_time; // If this is the first pass, initialize some values. - if (result.second) { - detail->name = name; - detail->type = op_type; - - detail->run_order = node_num; - + if (output_result.second) { outputs->resize(ns.output_size()); for (const auto& output : ns.output()) { const int32 slot = output.slot(); @@ -202,7 +194,6 @@ void StatSummarizer::ProcessStepStats(const StepStats& step_stats) { } (*outputs)[slot] = output.tensor_description(); } - detail->times_called = 0; } int64 curr_node_mem = 0; @@ -210,11 +201,10 @@ void StatSummarizer::ProcessStepStats(const StepStats& step_stats) { const int64 mem_usage = mem.total_bytes(); curr_node_mem += mem_usage; } - detail->mem_used.UpdateStat(curr_node_mem); - mem_total += curr_node_mem; + stats_calculator_->AddNodeStats(name, op_type, node_num, start_us, + rel_end_us, curr_node_mem); - ++detail->times_called; - stats_calculator_->UpdateDetails(details); + mem_total += curr_node_mem; Validate(outputs, ns); } diff --git a/tensorflow/core/util/stats_calculator.cc b/tensorflow/core/util/stats_calculator.cc index c4befbdb84..eb07754650 100644 --- a/tensorflow/core/util/stats_calculator.cc +++ b/tensorflow/core/util/stats_calculator.cc @@ -272,9 +272,24 @@ std::string StatsCalculator::GetOutputString() const { return stream.str(); } -void StatsCalculator::UpdateDetails( - const std::map& details) { - details_.insert(details.begin(), details.end()); +void StatsCalculator::AddNodeStats(const std::string& name, + const std::string& type, int64_t run_order, + int64_t start_us, int64_t rel_end_us, + int64_t mem_used) { + Detail* detail = nullptr; + if (details_.find(name) == details_.end()) { + details_.insert({name, {}}); + detail = &details_.at(name); + detail->type = type; + detail->name = name; + detail->run_order = run_order; + } else { + detail = &details_.at(name); + } + detail->start_us.UpdateStat(start_us); + detail->rel_end_us.UpdateStat(rel_end_us); + detail->mem_used.UpdateStat(mem_used); + detail->times_called++; } } // namespace tensorflow diff --git a/tensorflow/core/util/stats_calculator.h b/tensorflow/core/util/stats_calculator.h index 39cef816f1..e191737bb2 100644 --- a/tensorflow/core/util/stats_calculator.h +++ b/tensorflow/core/util/stats_calculator.h @@ -163,7 +163,10 @@ class StatsCalculator { }; const std::map& GetDetails() const { return details_; } - void UpdateDetails(const std::map& details); + + void AddNodeStats(const std::string& name, const std::string& type, + int64_t run_order, int64_t start_us, int64_t rel_end_us, + int64_t mem_used); private: void OrderNodesByMetric(SortingMetric sorting_metric, diff --git a/tensorflow/core/util/stats_calculator_test.cc b/tensorflow/core/util/stats_calculator_test.cc new file mode 100644 index 0000000000..00d7bfc2f9 --- /dev/null +++ b/tensorflow/core/util/stats_calculator_test.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 "tensorflow/core/util/stats_calculator.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace { + +using Detail = StatsCalculator::Detail; + +TEST(StatsCalculatorTest, TotalTimeMs) { + auto options = StatSummarizerOptions(); + StatsCalculator calc(options); + + EXPECT_EQ(0, calc.num_runs()); + calc.UpdateRunTotalUs(1); + + EXPECT_EQ(1, calc.num_runs()); + calc.UpdateRunTotalUs(2); + + EXPECT_EQ(2, calc.num_runs()); + auto run_time_us = calc.run_total_us(); + EXPECT_EQ(1, run_time_us.min()); + EXPECT_FLOAT_EQ(1.5, run_time_us.avg()); +} + +TEST(StatsCalculatorTest, AddNodeStatsUpdate) { + auto options = StatSummarizerOptions(); + StatsCalculator calc(options); + EXPECT_TRUE(calc.GetDetails().empty()); + + const int64_t node1_run_order = 1; + const int64_t run1_start_us = 1; + const int64_t run1_end_us = 2; + const int64_t run1_mem_used = 45; + calc.AddNodeStats("node1", "type_1", node1_run_order, run1_start_us, + run1_end_us, run1_mem_used); + ASSERT_EQ(1, calc.GetDetails().size()); + const Detail& detail = calc.GetDetails().at("node1"); + EXPECT_EQ(1, detail.times_called); + EXPECT_EQ("node1", detail.name); + EXPECT_EQ("type_1", detail.type); + EXPECT_EQ(node1_run_order, detail.run_order); + + const int64_t run2_start_us = 3; + const int64_t run2_end_us = 5; + const int64_t run2_mem_used = 145; + calc.AddNodeStats("node1", "type_1", node1_run_order, run2_start_us, + run2_end_us, run2_mem_used); + EXPECT_EQ(1, calc.GetDetails().size()); + + EXPECT_EQ(2, detail.times_called); + EXPECT_EQ("node1", detail.name); + EXPECT_EQ("type_1", detail.type); + EXPECT_EQ(node1_run_order, detail.run_order); + + EXPECT_EQ(run1_start_us + run2_start_us, detail.start_us.sum()); + EXPECT_EQ(run1_end_us + run2_end_us, detail.rel_end_us.sum()); + EXPECT_EQ(run1_mem_used + run2_mem_used, detail.mem_used.sum()); +} + +} // namespace +} // namespace tensorflow -- GitLab From 62a10974897c3cdc929a079f389f6770c767377a Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Fri, 20 Jul 2018 15:15:44 -0700 Subject: [PATCH 216/519] [XLA] Make ClientLibraryTestBase::AddParam work with the reference backend. Previously, AddParam only worked with the "real" backend -- we'd never pass the parameters to the reference backend, so it would always fail. PiperOrigin-RevId: 205461805 --- .../xla/tests/client_library_test_base.cc | 64 +++++++++++++++---- .../xla/tests/client_library_test_base.h | 6 +- 2 files changed, 55 insertions(+), 15 deletions(-) diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc index ef784da457..7a2e70d39f 100644 --- a/tensorflow/compiler/xla/tests/client_library_test_base.cc +++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc @@ -273,10 +273,16 @@ Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus( const Shape* shape_with_layout) { std::vector arguments(arguments_passed_in.begin(), arguments_passed_in.end()); + + // Transfer and use elements of arguments_, if the AddParam() API was used. + std::vector> owning_arguments; if (!arguments_.empty()) { CHECK(arguments.empty()); for (const auto& argument : arguments_) { - arguments.push_back(argument.get()); + owning_arguments.push_back( + client_->TransferToServer(MaybeConvertLiteralToBfloat16(argument)) + .ValueOrDie()); + arguments.push_back(owning_arguments.back().get()); } } @@ -331,10 +337,16 @@ Status ClientLibraryTestBase::ComputeAndCompareLiteralWithStatus( ErrorSpec error, const Shape* shape_with_layout) { std::vector arguments(arguments_passed_in.begin(), arguments_passed_in.end()); + + // Transfer and use elements of arguments_, if the AddParam() API was used. + std::vector> owning_arguments; if (!arguments_.empty()) { CHECK(arguments.empty()); for (const auto& argument : arguments_) { - arguments.push_back(argument.get()); + owning_arguments.push_back( + client_->TransferToServer(MaybeConvertLiteralToBfloat16(argument)) + .ValueOrDie()); + arguments.push_back(owning_arguments.back().get()); } } @@ -454,6 +466,14 @@ ClientLibraryTestBase::ComputeValueAndReference( // function. std::vector> argument_data; std::vector> ref_argument_data; + + // Use `arguments_` if the AddParam() API was used. Otherwise, use + // plain `arguments`. + if (!arguments_.empty()) { + CHECK_EQ(arguments.size(), 0); + arguments = arguments_; + } + for (const auto& arg : arguments) { TF_ASSIGN_OR_RETURN(auto data, client_->TransferToServer(arg.Clone())); TF_ASSIGN_OR_RETURN(auto ref_data, ref_client_->TransferToServer(arg)); @@ -552,10 +572,9 @@ ClientLibraryTestBase::CreatePatternedMatrixWithZeroPadding(int rows, int cols, XlaOp ClientLibraryTestBase::AddParam(const Literal& argument, XlaBuilder* builder) { - XlaOp data_handle; - arguments_.push_back(CreateParameterAndTransferLiteral( - arguments_.size(), argument, "", builder, &data_handle)); - return data_handle; + arguments_.push_back(argument.Clone()); + return Parameter(builder, /*parameter_number=*/arguments_.size() - 1, + MaybeConvertShapeToBfloat16(argument.shape()), ""); } XlaOp ClientLibraryTestBase::CreateConstantFromLiteral(const Literal& literal, @@ -575,22 +594,39 @@ ClientLibraryTestBase::CreateParameterAndTransferLiteral(int64 parameter_number, nullptr, builder, data_handle); } +Shape ClientLibraryTestBase::MaybeConvertShapeToBfloat16(const Shape& shape) { + if (!use_bfloat16_) { + return shape; + } + Shape new_shape = shape; + ShapeUtil::ForEachMutableSubshape(&new_shape, + [](Shape* subshape, const ShapeIndex&) { + if (subshape->element_type() == F32) { + subshape->set_element_type(BF16); + } + }); + return new_shape; +} + +Literal ClientLibraryTestBase::MaybeConvertLiteralToBfloat16( + const Literal& literal) { + if (use_bfloat16_) { + return std::move(*LiteralUtil::ConvertF32ToBF16(literal)); + } + return literal.Clone(); +} + std::unique_ptr ClientLibraryTestBase::CreateParameterAndTransferLiteral( int64 parameter_number, const Literal& literal, const string& name, const DeviceHandle* device_handle, XlaBuilder* builder, XlaOp* data_handle) { - const Literal* param_literal = &literal; - std::unique_ptr converted_literal; - if (use_bfloat16_) { - converted_literal = LiteralUtil::ConvertF32ToBF16(literal); - param_literal = converted_literal.get(); - } + Literal param_literal = MaybeConvertLiteralToBfloat16(literal); std::unique_ptr data = - client_->TransferToServer(*param_literal, device_handle) + client_->TransferToServer(param_literal, device_handle) .ConsumeValueOrDie(); *data_handle = - Parameter(builder, parameter_number, param_literal->shape(), name); + Parameter(builder, parameter_number, param_literal.shape(), name); return data; } diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h index fcc9347db5..f0f7ff1ea0 100644 --- a/tensorflow/compiler/xla/tests/client_library_test_base.h +++ b/tensorflow/compiler/xla/tests/client_library_test_base.h @@ -399,12 +399,16 @@ class ClientLibraryTestBase : public ::testing::Test { const string& error_message)>& verify_output, const Shape* output_with_layout = nullptr); + // Converts an f32 shape/literal to bf16 if use_bfloat16_ is true. + Literal MaybeConvertLiteralToBfloat16(const Literal& literal); + Shape MaybeConvertShapeToBfloat16(const Shape& shape); + // Whether to run tests with all float-type input/output converted to // bfloat16. bool use_bfloat16_ = false; // Arguments to be passed to the computation when it runs. - std::vector> arguments_; + std::vector arguments_; }; template -- GitLab From ee851755a687ec126280bb19e9c9b892b36e58a3 Mon Sep 17 00:00:00 2001 From: Jiri Simsa Date: Fri, 20 Jul 2018 15:26:00 -0700 Subject: [PATCH 217/519] Adding descriptor source test. PiperOrigin-RevId: 205463246 --- tensorflow/contrib/proto/BUILD | 12 -- .../contrib/proto/python/kernel_tests/BUILD | 27 +++ .../kernel_tests/descriptor_source_test.py | 36 ++++ .../descriptor_source_test_base.py | 176 ++++++++++++++++++ tensorflow/tools/pip_package/BUILD | 2 +- 5 files changed, 240 insertions(+), 13 deletions(-) create mode 100644 tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test.py create mode 100644 tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test_base.py diff --git a/tensorflow/contrib/proto/BUILD b/tensorflow/contrib/proto/BUILD index d45622174f..b27142cf4a 100644 --- a/tensorflow/contrib/proto/BUILD +++ b/tensorflow/contrib/proto/BUILD @@ -16,15 +16,3 @@ py_library( "//tensorflow/contrib/proto/python/ops:encode_proto_op_py", ], ) - -py_library( - name = "proto_pip", - data = if_static( - [], - otherwise = ["//tensorflow/contrib/proto/python/kernel_tests:libtestexample.so"], - ), - deps = [ - ":proto", - "//tensorflow/contrib/proto/python/kernel_tests:py_test_deps", - ], -) diff --git a/tensorflow/contrib/proto/python/kernel_tests/BUILD b/tensorflow/contrib/proto/python/kernel_tests/BUILD index 3c6fde23d2..125c1cee29 100644 --- a/tensorflow/contrib/proto/python/kernel_tests/BUILD +++ b/tensorflow/contrib/proto/python/kernel_tests/BUILD @@ -100,3 +100,30 @@ tf_cc_shared_object( ":test_example_proto_cc", ], ) + +py_library( + name = "descriptor_source_test_base", + testonly = 1, + srcs = ["descriptor_source_test_base.py"], + deps = [ + ":proto_op_test_base", + "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", + "@protobuf_archive//:protobuf_python", + ], +) + +tf_py_test( + name = "descriptor_source_test", + size = "small", + srcs = ["descriptor_source_test.py"], + additional_deps = [ + ":descriptor_source_test_base", + "//tensorflow/contrib/proto/python/ops:decode_proto_op_py", + "//tensorflow/contrib/proto/python/ops:encode_proto_op_py", + "//tensorflow/python:client_testlib", + ], + tags = [ + "no_pip", + ], +) diff --git a/tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test.py b/tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test.py new file mode 100644 index 0000000000..32ca318f73 --- /dev/null +++ b/tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test.py @@ -0,0 +1,36 @@ +# ============================================================================= +# 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 proto ops reading descriptors from other sources.""" +# Python3 preparedness imports. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.proto.python.kernel_tests import descriptor_source_test_base as test_base +from tensorflow.contrib.proto.python.ops import decode_proto_op +from tensorflow.contrib.proto.python.ops import encode_proto_op +from tensorflow.python.platform import test + + +class DescriptorSourceTest(test_base.DescriptorSourceTestBase): + + def __init__(self, methodName='runTest'): # pylint: disable=invalid-name + super(DescriptorSourceTest, self).__init__(decode_proto_op, encode_proto_op, + methodName) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test_base.py b/tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test_base.py new file mode 100644 index 0000000000..9a1c04af32 --- /dev/null +++ b/tensorflow/contrib/proto/python/kernel_tests/descriptor_source_test_base.py @@ -0,0 +1,176 @@ +# ============================================================================= +# 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 proto ops reading descriptors from other sources.""" +# Python3 preparedness imports. +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +import numpy as np + +from google.protobuf.descriptor_pb2 import FieldDescriptorProto +from google.protobuf.descriptor_pb2 import FileDescriptorSet +from tensorflow.contrib.proto.python.kernel_tests import proto_op_test_base as test_base +from tensorflow.python.framework import dtypes +from tensorflow.python.platform import test + + +class DescriptorSourceTestBase(test.TestCase): + """Base class for testing descriptor sources.""" + + def __init__(self, decode_module, encode_module, methodName='runTest'): # pylint: disable=invalid-name + """DescriptorSourceTestBase initializer. + + Args: + decode_module: a module containing the `decode_proto_op` method + encode_module: a module containing the `encode_proto_op` method + methodName: the name of the test method (same as for test.TestCase) + """ + + super(DescriptorSourceTestBase, self).__init__(methodName) + self._decode_module = decode_module + self._encode_module = encode_module + + # NOTE: We generate the descriptor programmatically instead of via a compiler + # because of differences between different versions of the compiler. + # + # The generated descriptor should capture the subset of `test_example.proto` + # used in `test_base.simple_test_case()`. + def _createDescriptorFile(self): + set_proto = FileDescriptorSet() + + file_proto = set_proto.file.add( + name='types.proto', + package='tensorflow', + syntax='proto3') + enum_proto = file_proto.enum_type.add(name='DataType') + enum_proto.value.add(name='DT_DOUBLE', number=0) + enum_proto.value.add(name='DT_BOOL', number=1) + + file_proto = set_proto.file.add( + name='test_example.proto', + package='tensorflow.contrib.proto', + dependency=['types.proto']) + message_proto = file_proto.message_type.add(name='TestCase') + message_proto.field.add( + name='values', + number=1, + type=FieldDescriptorProto.TYPE_MESSAGE, + type_name='.tensorflow.contrib.proto.TestValue', + label=FieldDescriptorProto.LABEL_REPEATED) + message_proto.field.add( + name='shapes', + number=2, + type=FieldDescriptorProto.TYPE_INT32, + label=FieldDescriptorProto.LABEL_REPEATED) + message_proto.field.add( + name='sizes', + number=3, + type=FieldDescriptorProto.TYPE_INT32, + label=FieldDescriptorProto.LABEL_REPEATED) + message_proto.field.add( + name='fields', + number=4, + type=FieldDescriptorProto.TYPE_MESSAGE, + type_name='.tensorflow.contrib.proto.FieldSpec', + label=FieldDescriptorProto.LABEL_REPEATED) + + message_proto = file_proto.message_type.add( + name='TestValue') + message_proto.field.add( + name='double_value', + number=1, + type=FieldDescriptorProto.TYPE_DOUBLE, + label=FieldDescriptorProto.LABEL_REPEATED) + message_proto.field.add( + name='bool_value', + number=2, + type=FieldDescriptorProto.TYPE_BOOL, + label=FieldDescriptorProto.LABEL_REPEATED) + + message_proto = file_proto.message_type.add( + name='FieldSpec') + message_proto.field.add( + name='name', + number=1, + type=FieldDescriptorProto.TYPE_STRING, + label=FieldDescriptorProto.LABEL_OPTIONAL) + message_proto.field.add( + name='dtype', + number=2, + type=FieldDescriptorProto.TYPE_ENUM, + type_name='.tensorflow.DataType', + label=FieldDescriptorProto.LABEL_OPTIONAL) + message_proto.field.add( + name='value', + number=3, + type=FieldDescriptorProto.TYPE_MESSAGE, + type_name='.tensorflow.contrib.proto.TestValue', + label=FieldDescriptorProto.LABEL_OPTIONAL) + + fn = os.path.join(self.get_temp_dir(), 'descriptor.pb') + with open(fn, 'wb') as f: + f.write(set_proto.SerializeToString()) + return fn + + def _testRoundtrip(self, descriptor_source): + # Numpy silently truncates the strings if you don't specify dtype=object. + in_bufs = np.array( + [test_base.ProtoOpTestBase.simple_test_case().SerializeToString()], + dtype=object) + message_type = 'tensorflow.contrib.proto.TestCase' + field_names = ['values', 'shapes', 'sizes', 'fields'] + tensor_types = [dtypes.string, dtypes.int32, dtypes.int32, dtypes.string] + + with self.test_session() as sess: + sizes, field_tensors = self._decode_module.decode_proto( + in_bufs, + message_type=message_type, + field_names=field_names, + output_types=tensor_types, + descriptor_source=descriptor_source) + + out_tensors = self._encode_module.encode_proto( + sizes, + field_tensors, + message_type=message_type, + field_names=field_names, + descriptor_source=descriptor_source) + + out_bufs, = sess.run([out_tensors]) + + # Check that the re-encoded tensor has the same shape. + self.assertEqual(in_bufs.shape, out_bufs.shape) + + # Compare the input and output. + for in_buf, out_buf in zip(in_bufs.flat, out_bufs.flat): + # Check that the input and output serialized messages are identical. + # If we fail here, there is a difference in the serialized + # representation but the new serialization still parses. This could + # be harmless (a change in map ordering?) or it could be bad (e.g. + # loss of packing in the encoding). + self.assertEqual(in_buf, out_buf) + + def testWithFileDescriptorSet(self): + # First try parsing with a local proto db, which should fail. + with self.assertRaisesOpError('No descriptor found for message type'): + self._testRoundtrip('local://') + + # Now try parsing with a FileDescriptorSet which contains the test proto. + descriptor_file = self._createDescriptorFile() + self._testRoundtrip(descriptor_file) diff --git a/tensorflow/tools/pip_package/BUILD b/tensorflow/tools/pip_package/BUILD index e661fb1adc..ab39ed8d69 100644 --- a/tensorflow/tools/pip_package/BUILD +++ b/tensorflow/tools/pip_package/BUILD @@ -78,7 +78,7 @@ COMMON_PIP_DEPS = [ "//tensorflow/contrib/labeled_tensor:labeled_tensor_pip", "//tensorflow/contrib/nn:nn_py", "//tensorflow/contrib/predictor:predictor_pip", - "//tensorflow/contrib/proto:proto_pip", + "//tensorflow/contrib/proto:proto", "//tensorflow/contrib/receptive_field:receptive_field_pip", "//tensorflow/contrib/rpc:rpc_pip", "//tensorflow/contrib/session_bundle:session_bundle_pip", -- GitLab From 8be889a3034b4dd5ea46330ffad185fc91901723 Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Fri, 20 Jul 2018 15:30:19 -0700 Subject: [PATCH 218/519] Comment about TfLiteDelegateParams PiperOrigin-RevId: 205463881 --- tensorflow/contrib/lite/context.h | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h index 1ff8843fa7..cbfce12d7e 100644 --- a/tensorflow/contrib/lite/context.h +++ b/tensorflow/contrib/lite/context.h @@ -464,6 +464,12 @@ typedef struct _TfLiteDelegate { } TfLiteDelegate; // WARNING: This is an experimental interface that is subject to change. +// +// Currently, TfLiteDelegateParams has to be allocated in a way that it's +// trivially destructable. It will be stored as `builtin_data` field in +// `TfLiteNode` of the delegate node. +// +// See also the `CreateDelegateParams` function in `interpreter.cc` details. typedef struct { TfLiteDelegate* delegate; TfLiteIntArray* nodes_to_replace; -- GitLab From f4f37efdc95adc4b2c6235479b89ddfbaf4b3eed Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 15:41:04 -0700 Subject: [PATCH 219/519] Update Grappler to use existing functions for retrieving a node's name and position. PiperOrigin-RevId: 205465354 --- .../core/grappler/costs/graph_properties.cc | 18 +- .../grappler/costs/graph_properties_test.cc | 38 +++ .../function_functional_while.pbtxt | 239 ++++++++++++++++++ 3 files changed, 285 insertions(+), 10 deletions(-) create mode 100644 tensorflow/core/grappler/costs/graph_properties_testdata/function_functional_while.pbtxt diff --git a/tensorflow/core/grappler/costs/graph_properties.cc b/tensorflow/core/grappler/costs/graph_properties.cc index 83a8326e79..231c7c63be 100644 --- a/tensorflow/core/grappler/costs/graph_properties.cc +++ b/tensorflow/core/grappler/costs/graph_properties.cc @@ -496,18 +496,11 @@ class SymbolicShapeRefiner { "supported."); } + // It is guaranteed that output_tensors does not contain any control + // inputs, so port_id >= 0. string out_tensor = out_arg.output_tensors[0]; - auto out_tensor_pieces = str_util::Split(out_tensor, ","); - string node_name = out_tensor_pieces[0]; int port_id; - - // Check if port_id was included in out_tensor - if (out_tensor_pieces.size() <= 1) { - port_id = 0; - } else if (!strings::safe_strto32(out_tensor_pieces[1], &port_id)) { - return errors::FailedPrecondition( - "Failed string to integer conversion for ", out_tensor_pieces[1]); - } + string node_name = ParseNodeName(out_tensor, &port_id); const NodeDef* retnode = gv.GetNode(node_name); if (retnode == nullptr) { @@ -516,6 +509,11 @@ class SymbolicShapeRefiner { } auto output_properties = gp.GetOutputProperties(retnode->name()); + if (port_id >= output_properties.size()) { + return errors::InvalidArgument( + out_tensor, " has invalid position ", port_id, + " (output_properties.size() = ", output_properties.size(), ")."); + } auto const& outprop = output_properties[port_id]; const TensorShapeProto& shape = outprop.shape(); ShapeHandle out; diff --git a/tensorflow/core/grappler/costs/graph_properties_test.cc b/tensorflow/core/grappler/costs/graph_properties_test.cc index 1be19d291a..5acfb56b05 100644 --- a/tensorflow/core/grappler/costs/graph_properties_test.cc +++ b/tensorflow/core/grappler/costs/graph_properties_test.cc @@ -887,6 +887,44 @@ TEST_F(GraphPropertiesTest, LargeFunctionStaticShapeInference) { EXPECT_EQ(8, in_prop3.shape().dim(3).size()); } +TEST_F(GraphPropertiesTest, LargeFunctionWithMultipleOutputs) { + // Test graph produced in python using: + /* + @function.Defun(noinline=True) + def MyFunc(): + @function.Defun(*[tf.float32] * 2) + def Cond(n, unused_x): + return n > 0 + + @function.Defun(*[tf.float32] * 2) + def Body(n, x): + return n - 1, x + n + + i = tf.constant(10) + return functional_ops.While([i, 0.], Cond, Body) + + with tf.Graph().as_default(): + z = MyFunc() + */ + GrapplerItem item; + string filename = io::JoinPath(testing::TensorFlowSrcRoot(), kTestDataPath, + "function_functional_while.pbtxt"); + TF_CHECK_OK(ReadGraphDefFromFile(filename, &item.graph)); + GraphProperties properties(item); + TF_CHECK_OK(properties.InferStatically(false)); + + const auto out_props = properties.GetOutputProperties("MyFunc_AenMyWWx1Us"); + EXPECT_EQ(2, out_props.size()); + + const OpInfo::TensorProperties& out_prop0 = out_props[0]; + EXPECT_EQ(DT_INT32, out_prop0.dtype()); + EXPECT_FALSE(out_prop0.shape().unknown_rank()); + + const OpInfo::TensorProperties& out_prop1 = out_props[1]; + EXPECT_EQ(DT_FLOAT, out_prop1.dtype()); + EXPECT_FALSE(out_prop1.shape().unknown_rank()); +} + TEST_F(GraphPropertiesTest, FunctionWithErrorStaticShapeInference) { GrapplerItem item; string filename = io::JoinPath(testing::TensorFlowSrcRoot(), kTestDataPath, diff --git a/tensorflow/core/grappler/costs/graph_properties_testdata/function_functional_while.pbtxt b/tensorflow/core/grappler/costs/graph_properties_testdata/function_functional_while.pbtxt new file mode 100644 index 0000000000..c94ee2f227 --- /dev/null +++ b/tensorflow/core/grappler/costs/graph_properties_testdata/function_functional_while.pbtxt @@ -0,0 +1,239 @@ +node { + name: "MyFunc_AenMyWWx1Us" + op: "MyFunc_AenMyWWx1Us" +} +library { + function { + signature { + name: "MyFunc_AenMyWWx1Us" + output_arg { + name: "while" + type: DT_INT32 + } + output_arg { + name: "while_0" + type: DT_FLOAT + } + is_stateful: true + } + node_def { + name: "Const" + op: "Const" + attr { + key: "dtype" + value { + type: DT_INT32 + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_INT32 + tensor_shape { + } + int_val: 10 + } + } + } + } + node_def { + name: "While/input_1" + op: "Const" + attr { + key: "dtype" + value { + type: DT_FLOAT + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_FLOAT + tensor_shape { + } + float_val: 0.0 + } + } + } + } + node_def { + name: "While" + op: "While" + input: "Const:output:0" + input: "While/input_1:output:0" + attr { + key: "T" + value { + list { + type: DT_INT32 + type: DT_FLOAT + } + } + } + attr { + key: "body" + value { + func { + name: "Body_8GOMGeZeK5c" + } + } + } + attr { + key: "cond" + value { + func { + name: "Cond_Xf5ttAHgUCg" + } + } + } + } + ret { + key: "while" + value: "While:output:0" + } + ret { + key: "while_0" + value: "While:output:1" + } + attr { + key: "_noinline" + value { + b: true + } + } + } + function { + signature { + name: "Body_8GOMGeZeK5c" + input_arg { + name: "n" + type: DT_FLOAT + } + input_arg { + name: "x" + type: DT_FLOAT + } + output_arg { + name: "sub" + type: DT_FLOAT + } + output_arg { + name: "add" + type: DT_FLOAT + } + } + node_def { + name: "sub/y" + op: "Const" + attr { + key: "dtype" + value { + type: DT_FLOAT + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_FLOAT + tensor_shape { + } + float_val: 1.0 + } + } + } + } + node_def { + name: "sub_0" + op: "Sub" + input: "n" + input: "sub/y:output:0" + attr { + key: "T" + value { + type: DT_FLOAT + } + } + } + node_def { + name: "add_0" + op: "Add" + input: "x" + input: "n" + attr { + key: "T" + value { + type: DT_FLOAT + } + } + } + ret { + key: "add" + value: "add_0:z:0" + } + ret { + key: "sub" + value: "sub_0:z:0" + } + } + function { + signature { + name: "Cond_Xf5ttAHgUCg" + input_arg { + name: "n" + type: DT_FLOAT + } + input_arg { + name: "unused_x" + type: DT_FLOAT + } + output_arg { + name: "greater" + type: DT_BOOL + } + } + node_def { + name: "Greater/y" + op: "Const" + attr { + key: "dtype" + value { + type: DT_FLOAT + } + } + attr { + key: "value" + value { + tensor { + dtype: DT_FLOAT + tensor_shape { + } + float_val: 0.0 + } + } + } + } + node_def { + name: "Greater" + op: "Greater" + input: "n" + input: "Greater/y:output:0" + attr { + key: "T" + value { + type: DT_FLOAT + } + } + } + ret { + key: "greater" + value: "Greater:z:0" + } + } +} +versions { + producer: 26 + min_consumer: 12 +} -- GitLab From 5e876a8c25819070d78aa96595943afa207a6671 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Fri, 20 Jul 2018 15:41:36 -0700 Subject: [PATCH 220/519] [XLA:GPU] Limit the number of shmem tiles XLA:GPU will use for 021 transposes. There's a limit to how much shared memory we can use. PiperOrigin-RevId: 205465441 --- .../xla/service/gpu/ir_emitter_unnested.cc | 34 ++++++++++++++++ tensorflow/compiler/xla/tests/fusion_test.cc | 40 +++++++++++++++++++ 2 files changed, 74 insertions(+) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 7100c9a08a..b3229303df 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -3243,6 +3243,40 @@ bool IrEmitterUnnested::CheckAndEmitHloWithTile021(HloInstruction* hlo) { return false; } + // Each of our shared memory tiles has 32*33 elements (so ~4kb, if the + // elements are of size 4 bytes), and CUDA has an architectural limit of 48kb + // shared memory per SM. (This is increased to 96kb in Volta, but we don't + // use this, in part because it eats into our L1 cache space.) + // + // For correctness we need to ensure that we don't make more than 48kb worth + // of shmem tiles per block. And for performance, we'd probably like to use + // significantly less, so that we can fit more than one block at a time on a + // gpu core. + // + // We say without benchmarks that we want at least 3 threads/block, + // corresponding to 3 shmem tiles if the elements are 32 bits wide. We choose + // which params get the shmem transpose treatment arbitrarily; it's not clear + // if there's a Right Choice. + // + // This is only sound if tiled transposes are the only place where we use + // shared memory in fusions. If in the future other fusile ops use shared + // memory, we'll have to adjust this heuristic. + constexpr int kMinBlocksPerCore = 3; + constexpr int64 kShmemPerCore = 48 * 1024; + int64 shmem_used = 0; + for (int64 i = 0; i < params_012.size(); ++i) { + const HloInstruction* operand = hlo->operand(params_012[i]); + shmem_used += + 32 * 33 * + ShapeUtil::ByteSizeOfPrimitiveType(operand->shape().element_type()); + + if (kMinBlocksPerCore * shmem_used > kShmemPerCore) { + // Erase this element and everything after it from params_012. + params_012.resize(i); + break; + } + } + VLOG(3) << "EmitHlo021Tile Emitting hlo tile 0-2-1" << hlo->ToString(); thunk_sequence_->emplace_back( BuildKernelThunk(hlo, /*implements_whole_instruction=*/true)); diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc index dc64477935..607bcdd51e 100644 --- a/tensorflow/compiler/xla/tests/fusion_test.cc +++ b/tensorflow/compiler/xla/tests/fusion_test.cc @@ -799,6 +799,46 @@ ENTRY main { *result)); } +class FusionClientLibraryTest : public ClientLibraryTestBase {}; + +XLA_TEST_F(FusionClientLibraryTest, ManyLayoutTransformations) { + // On the GPU backend, it's possible to have too many transposes within one + // fusion, causing the kernel to run out shared memory and thus not compile. + // We want to check that doesn't happen. + // + // To do this, we create a computation that computes + // + // P0 + P0*P1*P1 + P0*P2*P2 ... + // + // where even parameters have layout 1 and odd parameters have layout 2. + // + // Our goal is to tempt the backend into creating one giant multi-output + // fusion for the whole computation, including the transposes. Currently + // multi-output fusion only fuses fusions, so each of the terms in the sum + // needs to be a fusion itself, thus the contortions above. + constexpr int kNumParams = 25; + XlaBuilder b("ManyLayoutTransformations"); + + // This test produces values that overflow int32, which is UB, so use uint32, + // where overflow is OK. + Array2D arr(32, 32); + arr.FillUnique(); + std::unique_ptr l1 = LiteralUtil::CreateR2FromArray2D(arr)->Relayout( + LayoutUtil::MakeLayout({0, 1})); + + std::unique_ptr l2 = LiteralUtil::CreateR2FromArray2D(arr)->Relayout( + LayoutUtil::MakeLayout({1, 0})); + + XlaOp p0 = AddParam(*l1, &b); + XlaOp sum = p0; + for (int i = 1; i < kNumParams; ++i) { + auto pN = AddParam((i % 2 == 0 ? *l1 : *l2), &b); + sum = sum + p0 * pN * pN; + } + + ComputeAndCompare(&b, {}); +} + void BM_ParallelFusion(int num_iters) { // Simple element-wise computation to benchmark parallel task partitioning. tensorflow::testing::StopTiming(); -- GitLab From 6c528feaf820bdde820833ad24e05167adb5daa7 Mon Sep 17 00:00:00 2001 From: Katherine Wu Date: Fri, 20 Jul 2018 15:45:15 -0700 Subject: [PATCH 221/519] Automated rollback of commit 8257891f378027a1a7c0403ba6ba0aeb313496a0 PiperOrigin-RevId: 205466000 --- tensorflow/contrib/estimator/BUILD | 41 -- tensorflow/contrib/estimator/__init__.py | 5 - .../python/estimator/saved_model_estimator.py | 445 ------------------ .../estimator/saved_model_estimator_test.py | 369 --------------- tensorflow/python/estimator/estimator.py | 62 +-- tensorflow/python/framework/importer.py | 2 +- tensorflow/python/framework/meta_graph.py | 68 +-- tensorflow/python/saved_model/loader_impl.py | 13 +- tensorflow/python/saved_model/loader_test.py | 19 +- tensorflow/python/training/saver.py | 28 +- 10 files changed, 35 insertions(+), 1017 deletions(-) delete mode 100644 tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py delete mode 100644 tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD index 349f48f7f7..1aa3df8d8d 100644 --- a/tensorflow/contrib/estimator/BUILD +++ b/tensorflow/contrib/estimator/BUILD @@ -28,7 +28,6 @@ py_library( ":multi_head", ":replicate_model_fn", ":rnn", - ":saved_model_estimator", "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -466,43 +465,3 @@ py_test( "@absl_py//absl/testing:parameterized", ], ) - -py_library( - name = "saved_model_estimator", - srcs = ["python/estimator/saved_model_estimator.py"], - deps = [ - ":export", - "//tensorflow/python:framework_ops", - "//tensorflow/python:platform", - "//tensorflow/python:training", - "//tensorflow/python/estimator", - "//tensorflow/python/estimator:export", - "//tensorflow/python/estimator:model_fn", - "//tensorflow/python/saved_model", - ], -) - -py_test( - name = "saved_model_estimator_test", - size = "medium", - srcs = ["python/estimator/saved_model_estimator_test.py"], - srcs_version = "PY2AND3", - deps = [ - ":export", - ":saved_model_estimator", - "//tensorflow/python:array_ops", - "//tensorflow/python:client_testlib", - "//tensorflow/python:control_flow_ops", - "//tensorflow/python:framework_ops", - "//tensorflow/python:metrics", - "//tensorflow/python:platform", - "//tensorflow/python:state_ops", - "//tensorflow/python:training", - "//tensorflow/python:variables", - "//tensorflow/python/data/ops:dataset_ops", - "//tensorflow/python/estimator", - "//tensorflow/python/estimator:export_export", - "//tensorflow/python/estimator:export_output", - "//tensorflow/python/estimator:model_fn", - ], -) diff --git a/tensorflow/contrib/estimator/__init__.py b/tensorflow/contrib/estimator/__init__.py index e1453ae1d0..09fcfd66a1 100644 --- a/tensorflow/contrib/estimator/__init__.py +++ b/tensorflow/contrib/estimator/__init__.py @@ -33,8 +33,6 @@ from tensorflow.contrib.estimator.python.estimator.logit_fns import * from tensorflow.contrib.estimator.python.estimator.multi_head import * from tensorflow.contrib.estimator.python.estimator.replicate_model_fn import * from tensorflow.contrib.estimator.python.estimator.rnn import * -from tensorflow.contrib.estimator.python.estimator.saved_model_estimator import * -from tensorflow.python.estimator.export.export import * from tensorflow.python.util.all_util import remove_undocumented # pylint: enable=unused-import,line-too-long,wildcard-import @@ -72,9 +70,6 @@ _allowed_symbols = [ 'stop_if_higher_hook', 'stop_if_no_increase_hook', 'stop_if_no_decrease_hook', - 'build_raw_supervised_input_receiver_fn', - 'build_supervised_input_receiver_fn_from_input_fn', - 'SavedModelEstimator' ] remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py deleted file mode 100644 index 22188fe663..0000000000 --- a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py +++ /dev/null @@ -1,445 +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. -# ============================================================================== -"""Class that creates an Estimator from a SavedModel.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import six - -from tensorflow.python.estimator import estimator as estimator_lib -from tensorflow.python.estimator import model_fn as model_fn_lib -from tensorflow.python.estimator.export import export as export_lib -from tensorflow.python.estimator.export import export_output -from tensorflow.python.framework import dtypes -from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape -from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.saved_model import constants -from tensorflow.python.saved_model import loader_impl -from tensorflow.python.saved_model import signature_constants -from tensorflow.python.training import checkpoint_utils -from tensorflow.python.training import monitored_session -from tensorflow.python.training import training_util - - -class SavedModelEstimator(estimator_lib.Estimator): - """Create an Estimator from a SavedModel. - - Only SavedModels exported with - `tf.contrib.estimator.export_all_saved_models()` or - `tf.estimator.Estimator.export_savedmodel()` are supported for this class. - - Example with `tf.estimator.DNNClassifier`: - - **Step 1: Create and train DNNClassifier.** - ```python - feature1 = tf.feature_column.embedding_column( - tf.feature_column.categorical_column_with_vocabulary_list( - key='feature1', vocabulary_list=('green', 'yellow')), dimension=1) - feature2 = tf.feature_column.numeric_column(key='feature2', default_value=0.0) - - classifier = tf.estimator.DNNClassifier( - hidden_units=[4,2], feature_columns=[feature1, feature2]) - - def input_fn(): - features = {'feature1': tf.constant(['green', 'green', 'yellow']), - 'feature2': tf.constant([3.5, 4.2, 6.1])} - label = tf.constant([1., 0., 0.]) - return tf.data.Dataset.from_tensors((features, label)).repeat() - - classifier.train(input_fn=input_fn, steps=10) - ``` - - **Step 2: Export classifier.** - First, build functions that specify the expected inputs. - ```python - # During train and evaluation, both the features and labels should be defined. - supervised_input_receiver_fn = ( - tf.contrib.estimator.build_raw_supervised_input_receiver_fn( - {'feature1': tf.placeholder(dtype=tf.string, shape=[None]), - 'feature2': tf.placeholder(dtype=tf.float32, shape=[None])}, - tf.placeholder(dtype=tf.float32, shape=[None]))) - - # During predict mode, expect to receive a `tf.Example` proto, so a parsing - # function is used. - serving_input_receiver_fn = ( - tf.estimator.export.build_parsing_serving_input_receiver_fn( - tf.feature_column.make_parse_example_spec([feature1, feature2]))) - ``` - - Next, export the model as a SavedModel. A timestamped directory will be - created (for example `/tmp/export_all/1234567890`). - ```python - # Option 1: Save all modes (train, eval, predict) - export_dir = tf.contrib.estimator.export_all_saved_models( - classifier, '/tmp/export_all', - {tf.estimator.ModeKeys.TRAIN: supervised_input_receiver_fn, - tf.estimator.ModeKeys.EVAL: supervised_input_receiver_fn, - tf.estimator.ModeKeys.PREDICT: serving_input_receiver_fn}) - - # Option 2: Only export predict mode - export_dir = classifier.export_savedmodel( - '/tmp/export_predict', serving_input_receiver_fn) - ``` - - **Step 3: Create a SavedModelEstimator from the exported SavedModel.** - ```python - est = tf.contrib.estimator.SavedModelEstimator(export_dir) - - # If all modes were exported, you can immediately evaluate and predict, or - # continue training. Otherwise only predict is available. - eval_results = est.evaluate(input_fn=input_fn, steps=1) - print(eval_results) - - est.train(input_fn=input_fn, steps=20) - - def predict_input_fn(): - example = example_pb2.Example() - example.features.feature['feature1'].bytes_list.value.extend(['yellow']) - example.features.feature['feature2'].float_list.value.extend([1.]) - return {'inputs':tf.constant([example.SerializeToString()])} - - predictions = est.predict(predict_input_fn) - print(next(predictions)) - ``` - """ - - def __init__(self, saved_model_dir, model_dir=None): - """Initialize a SavedModelEstimator. - - The SavedModelEstimator loads its model function and variable values from - the graphs defined in the SavedModel. There is no option to pass in - `RunConfig` or `params` arguments, because the model function graph is - defined statically in the SavedModel. - - Args: - saved_model_dir: Directory containing SavedModel protobuf and subfolders. - model_dir: Directory to save new checkpoints during training. - - Raises: - NotImplementedError: If a DistributionStrategy is defined in the config. - Unless the SavedModelEstimator is subclassed, this shouldn't happen. - """ - checkpoint = estimator_lib._get_saved_model_ckpt(saved_model_dir) # pylint: disable=protected-access - vars_to_warm_start = [name for name, _ in - checkpoint_utils.list_variables(checkpoint)] - warm_start_settings = estimator_lib.WarmStartSettings( - ckpt_to_initialize_from=checkpoint, - vars_to_warm_start=vars_to_warm_start) - - super(SavedModelEstimator, self).__init__( - model_fn=self._model_fn_from_saved_model, model_dir=model_dir, - warm_start_from=warm_start_settings) - if self._distribution is not None: - raise NotImplementedError( - 'SavedModelEstimator currently does not support ' - 'DistributionStrategy.') - self.saved_model_dir = saved_model_dir - self.saved_model_loader = loader_impl.SavedModelLoader(saved_model_dir) - self._available_modes = self._extract_available_modes() - - def _extract_available_modes(self): - """Return list of modes found in SavedModel.""" - available_modes = [] - logging.info('Checking available modes for SavedModelEstimator.') - for mode in [model_fn_lib.ModeKeys.TRAIN, model_fn_lib.ModeKeys.EVAL, - model_fn_lib.ModeKeys.PREDICT]: - try: - self._get_meta_graph_def_for_mode(mode) - except RuntimeError: - logging.warning('%s mode not found in SavedModel.' % mode) - continue - - if self._get_signature_def_for_mode(mode) is not None: - available_modes.append(mode) - - logging.info('Available modes for Estimator: %s' % available_modes) - return available_modes - - def _validate_mode(self, mode): - """Make sure that mode can be run using the SavedModel.""" - if mode not in self._available_modes: - raise RuntimeError('%s mode is not available in the SavedModel. Use ' - 'saved_model_cli to check that the Metagraph for this ' - 'mode has been exported.' % mode) - - def _get_meta_graph_def_for_mode(self, mode): - tags = model_fn_lib.EXPORT_TAG_MAP[mode] - return self.saved_model_loader.get_meta_graph_def_from_tags(tags) - - def _get_signature_def_for_mode(self, mode): - meta_graph_def = self._get_meta_graph_def_for_mode(mode) - sig_def_key = (signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY - if mode == model_fn_lib.ModeKeys.PREDICT else mode) - if sig_def_key not in meta_graph_def.signature_def: - logging.warning('Metagraph for mode %s was found, but SignatureDef with' - ' key \"%s\" is missing.' % (mode, sig_def_key)) - return None - return meta_graph_def.signature_def[sig_def_key] - - def _create_and_assert_global_step(self, graph): - # Do nothing here. The global step variable will be created/loaded from the - # SavedModel. If a global step variable were created here, the result - # will be two duplicate global step variables, causing issues during - # the warm-start phase. - # Due to the global variable being created in the model function, this may - # cause issues when running DistributionStrategy. Thus, DistributionStrategy - # is not yet supported with SavedModelEstimator. - pass - - def _model_fn_from_saved_model(self, features, labels, mode): - """Load a SavedModel graph and return an EstimatorSpec.""" - # TODO(kathywu): Model function loads placeholders from the graph. Calling - # export_all_saved_models creates another placeholder for the inputs, on top - # of the original placeholders. There should be a way to avoid this. - self._validate_mode(mode) - - g = ops.get_default_graph() - if training_util.get_global_step(g) is not None: - raise RuntimeError( - 'Graph must not contain a global step tensor before the SavedModel is' - ' loaded. Please make sure that the input function does not create a ' - 'global step.') - - # Extract SignatureDef for information about the input and output tensors. - signature_def = self._get_signature_def_for_mode(mode) - - # Generate input map for replacing the inputs in the SavedModel graph with - # the provided features and labels. - input_map = _generate_input_map(signature_def, features, labels) - - # Create a list of the names of output tensors. When the graph is loaded, - # names of the output tensors may be remapped. This ensures that the correct - # tensors are returned in the EstimatorSpec. - output_tensor_names = [ - value.name for value in six.itervalues(signature_def.outputs)] - - # Load the graph. `output_tensors` contains output `Tensors` in the same - # same order as the `output_tensor_names` list. - tags = model_fn_lib.EXPORT_TAG_MAP[mode] - _, output_tensors = self.saved_model_loader.load_graph( - g, tags, input_map=input_map, return_elements=output_tensor_names) - - # Create a scaffold from the MetaGraphDef that contains ops to initialize - # the graph. This should mirror the steps from _add_meta_graph_for_mode(), - # which creates a MetaGraphDef from the EstimatorSpec's scaffold. - scaffold = monitored_session.Scaffold( - local_init_op=loader_impl._get_legacy_init_op_tensor( # pylint: disable=protected-access - self._get_meta_graph_def_for_mode(mode))) - - # Ensure that a global step tensor has been created. - global_step_tensor = training_util.get_global_step(g) - training_util.assert_global_step(global_step_tensor) - - # Extract values to return in the EstimatorSpec. - output_map = dict(zip(output_tensor_names, output_tensors)) - outputs = {key: output_map[value.name] - for key, value in six.iteritems(signature_def.outputs)} - - loss, predictions, metrics = _validate_and_extract_outputs( - mode, outputs, signature_def.method_name) - - train_op = ops.get_collection(constants.TRAIN_OP_KEY) - if len(train_op) > 1: - raise RuntimeError('Multiple ops found in the train_op collection.') - train_op = None if not train_op else train_op[0] - - _clear_saved_model_collections() - return model_fn_lib.EstimatorSpec( - scaffold=scaffold, - mode=mode, - loss=loss, - train_op=train_op, - predictions=predictions, - eval_metric_ops=metrics) - - -def _clear_saved_model_collections(): - """Clear collections that are expected empty when exporting a SavedModel. - - The SavedModel builder uses these collections to track ops necessary to - restore the graph state. These collections are expected to be empty before - MetaGraphs are added to the builder. - """ - del ops.get_collection_ref(constants.ASSETS_KEY)[:] - del ops.get_collection_ref(constants.LEGACY_INIT_OP_KEY)[:] - del ops.get_collection_ref(constants.MAIN_OP_KEY)[:] - del ops.get_collection_ref(constants.TRAIN_OP_KEY)[:] - - -def _generate_input_map(signature_def, features, labels): - """Return dict mapping an input tensor name to a feature or label tensor. - - Args: - signature_def: SignatureDef loaded from SavedModel - features: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or - `SparseTensor`, specifying the features to be passed to the model. - labels: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or - `SparseTensor`, specifying the labels to be passed to the model. May be - `None`. - - Returns: - dict mapping string names of inputs to features or labels tensors - - Raises: - ValueError: if SignatureDef inputs are not completely mapped by the input - features and labels. - """ - # pylint: disable=protected-access - if not isinstance(features, dict): - features = {export_lib._SINGLE_FEATURE_DEFAULT_NAME: features} - if labels is not None and not isinstance(labels, dict): - labels = {export_lib._SINGLE_LABEL_DEFAULT_NAME: labels} - # pylint: enable=protected-access - - inputs = signature_def.inputs - input_map = {} - for key, tensor_info in six.iteritems(inputs): - input_name = tensor_info.name - if ':' in input_name: - input_name = input_name[:input_name.find(':')] - - # When tensors are used as control inputs for operations, their names are - # prepended with a '^' character in the GraphDef. To handle possible control - # flow edge cases, control input names must be included in the input map. - control_dependency_name = '^' + input_name - - if key in features: - _check_same_dtype_and_shape(features[key], tensor_info, key) - input_map[input_name] = input_map[control_dependency_name] = features[key] - elif labels is not None and key in labels: - _check_same_dtype_and_shape(labels[key], tensor_info, key) - input_map[input_name] = input_map[control_dependency_name] = labels[key] - else: - raise ValueError( - 'Key \"%s\" not found in features or labels passed in to the model ' - 'function. All required keys: %s' % (key, inputs.keys())) - - return input_map - - -def _check_same_dtype_and_shape(tensor, tensor_info, name): - """Validate that tensor has the same properties as the TensorInfo proto. - - Args: - tensor: a `Tensor` object. - tensor_info: a `TensorInfo` proto. - name: Name of the input (to identify Tensor if an error is raised). - - Raises: - ValueError: If the tensor shape or dtype don't match the TensorInfo - """ - dtype_error = (tensor.dtype != dtypes.DType(tensor_info.dtype)) - shape_error = not tensor.shape.is_compatible_with(tensor_info.tensor_shape) - - if dtype_error or shape_error: - msg = 'Tensor shape and/or dtype validation failed for input %s:' % name - if dtype_error: - msg += ('\n\tExpected dtype: %s, Got: %s' - % (dtypes.DType(tensor_info.dtype), tensor.dtype)) - if shape_error: - msg += ('\n\tExpected shape: %s, Got: %s' - % (tensor_shape.TensorShape(tensor_info.tensor_shape), - tensor.shape)) - - raise ValueError(msg) - - -def _extract_eval_metrics(output_dict): - """Return a eval metric dict extracted from the output_dict. - - Eval metrics consist of a value tensor and an update op. Both must be in the - passed-in tensor dictionary for an eval metric to be added to the returned - dictionary. - - Args: - output_dict: a dict that maps strings to tensors. - - Returns: - dict mapping strings to (value, update_op) tuples. - """ - # pylint: disable=protected-access - metric_ops = {} - separator_char = export_output._SupervisedOutput._SEPARATOR_CHAR - - for key, tensor in six.iteritems(output_dict): - split_key = key.split(separator_char) - - # The metric name may contain the separator character, so recreate its name. - metric_name = separator_char.join(split_key[:-1]) - - if split_key[0] == export_output._SupervisedOutput.METRICS_NAME: - # If the key ends with the value suffix, and there is a corresponding - # key ending with the update_op suffix, then add tensors to metrics dict. - if split_key[-1] == export_output._SupervisedOutput.METRIC_VALUE_SUFFIX: - update_op = ''.join( - [metric_name, separator_char, - export_output._SupervisedOutput.METRIC_UPDATE_SUFFIX]) - if update_op in output_dict: - update_op_tensor = output_dict[update_op] - metric_ops[metric_name] = (tensor, update_op_tensor) - - # pylint: enable=protected-access - return metric_ops - - -def _validate_and_extract_outputs(mode, output_dict, method_name): - """Extract values from SignatureDef output dictionary. - - Args: - mode: One of the modes enumerated in `tf.estimator.ModeKeys`. - output_dict: dict of string SignatureDef keys to `Tensor`. - method_name: Method name of the SignatureDef as a string. - - Returns: - Tuple of ( - loss: `Tensor` object, - predictions: dictionary mapping string keys to `Tensor` objects, - metrics: dictionary mapping string keys to a tuple of two `Tensor` objects - ) - - Raises: - RuntimeError: raised if SignatureDef has an invalid method name for the mode - """ - # pylint: disable=protected-access - loss, predictions, metrics = None, None, None - - if mode == model_fn_lib.ModeKeys.PREDICT: - predictions = output_dict - else: - # Validate that the SignatureDef's method name matches the expected name for - # the given mode. - expected_method_name = signature_constants.SUPERVISED_TRAIN_METHOD_NAME - if mode == model_fn_lib.ModeKeys.EVAL: - expected_method_name = signature_constants.SUPERVISED_EVAL_METHOD_NAME - if method_name != expected_method_name: - raise RuntimeError( - 'Invalid SignatureDef method name for mode %s.\n\tExpected: %s\n\t' - 'Got: %s\nPlease ensure that the SavedModel was exported with ' - '`tf.contrib.estimator.export_all_saved_models()`.' % - (mode, expected_method_name, method_name)) - - # Extract loss, metrics and predictions from the output dict. - loss = output_dict[export_output._SupervisedOutput.LOSS_NAME] - metrics = _extract_eval_metrics(output_dict) - predictions = { - key: value for key, value in six.iteritems(output_dict) - if key.split(export_output._SupervisedOutput._SEPARATOR_CHAR)[0] == ( - export_output._SupervisedOutput.PREDICTIONS_NAME)} - - # pylint: enable=protected-access - return loss, predictions, metrics diff --git a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py deleted file mode 100644 index 718da1367c..0000000000 --- a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py +++ /dev/null @@ -1,369 +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 SavedModelEstimator.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import shutil -import tempfile - -from tensorflow.contrib.estimator.python.estimator import export as contrib_export -from tensorflow.contrib.estimator.python.estimator import saved_model_estimator -from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.estimator import estimator -from tensorflow.python.estimator import model_fn as model_fn_lib -from tensorflow.python.estimator.export import export -from tensorflow.python.estimator.export import export_output -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 control_flow_ops -from tensorflow.python.ops import metrics as metrics_lib -from tensorflow.python.ops import state_ops -from tensorflow.python.ops import variables -from tensorflow.python.platform import test -from tensorflow.python.training import monitored_session -from tensorflow.python.training import training - - -def dummy_input_fn(): - return dataset_ops.Dataset.from_tensors(( - {'x': constant_op.constant([[1], [-2]], dtype=dtypes.int64)}, - constant_op.constant([[4], [-3]], dtype=dtypes.float32))).repeat() - - -def dummy_input_fn_features_only(): - return dataset_ops.Dataset.from_tensors( - {'x': constant_op.constant([[5], [6]], dtype=dtypes.int64)}).repeat() - - -def dummy_supervised_receiver_fn(): - feature_spec = { - 'x': array_ops.placeholder( - dtype=dtypes.int64, shape=(2, 1), name='feature_x'), - } - label_spec = array_ops.placeholder( - dtype=dtypes.float32, shape=[2, 1], name='truth') - return export.build_raw_supervised_input_receiver_fn( - feature_spec, label_spec) - - -def dummy_serving_receiver_fn(): - feature_spec = {'x': array_ops.placeholder( - dtype=dtypes.int64, shape=(2, 1), name='feature_x'),} - return export.build_raw_serving_input_receiver_fn(feature_spec) - - -def model_fn_diff_modes(features, labels, mode): - _, _ = features, labels - v = variables.Variable(21, name='some_var') - train_op = None - loss = constant_op.constant(104) - if mode == model_fn_lib.ModeKeys.TRAIN: - loss = constant_op.constant(105) - predictions = constant_op.constant([501]) - train_op = control_flow_ops.group( - state_ops.assign_add(training.get_global_step(), 1), - state_ops.assign_add(v, 3)) - elif mode == model_fn_lib.ModeKeys.EVAL: - loss = constant_op.constant(106) - predictions = constant_op.constant([502]) - else: - loss = constant_op.constant(107) - predictions = constant_op.constant([503]) - return model_fn_lib.EstimatorSpec( - mode, - loss=loss, - train_op=train_op, - eval_metric_ops={ - 'abs_err': metrics_lib.mean_absolute_error( - constant_op.constant(0), predictions)}, - predictions=predictions) - - -class SavedModelEstimatorTest(test.TestCase): - - def setUp(self): - self.tmpdirs = [] - - def tearDown(self): - for tmpdir in self.tmpdirs: - # gfile.DeleteRecursively fails in the windows cmake test, so use shutil. - shutil.rmtree(tmpdir, ignore_errors=True) - self.tmpdirs = [] - - def _get_tmp_dir(self): - tmpdir = tempfile.mkdtemp() - self.tmpdirs.append(tmpdir) - return tmpdir - - def _export_estimator(self, train=True, evaluate=True, predict=True, - model_fn=model_fn_diff_modes): - est = estimator.Estimator(model_fn, self._get_tmp_dir()) - est.train(input_fn=dummy_input_fn, steps=10) - - input_receiver_fn_map = {} - if train: - input_receiver_fn_map[model_fn_lib.ModeKeys.TRAIN] = ( - dummy_supervised_receiver_fn()) - if evaluate: - input_receiver_fn_map[model_fn_lib.ModeKeys.EVAL] = ( - dummy_supervised_receiver_fn()) - if predict: - input_receiver_fn_map[model_fn_lib.ModeKeys.PREDICT] = ( - dummy_serving_receiver_fn()) - - export_base_path = self._get_tmp_dir() - export_dir = contrib_export.export_all_saved_models( - est, export_base_path, input_receiver_fn_map) - return export_dir - - def test_load_all_modes(self): - sme = saved_model_estimator.SavedModelEstimator( - self._export_estimator(), self._get_tmp_dir()) - sme.train(input_fn=dummy_input_fn, steps=1) - sme.train(input_fn=dummy_input_fn, steps=2) - self.assertEqual(13, sme.get_variable_value('global_step')) - self.assertEqual(60, sme.get_variable_value('some_var')) - - eval_results = sme.evaluate(dummy_input_fn, steps=5) - - self.assertEqual(13, eval_results['global_step']) - self.assertEqual(106, eval_results['loss']) - self.assertEqual(502, eval_results['metrics/abs_err']) - - predictions = next(sme.predict(dummy_input_fn_features_only)) - self.assertDictEqual({'output': 503}, predictions) - - def test_load_all_modes_no_train(self): - """Ensure that all functions can be used without requiring a ckpt.""" - sme = saved_model_estimator.SavedModelEstimator( - self._export_estimator(), self._get_tmp_dir()) - eval_results = sme.evaluate(dummy_input_fn, steps=5) - self.assertEqual(10, eval_results['global_step']) - self.assertEqual(106, eval_results['loss']) - self.assertEqual(502, eval_results['metrics/abs_err']) - - predictions = next(sme.predict(dummy_input_fn_features_only)) - self.assertDictEqual({'output': 503}, predictions) - - def test_partial_exported_estimator(self): - sme1 = saved_model_estimator.SavedModelEstimator( - self._export_estimator(train=False, predict=False), self._get_tmp_dir()) - sme1.evaluate(dummy_input_fn, steps=5) - with self.assertRaisesRegexp(RuntimeError, 'train mode is not available'): - sme1.train(input_fn=dummy_input_fn, steps=1) - with self.assertRaisesRegexp(RuntimeError, 'infer mode is not available'): - next(sme1.predict(dummy_input_fn_features_only)) - - sme2 = saved_model_estimator.SavedModelEstimator( - self._export_estimator(evaluate=False), self._get_tmp_dir()) - sme2.train(input_fn=dummy_input_fn, steps=1) - next(sme2.predict(dummy_input_fn_features_only)) - with self.assertRaisesRegexp(RuntimeError, 'eval mode is not available'): - sme2.evaluate(dummy_input_fn, steps=5) - - def test_with_incorrect_input(self): - sme = saved_model_estimator.SavedModelEstimator( - self._export_estimator(), self._get_tmp_dir()) - - def bad_shape_input_fn(): - return dataset_ops.Dataset.from_tensors(( - {'x': constant_op.constant([1, 2], dtype=dtypes.int64)}, - constant_op.constant([1, 2], dtype=dtypes.float32))) - - with self.assertRaisesRegexp(ValueError, 'Expected shape'): - sme.train(bad_shape_input_fn, steps=1) - - def bad_dtype_input_fn(): - return dataset_ops.Dataset.from_tensors(( - {'x': constant_op.constant([[1], [1]], dtype=dtypes.int32)}, - constant_op.constant([[1], [1]], dtype=dtypes.int64))) - - with self.assertRaisesRegexp(ValueError, 'Expected dtype'): - sme.train(bad_dtype_input_fn, steps=1) - - def test_input_fn_with_global_step(self): - sme = saved_model_estimator.SavedModelEstimator( - self._export_estimator(), self._get_tmp_dir()) - - def bad_input_fn(): - training.get_or_create_global_step() - return dataset_ops.Dataset.from_tensors(( - {'x': constant_op.constant([[1], [1]], dtype=dtypes.int64)}, - constant_op.constant([[1], [1]], dtype=dtypes.float32))) - - with self.assertRaisesRegexp(RuntimeError, - 'Graph must not contain a global step tensor'): - sme.train(bad_input_fn, steps=1) - - def test_re_export_saved_model_serving_only(self): - sme = saved_model_estimator.SavedModelEstimator( - self._export_estimator(), self._get_tmp_dir()) - sme.train(dummy_input_fn, steps=3) - self.assertEqual(13, sme.get_variable_value('global_step')) - self.assertEqual(60, sme.get_variable_value('some_var')) - - predictions = next(sme.predict(dummy_input_fn_features_only)) - self.assertDictEqual({'output': 503}, predictions) - - # Export SavedModel, and test that the variable and prediction values are - # the same. - sme_export_dir = sme.export_savedmodel( - self._get_tmp_dir(), dummy_serving_receiver_fn()) - - sme2 = saved_model_estimator.SavedModelEstimator( - sme_export_dir, self._get_tmp_dir()) - self.assertEqual(60, sme.get_variable_value('some_var')) - self.assertEqual(13, sme.get_variable_value('global_step')) - - predictions = next(sme2.predict(dummy_input_fn_features_only)) - self.assertDictEqual({'output': 503}, predictions) - - def test_re_export_saved_model(self): - sme = saved_model_estimator.SavedModelEstimator( - self._export_estimator(), self._get_tmp_dir()) - self.assertDictEqual( - {'loss': 106, 'metrics/abs_err': 502, 'global_step': 10}, - sme.evaluate(dummy_input_fn, steps=1)) - - sme.train(dummy_input_fn, steps=3) - self.assertDictEqual( - {'loss': 106, 'metrics/abs_err': 502, 'global_step': 13}, - sme.evaluate(dummy_input_fn, steps=1)) - self.assertEqual(60, sme.get_variable_value('some_var')) - - predictions = next(sme.predict(dummy_input_fn_features_only)) - self.assertDictEqual({'output': 503}, predictions) - - # Export SavedModel for all modes - input_receiver_fn_map = { - model_fn_lib.ModeKeys.TRAIN: dummy_supervised_receiver_fn(), - model_fn_lib.ModeKeys.EVAL: dummy_supervised_receiver_fn(), - model_fn_lib.ModeKeys.PREDICT: dummy_serving_receiver_fn()} - sme_export_dir = contrib_export.export_all_saved_models( - sme, self._get_tmp_dir(), input_receiver_fn_map) - - sme2 = saved_model_estimator.SavedModelEstimator( - sme_export_dir, self._get_tmp_dir()) - self.assertDictEqual( - {'loss': 106, 'metrics/abs_err': 502, 'global_step': 13}, - sme.evaluate(dummy_input_fn, steps=1)) - self.assertEqual(60, sme.get_variable_value('some_var')) - - sme.train(dummy_input_fn, steps=7) - self.assertEqual(20, sme.get_variable_value('global_step')) - - predictions = next(sme2.predict(dummy_input_fn_features_only)) - self.assertDictEqual({'output': 503}, predictions) - - def test_load_saved_model_from_serving_only(self): - def model_fn(features, labels, mode): - _, _ = features, labels - return model_fn_lib.EstimatorSpec( - mode, - loss=constant_op.constant([103]), - train_op=state_ops.assign_add(training.get_global_step(), 1), - predictions=constant_op.constant([502]), - export_outputs={'test': export_output.ClassificationOutput( - constant_op.constant([[32.]]))}) - - est = estimator.Estimator(model_fn, self._get_tmp_dir()) - est.train(input_fn=dummy_input_fn, steps=10) - - def serving_input_receiver_fn(): - return export.ServingInputReceiver( - {'test-features': constant_op.constant([[1], [1]])}, - array_ops.placeholder(dtype=dtypes.string)) - - export_dir = est.export_savedmodel( - self._get_tmp_dir(), serving_input_receiver_fn) - - sme = saved_model_estimator.SavedModelEstimator( - export_dir, self._get_tmp_dir()) - - def input_fn(): - return {'inputs': constant_op.constant('someinputstr')} - - prediction = next(sme.predict(input_fn)) - self.assertDictEqual({'scores': 32}, prediction) - - def test_with_local_init_op(self): - def model_fn(features, labels, mode): - _, _ = features, labels - v = variables.Variable(21, name='some_var') - scaffold = monitored_session.Scaffold( - local_init_op=state_ops.assign_add(v, -3).op - ) - return model_fn_lib.EstimatorSpec( - mode, - scaffold=scaffold, - train_op=state_ops.assign_add(training.get_global_step(), 1), - loss=array_ops.identity(v)) - export_dir = self._export_estimator(predict=False, model_fn=model_fn) - sme = saved_model_estimator.SavedModelEstimator( - export_dir, self._get_tmp_dir()) - - eval_results1 = sme.evaluate(dummy_input_fn, steps=2) - self.assertEqual(15, eval_results1['loss']) - - sme.train(dummy_input_fn, steps=1) - self.assertEqual(15, sme.get_variable_value('some_var')) - - eval_results2 = sme.evaluate(dummy_input_fn, steps=5) - self.assertEqual(12, eval_results2['loss']) - - def test_with_working_input_fn(self): - def model_fn(features, labels, mode): - loss = None - if labels is not None: - loss = labels[0][0] + labels[1][0] - return model_fn_lib.EstimatorSpec( - mode, - loss=loss, - train_op=state_ops.assign_add(training.get_global_step(), 1), - predictions={'features_0': array_ops.identity([features['x'][0][0]]), - 'features_1': array_ops.identity([features['x'][1][0]])}) - - sme = saved_model_estimator.SavedModelEstimator( - self._export_estimator(model_fn=model_fn), self._get_tmp_dir()) - eval_results = sme.evaluate(dummy_input_fn, steps=1) - self.assertEqual(1, eval_results['loss']) - - predictions = next(sme.predict(dummy_input_fn_features_only)) - self.assertDictEqual({'features_0': 5, 'features_1': 6}, predictions) - - def test_control_dependency(self): - # Control dependencies are saved with "^" appended to the start of the input - # name. The input map must include control dependencies as well. - def model_fn(features, labels, mode): - _ = labels - with ops.control_dependencies([features['x']]): - loss = features['x'][1][0] - return model_fn_lib.EstimatorSpec( - mode, - loss=loss, - train_op=state_ops.assign_add(training.get_global_step(), 1)) - sme = saved_model_estimator.SavedModelEstimator( - self._export_estimator(train=False, predict=False, model_fn=model_fn), - self._get_tmp_dir()) - sme.evaluate(dummy_input_fn, steps=1) # Should run without error - - -if __name__ == '__main__': - test.main() diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 148fcf61fa..2fd6f6fab9 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -568,14 +568,13 @@ class Estimator(object): def _assert_members_are_not_overridden(self): """Asserts members of `Estimator` are not overridden.""" allowed_overrides = set([ - '_call_input_fn', '_call_model_fn', + '_call_input_fn', '_create_global_step', '_convert_train_steps_to_hooks', '_convert_eval_steps_to_hooks', - '_create_global_step', '_create_and_assert_global_step', '_tf_api_names', '_tf_api_names_v1', '_estimator_api_names', '_estimator_api_names_v1', '_estimator_api_constants', '_estimator_api_constants_v1', '_validate_features_in_predict_input', - '_add_meta_graph_for_mode' + '_call_model_fn', '_add_meta_graph_for_mode' ]) estimator_members = set([m for m in Estimator.__dict__.keys() if not m.startswith('__')]) @@ -902,10 +901,9 @@ class Estimator(object): with tf_session.Session(config=self._session_config) as session: - if estimator_spec.scaffold.local_init_op is not None: - local_init_op = estimator_spec.scaffold.local_init_op - else: - local_init_op = monitored_session.Scaffold.default_local_init_op() + local_init_op = ( + estimator_spec.scaffold.local_init_op or + monitored_session.Scaffold.default_local_init_op()) # This saver will be used both for restoring variables now, # and in saving out the metagraph below. This ensures that any @@ -1156,15 +1154,14 @@ class Estimator(object): worker_hooks = [] with ops.Graph().as_default() as g, g.device(self._device_fn): random_seed.set_random_seed(self._config.tf_random_seed) - self._create_and_assert_global_step(g) + global_step_tensor = self._create_and_assert_global_step(g) + training_util._get_or_create_global_step_read() # pylint: disable=protected-access features, labels, input_hooks = ( self._get_features_and_labels_from_input_fn( input_fn, model_fn_lib.ModeKeys.TRAIN)) worker_hooks.extend(input_hooks) estimator_spec = self._call_model_fn( features, labels, model_fn_lib.ModeKeys.TRAIN, self.config) - global_step_tensor = training_util.get_global_step(g) - training_util._get_or_create_global_step_read() # pylint: disable=protected-access return self._train_with_estimator_spec(estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners) @@ -1367,8 +1364,10 @@ class Estimator(object): def _train_with_estimator_spec(self, estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners): """Train a model with the given Estimator Spec.""" - self._maybe_warm_start(self.latest_checkpoint()) - + if self._warm_start_settings: + logging.info('Warm-starting with WarmStartSettings: %s' % + (self._warm_start_settings,)) + warm_starting_util.warm_start(*self._warm_start_settings) # Check if the user created a loss summary, and add one if they didn't. # We assume here that the summary is called 'loss'. If it is not, we will # make another one with the name 'loss' to ensure it shows up in the right @@ -1449,13 +1448,13 @@ class Estimator(object): def _evaluate_build_graph(self, input_fn, hooks=None, checkpoint_path=None): """Builds the graph and related hooks to run evaluation.""" random_seed.set_random_seed(self._config.tf_random_seed) - self._create_and_assert_global_step(ops.get_default_graph()) + global_step_tensor = self._create_and_assert_global_step( + ops.get_default_graph()) features, labels, input_hooks = ( self._get_features_and_labels_from_input_fn(input_fn, model_fn_lib.ModeKeys.EVAL)) estimator_spec = self._call_model_fn( features, labels, model_fn_lib.ModeKeys.EVAL, self.config) - global_step_tensor = training_util.get_global_step(ops.get_default_graph()) # Call to warm_start has to be after model_fn is called. self._maybe_warm_start(checkpoint_path) @@ -1481,21 +1480,7 @@ class Estimator(object): all_hooks.extend(hooks) all_hooks.extend(list(estimator_spec.evaluation_hooks or [])) - # New local variables have been added, so update the estimator spec's - # local init op if it was defined. - scaffold = estimator_spec.scaffold - if estimator_spec.scaffold and estimator_spec.scaffold.local_init_op: - # Ensure that eval step has been created before updating local init op. - evaluation._get_or_create_eval_step() # pylint: disable=protected-access - - scaffold = monitored_session.Scaffold( - local_init_op=control_flow_ops.group( - estimator_spec.scaffold.local_init_op, - monitored_session.Scaffold.default_local_init_op()), - copy_from_scaffold=scaffold - ) - - return scaffold, update_op, eval_dict, all_hooks + return estimator_spec.scaffold, update_op, eval_dict, all_hooks def _evaluate_run(self, checkpoint_path, scaffold, update_op, eval_dict, all_hooks, output_dir): @@ -1926,19 +1911,6 @@ class WarmStartSettings( ) -def _get_saved_model_ckpt(saved_model_dir): - """Return path to variables checkpoint in a SavedModel directory.""" - if not gfile.Exists( - os.path.join(compat.as_bytes(saved_model_dir), - compat.as_bytes('variables/variables.index'))): - raise ValueError('Directory provided has an invalid SavedModel format: %s' - % saved_model_dir) - return os.path.join( - compat.as_bytes(saved_model_dir), - compat.as_bytes('{}/{}'.format(constants.VARIABLES_DIRECTORY, - constants.VARIABLES_FILENAME))) - - def _get_default_warm_start_settings(warm_start_from): """Returns default WarmStartSettings. @@ -1962,8 +1934,10 @@ def _get_default_warm_start_settings(warm_start_from): if gfile.Exists(os.path.join(compat.as_bytes(warm_start_from), compat.as_bytes('variables/variables.index'))): logging.info('Warm-starting from a SavedModel') - return WarmStartSettings( - ckpt_to_initialize_from=_get_saved_model_ckpt(warm_start_from)) + return WarmStartSettings(ckpt_to_initialize_from=os.path.join( + compat.as_bytes(warm_start_from), + compat.as_bytes('{}/{}'.format(constants.VARIABLES_DIRECTORY, + constants.VARIABLES_FILENAME)))) return WarmStartSettings(ckpt_to_initialize_from=warm_start_from) elif isinstance(warm_start_from, WarmStartSettings): return warm_start_from diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py index 687bfebd43..699d2b70d1 100644 --- a/tensorflow/python/framework/importer.py +++ b/tensorflow/python/framework/importer.py @@ -205,7 +205,7 @@ def _PopulateTFImportGraphDefOptions(options, prefix, input_map, for input_src, input_dst in input_map.items(): input_src = compat.as_str(input_src) if input_src.startswith('^'): - src_name = compat.as_str(input_src[1:]) + src_name = compat.as_bytes(input_src[1:]) dst_op = input_dst._as_tf_output().oper # pylint: disable=protected-access c_api.TF_ImportGraphDefOptionsRemapControlDependency( options, src_name, dst_op) diff --git a/tensorflow/python/framework/meta_graph.py b/tensorflow/python/framework/meta_graph.py index 33631282bd..923e76fc9c 100644 --- a/tensorflow/python/framework/meta_graph.py +++ b/tensorflow/python/framework/meta_graph.py @@ -696,67 +696,6 @@ def import_scoped_meta_graph(meta_graph_or_file, Raises: ValueError: If the graph_def contains unbound inputs. """ - return import_scoped_meta_graph_with_return_elements( - meta_graph_or_file, clear_devices, graph, import_scope, input_map, - unbound_inputs_col_name, restore_collections_predicate)[0] - - -def import_scoped_meta_graph_with_return_elements( - meta_graph_or_file, - clear_devices=False, - graph=None, - import_scope=None, - input_map=None, - unbound_inputs_col_name="unbound_inputs", - restore_collections_predicate=(lambda key: True), - return_elements=None): - """Imports graph from `MetaGraphDef` and returns vars and return elements. - - This function takes a `MetaGraphDef` protocol buffer as input. If - the argument is a file containing a `MetaGraphDef` protocol buffer , - it constructs a protocol buffer from the file content. The function - then adds all the nodes from the `graph_def` field to the - current graph, recreates the desired collections, and returns a dictionary of - all the Variables imported into the name scope. - - In combination with `export_scoped_meta_graph()`, this function can be used to - - * Serialize a graph along with other Python objects such as `QueueRunner`, - `Variable` into a `MetaGraphDef`. - - * Restart training from a saved graph and checkpoints. - - * Run inference from a saved graph and checkpoints. - - Args: - meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including - the path) containing a `MetaGraphDef`. - clear_devices: Boolean which controls whether to clear device information - from graph_def. Default false. - graph: The `Graph` to import into. If `None`, use the default graph. - import_scope: Optional `string`. Name scope into which to import the - subgraph. If `None`, the graph is imported to the root name scope. - input_map: A dictionary mapping input names (as strings) in `graph_def` to - `Tensor` objects. The values of the named input tensors in the imported - graph will be re-mapped to the respective `Tensor` values. - unbound_inputs_col_name: Collection name for looking up unbound inputs. - restore_collections_predicate: a predicate on collection names. A collection - named c (i.e whose key is c) will be restored iff - 1) `restore_collections_predicate(c)` is True, and - 2) `c != unbound_inputs_col_name`. - return_elements: A list of strings containing operation names in the - `MetaGraphDef` that will be returned as `Operation` objects; and/or - tensor names in `MetaGraphDef` that will be returned as `Tensor` objects. - - Returns: - A tuple of ( - dictionary of all the `Variables` imported into the name scope, - list of `Operation` or `Tensor` objects from the `return_elements` list). - - Raises: - ValueError: If the graph_def contains unbound inputs. - - """ if context.executing_eagerly(): raise ValueError("Exporting/importing meta graphs is not supported when " "eager execution is enabled.") @@ -798,12 +737,11 @@ def import_scoped_meta_graph_with_return_elements( scope_to_prepend_to_names = graph.unique_name( import_scope or "", mark_as_used=False) - imported_return_elements = importer.import_graph_def( + importer.import_graph_def( input_graph_def, name=(import_scope or scope_to_prepend_to_names), input_map=input_map, - producer_op_list=producer_op_list, - return_elements=return_elements) + producer_op_list=producer_op_list) # Restores all the other collections. variable_objects = {} @@ -868,7 +806,7 @@ def import_scoped_meta_graph_with_return_elements( for v in variables: var_list[ops.strip_name_scope(v.name, scope_to_prepend_to_names)] = v - return var_list, imported_return_elements + return var_list def export_scoped_meta_graph(filename=None, diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index 685a913f9c..e5f649fdab 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -284,15 +284,12 @@ class SavedModelLoader(object): **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph. Returns: - A tuple of - * Saver defined by the MetaGraph, which can be used to restore the - variable values. - * List of `Operation`/`Tensor` objects returned from - `tf.import_graph_def` (may be `None`). + Saver defined by the MetaGraph, which can be used to restore the variable + values. """ meta_graph_def = self.get_meta_graph_def_from_tags(tags) with graph.as_default(): - return tf_saver._import_meta_graph_with_return_elements( # pylint: disable=protected-access + return tf_saver.import_meta_graph( meta_graph_def, import_scope=import_scope, **saver_kwargs) def restore_variables(self, sess, saver, import_scope=None): @@ -364,8 +361,8 @@ class SavedModelLoader(object): `MetagraphDef` proto of the graph that was loaded. """ with sess.graph.as_default(): - saver, _ = self.load_graph(sess.graph, tags, import_scope, - **saver_kwargs) + saver = self.load_graph(sess.graph, tags, import_scope, + **saver_kwargs) self.restore_variables(sess, saver, import_scope) self.run_init_ops(sess, tags, import_scope) return self.get_meta_graph_def_from_tags(tags) diff --git a/tensorflow/python/saved_model/loader_test.py b/tensorflow/python/saved_model/loader_test.py index 9a0b276a4b..ce18859f6b 100644 --- a/tensorflow/python/saved_model/loader_test.py +++ b/tensorflow/python/saved_model/loader_test.py @@ -111,8 +111,7 @@ class SavedModelLoaderTest(test.TestCase): def test_load_with_import_scope(self): loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) with self.test_session(graph=ops.Graph()) as sess: - saver, _ = loader.load_graph( - sess.graph, ["foo_graph"], import_scope="baz") + saver = loader.load_graph(sess.graph, ["foo_graph"], import_scope="baz") # The default saver should not work when the import scope is set. with self.assertRaises(errors.NotFoundError): @@ -150,7 +149,7 @@ class SavedModelLoaderTest(test.TestCase): def test_run_init_op(self): loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) graph = ops.Graph() - saver, _ = loader.load_graph(graph, ["foo_graph"]) + saver = loader.load_graph(graph, ["foo_graph"]) with self.test_session(graph=graph) as sess: loader.restore_variables(sess, saver) self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) @@ -204,7 +203,7 @@ class SavedModelLoaderTest(test.TestCase): loader = loader_impl.SavedModelLoader(path) with self.test_session(graph=ops.Graph()) as sess: - saver, _ = loader.load_graph(sess.graph, ["foo_graph"]) + saver = loader.load_graph(sess.graph, ["foo_graph"]) self.assertFalse(variables._all_saveable_objects()) self.assertIsNotNone(saver) @@ -213,18 +212,6 @@ class SavedModelLoaderTest(test.TestCase): self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) - def test_load_saved_model_graph_with_return_elements(self): - """Ensure that the correct elements are returned.""" - loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) - graph = ops.Graph() - _, ret = loader.load_graph(graph, ["foo_graph"], - return_elements=["y:0", "x:0"]) - - self.assertEqual(graph.get_tensor_by_name("y:0"), ret[0]) - self.assertEqual(graph.get_tensor_by_name("x:0"), ret[1]) - - with self.assertRaisesRegexp(ValueError, "not found in graph"): - loader.load_graph(graph, ["foo_graph"], return_elements=["z:0"]) if __name__ == "__main__": test.main() diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 3a06a52812..11510d9928 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -1928,14 +1928,6 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False, execution is enabled. @end_compatibility """ # pylint: disable=g-doc-exception - return _import_meta_graph_with_return_elements( - meta_graph_or_file, clear_devices, import_scope, **kwargs)[0] - - -def _import_meta_graph_with_return_elements( - meta_graph_or_file, clear_devices=False, import_scope=None, - return_elements=None, **kwargs): - """Import MetaGraph, and return both a saver and returned elements.""" if context.executing_eagerly(): raise RuntimeError("Exporting/importing meta graphs is not supported when " "eager execution is enabled. No graph exists when eager " @@ -1945,22 +1937,12 @@ def _import_meta_graph_with_return_elements( else: meta_graph_def = meta_graph_or_file - imported_vars, imported_return_elements = ( - meta_graph.import_scoped_meta_graph_with_return_elements( - meta_graph_def, - clear_devices=clear_devices, - import_scope=import_scope, - return_elements=return_elements, - **kwargs)) - - saver = _create_saver_from_imported_meta_graph( - meta_graph_def, import_scope, imported_vars) - return saver, imported_return_elements - + imported_vars = meta_graph.import_scoped_meta_graph( + meta_graph_def, + clear_devices=clear_devices, + import_scope=import_scope, + **kwargs) -def _create_saver_from_imported_meta_graph( - meta_graph_def, import_scope, imported_vars): - """Return a saver for restoring variable values to an imported MetaGraph.""" if meta_graph_def.HasField("saver_def"): # Infer the scope that is prepended by `import_scoped_meta_graph`. scope = import_scope -- GitLab From 41781bad97698c29cd74203cef465d2adb2f04e8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 16:23:13 -0700 Subject: [PATCH 222/519] Add support for computing Softmax activation over tensors of rank 1. PiperOrigin-RevId: 205470922 --- .../contrib/lite/kernels/activations.cc | 53 +++++++++++++++---- .../contrib/lite/kernels/activations_test.cc | 23 ++++++++ 2 files changed, 65 insertions(+), 11 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc index 99f81c4a8a..d5ac2a7814 100644 --- a/tensorflow/contrib/lite/kernels/activations.cc +++ b/tensorflow/contrib/lite/kernels/activations.cc @@ -186,8 +186,8 @@ TfLiteStatus SoftmaxPrepare(TfLiteContext* context, TfLiteNode* node) { TfLiteTensor* output = GetOutput(context, node, 0); TF_LITE_ENSURE_EQ(context, input->type, output->type); - TF_LITE_ENSURE(context, - NumDimensions(input) == 2 || NumDimensions(input) == 4); + const int num_dims = NumDimensions(input); + TF_LITE_ENSURE(context, num_dims == 1 || num_dims == 2 || num_dims == 4); if (input->type == kTfLiteUInt8) { TF_LITE_ENSURE_EQ(context, output->params.zero_point, 0); @@ -365,13 +365,9 @@ TfLiteStatus SigmoidEval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } -// Takes a 2D tensor and perform softmax along the second dimension. -void Softmax2DFloat(const TfLiteTensor* input, TfLiteTensor* output, - TfLiteSoftmaxParams* params) { - const int batch_size = input->dims->data[0]; - const int input_size = input->dims->data[1]; - float* in = input->data.f; - float* out = output->data.f; +// Performs softmax along the input of size (input_size * batch_size). +void Softmax(const float* in, const int input_size, const int batch_size, + const float beta, float* out) { TF_LITE_ASSERT(input_size > 0); // For each batch @@ -385,7 +381,7 @@ void Softmax2DFloat(const TfLiteTensor* input, TfLiteTensor* output, // Compute the normalized sum of exps. float exp_sum = 0.0; for (int i = 0; i < input_size; i++) { - out[i] = std::exp((in[i] - max_coeff) * params->beta); + out[i] = std::exp((in[i] - max_coeff) * beta); exp_sum += out[i]; } @@ -401,6 +397,33 @@ void Softmax2DFloat(const TfLiteTensor* input, TfLiteTensor* output, } } +// Takes a 1D tensor and performs softmax along it. +void Softmax1DFloat(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params) { + const int input_size = input->dims->data[0]; + Softmax(input->data.f, input_size, 1, params->beta, output->data.f); +} + +// Takes a 2D tensor and perform softmax along the last dimension. +void Softmax2DFloat(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params) { + const int batch_size = input->dims->data[0]; + const int input_size = input->dims->data[1]; + Softmax(input->data.f, input_size, batch_size, params->beta, output->data.f); +} + +void Softmax1DQuantized(const TfLiteTensor* input, TfLiteTensor* output, + TfLiteSoftmaxParams* params, OpData* data) { + // TODO(ahentz): this is arguably a dirty trick. Since the implementation + // always traverses the last dimension of a 4D tensor, we will pretend our 1D + // tensor is 4D in a special way. We will convert a (Y) shape into a (1, + // 1, 1, Y) shape. + const int input_size = input->dims->data[0]; + optimized_ops::Softmax( + GetTensorData(input), GetTensorShape({1, 1, 1, input_size}), + data->input_multiplier, data->input_left_shift, data->diff_min, + GetTensorData(output), GetTensorShape({1, 1, 1, input_size})); +} void Softmax2DQuantized(const TfLiteTensor* input, TfLiteTensor* output, TfLiteSoftmaxParams* params, OpData* data) { // TODO(ahentz): this is arguably a dirty trick. Since the implementation @@ -443,6 +466,10 @@ TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { // dimensions. switch (input->type) { case kTfLiteFloat32: { + if (NumDimensions(input) == 1) { + Softmax1DFloat(input, output, params); + return kTfLiteOk; + } if (NumDimensions(input) == 2) { Softmax2DFloat(input, output, params); return kTfLiteOk; @@ -452,11 +479,15 @@ TfLiteStatus SoftmaxEval(TfLiteContext* context, TfLiteNode* node) { return kTfLiteOk; } context->ReportError( - context, "Only 2D and 4D tensors supported currently, got %dD.", + context, "Only 1D, 2D and 4D tensors supported currently, got %dD.", NumDimensions(input)); return kTfLiteError; } case kTfLiteUInt8: { + if (NumDimensions(input) == 1) { + Softmax1DQuantized(input, output, params, data); + return kTfLiteOk; + } if (NumDimensions(input) == 2) { Softmax2DQuantized(input, output, params, data); return kTfLiteOk; diff --git a/tensorflow/contrib/lite/kernels/activations_test.cc b/tensorflow/contrib/lite/kernels/activations_test.cc index 587e1303da..083cdf78d7 100644 --- a/tensorflow/contrib/lite/kernels/activations_test.cc +++ b/tensorflow/contrib/lite/kernels/activations_test.cc @@ -339,6 +339,29 @@ TEST(QuantizedActivationsOpTest, Softmax4D) { kQuantizedTolerance))); } +TEST(FloatActivationsOpTest, Softmax1D) { + FloatActivationsOpModel m(0.1, + /*input=*/{TensorType_FLOAT32, {8}}); + m.SetInput({0, -6, 2, 4, 3, -2, 10, 1}); + m.Invoke(); + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {.09752, .05352, .11911, .14548, .13164, .07984, .26509, .10778}))); +} + +TEST(QuantizedActivationsOpTest, Softmax1D) { + QuantizedActivationsOpModel m(0.1, + /*input=*/{TensorType_UINT8, {8}, -10, 10}); + m.SetInput({0, -6, 2, 4, 3, -2, 10, 1}); + m.Invoke(); + EXPECT_THAT( + m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({0.09766, 0.05469, 0.12109, 0.14453, + 0.13281, 0.07813, 0.26563, 0.10938}, + kQuantizedTolerance))); +} + TEST(FloatActivationsOpTest, Softmax2D) { FloatActivationsOpModel m(0.1, /*input=*/{TensorType_FLOAT32, {2, 4}}); -- GitLab From a4bab4517eddef07236529c0141e85bcae06ad74 Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Fri, 20 Jul 2018 16:23:51 -0700 Subject: [PATCH 223/519] Align TFLite tensors to 64 bytes for EIGEN_DONT_ALIGN PiperOrigin-RevId: 205471025 --- tensorflow/contrib/lite/arena_planner.cc | 25 +++++++------------ tensorflow/contrib/lite/arena_planner.h | 10 +++++++- tensorflow/contrib/lite/arena_planner_test.cc | 8 +++--- tensorflow/contrib/lite/kernels/BUILD | 1 + .../contrib/lite/kernels/eigen_support.cc | 11 ++++++++ .../contrib/lite/simple_memory_arena.cc | 2 +- 6 files changed, 36 insertions(+), 21 deletions(-) diff --git a/tensorflow/contrib/lite/arena_planner.cc b/tensorflow/contrib/lite/arena_planner.cc index 16a0e71624..02442575b3 100644 --- a/tensorflow/contrib/lite/arena_planner.cc +++ b/tensorflow/contrib/lite/arena_planner.cc @@ -17,14 +17,6 @@ limitations under the License. namespace tflite { -namespace { - -// Memory allocation tuning -constexpr const int kDefaultArenaAlignment = 64; -constexpr const int kDefaultTensorAlignment = 4; - -} // namespace - struct AllocationInfo { // The node index requesting this allocation. int node; @@ -36,13 +28,16 @@ struct AllocationInfo { ArenaPlanner::ArenaPlanner(TfLiteContext* context, std::unique_ptr graph_info, - bool preserve_inputs, bool preserve_intermediates) + bool preserve_inputs, bool preserve_intermediates, + int tensor_alignment) : context_(context), graph_info_(std::move(graph_info)), arena_(kDefaultArenaAlignment), persistent_arena_(kDefaultArenaAlignment), preserve_inputs_(preserve_inputs), - preserve_intermediates_(preserve_intermediates) {} + preserve_intermediates_(preserve_intermediates), + tensor_alignment_(tensor_alignment) {} + ArenaPlanner::~ArenaPlanner() {} int64_t ArenaPlanner::BasePointer(TfLiteAllocationType type) { @@ -264,14 +259,12 @@ TfLiteStatus ArenaPlanner::ResolveTensorAllocation(int tensor_index) { TfLiteStatus ArenaPlanner::CalculateTensorAllocation(int tensor_index) { TfLiteTensor& tensor = *graph_info_->tensor(tensor_index); if (tensor.allocation_type == kTfLiteArenaRw) { - TF_LITE_ENSURE_STATUS(arena_.Allocate(context_, kDefaultTensorAlignment, - tensor.bytes, - &allocs_[tensor_index])); + TF_LITE_ENSURE_STATUS(arena_.Allocate( + context_, tensor_alignment_, tensor.bytes, &allocs_[tensor_index])); } if (tensor.allocation_type == kTfLiteArenaRwPersistent) { - TF_LITE_ENSURE_STATUS( - persistent_arena_.Allocate(context_, kDefaultTensorAlignment, - tensor.bytes, &allocs_[tensor_index])); + TF_LITE_ENSURE_STATUS(persistent_arena_.Allocate( + context_, tensor_alignment_, tensor.bytes, &allocs_[tensor_index])); } return kTfLiteOk; } diff --git a/tensorflow/contrib/lite/arena_planner.h b/tensorflow/contrib/lite/arena_planner.h index 82c866734f..55003cf4e9 100644 --- a/tensorflow/contrib/lite/arena_planner.h +++ b/tensorflow/contrib/lite/arena_planner.h @@ -25,6 +25,10 @@ limitations under the License. namespace tflite { +// Memory allocation tuning +constexpr const int kDefaultArenaAlignment = 64; +constexpr const int kDefaultTensorAlignment = 64; + struct AllocationInfo; // A memory planner that makes all the allocations using arenas. @@ -47,7 +51,8 @@ class ArenaPlanner : public MemoryPlanner { // graph will not share memory with any other tensor, effectively preserving // them until the end of inference. ArenaPlanner(TfLiteContext* context, std::unique_ptr graph_info, - bool preserve_inputs, bool preserve_intermediates); + bool preserve_inputs, bool preserve_intermediates, + int tensor_alignment = kDefaultTensorAlignment); ~ArenaPlanner() override; ArenaPlanner(const ArenaPlanner&) = delete; ArenaPlanner& operator=(const ArenaPlanner&) = delete; @@ -112,6 +117,9 @@ class ArenaPlanner : public MemoryPlanner { // If true, then no overlapping of memory areas is done, meaning intermediates // results can be queried after running (modulo running delegates). bool preserve_intermediates_; + + // Number of bytes that tensor buffers should be aligned to. + int tensor_alignment_; }; } // namespace tflite diff --git a/tensorflow/contrib/lite/arena_planner_test.cc b/tensorflow/contrib/lite/arena_planner_test.cc index 1adb426d58..7d7c41289c 100644 --- a/tensorflow/contrib/lite/arena_planner_test.cc +++ b/tensorflow/contrib/lite/arena_planner_test.cc @@ -24,6 +24,8 @@ limitations under the License. namespace tflite { namespace { +constexpr const int kTensorAlignment = 4; + // A simple op to be used in tests, as syntactic sugar. class TestOp { public: @@ -156,7 +158,7 @@ class ArenaPlannerTest : public ::testing::Test { context_.ReportError = ReportError; planner_.reset(new ArenaPlanner( &context_, std::unique_ptr(new TestGraphInfo(graph)), - preserve_inputs, /*preserve intermediates*/ false)); + preserve_inputs, /*preserve intermediates*/ false, kTensorAlignment)); CHECK(planner_->ResetAllocations() == kTfLiteOk); CHECK(planner_->PlanAllocations() == kTfLiteOk); } @@ -178,8 +180,8 @@ class ArenaPlannerTest : public ::testing::Test { const TfLiteTensor& tensor = (*graph_->tensors())[tensor_index]; int64_t offset = GetOffset(tensor_index) + tensor.bytes; // We must make sure the offset is aligned to kDefaultArenaAlignment. - if (offset % 4 != 0) { - offset += 4 - offset % 4; + if (offset % kTensorAlignment != 0) { + offset += kTensorAlignment - offset % kTensorAlignment; } return offset; }; diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index ad30624f40..9549b4445d 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -58,6 +58,7 @@ cc_library( }), deps = [ ":op_macros", + "//tensorflow/contrib/lite:arena_planner", "//tensorflow/contrib/lite:context", "//tensorflow/contrib/lite/kernels/internal:optimized", ], diff --git a/tensorflow/contrib/lite/kernels/eigen_support.cc b/tensorflow/contrib/lite/kernels/eigen_support.cc index 4f0d020793..e542ad0765 100644 --- a/tensorflow/contrib/lite/kernels/eigen_support.cc +++ b/tensorflow/contrib/lite/kernels/eigen_support.cc @@ -16,6 +16,7 @@ limitations under the License. #include +#include "tensorflow/contrib/lite/arena_planner.h" #include "tensorflow/contrib/lite/kernels/internal/optimized/eigen_spatial_convolutions.h" #include "tensorflow/contrib/lite/kernels/op_macros.h" @@ -23,6 +24,16 @@ namespace tflite { namespace eigen_support { namespace { +#ifndef EIGEN_DONT_ALIGN +// Eigen may require buffers to be algiend to 16, 32 or 64 bytes depending on +// hardware architecture and build configurations. +// If the static assertion fails, try to increase `kDefaultTensorAlignment` to +// in `arena_planner.h` to 32 or 64. +static_assert( + kDefaultTensorAlignment % EIGEN_MAX_ALIGN_BYTES == 0, + "kDefaultArenaAlignment doesn't comply with Eigen alignment requirement."); +#endif // EIGEN_DONT_ALIGN + // We have a single global threadpool for all convolution operations. This means // that inferences started from different threads may block each other, but // since the underlying resource of CPU cores should be consumed by the diff --git a/tensorflow/contrib/lite/simple_memory_arena.cc b/tensorflow/contrib/lite/simple_memory_arena.cc index 4eaf6f1bfe..24593d2a67 100644 --- a/tensorflow/contrib/lite/simple_memory_arena.cc +++ b/tensorflow/contrib/lite/simple_memory_arena.cc @@ -34,7 +34,7 @@ namespace tflite { TfLiteStatus SimpleMemoryArena::Allocate(TfLiteContext* context, size_t alignment, size_t size, ArenaAlloc* new_alloc) { - TF_LITE_ENSURE(context, alignment < arena_alignment_); + TF_LITE_ENSURE(context, alignment <= arena_alignment_); if (size == 0) { new_alloc->offset = 0; -- GitLab From 9e61678787d329322dd729db92e833c874bdf835 Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Fri, 20 Jul 2018 16:26:54 -0700 Subject: [PATCH 224/519] TFLite Reshape - Uses shape input tensor Now it: (1) Use shape (2nd input tensor) if it exists. Mark the output as a dynamic tensor if shape tensor isn't constant. (2) Fallback to `TfLiteReshapeParams.shape` if the shape input tensor doesn't exist. PiperOrigin-RevId: 205471380 --- tensorflow/contrib/lite/kernels/reshape.cc | 69 +++++++++++++++---- .../contrib/lite/testing/generate_examples.py | 27 ++++++-- 2 files changed, 75 insertions(+), 21 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/reshape.cc b/tensorflow/contrib/lite/kernels/reshape.cc index 3287040695..99ecc16093 100644 --- a/tensorflow/contrib/lite/kernels/reshape.cc +++ b/tensorflow/contrib/lite/kernels/reshape.cc @@ -25,16 +25,11 @@ namespace builtin { namespace reshape { constexpr int kInputTensor = 0; +constexpr int kShapeTensor = 1; constexpr int kOutputTensor = 0; -TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { - auto* params = reinterpret_cast(node->builtin_data); - - // TODO(ahentz): we are often given a tensor with the shape but we only pay - // attention to what the shape specified in 'params'. - TF_LITE_ENSURE(context, NumInputs(node) == 1 || NumInputs(node) == 2); - TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); - +TfLiteStatus ResizeOutput(TfLiteContext* context, TfLiteNode* node, + TfLiteIntArray* output_shape) { const TfLiteTensor* input = GetInput(context, node, kInputTensor); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); @@ -47,32 +42,76 @@ TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { num_input_elements *= SizeOfDimension(input, i); } - TfLiteIntArray* output_size = TfLiteIntArrayCreate(params->num_dimensions); int num_output_elements = 1; int stretch_dim = -1; - for (int i = 0; i < params->num_dimensions; ++i) { - int value = params->shape[i]; + for (int i = 0; i < output_shape->size; ++i) { + int value = output_shape->data[i]; if (value == -1) { TF_LITE_ENSURE_EQ(context, stretch_dim, -1); stretch_dim = i; } else { num_output_elements *= value; - output_size->data[i] = value; } } if (stretch_dim != -1) { - output_size->data[stretch_dim] = num_input_elements / num_output_elements; - num_output_elements *= output_size->data[stretch_dim]; + output_shape->data[stretch_dim] = num_input_elements / num_output_elements; + num_output_elements *= output_shape->data[stretch_dim]; } TF_LITE_ENSURE_EQ(context, num_input_elements, num_output_elements); - return context->ResizeTensor(context, output, output_size); + return context->ResizeTensor(context, output, output_shape); +} + +TfLiteStatus ResizeOutputWithShapeTensor(TfLiteContext* context, + TfLiteNode* node) { + const TfLiteTensor* shape = GetInput(context, node, kShapeTensor); + + TfLiteIntArray* output_shape = TfLiteIntArrayCreate(shape->dims->data[0]); + for (int i = 0; i < output_shape->size; ++i) { + output_shape->data[i] = shape->data.i32[i]; + } + return ResizeOutput(context, node, output_shape); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + auto* params = reinterpret_cast(node->builtin_data); + + TF_LITE_ENSURE(context, NumInputs(node) == 1 || NumInputs(node) == 2); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + // Attempt to use shape tensor if it exists. + if (NumInputs(node) == 2) { + const TfLiteTensor* shape = GetInput(context, node, kShapeTensor); + // Check if the shape tensor is valid. + if (shape->dims->size == 1 && shape->type == kTfLiteInt32) { + // Set the output tensor as dynamic if the shape isn't constnat. + if (!IsConstantTensor(shape)) { + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + SetTensorToDynamic(output); + return kTfLiteOk; + } + // Shape is constant. Resize now. + return ResizeOutputWithShapeTensor(context, node); + } + } + // The function is returned above this line if the shape tensor is usable. + // Now fallback to the shape parameter in `TfLiteReshapeParams`. + + TfLiteIntArray* output_shape = TfLiteIntArrayCreate(params->num_dimensions); + for (int i = 0; i < params->num_dimensions; ++i) { + output_shape->data[i] = params->shape[i]; + } + return ResizeOutput(context, node, output_shape); } TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { const TfLiteTensor* input = GetInput(context, node, kInputTensor); TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + if (IsDynamicTensor(output)) { + TF_LITE_ENSURE_OK(context, ResizeOutputWithShapeTensor(context, node)); + } + memcpy(output->data.raw, input->data.raw, input->bytes); return kTfLiteOk; diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 32d04c0717..a91ff8626a 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -1595,19 +1595,34 @@ def make_reshape_tests(zip_path): "dtype": [tf.float32, tf.int32], "input_shape": [[3, 4, 5, 7], [4, 105], [21, 5, 2, 2], [420]], "output_shape": [[15, 28], [420], [1, -1, 5, 7], [-1]], + "constant_shape": [True, False], }] def build_graph(parameters): input_tensor = tf.placeholder(dtype=parameters["dtype"], name="input", shape=parameters["input_shape"]) - out = tf.reshape(input_tensor, shape=parameters["output_shape"]) - return [input_tensor], [out] + + # Get shape as either a placeholder or constants. + if parameters["constant_shape"]: + output_shape = parameters["output_shape"] + input_tensors = [input_tensor] + else: + # The shape of the shape tensor. + shape_tensor_shape = [len(parameters["output_shape"])] + output_shape = tf.placeholder( + dtype=tf.int32, name="output_shape", shape=shape_tensor_shape) + input_tensors = [input_tensor, output_shape] + out = tf.reshape(input_tensor, shape=output_shape) + return input_tensors, [out] def build_inputs(parameters, sess, inputs, outputs): - input_values = create_tensor_data(parameters["dtype"], - parameters["input_shape"]) - return [input_values], sess.run( - outputs, feed_dict=dict(zip(inputs, [input_values]))) + values = [ + create_tensor_data(parameters["dtype"], parameters["input_shape"]) + ] + if not parameters["constant_shape"]: + values.append(np.array(parameters["output_shape"])) + + return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) -- GitLab From a4b95884f870a040038e530c978239999933acd9 Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Fri, 20 Jul 2018 16:27:29 -0700 Subject: [PATCH 225/519] TFLite Python: Make resize_input_tensor accept list/tuple sizes. PiperOrigin-RevId: 205471451 --- tensorflow/contrib/lite/python/BUILD | 1 + tensorflow/contrib/lite/python/interpreter.py | 6 +++++- tensorflow/contrib/lite/python/interpreter_test.py | 2 +- 3 files changed, 7 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/lite/python/BUILD b/tensorflow/contrib/lite/python/BUILD index 727fbff38e..860aff9e7e 100644 --- a/tensorflow/contrib/lite/python/BUILD +++ b/tensorflow/contrib/lite/python/BUILD @@ -20,6 +20,7 @@ py_library( deps = [ "//tensorflow/contrib/lite/python/interpreter_wrapper:tensorflow_wrap_interpreter_wrapper", "//tensorflow/python:util", + "//third_party/py/numpy", ], ) diff --git a/tensorflow/contrib/lite/python/interpreter.py b/tensorflow/contrib/lite/python/interpreter.py index e1981ceae2..3243bddac8 100644 --- a/tensorflow/contrib/lite/python/interpreter.py +++ b/tensorflow/contrib/lite/python/interpreter.py @@ -18,6 +18,7 @@ from __future__ import division from __future__ import print_function import sys +import numpy as np from tensorflow.python.util.lazy_loader import LazyLoader # Lazy load since some of the performance benchmark skylark rules @@ -162,6 +163,9 @@ class Interpreter(object): ValueError: If the interpreter could not resize the input tensor. """ self._ensure_safe() + # `ResizeInputTensor` now only accepts int32 numpy array as `tensor_size + # parameter. + tensor_size = np.array(tensor_size, dtype=np.int32) self._interpreter.ResizeInputTensor(input_index, tensor_size) def get_output_details(self): @@ -204,7 +208,7 @@ class Interpreter(object): for i in range(10): input().fill(3.) interpreter.invoke() - print("inference %s" % output) + print("inference %s" % output()) Notice how this function avoids making a numpy array directly. This is because it is important to not hold actual numpy views to the data longer diff --git a/tensorflow/contrib/lite/python/interpreter_test.py b/tensorflow/contrib/lite/python/interpreter_test.py index 95fa4b8584..e77d52ca99 100644 --- a/tensorflow/contrib/lite/python/interpreter_test.py +++ b/tensorflow/contrib/lite/python/interpreter_test.py @@ -83,7 +83,7 @@ class InterpreterTest(test_util.TensorFlowTestCase): test_input = np.array([[1, 2, 3, 4]], dtype=np.uint8) expected_output = np.array([[4, 3, 2, 1]], dtype=np.uint8) interpreter.resize_tensor_input(input_details[0]['index'], - np.array(test_input.shape, dtype=np.int32)) + test_input.shape) interpreter.allocate_tensors() interpreter.set_tensor(input_details[0]['index'], test_input) interpreter.invoke() -- GitLab From 0cc0166a97f95499f0af673f3004d6bb748dc7e4 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Fri, 20 Jul 2018 16:34:46 -0700 Subject: [PATCH 226/519] Relax dependency checking for custom op libraries These checks were necessary when we used RTLD_GLOBAL to expose TF symbols to custom ops, since :framework and :lib pulled in implementations. They're now header-only. Ideally we'd switch the checks to framework_internal_impl and lib_internal_impl, but that would require visibility for those rules (thus making it more likely they'd get included in silly places). So this change disables the check for dynamic builds, on the theory that accidentally relying on implementation rules is much more difficult than it was with a static build. Should allow tf_custom_op_libraries to depend on GPU kernels (which depend on core:gpu_lib which depends on :framework). PiperOrigin-RevId: 205472434 --- tensorflow/tensorflow.bzl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 954940642b..26970c8cb0 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -1359,7 +1359,7 @@ def tf_custom_op_library(name, srcs=[], gpu_srcs=[], deps=[], linkopts=[]): name=name, srcs=srcs, deps=deps + if_cuda(cuda_deps), - data=[name + "_check_deps"], + data=if_static([name + "_check_deps"]), copts=tf_copts(is_external=True), features = ["windows_export_all_symbols"], linkopts=linkopts + select({ -- GitLab From 8741006018326350467fe86785d98963ff9e983e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 16:39:46 -0700 Subject: [PATCH 227/519] Automated rollback of commit 265292420de30f24805d28886d403dc42d3685b3 PiperOrigin-RevId: 205472990 --- .../eager/python/examples/revnet/BUILD | 36 -- .../eager/python/examples/revnet/blocks.py | 374 ++++++------------ .../python/examples/revnet/cifar_input.py | 2 +- .../eager/python/examples/revnet/config.py | 16 +- .../eager/python/examples/revnet/main.py | 82 ++-- .../python/examples/revnet/main_estimator.py | 200 ---------- .../examples/revnet/main_estimator_tpu.py | 328 --------------- .../eager/python/examples/revnet/revnet.py | 110 ++++-- .../python/examples/revnet/revnet_test.py | 25 +- 9 files changed, 268 insertions(+), 905 deletions(-) delete mode 100644 tensorflow/contrib/eager/python/examples/revnet/main_estimator.py delete mode 100644 tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py diff --git a/tensorflow/contrib/eager/python/examples/revnet/BUILD b/tensorflow/contrib/eager/python/examples/revnet/BUILD index 3316dc1114..0c0e4c0eb9 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/BUILD +++ b/tensorflow/contrib/eager/python/examples/revnet/BUILD @@ -113,39 +113,3 @@ py_binary( "//tensorflow:tensorflow_py", ], ) - -py_binary( - name = "main_estimator", - srcs = ["main_estimator.py"], - srcs_version = "PY2AND3", - deps = [ - ":cifar_input", - ":main", - ":revnet", - "//tensorflow:tensorflow_py", - ], -) - -py_library( - name = "main_estimator_lib", - srcs = ["main_estimator.py"], - srcs_version = "PY2AND3", - deps = [ - ":cifar_input", - ":main", - ":revnet", - "//tensorflow:tensorflow_py", - ], -) - -py_library( - name = "main_estimator_tpu_lib", - srcs = ["main_estimator_tpu.py"], - srcs_version = "PY2AND3", - deps = [ - ":cifar_input", - ":main", - ":revnet", - "//tensorflow:tensorflow_py", - ], -) diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py index 639bb06a34..306096e9f8 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -24,9 +24,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import functools -import operator - import tensorflow as tf from tensorflow.contrib.eager.python.examples.revnet import ops @@ -48,7 +45,7 @@ class RevBlock(tf.keras.Model): bottleneck=False, fused=True, dtype=tf.float32): - """Initialization. + """Initialize RevBlock. Args: n_res: number of residual blocks @@ -102,6 +99,7 @@ class RevBlock(tf.keras.Model): if i == 0: # First block usually contains downsampling that can't be reversed with tf.GradientTape() as tape: + x = tf.identity(x) tape.watch(x) y = block(x, training=training) @@ -123,6 +121,16 @@ class _Residual(tf.keras.Model): """Single residual block contained in a _RevBlock. Each `_Residual` object has two _ResidualInner objects, corresponding to the `F` and `G` functions in the paper. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC", + bottleneck: use bottleneck residual if True + fused: use fused batch normalization if True + dtype: float16, float32, or float64 """ def __init__(self, @@ -134,18 +142,6 @@ class _Residual(tf.keras.Model): bottleneck=False, fused=True, dtype=tf.float32): - """Initialization. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC", - bottleneck: use bottleneck residual if True - fused: use fused batch normalization if True - dtype: float16, float32, or float64 - """ super(_Residual, self).__init__() self.filters = filters @@ -200,6 +196,7 @@ class _Residual(tf.keras.Model): dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=self.axis) with tf.GradientTape(persistent=True) as tape: + y = tf.identity(y) tape.watch(y) y1, y2 = tf.split(y, num_or_size_splits=2, axis=self.axis) z1 = y1 @@ -230,252 +227,131 @@ class _Residual(tf.keras.Model): return x, dx, grads, vars_ -# Ideally, the following should be wrapped in `tf.keras.Sequential`, however -# there are subtle issues with its placeholder insertion policy and batch norm -class _BottleneckResidualInner(tf.keras.Model): +def _BottleneckResidualInner(filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True, + dtype=tf.float32): """Single bottleneck residual inner function contained in _Resdual. Corresponds to the `F`/`G` functions in the paper. Suitable for training on ImageNet dataset. - """ - - def __init__(self, - filters, - strides, - input_shape, - batch_norm_first=True, - data_format="channels_first", - fused=True, - dtype=tf.float32): - """Initialization. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC" - fused: use fused batch normalization if True - dtype: float16, float32, or float64 - """ - super(_BottleneckResidualInner, self).__init__() - axis = 1 if data_format == "channels_first" else 3 - if batch_norm_first: - self.batch_norm_0 = tf.keras.layers.BatchNormalization( - axis=axis, input_shape=input_shape, fused=fused, dtype=dtype) - - self.conv2d_1 = tf.keras.layers.Conv2D( - filters=filters // 4, - kernel_size=1, - strides=strides, - input_shape=input_shape, - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype) - self.batch_norm_1 = tf.keras.layers.BatchNormalization( - axis=axis, fused=fused, dtype=dtype) - - self.conv2d_2 = tf.keras.layers.Conv2D( - filters=filters // 4, - kernel_size=3, - strides=(1, 1), - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype) - self.batch_norm_2 = tf.keras.layers.BatchNormalization( - axis=axis, fused=fused, dtype=dtype) - self.conv2d_3 = tf.keras.layers.Conv2D( - filters=filters, - kernel_size=1, - strides=(1, 1), - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype) - - self.batch_norm_first = batch_norm_first - - def call(self, x, training=True): - net = x - if self.batch_norm_first: - net = self.batch_norm_0(net, training=training) - net = tf.nn.relu(net) - - net = self.conv2d_1(net) - net = self.batch_norm_1(net, training=training) - net = tf.nn.relu(net) - - net = self.conv2d_2(net) - net = self.batch_norm_2(net, training=training) - net = tf.nn.relu(net) + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + + Returns: + A keras model + """ - net = self.conv2d_3(net) + axis = 1 if data_format == "channels_first" else 3 + model = tf.keras.Sequential() + if batch_norm_first: + model.add( + tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused, dtype=dtype)) + model.add(tf.keras.layers.Activation("relu")) + model.add( + tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=1, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype)) + + model.add( + tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) + model.add(tf.keras.layers.Activation("relu")) + model.add( + tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype)) + + model.add( + tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) + model.add(tf.keras.layers.Activation("relu")) + model.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=1, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype)) - return net + return model -class _ResidualInner(tf.keras.Model): +def _ResidualInner(filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True, + dtype=tf.float32): """Single residual inner function contained in _ResdualBlock. Corresponds to the `F`/`G` functions in the paper. - """ - - def __init__(self, - filters, - strides, - input_shape, - batch_norm_first=True, - data_format="channels_first", - fused=True, - dtype=tf.float32): - """Initialization. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC" - fused: use fused batch normalization if True - dtype: float16, float32, or float64 - """ - super(_ResidualInner, self).__init__() - axis = 1 if data_format == "channels_first" else 3 - if batch_norm_first: - self.batch_norm_0 = tf.keras.layers.BatchNormalization( - axis=axis, input_shape=input_shape, fused=fused, dtype=dtype) - self.conv2d_1 = tf.keras.layers.Conv2D( - filters=filters, - kernel_size=3, - strides=strides, - input_shape=input_shape, - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype) - self.batch_norm_1 = tf.keras.layers.BatchNormalization( - axis=axis, fused=fused, dtype=dtype) - - self.conv2d_2 = tf.keras.layers.Conv2D( - filters=filters, - kernel_size=3, - strides=(1, 1), - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype) - - self.batch_norm_first = batch_norm_first - - def call(self, x, training=True): - net = x - if self.batch_norm_first: - net = self.batch_norm_0(net, training=training) - net = tf.nn.relu(net) - - net = self.conv2d_1(net) - net = self.batch_norm_1(net, training=training) - - net = self.conv2d_2(net) - - return net + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + + Returns: + A keras model + """ -class InitBlock(tf.keras.Model): - """Initial block of RevNet.""" - - def __init__(self, config): - """Initialization. - - Args: - config: tf.contrib.training.HParams object; specifies hyperparameters - """ - super(InitBlock, self).__init__() - self.config = config - self.axis = 1 if self.config.data_format == "channels_first" else 3 - self.conv2d = tf.keras.layers.Conv2D( - filters=self.config.init_filters, - kernel_size=self.config.init_kernel, - strides=(self.config.init_stride, self.config.init_stride), - data_format=self.config.data_format, - use_bias=False, - padding="SAME", - input_shape=self.config.input_shape, - dtype=self.config.dtype) - self.batch_norm = tf.keras.layers.BatchNormalization( - axis=self.axis, fused=self.config.fused, dtype=self.config.dtype) - self.activation = tf.keras.layers.Activation("relu") - - if self.config.init_max_pool: - self.max_pool = tf.keras.layers.MaxPooling2D( - pool_size=(3, 3), - strides=(2, 2), + axis = 1 if data_format == "channels_first" else 3 + model = tf.keras.Sequential() + if batch_norm_first: + model.add( + tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused, dtype=dtype)) + model.add(tf.keras.layers.Activation("relu")) + model.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, padding="SAME", - data_format=self.config.data_format, - dtype=self.config.dtype) - - def call(self, x, training=True): - net = x - net = self.conv2d(net) - net = self.batch_norm(net, training=training) - net = self.activation(net) - - if self.config.init_max_pool: - net = self.max_pool(net) - - return net - - -class FinalBlock(tf.keras.Model): - """Final block of RevNet.""" - - def __init__(self, config): - """Initialization. - - Args: - config: tf.contrib.training.HParams object; specifies hyperparameters + dtype=dtype)) + + model.add( + tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) + model.add(tf.keras.layers.Activation("relu")) + model.add( + tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype)) - Raises: - ValueError: Unsupported data format - """ - super(FinalBlock, self).__init__() - self.config = config - self.axis = 1 if self.config.data_format == "channels_first" else 3 - - f = self.config.filters[-1] # Number of filters - r = functools.reduce(operator.mul, self.config.strides, 1) # Reduce ratio - r *= self.config.init_stride - if self.config.init_max_pool: - r *= 2 - - if self.config.data_format == "channels_first": - w, h = self.config.input_shape[1], self.config.input_shape[2] - input_shape = (f, w // r, h // r) - elif self.config.data_format == "channels_last": - w, h = self.config.input_shape[0], self.config.input_shape[1] - input_shape = (w // r, h // r, f) - else: - raise ValueError("Data format should be either `channels_first`" - " or `channels_last`") - self.batch_norm = tf.keras.layers.BatchNormalization( - axis=self.axis, - input_shape=input_shape, - fused=self.config.fused, - dtype=self.config.dtype) - self.activation = tf.keras.layers.Activation("relu") - self.global_avg_pool = tf.keras.layers.GlobalAveragePooling2D( - data_format=self.config.data_format, dtype=self.config.dtype) - self.dense = tf.keras.layers.Dense( - self.config.n_classes, dtype=self.config.dtype) - - def call(self, x, training=True): - net = x - net = self.batch_norm(net, training=training) - net = self.activation(net) - net = self.global_avg_pool(net) - net = self.dense(net) - - return net + return model diff --git a/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py index e9672f13e1..b6d4c35bfd 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py +++ b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py @@ -111,6 +111,6 @@ def get_ds_from_tfrecords(data_dir, }[split] dataset = dataset.shuffle(size) - dataset = dataset.batch(batch_size, drop_remainder=True) + dataset = dataset.batch(batch_size) return dataset diff --git a/tensorflow/contrib/eager/python/examples/revnet/config.py b/tensorflow/contrib/eager/python/examples/revnet/config.py index 1532c7b67b..3d93fa955a 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/config.py +++ b/tensorflow/contrib/eager/python/examples/revnet/config.py @@ -27,16 +27,17 @@ from __future__ import division from __future__ import print_function import tensorflow as tf +tfe = tf.contrib.eager def get_hparams_cifar_38(): """RevNet-38 configurations for CIFAR-10/CIFAR-100.""" config = tf.contrib.training.HParams() - # Hyperparameters from the RevNet paper config.add_hparam("init_filters", 32) config.add_hparam("init_kernel", 3) config.add_hparam("init_stride", 1) + config.add_hparam("n_classes", 10) config.add_hparam("n_rev_blocks", 3) config.add_hparam("n_res", [3, 3, 3]) config.add_hparam("filters", [32, 64, 112]) @@ -45,7 +46,7 @@ def get_hparams_cifar_38(): config.add_hparam("bottleneck", False) config.add_hparam("fused", True) config.add_hparam("init_max_pool", False) - if tf.test.is_gpu_available() > 0: + if tfe.num_gpus() > 0: config.add_hparam("input_shape", (3, 32, 32)) config.add_hparam("data_format", "channels_first") else: @@ -70,16 +71,6 @@ def get_hparams_cifar_38(): config.add_hparam("iters_per_epoch", 50000 // config.batch_size) config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) - # Customized TPU hyperparameters due to differing batch size caused by - # TPU architecture specifics - # Suggested batch sizes to reduce overhead from excessive tensor padding - # https://cloud.google.com/tpu/docs/troubleshooting - config.add_hparam("tpu_batch_size", 128) - config.add_hparam("tpu_eval_batch_size", 1024) - config.add_hparam("tpu_iters_per_epoch", 50000 // config.tpu_batch_size) - config.add_hparam("tpu_epochs", - config.max_train_iter // config.tpu_iters_per_epoch) - return config @@ -110,6 +101,7 @@ def get_hparams_imagenet_56(): config.add_hparam("init_filters", 128) config.add_hparam("init_kernel", 7) config.add_hparam("init_stride", 2) + config.add_hparam("n_classes", 1000) config.add_hparam("n_rev_blocks", 4) config.add_hparam("n_res", [2, 2, 2, 2]) config.add_hparam("filters", [128, 256, 512, 832]) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main.py b/tensorflow/contrib/eager/python/examples/revnet/main.py index 1a4fd45c8b..e2f43b03f9 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/main.py +++ b/tensorflow/contrib/eager/python/examples/revnet/main.py @@ -31,11 +31,8 @@ tfe = tf.contrib.eager def main(_): """Eager execution workflow with RevNet trained on CIFAR-10.""" - tf.enable_eager_execution() - - config = get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) - ds_train, ds_train_one_shot, ds_validation, ds_test = get_datasets( - data_dir=FLAGS.data_dir, config=config) + config = get_config() + ds_train, ds_train_one_shot, ds_validation, ds_test = get_datasets(config) model = revnet.RevNet(config=config) global_step = tf.train.get_or_create_global_step() # Ensure correct summary global_step.assign(1) @@ -55,17 +52,23 @@ def main(_): "with global_step: {}".format(latest_path, global_step.numpy())) sys.stdout.flush() + if FLAGS.manual_grad: + print("Using manual gradients.") + else: + print("Not using manual gradients.") + sys.stdout.flush() + for x, y in ds_train: train_one_iter(model, x, y, optimizer, global_step=global_step) if global_step.numpy() % config.log_every == 0: + it_train = ds_train_one_shot.make_one_shot_iterator() it_test = ds_test.make_one_shot_iterator() + acc_train, loss_train = evaluate(model, it_train) acc_test, loss_test = evaluate(model, it_test) if FLAGS.validate: - it_train = ds_train_one_shot.make_one_shot_iterator() it_validation = ds_validation.make_one_shot_iterator() - acc_train, loss_train = evaluate(model, it_train) acc_validation, loss_validation = evaluate(model, it_validation) print("Iter {}, " "training set accuracy {:.4f}, loss {:.4f}; " @@ -74,8 +77,11 @@ def main(_): global_step.numpy(), acc_train, loss_train, acc_validation, loss_validation, acc_test, loss_test)) else: - print("Iter {}, test accuracy {:.4f}, loss {:.4f}".format( - global_step.numpy(), acc_test, loss_test)) + print("Iter {}, " + "training set accuracy {:.4f}, loss {:.4f}; " + "test accuracy {:.4f}, loss {:.4f}".format( + global_step.numpy(), acc_train, loss_train, acc_test, + loss_test)) sys.stdout.flush() if FLAGS.train_dir: @@ -97,38 +103,34 @@ def main(_): sys.stdout.flush() -def get_config(config_name="revnet-38", dataset="cifar-10"): +def get_config(): """Return configuration.""" - print("Config: {}".format(config_name)) + print("Config: {}".format(FLAGS.config)) sys.stdout.flush() config = { "revnet-38": config_.get_hparams_cifar_38(), "revnet-110": config_.get_hparams_cifar_110(), "revnet-164": config_.get_hparams_cifar_164(), - }[config_name] + }[FLAGS.config] - if dataset == "cifar-10": - config.add_hparam("n_classes", 10) - config.add_hparam("dataset", "cifar-10") - else: - config.add_hparam("n_classes", 100) - config.add_hparam("dataset", "cifar-100") + if FLAGS.dataset == "cifar-100": + config.n_classes = 100 return config -def get_datasets(data_dir, config): +def get_datasets(config): """Return dataset.""" - if data_dir is None: + if FLAGS.data_dir is None: raise ValueError("No supplied data directory") - if not os.path.exists(data_dir): - raise ValueError("Data directory {} does not exist".format(data_dir)) - if config.dataset not in ["cifar-10", "cifar-100"]: - raise ValueError("Unknown dataset {}".format(config.dataset)) + if not os.path.exists(FLAGS.data_dir): + raise ValueError("Data directory {} does not exist".format(FLAGS.data_dir)) + if FLAGS.dataset not in ["cifar-10", "cifar-100"]: + raise ValueError("Unknown dataset {}".format(FLAGS.dataset)) - print("Training on {} dataset.".format(config.dataset)) + print("Training on {} dataset.".format(FLAGS.dataset)) sys.stdout.flush() - data_dir = os.path.join(data_dir, config.dataset) + data_dir = os.path.join(FLAGS.data_dir, FLAGS.dataset) if FLAGS.validate: # 40k Training set ds_train = cifar_input.get_ds_from_tfrecords( @@ -166,7 +168,7 @@ def get_datasets(data_dir, config): prefetch=config.batch_size) ds_validation = None - # Always compute loss and accuracy on whole test set + # Always compute loss and accuracy on whole training and test set ds_train_one_shot = cifar_input.get_ds_from_tfrecords( data_dir=data_dir, split="train_all", @@ -194,11 +196,19 @@ def get_datasets(data_dir, config): def train_one_iter(model, inputs, labels, optimizer, global_step=None): """Train for one iteration.""" - grads, vars_, logits, loss = model.compute_gradients( - inputs, labels, training=True) - optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) + if FLAGS.manual_grad: + grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) + optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) + else: # For correctness validation + with tf.GradientTape() as tape: + logits, _ = model(inputs, training=True) + loss = model.compute_loss(logits=logits, labels=labels) + tf.logging.info("Logits are placed on device: {}".format(logits.device)) + grads = tape.gradient(loss, model.trainable_variables) + optimizer.apply_gradients( + zip(grads, model.trainable_variables), global_step=global_step) - return logits, loss + return loss.numpy() def evaluate(model, iterator): @@ -231,14 +241,16 @@ if __name__ == "__main__": "validate", default=False, help="[Optional] Use the validation set or not for hyperparameter search") + flags.DEFINE_boolean( + "manual_grad", + default=False, + help="[Optional] Use manual gradient graph to save memory") flags.DEFINE_string( "dataset", default="cifar-10", help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") flags.DEFINE_string( - "config", - default="revnet-38", - help="[Optional] Architecture of network. " - "Other options include `revnet-110` and `revnet-164`") + "config", default="revnet-38", help="[Optional] Architecture of network.") FLAGS = flags.FLAGS + tf.enable_eager_execution() tf.app.run(main) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py b/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py deleted file mode 100644 index c875e8da6d..0000000000 --- a/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py +++ /dev/null @@ -1,200 +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. -# ============================================================================== -"""Estimator workflow with RevNet train on CIFAR-10.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os - -from absl import flags -import tensorflow as tf -from tensorflow.contrib.eager.python.examples.revnet import cifar_input -from tensorflow.contrib.eager.python.examples.revnet import main as main_ -from tensorflow.contrib.eager.python.examples.revnet import revnet - - -def model_fn(features, labels, mode, params): - """Function specifying the model that is required by the `tf.estimator` API. - - Args: - features: Input images - labels: Labels of images - mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT' - params: A dictionary of extra parameter that might be passed - - Returns: - An instance of `tf.estimator.EstimatorSpec` - """ - - inputs = features - if isinstance(inputs, dict): - inputs = features["image"] - - config = params["config"] - model = revnet.RevNet(config=config) - - if mode == tf.estimator.ModeKeys.TRAIN: - global_step = tf.train.get_or_create_global_step() - learning_rate = tf.train.piecewise_constant( - global_step, config.lr_decay_steps, config.lr_list) - optimizer = tf.train.MomentumOptimizer( - learning_rate, momentum=config.momentum) - grads, vars_, logits, loss = model.compute_gradients( - inputs, labels, training=True) - train_op = optimizer.apply_gradients( - zip(grads, vars_), global_step=global_step) - - return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op) - else: - logits, _ = model(inputs, training=False) - predictions = tf.argmax(logits, axis=1) - probabilities = tf.nn.softmax(logits) - loss = model.compute_loss(labels=labels, logits=logits) - - if mode == tf.estimator.ModeKeys.EVAL: - return tf.estimator.EstimatorSpec( - mode=mode, - loss=loss, - eval_metric_ops={ - "accuracy": - tf.metrics.accuracy(labels=labels, predictions=predictions) - }) - - else: # mode == tf.estimator.ModeKeys.PREDICT - result = { - "classes": predictions, - "probabilities": probabilities, - } - - return tf.estimator.EstimatorSpec( - mode=mode, - predictions=predictions, - export_outputs={ - "classify": tf.estimator.export.PredictOutput(result) - }) - - -def get_input_fn(config, data_dir, split): - """Get the input function that is required by the `tf.estimator` API. - - Args: - config: Customized hyperparameters - data_dir: Directory where the data is stored - split: One of `train`, `validation`, `train_all`, and `test` - - Returns: - Input function required by the `tf.estimator` API - """ - - data_dir = os.path.join(data_dir, config.dataset) - # Fix split-dependent hyperparameters - if split == "train_all" or split == "train": - data_aug = True - batch_size = config.batch_size - epochs = config.epochs - shuffle = True - prefetch = config.batch_size - else: - data_aug = False - batch_size = config.eval_batch_size - epochs = 1 - shuffle = False - prefetch = config.eval_batch_size - - def input_fn(): - """Input function required by the `tf.estimator.Estimator` API.""" - return cifar_input.get_ds_from_tfrecords( - data_dir=data_dir, - split=split, - data_aug=data_aug, - batch_size=batch_size, - epochs=epochs, - shuffle=shuffle, - prefetch=prefetch, - data_format=config.data_format) - - return input_fn - - -def main(argv): - FLAGS = argv[0] # pylint:disable=invalid-name,redefined-outer-name - tf.logging.set_verbosity(tf.logging.INFO) - - # RevNet specific configuration - config = main_.get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) - - # Estimator specific configuration - run_config = tf.estimator.RunConfig( - model_dir=FLAGS.train_dir, # Directory for storing checkpoints - tf_random_seed=config.seed, - save_summary_steps=config.log_every, - save_checkpoints_steps=config.log_every, - session_config=None, # Using default - keep_checkpoint_max=100, - keep_checkpoint_every_n_hours=10000, # Using default - log_step_count_steps=config.log_every, - train_distribute=None # Default not use distribution strategy - ) - - # Construct estimator - revnet_estimator = tf.estimator.Estimator( - model_fn=model_fn, - model_dir=FLAGS.train_dir, - config=run_config, - params={"config": config}) - - # Construct input functions - train_input_fn = get_input_fn( - config=config, data_dir=FLAGS.data_dir, split="train_all") - eval_input_fn = get_input_fn( - config=config, data_dir=FLAGS.data_dir, split="test") - - # Train and evaluate estimator - revnet_estimator.train(input_fn=train_input_fn) - revnet_estimator.evaluate(input_fn=eval_input_fn) - - if FLAGS.export: - input_shape = (None,) + config.input_shape - inputs = tf.placeholder(tf.float32, shape=input_shape) - input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({ - "image": inputs - }) - revnet_estimator.export_savedmodel(FLAGS.train_dir, input_fn) - - -if __name__ == "__main__": - flags.DEFINE_string( - "data_dir", default=None, help="Directory to load tfrecords") - flags.DEFINE_string( - "train_dir", - default=None, - help="[Optional] Directory to store the training information") - flags.DEFINE_string( - "dataset", - default="cifar-10", - help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") - flags.DEFINE_boolean( - "export", - default=False, - help="[Optional] Export the model for serving if True") - flags.DEFINE_string( - "config", - default="revnet-38", - help="[Optional] Architecture of network. " - "Other options include `revnet-110` and `revnet-164`") - FLAGS = flags.FLAGS - tf.app.run(main=main, argv=[FLAGS]) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py b/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py deleted file mode 100644 index f1e1e530df..0000000000 --- a/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py +++ /dev/null @@ -1,328 +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. -# ============================================================================== -"""Cloud TPU Estimator workflow with RevNet train on CIFAR-10.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import os -import time - -from absl import flags -import tensorflow as tf -from tensorflow.contrib.eager.python.examples.revnet import cifar_input -from tensorflow.contrib.eager.python.examples.revnet import main as main_ -from tensorflow.contrib.eager.python.examples.revnet import revnet -from tensorflow.contrib.training.python.training import evaluation -from tensorflow.python.estimator import estimator as estimator_ - - -def model_fn(features, labels, mode, params): - """Model function required by the `tf.contrib.tpu.TPUEstimator` API. - - Args: - features: Input images - labels: Labels of images - mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT' - params: A dictionary of extra parameter that might be passed - - Returns: - An instance of `tf.contrib.tpu.TPUEstimatorSpec` - """ - - inputs = features - if isinstance(inputs, dict): - inputs = features["image"] - - FLAGS = params["FLAGS"] # pylint:disable=invalid-name,redefined-outer-name - config = params["config"] - model = revnet.RevNet(config=config) - - if mode == tf.estimator.ModeKeys.TRAIN: - global_step = tf.train.get_or_create_global_step() - learning_rate = tf.train.piecewise_constant( - global_step, config.lr_decay_steps, config.lr_list) - optimizer = tf.train.MomentumOptimizer( - learning_rate, momentum=config.momentum) - - if FLAGS.use_tpu: - optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer) - - # Define gradients - grads, vars_, logits, loss = model.compute_gradients( - inputs, labels, training=True) - train_op = optimizer.apply_gradients( - zip(grads, vars_), global_step=global_step) - - names = [v.name for v in model.variables] - tf.logging.warn("{}".format(names)) - - return tf.contrib.tpu.TPUEstimatorSpec( - mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op) - - if mode == tf.estimator.ModeKeys.EVAL: - logits, _ = model(inputs, training=False) - loss = model.compute_loss(labels=labels, logits=logits) - - def metric_fn(labels, logits): - predictions = tf.argmax(logits, axis=1) - accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions) - return { - "accuracy": accuracy, - } - - return tf.contrib.tpu.TPUEstimatorSpec( - mode=mode, loss=loss, eval_metrics=(metric_fn, [labels, logits])) - - if mode == tf.estimator.ModeKeys.PREDICT: - logits, _ = model(inputs, training=False) - predictions = { - "classes": tf.argmax(logits, axis=1), - "probabilities": tf.nn.softmax(logits), - } - - return tf.contrib.tpu.TPUEstimatorSpec( - mode=mode, - predictions=predictions, - export_outputs={ - "classify": tf.estimator.export.PredictOutput(predictions) - }) - - -def get_input_fn(config, data_dir, split): - """Get the input function required by the `tf.contrib.tpu.TPUEstimator` API. - - Args: - config: Customized hyperparameters - data_dir: Directory where the data is stored - split: One of `train`, `validation`, `train_all`, and `test` - - Returns: - Input function required by the `tf.contrib.tpu.TPUEstimator` API - """ - - data_dir = os.path.join(data_dir, config.dataset) - # Fix split-dependent hyperparameters - if split == "train_all" or split == "train": - data_aug = True - epochs = config.tpu_epochs - shuffle = True - else: - data_aug = False - epochs = 1 - shuffle = False - - def input_fn(params): - """Input function required by the `tf.contrib.tpu.TPUEstimator` API.""" - batch_size = params["batch_size"] - return cifar_input.get_ds_from_tfrecords( - data_dir=data_dir, - split=split, - data_aug=data_aug, - batch_size=batch_size, # per-shard batch size - epochs=epochs, - shuffle=shuffle, - prefetch=batch_size, # per-shard batch size - data_format=config.data_format) - - return input_fn - - -def main(argv): - FLAGS = argv[0] # pylint:disable=invalid-name,redefined-outer-name - tf.logging.set_verbosity(tf.logging.INFO) - - # RevNet specific configuration - config = main_.get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) - - if FLAGS.use_tpu: - tf.logging.info("Using TPU.") - tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( - FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) - else: - tpu_cluster_resolver = None - - # TPU specific configuration - tpu_config = tf.contrib.tpu.TPUConfig( - # Recommended to be set as number of global steps for next checkpoint - iterations_per_loop=FLAGS.iterations_per_loop, - num_shards=FLAGS.num_shards) - - # Estimator specific configuration - run_config = tf.contrib.tpu.RunConfig( - cluster=tpu_cluster_resolver, - model_dir=FLAGS.model_dir, - session_config=tf.ConfigProto( - allow_soft_placement=True, log_device_placement=False), - tpu_config=tpu_config, - ) - - # Construct TPU Estimator - estimator = tf.contrib.tpu.TPUEstimator( - model_fn=model_fn, - use_tpu=FLAGS.use_tpu, - train_batch_size=config.tpu_batch_size, - eval_batch_size=config.tpu_eval_batch_size, - config=run_config, - params={ - "FLAGS": FLAGS, - "config": config, - }) - - # Construct input functions - train_input_fn = get_input_fn( - config=config, data_dir=FLAGS.data_dir, split="train_all") - eval_input_fn = get_input_fn( - config=config, data_dir=FLAGS.data_dir, split="test") - - # Disabling a range within an else block currently doesn't work - # due to https://github.com/PyCQA/pylint/issues/872 - # pylint: disable=protected-access - if FLAGS.mode == "eval": - # TPUEstimator.evaluate *requires* a steps argument. - # Note that the number of examples used during evaluation is - # --eval_steps * --batch_size. - # So if you change --batch_size then change --eval_steps too. - eval_steps = 10000 // config.tpu_eval_batch_size - - # Run evaluation when there's a new checkpoint - for ckpt in evaluation.checkpoints_iterator( - FLAGS.model_dir, timeout=FLAGS.eval_timeout): - tf.logging.info("Starting to evaluate.") - try: - start_timestamp = time.time() # This time will include compilation time - eval_results = estimator.evaluate( - input_fn=eval_input_fn, steps=eval_steps, checkpoint_path=ckpt) - elapsed_time = int(time.time() - start_timestamp) - tf.logging.info("Eval results: %s. Elapsed seconds: %d" % - (eval_results, elapsed_time)) - - # Terminate eval job when final checkpoint is reached - current_step = int(os.path.basename(ckpt).split("-")[1]) - if current_step >= config.max_train_iter: - tf.logging.info( - "Evaluation finished after training step %d" % current_step) - break - - except tf.errors.NotFoundError: - # Since the coordinator is on a different job than the TPU worker, - # sometimes the TPU worker does not finish initializing until long after - # the CPU job tells it to start evaluating. In this case, the checkpoint - # file could have been deleted already. - tf.logging.info( - "Checkpoint %s no longer exists, skipping checkpoint" % ckpt) - - else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval' - current_step = estimator_._load_global_step_from_checkpoint_dir( - FLAGS.model_dir) - tf.logging.info("Training for %d steps . Current" - " step %d." % (config.max_train_iter, current_step)) - - start_timestamp = time.time() # This time will include compilation time - if FLAGS.mode == "train": - estimator.train(input_fn=train_input_fn, max_steps=config.max_train_iter) - else: - eval_steps = 10000 // config.tpu_eval_batch_size - assert FLAGS.mode == "train_and_eval" - while current_step < config.max_train_iter: - # Train for up to steps_per_eval number of steps. - # At the end of training, a checkpoint will be written to --model_dir. - next_checkpoint = min(current_step + FLAGS.steps_per_eval, - config.max_train_iter) - estimator.train(input_fn=train_input_fn, max_steps=next_checkpoint) - current_step = next_checkpoint - - # Evaluate the model on the most recent model in --model_dir. - # Since evaluation happens in batches of --eval_batch_size, some images - # may be consistently excluded modulo the batch size. - tf.logging.info("Starting to evaluate.") - eval_results = estimator.evaluate( - input_fn=eval_input_fn, steps=eval_steps) - tf.logging.info("Eval results: %s" % eval_results) - - elapsed_time = int(time.time() - start_timestamp) - tf.logging.info("Finished training up to step %d. Elapsed seconds %d." % - (config.max_train_iter, elapsed_time)) - # pylint: enable=protected-access - - -if __name__ == "__main__": - # Cloud TPU Cluster Resolver flags - flags.DEFINE_string( - "tpu", - default=None, - help="The Cloud TPU to use for training. This should be either the name " - "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " - "url.") - flags.DEFINE_string( - "tpu_zone", - default=None, - help="[Optional] GCE zone where the Cloud TPU is located in. If not " - "specified, we will attempt to automatically detect the GCE project from " - "metadata.") - flags.DEFINE_string( - "gcp_project", - default=None, - help="[Optional] Project name for the Cloud TPU-enabled project. If not " - "specified, we will attempt to automatically detect the GCE project from " - "metadata.") - - # Model specific parameters - flags.DEFINE_string( - "data_dir", default=None, help="Directory to load tfrecords") - flags.DEFINE_string( - "model_dir", - default=None, - help="[Optional] Directory to store the model information") - flags.DEFINE_string( - "dataset", - default="cifar-10", - help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") - flags.DEFINE_string( - "config", - default="revnet-38", - help="[Optional] Architecture of network. " - "Other options include `revnet-110` and `revnet-164`") - flags.DEFINE_boolean( - "use_tpu", default=True, help="[Optional] Whether to use TPU") - flags.DEFINE_integer( - "num_shards", default=8, help="Number of shards (TPU chips).") - flags.DEFINE_integer( - "iterations_per_loop", - default=100, - help=( - "Number of steps to run on TPU before feeding metrics to the CPU." - " If the number of iterations in the loop would exceed the number of" - " train steps, the loop will exit before reaching" - " --iterations_per_loop. The larger this value is, the higher the" - " utilization on the TPU.")) - flags.DEFINE_string( - "mode", - default="train_and_eval", - help="[Optional] Mode to run: train, eval, train_and_eval") - flags.DEFINE_integer( - "eval_timeout", 60 * 60 * 24, - "Maximum seconds between checkpoints before evaluation terminates.") - flags.DEFINE_integer( - "steps_per_eval", - default=1000, - help=( - "Controls how often evaluation is performed. Since evaluation is" - " fairly expensive, it is advised to evaluate as infrequently as" - " possible (i.e. up to --train_steps, which evaluates the model only" - " after finishing the entire training regime).")) - FLAGS = flags.FLAGS - tf.app.run(main=main, argv=[FLAGS]) diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet.py b/tensorflow/contrib/eager/python/examples/revnet/revnet.py index a3c2f7dbec..af0d20fa72 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet.py @@ -24,6 +24,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import functools +import operator + import six import tensorflow as tf from tensorflow.contrib.eager.python.examples.revnet import blocks @@ -42,9 +45,71 @@ class RevNet(tf.keras.Model): self.axis = 1 if config.data_format == "channels_first" else 3 self.config = config - self._init_block = blocks.InitBlock(config=self.config) - self._final_block = blocks.FinalBlock(config=self.config) + self._init_block = self._construct_init_block() self._block_list = self._construct_intermediate_blocks() + self._final_block = self._construct_final_block() + + def _construct_init_block(self): + init_block = tf.keras.Sequential( + [ + tf.keras.layers.Conv2D( + filters=self.config.init_filters, + kernel_size=self.config.init_kernel, + strides=(self.config.init_stride, self.config.init_stride), + data_format=self.config.data_format, + use_bias=False, + padding="SAME", + input_shape=self.config.input_shape, + dtype=self.config.dtype), + tf.keras.layers.BatchNormalization( + axis=self.axis, + fused=self.config.fused, + dtype=self.config.dtype), + tf.keras.layers.Activation("relu"), + ], + name="init") + if self.config.init_max_pool: + init_block.add( + tf.keras.layers.MaxPooling2D( + pool_size=(3, 3), + strides=(2, 2), + padding="SAME", + data_format=self.config.data_format, + dtype=self.config.dtype)) + return init_block + + def _construct_final_block(self): + f = self.config.filters[-1] # Number of filters + r = functools.reduce(operator.mul, self.config.strides, 1) # Reduce ratio + r *= self.config.init_stride + if self.config.init_max_pool: + r *= 2 + + if self.config.data_format == "channels_first": + w, h = self.config.input_shape[1], self.config.input_shape[2] + input_shape = (f, w // r, h // r) + elif self.config.data_format == "channels_last": + w, h = self.config.input_shape[0], self.config.input_shape[1] + input_shape = (w // r, h // r, f) + else: + raise ValueError("Data format should be either `channels_first`" + " or `channels_last`") + + final_block = tf.keras.Sequential( + [ + tf.keras.layers.BatchNormalization( + axis=self.axis, + input_shape=input_shape, + fused=self.config.fused, + dtype=self.config.dtype), + tf.keras.layers.Activation("relu"), + tf.keras.layers.GlobalAveragePooling2D( + data_format=self.config.data_format, dtype=self.config.dtype), + tf.keras.layers.Dense( + self.config.n_classes, dtype=self.config.dtype) + ], + name="final") + return final_block def _construct_intermediate_blocks(self): # Precompute input shape after initial block @@ -141,20 +206,13 @@ class RevNet(tf.keras.Model): l2_reg: Apply l2 regularization Returns: - A tuple with the first entry being a list of all gradients, the second - entry being a list of respective variables, the third being the logits, - and the forth being the loss + list of tuples each being (grad, var) for optimizer to use """ - # Run forward pass to record hidden states + # Run forward pass to record hidden states; avoid updating running averages vars_and_vals = self.get_moving_stats() _, saved_hidden = self.call(inputs, training=training) - if tf.executing_eagerly(): - # Restore moving averages when executing eagerly to avoid updating twice - self.restore_moving_stats(vars_and_vals) - else: - # Fetch batch norm updates in graph mode - updates = self.get_updates_for(inputs) + self.restore_moving_stats(vars_and_vals) grads_all = [] vars_all = [] @@ -162,8 +220,9 @@ class RevNet(tf.keras.Model): # Manually backprop through last block x = saved_hidden[-1] with tf.GradientTape() as tape: + x = tf.identity(x) tape.watch(x) - # Running stats updated here + # Running stats updated below logits = self._final_block(x, training=training) loss = self.compute_loss(logits, labels) @@ -177,7 +236,6 @@ class RevNet(tf.keras.Model): for block in reversed(self._block_list): y = saved_hidden.pop() x = saved_hidden[-1] - # Running stats updated here dy, grads, vars_ = block.backward_grads_and_vars( x, y, dy, training=training) grads_all += grads @@ -189,7 +247,8 @@ class RevNet(tf.keras.Model): assert not saved_hidden # Cleared after backprop with tf.GradientTape() as tape: - # Running stats updated here + x = tf.identity(x) + # Running stats updated below y = self._init_block(x, training=training) grads_all += tape.gradient( @@ -200,13 +259,7 @@ class RevNet(tf.keras.Model): if l2_reg: grads_all = self._apply_weight_decay(grads_all, vars_all) - if not tf.executing_eagerly(): - # Force updates to be executed before gradient computation in graph mode - # This does nothing when the function is wrapped in defun - with tf.control_dependencies(updates): - grads_all[0] = tf.identity(grads_all[0]) - - return grads_all, vars_all, logits, loss + return grads_all, vars_all, loss def _apply_weight_decay(self, grads, vars_): """Update gradients to reflect weight decay.""" @@ -231,10 +284,8 @@ class RevNet(tf.keras.Model): n = v.name return n.endswith("moving_mean:0") or n.endswith("moving_variance:0") - device = "/gpu:0" if tf.test.is_gpu_available() else "/cpu:0" - with tf.device(device): - for v in filter(_is_moving_var, self.variables): - vars_and_vals[v] = v.read_value() + for v in filter(_is_moving_var, self.variables): + vars_and_vals[v] = v.read_value() return vars_and_vals @@ -246,8 +297,5 @@ class RevNet(tf.keras.Model): Args: vars_and_vals: The dictionary mapping variables to their previous values. """ - device = "/gpu:0" if tf.test.is_gpu_available() else "/cpu:0" - with tf.device(device): - for var_, val in six.iteritems(vars_and_vals): - # `assign` causes a copy to GPU (if variable is already on GPU) - var_.assign(val) + for var_, val in six.iteritems(vars_and_vals): + var_.assign(val) diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py index 26b0847523..b0d0a5486d 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py @@ -31,11 +31,10 @@ tfe = tf.contrib.eager def train_one_iter(model, inputs, labels, optimizer, global_step=None): """Train for one iteration.""" - grads, vars_, logits, loss = model.compute_gradients( - inputs, labels, training=True) + grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) - return logits, loss + return loss class RevNetTest(tf.test.TestCase): @@ -43,8 +42,6 @@ class RevNetTest(tf.test.TestCase): def setUp(self): super(RevNetTest, self).setUp() config = config_.get_hparams_cifar_38() - config.add_hparam("n_classes", 10) - config.add_hparam("dataset", "cifar-10") # Reconstruction could cause numerical error, use double precision for tests config.dtype = tf.float64 config.fused = False # Fused batch norm does not support tf.float64 @@ -97,7 +94,7 @@ class RevNetTest(tf.test.TestCase): def test_compute_gradients(self): """Test `compute_gradients` function.""" self.model(self.x, training=False) # Initialize model - grads, vars_, logits, loss = self.model.compute_gradients( + grads, vars_, loss = self.model.compute_gradients( inputs=self.x, labels=self.t, training=True, l2_reg=True) self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) @@ -122,7 +119,7 @@ class RevNetTest(tf.test.TestCase): def test_compute_gradients_defun(self): """Test `compute_gradients` function with defun.""" compute_gradients = tfe.defun(self.model.compute_gradients) - grads, vars_, _, _ = compute_gradients(self.x, self.t, training=True) + grads, vars_, _ = compute_gradients(self.x, self.t, training=True) self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) self.assertEqual(len(grads), len(vars_)) @@ -134,9 +131,6 @@ class RevNetTest(tf.test.TestCase): """Test model training in graph mode.""" with tf.Graph().as_default(): config = config_.get_hparams_cifar_38() - config.add_hparam("n_classes", 10) - config.add_hparam("dataset", "cifar-10") - x = tf.random_normal( shape=(self.config.batch_size,) + self.config.input_shape) t = tf.random_uniform( @@ -146,10 +140,15 @@ class RevNetTest(tf.test.TestCase): dtype=tf.int32) global_step = tf.Variable(0., trainable=False) model = revnet.RevNet(config=config) - grads_all, vars_all, _, _ = model.compute_gradients(x, t, training=True) + model(x) + updates = model.get_updates_for(x) + + x_ = tf.identity(x) + grads_all, vars_all, _ = model.compute_gradients(x_, t, training=True) optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) - train_op = optimizer.apply_gradients( - zip(grads_all, vars_all), global_step=global_step) + with tf.control_dependencies(updates): + train_op = optimizer.apply_gradients( + zip(grads_all, vars_all), global_step=global_step) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) -- GitLab From 9dbbea7a4327cd694ccff1f9edf6ae1af4329362 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 16:59:11 -0700 Subject: [PATCH 228/519] Add gpu_event_mgr::WarnIfInCallback() analysis function, included by compiler option only. Useful for tracking down performance problems caused by misuse of EventMgr::ThenExecute callbacks. PiperOrigin-RevId: 205475177 --- tensorflow/core/BUILD | 18 ++++- .../core/common_runtime/gpu/gpu_event_mgr.cc | 74 ++++++++++++++++++- .../core/common_runtime/gpu/gpu_event_mgr.h | 22 ++++++ .../common_runtime/gpu/gpu_event_mgr_test.cc | 23 ++++++ 4 files changed, 133 insertions(+), 4 deletions(-) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 870bde7bc8..17e6ccda14 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -3746,7 +3746,6 @@ tf_cc_tests_gpu( "common_runtime/gpu/gpu_bfc_allocator_test.cc", "common_runtime/gpu/gpu_device_test.cc", "common_runtime/gpu/gpu_id_manager_test.cc", - "common_runtime/gpu/gpu_event_mgr_test.cc", "common_runtime/gpu/pool_allocator_test.cc", ], linkstatic = tf_kernel_tests_linkstatic(), @@ -3770,6 +3769,23 @@ tf_cc_tests_gpu( ], ) +tf_cc_test_gpu( + name = "gpu_event_mgr_test", + srcs = ["common_runtime/gpu/gpu_event_mgr_test.cc"], + linkstatic = tf_kernel_tests_linkstatic(), + tags = tf_cuda_tests_tags(), + deps = [ + ":framework", + ":framework_internal", + ":lib", + ":lib_internal", + ":protos_all_cc", + ":test", + ":test_main", + ":testlib", + ], +) + tf_cuda_cc_test( name = "gpu_device_unified_memory_test", size = "small", diff --git a/tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc b/tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc index 4898448476..3c1c31aa73 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_event_mgr.cc @@ -15,11 +15,80 @@ limitations under the License. #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" +#include "tensorflow/core/platform/stacktrace.h" #include "tensorflow/core/platform/stream_executor.h" #include "tensorflow/core/protobuf/config.pb.h" namespace tensorflow { +namespace { +// The EventMgr has 1 thread for the polling loop and one to execute +// event callback functions. Issues for reconsideration: +// - Is this the right number of threads? +// - Should EventMgrs be shared between GPUDevices on a multi-GPU machine? +static const int kNumThreads = 2; +} // namespace + +namespace gpu_event_mgr { +class ThreadLabel { + public: + static const char* GetValue() { return value_; } + + // v must be a static const because value_ will capture and use its value + // until reset or thread terminates. + static void SetValue(const char* v) { value_ = v; } + + private: + static thread_local const char* value_; +}; +thread_local const char* ThreadLabel::value_ = ""; + +void WarnIfInCallback(std::function f) { + const char* label = ThreadLabel::GetValue(); + if (label && !strcmp(label, "gpu_event_mgr")) { + if (f) { + f(); + } else { + LOG(WARNING) << "Executing inside EventMgr callback thread: " + << CurrentStackTrace(); + } + } +} + +void InitThreadpoolLabels(thread::ThreadPool* threadpool) { + static const char* label = "gpu_event_mgr"; + mutex mu; + int init_count = 0; + condition_variable all_initialized; + int exit_count = 0; + condition_variable ready_to_exit; + const int num_threads = threadpool->NumThreads(); + for (int i = 0; i < num_threads; ++i) { + threadpool->Schedule([num_threads, &mu, &init_count, &all_initialized, + &exit_count, &ready_to_exit]() { + gpu_event_mgr::ThreadLabel::SetValue(label); + mutex_lock l(mu); + ++init_count; + if (init_count == num_threads) { + all_initialized.notify_all(); + } + while (init_count < num_threads) { + all_initialized.wait(l); + } + if (++exit_count == num_threads) { + ready_to_exit.notify_all(); + } + }); + } + { + mutex_lock l(mu); + while (exit_count < num_threads) { + ready_to_exit.wait(l); + } + } +} +} // namespace gpu_event_mgr + EventMgr::EventMgr(se::StreamExecutor* se, const GPUOptions& gpu_options) : exec_(se), deferred_bytes_threshold_(gpu_options.deferred_deletion_bytes() @@ -31,9 +100,8 @@ EventMgr::EventMgr(se::StreamExecutor* se, const GPUOptions& gpu_options) accumulated_stream_(nullptr), accumulated_tensors_(new TensorReferenceVector), accumulated_tensor_bytes_(0), - // threadpool_ has 1 thread for the polling loop, and one to execute - // event callback functions. Maybe we should have more? - threadpool_(Env::Default(), "GPU_Event_Manager", 2) { + threadpool_(Env::Default(), "GPU_Event_Manager", kNumThreads) { + gpu_event_mgr::InitThreadpoolLabels(&threadpool_); StartPollingLoop(); } diff --git a/tensorflow/core/common_runtime/gpu/gpu_event_mgr.h b/tensorflow/core/common_runtime/gpu/gpu_event_mgr.h index b26f88a201..f0a109cc10 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_event_mgr.h +++ b/tensorflow/core/common_runtime/gpu/gpu_event_mgr.h @@ -39,6 +39,25 @@ namespace tensorflow { class GPUOptions; +// The callback provided to EventMgr::ThenExecute must not block or take a long +// time. If it does, performance may be impacted and GPU memory may be +// exhausted. This macro is for checking that an EventMgr thread is not +// accidentally entering blocking parts of the code, e.g. the RPC subsystem. +// +// Intended use is something like +// +// void RespondToAnRPC(Params* params) { +// WARN_IF_IN_EVENT_MGR_THREAD; +// if (params->status.ok()) { ... +// +namespace gpu_event_mgr { +// Logs a stack trace if current execution thread belongs to this EventMgr +// object. If f is not nullptr, executes instead of logging the stack trace. +// trace. +void WarnIfInCallback(std::function f); +} // namespace gpu_event_mgr +#define WARN_IF_IN_EVENT_MGR_THREAD gpu_event_mgr::WarnIfInCallback(nullptr) + // An object to keep track of pending Events in the StreamExecutor streams // and associated Tensors that cannot safely be deleted until the associated // Events are recorded. @@ -74,6 +93,9 @@ class EventMgr { FreeMemory(to_free); } + // Execute func when all pending stream actions have completed. + // func must be brief and non-blocking since it executes in the one + // thread used for all such callbacks and also buffer deletions. inline void ThenExecute(se::Stream* stream, std::function func) { ToFreeVector to_free; { diff --git a/tensorflow/core/common_runtime/gpu/gpu_event_mgr_test.cc b/tensorflow/core/common_runtime/gpu/gpu_event_mgr_test.cc index c5ff6c97a1..d2adf699f5 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_event_mgr_test.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_event_mgr_test.cc @@ -19,6 +19,7 @@ limitations under the License. #include #include "tensorflow/core/common_runtime/gpu/gpu_init.h" +#include "tensorflow/core/lib/core/notification.h" #include "tensorflow/core/platform/stream_executor.h" #include "tensorflow/core/platform/test.h" #include "tensorflow/core/protobuf/config.pb.h" @@ -243,6 +244,28 @@ TEST(EventMgr, NonEmptyShutdown) { } } +// Tests that WarnIfInCallback() triggers correctly. +TEST(EventMgr, WarnIfInCallback) { + auto stream_exec = GPUMachineManager()->ExecutorForDevice(0).ValueOrDie(); + EventMgr em(stream_exec, GPUOptions()); + TEST_EventMgrHelper th(&em); + std::unique_ptr stream(new se::Stream(stream_exec)); + CHECK(stream); + stream->Init(); + bool hit = false; + gpu_event_mgr::WarnIfInCallback([&hit] { hit = true; }); + EXPECT_FALSE(hit); + Notification note; + em.ThenExecute(stream.get(), [&hit, ¬e]() { + gpu_event_mgr::WarnIfInCallback([&hit, ¬e] { + hit = true; + note.Notify(); + }); + }); + note.WaitForNotification(); + EXPECT_TRUE(hit); +} + } // namespace } // namespace tensorflow -- GitLab From c66cc3cf0d6a752430f37b8798a8adaa973875d0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 17:10:14 -0700 Subject: [PATCH 229/519] build_tf_windows.sh: Add --test_target option to control which tests to run PiperOrigin-RevId: 205476590 --- .../windows/cpu/pip/build_tf_windows.sh | 37 ++++++++++++------- .../windows/gpu/pip/build_tf_windows.sh | 37 ++++++++++++------- 2 files changed, 46 insertions(+), 28 deletions(-) diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index 42f58deb42..22d389fd44 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -53,20 +53,31 @@ function cleanup { } trap cleanup EXIT -skip_test=0 -release_build=0 +PY_TEST_DIR="py_test_dir" +SKIP_TEST=0 +RELEASE_BUILD=0 +TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/... " + + "//${PY_TEST_DIR}tensorflow/contrib/... " + +# --skip_test Skip running tests +# --enable_remote_cache Add options to enable remote cache for build and test +# --release_build Build for release, compilation time will be longer to +# ensure performance +# --test_core_only Use tensorflow/python/... as test target +# --test_contrib_only Use tensorflow/contrib/... as test target for ARG in "$@"; do - if [[ "$ARG" == --skip_test ]]; then - skip_test=1 - elif [[ "$ARG" == --enable_remote_cache ]]; then - set_remote_cache_options - elif [[ "$ARG" == --release_build ]]; then - release_build=1 - fi + case "$ARG" in + --skip_test) SKIP_TEST=1 ;; + --enable_remote_cache) set_remote_cache_options ;; + --release_build) RELEASE_BUILD=1 ;; + --test_core_only) TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/..." ;; + --test_contrib_only) TEST_TARGET="//${PY_TEST_DIR}tensorflow/contrib/..." ;; + *) + esac done -if [[ "$release_build" == 1 ]]; then +if [[ "$RELEASE_BUILD" == 1 ]]; then # Overriding eigen strong inline speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc # by 20 minutes. See https://github.com/tensorflow/tensorflow/issues/10521 # Because this hurts the performance of TF, we don't override it in release build. @@ -86,12 +97,11 @@ run_configure_for_cpu_build bazel build --announce_rc --config=opt tensorflow/tools/pip_package:build_pip_package || exit $? -if [[ "$skip_test" == 1 ]]; then +if [[ "$SKIP_TEST" == 1 ]]; then exit 0 fi # Create a python test directory to avoid package name conflict -PY_TEST_DIR="py_test_dir" create_python_test_dir "${PY_TEST_DIR}" ./bazel-bin/tensorflow/tools/pip_package/build_pip_package "$PWD/${PY_TEST_DIR}" @@ -112,5 +122,4 @@ bazel test --announce_rc --config=opt -k --test_output=errors \ --test_size_filters=small,medium \ --jobs="${N_JOBS}" --test_timeout="300,450,1200,3600" \ --flaky_test_attempts=3 \ - //${PY_TEST_DIR}/tensorflow/python/... \ - //${PY_TEST_DIR}/tensorflow/contrib/... + ${TEST_TARGET} diff --git a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh index 2a8c2d9167..682a396d10 100644 --- a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh @@ -53,20 +53,31 @@ function cleanup { } trap cleanup EXIT -skip_test=0 -release_build=0 +PY_TEST_DIR="py_test_dir" +SKIP_TEST=0 +RELEASE_BUILD=0 +TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/... " + + "//${PY_TEST_DIR}tensorflow/contrib/... " + +# --skip_test Skip running tests +# --enable_remote_cache Add options to enable remote cache for build and test +# --release_build Build for release, compilation time will be longer to +# ensure performance +# --test_core_only Use tensorflow/python/... as test target +# --test_contrib_only Use tensorflow/contrib/... as test target for ARG in "$@"; do - if [[ "$ARG" == --skip_test ]]; then - skip_test=1 - elif [[ "$ARG" == --enable_remote_cache ]]; then - set_remote_cache_options - elif [[ "$ARG" == --release_build ]]; then - release_build=1 - fi + case "$ARG" in + --skip_test) SKIP_TEST=1 ;; + --enable_remote_cache) set_remote_cache_options ;; + --release_build) RELEASE_BUILD=1 ;; + --test_core_only) TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/..." ;; + --test_contrib_only) TEST_TARGET="//${PY_TEST_DIR}tensorflow/contrib/..." ;; + *) + esac done -if [[ "$release_build" == 1 ]]; then +if [[ "$RELEASE_BUILD" == 1 ]]; then # Overriding eigen strong inline speeds up the compiling of conv_grad_ops_3d.cc and conv_ops_3d.cc # by 20 minutes. See https://github.com/tensorflow/tensorflow/issues/10521 # Because this hurts the performance of TF, we don't override it in release build. @@ -89,12 +100,11 @@ run_configure_for_gpu_build bazel build --announce_rc --config=opt tensorflow/tools/pip_package:build_pip_package || exit $? -if [[ "$skip_test" == 1 ]]; then +if [[ "$SKIP_TEST" == 1 ]]; then exit 0 fi # Create a python test directory to avoid package name conflict -PY_TEST_DIR="py_test_dir" create_python_test_dir "${PY_TEST_DIR}" ./bazel-bin/tensorflow/tools/pip_package/build_pip_package "$PWD/${PY_TEST_DIR}" @@ -117,5 +127,4 @@ bazel test --announce_rc --config=opt -k --test_output=errors \ --test_size_filters=small,medium \ --local_test_jobs=$TF_GPU_COUNT --test_timeout="300,450,1200,3600" \ --flaky_test_attempts=3 \ - //${PY_TEST_DIR}/tensorflow/python/... \ - //${PY_TEST_DIR}/tensorflow/contrib/... + ${TEST_TARGET} -- GitLab From 57e5dfa76a32ff0ee6ec4b72a2461487b7969a3e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 17:10:38 -0700 Subject: [PATCH 230/519] Avoid argmax/argmin divide by 0 when the output tensor is an empty tensor. PiperOrigin-RevId: 205476629 --- tensorflow/core/kernels/argmax_op.cc | 4 ++++ tensorflow/python/kernel_tests/argmax_op_test.py | 7 +++++++ 2 files changed, 11 insertions(+) diff --git a/tensorflow/core/kernels/argmax_op.cc b/tensorflow/core/kernels/argmax_op.cc index adc573e40c..c731b64993 100644 --- a/tensorflow/core/kernels/argmax_op.cc +++ b/tensorflow/core/kernels/argmax_op.cc @@ -76,6 +76,10 @@ class ArgOp : public OpKernel { Tensor* output = nullptr; OP_REQUIRES_OK(context, context->allocate_output(0, output_shape, &output)); + if (output_shape.num_elements() == 0) { + return; + } + #define HANDLE_DIM(NDIM) \ case NDIM: \ ArgFunctor::Reduce##NDIM(context->eigen_device(), \ diff --git a/tensorflow/python/kernel_tests/argmax_op_test.py b/tensorflow/python/kernel_tests/argmax_op_test.py index ce06769902..1202c463e8 100644 --- a/tensorflow/python/kernel_tests/argmax_op_test.py +++ b/tensorflow/python/kernel_tests/argmax_op_test.py @@ -20,6 +20,7 @@ from __future__ import print_function import numpy as np from tensorflow.python.framework import dtypes +from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.platform import test @@ -115,6 +116,12 @@ class ArgMaxTest(test.TestCase): ans = op([1]).eval() self.assertAllEqual(ans, 0) + def testOutputEmpty(self): + with self.test_session(): + for op in math_ops.argmin, math_ops.argmax: + ret = op(array_ops.zeros(shape=[1, 0, 2]), axis=-1).eval() + self.assertEqual(ret.shape, (1, 0)) + if __name__ == "__main__": test.main() -- GitLab From b840e5ac84319e6e091a0f9351b7691390275f2f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 17:44:31 -0700 Subject: [PATCH 231/519] [XLA] add BitcastConvertType to local Python client PiperOrigin-RevId: 205479860 --- .../xla/python/local_computation_builder.cc | 5 ++++ .../xla/python/local_computation_builder.h | 3 ++ .../xla/python/local_computation_builder.i | 1 + tensorflow/compiler/xla/python/xla_client.py | 12 ++++++++ .../compiler/xla/python/xla_client_test.py | 28 +++++++++++++++++++ 5 files changed, 49 insertions(+) diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index f25348e735..8aefc4cd5e 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -486,6 +486,11 @@ LocalOp LocalComputationBuilder::ConvertElementType( return xla::ConvertElementType(operand.op(), new_element_type); } +LocalOp LocalComputationBuilder::BitcastConvertType( + const LocalOp& operand, PrimitiveType new_element_type) { + return xla::BitcastConvertType(operand.op(), new_element_type); +} + LocalOp LocalComputationBuilder::Call( const LocalComputation& local_computation, tensorflow::gtl::ArraySlice operands) { diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index 0e0d8ac29a..dd9e2fbe72 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -259,6 +259,9 @@ class LocalComputationBuilder { LocalOp ConvertElementType(const LocalOp& operand, PrimitiveType new_element_type); + LocalOp BitcastConvertType(const LocalOp& operand, + PrimitiveType new_element_type); + LocalOp Call(const LocalComputation& local_computation, tensorflow::gtl::ArraySlice operands); diff --git a/tensorflow/compiler/xla/python/local_computation_builder.i b/tensorflow/compiler/xla/python/local_computation_builder.i index eeccbd7cfa..9b8b0aa7f2 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.i +++ b/tensorflow/compiler/xla/python/local_computation_builder.i @@ -957,6 +957,7 @@ tensorflow::ImportNumpy(); %unignore xla::swig::LocalComputationBuilder::Tuple; %unignore xla::swig::LocalComputationBuilder::GetTupleElement; %unignore xla::swig::LocalComputationBuilder::ConvertElementType; +%unignore xla::swig::LocalComputationBuilder::BitcastConvertType; %unignore xla::swig::LocalComputationBuilder::Call; %unignore xla::swig::LocalComputationBuilder::Transpose; %unignore xla::swig::LocalComputationBuilder::Rev; diff --git a/tensorflow/compiler/xla/python/xla_client.py b/tensorflow/compiler/xla/python/xla_client.py index ef043e4ca0..c0105b385b 100644 --- a/tensorflow/compiler/xla/python/xla_client.py +++ b/tensorflow/compiler/xla/python/xla_client.py @@ -721,6 +721,18 @@ class ComputationBuilder(object): """ return self._client.ConvertElementType(operand, new_element_type) + def BitcastConvertType(self, operand, new_element_type): + """Enqueues a bitcast type conversion operation onto the computation. + + Args: + operand: the operand to convert. + new_element_type: the target primitive type. + + Returns: + A LocalOp representing the added conversion op. + """ + return self._client.BitcastConvertType(operand, new_element_type) + def GetShape(self, operand): return _wrap_shape(self._client.GetShape(operand)) diff --git a/tensorflow/compiler/xla/python/xla_client_test.py b/tensorflow/compiler/xla/python/xla_client_test.py index 93177aa647..fd98e19457 100644 --- a/tensorflow/compiler/xla/python/xla_client_test.py +++ b/tensorflow/compiler/xla/python/xla_client_test.py @@ -489,6 +489,34 @@ class SingleOpTest(LocalComputationTest): for src_dtype, dst_dtype in itertools.product(xla_types, xla_types): _ConvertAndTest(x, src_dtype, dst_dtype) + def testBitcastConvertType(self): + xla_x32_types = { + np.int32: xla_client.xla_data_pb2.S32, + np.float32: xla_client.xla_data_pb2.F32, + } + + xla_x64_types = { + np.int64: xla_client.xla_data_pb2.S64, + np.float64: xla_client.xla_data_pb2.F64, + } + + def _ConvertAndTest(template, src_dtype, dst_dtype, dst_etype): + c = self._NewComputation() + x = c.Constant(np.array(template, dtype=src_dtype)) + c.BitcastConvertType(x, dst_etype) + + result = c.Build().Compile().Execute() + expected = np.array(template, src_dtype).view(dst_dtype) + + self.assertEqual(result.shape, expected.shape) + self.assertEqual(result.dtype, expected.dtype) + np.testing.assert_equal(result, expected) + + x = [0, 1, 0, 0, 1] + for xla_types in [xla_x32_types, xla_x64_types]: + for src_dtype, dst_dtype in itertools.product(xla_types, xla_types): + _ConvertAndTest(x, src_dtype, dst_dtype, xla_types[dst_dtype]) + def testCrossReplicaSumOneReplica(self): samples = [ NumpyArrayF32(42.0), -- GitLab From 5c15427443506d4beafc8223fbe665024191464a Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Fri, 20 Jul 2018 17:57:34 -0700 Subject: [PATCH 232/519] BEGIN_PUBLIC Fix broken benchmark. END_PUBLIC Automated rollback of commit 8741006018326350467fe86785d98963ff9e983e PiperOrigin-RevId: 205480787 --- .../eager/python/examples/revnet/BUILD | 36 ++ .../eager/python/examples/revnet/blocks.py | 374 ++++++++++++------ .../python/examples/revnet/cifar_input.py | 2 +- .../eager/python/examples/revnet/config.py | 16 +- .../eager/python/examples/revnet/main.py | 82 ++-- .../python/examples/revnet/main_estimator.py | 200 ++++++++++ .../examples/revnet/main_estimator_tpu.py | 328 +++++++++++++++ .../eager/python/examples/revnet/revnet.py | 112 ++---- .../python/examples/revnet/revnet_test.py | 29 +- 9 files changed, 910 insertions(+), 269 deletions(-) create mode 100644 tensorflow/contrib/eager/python/examples/revnet/main_estimator.py create mode 100644 tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py diff --git a/tensorflow/contrib/eager/python/examples/revnet/BUILD b/tensorflow/contrib/eager/python/examples/revnet/BUILD index 0c0e4c0eb9..3316dc1114 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/BUILD +++ b/tensorflow/contrib/eager/python/examples/revnet/BUILD @@ -113,3 +113,39 @@ py_binary( "//tensorflow:tensorflow_py", ], ) + +py_binary( + name = "main_estimator", + srcs = ["main_estimator.py"], + srcs_version = "PY2AND3", + deps = [ + ":cifar_input", + ":main", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "main_estimator_lib", + srcs = ["main_estimator.py"], + srcs_version = "PY2AND3", + deps = [ + ":cifar_input", + ":main", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "main_estimator_tpu_lib", + srcs = ["main_estimator_tpu.py"], + srcs_version = "PY2AND3", + deps = [ + ":cifar_input", + ":main", + ":revnet", + "//tensorflow:tensorflow_py", + ], +) diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py index 306096e9f8..639bb06a34 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -24,6 +24,9 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import functools +import operator + import tensorflow as tf from tensorflow.contrib.eager.python.examples.revnet import ops @@ -45,7 +48,7 @@ class RevBlock(tf.keras.Model): bottleneck=False, fused=True, dtype=tf.float32): - """Initialize RevBlock. + """Initialization. Args: n_res: number of residual blocks @@ -99,7 +102,6 @@ class RevBlock(tf.keras.Model): if i == 0: # First block usually contains downsampling that can't be reversed with tf.GradientTape() as tape: - x = tf.identity(x) tape.watch(x) y = block(x, training=training) @@ -121,16 +123,6 @@ class _Residual(tf.keras.Model): """Single residual block contained in a _RevBlock. Each `_Residual` object has two _ResidualInner objects, corresponding to the `F` and `G` functions in the paper. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC", - bottleneck: use bottleneck residual if True - fused: use fused batch normalization if True - dtype: float16, float32, or float64 """ def __init__(self, @@ -142,6 +134,18 @@ class _Residual(tf.keras.Model): bottleneck=False, fused=True, dtype=tf.float32): + """Initialization. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC", + bottleneck: use bottleneck residual if True + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + """ super(_Residual, self).__init__() self.filters = filters @@ -196,7 +200,6 @@ class _Residual(tf.keras.Model): dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=self.axis) with tf.GradientTape(persistent=True) as tape: - y = tf.identity(y) tape.watch(y) y1, y2 = tf.split(y, num_or_size_splits=2, axis=self.axis) z1 = y1 @@ -227,131 +230,252 @@ class _Residual(tf.keras.Model): return x, dx, grads, vars_ -def _BottleneckResidualInner(filters, - strides, - input_shape, - batch_norm_first=True, - data_format="channels_first", - fused=True, - dtype=tf.float32): +# Ideally, the following should be wrapped in `tf.keras.Sequential`, however +# there are subtle issues with its placeholder insertion policy and batch norm +class _BottleneckResidualInner(tf.keras.Model): """Single bottleneck residual inner function contained in _Resdual. Corresponds to the `F`/`G` functions in the paper. Suitable for training on ImageNet dataset. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC" - fused: use fused batch normalization if True - dtype: float16, float32, or float64 - - Returns: - A keras model """ - axis = 1 if data_format == "channels_first" else 3 - model = tf.keras.Sequential() - if batch_norm_first: - model.add( - tf.keras.layers.BatchNormalization( - axis=axis, input_shape=input_shape, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters // 4, - kernel_size=1, - strides=strides, - input_shape=input_shape, - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) - - model.add( - tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters // 4, - kernel_size=3, - strides=(1, 1), - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) - - model.add( - tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters, - kernel_size=1, - strides=(1, 1), - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) + def __init__(self, + filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True, + dtype=tf.float32): + """Initialization. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + """ + super(_BottleneckResidualInner, self).__init__() + axis = 1 if data_format == "channels_first" else 3 + if batch_norm_first: + self.batch_norm_0 = tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused, dtype=dtype) + + self.conv2d_1 = tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=1, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + self.batch_norm_1 = tf.keras.layers.BatchNormalization( + axis=axis, fused=fused, dtype=dtype) + + self.conv2d_2 = tf.keras.layers.Conv2D( + filters=filters // 4, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + + self.batch_norm_2 = tf.keras.layers.BatchNormalization( + axis=axis, fused=fused, dtype=dtype) + self.conv2d_3 = tf.keras.layers.Conv2D( + filters=filters, + kernel_size=1, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + + self.batch_norm_first = batch_norm_first + + def call(self, x, training=True): + net = x + if self.batch_norm_first: + net = self.batch_norm_0(net, training=training) + net = tf.nn.relu(net) + + net = self.conv2d_1(net) + net = self.batch_norm_1(net, training=training) + net = tf.nn.relu(net) + + net = self.conv2d_2(net) + net = self.batch_norm_2(net, training=training) + net = tf.nn.relu(net) - return model + net = self.conv2d_3(net) + return net -def _ResidualInner(filters, - strides, - input_shape, - batch_norm_first=True, - data_format="channels_first", - fused=True, - dtype=tf.float32): + +class _ResidualInner(tf.keras.Model): """Single residual inner function contained in _ResdualBlock. Corresponds to the `F`/`G` functions in the paper. - - Args: - filters: output filter size - strides: length 2 list/tuple of integers for height and width strides - input_shape: length 3 list/tuple of integers - batch_norm_first: whether to apply activation and batch norm before conv - data_format: tensor data format, "NCHW"/"NHWC" - fused: use fused batch normalization if True - dtype: float16, float32, or float64 - - Returns: - A keras model """ - axis = 1 if data_format == "channels_first" else 3 - model = tf.keras.Sequential() - if batch_norm_first: - model.add( - tf.keras.layers.BatchNormalization( - axis=axis, input_shape=input_shape, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters, - kernel_size=3, - strides=strides, - input_shape=input_shape, - data_format=data_format, - use_bias=False, - padding="SAME", - dtype=dtype)) - - model.add( - tf.keras.layers.BatchNormalization(axis=axis, fused=fused, dtype=dtype)) - model.add(tf.keras.layers.Activation("relu")) - model.add( - tf.keras.layers.Conv2D( - filters=filters, - kernel_size=3, - strides=(1, 1), - data_format=data_format, - use_bias=False, + def __init__(self, + filters, + strides, + input_shape, + batch_norm_first=True, + data_format="channels_first", + fused=True, + dtype=tf.float32): + """Initialization. + + Args: + filters: output filter size + strides: length 2 list/tuple of integers for height and width strides + input_shape: length 3 list/tuple of integers + batch_norm_first: whether to apply activation and batch norm before conv + data_format: tensor data format, "NCHW"/"NHWC" + fused: use fused batch normalization if True + dtype: float16, float32, or float64 + """ + super(_ResidualInner, self).__init__() + axis = 1 if data_format == "channels_first" else 3 + if batch_norm_first: + self.batch_norm_0 = tf.keras.layers.BatchNormalization( + axis=axis, input_shape=input_shape, fused=fused, dtype=dtype) + self.conv2d_1 = tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=strides, + input_shape=input_shape, + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + self.batch_norm_1 = tf.keras.layers.BatchNormalization( + axis=axis, fused=fused, dtype=dtype) + + self.conv2d_2 = tf.keras.layers.Conv2D( + filters=filters, + kernel_size=3, + strides=(1, 1), + data_format=data_format, + use_bias=False, + padding="SAME", + dtype=dtype) + + self.batch_norm_first = batch_norm_first + + def call(self, x, training=True): + net = x + if self.batch_norm_first: + net = self.batch_norm_0(net, training=training) + net = tf.nn.relu(net) + + net = self.conv2d_1(net) + net = self.batch_norm_1(net, training=training) + + net = self.conv2d_2(net) + + return net + + +class InitBlock(tf.keras.Model): + """Initial block of RevNet.""" + + def __init__(self, config): + """Initialization. + + Args: + config: tf.contrib.training.HParams object; specifies hyperparameters + """ + super(InitBlock, self).__init__() + self.config = config + self.axis = 1 if self.config.data_format == "channels_first" else 3 + self.conv2d = tf.keras.layers.Conv2D( + filters=self.config.init_filters, + kernel_size=self.config.init_kernel, + strides=(self.config.init_stride, self.config.init_stride), + data_format=self.config.data_format, + use_bias=False, + padding="SAME", + input_shape=self.config.input_shape, + dtype=self.config.dtype) + self.batch_norm = tf.keras.layers.BatchNormalization( + axis=self.axis, fused=self.config.fused, dtype=self.config.dtype) + self.activation = tf.keras.layers.Activation("relu") + + if self.config.init_max_pool: + self.max_pool = tf.keras.layers.MaxPooling2D( + pool_size=(3, 3), + strides=(2, 2), padding="SAME", - dtype=dtype)) + data_format=self.config.data_format, + dtype=self.config.dtype) + + def call(self, x, training=True): + net = x + net = self.conv2d(net) + net = self.batch_norm(net, training=training) + net = self.activation(net) + + if self.config.init_max_pool: + net = self.max_pool(net) + + return net + - return model +class FinalBlock(tf.keras.Model): + """Final block of RevNet.""" + + def __init__(self, config): + """Initialization. + + Args: + config: tf.contrib.training.HParams object; specifies hyperparameters + + Raises: + ValueError: Unsupported data format + """ + super(FinalBlock, self).__init__() + self.config = config + self.axis = 1 if self.config.data_format == "channels_first" else 3 + + f = self.config.filters[-1] # Number of filters + r = functools.reduce(operator.mul, self.config.strides, 1) # Reduce ratio + r *= self.config.init_stride + if self.config.init_max_pool: + r *= 2 + + if self.config.data_format == "channels_first": + w, h = self.config.input_shape[1], self.config.input_shape[2] + input_shape = (f, w // r, h // r) + elif self.config.data_format == "channels_last": + w, h = self.config.input_shape[0], self.config.input_shape[1] + input_shape = (w // r, h // r, f) + else: + raise ValueError("Data format should be either `channels_first`" + " or `channels_last`") + self.batch_norm = tf.keras.layers.BatchNormalization( + axis=self.axis, + input_shape=input_shape, + fused=self.config.fused, + dtype=self.config.dtype) + self.activation = tf.keras.layers.Activation("relu") + self.global_avg_pool = tf.keras.layers.GlobalAveragePooling2D( + data_format=self.config.data_format, dtype=self.config.dtype) + self.dense = tf.keras.layers.Dense( + self.config.n_classes, dtype=self.config.dtype) + + def call(self, x, training=True): + net = x + net = self.batch_norm(net, training=training) + net = self.activation(net) + net = self.global_avg_pool(net) + net = self.dense(net) + + return net diff --git a/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py index b6d4c35bfd..e9672f13e1 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py +++ b/tensorflow/contrib/eager/python/examples/revnet/cifar_input.py @@ -111,6 +111,6 @@ def get_ds_from_tfrecords(data_dir, }[split] dataset = dataset.shuffle(size) - dataset = dataset.batch(batch_size) + dataset = dataset.batch(batch_size, drop_remainder=True) return dataset diff --git a/tensorflow/contrib/eager/python/examples/revnet/config.py b/tensorflow/contrib/eager/python/examples/revnet/config.py index 3d93fa955a..1532c7b67b 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/config.py +++ b/tensorflow/contrib/eager/python/examples/revnet/config.py @@ -27,17 +27,16 @@ from __future__ import division from __future__ import print_function import tensorflow as tf -tfe = tf.contrib.eager def get_hparams_cifar_38(): """RevNet-38 configurations for CIFAR-10/CIFAR-100.""" config = tf.contrib.training.HParams() + # Hyperparameters from the RevNet paper config.add_hparam("init_filters", 32) config.add_hparam("init_kernel", 3) config.add_hparam("init_stride", 1) - config.add_hparam("n_classes", 10) config.add_hparam("n_rev_blocks", 3) config.add_hparam("n_res", [3, 3, 3]) config.add_hparam("filters", [32, 64, 112]) @@ -46,7 +45,7 @@ def get_hparams_cifar_38(): config.add_hparam("bottleneck", False) config.add_hparam("fused", True) config.add_hparam("init_max_pool", False) - if tfe.num_gpus() > 0: + if tf.test.is_gpu_available() > 0: config.add_hparam("input_shape", (3, 32, 32)) config.add_hparam("data_format", "channels_first") else: @@ -71,6 +70,16 @@ def get_hparams_cifar_38(): config.add_hparam("iters_per_epoch", 50000 // config.batch_size) config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) + # Customized TPU hyperparameters due to differing batch size caused by + # TPU architecture specifics + # Suggested batch sizes to reduce overhead from excessive tensor padding + # https://cloud.google.com/tpu/docs/troubleshooting + config.add_hparam("tpu_batch_size", 128) + config.add_hparam("tpu_eval_batch_size", 1024) + config.add_hparam("tpu_iters_per_epoch", 50000 // config.tpu_batch_size) + config.add_hparam("tpu_epochs", + config.max_train_iter // config.tpu_iters_per_epoch) + return config @@ -101,7 +110,6 @@ def get_hparams_imagenet_56(): config.add_hparam("init_filters", 128) config.add_hparam("init_kernel", 7) config.add_hparam("init_stride", 2) - config.add_hparam("n_classes", 1000) config.add_hparam("n_rev_blocks", 4) config.add_hparam("n_res", [2, 2, 2, 2]) config.add_hparam("filters", [128, 256, 512, 832]) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main.py b/tensorflow/contrib/eager/python/examples/revnet/main.py index e2f43b03f9..1a4fd45c8b 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/main.py +++ b/tensorflow/contrib/eager/python/examples/revnet/main.py @@ -31,8 +31,11 @@ tfe = tf.contrib.eager def main(_): """Eager execution workflow with RevNet trained on CIFAR-10.""" - config = get_config() - ds_train, ds_train_one_shot, ds_validation, ds_test = get_datasets(config) + tf.enable_eager_execution() + + config = get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) + ds_train, ds_train_one_shot, ds_validation, ds_test = get_datasets( + data_dir=FLAGS.data_dir, config=config) model = revnet.RevNet(config=config) global_step = tf.train.get_or_create_global_step() # Ensure correct summary global_step.assign(1) @@ -52,23 +55,17 @@ def main(_): "with global_step: {}".format(latest_path, global_step.numpy())) sys.stdout.flush() - if FLAGS.manual_grad: - print("Using manual gradients.") - else: - print("Not using manual gradients.") - sys.stdout.flush() - for x, y in ds_train: train_one_iter(model, x, y, optimizer, global_step=global_step) if global_step.numpy() % config.log_every == 0: - it_train = ds_train_one_shot.make_one_shot_iterator() it_test = ds_test.make_one_shot_iterator() - acc_train, loss_train = evaluate(model, it_train) acc_test, loss_test = evaluate(model, it_test) if FLAGS.validate: + it_train = ds_train_one_shot.make_one_shot_iterator() it_validation = ds_validation.make_one_shot_iterator() + acc_train, loss_train = evaluate(model, it_train) acc_validation, loss_validation = evaluate(model, it_validation) print("Iter {}, " "training set accuracy {:.4f}, loss {:.4f}; " @@ -77,11 +74,8 @@ def main(_): global_step.numpy(), acc_train, loss_train, acc_validation, loss_validation, acc_test, loss_test)) else: - print("Iter {}, " - "training set accuracy {:.4f}, loss {:.4f}; " - "test accuracy {:.4f}, loss {:.4f}".format( - global_step.numpy(), acc_train, loss_train, acc_test, - loss_test)) + print("Iter {}, test accuracy {:.4f}, loss {:.4f}".format( + global_step.numpy(), acc_test, loss_test)) sys.stdout.flush() if FLAGS.train_dir: @@ -103,34 +97,38 @@ def main(_): sys.stdout.flush() -def get_config(): +def get_config(config_name="revnet-38", dataset="cifar-10"): """Return configuration.""" - print("Config: {}".format(FLAGS.config)) + print("Config: {}".format(config_name)) sys.stdout.flush() config = { "revnet-38": config_.get_hparams_cifar_38(), "revnet-110": config_.get_hparams_cifar_110(), "revnet-164": config_.get_hparams_cifar_164(), - }[FLAGS.config] + }[config_name] - if FLAGS.dataset == "cifar-100": - config.n_classes = 100 + if dataset == "cifar-10": + config.add_hparam("n_classes", 10) + config.add_hparam("dataset", "cifar-10") + else: + config.add_hparam("n_classes", 100) + config.add_hparam("dataset", "cifar-100") return config -def get_datasets(config): +def get_datasets(data_dir, config): """Return dataset.""" - if FLAGS.data_dir is None: + if data_dir is None: raise ValueError("No supplied data directory") - if not os.path.exists(FLAGS.data_dir): - raise ValueError("Data directory {} does not exist".format(FLAGS.data_dir)) - if FLAGS.dataset not in ["cifar-10", "cifar-100"]: - raise ValueError("Unknown dataset {}".format(FLAGS.dataset)) + if not os.path.exists(data_dir): + raise ValueError("Data directory {} does not exist".format(data_dir)) + if config.dataset not in ["cifar-10", "cifar-100"]: + raise ValueError("Unknown dataset {}".format(config.dataset)) - print("Training on {} dataset.".format(FLAGS.dataset)) + print("Training on {} dataset.".format(config.dataset)) sys.stdout.flush() - data_dir = os.path.join(FLAGS.data_dir, FLAGS.dataset) + data_dir = os.path.join(data_dir, config.dataset) if FLAGS.validate: # 40k Training set ds_train = cifar_input.get_ds_from_tfrecords( @@ -168,7 +166,7 @@ def get_datasets(config): prefetch=config.batch_size) ds_validation = None - # Always compute loss and accuracy on whole training and test set + # Always compute loss and accuracy on whole test set ds_train_one_shot = cifar_input.get_ds_from_tfrecords( data_dir=data_dir, split="train_all", @@ -196,19 +194,11 @@ def get_datasets(config): def train_one_iter(model, inputs, labels, optimizer, global_step=None): """Train for one iteration.""" - if FLAGS.manual_grad: - grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) - optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) - else: # For correctness validation - with tf.GradientTape() as tape: - logits, _ = model(inputs, training=True) - loss = model.compute_loss(logits=logits, labels=labels) - tf.logging.info("Logits are placed on device: {}".format(logits.device)) - grads = tape.gradient(loss, model.trainable_variables) - optimizer.apply_gradients( - zip(grads, model.trainable_variables), global_step=global_step) + grads, vars_, logits, loss = model.compute_gradients( + inputs, labels, training=True) + optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) - return loss.numpy() + return logits, loss def evaluate(model, iterator): @@ -241,16 +231,14 @@ if __name__ == "__main__": "validate", default=False, help="[Optional] Use the validation set or not for hyperparameter search") - flags.DEFINE_boolean( - "manual_grad", - default=False, - help="[Optional] Use manual gradient graph to save memory") flags.DEFINE_string( "dataset", default="cifar-10", help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") flags.DEFINE_string( - "config", default="revnet-38", help="[Optional] Architecture of network.") + "config", + default="revnet-38", + help="[Optional] Architecture of network. " + "Other options include `revnet-110` and `revnet-164`") FLAGS = flags.FLAGS - tf.enable_eager_execution() tf.app.run(main) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py b/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py new file mode 100644 index 0000000000..4868f1931f --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py @@ -0,0 +1,200 @@ +# 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. +# ============================================================================== +"""Estimator workflow with RevNet train on CIFAR-10.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from absl import flags +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import cifar_input +from tensorflow.contrib.eager.python.examples.revnet import main as main_ +from tensorflow.contrib.eager.python.examples.revnet import revnet + + +def model_fn(features, labels, mode, params): + """Function specifying the model that is required by the `tf.estimator` API. + + Args: + features: Input images + labels: Labels of images + mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT' + params: A dictionary of extra parameter that might be passed + + Returns: + An instance of `tf.estimator.EstimatorSpec` + """ + + inputs = features + if isinstance(inputs, dict): + inputs = features["image"] + + config = params["config"] + model = revnet.RevNet(config=config) + + if mode == tf.estimator.ModeKeys.TRAIN: + global_step = tf.train.get_or_create_global_step() + learning_rate = tf.train.piecewise_constant( + global_step, config.lr_decay_steps, config.lr_list) + optimizer = tf.train.MomentumOptimizer( + learning_rate, momentum=config.momentum) + grads, vars_, logits, loss = model.compute_gradients( + inputs, labels, training=True) + train_op = optimizer.apply_gradients( + zip(grads, vars_), global_step=global_step) + + return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op) + else: + logits, _ = model(inputs, training=False) + predictions = tf.argmax(logits, axis=1) + probabilities = tf.nn.softmax(logits) + + if mode == tf.estimator.ModeKeys.EVAL: + loss = model.compute_loss(labels=labels, logits=logits) + return tf.estimator.EstimatorSpec( + mode=mode, + loss=loss, + eval_metric_ops={ + "accuracy": + tf.metrics.accuracy(labels=labels, predictions=predictions) + }) + + else: # mode == tf.estimator.ModeKeys.PREDICT + result = { + "classes": predictions, + "probabilities": probabilities, + } + + return tf.estimator.EstimatorSpec( + mode=mode, + predictions=predictions, + export_outputs={ + "classify": tf.estimator.export.PredictOutput(result) + }) + + +def get_input_fn(config, data_dir, split): + """Get the input function that is required by the `tf.estimator` API. + + Args: + config: Customized hyperparameters + data_dir: Directory where the data is stored + split: One of `train`, `validation`, `train_all`, and `test` + + Returns: + Input function required by the `tf.estimator` API + """ + + data_dir = os.path.join(data_dir, config.dataset) + # Fix split-dependent hyperparameters + if split == "train_all" or split == "train": + data_aug = True + batch_size = config.batch_size + epochs = config.epochs + shuffle = True + prefetch = config.batch_size + else: + data_aug = False + batch_size = config.eval_batch_size + epochs = 1 + shuffle = False + prefetch = config.eval_batch_size + + def input_fn(): + """Input function required by the `tf.estimator.Estimator` API.""" + return cifar_input.get_ds_from_tfrecords( + data_dir=data_dir, + split=split, + data_aug=data_aug, + batch_size=batch_size, + epochs=epochs, + shuffle=shuffle, + prefetch=prefetch, + data_format=config.data_format) + + return input_fn + + +def main(argv): + FLAGS = argv[0] # pylint:disable=invalid-name,redefined-outer-name + tf.logging.set_verbosity(tf.logging.INFO) + + # RevNet specific configuration + config = main_.get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) + + # Estimator specific configuration + run_config = tf.estimator.RunConfig( + model_dir=FLAGS.train_dir, # Directory for storing checkpoints + tf_random_seed=config.seed, + save_summary_steps=config.log_every, + save_checkpoints_steps=config.log_every, + session_config=None, # Using default + keep_checkpoint_max=100, + keep_checkpoint_every_n_hours=10000, # Using default + log_step_count_steps=config.log_every, + train_distribute=None # Default not use distribution strategy + ) + + # Construct estimator + revnet_estimator = tf.estimator.Estimator( + model_fn=model_fn, + model_dir=FLAGS.train_dir, + config=run_config, + params={"config": config}) + + # Construct input functions + train_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="train_all") + eval_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="test") + + # Train and evaluate estimator + revnet_estimator.train(input_fn=train_input_fn) + revnet_estimator.evaluate(input_fn=eval_input_fn) + + if FLAGS.export: + input_shape = (None,) + config.input_shape + inputs = tf.placeholder(tf.float32, shape=input_shape) + input_fn = tf.estimator.export.build_raw_serving_input_receiver_fn({ + "image": inputs + }) + revnet_estimator.export_savedmodel(FLAGS.train_dir, input_fn) + + +if __name__ == "__main__": + flags.DEFINE_string( + "data_dir", default=None, help="Directory to load tfrecords") + flags.DEFINE_string( + "train_dir", + default=None, + help="[Optional] Directory to store the training information") + flags.DEFINE_string( + "dataset", + default="cifar-10", + help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") + flags.DEFINE_boolean( + "export", + default=False, + help="[Optional] Export the model for serving if True") + flags.DEFINE_string( + "config", + default="revnet-38", + help="[Optional] Architecture of network. " + "Other options include `revnet-110` and `revnet-164`") + FLAGS = flags.FLAGS + tf.app.run(main=main, argv=[FLAGS]) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py b/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py new file mode 100644 index 0000000000..d809bcd287 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py @@ -0,0 +1,328 @@ +# 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. +# ============================================================================== +"""Cloud TPU Estimator workflow with RevNet train on CIFAR-10.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import time + +from absl import flags +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.revnet import cifar_input +from tensorflow.contrib.eager.python.examples.revnet import main as main_ +from tensorflow.contrib.eager.python.examples.revnet import revnet +from tensorflow.contrib.training.python.training import evaluation +from tensorflow.python.estimator import estimator as estimator_ + + +def model_fn(features, labels, mode, params): + """Model function required by the `tf.contrib.tpu.TPUEstimator` API. + + Args: + features: Input images + labels: Labels of images + mode: One of `ModeKeys.TRAIN`, `ModeKeys.EVAL` or 'ModeKeys.PREDICT' + params: A dictionary of extra parameter that might be passed + + Returns: + An instance of `tf.contrib.tpu.TPUEstimatorSpec` + """ + + inputs = features + if isinstance(inputs, dict): + inputs = features["image"] + + FLAGS = params["FLAGS"] # pylint:disable=invalid-name,redefined-outer-name + config = params["config"] + model = revnet.RevNet(config=config) + + if mode == tf.estimator.ModeKeys.TRAIN: + global_step = tf.train.get_or_create_global_step() + learning_rate = tf.train.piecewise_constant( + global_step, config.lr_decay_steps, config.lr_list) + optimizer = tf.train.MomentumOptimizer( + learning_rate, momentum=config.momentum) + + if FLAGS.use_tpu: + optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer) + + # Define gradients + grads, vars_, logits, loss = model.compute_gradients( + inputs, labels, training=True) + train_op = optimizer.apply_gradients( + zip(grads, vars_), global_step=global_step) + + names = [v.name for v in model.variables] + tf.logging.warn("{}".format(names)) + + return tf.contrib.tpu.TPUEstimatorSpec( + mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op) + + elif mode == tf.estimator.ModeKeys.EVAL: + logits, _ = model(inputs, training=False) + loss = model.compute_loss(labels=labels, logits=logits) + + def metric_fn(labels, logits): + predictions = tf.argmax(logits, axis=1) + accuracy = tf.metrics.accuracy(labels=labels, predictions=predictions) + return { + "accuracy": accuracy, + } + + return tf.contrib.tpu.TPUEstimatorSpec( + mode=mode, loss=loss, eval_metrics=(metric_fn, [labels, logits])) + + else: # Predict or export + logits, _ = model(inputs, training=False) + predictions = { + "classes": tf.argmax(logits, axis=1), + "probabilities": tf.nn.softmax(logits), + } + + return tf.contrib.tpu.TPUEstimatorSpec( + mode=mode, + predictions=predictions, + export_outputs={ + "classify": tf.estimator.export.PredictOutput(predictions) + }) + + +def get_input_fn(config, data_dir, split): + """Get the input function required by the `tf.contrib.tpu.TPUEstimator` API. + + Args: + config: Customized hyperparameters + data_dir: Directory where the data is stored + split: One of `train`, `validation`, `train_all`, and `test` + + Returns: + Input function required by the `tf.contrib.tpu.TPUEstimator` API + """ + + data_dir = os.path.join(data_dir, config.dataset) + # Fix split-dependent hyperparameters + if split == "train_all" or split == "train": + data_aug = True + epochs = config.tpu_epochs + shuffle = True + else: + data_aug = False + epochs = 1 + shuffle = False + + def input_fn(params): + """Input function required by the `tf.contrib.tpu.TPUEstimator` API.""" + batch_size = params["batch_size"] + return cifar_input.get_ds_from_tfrecords( + data_dir=data_dir, + split=split, + data_aug=data_aug, + batch_size=batch_size, # per-shard batch size + epochs=epochs, + shuffle=shuffle, + prefetch=batch_size, # per-shard batch size + data_format=config.data_format) + + return input_fn + + +def main(argv): + FLAGS = argv[0] # pylint:disable=invalid-name,redefined-outer-name + tf.logging.set_verbosity(tf.logging.INFO) + + # RevNet specific configuration + config = main_.get_config(config_name=FLAGS.config, dataset=FLAGS.dataset) + + if FLAGS.use_tpu: + tf.logging.info("Using TPU.") + tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver( + FLAGS.tpu, zone=FLAGS.tpu_zone, project=FLAGS.gcp_project) + else: + tpu_cluster_resolver = None + + # TPU specific configuration + tpu_config = tf.contrib.tpu.TPUConfig( + # Recommended to be set as number of global steps for next checkpoint + iterations_per_loop=FLAGS.iterations_per_loop, + num_shards=FLAGS.num_shards) + + # Estimator specific configuration + run_config = tf.contrib.tpu.RunConfig( + cluster=tpu_cluster_resolver, + model_dir=FLAGS.model_dir, + session_config=tf.ConfigProto( + allow_soft_placement=True, log_device_placement=False), + tpu_config=tpu_config, + ) + + # Construct TPU Estimator + estimator = tf.contrib.tpu.TPUEstimator( + model_fn=model_fn, + use_tpu=FLAGS.use_tpu, + train_batch_size=config.tpu_batch_size, + eval_batch_size=config.tpu_eval_batch_size, + config=run_config, + params={ + "FLAGS": FLAGS, + "config": config, + }) + + # Construct input functions + train_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="train_all") + eval_input_fn = get_input_fn( + config=config, data_dir=FLAGS.data_dir, split="test") + + # Disabling a range within an else block currently doesn't work + # due to https://github.com/PyCQA/pylint/issues/872 + # pylint: disable=protected-access + if FLAGS.mode == "eval": + # TPUEstimator.evaluate *requires* a steps argument. + # Note that the number of examples used during evaluation is + # --eval_steps * --batch_size. + # So if you change --batch_size then change --eval_steps too. + eval_steps = 10000 // config.tpu_eval_batch_size + + # Run evaluation when there's a new checkpoint + for ckpt in evaluation.checkpoints_iterator( + FLAGS.model_dir, timeout=FLAGS.eval_timeout): + tf.logging.info("Starting to evaluate.") + try: + start_timestamp = time.time() # This time will include compilation time + eval_results = estimator.evaluate( + input_fn=eval_input_fn, steps=eval_steps, checkpoint_path=ckpt) + elapsed_time = int(time.time() - start_timestamp) + tf.logging.info("Eval results: %s. Elapsed seconds: %d" % + (eval_results, elapsed_time)) + + # Terminate eval job when final checkpoint is reached + current_step = int(os.path.basename(ckpt).split("-")[1]) + if current_step >= config.max_train_iter: + tf.logging.info( + "Evaluation finished after training step %d" % current_step) + break + + except tf.errors.NotFoundError: + # Since the coordinator is on a different job than the TPU worker, + # sometimes the TPU worker does not finish initializing until long after + # the CPU job tells it to start evaluating. In this case, the checkpoint + # file could have been deleted already. + tf.logging.info( + "Checkpoint %s no longer exists, skipping checkpoint" % ckpt) + + else: # FLAGS.mode == 'train' or FLAGS.mode == 'train_and_eval' + current_step = estimator_._load_global_step_from_checkpoint_dir( + FLAGS.model_dir) + tf.logging.info("Training for %d steps . Current" + " step %d." % (config.max_train_iter, current_step)) + + start_timestamp = time.time() # This time will include compilation time + if FLAGS.mode == "train": + estimator.train(input_fn=train_input_fn, max_steps=config.max_train_iter) + else: + eval_steps = 10000 // config.tpu_eval_batch_size + assert FLAGS.mode == "train_and_eval" + while current_step < config.max_train_iter: + # Train for up to steps_per_eval number of steps. + # At the end of training, a checkpoint will be written to --model_dir. + next_checkpoint = min(current_step + FLAGS.steps_per_eval, + config.max_train_iter) + estimator.train(input_fn=train_input_fn, max_steps=next_checkpoint) + current_step = next_checkpoint + + # Evaluate the model on the most recent model in --model_dir. + # Since evaluation happens in batches of --eval_batch_size, some images + # may be consistently excluded modulo the batch size. + tf.logging.info("Starting to evaluate.") + eval_results = estimator.evaluate( + input_fn=eval_input_fn, steps=eval_steps) + tf.logging.info("Eval results: %s" % eval_results) + + elapsed_time = int(time.time() - start_timestamp) + tf.logging.info("Finished training up to step %d. Elapsed seconds %d." % + (config.max_train_iter, elapsed_time)) + # pylint: enable=protected-access + + +if __name__ == "__main__": + # Cloud TPU Cluster Resolver flags + flags.DEFINE_string( + "tpu", + default=None, + help="The Cloud TPU to use for training. This should be either the name " + "used when creating the Cloud TPU, or a grpc://ip.address.of.tpu:8470 " + "url.") + flags.DEFINE_string( + "tpu_zone", + default=None, + help="[Optional] GCE zone where the Cloud TPU is located in. If not " + "specified, we will attempt to automatically detect the GCE project from " + "metadata.") + flags.DEFINE_string( + "gcp_project", + default=None, + help="[Optional] Project name for the Cloud TPU-enabled project. If not " + "specified, we will attempt to automatically detect the GCE project from " + "metadata.") + + # Model specific parameters + flags.DEFINE_string( + "data_dir", default=None, help="Directory to load tfrecords") + flags.DEFINE_string( + "model_dir", + default=None, + help="[Optional] Directory to store the model information") + flags.DEFINE_string( + "dataset", + default="cifar-10", + help="[Optional] The dataset used; either `cifar-10` or `cifar-100`") + flags.DEFINE_string( + "config", + default="revnet-38", + help="[Optional] Architecture of network. " + "Other options include `revnet-110` and `revnet-164`") + flags.DEFINE_boolean( + "use_tpu", default=True, help="[Optional] Whether to use TPU") + flags.DEFINE_integer( + "num_shards", default=8, help="Number of shards (TPU chips).") + flags.DEFINE_integer( + "iterations_per_loop", + default=100, + help=( + "Number of steps to run on TPU before feeding metrics to the CPU." + " If the number of iterations in the loop would exceed the number of" + " train steps, the loop will exit before reaching" + " --iterations_per_loop. The larger this value is, the higher the" + " utilization on the TPU.")) + flags.DEFINE_string( + "mode", + default="train_and_eval", + help="[Optional] Mode to run: train, eval, train_and_eval") + flags.DEFINE_integer( + "eval_timeout", 60 * 60 * 24, + "Maximum seconds between checkpoints before evaluation terminates.") + flags.DEFINE_integer( + "steps_per_eval", + default=1000, + help=( + "Controls how often evaluation is performed. Since evaluation is" + " fairly expensive, it is advised to evaluate as infrequently as" + " possible (i.e. up to --train_steps, which evaluates the model only" + " after finishing the entire training regime).")) + FLAGS = flags.FLAGS + tf.app.run(main=main, argv=[FLAGS]) diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet.py b/tensorflow/contrib/eager/python/examples/revnet/revnet.py index af0d20fa72..b1cb312b74 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet.py @@ -24,9 +24,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import functools -import operator - import six import tensorflow as tf from tensorflow.contrib.eager.python.examples.revnet import blocks @@ -45,71 +42,9 @@ class RevNet(tf.keras.Model): self.axis = 1 if config.data_format == "channels_first" else 3 self.config = config - self._init_block = self._construct_init_block() + self._init_block = blocks.InitBlock(config=self.config) + self._final_block = blocks.FinalBlock(config=self.config) self._block_list = self._construct_intermediate_blocks() - self._final_block = self._construct_final_block() - - def _construct_init_block(self): - init_block = tf.keras.Sequential( - [ - tf.keras.layers.Conv2D( - filters=self.config.init_filters, - kernel_size=self.config.init_kernel, - strides=(self.config.init_stride, self.config.init_stride), - data_format=self.config.data_format, - use_bias=False, - padding="SAME", - input_shape=self.config.input_shape, - dtype=self.config.dtype), - tf.keras.layers.BatchNormalization( - axis=self.axis, - fused=self.config.fused, - dtype=self.config.dtype), - tf.keras.layers.Activation("relu"), - ], - name="init") - if self.config.init_max_pool: - init_block.add( - tf.keras.layers.MaxPooling2D( - pool_size=(3, 3), - strides=(2, 2), - padding="SAME", - data_format=self.config.data_format, - dtype=self.config.dtype)) - return init_block - - def _construct_final_block(self): - f = self.config.filters[-1] # Number of filters - r = functools.reduce(operator.mul, self.config.strides, 1) # Reduce ratio - r *= self.config.init_stride - if self.config.init_max_pool: - r *= 2 - - if self.config.data_format == "channels_first": - w, h = self.config.input_shape[1], self.config.input_shape[2] - input_shape = (f, w // r, h // r) - elif self.config.data_format == "channels_last": - w, h = self.config.input_shape[0], self.config.input_shape[1] - input_shape = (w // r, h // r, f) - else: - raise ValueError("Data format should be either `channels_first`" - " or `channels_last`") - - final_block = tf.keras.Sequential( - [ - tf.keras.layers.BatchNormalization( - axis=self.axis, - input_shape=input_shape, - fused=self.config.fused, - dtype=self.config.dtype), - tf.keras.layers.Activation("relu"), - tf.keras.layers.GlobalAveragePooling2D( - data_format=self.config.data_format, dtype=self.config.dtype), - tf.keras.layers.Dense( - self.config.n_classes, dtype=self.config.dtype) - ], - name="final") - return final_block def _construct_intermediate_blocks(self): # Precompute input shape after initial block @@ -206,13 +141,20 @@ class RevNet(tf.keras.Model): l2_reg: Apply l2 regularization Returns: - list of tuples each being (grad, var) for optimizer to use + A tuple with the first entry being a list of all gradients, the second + entry being a list of respective variables, the third being the logits, + and the forth being the loss """ - # Run forward pass to record hidden states; avoid updating running averages + # Run forward pass to record hidden states vars_and_vals = self.get_moving_stats() - _, saved_hidden = self.call(inputs, training=training) - self.restore_moving_stats(vars_and_vals) + _, saved_hidden = self(inputs, training=training) # pylint:disable=not-callable + if tf.executing_eagerly(): + # Restore moving averages when executing eagerly to avoid updating twice + self.restore_moving_stats(vars_and_vals) + else: + # Fetch batch norm updates in graph mode + updates = self.get_updates_for(inputs) grads_all = [] vars_all = [] @@ -220,9 +162,8 @@ class RevNet(tf.keras.Model): # Manually backprop through last block x = saved_hidden[-1] with tf.GradientTape() as tape: - x = tf.identity(x) tape.watch(x) - # Running stats updated below + # Running stats updated here logits = self._final_block(x, training=training) loss = self.compute_loss(logits, labels) @@ -236,6 +177,7 @@ class RevNet(tf.keras.Model): for block in reversed(self._block_list): y = saved_hidden.pop() x = saved_hidden[-1] + # Running stats updated here dy, grads, vars_ = block.backward_grads_and_vars( x, y, dy, training=training) grads_all += grads @@ -247,8 +189,7 @@ class RevNet(tf.keras.Model): assert not saved_hidden # Cleared after backprop with tf.GradientTape() as tape: - x = tf.identity(x) - # Running stats updated below + # Running stats updated here y = self._init_block(x, training=training) grads_all += tape.gradient( @@ -259,7 +200,13 @@ class RevNet(tf.keras.Model): if l2_reg: grads_all = self._apply_weight_decay(grads_all, vars_all) - return grads_all, vars_all, loss + if not tf.executing_eagerly(): + # Force updates to be executed before gradient computation in graph mode + # This does nothing when the function is wrapped in defun + with tf.control_dependencies(updates): + grads_all[0] = tf.identity(grads_all[0]) + + return grads_all, vars_all, logits, loss def _apply_weight_decay(self, grads, vars_): """Update gradients to reflect weight decay.""" @@ -284,8 +231,10 @@ class RevNet(tf.keras.Model): n = v.name return n.endswith("moving_mean:0") or n.endswith("moving_variance:0") - for v in filter(_is_moving_var, self.variables): - vars_and_vals[v] = v.read_value() + device = "/gpu:0" if tf.test.is_gpu_available() else "/cpu:0" + with tf.device(device): + for v in filter(_is_moving_var, self.variables): + vars_and_vals[v] = v.read_value() return vars_and_vals @@ -297,5 +246,8 @@ class RevNet(tf.keras.Model): Args: vars_and_vals: The dictionary mapping variables to their previous values. """ - for var_, val in six.iteritems(vars_and_vals): - var_.assign(val) + device = "/gpu:0" if tf.test.is_gpu_available() else "/cpu:0" + with tf.device(device): + for var_, val in six.iteritems(vars_and_vals): + # `assign` causes a copy to GPU (if variable is already on GPU) + var_.assign(val) diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py index b0d0a5486d..2dc7b9fd70 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py @@ -31,10 +31,11 @@ tfe = tf.contrib.eager def train_one_iter(model, inputs, labels, optimizer, global_step=None): """Train for one iteration.""" - grads, vars_, loss = model.compute_gradients(inputs, labels, training=True) + grads, vars_, logits, loss = model.compute_gradients( + inputs, labels, training=True) optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) - return loss + return logits, loss class RevNetTest(tf.test.TestCase): @@ -42,6 +43,8 @@ class RevNetTest(tf.test.TestCase): def setUp(self): super(RevNetTest, self).setUp() config = config_.get_hparams_cifar_38() + config.add_hparam("n_classes", 10) + config.add_hparam("dataset", "cifar-10") # Reconstruction could cause numerical error, use double precision for tests config.dtype = tf.float64 config.fused = False # Fused batch norm does not support tf.float64 @@ -94,7 +97,7 @@ class RevNetTest(tf.test.TestCase): def test_compute_gradients(self): """Test `compute_gradients` function.""" self.model(self.x, training=False) # Initialize model - grads, vars_, loss = self.model.compute_gradients( + grads, vars_, logits, loss = self.model.compute_gradients( inputs=self.x, labels=self.t, training=True, l2_reg=True) self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) @@ -119,7 +122,7 @@ class RevNetTest(tf.test.TestCase): def test_compute_gradients_defun(self): """Test `compute_gradients` function with defun.""" compute_gradients = tfe.defun(self.model.compute_gradients) - grads, vars_, _ = compute_gradients(self.x, self.t, training=True) + grads, vars_, _, _ = compute_gradients(self.x, self.t, training=True) self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) self.assertEqual(len(grads), len(vars_)) @@ -131,6 +134,9 @@ class RevNetTest(tf.test.TestCase): """Test model training in graph mode.""" with tf.Graph().as_default(): config = config_.get_hparams_cifar_38() + config.add_hparam("n_classes", 10) + config.add_hparam("dataset", "cifar-10") + x = tf.random_normal( shape=(self.config.batch_size,) + self.config.input_shape) t = tf.random_uniform( @@ -140,15 +146,10 @@ class RevNetTest(tf.test.TestCase): dtype=tf.int32) global_step = tf.Variable(0., trainable=False) model = revnet.RevNet(config=config) - model(x) - updates = model.get_updates_for(x) - - x_ = tf.identity(x) - grads_all, vars_all, _ = model.compute_gradients(x_, t, training=True) + grads_all, vars_all, _, _ = model.compute_gradients(x, t, training=True) optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) - with tf.control_dependencies(updates): - train_op = optimizer.apply_gradients( - zip(grads_all, vars_all), global_step=global_step) + train_op = optimizer.apply_gradients( + zip(grads_all, vars_all), global_step=global_step) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) @@ -222,6 +223,8 @@ class RevNetBenchmark(tf.test.Benchmark): execution_mode=None, compiled=False): config = config_.get_hparams_imagenet_56() + config.add_hparam("n_classes", 1000) + config.add_hparam("dataset", "ImageNet") with tfe.execution_mode(execution_mode): device, data_format = device_and_format model = revnet.RevNet(config=config) @@ -267,6 +270,8 @@ class RevNetBenchmark(tf.test.Benchmark): execution_mode=None, compiled=False): config = config_.get_hparams_imagenet_56() + config.add_hparam("n_classes", 1000) + config.add_hparam("dataset", "ImageNet") with tfe.execution_mode(execution_mode): device, data_format = device_and_format for batch_size in self._train_batch_sizes(): -- GitLab From a8f3646377af20101b180a5f64df3f0066a9653b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 20 Jul 2018 18:01:30 -0700 Subject: [PATCH 233/519] [TF:XLA] Enable better fusion-operand sharing in copy elision. Make use of the back-end specific fusion-operand sharing functions in copy insertion. This allows potentially better copy insertion/elision. PiperOrigin-RevId: 205481101 --- .../compiler/xla/service/copy_insertion.cc | 77 +++++++++---------- .../compiler/xla/service/copy_insertion.h | 22 ++++-- .../compiler/xla/service/hlo_alias_analysis.h | 2 +- .../xla/service/hlo_alias_analysis_test.cc | 4 +- .../xla/service/hlo_rematerialization.cc | 11 +-- .../xla/service/hlo_rematerialization.h | 10 ++- .../xla/service/hlo_rematerialization_test.cc | 2 +- 7 files changed, 69 insertions(+), 59 deletions(-) diff --git a/tensorflow/compiler/xla/service/copy_insertion.cc b/tensorflow/compiler/xla/service/copy_insertion.cc index ca2a78da67..36fb9b43aa 100644 --- a/tensorflow/compiler/xla/service/copy_insertion.cc +++ b/tensorflow/compiler/xla/service/copy_insertion.cc @@ -351,26 +351,6 @@ Status StripControlDependenciesFrom(HloInstruction* instruction) { return Status::OK(); } -// Add kCopy instructions to the given module to guarantee there is no -// live-range interference. Generally interference can only occur around kWhile -// instructions which have update-in-place semantics. -Status AddCopiesToResolveInterference(HloModule* module) { - TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, - HloAliasAnalysis::Run(module)); - - for (HloComputation* computation : module->computations()) { - for (HloInstruction* instruction : computation->instructions()) { - if (instruction->opcode() == HloOpcode::kWhile) { - TF_RETURN_IF_ERROR(AddCopiesForWhile(*alias_analysis, instruction)); - } else if (instruction->opcode() == HloOpcode::kConditional) { - TF_RETURN_IF_ERROR( - AddCopiesForConditional(*alias_analysis, instruction)); - } - } - } - return Status::OK(); -} - // Class for removing unnecessary copies from the module. // // kCopy instructions are added conservatively to guarantee no live range @@ -945,6 +925,36 @@ class CopyRemover { BufferValueTracker buffer_value_tracker_; }; +void MaybeDumpModule(const string& message, const HloModule& module) { + if (VLOG_IS_ON(3)) { + VLOG(3) << message; + XLA_VLOG_LINES(3, module.ToString()); + hlo_graph_dumper::MaybeDumpHloModule(module, message); + } +} + +} // namespace + +// Add kCopy instructions to the given module to guarantee there is no +// live-range interference. Generally interference can only occur around kWhile +// instructions which have update-in-place semantics. +Status CopyInsertion::AddCopiesToResolveInterference(HloModule* module) { + TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, + HloAliasAnalysis::Run(module, fusion_can_share_buffer_)); + + for (HloComputation* computation : module->computations()) { + for (HloInstruction* instruction : computation->instructions()) { + if (instruction->opcode() == HloOpcode::kWhile) { + TF_RETURN_IF_ERROR(AddCopiesForWhile(*alias_analysis, instruction)); + } else if (instruction->opcode() == HloOpcode::kConditional) { + TF_RETURN_IF_ERROR( + AddCopiesForConditional(*alias_analysis, instruction)); + } + } + } + return Status::OK(); +} + // Add copies to address special constraints on the roots of computations not // related to live range interference: // @@ -955,9 +965,10 @@ class CopyRemover { // // (3) Constants and parameters cannot be live out of the entry computation // -Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { +Status CopyInsertion::AddSpecialCaseCopies(const CallGraph& call_graph, + HloModule* module) { TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, - HloAliasAnalysis::Run(module)); + HloAliasAnalysis::Run(module, fusion_can_share_buffer_)); // Identify which shape indices of which instructions need to be copied. Store // these results in 'instructions_to_copy'. @@ -1065,32 +1076,20 @@ Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module) { return Status::OK(); } -Status VerifyNoLiveRangeInterference(HloModule* module) { +Status CopyInsertion::VerifyNoLiveRangeInterference(HloModule* module) { TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, - HloAliasAnalysis::Run(module)); + HloAliasAnalysis::Run(module, fusion_can_share_buffer_)); DependencyHloOrdering ordering(module); TF_RET_CHECK(!alias_analysis->HasLiveRangeInterference(ordering)); return Status::OK(); } -void MaybeDumpModule(const string& message, const HloModule& module) { - if (VLOG_IS_ON(3)) { - VLOG(3) << message; - XLA_VLOG_LINES(3, module.ToString()); - hlo_graph_dumper::MaybeDumpHloModule(module, message); - } -} - -} // namespace - -Status RemoveUnnecessaryCopies( - const HloOrdering& ordering, HloModule* module, - const HloDataflowAnalysis::FusionCanShareBufferFunction& - fusion_can_share_buffer) { +Status CopyInsertion::RemoveUnnecessaryCopies(const HloOrdering& ordering, + HloModule* module) { MaybeDumpModule("after adding copies to resolve interference", *module); TF_ASSIGN_OR_RETURN(std::unique_ptr alias_analysis, - HloAliasAnalysis::Run(module, fusion_can_share_buffer)); + HloAliasAnalysis::Run(module, fusion_can_share_buffer_)); CopyRemover copy_remover(*alias_analysis, ordering, module); XLA_VLOG_LINES(3, copy_remover.ToString()); diff --git a/tensorflow/compiler/xla/service/copy_insertion.h b/tensorflow/compiler/xla/service/copy_insertion.h index e1973db928..5ba64b78a3 100644 --- a/tensorflow/compiler/xla/service/copy_insertion.h +++ b/tensorflow/compiler/xla/service/copy_insertion.h @@ -71,20 +71,26 @@ class CopyInsertion : public HloPassInterface { // TODO(b/62548313): Remove this when buffer assignment is module-scoped. static StatusOr AddCopiesForBufferAssignment(HloModule* module); + // Try to remove as many copies from the module as possible without + // introducing live range interference. Only copy instructions that are + // eligible for copy elision are considered for removal. + Status RemoveUnnecessaryCopies(const HloOrdering& ordering, + HloModule* module); + private: + // Verifies that no HLO values have interfering live ranged assuming the + // ordering used by copy insertion. + Status VerifyNoLiveRangeInterference(HloModule* module); + + Status AddCopiesToResolveInterference(HloModule* module); + + Status AddSpecialCaseCopies(const CallGraph& call_graph, HloModule* module); + // Backend specific function that decides whether a fusion can share buffer // with its operand. HloDataflowAnalysis::FusionCanShareBufferFunction fusion_can_share_buffer_; }; -// Try to remove as many copies from the module as possible without introducing -// live range interference. Only copy instructions that are eligible for -// copy elision are considered for removal. -Status RemoveUnnecessaryCopies( - const HloOrdering& ordering, HloModule* module, - const HloDataflowAnalysis::FusionCanShareBufferFunction& - fusion_can_share_buffer = nullptr); - } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_SERVICE_COPY_INSERTION_H_ diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis.h b/tensorflow/compiler/xla/service/hlo_alias_analysis.h index afb0c20f0c..1fea544730 100644 --- a/tensorflow/compiler/xla/service/hlo_alias_analysis.h +++ b/tensorflow/compiler/xla/service/hlo_alias_analysis.h @@ -42,7 +42,7 @@ class HloAliasAnalysis { static StatusOr> Run( HloModule* module, const HloDataflowAnalysis::FusionCanShareBufferFunction& - fusion_can_share_buffer = nullptr); + fusion_can_share_buffer); string ToString() const; diff --git a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc index 403d4df6b5..da94ab5346 100644 --- a/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_alias_analysis_test.cc @@ -47,7 +47,9 @@ class HloAliasAnalysisTest : public HloTestBase { // reference to the generated analysis stored in analysis_. HloAliasAnalysis& RunAnalysis() { hlo_graph_dumper::MaybeDumpHloModule(*module_, "Before alias analysis"); - analysis_ = HloAliasAnalysis::Run(module_.get()).ConsumeValueOrDie(); + analysis_ = HloAliasAnalysis::Run(module_.get(), + /*fusion_can_share_buffer=*/nullptr) + .ConsumeValueOrDie(); return *analysis_; } diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.cc b/tensorflow/compiler/xla/service/hlo_rematerialization.cc index 59a8800a7d..cf0be30c7a 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.cc @@ -1203,7 +1203,7 @@ StatusOr HloRematerialization::RematerializeComputation( StatusOr HloRematerialization::Run( HloModule* module, SequentialHloOrdering::HloModuleSequence* sequence, int64 memory_limit_bytes, RematerializationSizes* sizes, - bool run_copy_elision) { + CopyInsertion* copy_insertion) { // The sequence is constructed entirely by this method. TF_RET_CHECK(sequence->empty()); @@ -1238,13 +1238,14 @@ StatusOr HloRematerialization::Run( return size_function_(buffer.shape()); }, scheduler_algorithm_)); - if (run_copy_elision) { + if (copy_insertion) { // We run a separate pass of copy elision here because the sequential // ordering from the HLO schedule allows for more copies to be eliminated. // TODO(b/80249101): Instead of a separate copy elision pass, use the // ordering from the HLO schedule directly for copy insertion. SequentialHloOrdering ordering(module, *sequence); - TF_RETURN_IF_ERROR(RemoveUnnecessaryCopies(ordering, module)); + TF_RETURN_IF_ERROR( + copy_insertion->RemoveUnnecessaryCopies(ordering, module)); } // Compute peak memory usage of all computations in the module called in a @@ -1349,10 +1350,10 @@ StatusOr HloRematerialization::Run( int64 memory_limit_bytes, HloModule* hlo_module, MemorySchedulerAlgorithm scheduler_algorithm, SequentialHloOrdering::HloModuleSequence* sequence, - RematerializationSizes* sizes, bool run_copy_elision) { + RematerializationSizes* sizes, CopyInsertion* copy_insertion) { HloRematerialization remat(scheduler_algorithm, size_function); return remat.Run(hlo_module, sequence, memory_limit_bytes, sizes, - run_copy_elision); + copy_insertion); } } // namespace xla diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization.h b/tensorflow/compiler/xla/service/hlo_rematerialization.h index 59b4cf5dcc..2ec004350a 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization.h +++ b/tensorflow/compiler/xla/service/hlo_rematerialization.h @@ -17,6 +17,7 @@ #include "tensorflow/compiler/xla/service/buffer_liveness.h" #include "tensorflow/compiler/xla/service/call_graph.h" +#include "tensorflow/compiler/xla/service/copy_insertion.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" @@ -57,8 +58,9 @@ class HloRematerialization { // sizes: Optional outparam that indicates the peak memory usage of the HLO // module before/after rematerialization. // - // run_copy_elision: Enable copy elision. This pass is used to eliminate - // copies that were inserted before HLO scheduling. + // copy_insertion: If non-null, run copy elision after scheduling. This + // pass is used to eliminate copies that were inserted by copy insertion + // before HLO scheduling. // // TODO(b/80249101): Remove the 'run_copy_elision' parameter when copy // insertion is integrated with HLO scheduling. @@ -74,7 +76,7 @@ class HloRematerialization { const ShapeSizeFunction& size_function, int64 memory_limit_bytes, HloModule* hlo_module, MemorySchedulerAlgorithm scheduler_algorithm, SequentialHloOrdering::HloModuleSequence* sequence, - RematerializationSizes* sizes, bool run_copy_elision = true); + RematerializationSizes* sizes, CopyInsertion* copy_insertion = nullptr); protected: HloRematerialization(MemorySchedulerAlgorithm scheduler_algorithm, @@ -90,7 +92,7 @@ class HloRematerialization { StatusOr Run(HloModule* module, SequentialHloOrdering::HloModuleSequence* sequence, int64 memory_limit, RematerializationSizes* sizes, - bool run_copy_elision); + CopyInsertion* copy_insertion); // Rematerializes instructions within the given computation. 'order' is the // order in which the computation's instructions will be emitted in the diff --git a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc index cd131147e6..ac8c97d380 100644 --- a/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc +++ b/tensorflow/compiler/xla/service/hlo_rematerialization_test.cc @@ -147,7 +147,7 @@ class HloRematerializationTest : public HloTestBase { TF_EXPECT_OK(verifier().Run(module).status()); return HloRematerialization::RematerializeAndSchedule( ByteSizeOf, memory_limit_bytes, module, DefaultMemoryScheduler, - sequence, /*sizes=*/nullptr, /*run_copy_elision=*/false); + sequence, /*sizes=*/nullptr); } // Various shapes used in the canned computations. -- GitLab From 2a9c19d44c5e3ec80a859bf65219c0ff2fc451f0 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Fri, 20 Jul 2018 21:59:00 -0700 Subject: [PATCH 234/519] Fix typo in windows build scripts. --- tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh | 4 ++-- tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index 22d389fd44..9883bb622c 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -57,8 +57,8 @@ PY_TEST_DIR="py_test_dir" SKIP_TEST=0 RELEASE_BUILD=0 -TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/... " - + "//${PY_TEST_DIR}tensorflow/contrib/... " +TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/... \ + //${PY_TEST_DIR}tensorflow/contrib/... " # --skip_test Skip running tests # --enable_remote_cache Add options to enable remote cache for build and test diff --git a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh index 682a396d10..57463e3366 100644 --- a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh @@ -57,8 +57,8 @@ PY_TEST_DIR="py_test_dir" SKIP_TEST=0 RELEASE_BUILD=0 -TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/... " - + "//${PY_TEST_DIR}tensorflow/contrib/... " +TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/... \ + //${PY_TEST_DIR}tensorflow/contrib/... " # --skip_test Skip running tests # --enable_remote_cache Add options to enable remote cache for build and test -- GitLab From 3153ebe72a5dd5c5848122ddc3c883aab14b5cf1 Mon Sep 17 00:00:00 2001 From: Guangda Lai Date: Fri, 20 Jul 2018 22:03:40 -0700 Subject: [PATCH 235/519] Exclude (some) tensorrt tests from mac build and cuda_on_cpu build. PiperOrigin-RevId: 205494168 --- tensorflow/contrib/tensorrt/BUILD | 22 ++++++++++++++++++---- 1 file changed, 18 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index 7999f718e3..08b267c11a 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -32,7 +32,10 @@ tf_cuda_cc_test( name = "tensorrt_test_cc", size = "small", srcs = ["tensorrt_test.cc"], - tags = ["no_windows"], + tags = [ + "no_windows", + "nomac", + ], deps = [ "//tensorflow/core:lib", "//tensorflow/core:test", @@ -234,7 +237,10 @@ tf_cc_test( name = "trt_allocator_test", size = "small", srcs = ["resources/trt_allocator_test.cc"], - tags = ["no_windows"], + tags = [ + "no_windows", + "nomac", + ], deps = [ ":trt_allocator", "//tensorflow/core:test", @@ -302,7 +308,10 @@ tf_cc_test( name = "segment_test", size = "small", srcs = ["segment/segment_test.cc"], - tags = ["no_windows"], + tags = [ + "no_windows", + "nomac", + ], deps = [ ":segment", "//tensorflow/c:c_api", @@ -338,7 +347,11 @@ tf_cuda_cc_test( name = "trt_plugin_factory_test", size = "small", srcs = ["plugin/trt_plugin_factory_test.cc"], - tags = ["no_windows"], + tags = [ + "no_cuda_on_cpu_tap", + "no_windows", + "nomac", + ], deps = [ ":trt_plugins", "//tensorflow/core:lib", @@ -382,6 +395,7 @@ cuda_py_tests( "//tensorflow/python:framework_test_lib", ], tags = [ + "no_cuda_on_cpu_tap", "no_windows", "nomac", ], -- GitLab From 7eb2bce1eb94ecc24aab02af90f51700500308cd Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Fri, 20 Jul 2018 22:44:17 -0700 Subject: [PATCH 236/519] Enable a few oss profiler tests. PiperOrigin-RevId: 205495906 --- tensorflow/contrib/lite/profiling/BUILD | 2 -- 1 file changed, 2 deletions(-) diff --git a/tensorflow/contrib/lite/profiling/BUILD b/tensorflow/contrib/lite/profiling/BUILD index b29ca330dc..1172722f7a 100644 --- a/tensorflow/contrib/lite/profiling/BUILD +++ b/tensorflow/contrib/lite/profiling/BUILD @@ -20,7 +20,6 @@ cc_test( srcs = ["profiler_test.cc"], copts = ["-DTFLITE_PROFILING_ENABLED"], defines = ["TFLITE_PROFILING_ENABLED"], - tags = ["no_oss"], deps = [ ":profiler", "//tensorflow/contrib/lite/testing:util", @@ -77,7 +76,6 @@ cc_test( srcs = ["profile_buffer_test.cc"], copts = ["-DTFLITE_PROFILING_ENABLED"], defines = ["TFLITE_PROFILING_ENABLED"], - tags = ["no_oss"], deps = [ ":profile_buffer", "//tensorflow/contrib/lite/testing:util", -- GitLab From f0b0f4d485a60e703212a3f14d84d3e479504bd0 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Fri, 20 Jul 2018 22:56:57 -0700 Subject: [PATCH 237/519] Fix more typos. --- .../tools/ci_build/windows/cpu/pip/build_tf_windows.sh | 8 ++++---- .../tools/ci_build/windows/gpu/pip/build_tf_windows.sh | 8 ++++---- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh index 9883bb622c..47e0e5dd59 100644 --- a/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/cpu/pip/build_tf_windows.sh @@ -57,8 +57,8 @@ PY_TEST_DIR="py_test_dir" SKIP_TEST=0 RELEASE_BUILD=0 -TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/... \ - //${PY_TEST_DIR}tensorflow/contrib/... " +TEST_TARGET="//${PY_TEST_DIR}/tensorflow/python/... \ + //${PY_TEST_DIR}/tensorflow/contrib/... " # --skip_test Skip running tests # --enable_remote_cache Add options to enable remote cache for build and test @@ -71,8 +71,8 @@ for ARG in "$@"; do --skip_test) SKIP_TEST=1 ;; --enable_remote_cache) set_remote_cache_options ;; --release_build) RELEASE_BUILD=1 ;; - --test_core_only) TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/..." ;; - --test_contrib_only) TEST_TARGET="//${PY_TEST_DIR}tensorflow/contrib/..." ;; + --test_core_only) TEST_TARGET="//${PY_TEST_DIR}/tensorflow/python/..." ;; + --test_contrib_only) TEST_TARGET="//${PY_TEST_DIR}/tensorflow/contrib/..." ;; *) esac done diff --git a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh index 57463e3366..e3eee11080 100644 --- a/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh +++ b/tensorflow/tools/ci_build/windows/gpu/pip/build_tf_windows.sh @@ -57,8 +57,8 @@ PY_TEST_DIR="py_test_dir" SKIP_TEST=0 RELEASE_BUILD=0 -TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/... \ - //${PY_TEST_DIR}tensorflow/contrib/... " +TEST_TARGET="//${PY_TEST_DIR}/tensorflow/python/... \ + //${PY_TEST_DIR}/tensorflow/contrib/... " # --skip_test Skip running tests # --enable_remote_cache Add options to enable remote cache for build and test @@ -71,8 +71,8 @@ for ARG in "$@"; do --skip_test) SKIP_TEST=1 ;; --enable_remote_cache) set_remote_cache_options ;; --release_build) RELEASE_BUILD=1 ;; - --test_core_only) TEST_TARGET="//${PY_TEST_DIR}tensorflow/python/..." ;; - --test_contrib_only) TEST_TARGET="//${PY_TEST_DIR}tensorflow/contrib/..." ;; + --test_core_only) TEST_TARGET="//${PY_TEST_DIR}/tensorflow/python/..." ;; + --test_contrib_only) TEST_TARGET="//${PY_TEST_DIR}/tensorflow/contrib/..." ;; *) esac done -- GitLab From 19354d18bb2ce412409f4d79ec4fa23dd9057d8b Mon Sep 17 00:00:00 2001 From: Shashi Shekhar Date: Fri, 20 Jul 2018 23:21:37 -0700 Subject: [PATCH 238/519] Add a simple test for benchmark. PiperOrigin-RevId: 205497641 --- tensorflow/contrib/lite/tools/benchmark/BUILD | 33 ++++++++- .../lite/tools/benchmark/benchmark_model.cc | 12 ++- .../lite/tools/benchmark/benchmark_model.h | 7 +- .../lite/tools/benchmark/benchmark_test.cc | 74 +++++++++++++++++++ .../tools/benchmark/benchmark_tflite_model.cc | 6 +- .../tools/benchmark/benchmark_tflite_model.h | 4 +- 6 files changed, 121 insertions(+), 15 deletions(-) create mode 100644 tensorflow/contrib/lite/tools/benchmark/benchmark_test.cc diff --git a/tensorflow/contrib/lite/tools/benchmark/BUILD b/tensorflow/contrib/lite/tools/benchmark/BUILD index 810e25961f..2cb07eb6ec 100644 --- a/tensorflow/contrib/lite/tools/benchmark/BUILD +++ b/tensorflow/contrib/lite/tools/benchmark/BUILD @@ -10,11 +10,16 @@ load("//tensorflow/contrib/lite:build_def.bzl", "tflite_copts") common_copts = ["-Wall"] + tflite_copts() +cc_library( + name = "logging", + hdrs = ["logging.h"], + copts = common_copts, +) + cc_binary( name = "benchmark_model", srcs = [ "benchmark_main.cc", - "logging.h", ], copts = common_copts, linkopts = tflite_linkopts() + select({ @@ -26,6 +31,26 @@ cc_binary( }), deps = [ ":benchmark_tflite_model_lib", + ":logging", + ], +) + +cc_test( + name = "benchmark_test", + srcs = ["benchmark_test.cc"], + args = [ + "--graph=$(location //tensorflow/contrib/lite:testdata/multi_add.bin)", + ], + data = ["//tensorflow/contrib/lite:testdata/multi_add.bin"], + tags = [ + "tflite_not_portable_android", + "tflite_not_portable_ios", + ], + deps = [ + ":benchmark_tflite_model_lib", + ":command_line_flags", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", ], ) @@ -40,7 +65,6 @@ cc_test( name = "command_line_flags_test", srcs = ["command_line_flags_test.cc"], copts = common_copts, - tags = ["no_oss"], visibility = ["//visibility:private"], deps = [ ":command_line_flags", @@ -59,6 +83,7 @@ cc_library( copts = common_copts, deps = [ ":benchmark_model_lib", + ":logging", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/kernels:builtin_ops", @@ -71,23 +96,23 @@ cc_library( name = "benchmark_params", srcs = [ "benchmark_params.cc", - "logging.h", ], hdrs = ["benchmark_params.h"], copts = common_copts, + deps = [":logging"], ) cc_library( name = "benchmark_model_lib", srcs = [ "benchmark_model.cc", - "logging.h", ], hdrs = ["benchmark_model.h"], copts = common_copts, deps = [ ":benchmark_params", ":command_line_flags", + ":logging", "//tensorflow/contrib/lite:framework", "//tensorflow/contrib/lite:string_util", "//tensorflow/contrib/lite/kernels:builtin_ops", diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc index 19b9a9c7ba..f86c0445b0 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.cc @@ -84,7 +84,7 @@ std::vector BenchmarkModel::GetFlags() { }; } -void BenchmarkModel::LogFlags() { +void BenchmarkModel::LogParams() { TFLITE_LOG(INFO) << "Num runs: [" << params_.Get("num_runs") << "]"; TFLITE_LOG(INFO) << "Inter-run delay (seconds): [" << params_.Get("run_delay") << "]"; @@ -122,12 +122,18 @@ Stat BenchmarkModel::Run(int num_times, RunType run_type) { return run_stats; } +bool BenchmarkModel::ValidateParams() { return true; } + void BenchmarkModel::Run(int argc, char **argv) { if (!ParseFlags(argc, argv)) { return; } + Run(); +} - LogFlags(); +void BenchmarkModel::Run() { + ValidateParams(); + LogParams(); listeners_.OnBenchmarkStart(params_); int64_t initialization_start_us = profiling::time::NowMicros(); @@ -155,7 +161,7 @@ bool BenchmarkModel::ParseFlags(int argc, char **argv) { TFLITE_LOG(ERROR) << usage; return false; } - return ValidateFlags(); + return true; } } // namespace benchmark diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h index 3c7063b2d4..677a1ee68c 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_model.h @@ -137,16 +137,17 @@ class BenchmarkModel { BenchmarkModel(); BenchmarkModel(BenchmarkParams params) : params_(std::move(params)) {} virtual ~BenchmarkModel() {} - bool ParseFlags(int argc, char** argv); virtual void Init() = 0; void Run(int argc, char** argv); + virtual void Run(); void AddListener(BenchmarkListener* listener) { listeners_.AddListener(listener); } protected: - virtual void LogFlags(); - virtual bool ValidateFlags() { return true; } + virtual void LogParams(); + virtual bool ValidateParams(); + bool ParseFlags(int argc, char** argv); virtual std::vector GetFlags(); virtual uint64_t ComputeInputBytes() = 0; virtual tensorflow::Stat Run(int num_times, RunType run_type); diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_test.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_test.cc new file mode 100644 index 0000000000..b697bb394d --- /dev/null +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_test.cc @@ -0,0 +1,74 @@ +/* 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 +#include +#include + +#include +#include +#include "tensorflow/contrib/lite/testing/util.h" +#include "tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h" +#include "tensorflow/contrib/lite/tools/benchmark/command_line_flags.h" + +namespace { +const std::string* g_model_path = nullptr; +} + +namespace tflite { +namespace benchmark { +namespace { + +BenchmarkParams CreateParams() { + BenchmarkParams params; + params.AddParam("num_runs", BenchmarkParam::Create(2)); + params.AddParam("run_delay", BenchmarkParam::Create(-1.0f)); + params.AddParam("num_threads", BenchmarkParam::Create(1)); + params.AddParam("benchmark_name", BenchmarkParam::Create("")); + params.AddParam("output_prefix", BenchmarkParam::Create("")); + params.AddParam("warmup_runs", BenchmarkParam::Create(1)); + params.AddParam("graph", BenchmarkParam::Create(*g_model_path)); + params.AddParam("input_layer", BenchmarkParam::Create("")); + params.AddParam("input_layer_shape", BenchmarkParam::Create("")); + params.AddParam("use_nnapi", BenchmarkParam::Create(false)); + return params; +} + +TEST(BenchmarkTest, DoesntCrash) { + ASSERT_THAT(g_model_path, testing::NotNull()); + + BenchmarkTfLiteModel benchmark(CreateParams()); + benchmark.Run(); +} + +} // namespace +} // namespace benchmark +} // namespace tflite + +int main(int argc, char** argv) { + std::string model_path; + std::vector flags = { + tflite::Flag::CreateFlag("graph", &model_path, "Path to model file.")}; + g_model_path = &model_path; + const bool parse_result = + tflite::Flags::Parse(&argc, const_cast(argv), flags); + if (!parse_result) { + std::cerr << tflite::Flags::Usage(argv[0], flags); + return 1; + } + + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc index 73affc26b0..7f97f5d0cd 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.cc @@ -198,8 +198,8 @@ std::vector BenchmarkTfLiteModel::GetFlags() { return flags; } -void BenchmarkTfLiteModel::LogFlags() { - BenchmarkModel::LogFlags(); +void BenchmarkTfLiteModel::LogParams() { + BenchmarkModel::LogParams(); TFLITE_LOG(INFO) << "Graph: [" << params_.Get("graph") << "]"; TFLITE_LOG(INFO) << "Input layers: [" << params_.Get("input_layer") << "]"; @@ -208,7 +208,7 @@ void BenchmarkTfLiteModel::LogFlags() { TFLITE_LOG(INFO) << "Use nnapi : [" << params_.Get("use_nnapi") << "]"; } -bool BenchmarkTfLiteModel::ValidateFlags() { +bool BenchmarkTfLiteModel::ValidateParams() { if (params_.Get("graph").empty()) { TFLITE_LOG(ERROR) << "Please specify the name of your TF Lite input file with --graph"; diff --git a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h index 50cc3f24b3..9931dcbafe 100644 --- a/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h +++ b/tensorflow/contrib/lite/tools/benchmark/benchmark_tflite_model.h @@ -54,8 +54,8 @@ class BenchmarkTfLiteModel : public BenchmarkModel { BenchmarkTfLiteModel(BenchmarkParams params); std::vector GetFlags() override; - void LogFlags() override; - bool ValidateFlags() override; + void LogParams() override; + bool ValidateParams() override; uint64_t ComputeInputBytes() override; void Init() override; void RunImpl() override; -- GitLab From db866b6fb02a3b7855f521fdb4175b2d1b217259 Mon Sep 17 00:00:00 2001 From: qwertWZ Date: Sat, 21 Jul 2018 15:22:29 +0800 Subject: [PATCH 239/519] Add a missing left bracket in comments --- tensorflow/contrib/rnn/python/ops/rnn.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/rnn/python/ops/rnn.py b/tensorflow/contrib/rnn/python/ops/rnn.py index 2f0caadda3..0266b72dcb 100644 --- a/tensorflow/contrib/rnn/python/ops/rnn.py +++ b/tensorflow/contrib/rnn/python/ops/rnn.py @@ -175,7 +175,7 @@ def stack_bidirectional_dynamic_rnn(cells_fw, Returns: A tuple (outputs, output_state_fw, output_state_bw) where: outputs: Output `Tensor` shaped: - `batch_size, max_time, layers_output]`. Where layers_output + `[batch_size, max_time, layers_output]`. Where layers_output are depth-concatenated forward and backward outputs. output_states_fw is the final states, one tensor per layer, of the forward rnn. -- GitLab From fb8d1ca4eaefe58d42c27b6fc676f64f137f4675 Mon Sep 17 00:00:00 2001 From: Ray Kim Date: Sat, 21 Jul 2018 21:42:15 +0900 Subject: [PATCH 240/519] fixed build error on gcc-7 --- tensorflow/compiler/xla/service/gpu/xfeed_queue.h | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/compiler/xla/service/gpu/xfeed_queue.h b/tensorflow/compiler/xla/service/gpu/xfeed_queue.h index 737c7eb025..dd46ff433b 100644 --- a/tensorflow/compiler/xla/service/gpu/xfeed_queue.h +++ b/tensorflow/compiler/xla/service/gpu/xfeed_queue.h @@ -17,6 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_XFEED_QUEUE_H_ #include +#include #include #include "tensorflow/core/platform/mutex.h" -- GitLab From aa5d3126ced57f8117678bb1cb5cc41e2a72eb9a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sat, 21 Jul 2018 10:11:49 -0700 Subject: [PATCH 241/519] Support while ops to be CSEd in HLO. Do so if they have same bodies, conditions, and init conditions. PiperOrigin-RevId: 205524367 --- .../compiler/xla/service/hlo_cse_test.cc | 179 +++++++++++++++++- .../compiler/xla/service/hlo_instruction.cc | 11 +- 2 files changed, 187 insertions(+), 3 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_cse_test.cc b/tensorflow/compiler/xla/service/hlo_cse_test.cc index 76b9c66651..90fbaa37c5 100644 --- a/tensorflow/compiler/xla/service/hlo_cse_test.cc +++ b/tensorflow/compiler/xla/service/hlo_cse_test.cc @@ -239,7 +239,7 @@ TEST_F(HloCseTest, IdenticalInstructions) { EXPECT_EQ(5, computation->instruction_count()); EXPECT_THAT(tuple, op::Tuple(exp1, exp2, exp3)); - HloCSE cse(/*is_layout_sensitive=*/false); + HloCSE cse(/*is_layout_sensitive=*/true); EXPECT_TRUE(cse.Run(module.get()).ValueOrDie()); EXPECT_EQ(3, computation->instruction_count()); @@ -248,6 +248,183 @@ TEST_F(HloCseTest, IdenticalInstructions) { EXPECT_THAT(tuple, op::Tuple(first_operand, first_operand, first_operand)); } +// Test two identical while loops with same inputs +TEST_F(HloCseTest, WhileLoopsIdenticalConditionsAndBodiesSameInput) { + auto module = ParseHloString(R"( + HloModule WhileLoopsIdenticalConditionsAndBodiesSameInput + + %body (param: (f32[], f32[])) -> (f32[], f32[]) { + %param = (f32[], f32[]) parameter(0) + %get-tuple-element = f32[] get-tuple-element((f32[], f32[]) %param), +index=0 %get-tuple-element.1 = f32[] get-tuple-element((f32[], f32[]) %param), +index=1 %add = f32[] add(f32[] %get-tuple-element, f32[] %get-tuple-element.1) + ROOT %tuple = (f32[], f32[]) tuple(f32[] %get-tuple-element, f32[] %add) + } + + %condition (param.1: (f32[], f32[])) -> pred[] { + %param.1 = (f32[], f32[]) parameter(0) + ROOT %constant = pred[] constant(false) + } + + %condition.1 (param.2: (f32[], f32[])) -> pred[] { + %param.2 = (f32[], f32[]) parameter(0) + ROOT %constant.1 = pred[] constant(false) + } + + ENTRY %WhileLoopsIdenticalConditionsAndBodiesSameInput () -> (f32[], f32[]) +{ %constant.2 = f32[] constant(1) %constant.3 = f32[] constant(2) %tuple.1 = +(f32[], f32[]) tuple(f32[] %constant.2, f32[] %constant.3) %while = (f32[], +f32[]) while((f32[], f32[]) %tuple.1), condition=%condition, body=%body ROOT +%while.1 = (f32[], f32[]) while((f32[], f32[]) %tuple.1), +condition=%condition.1, body=%body + } + )") + .ValueOrDie(); + + auto computation = module->entry_computation(); + + EXPECT_EQ(5, computation->instruction_count()); + HloCSE cse(true); + EXPECT_TRUE(cse.Run(module.get()).ValueOrDie()); + EXPECT_EQ(4, computation->instruction_count()); +} + +// Test two while loops with same conditions, same inputs, but different +// bodies +TEST_F(HloCseTest, WhileLoopsIdenticalConditionsSameInputAndDifferentBodies) { + auto module = ParseHloString(R"( + HloModule WhileLoopsIdenticalConditionsSameInputAndDifferentBodies + + %body (param: (f32[], f32[])) -> (f32[], f32[]) { + %param = (f32[], f32[]) parameter(0) + %get-tuple-element = f32[] get-tuple-element((f32[], f32[]) %param), +index=0 %get-tuple-element.1 = f32[] get-tuple-element((f32[], f32[]) %param), +index=1 %add = f32[] add(f32[] %get-tuple-element, f32[] %get-tuple-element.1) + ROOT %tuple = (f32[], f32[]) tuple(f32[] %get-tuple-element, f32[] %add) + } + + %body2 (param.1: (f32[], f32[])) -> (f32[], f32[]) { + %param.1 = (f32[], f32[]) parameter(0) + %get-tuple-element.2 = f32[] get-tuple-element((f32[], f32[]) %param.1), +index=0 %get-tuple-element.3 = f32[] get-tuple-element((f32[], f32[]) %param.1), +index=1 %sub = f32[] subtract(f32[] %get-tuple-element.2, f32[] +%get-tuple-element.3) ROOT %tuple.2 = (f32[], f32[]) tuple(f32[] +%get-tuple-element.2, f32[] %sub) + } + + %condition (param.2: (f32[], f32[])) -> pred[] { + %param.2 = (f32[], f32[]) parameter(0) + ROOT %constant = pred[] constant(false) + } + + %condition.1 (param.3: (f32[], f32[])) -> pred[] { + %param.3 = (f32[], f32[]) parameter(0) + ROOT %constant.1 = pred[] constant(false) + } + + ENTRY %WhileLoopsIdenticalConditionsSameInputAndDifferentBodies () -> +(f32[], f32[]) { %constant.2 = f32[] constant(1) %constant.3 = f32[] constant(2) + %tuple.1 = (f32[], f32[]) tuple(f32[] %constant.2, f32[] %constant.3) + %while = (f32[], f32[]) while((f32[], f32[]) %tuple.1), +condition=%condition, body=%body ROOT %while.1 = (f32[], f32[]) while((f32[], +f32[]) %tuple.1), condition=%condition.1, body=%body2 + } + )") + .ValueOrDie(); + + auto computation = module->entry_computation(); + + EXPECT_EQ(5, computation->instruction_count()); + HloCSE cse(true); + EXPECT_FALSE(cse.Run(module.get()).ValueOrDie()); + EXPECT_EQ(5, computation->instruction_count()); +} + +// Test two identical while loops with different inputs +TEST_F(HloCseTest, WhileLoopsIdenticalConditionsAndBodiesDifferentInput) { + auto module = ParseHloString(R"( + HloModule WhileLoopsIdenticalConditionsAndBodiesDifferentInput + + %body (param: (f32[], f32[])) -> (f32[], f32[]) { + %param = (f32[], f32[]) parameter(0) + %get-tuple-element = f32[] get-tuple-element((f32[], f32[]) %param), +index=0 %get-tuple-element.1 = f32[] get-tuple-element((f32[], f32[]) %param), +index=1 %add = f32[] add(f32[] %get-tuple-element, f32[] %get-tuple-element.1) + ROOT %tuple = (f32[], f32[]) tuple(f32[] %get-tuple-element, f32[] %add) + } + + %condition (param.1: (f32[], f32[])) -> pred[] { + %param.1 = (f32[], f32[]) parameter(0) + ROOT %constant = pred[] constant(false) + } + + %condition.1 (param.2: (f32[], f32[])) -> pred[] { + %param.2 = (f32[], f32[]) parameter(0) + ROOT %constant.1 = pred[] constant(false) + } + + ENTRY %WhileLoopsIdenticalConditionsAndBodiesDifferentInput () -> (f32[], +f32[]) { %constant.2 = f32[] constant(1) %constant.3 = f32[] constant(2) + %tuple.1 = (f32[], f32[]) tuple(f32[] %constant.2, f32[] %constant.3) + %while = (f32[], f32[]) while((f32[], f32[]) %tuple.1), +condition=%condition, body=%body %constant.4 = f32[] constant(1) %constant.5 = +f32[] constant(2) %tuple.2 = (f32[], f32[]) tuple(f32[] %constant.4, f32[] +%constant.5) ROOT %while.1 = (f32[], f32[]) while((f32[], f32[]) %tuple.2), +condition=%condition.1, body=%body + } + + )") + .ValueOrDie(); + + auto computation = module->entry_computation(); + + EXPECT_EQ(8, computation->instruction_count()); + HloCSE cse(true); + EXPECT_FALSE(cse.Run(module.get()).ValueOrDie()); + EXPECT_EQ(8, computation->instruction_count()); +} + +// Test two while loops with identical bodies and same inputs, but different +// conditions +TEST_F(HloCseTest, WhileLoopsIdenticalBodiesAndInputDifferntConditions) { + auto module = ParseHloString(R"( + HloModule WhileLoopsIdenticalBodiesAndInputDifferntConditions + + %body (param: (f32[], f32[])) -> (f32[], f32[]) { + %param = (f32[], f32[]) parameter(0) + %get-tuple-element = f32[] get-tuple-element((f32[], f32[]) %param), +index=0 %get-tuple-element.1 = f32[] get-tuple-element((f32[], f32[]) %param), +index=1 %add = f32[] add(f32[] %get-tuple-element, f32[] %get-tuple-element.1) + ROOT %tuple = (f32[], f32[]) tuple(f32[] %get-tuple-element, f32[] %add) + } + + %condition (param.1: (f32[], f32[])) -> pred[] { + %param.1 = (f32[], f32[]) parameter(0) + ROOT %constant = pred[] constant(false) + } + + %condition.1 (param.2: (f32[], f32[])) -> pred[] { + %param.2 = (f32[], f32[]) parameter(0) + ROOT %constant.1 = pred[] constant(true) + } + + ENTRY %WhileLoopsIdenticalBodiesAndInputDifferntConditions () -> (f32[], +f32[]) { %constant.2 = f32[] constant(1) %constant.3 = f32[] constant(2) + %tuple.1 = (f32[], f32[]) tuple(f32[] %constant.2, f32[] %constant.3) + %while = (f32[], f32[]) while((f32[], f32[]) %tuple.1), +condition=%condition, body=%body ROOT %while.1 = (f32[], f32[]) while((f32[], +f32[]) %tuple.1), condition=%condition.1, body=%body + })") + .ValueOrDie(); + + auto computation = module->entry_computation(); + + EXPECT_EQ(5, computation->instruction_count()); + HloCSE cse(true); + EXPECT_FALSE(cse.Run(module.get()).ValueOrDie()); + EXPECT_EQ(5, computation->instruction_count()); +} + TEST_F(HloCseTest, IdenticalInstructionsDifferentLayoutsSensitive) { // Test that two identical instructions with different layouts are *not* // commoned if the pass is layout sensitive. diff --git a/tensorflow/compiler/xla/service/hlo_instruction.cc b/tensorflow/compiler/xla/service/hlo_instruction.cc index 7685c822f4..8b9bdd2f46 100644 --- a/tensorflow/compiler/xla/service/hlo_instruction.cc +++ b/tensorflow/compiler/xla/service/hlo_instruction.cc @@ -1522,8 +1522,7 @@ bool HloInstruction::IdenticalSlowPath( case HloOpcode::kTupleSelect: return true; - // These opcodes have complex or special behavior so just return false. - case HloOpcode::kWhile: + // This opcode has complex or special behavior so just return false. case HloOpcode::kAfterAll: return false; @@ -1539,6 +1538,14 @@ bool HloInstruction::IdenticalSlowPath( return eq_computations(true_computation(), other.true_computation()) && eq_computations(false_computation(), other.false_computation()); + case HloOpcode::kWhile: { + if (eq_computations(while_body(), other.while_body()) && + eq_computations(while_condition(), other.while_condition())) { + return true; + } + return false; + } + case HloOpcode::kDomain: return operand_side_metadata().Matches(other.operand_side_metadata()) && user_side_metadata().Matches(other.user_side_metadata()); -- GitLab From 2279279fd15369e361a02fb09a1df41e08a34aae Mon Sep 17 00:00:00 2001 From: Brennan Saeta Date: Sat, 21 Jul 2018 12:55:03 -0700 Subject: [PATCH 242/519] [tf.data / Bigtable] Document use of the Cloud Bigtable API PiperOrigin-RevId: 205530581 --- tensorflow/contrib/bigtable/README.md | 344 +++++++++++++++++++++++++- 1 file changed, 342 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/bigtable/README.md b/tensorflow/contrib/bigtable/README.md index ef3c60069e..b2c0460f04 100644 --- a/tensorflow/contrib/bigtable/README.md +++ b/tensorflow/contrib/bigtable/README.md @@ -1,10 +1,350 @@ # Bigtable # -[Google Cloud Bigtable](https://cloud.google.com/bigtable/) is a high +[Cloud Bigtable](https://cloud.google.com/bigtable/) is a high performance storage system that can store and serve training data. This contrib package contains an experimental integration with TensorFlow. > **Status: Highly experimental.** The current implementation is very much in > flux. Please use at your own risk! :-) - +The TensorFlow integration with Cloud Bigtable is optimized for common +TensorFlow usage and workloads. It is currently optimized for reading from Cloud +Bigtable at high speed, in particular to feed modern accelerators. For +general-purpose Cloud Bigtable +APIs, see the [official Cloud Bigtable client library documentation][clientdoc]. + +[clientdoc]: https://cloud.google.com/bigtable/docs/reference/libraries + +## Sample Use + +There are three main reading styles supported by the `BigTable` class: + + 1. **Reading keys**: Read only the row keys in a table. Keys are returned in + sorted order from the table. Most key reading operations retrieve all keys + in a contiguous range, however the `sample_keys` operation skips keys, and + operates on the whole table (and not a contiguous subset). + 2. **Retrieving a row's values**: Given a row key, look up the data associated + with a defined set of columns. This operation takes advantage of Cloud + Bigtable's low-latency and excellent support for random access. + 3. **Scanning ranges**: Given a contiguous range of rows retrieve both the row + key and the data associated with a fixed set of columns. This operation + takes advantage of Cloud Bigtable's high throughput scans, and is the most + efficient way to read data. + +When using the Cloud Bigtable API, the workflow is: + + 1. Create a `BigtableClient` object. + 2. Use the `BigtableClient` to create `BigTable` objects corresponding to each + table in the Bigtable instance you would like to access. + 3. Call methods on the `BigTable` object to create `tf.data.Dataset`s to + retrieve data. + +The following is an example for how to read all row keys with the prefix +`train-`. + +```python +import tensorflow as tf + +GCP_PROJECT_ID = '' +BIGTABLE_INSTANCE_ID = '' +BIGTABLE_TABLE_NAME = '' +PREFIX = 'train-' + +def main(): + client = tf.contrib.cloud.BigtableClient(GCP_PROJECT_ID, BIGTABLE_INSTANCE_ID) + table = client.table(BIGTABLE_TABLE_NAME) + dataset = table.keys_by_prefix_dataset(PREFIX) + iterator = dataset.make_initializable_iterator() + get_next_op = iterator.get_next() + + with tf.Session() as sess: + print('Initializing the iterator.') + sess.run(iterator.initializer) + print('Retrieving rows:') + row_index = 0 + while True: + try: + row_key = sess.run(get_next_op) + print('Row key %d: %s' % (row_index, row_key)) + row_index += 1 + except tf.errors.OutOfRangeError: + print('Finished reading data!') + break + +if __name__ == '__main__': + main() + +``` + +### Reading row keys + +Read only the row keys in a table. Keys are returned in sorted order from the +table. Most key reading operations retrieve all keys in a contiguous range, +however the `sample_keys` operation skips keys, and operates on the whole table +(and not a contiguous subset). + +There are 3 methods to retrieve row keys: + + - `table.keys_by_range_dataset(start, end)`: Retrieve row keys starting with + `start`, and ending with `end`. The range is "half-open", and thus it + includes `start` if `start` is present in the table. It does not include + `end`. + - `table.keys_by_prefix_dataset(prefix)`: Retrieves all row keys that start + with `prefix`. It includes the row key `prefix` if present in the table. + - `table.sample_keys()`: Retrieves a sampling of keys from the underlying + table. This is often useful in conjunction with parallel scans. + +### Reading cell values given a row key + +Given a dataset producing row keys, you can use the `table.lookup_columns` +transformation to retrieve values. Example: + +```python +key_dataset = tf.data.Dataset.from_tensor_slices([ + 'row_key_1', + 'other_row_key', + 'final_row_key', +]) +values_dataset = key_dataset.apply( + table.lookup_columns(('my_column_family', 'column_name'), + ('other_cf', 'col'))) +training_data = values_dataset.map(my_parsing_function) # ... +``` + +### Scanning ranges +Given a contiguous range of rows retrieve both the row key and the data +associated with a fixed set of columns. Scanning is the most efficient way to +retrieve data from Cloud Bigtable and is thus a very common API for high +performance data pipelines. To construct a scanning `tf.data.Dataset` from a +`BigTable` object, call one of the following methods: + + - `table.scan_prefix(prefix, ...)` + - `table.scan_range(start, end, ...)` + - `table.parallel_scan_prefix(prefix, ...)` + - `table.parallel_scan_range(start, end, ...)` + +Aside from the specification of the contiguous range of rows, they all take the +following arguments: + + - `probability`: (Optional.) A float between 0 (exclusive) and 1 (inclusive). + A non-1 value indicates to probabilistically sample rows with the + provided probability. + - `columns`: The columns to read. (See below.) + - `**kwargs`: The columns to read. (See below.) + +In addition the two parallel operations accept the following optional argument: +`num_parallel_scans` which configures the number of parallel Cloud Bigtable scan +operations to run. A reasonable default is automatically chosen for small +Cloud Bigtable clusters. If you have a large cluster, or an extremely demanding +workload, you can tune this value to optimize performance. + +#### Specifying columns to read when scanning + +All of the scan operations allow you to specify the column family and columns +in the same ways. + +##### Using `columns` + +The first way to specify the data to read is via the `columns` parameter. The +value should be a tuple (or list of tuples) of strings. The first string in the +tuple is the column family, and the second string in the tuple is the column +qualifier. + +##### Using `**kwargs` + +The second way to specify the data to read is via the `**kwargs` parameter, +which you can use to specify keyword arguments corresponding to the columns that +you want to read. The keyword to use is the column family name, and the argument +value should be either a string, or a tuple of strings, specifying the column +qualifiers (column names). + +Although using `**kwargs` has the advantage of requiring less typing, it is not +future-proof in all cases. (If we add a new parameter to the scan functions that +has the same name as your column family, your code will break.) + +##### Examples + +Below are two equivalent snippets for how to specify which columns to read: + +```python +ds1 = table.scan_range("row_start", "row_end", columns=[("cfa", "c1"), + ("cfa", "c2"), + ("cfb", "c3")]) +ds2 = table.scan_range("row_start", "row_end", cfa=["c1", "c2"], cfb="c3") +``` + +In this example, we are reading 3 columns from a total of 2 column families. +From the `cfa` column family, we are reading columns `c1`, and `c2`. From the +second column family (`cfb`), we are reading `c3`. Both `ds1` and `ds2` will +output elements of the following types (`tf.string`, `tf.string`, `tf.string`, +`tf.string`). The first `tf.string` is the row key, the second `tf.string` is +the latest data in cell `cfa:c1`, the third corresponds to `cfa:c2`, and the +final one is `cfb:c3`. + +#### Determinism when scanning + +While the non-parallel scan operations are fully deterministic, the parallel +scan operations are not. If you would like to scan in parallel without losing +determinism, you can build up the `parallel_interleave` yourself. As an example, +say we wanted to scan all rows between `training_data_00000`, and +`training_data_90000`, we can use the following code snippet: + +```python +table = # ... +columns = [('cf1', 'col1'), ('cf1', 'col2')] +NUM_PARALLEL_READS = # ... +ds = tf.data.Dataset.range(9).shuffle(10) +def interleave_fn(index): + # Given a starting index, create 2 strings to be the start and end + start_idx = index + end_idx = index + 1 + start_idx_str = tf.as_string(start_idx * 10000, width=5, fill='0') + end_idx_str = tf.as_string(end_idx * 10000, width=5, fill='0') + start = tf.string_join(['training_data_', start_idx_str]) + end = tf.string_join(['training_data_', end_idx_str]) + return table.scan_range(start_idx, end_idx, columns=columns) +ds = ds.apply(tf.contrib.data.parallel_interleave( + interleave_fn, cycle_length=NUM_PARALLEL_READS, prefetch_input_elements=1)) +``` + +> Note: you should divide up the key range into more sub-ranges for increased +> parallelism. + +## Writing to Cloud Bigtable + +In order to simplify getting started, this package provides basic support for +writing data into Cloud Bigtable. + +> Note: The implementation is not optimized for performance! Please consider +> using alternative frameworks such as Apache Beam / Cloud Dataflow for +> production workloads. + +Below is an example for how to write a trivial dataset into Cloud Bigtable. + +```python +import tensorflow as tf + +GCP_PROJECT_ID = '' +BIGTABLE_INSTANCE_ID = '' +BIGTABLE_TABLE_NAME = '' +COLUMN_FAMILY = '' +COLUMN_QUALIFIER = '' + +def make_dataset(): + """Makes a dataset to write to Cloud Bigtable.""" + return tf.data.Dataset.from_tensor_slices([ + 'training_data_1', + 'training_data_2', + 'training_data_3', + ]) + +def make_row_key_dataset(): + """Makes a dataset of strings used for row keys. + + The strings are of the form: `fake-data-` followed by a sequential counter. + For example, this dataset would contain the following elements: + + - fake-data-00000001 + - fake-data-00000002 + - ... + - fake-data-23498103 + """ + counter_dataset = tf.contrib.data.Counter() + width = 8 + row_key_prefix = 'fake-data-' + ds = counter_dataset.map(lambda index: tf.as_string(index, + width=width, + fill='0')) + ds = ds.map(lambda idx_str: tf.string_join([row_key_prefix, idx_str])) + return ds + + +def main(): + client = tf.contrib.cloud.BigtableClient(GCP_PROJECT_ID, BIGTABLE_INSTANCE_ID) + table = client.table(BIGTABLE_TABLE_NAME) + dataset = make_dataset() + index_dataset = make_row_key_dataset() + aggregate_dataset = tf.data.Dataset.zip((index_dataset, dataset)) + write_op = table.write(aggregate_dataset, column_families=[COLUMN_FAMILY], + columns=[COLUMN_QUALIFIER]) + + with tf.Session() as sess: + print('Starting transfer.') + sess.run(write_op) + print('Transfer complete.') + +if __name__ == '__main__': + main() +``` + +## Sample applications and architectures + +While most machine learning applications are well suited by a high performance +distributed file system, there are certain applications where using Cloud +Bigtable works extremely well. + +### Perfect Shuffling + +Normally, training data is stored in flat files, and a combination of +(1) `tf.data.Dataset.interleave` (or `parallel_interleave`), (2) +`tf.data.Dataset.shuffle`, and (3) writing the data in an unsorted order in the +data files in the first place, provides enough randomization to ensure models +train efficiently. However, if you would like perfect shuffling, you can use +Cloud Bigtable's low-latency random access capabilities. Create a +`tf.data.Dataset` that generates the keys in a perfectly random order (or read +all the keys into memory and use a shuffle buffer sized to fit all of them for a +perfect random shuffle using `tf.data.Dataset.shuffle`), and then use +`lookup_columns` to retrieve the training data. + +### Distributed Reinforcement Learning + +Sophisticated reinforcement learning algorithms are commonly trained across a +distributed cluster. (See [IMPALA by DeepMind][impala].) One part of the cluster +runs self-play, while the other part of the cluster learns a new version of the +model based on the training data generated by self-play. The new model version +is then distributed to the self-play half of the cluster, and new training data +is generated to continue the cycle. + +In such a configuration, because there is value in training on the freshest +examples, a storage service like Cloud Bigtable can be used to store and +serve the generated training data. When using Cloud Bigtable, there is no need +to aggregate the examples into large batch files, but the examples can instead +be written as soon as they are generated, and then retrieved at high speed. + +[impala]: https://arxiv.org/abs/1802.01561 + +## Common Gotchas! + +### gRPC Certificates + +If you encounter a log line that includes the following: + +``` +"description":"Failed to load file", [...], +"filename":"/usr/share/grpc/roots.pem" +``` + +you likely need to copy the [gRPC roots.pem file][grpcPem] to +`/usr/share/grpc/roots.pem` on your local machine. + +[grpcPem]: https://github.com/grpc/grpc/blob/master/etc/roots.pem + +### Permission denied errors + +The TensorFlow Cloud Bigtable client will search for credentials to use in the +process's environment. It will use the first credentials it finds if multiple +are available. + + - **Compute Engine**: When running on Compute Engine, the client will often use + the service account from the virtual machine's metadata service. Be sure to + authorize your Compute Engine VM to have access to the Cloud Bigtable service + when creating your VM. + - **Cloud TPU**: Your Cloud TPUs run with the designated Cloud TPU service + account dedicated to your GCP project. Ensure the service account has been + authorized via the Cloud Console to access your Cloud Bigtable instances. + +### `BigTable` vs Bigtable? + +Cloud Bigtable is spelled with a lower-case (aka common) `t`. The Python class +`BigTable`, however is short for `BigtableTable`, and thus uses an upper-case +(aka capital) `T`. -- GitLab From b7bbe64b5fde8a909d4410f758244a6703f84780 Mon Sep 17 00:00:00 2001 From: Miguel Mota Date: Sat, 21 Jul 2018 19:50:58 -0700 Subject: [PATCH 243/519] Update readme with working bazel config flag --- tensorflow/go/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/go/README.md b/tensorflow/go/README.md index e251356ec8..288a32530a 100644 --- a/tensorflow/go/README.md +++ b/tensorflow/go/README.md @@ -46,7 +46,7 @@ from source. ```sh cd ${GOPATH}/src/github.com/tensorflow/tensorflow ./configure - bazel build --config opt //tensorflow:libtensorflow.so + bazel build -c opt //tensorflow:libtensorflow.so ``` This can take a while (tens of minutes, more if also building for GPU). -- GitLab From e578eb00f2eedae6ce25eabdbf349581f22c4df9 Mon Sep 17 00:00:00 2001 From: Anirudh Koul Date: Sat, 21 Jul 2018 20:23:15 -0700 Subject: [PATCH 244/519] Fixed typo in TOCO command line doc --- tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md index 18b7848db8..4bf47aa3c4 100644 --- a/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md +++ b/tensorflow/contrib/lite/toco/g3doc/cmdline_examples.md @@ -36,7 +36,7 @@ There are two approaches to running TOCO via command line. * `tflite_convert`: Starting from TensorFlow 1.9, the command-line tool `tflite_convert` will be installed as part of the Python package. All of the examples below use `tflite_convert` for simplicity. - * Example: `tflite --output_file=...` + * Example: `tflite_convert --output_file=...` * `bazel`: In order to run the latest version of TOCO, [clone the TensorFlow repository](https://www.tensorflow.org/install/install_sources#clone_the_tensorflow_repository) and use `bazel`. This is the recommended approach for converting models that -- GitLab From f31939d24e3c544933b98ef48fac9ccac5679e05 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sat, 21 Jul 2018 22:04:15 -0700 Subject: [PATCH 245/519] Add function crf_multitag_sequence_score which enables calculating scores with more than one tag at each index. PiperOrigin-RevId: 205551004 --- tensorflow/contrib/crf/__init__.py | 2 + .../crf/python/kernel_tests/crf_test.py | 62 +++++++++++++++++-- tensorflow/contrib/crf/python/ops/crf.py | 52 +++++++++++++++- 3 files changed, 109 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/crf/__init__.py b/tensorflow/contrib/crf/__init__.py index 046c509626..615e62b16f 100644 --- a/tensorflow/contrib/crf/__init__.py +++ b/tensorflow/contrib/crf/__init__.py @@ -20,6 +20,7 @@ See the @{$python/contrib.crf} guide. @@crf_decode @@crf_log_likelihood @@crf_log_norm +@@crf_multitag_sequence_score @@crf_sequence_score @@crf_unary_score @@CrfDecodeBackwardRnnCell @@ -36,6 +37,7 @@ from tensorflow.contrib.crf.python.ops.crf import crf_binary_score from tensorflow.contrib.crf.python.ops.crf import crf_decode from tensorflow.contrib.crf.python.ops.crf import crf_log_likelihood from tensorflow.contrib.crf.python.ops.crf import crf_log_norm +from tensorflow.contrib.crf.python.ops.crf import crf_multitag_sequence_score from tensorflow.contrib.crf.python.ops.crf import crf_sequence_score from tensorflow.contrib.crf.python.ops.crf import crf_unary_score from tensorflow.contrib.crf.python.ops.crf import CrfDecodeBackwardRnnCell diff --git a/tensorflow/contrib/crf/python/kernel_tests/crf_test.py b/tensorflow/contrib/crf/python/kernel_tests/crf_test.py index 74f2ec22ff..f56a973f6f 100644 --- a/tensorflow/contrib/crf/python/kernel_tests/crf_test.py +++ b/tensorflow/contrib/crf/python/kernel_tests/crf_test.py @@ -31,6 +31,15 @@ from tensorflow.python.platform import test class CrfTest(test.TestCase): + def calculateSequenceScore(self, inputs, transition_params, tag_indices, + sequence_lengths): + expected_unary_score = sum( + inputs[i][tag_indices[i]] for i in range(sequence_lengths)) + expected_binary_score = sum( + transition_params[tag_indices[i], tag_indices[i + 1]] + for i in range(sequence_lengths - 1)) + return expected_unary_score + expected_binary_score + def testCrfSequenceScore(self): transition_params = np.array( [[-3, 5, -2], [3, 4, 1], [1, 2, 1]], dtype=np.float32) @@ -60,14 +69,55 @@ class CrfTest(test.TestCase): transition_params=constant_op.constant(transition_params)) sequence_score = array_ops.squeeze(sequence_score, [0]) tf_sequence_score = sess.run(sequence_score) - expected_unary_score = sum(inputs[i][tag_indices[i]] - for i in range(sequence_lengths)) - expected_binary_score = sum( - transition_params[tag_indices[i], tag_indices[i + 1]] - for i in range(sequence_lengths - 1)) - expected_sequence_score = expected_unary_score + expected_binary_score + expected_sequence_score = self.calculateSequenceScore( + inputs, transition_params, tag_indices, sequence_lengths) self.assertAllClose(tf_sequence_score, expected_sequence_score) + def testCrfMultiTagSequenceScore(self): + transition_params = np.array( + [[-3, 5, -2], [3, 4, 1], [1, 2, 1]], dtype=np.float32) + # Test both the length-1 and regular cases. + sequence_lengths_list = [ + np.array(3, dtype=np.int32), + np.array(1, dtype=np.int32) + ] + inputs_list = [ + np.array([[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], + dtype=np.float32), + np.array([[4, 5, -3]], + dtype=np.float32), + ] + tag_bitmap_list = [ + np.array( + [[True, True, False], [True, False, True], [False, True, True], + [True, False, True]], + dtype=np.bool), + np.array([[True, True, False]], dtype=np.bool) + ] + for sequence_lengths, inputs, tag_bitmap in zip( + sequence_lengths_list, inputs_list, tag_bitmap_list): + with self.test_session() as sess: + sequence_score = crf.crf_multitag_sequence_score( + inputs=array_ops.expand_dims(inputs, 0), + tag_bitmap=array_ops.expand_dims(tag_bitmap, 0), + sequence_lengths=array_ops.expand_dims(sequence_lengths, 0), + transition_params=constant_op.constant(transition_params)) + sequence_score = array_ops.squeeze(sequence_score, [0]) + tf_sum_sequence_score = sess.run(sequence_score) + all_indices_list = [ + single_index_bitmap.nonzero()[0] + for single_index_bitmap in tag_bitmap[:sequence_lengths] + ] + expected_sequence_scores = [ + self.calculateSequenceScore(inputs, transition_params, indices, + sequence_lengths) + for indices in itertools.product(*all_indices_list) + ] + expected_log_sum_exp_sequence_scores = np.logaddexp.reduce( + expected_sequence_scores) + self.assertAllClose(tf_sum_sequence_score, + expected_log_sum_exp_sequence_scores) + def testCrfUnaryScore(self): inputs = np.array( [[4, 5, -3], [3, -1, 3], [-1, 2, 1], [0, 0, 0]], dtype=np.float32) diff --git a/tensorflow/contrib/crf/python/ops/crf.py b/tensorflow/contrib/crf/python/ops/crf.py index 2d2cbdc199..8a7ff61bc8 100644 --- a/tensorflow/contrib/crf/python/ops/crf.py +++ b/tensorflow/contrib/crf/python/ops/crf.py @@ -67,7 +67,7 @@ __all__ = [ "crf_sequence_score", "crf_log_norm", "crf_log_likelihood", "crf_unary_score", "crf_binary_score", "CrfForwardRnnCell", "viterbi_decode", "crf_decode", "CrfDecodeForwardRnnCell", - "CrfDecodeBackwardRnnCell" + "CrfDecodeBackwardRnnCell", "crf_multitag_sequence_score" ] @@ -114,6 +114,56 @@ def crf_sequence_score(inputs, tag_indices, sequence_lengths, false_fn=_multi_seq_fn) +def crf_multitag_sequence_score(inputs, tag_bitmap, sequence_lengths, + transition_params): + """Computes the unnormalized score of all tag sequences matching tag_bitmap. + + tag_bitmap enables more than one tag to be considered correct at each time + step. This is useful when an observed output at a given time step is + consistent with more than one tag, and thus the log likelihood of that + observation must take into account all possible consistent tags. + + Using one-hot vectors in tag_bitmap gives results identical to + crf_sequence_score. + + Args: + inputs: A [batch_size, max_seq_len, num_tags] tensor of unary potentials + to use as input to the CRF layer. + tag_bitmap: A [batch_size, max_seq_len, num_tags] boolean tensor + representing all active tags at each index for which to calculate the + unnormalized score. + sequence_lengths: A [batch_size] vector of true sequence lengths. + transition_params: A [num_tags, num_tags] transition matrix. + Returns: + sequence_scores: A [batch_size] vector of unnormalized sequence scores. + """ + + # If max_seq_len is 1, we skip the score calculation and simply gather the + # unary potentials of all active tags. + def _single_seq_fn(): + filtered_inputs = array_ops.where( + tag_bitmap, inputs, + array_ops.fill(array_ops.shape(inputs), float("-inf"))) + return math_ops.reduce_logsumexp( + filtered_inputs, axis=[1, 2], keepdims=False) + + def _multi_seq_fn(): + # Compute the logsumexp of all scores of sequences matching the given tags. + filtered_inputs = array_ops.where( + tag_bitmap, inputs, + array_ops.fill(array_ops.shape(inputs), float("-inf"))) + return crf_log_norm( + inputs=filtered_inputs, + sequence_lengths=sequence_lengths, + transition_params=transition_params) + + return utils.smart_cond( + pred=math_ops.equal(inputs.shape[1].value or array_ops.shape(inputs)[1], + 1), + true_fn=_single_seq_fn, + false_fn=_multi_seq_fn) + + def crf_log_norm(inputs, sequence_lengths, transition_params): """Computes the normalization for a CRF. -- GitLab From 571f7a2488f653ed0647d55ed62dd51473d3eaa3 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Sat, 21 Jul 2018 23:05:38 -0700 Subject: [PATCH 246/519] Fix formatting issues --- tensorflow/contrib/tensorrt/BUILD | 5 ++++- tensorflow/contrib/tensorrt/segment/segment.cc | 16 ++++++++-------- 2 files changed, 12 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index cb2daa7b12..e3248699dd 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -278,11 +278,14 @@ tf_cc_test( tags = ["no_windows"], deps = [ ":segment", - "//tensorflow/c:c_api", + "//tensorflow/cc:cc_ops", + "//tensorflow/cc:scope", + "//tensorflow/core:core_cpu", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", "//tensorflow/core:test", "//tensorflow/core:test_main", + "//tensorflow/core:testlib", ], ) diff --git a/tensorflow/contrib/tensorrt/segment/segment.cc b/tensorflow/contrib/tensorrt/segment/segment.cc index 92807bed14..008fffc954 100644 --- a/tensorflow/contrib/tensorrt/segment/segment.cc +++ b/tensorflow/contrib/tensorrt/segment/segment.cc @@ -562,8 +562,8 @@ tensorflow::Status SegmentGraph( // input, for output nodes remove all its output. In this way, for common // cases the number of removed nodes should be minimum. auto remove_nodes = [&segment_nodes]( - bool is_input_nodes, - std::deque* que) { + bool is_input_nodes, + std::deque* que) { // Run a BFS on the queue to find all the input/output nodes. std::set visited; while (!que->empty()) { @@ -571,13 +571,14 @@ tensorflow::Status SegmentGraph( que->pop_front(); if (!visited.insert(node).second) continue; segment_nodes.erase(node); - for (auto in : is_input_nodes ? node->in_nodes() : node->out_nodes()) { + for (auto in : + is_input_nodes ? node->in_nodes() : node->out_nodes()) { if (segment_nodes.count(in)) { que->push_back(in); VLOG(2) << "Need to remove node " << in->name() - << " because one of its " - << (is_input_nodes ? "output" : "input") - << " nodes in the graph was removed: " << node->name(); + << " because one of its " + << (is_input_nodes ? "output" : "input") + << " nodes in the graph was removed: " << node->name(); } } } @@ -599,8 +600,7 @@ tensorflow::Status SegmentGraph( } // Don't use small segments. - if (static_cast(segment_nodes.size()) < - options.minimum_segment_size) { + if (static_cast(segment_nodes.size()) < options.minimum_segment_size) { VLOG(1) << "Segment " << segments->size() << " has only " << segment_nodes.size() << " nodes, dropping"; continue; -- GitLab From 88e560d6fadc1cf23519b00a9de5ed7c973536fd Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Sun, 22 Jul 2018 00:16:05 -0700 Subject: [PATCH 247/519] Use paramaterized tests in `train_test.py`. PiperOrigin-RevId: 205555784 --- tensorflow/contrib/gan/BUILD | 2 + tensorflow/contrib/gan/python/train_test.py | 571 ++++++++------------ 2 files changed, 219 insertions(+), 354 deletions(-) diff --git a/tensorflow/contrib/gan/BUILD b/tensorflow/contrib/gan/BUILD index c8c2af49d4..781e4ae4d7 100644 --- a/tensorflow/contrib/gan/BUILD +++ b/tensorflow/contrib/gan/BUILD @@ -57,6 +57,7 @@ py_library( py_test( name = "train_test", srcs = ["python/train_test.py"], + shard_count = 50, srcs_version = "PY2AND3", tags = ["notsan"], deps = [ @@ -80,6 +81,7 @@ py_test( "//tensorflow/python:variables", "//tensorflow/python/ops/distributions", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/contrib/gan/python/train_test.py b/tensorflow/contrib/gan/python/train_test.py index 93a12af944..cd99a33c03 100644 --- a/tensorflow/contrib/gan/python/train_test.py +++ b/tensorflow/contrib/gan/python/train_test.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized import numpy as np from tensorflow.contrib import layers @@ -296,38 +297,24 @@ def get_tensor_pool_fn_for_infogan(pool_size): return tensor_pool_fn_impl -class GANModelTest(test.TestCase): +class GANModelTest(test.TestCase, parameterized.TestCase): """Tests for `gan_model`.""" - def _test_output_type_helper(self, create_fn, tuple_type): - self.assertTrue(isinstance(create_fn(), tuple_type)) - - def test_output_type_gan(self): - self._test_output_type_helper(get_gan_model, namedtuples.GANModel) - - def test_output_type_callable_gan(self): - self._test_output_type_helper(get_callable_gan_model, namedtuples.GANModel) - - def test_output_type_infogan(self): - self._test_output_type_helper(get_infogan_model, namedtuples.InfoGANModel) - - def test_output_type_callable_infogan(self): - self._test_output_type_helper(get_callable_infogan_model, - namedtuples.InfoGANModel) - - def test_output_type_acgan(self): - self._test_output_type_helper(get_acgan_model, namedtuples.ACGANModel) - - def test_output_type_callable_acgan(self): - self._test_output_type_helper(get_callable_acgan_model, - namedtuples.ACGANModel) - - def test_output_type_cyclegan(self): - self._test_output_type_helper(get_cyclegan_model, namedtuples.CycleGANModel) - - def test_output_type_callable_cyclegan(self): - self._test_output_type_helper(get_callable_cyclegan_model, - namedtuples.CycleGANModel) + @parameterized.named_parameters( + ('gan', get_gan_model, namedtuples.GANModel), + ('callable_gan', get_callable_gan_model, namedtuples.GANModel), + ('infogan', get_infogan_model, namedtuples.InfoGANModel), + ('callable_infogan', get_callable_infogan_model, + namedtuples.InfoGANModel), + ('acgan', get_acgan_model, namedtuples.ACGANModel), + ('callable_acgan', get_callable_acgan_model, namedtuples.ACGANModel), + ('cyclegan', get_cyclegan_model, namedtuples.CycleGANModel), + ('callable_cyclegan', get_callable_cyclegan_model, + namedtuples.CycleGANModel), + ) + def test_output_type(self, create_fn, expected_tuple_type): + """Test that output type is as expected.""" + self.assertIsInstance(create_fn(), expected_tuple_type) def test_no_shape_check(self): @@ -484,53 +471,55 @@ class StarGANModelTest(test.TestCase): disc_gen_label.shape) -class GANLossTest(test.TestCase): +class GANLossTest(test.TestCase, parameterized.TestCase): """Tests for `gan_loss`.""" - # Test output type. - def _test_output_type_helper(self, get_gan_model_fn): + @parameterized.named_parameters( + ('gan', get_gan_model), + ('callable_gan', get_callable_gan_model), + ('infogan', get_infogan_model), + ('callable_infogan', get_callable_infogan_model), + ('acgan', get_acgan_model), + ('callable_acgan', get_callable_acgan_model), + ) + def test_output_type(self, get_gan_model_fn): + """Test output type.""" loss = train.gan_loss(get_gan_model_fn(), add_summaries=True) - self.assertTrue(isinstance(loss, namedtuples.GANLoss)) - self.assertGreater(len(ops.get_collection(ops.GraphKeys.SUMMARIES)), 0) - - def test_output_type_gan(self): - self._test_output_type_helper(get_gan_model) - - def test_output_type_callable_gan(self): - self._test_output_type_helper(get_callable_gan_model) - - def test_output_type_infogan(self): - self._test_output_type_helper(get_infogan_model) - - def test_output_type_callable_infogan(self): - self._test_output_type_helper(get_callable_infogan_model) - - def test_output_type_acgan(self): - self._test_output_type_helper(get_acgan_model) - - def test_output_type_callable_acgan(self): - self._test_output_type_helper(get_callable_acgan_model) - - def test_output_type_cyclegan(self): - loss = train.cyclegan_loss(create_cyclegan_model(), add_summaries=True) - self.assertIsInstance(loss, namedtuples.CycleGANLoss) + self.assertIsInstance(loss, namedtuples.GANLoss) self.assertGreater(len(ops.get_collection(ops.GraphKeys.SUMMARIES)), 0) - def test_output_type_callable_cyclegan(self): - loss = train.cyclegan_loss( - create_callable_cyclegan_model(), add_summaries=True) + @parameterized.named_parameters( + ('cyclegan', create_cyclegan_model), + ('callable_cyclegan', create_callable_cyclegan_model), + ) + def test_cyclegan_output_type(self, get_gan_model_fn): + loss = train.cyclegan_loss(get_gan_model_fn(), add_summaries=True) self.assertIsInstance(loss, namedtuples.CycleGANLoss) self.assertGreater(len(ops.get_collection(ops.GraphKeys.SUMMARIES)), 0) - # Test gradient penalty option. - def _test_grad_penalty_helper(self, create_gan_model_fn, one_sided=False): + @parameterized.named_parameters( + ('gan', create_gan_model, False), + ('gan_one_sided', create_gan_model, True), + ('callable_gan', create_callable_gan_model, False), + ('callable_gan_one_sided', create_callable_gan_model, True), + ('infogan', create_infogan_model, False), + ('infogan_one_sided', create_infogan_model, True), + ('callable_infogan', create_callable_infogan_model, False), + ('callable_infogan_one_sided', create_callable_infogan_model, True), + ('acgan', create_acgan_model, False), + ('acgan_one_sided', create_acgan_model, True), + ('callable_acgan', create_callable_acgan_model, False), + ('callable_acgan_one_sided', create_callable_acgan_model, True), + ) + def test_grad_penalty(self, create_gan_model_fn, one_sided): + """Test gradient penalty option.""" model = create_gan_model_fn() loss = train.gan_loss(model) loss_gp = train.gan_loss( model, gradient_penalty_weight=1.0, gradient_penalty_one_sided=one_sided) - self.assertTrue(isinstance(loss_gp, namedtuples.GANLoss)) + self.assertIsInstance(loss_gp, namedtuples.GANLoss) # Check values. with self.test_session(use_gpu=True) as sess: @@ -541,59 +530,28 @@ class GANLossTest(test.TestCase): [loss.discriminator_loss, loss_gp.discriminator_loss]) self.assertEqual(loss_gen_np, loss_gen_gp_np) - self.assertTrue(loss_dis_np < loss_dis_gp_np) - - def test_grad_penalty_gan(self): - self._test_grad_penalty_helper(create_gan_model) - - def test_grad_penalty_callable_gan(self): - self._test_grad_penalty_helper(create_callable_gan_model) - - def test_grad_penalty_infogan(self): - self._test_grad_penalty_helper(create_infogan_model) - - def test_grad_penalty_callable_infogan(self): - self._test_grad_penalty_helper(create_callable_infogan_model) - - def test_grad_penalty_acgan(self): - self._test_grad_penalty_helper(create_acgan_model) - - def test_grad_penalty_callable_acgan(self): - self._test_grad_penalty_helper(create_callable_acgan_model) - - def test_grad_penalty_one_sided_gan(self): - self._test_grad_penalty_helper(create_gan_model, one_sided=True) - - def test_grad_penalty_one_sided_callable_gan(self): - self._test_grad_penalty_helper(create_callable_gan_model, one_sided=True) - - def test_grad_penalty_one_sided_infogan(self): - self._test_grad_penalty_helper(create_infogan_model, one_sided=True) - - def test_grad_penalty_one_sided_callable_infogan(self): - self._test_grad_penalty_helper( - create_callable_infogan_model, one_sided=True) - - def test_grad_penalty_one_sided_acgan(self): - self._test_grad_penalty_helper(create_acgan_model, one_sided=True) - - def test_grad_penalty_one_sided_callable_acgan(self): - self._test_grad_penalty_helper(create_callable_acgan_model, one_sided=True) - - # Test mutual information penalty option. - def _test_mutual_info_penalty_helper(self, create_gan_model_fn): + self.assertLess(loss_dis_np, loss_dis_gp_np) + + @parameterized.named_parameters( + ('infogan', get_infogan_model), + ('callable_infogan', get_callable_infogan_model), + ) + def test_mutual_info_penalty(self, create_gan_model_fn): + """Test mutual information penalty option.""" train.gan_loss( create_gan_model_fn(), mutual_information_penalty_weight=constant_op.constant(1.0)) - def test_mutual_info_penalty_infogan(self): - self._test_mutual_info_penalty_helper(get_infogan_model) - - def test_mutual_info_penalty_callable_infogan(self): - self._test_mutual_info_penalty_helper(get_callable_infogan_model) - - # Test regularization loss. - def _test_regularization_helper(self, get_gan_model_fn): + @parameterized.named_parameters( + ('gan', get_gan_model), + ('callable_gan', get_callable_gan_model), + ('infogan', get_infogan_model), + ('callable_infogan', get_callable_infogan_model), + ('acgan', get_acgan_model), + ('callable_acgan', get_callable_acgan_model), + ) + def test_regularization_helper(self, get_gan_model_fn): + """Test regularization loss.""" # Evaluate losses without regularization. no_reg_loss = train.gan_loss(get_gan_model_fn()) with self.test_session(use_gpu=True): @@ -616,33 +574,19 @@ class GANLossTest(test.TestCase): self.assertEqual(3.0, reg_loss_gen_np - no_reg_loss_gen_np) self.assertEqual(2.0, reg_loss_dis_np - no_reg_loss_dis_np) - def test_regularization_gan(self): - self._test_regularization_helper(get_gan_model) - - def test_regularization_callable_gan(self): - self._test_regularization_helper(get_callable_gan_model) - - def test_regularization_infogan(self): - self._test_regularization_helper(get_infogan_model) - - def test_regularization_callable_infogan(self): - self._test_regularization_helper(get_callable_infogan_model) - - def test_regularization_acgan(self): - self._test_regularization_helper(get_acgan_model) - - def test_regularization_callable_acgan(self): - self._test_regularization_helper(get_callable_acgan_model) - - # Test that ACGan models work. - def _test_acgan_helper(self, create_gan_model_fn): + @parameterized.named_parameters( + ('notcallable', create_acgan_model), + ('callable', create_callable_acgan_model), + ) + def test_acgan(self, create_gan_model_fn): + """Test that ACGAN models work.""" model = create_gan_model_fn() loss = train.gan_loss(model) loss_ac_gen = train.gan_loss(model, aux_cond_generator_weight=1.0) loss_ac_dis = train.gan_loss(model, aux_cond_discriminator_weight=1.0) - self.assertTrue(isinstance(loss, namedtuples.GANLoss)) - self.assertTrue(isinstance(loss_ac_gen, namedtuples.GANLoss)) - self.assertTrue(isinstance(loss_ac_dis, namedtuples.GANLoss)) + self.assertIsInstance(loss, namedtuples.GANLoss) + self.assertIsInstance(loss_ac_gen, namedtuples.GANLoss) + self.assertIsInstance(loss_ac_dis, namedtuples.GANLoss) # Check values. with self.test_session(use_gpu=True) as sess: @@ -656,20 +600,18 @@ class GANLossTest(test.TestCase): loss_ac_dis.discriminator_loss ]) - self.assertTrue(loss_gen_np < loss_dis_np) + self.assertLess(loss_gen_np, loss_dis_np) self.assertTrue(np.isscalar(loss_ac_gen_gen_np)) self.assertTrue(np.isscalar(loss_ac_dis_gen_np)) self.assertTrue(np.isscalar(loss_ac_gen_dis_np)) self.assertTrue(np.isscalar(loss_ac_dis_dis_np)) - def test_acgan(self): - self._test_acgan_helper(create_acgan_model) - - def test_callable_acgan(self): - self._test_acgan_helper(create_callable_acgan_model) - - # Test that CycleGan models work. - def _test_cyclegan_helper(self, create_gan_model_fn): + @parameterized.named_parameters( + ('notcallable', create_cyclegan_model), + ('callable', create_callable_cyclegan_model), + ) + def test_cyclegan(self, create_gan_model_fn): + """Test that CycleGan models work.""" model = create_gan_model_fn() loss = train.cyclegan_loss(model) self.assertIsInstance(loss, namedtuples.CycleGANLoss) @@ -690,11 +632,46 @@ class GANLossTest(test.TestCase): self.assertTrue(np.isscalar(loss_y2x_gen_np)) self.assertTrue(np.isscalar(loss_y2x_dis_np)) - def test_cyclegan(self): - self._test_cyclegan_helper(create_cyclegan_model) + @parameterized.named_parameters( + ('gan', create_gan_model), + ('callable_gan', create_callable_gan_model), + ('infogan', create_infogan_model), + ('callable_infogan', create_callable_infogan_model), + ('acgan', create_acgan_model), + ('callable_acgan', create_callable_acgan_model), + ) + def test_tensor_pool(self, create_gan_model_fn): + """Test tensor pool option.""" + model = create_gan_model_fn() + if isinstance(model, namedtuples.InfoGANModel): + tensor_pool_fn = get_tensor_pool_fn_for_infogan(pool_size=5) + else: + tensor_pool_fn = get_tensor_pool_fn(pool_size=5) + loss = train.gan_loss(model, tensor_pool_fn=tensor_pool_fn) + self.assertIsInstance(loss, namedtuples.GANLoss) + + # Check values. + with self.test_session(use_gpu=True) as sess: + variables.global_variables_initializer().run() + for _ in range(10): + sess.run([loss.generator_loss, loss.discriminator_loss]) + + def test_doesnt_crash_when_in_nested_scope(self): + with variable_scope.variable_scope('outer_scope'): + gan_model = train.gan_model( + generator_model, + discriminator_model, + real_data=array_ops.zeros([1, 2]), + generator_inputs=random_ops.random_normal([1, 2])) + + # This should work inside a scope. + train.gan_loss(gan_model, gradient_penalty_weight=1.0) + + # This should also work outside a scope. + train.gan_loss(gan_model, gradient_penalty_weight=1.0) + - def test_callable_cyclegan(self): - self._test_cyclegan_helper(create_callable_cyclegan_model) +class TensorPoolAdjusteModelTest(test.TestCase): def _check_tensor_pool_adjusted_model_outputs(self, tensor1, tensor2, pool_size): @@ -714,115 +691,77 @@ class GANLossTest(test.TestCase): # pool). self.assertTrue(any([(v == t2).all() for v in history_values])) - # Test `_tensor_pool_adjusted_model` for gan model. - def test_tensor_pool_adjusted_model_gan(self): - model = create_gan_model() - - new_model = train._tensor_pool_adjusted_model(model, None) + def _make_new_model_and_check(self, model, pool_size, + pool_fn=get_tensor_pool_fn): + new_model = train._tensor_pool_adjusted_model( + model, pool_fn(pool_size=pool_size)) # 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0' self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.VARIABLES))) + self.assertIsNot(new_model.discriminator_gen_outputs, + model.discriminator_gen_outputs) + + return new_model + + def test_tensor_pool_adjusted_model_no_pool(self): + """Test `_tensor_pool_adjusted_model` for no pool size.""" + model = create_gan_model() + new_model = train._tensor_pool_adjusted_model(model, None) + + # Check values. self.assertIs(new_model.discriminator_gen_outputs, model.discriminator_gen_outputs) + def test_tensor_pool_adjusted_model_gan(self): + """Test `_tensor_pool_adjusted_model` for gan model.""" pool_size = 5 - new_model = train._tensor_pool_adjusted_model( - model, get_tensor_pool_fn(pool_size=pool_size)) - self.assertIsNot(new_model.discriminator_gen_outputs, - model.discriminator_gen_outputs) + model = create_gan_model() + new_model = self._make_new_model_and_check(model, pool_size) + # Check values. self._check_tensor_pool_adjusted_model_outputs( model.discriminator_gen_outputs, new_model.discriminator_gen_outputs, pool_size) - # Test _tensor_pool_adjusted_model for infogan model. def test_tensor_pool_adjusted_model_infogan(self): + """Test _tensor_pool_adjusted_model for infogan model.""" + pool_size = 5 model = create_infogan_model() + new_model = self._make_new_model_and_check( + model, pool_size, pool_fn=get_tensor_pool_fn_for_infogan) - pool_size = 5 - new_model = train._tensor_pool_adjusted_model( - model, get_tensor_pool_fn_for_infogan(pool_size=pool_size)) - # 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0' - self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.VARIABLES))) - self.assertIsNot(new_model.discriminator_gen_outputs, - model.discriminator_gen_outputs) + # Check values. self.assertIsNot(new_model.predicted_distributions, model.predicted_distributions) - # Check values. self._check_tensor_pool_adjusted_model_outputs( model.discriminator_gen_outputs, new_model.discriminator_gen_outputs, pool_size) - # Test _tensor_pool_adjusted_model for acgan model. def test_tensor_pool_adjusted_model_acgan(self): + """Test _tensor_pool_adjusted_model for acgan model.""" + pool_size = 5 model = create_acgan_model() + new_model = self._make_new_model_and_check(model, pool_size) - pool_size = 5 - new_model = train._tensor_pool_adjusted_model( - model, get_tensor_pool_fn(pool_size=pool_size)) - # 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0' - self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.VARIABLES))) - self.assertIsNot(new_model.discriminator_gen_outputs, - model.discriminator_gen_outputs) + # Check values. self.assertIsNot(new_model.discriminator_gen_classification_logits, model.discriminator_gen_classification_logits) - # Check values. self._check_tensor_pool_adjusted_model_outputs( model.discriminator_gen_outputs, new_model.discriminator_gen_outputs, pool_size) - # Test tensor pool. - def _test_tensor_pool_helper(self, create_gan_model_fn): - model = create_gan_model_fn() - if isinstance(model, namedtuples.InfoGANModel): - tensor_pool_fn = get_tensor_pool_fn_for_infogan(pool_size=5) - else: - tensor_pool_fn = get_tensor_pool_fn(pool_size=5) - loss = train.gan_loss(model, tensor_pool_fn=tensor_pool_fn) - self.assertTrue(isinstance(loss, namedtuples.GANLoss)) - - # Check values. - with self.test_session(use_gpu=True) as sess: - variables.global_variables_initializer().run() - for _ in range(10): - sess.run([loss.generator_loss, loss.discriminator_loss]) - - def test_tensor_pool_gan(self): - self._test_tensor_pool_helper(create_gan_model) - - def test_tensor_pool_callable_gan(self): - self._test_tensor_pool_helper(create_callable_gan_model) - - def test_tensor_pool_infogan(self): - self._test_tensor_pool_helper(create_infogan_model) - - def test_tensor_pool_callable_infogan(self): - self._test_tensor_pool_helper(create_callable_infogan_model) - - def test_tensor_pool_acgan(self): - self._test_tensor_pool_helper(create_acgan_model) - - def test_tensor_pool_callable_acgan(self): - self._test_tensor_pool_helper(create_callable_acgan_model) - - def test_doesnt_crash_when_in_nested_scope(self): - with variable_scope.variable_scope('outer_scope'): - gan_model = train.gan_model( - generator_model, - discriminator_model, - real_data=array_ops.zeros([1, 2]), - generator_inputs=random_ops.random_normal([1, 2])) - - # This should work inside a scope. - train.gan_loss(gan_model, gradient_penalty_weight=1.0) - - # This should also work outside a scope. - train.gan_loss(gan_model, gradient_penalty_weight=1.0) - -class GANTrainOpsTest(test.TestCase): +class GANTrainOpsTest(test.TestCase, parameterized.TestCase): """Tests for `gan_train_ops`.""" - def _test_output_type_helper(self, create_gan_model_fn): + @parameterized.named_parameters( + ('gan', create_gan_model), + ('callable_gan', create_callable_gan_model), + ('infogan', create_infogan_model), + ('callable_infogan', create_callable_infogan_model), + ('acgan', create_acgan_model), + ('callable_acgan', create_callable_acgan_model), + ) + def test_output_type(self, create_gan_model_fn): model = create_gan_model_fn() loss = train.gan_loss(model) @@ -836,28 +775,24 @@ class GANTrainOpsTest(test.TestCase): summarize_gradients=True, colocate_gradients_with_ops=True) - self.assertTrue(isinstance(train_ops, namedtuples.GANTrainOps)) - - def test_output_type_gan(self): - self._test_output_type_helper(create_gan_model) - - def test_output_type_callable_gan(self): - self._test_output_type_helper(create_callable_gan_model) - - def test_output_type_infogan(self): - self._test_output_type_helper(create_infogan_model) - - def test_output_type_callable_infogan(self): - self._test_output_type_helper(create_callable_infogan_model) - - def test_output_type_acgan(self): - self._test_output_type_helper(create_acgan_model) - - def test_output_type_callable_acgan(self): - self._test_output_type_helper(create_callable_acgan_model) + self.assertIsInstance(train_ops, namedtuples.GANTrainOps) # TODO(joelshor): Add a test to check that custom update op is run. - def _test_unused_update_ops(self, create_gan_model_fn, provide_update_ops): + @parameterized.named_parameters( + ('gan', create_gan_model, False), + ('gan_provideupdates', create_gan_model, True), + ('callable_gan', create_callable_gan_model, False), + ('callable_gan_provideupdates', create_callable_gan_model, True), + ('infogan', create_infogan_model, False), + ('infogan_provideupdates', create_infogan_model, True), + ('callable_infogan', create_callable_infogan_model, False), + ('callable_infogan_provideupdates', create_callable_infogan_model, True), + ('acgan', create_acgan_model, False), + ('acgan_provideupdates', create_acgan_model, True), + ('callable_acgan', create_callable_acgan_model, False), + ('callable_acgan_provideupdates', create_callable_acgan_model, True), + ) + def test_unused_update_ops(self, create_gan_model_fn, provide_update_ops): model = create_gan_model_fn() loss = train.gan_loss(model) @@ -904,45 +839,16 @@ class GANTrainOpsTest(test.TestCase): self.assertEqual(1, gen_update_count.eval()) self.assertEqual(1, dis_update_count.eval()) - def test_unused_update_ops_gan(self): - self._test_unused_update_ops(create_gan_model, False) - - def test_unused_update_ops_gan_provideupdates(self): - self._test_unused_update_ops(create_gan_model, True) - - def test_unused_update_ops_callable_gan(self): - self._test_unused_update_ops(create_callable_gan_model, False) - - def test_unused_update_ops_callable_gan_provideupdates(self): - self._test_unused_update_ops(create_callable_gan_model, True) - - def test_unused_update_ops_infogan(self): - self._test_unused_update_ops(create_infogan_model, False) - - def test_unused_update_ops_infogan_provideupdates(self): - self._test_unused_update_ops(create_infogan_model, True) - - def test_unused_update_ops_callable_infogan(self): - self._test_unused_update_ops(create_callable_infogan_model, False) - - def test_unused_update_ops_callable_infogan_provideupdates(self): - self._test_unused_update_ops(create_callable_infogan_model, True) - - def test_unused_update_ops_acgan(self): - self._test_unused_update_ops(create_acgan_model, False) - - def test_unused_update_ops_acgan_provideupdates(self): - self._test_unused_update_ops(create_acgan_model, True) - - def test_unused_update_ops_callable_acgan(self): - self._test_unused_update_ops(create_callable_acgan_model, False) - - def test_unused_update_ops_callable_acgan_provideupdates(self): - self._test_unused_update_ops(create_callable_acgan_model, True) - - def _test_sync_replicas_helper(self, - create_gan_model_fn, - create_global_step=False): + @parameterized.named_parameters( + ('gan', create_gan_model, False), + ('callable_gan', create_callable_gan_model, False), + ('infogan', create_infogan_model, False), + ('callable_infogan', create_callable_infogan_model, False), + ('acgan', create_acgan_model, False), + ('callable_acgan', create_callable_acgan_model, False), + ('gan_canbeint32', create_gan_model, True), + ) + def test_sync_replicas(self, create_gan_model_fn, create_global_step): model = create_gan_model_fn() loss = train.gan_loss(model) num_trainable_vars = len(variables_lib.get_trainable_variables()) @@ -956,7 +862,7 @@ class GANTrainOpsTest(test.TestCase): d_opt = get_sync_optimizer() train_ops = train.gan_train_ops( model, loss, generator_optimizer=g_opt, discriminator_optimizer=d_opt) - self.assertTrue(isinstance(train_ops, namedtuples.GANTrainOps)) + self.assertIsInstance(train_ops, namedtuples.GANTrainOps) # No new trainable variables should have been added. self.assertEqual(num_trainable_vars, len(variables_lib.get_trainable_variables())) @@ -994,29 +900,8 @@ class GANTrainOpsTest(test.TestCase): coord.request_stop() coord.join(g_threads + d_threads) - def test_sync_replicas_gan(self): - self._test_sync_replicas_helper(create_gan_model) - - def test_sync_replicas_callable_gan(self): - self._test_sync_replicas_helper(create_callable_gan_model) - - def test_sync_replicas_infogan(self): - self._test_sync_replicas_helper(create_infogan_model) - - def test_sync_replicas_callable_infogan(self): - self._test_sync_replicas_helper(create_callable_infogan_model) - - def test_sync_replicas_acgan(self): - self._test_sync_replicas_helper(create_acgan_model) - - def test_sync_replicas_callable_acgan(self): - self._test_sync_replicas_helper(create_callable_acgan_model) - def test_global_step_can_be_int32(self): - self._test_sync_replicas_helper(create_gan_model, create_global_step=True) - - -class GANTrainTest(test.TestCase): +class GANTrainTest(test.TestCase, parameterized.TestCase): """Tests for `gan_train`.""" def _gan_train_ops(self, generator_add, discriminator_add): @@ -1032,7 +917,15 @@ class GANTrainTest(test.TestCase): global_step_inc_op=step.assign_add(1)) return train_ops - def _test_run_helper(self, create_gan_model_fn): + @parameterized.named_parameters( + ('gan', create_gan_model), + ('callable_gan', create_callable_gan_model), + ('infogan', create_infogan_model), + ('callable_infogan', create_callable_infogan_model), + ('acgan', create_acgan_model), + ('callable_acgan', create_callable_acgan_model), + ) + def test_run_helper(self, create_gan_model_fn): random_seed.set_random_seed(1234) model = create_gan_model_fn() loss = train.gan_loss(model) @@ -1048,26 +941,12 @@ class GANTrainTest(test.TestCase): self.assertTrue(np.isscalar(final_step)) self.assertEqual(2, final_step) - def test_run_gan(self): - self._test_run_helper(create_gan_model) - - def test_run_callable_gan(self): - self._test_run_helper(create_callable_gan_model) - - def test_run_infogan(self): - self._test_run_helper(create_infogan_model) - - def test_run_callable_infogan(self): - self._test_run_helper(create_callable_infogan_model) - - def test_run_acgan(self): - self._test_run_helper(create_acgan_model) - - def test_run_callable_acgan(self): - self._test_run_helper(create_callable_acgan_model) - - # Test multiple train steps. - def _test_multiple_steps_helper(self, get_hooks_fn_fn): + @parameterized.named_parameters( + ('seq_train_steps', train.get_sequential_train_hooks), + ('efficient_seq_train_steps', train.get_joint_train_hooks), + ) + def test_multiple_steps(self, get_hooks_fn_fn): + """Test multiple train steps.""" train_ops = self._gan_train_ops(generator_add=10, discriminator_add=100) train_steps = namedtuples.GANTrainSteps( generator_train_steps=3, discriminator_train_steps=4) @@ -1080,12 +959,6 @@ class GANTrainTest(test.TestCase): self.assertTrue(np.isscalar(final_step)) self.assertEqual(1 + 3 * 10 + 4 * 100, final_step) - def test_multiple_steps_seq_train_steps(self): - self._test_multiple_steps_helper(train.get_sequential_train_hooks) - - def test_multiple_steps_efficient_seq_train_steps(self): - self._test_multiple_steps_helper(train.get_joint_train_hooks) - def test_supervisor_run_gan_model_train_ops_multiple_steps(self): step = training_util.create_global_step() train_ops = namedtuples.GANTrainOps( @@ -1105,10 +978,18 @@ class GANTrainTest(test.TestCase): self.assertEqual(17.0, final_loss) -class PatchGANTest(test.TestCase): +class PatchGANTest(test.TestCase, parameterized.TestCase): """Tests that functions work on PatchGAN style output.""" - def _test_patchgan_helper(self, create_gan_model_fn): + @parameterized.named_parameters( + ('gan', create_gan_model), + ('callable_gan', create_callable_gan_model), + ('infogan', create_infogan_model), + ('callable_infogan', create_callable_infogan_model), + ('acgan', create_acgan_model), + ('callable_acgan', create_callable_acgan_model), + ) + def test_patchgan(self, create_gan_model_fn): """Ensure that patch-based discriminators work end-to-end.""" random_seed.set_random_seed(1234) model = create_gan_model_fn() @@ -1125,24 +1006,6 @@ class PatchGANTest(test.TestCase): self.assertTrue(np.isscalar(final_step)) self.assertEqual(2, final_step) - def test_patchgan_gan(self): - self._test_patchgan_helper(create_gan_model) - - def test_patchgan_callable_gan(self): - self._test_patchgan_helper(create_callable_gan_model) - - def test_patchgan_infogan(self): - self._test_patchgan_helper(create_infogan_model) - - def test_patchgan_callable_infogan(self): - self._test_patchgan_helper(create_callable_infogan_model) - - def test_patchgan_acgan(self): - self._test_patchgan_helper(create_acgan_model) - - def test_patchgan_callable_acgan(self): - self._test_patchgan_helper(create_callable_acgan_model) - if __name__ == '__main__': test.main() -- GitLab From 162304f9da4114f5ed3f0e4c27929413e7abc965 Mon Sep 17 00:00:00 2001 From: Misha Brukman Date: Sun, 22 Jul 2018 12:48:00 -0700 Subject: [PATCH 248/519] [tf.data / Bigtable] Renamed BigTable class to BigtableTable for clarity This removes the confusion between BigTable and Bigtable naming. Also cleaned up all other uses of BigTable in error messages. PiperOrigin-RevId: 205586899 --- tensorflow/contrib/bigtable/README.md | 16 +++++----------- tensorflow/contrib/bigtable/__init__.py | 6 +++--- .../bigtable/kernels/bigtable_kernels.cc | 2 +- .../contrib/bigtable/kernels/bigtable_lib.cc | 8 ++++---- .../python/kernel_tests/bigtable_ops_test.py | 2 +- .../contrib/bigtable/python/ops/bigtable_api.py | 17 +++++++++-------- tensorflow/contrib/cloud/README.md | 4 ++-- tensorflow/contrib/cloud/__init__.py | 4 ++-- 8 files changed, 27 insertions(+), 32 deletions(-) diff --git a/tensorflow/contrib/bigtable/README.md b/tensorflow/contrib/bigtable/README.md index b2c0460f04..d7c71a20ed 100644 --- a/tensorflow/contrib/bigtable/README.md +++ b/tensorflow/contrib/bigtable/README.md @@ -17,7 +17,7 @@ APIs, see the [official Cloud Bigtable client library documentation][clientdoc]. ## Sample Use -There are three main reading styles supported by the `BigTable` class: +There are three main reading styles supported by the `BigtableTable` class: 1. **Reading keys**: Read only the row keys in a table. Keys are returned in sorted order from the table. Most key reading operations retrieve all keys @@ -34,9 +34,9 @@ There are three main reading styles supported by the `BigTable` class: When using the Cloud Bigtable API, the workflow is: 1. Create a `BigtableClient` object. - 2. Use the `BigtableClient` to create `BigTable` objects corresponding to each - table in the Bigtable instance you would like to access. - 3. Call methods on the `BigTable` object to create `tf.data.Dataset`s to + 2. Use the `BigtableClient` to create `BigtableTable` objects corresponding to + each table in the Cloud Bigtable instance you would like to access. + 3. Call methods on the `BigtableTable` object to create `tf.data.Dataset`s to retrieve data. The following is an example for how to read all row keys with the prefix @@ -116,7 +116,7 @@ Given a contiguous range of rows retrieve both the row key and the data associated with a fixed set of columns. Scanning is the most efficient way to retrieve data from Cloud Bigtable and is thus a very common API for high performance data pipelines. To construct a scanning `tf.data.Dataset` from a -`BigTable` object, call one of the following methods: +`BigtableTable` object, call one of the following methods: - `table.scan_prefix(prefix, ...)` - `table.scan_range(start, end, ...)` @@ -342,9 +342,3 @@ are available. - **Cloud TPU**: Your Cloud TPUs run with the designated Cloud TPU service account dedicated to your GCP project. Ensure the service account has been authorized via the Cloud Console to access your Cloud Bigtable instances. - -### `BigTable` vs Bigtable? - -Cloud Bigtable is spelled with a lower-case (aka common) `t`. The Python class -`BigTable`, however is short for `BigtableTable`, and thus uses an upper-case -(aka capital) `T`. diff --git a/tensorflow/contrib/bigtable/__init__.py b/tensorflow/contrib/bigtable/__init__.py index 7df054637c..b7d89c9842 100644 --- a/tensorflow/contrib/bigtable/__init__.py +++ b/tensorflow/contrib/bigtable/__init__.py @@ -18,7 +18,7 @@ This contrib package allows TensorFlow to interface directly with Cloud Bigtable for high-speed data loading. @@BigtableClient -@@BigTable +@@BigtableTable """ @@ -26,14 +26,14 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigTable from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigtableClient +from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigtableTable from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ - 'BigTable', 'BigtableClient', + 'BigtableTable', ] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc index 70923e6287..a6755a3496 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_kernels.cc @@ -276,7 +276,7 @@ class ToBigtableOp : public AsyncOpKernel { } OP_REQUIRES_ASYNC( ctx, failures.empty() && mutation_status.ok(), - errors::Unknown("Failure while writing to BigTable: ", + errors::Unknown("Failure while writing to Cloud Bigtable: ", mutation_status.error_code(), " - ", mutation_status.error_message(), " (", mutation_status.error_details(), diff --git a/tensorflow/contrib/bigtable/kernels/bigtable_lib.cc b/tensorflow/contrib/bigtable/kernels/bigtable_lib.cc index 2514575f30..67bf14c176 100644 --- a/tensorflow/contrib/bigtable/kernels/bigtable_lib.cc +++ b/tensorflow/contrib/bigtable/kernels/bigtable_lib.cc @@ -27,10 +27,10 @@ Status GrpcStatusToTfStatus(const ::grpc::Status& status) { status.error_code() == ::grpc::StatusCode::OUT_OF_RANGE) { grpc_code = ::grpc::StatusCode::INTERNAL; } - return Status( - static_cast<::tensorflow::error::Code>(status.error_code()), - strings::StrCat("Error reading from BigTable: ", status.error_message(), - " (Details: ", status.error_details(), ")")); + return Status(static_cast<::tensorflow::error::Code>(status.error_code()), + strings::StrCat("Error reading from Cloud Bigtable: ", + status.error_message(), + " (Details: ", status.error_details(), ")")); } string RegexFromStringSet(const std::vector& strs) { diff --git a/tensorflow/contrib/bigtable/python/kernel_tests/bigtable_ops_test.py b/tensorflow/contrib/bigtable/python/kernel_tests/bigtable_ops_test.py index 2f20064619..e36f7f32c6 100644 --- a/tensorflow/contrib/bigtable/python/kernel_tests/bigtable_ops_test.py +++ b/tensorflow/contrib/bigtable/python/kernel_tests/bigtable_ops_test.py @@ -44,7 +44,7 @@ class BigtableOpsTest(test.TestCase): def setUp(self): self._client = gen_bigtable_test_ops.bigtable_test_client() table = gen_bigtable_ops.bigtable_table(self._client, "testtable") - self._table = bigtable.BigTable("testtable", None, table) + self._table = bigtable.BigtableTable("testtable", None, table) def _makeSimpleDataset(self): output_rows = dataset_ops.Dataset.from_tensor_slices(self.COMMON_ROW_KEYS) diff --git a/tensorflow/contrib/bigtable/python/ops/bigtable_api.py b/tensorflow/contrib/bigtable/python/ops/bigtable_api.py index 9f73b7223c..fd30aa8bbb 100644 --- a/tensorflow/contrib/bigtable/python/ops/bigtable_api.py +++ b/tensorflow/contrib/bigtable/python/ops/bigtable_api.py @@ -94,7 +94,7 @@ class BigtableClient(object): project_id, instance_id, connection_pool_size, max_receive_message_size) def table(self, name, snapshot=None): - """Opens a table and returns a `BigTable` object. + """Opens a table and returns a `BigtableTable` object. Args: name: A `tf.string` `tf.Tensor` name of the table to open. @@ -102,19 +102,20 @@ class BigtableClient(object): request the creation of a snapshot. (Note: currently unimplemented.) Returns: - A `BigTable` python object representing the operations available on the - table. + A `BigtableTable` python object representing the operations available on + the table. """ # TODO(saeta): Implement snapshot functionality. table = gen_bigtable_ops.bigtable_table(self._resource, name) - return BigTable(name, snapshot, table) + return BigtableTable(name, snapshot, table) -class BigTable(object): - """BigTable is the entrypoint for reading and writing data in Cloud Bigtable. +class BigtableTable(object): + """BigtableTable is the entrypoint for reading and writing data in Cloud + Bigtable. - This BigTable class is the python representation of the Cloud Bigtable table - within TensorFlow. Methods on this class allow data to be read from and + This BigtableTable class is the Python representation of the Cloud Bigtable + table within TensorFlow. Methods on this class allow data to be read from and written to the Cloud Bigtable service in flexible and high performance manners. """ diff --git a/tensorflow/contrib/cloud/README.md b/tensorflow/contrib/cloud/README.md index 134ce057f4..a80d8965f3 100644 --- a/tensorflow/contrib/cloud/README.md +++ b/tensorflow/contrib/cloud/README.md @@ -1,8 +1,8 @@ # Cloud # -## BigTable ## +## Cloud Bigtable ## -[Google Cloud BigTable](https://cloud.google.com/bigtable/) is a high +[Google Cloud Bigtable](https://cloud.google.com/bigtable/) is a high performance storage system that can store and serve training data. This contrib package contains an experimental integration with TensorFlow. diff --git a/tensorflow/contrib/cloud/__init__.py b/tensorflow/contrib/cloud/__init__.py index af81106a68..8efd259946 100644 --- a/tensorflow/contrib/cloud/__init__.py +++ b/tensorflow/contrib/cloud/__init__.py @@ -25,8 +25,8 @@ from tensorflow.contrib.cloud.python.ops.bigquery_reader_ops import * from tensorflow.contrib.cloud.python.ops.gcs_config_ops import * if os.name != 'nt': - from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigTable from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigtableClient + from tensorflow.contrib.bigtable.python.ops.bigtable_api import BigtableTable del os @@ -34,8 +34,8 @@ from tensorflow.python.util.all_util import remove_undocumented _allowed_symbols = [ 'BigQueryReader', - 'BigTable', 'BigtableClient', + 'BigtableTable', 'BlockCacheParams', 'configure_colab_session', 'configure_gcs', -- GitLab From 012f97121441f936b5262b98e2ca488c0c92422f Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Sun, 22 Jul 2018 13:41:51 -0700 Subject: [PATCH 249/519] Add synchronization and aggregation arguments to variable creation methods in contrib/layers. PiperOrigin-RevId: 205588849 --- .../contrib/framework/python/ops/variables.py | 97 ++++++++++++++----- .../contrib/layers/python/layers/layers.py | 33 ++++--- 2 files changed, 94 insertions(+), 36 deletions(-) diff --git a/tensorflow/contrib/framework/python/ops/variables.py b/tensorflow/contrib/framework/python/ops/variables.py index e8e3180019..322d5c335e 100644 --- a/tensorflow/contrib/framework/python/ops/variables.py +++ b/tensorflow/contrib/framework/python/ops/variables.py @@ -34,6 +34,7 @@ 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.ops import variable_scope +from tensorflow.python.ops import variables from tensorflow.python.platform import resource_loader from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import saver as tf_saver @@ -199,10 +200,20 @@ def global_variable(initial_value, @contrib_add_arg_scope -def variable(name, shape=None, dtype=None, initializer=None, - regularizer=None, trainable=True, collections=None, - caching_device=None, device=None, - partitioner=None, custom_getter=None, use_resource=None): +def variable(name, + shape=None, + dtype=None, + initializer=None, + regularizer=None, + trainable=True, + collections=None, + caching_device=None, + device=None, + partitioner=None, + custom_getter=None, + use_resource=None, + synchronization=variables.VariableSynchronization.AUTO, + aggregation=variables.VariableAggregation.NONE): """Gets an existing variable with these parameters or creates a new one. Args: @@ -228,6 +239,15 @@ def variable(name, shape=None, dtype=None, initializer=None, custom_getter: Callable that allows overwriting the internal get_variable method and has to have the same signature. use_resource: If `True` use a ResourceVariable instead of a Variable. + synchronization: Indicates when a distributed a variable will be + aggregated. Accepted values are constants defined in the class + @{tf.VariableSynchronization}. By default the synchronization is set to + `AUTO` and the current `DistributionStrategy` chooses + when to synchronize. If `synchronization` is set to `ON_READ`, + `trainable` must not be set to `True`. + aggregation: Indicates how a distributed variable will be aggregated. + Accepted values are constants defined in the class + @{tf.VariableAggregation}. Returns: The created or existing variable. @@ -242,21 +262,36 @@ def variable(name, shape=None, dtype=None, initializer=None, getter = functools.partial(custom_getter, reuse=variable_scope.get_variable_scope().reuse) with ops.device(device or ''): - return getter(name, shape=shape, dtype=dtype, - initializer=initializer, - regularizer=regularizer, - trainable=trainable, - collections=collections, - caching_device=caching_device, - partitioner=partitioner, - use_resource=use_resource) + return getter( + name, + shape=shape, + dtype=dtype, + initializer=initializer, + regularizer=regularizer, + trainable=trainable, + collections=collections, + caching_device=caching_device, + partitioner=partitioner, + use_resource=use_resource, + synchronization=synchronization, + aggregation=aggregation) @contrib_add_arg_scope -def model_variable(name, shape=None, dtype=dtypes.float32, initializer=None, - regularizer=None, trainable=True, collections=None, - caching_device=None, device=None, partitioner=None, - custom_getter=None, use_resource=None): +def model_variable(name, + shape=None, + dtype=dtypes.float32, + initializer=None, + regularizer=None, + trainable=True, + collections=None, + caching_device=None, + device=None, + partitioner=None, + custom_getter=None, + use_resource=None, + synchronization=variables.VariableSynchronization.AUTO, + aggregation=variables.VariableAggregation.NONE): """Gets an existing model variable with these parameters or creates a new one. Args: @@ -283,18 +318,36 @@ def model_variable(name, shape=None, dtype=dtypes.float32, initializer=None, custom_getter: Callable that allows overwriting the internal get_variable method and has to have the same signature. use_resource: If `True` use a ResourceVariable instead of a Variable. + synchronization: Indicates when a distributed a variable will be + aggregated. Accepted values are constants defined in the class + @{tf.VariableSynchronization}. By default the synchronization is set to + `AUTO` and the current `DistributionStrategy` chooses + when to synchronize. If `synchronization` is set to `ON_READ`, + `trainable` must not be set to `True`. + aggregation: Indicates how a distributed variable will be aggregated. + Accepted values are constants defined in the class + @{tf.VariableAggregation}. Returns: The created or existing variable. """ collections = list(collections or []) collections += [ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.MODEL_VARIABLES] - var = variable(name, shape=shape, dtype=dtype, - initializer=initializer, regularizer=regularizer, - trainable=trainable, collections=collections, - caching_device=caching_device, device=device, - partitioner=partitioner, custom_getter=custom_getter, - use_resource=use_resource) + var = variable( + name, + shape=shape, + dtype=dtype, + initializer=initializer, + regularizer=regularizer, + trainable=trainable, + collections=collections, + caching_device=caching_device, + device=device, + partitioner=partitioner, + custom_getter=custom_getter, + use_resource=use_resource, + synchronization=synchronization, + aggregation=aggregation) return var diff --git a/tensorflow/contrib/layers/python/layers/layers.py b/tensorflow/contrib/layers/python/layers/layers.py index beeabd6b65..dd602cf3a9 100644 --- a/tensorflow/contrib/layers/python/layers/layers.py +++ b/tensorflow/contrib/layers/python/layers/layers.py @@ -1702,19 +1702,22 @@ def _inner_flatten(inputs, new_rank, output_collections=None, scope=None): return utils.collect_named_outputs(output_collections, sc, flattened) -def _model_variable_getter(getter, - name, - shape=None, - dtype=None, - initializer=None, - regularizer=None, - trainable=True, - collections=None, - caching_device=None, - partitioner=None, - rename=None, - use_resource=None, - **_): +def _model_variable_getter( + getter, + name, + shape=None, + dtype=None, + initializer=None, + regularizer=None, + trainable=True, + collections=None, + caching_device=None, + partitioner=None, + rename=None, + use_resource=None, + synchronization=tf_variables.VariableSynchronization.AUTO, + aggregation=tf_variables.VariableAggregation.NONE, + **_): """Getter that uses model_variable for compatibility with core layers.""" short_name = name.split('/')[-1] if rename and short_name in rename: @@ -1732,7 +1735,9 @@ def _model_variable_getter(getter, caching_device=caching_device, partitioner=partitioner, custom_getter=getter, - use_resource=use_resource) + use_resource=use_resource, + synchronization=synchronization, + aggregation=aggregation) def _build_variable_getter(rename=None): -- GitLab From 89e06304aad35bfb019a8c10f39fc1ead83e0f99 Mon Sep 17 00:00:00 2001 From: Anjali Sridhar Date: Sun, 22 Jul 2018 16:32:41 -0700 Subject: [PATCH 250/519] Add support for `is_tensor_like` property to DistributedValues and add support for calling `assign` on TowerLocalVariables. PiperOrigin-RevId: 205595323 --- .../python/mirrored_strategy_multigpu_test.py | 69 +++++++++++++++++++ .../contrib/distribute/python/values.py | 33 ++++++--- .../contrib/distribute/python/values_test.py | 25 +++++++ tensorflow/python/framework/tensor_util.py | 9 ++- 4 files changed, 123 insertions(+), 13 deletions(-) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py index 6a14b833d2..9807ce4351 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py @@ -967,5 +967,74 @@ class MirroredAndTowerLocalVariableInitializerTest(test.TestCase): self.evaluate(tower_local_var.initializer) self.assertTrue(self.evaluate(tower_local_var.is_initialized())) + +class TowerLocalVariableAssignTest(test.TestCase): + config = config_pb2.ConfigProto() + config.allow_soft_placement = True + + def _skip_eager_if_gpus_less_than(self, num_gpus): + if context.num_gpus() < num_gpus and context.executing_eagerly(): + self.skipTest("Enough GPUs not available for this test in eager mode.") + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignTowerLocalVarSumAggregation(self): + self._skip_eager_if_gpus_less_than(1) + def model_fn(): + v_sum = variable_scope.variable( + 1.0, + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.SUM) + return v_sum + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + tower_local_var = dist.call_for_each_tower(model_fn, + run_concurrently=False) + self.assertTrue(isinstance(tower_local_var, values.TowerLocalVariable)) + self.evaluate(variables.global_variables_initializer()) + # Each tower has a value of 1.0 assigned to it in tower context. + # When we read the value using `read_var` we should see the SUM of each of + # values on each of the towers. + self.assertEqual(2.0, self.evaluate(dist.read_var(tower_local_var))) + # Assigning 6.0 in cross tower context will assign a value of + # 6.0/num_towers to each tower. + tlv_ops = tower_local_var.assign(6.0) + self.evaluate(tlv_ops) + # On reading the tower local var we should get the assigned value back. + # The value on all the towers are added before being returned by + # `read_var`. + self.assertEqual(6.0, self.evaluate(dist.read_var(tower_local_var))) + + @test_util.run_in_graph_and_eager_modes(config=config) + def testAssignTowerLocalVarMeanAggregation(self): + self._skip_eager_if_gpus_less_than(1) + def model_fn(): + v_sum = variable_scope.variable( + 1.0, + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.MEAN) + return v_sum + + dist = mirrored_strategy.MirroredStrategy( + ["/device:GPU:0", "/device:CPU:0"]) + + with dist.scope(): + tower_local_var = dist.call_for_each_tower(model_fn, + run_concurrently=False) + self.assertTrue(isinstance(tower_local_var, values.TowerLocalVariable)) + self.evaluate(variables.global_variables_initializer()) + # Each tower has a value of 1.0 assigned to it in tower context. + # When we read the value using `read_var` we should see the MEAN of values + # on all towers which is the value assigned in tower context. + self.assertEqual(1.0, self.evaluate(dist.read_var(tower_local_var))) + tlv_ops = tower_local_var.assign(6.0) + self.evaluate(tlv_ops) + # On reading the tower local var we should get the MEAN of all values + # which is equal to the value assigned. + self.assertEqual(6.0, self.evaluate(dist.read_var(tower_local_var))) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index 3162aebf5b..47dcf679c2 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -30,6 +30,7 @@ from tensorflow.contrib.distribute.python import prefetching_ops_v2 from tensorflow.python.eager import context from tensorflow.python.framework import device as tf_device 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 math_ops @@ -77,6 +78,13 @@ class DistributedValues(object): def devices(self): return list(self._index.keys()) + @property + def is_tensor_like(self): + for v in self._index.values(): + if not tensor_util.is_tensor(v): + return False + return True + def __str__(self): return "%s:%s" % (self.__class__.__name__, self._index) @@ -352,6 +360,7 @@ class MirroredVariable(DistributedVariable, Mirrored, return distribute_lib.get_distribution_strategy().update( self, f, *args, **kwargs) else: + _assert_tower_context() # We are calling an assign function on the mirrored variable in tower # context. # We reduce the value we want to assign/add/sub. More details about how we @@ -448,14 +457,7 @@ class _TowerLocalSaveable(saver.BaseSaverBuilder.SaveableObject): def restore(self, restored_tensors, restored_shapes): """Restore the same value into all variables.""" tensor, = restored_tensors - # To preserve the sum across save and restore, we have to divide the - # total across all devices when restoring a variable that was summed - # when saving. - if self._tower_local_variable.aggregation == vs.VariableAggregation.SUM: - tensor *= 1. / len(self._tower_local_variable.devices) - return control_flow_ops.group([ - _assign_on_device(d, v, tensor) - for d, v in six.iteritems(self._tower_local_variable._index)]) # pylint: disable=protected-access + return self._tower_local_variable.assign(tensor) def _assert_tower_context(): @@ -482,8 +484,19 @@ class TowerLocalVariable(DistributedVariable, PerDevice, return self.get().assign_add(*args, **kwargs) def assign(self, *args, **kwargs): - _assert_tower_context() - return self.get().assign(*args, **kwargs) + if distribute_lib.get_cross_tower_context(): + # To preserve the sum across save and restore, we have to divide the + # total across all devices when restoring a variable that was summed + # when saving. + tensor = args[0] + if self._aggregation == vs.VariableAggregation.SUM: + tensor *= 1. / len(self.devices) + return control_flow_ops.group( + [_assign_on_device(d, v, tensor) + for d, v in six.iteritems(self._index)]) + else: + _assert_tower_context() + return self.get().assign(*args, **kwargs) @property def aggregation(self): diff --git a/tensorflow/contrib/distribute/python/values_test.py b/tensorflow/contrib/distribute/python/values_test.py index 8e44f2fea1..91a43d4999 100644 --- a/tensorflow/contrib/distribute/python/values_test.py +++ b/tensorflow/contrib/distribute/python/values_test.py @@ -32,6 +32,7 @@ from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import random_ops @@ -79,6 +80,30 @@ class DistributedValuesTest(test.TestCase): with self.assertRaises(AssertionError): v = values.DistributedValues({"/device:cpu:0": 42}) + def testIsTensorLike(self): + with context.graph_mode(), \ + ops.Graph().as_default(), \ + ops.device("/device:CPU:0"): + one = constant_op.constant(1) + two = constant_op.constant(2) + v = values.DistributedValues({"/device:CPU:0": one, "/device:GPU:0": two}) + self.assertEqual(two, v.get("/device:GPU:0")) + self.assertEqual(one, v.get()) + self.assertTrue(v.is_tensor_like) + self.assertTrue(tensor_util.is_tensor(v)) + + def testIsTensorLikeWithAConstant(self): + with context.graph_mode(), \ + ops.Graph().as_default(), \ + ops.device("/device:CPU:0"): + one = constant_op.constant(1) + two = 2.0 + v = values.DistributedValues({"/device:CPU:0": one, "/device:GPU:0": two}) + self.assertEqual(two, v.get("/device:GPU:0")) + self.assertEqual(one, v.get()) + self.assertFalse(v.is_tensor_like) + self.assertFalse(tensor_util.is_tensor(v)) + class DistributedDelegateTest(test.TestCase): diff --git a/tensorflow/python/framework/tensor_util.py b/tensorflow/python/framework/tensor_util.py index ca63efbc84..8c9dfce7cc 100644 --- a/tensorflow/python/framework/tensor_util.py +++ b/tensorflow/python/framework/tensor_util.py @@ -935,8 +935,10 @@ def constant_value_as_shape(tensor): # pylint: disable=invalid-name def is_tensor(x): # pylint: disable=invalid-name """Check whether `x` is of tensor type. - Check whether an object is a tensor. Equivalent to - `isinstance(x, [tf.Tensor, tf.SparseTensor, tf.Variable])`. + Check whether an object is a tensor. This check is equivalent to calling + `isinstance(x, [tf.Tensor, tf.SparseTensor, tf.Variable])` and also checks + if all the component variables of a MirroredVariable or a TowerLocalVariable + are tensors. Args: x: A python object to check. @@ -944,4 +946,5 @@ def is_tensor(x): # pylint: disable=invalid-name Returns: `True` if `x` is a tensor, `False` if not. """ - return isinstance(x, ops._TensorLike) or ops.is_dense_tensor_like(x) # pylint: disable=protected-access + return (isinstance(x, ops._TensorLike) or ops.is_dense_tensor_like(x) or # pylint: disable=protected-access + (hasattr(x, "is_tensor_like") and x.is_tensor_like)) -- GitLab From 21d0205916eded7e2bf2f26e43dd41b2f86cba3f Mon Sep 17 00:00:00 2001 From: David Majnemer Date: Mon, 23 Jul 2018 06:15:33 -0700 Subject: [PATCH 251/519] [XLA:CPU,GPU] Implement more cases of convert This adds support for {S32,U32,F32} -> PRED and adds test for several other cases as well. PiperOrigin-RevId: 205650630 --- .../xla/service/elemental_ir_emitter.cc | 18 ++++- tensorflow/compiler/xla/tests/convert_test.cc | 70 ++++++++++++++++++- 2 files changed, 83 insertions(+), 5 deletions(-) diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index 1eedd85363..b58b87a978 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -222,10 +222,17 @@ StatusOr ElementalIrEmitter::EmitIntegerUnaryOp( case HloOpcode::kConvert: { PrimitiveType from_type = op->operand(0)->shape().element_type(); PrimitiveType to_type = op->shape().element_type(); - CHECK(primitive_util::IsIntegralType(from_type) || from_type == PRED); + CHECK(primitive_util::IsIntegralType(from_type) || from_type == PRED) + << from_type; if (from_type == to_type) { return operand_value; } + if (to_type == PRED) { + return b_->CreateZExt( + b_->CreateICmpNE(operand_value, llvm::ConstantInt::get( + operand_value->getType(), 0)), + llvm_ir::PrimitiveTypeToIrType(PRED, module_)); + } if (primitive_util::IsIntegralType(to_type)) { return b_->CreateIntCast( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_), @@ -342,7 +349,7 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( case HloOpcode::kConvert: { PrimitiveType from_type = op->operand(0)->shape().element_type(); PrimitiveType to_type = op->shape().element_type(); - CHECK(primitive_util::IsFloatingPointType(from_type)); + CHECK(primitive_util::IsFloatingPointType(from_type)) << from_type; if (from_type == to_type) { return operand_value; } @@ -369,6 +376,13 @@ StatusOr ElementalIrEmitter::EmitFloatUnaryOp( if (from_type == F32 && to_type == BF16) { return EmitF32ToBF16(operand_value, b_); } + if (to_type == PRED) { + return b_->CreateZExt( + b_->CreateFCmpUNE( + operand_value, + llvm::ConstantFP::get(operand_value->getType(), 0.0)), + llvm_ir::PrimitiveTypeToIrType(PRED, module_)); + } if (primitive_util::IsFloatingPointType(to_type)) { return b_->CreateFPCast( operand_value, llvm_ir::PrimitiveTypeToIrType(to_type, module_)); diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc index dca57fd1c7..0fb6853e3f 100644 --- a/tensorflow/compiler/xla/tests/convert_test.cc +++ b/tensorflow/compiler/xla/tests/convert_test.cc @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include #include #include #include @@ -52,13 +53,67 @@ TEST_F(ConvertTest, ConvertR1S32ToR1S32) { ComputeAndCompareR1(&builder, expected, {}); } +TEST_F(ConvertTest, ConvertR1S32ToR1U32) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42, 64}); + ConvertElementType(a, U32); + + std::vector expected = {42, 64}; + ComputeAndCompareR1(&builder, expected, {}); +} + +TEST_F(ConvertTest, ConvertR1S32ToR1PRED) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42, 0, -64}); + ConvertElementType(a, PRED); + + std::array expected = {true, false, true}; + ComputeAndCompareR1(&builder, expected, {}); +} + +TEST_F(ConvertTest, ConvertR1U32ToR1U32) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42, 64}); + ConvertElementType(a, U32); + + std::vector expected = {42, 64}; + ComputeAndCompareR1(&builder, expected, {}); +} + +TEST_F(ConvertTest, ConvertR1U32ToR1S32) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42, 64}); + ConvertElementType(a, S32); + + std::vector expected = {42, 64}; + ComputeAndCompareR1(&builder, expected, {}); +} + +TEST_F(ConvertTest, ConvertR1U32ToR1PRED) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42, 0, 64}); + ConvertElementType(a, PRED); + + std::array expected = {true, false, true}; + ComputeAndCompareR1(&builder, expected, {}); +} + TEST_F(ConvertTest, ConvertR1F32ToR1F32) { XlaBuilder builder(TestName()); auto a = ConstantR1(&builder, {42.0f, 64.0f}); ConvertElementType(a, F32); std::vector expected = {42.0f, 64.0f}; - ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); + ComputeAndCompareR1(&builder, expected, {}); +} + +TEST_F(ConvertTest, ConvertR1F32ToR1PRED) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {42.0f, 0.0f, 64.0f}); + ConvertElementType(a, PRED); + + std::array expected = {true, false, true}; + ComputeAndCompareR1(&builder, expected, {}); } TEST_F(ConvertTest, ConvertR1S32ToR1F32) { @@ -67,7 +122,7 @@ TEST_F(ConvertTest, ConvertR1S32ToR1F32) { ConvertElementType(a, F32); std::vector expected = {42.0f, 64.0f}; - ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); + ComputeAndCompareR1(&builder, expected, {}); } TEST_F(ConvertTest, ConvertR1PREDToR1S32) { @@ -79,6 +134,15 @@ TEST_F(ConvertTest, ConvertR1PREDToR1S32) { ComputeAndCompareR1(&builder, expected, {}); } +TEST_F(ConvertTest, ConvertR1PREDToR1U32) { + XlaBuilder builder(TestName()); + auto a = ConstantR1(&builder, {true, false, true}); + ConvertElementType(a, U32); + + std::vector expected = {1, 0, 1}; + ComputeAndCompareR1(&builder, expected, {}); +} + TEST_F(ConvertTest, ConvertR1PREDToR1F32) { XlaBuilder builder(TestName()); auto a = ConstantR1(&builder, {true, false, true}); @@ -94,7 +158,7 @@ XLA_TEST_F(ConvertTest, ConvertR1S0S32ToR1S0F32) { ConvertElementType(a, F32); std::vector expected = {}; - ComputeAndCompareR1(&builder, expected, {}, ErrorSpec(0.0001)); + ComputeAndCompareR1(&builder, expected, {}); } TEST_F(ConvertTest, ConvertR1F32ToR1S32) { -- GitLab From 7aef462279657517377e0da15b90b3f3f5be16e1 Mon Sep 17 00:00:00 2001 From: James Keeling Date: Mon, 23 Jul 2018 07:12:57 -0700 Subject: [PATCH 252/519] Add GetFilteredRegisteredKernels and refactor GetFilteredRegisteredKernels makes it easier for users to query at runtime which kernels are available which match some predicate. The most common usage will be querying which kernels are available for a given op, so we add the specialized GetRegisteredKernelsForOp. This is part of the work to make available kernels possible to query, to support Swift For TensorFlow. There are also a number of github issues asking for the functionality. I will add C API and Python API support in upcoming changes. PiperOrigin-RevId: 205656251 --- tensorflow/core/framework/op_kernel.cc | 45 +++++++++++++-------- tensorflow/core/framework/op_kernel.h | 7 ++++ tensorflow/core/framework/op_kernel_test.cc | 18 ++++++++- 3 files changed, 52 insertions(+), 18 deletions(-) diff --git a/tensorflow/core/framework/op_kernel.cc b/tensorflow/core/framework/op_kernel.cc index 58feec90f0..507aa9e447 100644 --- a/tensorflow/core/framework/op_kernel.cc +++ b/tensorflow/core/framework/op_kernel.cc @@ -1061,40 +1061,51 @@ Status SupportedDeviceTypesForNode( } void LogAllRegisteredKernels() { - for (const auto& key_registration : *GlobalKernelRegistryTyped()) { - const KernelDef& kernel_def(key_registration.second.def); + KernelList kernel_list = GetAllRegisteredKernels(); + for (const auto& kernel_def : kernel_list.kernel()) { LOG(INFO) << "OpKernel ('" << ProtoShortDebugString(kernel_def) << "')"; } } KernelList GetAllRegisteredKernels() { + return GetFilteredRegisteredKernels([](const KernelDef& k) { return true; }); +} + +KernelList GetFilteredRegisteredKernels( + const std::function& predicate) { const KernelRegistry* const typed_registry = GlobalKernelRegistryTyped(); KernelList kernel_list; kernel_list.mutable_kernel()->Reserve(typed_registry->size()); for (const auto& p : *typed_registry) { - *kernel_list.add_kernel() = p.second.def; + const KernelDef& kernel_def = p.second.def; + if (predicate(kernel_def)) { + *kernel_list.add_kernel() = kernel_def; + } } return kernel_list; } +KernelList GetRegisteredKernelsForOp(StringPiece op_name) { + auto op_pred = [op_name](const KernelDef& k) { return k.op() == op_name; }; + return GetFilteredRegisteredKernels(op_pred); +} + string KernelsRegisteredForOp(StringPiece op_name) { + KernelList kernel_list = GetRegisteredKernelsForOp(op_name); + if (kernel_list.kernel_size() == 0) return " \n"; string ret; - for (const auto& key_registration : *GlobalKernelRegistryTyped()) { - const KernelDef& kernel_def(key_registration.second.def); - if (kernel_def.op() == op_name) { - strings::StrAppend(&ret, " device='", kernel_def.device_type(), "'"); - if (!kernel_def.label().empty()) { - strings::StrAppend(&ret, "; label='", kernel_def.label(), "'"); - } - for (int i = 0; i < kernel_def.constraint_size(); ++i) { - strings::StrAppend( - &ret, "; ", kernel_def.constraint(i).name(), " in ", - SummarizeAttrValue(kernel_def.constraint(i).allowed_values())); - } - strings::StrAppend(&ret, "\n"); + for (const auto& kernel_def : kernel_list.kernel()) { + strings::StrAppend(&ret, " device='", kernel_def.device_type(), "'"); + if (!kernel_def.label().empty()) { + strings::StrAppend(&ret, "; label='", kernel_def.label(), "'"); + } + for (int i = 0; i < kernel_def.constraint_size(); ++i) { + strings::StrAppend( + &ret, "; ", kernel_def.constraint(i).name(), " in ", + SummarizeAttrValue(kernel_def.constraint(i).allowed_values())); } + strings::StrAppend(&ret, "\n"); } - if (ret.empty()) return " \n"; return ret; } diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h index d9fe42fcbb..1fc5e9908e 100644 --- a/tensorflow/core/framework/op_kernel.h +++ b/tensorflow/core/framework/op_kernel.h @@ -1304,6 +1304,13 @@ void LogAllRegisteredKernels(); // Gets a list of all registered kernels. KernelList GetAllRegisteredKernels(); +// Gets a list of all registered kernels for which predicate returns true +KernelList GetFilteredRegisteredKernels( + const std::function& predicate); + +// Gets a list of all registered kernels for a given op +KernelList GetRegisteredKernelsForOp(StringPiece op_name); + namespace kernel_factory { class OpKernelRegistrar { diff --git a/tensorflow/core/framework/op_kernel_test.cc b/tensorflow/core/framework/op_kernel_test.cc index b76a3400a8..83dda6579b 100644 --- a/tensorflow/core/framework/op_kernel_test.cc +++ b/tensorflow/core/framework/op_kernel_test.cc @@ -965,7 +965,8 @@ BENCHMARK(BM_ConcatInputRange); BENCHMARK(BM_SelectInputRange); TEST(RegisteredKernels, CanCallGetAllRegisteredKernels) { - auto all_registered_kernels = GetAllRegisteredKernels().kernel(); + auto kernel_list = GetAllRegisteredKernels(); + auto all_registered_kernels = kernel_list.kernel(); auto has_name_test1 = [](const KernelDef& k) { return k.op() == "Test1"; }; // Verify we can find the "Test1" op registered above @@ -986,5 +987,20 @@ TEST(RegisteredKernels, CanLogAllRegisteredKernels) { tensorflow::LogAllRegisteredKernels(); } +TEST(RegisteredKernels, GetFilteredRegisteredKernels) { + auto has_name_test1 = [](const KernelDef& k) { return k.op() == "Test1"; }; + auto kernel_list = GetFilteredRegisteredKernels(has_name_test1); + ASSERT_EQ(kernel_list.kernel_size(), 1); + EXPECT_EQ(kernel_list.kernel(0).op(), "Test1"); + EXPECT_EQ(kernel_list.kernel(0).device_type(), "CPU"); +} + +TEST(RegisteredKernels, GetRegisteredKernelsForOp) { + auto kernel_list = GetRegisteredKernelsForOp("Test1"); + ASSERT_EQ(kernel_list.kernel_size(), 1); + EXPECT_EQ(kernel_list.kernel(0).op(), "Test1"); + EXPECT_EQ(kernel_list.kernel(0).device_type(), "CPU"); +} + } // namespace } // namespace tensorflow -- GitLab From 8647db865ce41361413a2eb4c3b4d0ba404dd4e0 Mon Sep 17 00:00:00 2001 From: James Keeling Date: Mon, 23 Jul 2018 07:56:46 -0700 Subject: [PATCH 253/519] Add C API for kernel info This is part of the work to make available kernels possible to query, to support Swift For TensorFlow. There are also a number of github issues asking for the functionality. PiperOrigin-RevId: 205660862 --- tensorflow/c/c_api.cc | 24 +++++++++++++++++ tensorflow/c/c_api.h | 12 +++++++++ tensorflow/c/c_api_test.cc | 53 ++++++++++++++++++++++++++++++++++++++ 3 files changed, 89 insertions(+) diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc index a3003953a3..1b937883c8 100644 --- a/tensorflow/c/c_api.cc +++ b/tensorflow/c/c_api.cc @@ -33,6 +33,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/eval_const_tensor.h" #include "tensorflow/core/common_runtime/shape_refiner.h" #include "tensorflow/core/framework/allocation_description.pb.h" +#include "tensorflow/core/framework/kernel_def.pb.h" #include "tensorflow/core/framework/log_memory.h" #include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/framework/op_kernel.h" @@ -2729,4 +2730,27 @@ TF_Buffer* TF_ApiDefMapGet(TF_ApiDefMap* api_def_map, const char* name, return ret; #endif // __ANDROID__ } + +TF_Buffer* TF_GetAllRegisteredKernels(TF_Status* status) { + tensorflow::KernelList kernel_list = tensorflow::GetAllRegisteredKernels(); + TF_Buffer* ret = TF_NewBuffer(); + status->status = MessageToBuffer(kernel_list, ret); + if (!status->status.ok()) { + TF_DeleteBuffer(ret); + return nullptr; + } + return ret; +} + +TF_Buffer* TF_GetRegisteredKernelsForOp(const char* name, TF_Status* status) { + tensorflow::KernelList kernel_list = + tensorflow::GetRegisteredKernelsForOp(name); + TF_Buffer* ret = TF_NewBuffer(); + status->status = MessageToBuffer(kernel_list, ret); + if (!status->status.ok()) { + TF_DeleteBuffer(ret); + return nullptr; + } + return ret; +} } // end extern "C" diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h index fddc09d45e..c5035e0e41 100644 --- a/tensorflow/c/c_api.h +++ b/tensorflow/c/c_api.h @@ -1610,6 +1610,18 @@ TF_CAPI_EXPORT extern TF_Buffer* TF_ApiDefMapGet(TF_ApiDefMap* api_def_map, size_t name_len, TF_Status* status); +// -------------------------------------------------------------------------- +// Kernel definition information. + +// Returns a serialized KernelList protocol buffer containing KernelDefs for all +// registered kernels. +TF_CAPI_EXPORT extern TF_Buffer* TF_GetAllRegisteredKernels(TF_Status* status); + +// Returns a serialized KernelList protocol buffer containing KernelDefs for all +// kernels registered for the operation named `name`. +TF_CAPI_EXPORT extern TF_Buffer* TF_GetRegisteredKernelsForOp( + const char* name, TF_Status* status); + #ifdef __cplusplus } /* end extern "C" */ #endif diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc index bc04b53fbb..c470ab5649 100644 --- a/tensorflow/c/c_api_test.cc +++ b/tensorflow/c/c_api_test.cc @@ -29,9 +29,11 @@ limitations under the License. #include "tensorflow/core/framework/api_def.pb.h" #include "tensorflow/core/framework/common_shape_fns.h" #include "tensorflow/core/framework/graph.pb_text.h" +#include "tensorflow/core/framework/kernel_def.pb.h" #include "tensorflow/core/framework/node_def.pb_text.h" #include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/framework/op.h" +#include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/partial_tensor_shape.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.pb.h" @@ -2312,6 +2314,57 @@ TEST(TestApiDef, TestCreateApiDefWithOverwrites) { TF_DeleteLibraryHandle(lib); } +class DummyKernel : public tensorflow::OpKernel { + public: + explicit DummyKernel(tensorflow::OpKernelConstruction* context) + : OpKernel(context) {} + void Compute(tensorflow::OpKernelContext* context) override {} +}; + +// Test we can query kernels +REGISTER_OP("TestOpWithSingleKernel") + .Input("a: float") + .Input("b: float") + .Output("o: float"); +REGISTER_KERNEL_BUILDER( + Name("TestOpWithSingleKernel").Device(tensorflow::DEVICE_CPU), DummyKernel); + +TEST(TestKernel, TestGetAllRegisteredKernels) { + TF_Status* status = TF_NewStatus(); + TF_Buffer* kernel_list_buf = TF_GetAllRegisteredKernels(status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + KernelList kernel_list; + kernel_list.ParseFromArray(kernel_list_buf->data, kernel_list_buf->length); + ASSERT_GT(kernel_list.kernel_size(), 0); + TF_DeleteBuffer(kernel_list_buf); + TF_DeleteStatus(status); +} + +TEST(TestKernel, TestGetRegisteredKernelsForOp) { + TF_Status* status = TF_NewStatus(); + TF_Buffer* kernel_list_buf = + TF_GetRegisteredKernelsForOp("TestOpWithSingleKernel", status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + KernelList kernel_list; + kernel_list.ParseFromArray(kernel_list_buf->data, kernel_list_buf->length); + ASSERT_EQ(kernel_list.kernel_size(), 1); + EXPECT_EQ(kernel_list.kernel(0).op(), "TestOpWithSingleKernel"); + EXPECT_EQ(kernel_list.kernel(0).device_type(), "CPU"); + TF_DeleteBuffer(kernel_list_buf); + TF_DeleteStatus(status); +} + +TEST(TestKernel, TestGetRegisteredKernelsForOpNoKernels) { + TF_Status* status = TF_NewStatus(); + TF_Buffer* kernel_list_buf = TF_GetRegisteredKernelsForOp("Unknown", status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + KernelList kernel_list; + kernel_list.ParseFromArray(kernel_list_buf->data, kernel_list_buf->length); + ASSERT_EQ(kernel_list.kernel_size(), 0); + TF_DeleteBuffer(kernel_list_buf); + TF_DeleteStatus(status); +} + #undef EXPECT_TF_META } // namespace -- GitLab From 32fe0302b0cf02d6cc3ae6cf67b233ad65c74bfe Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 08:03:04 -0700 Subject: [PATCH 254/519] Delegate L2Norm to nnapi. PiperOrigin-RevId: 205661557 --- .../lite/delegates/nnapi/nnapi_delegate.cc | 12 +++++++ .../delegates/nnapi/nnapi_delegate_test.cc | 35 +++++++++++++++++++ tensorflow/contrib/lite/nnapi_delegate.cc | 9 ++++- 3 files changed, 55 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc index f0d16575ec..0c7f6d3125 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc @@ -452,6 +452,18 @@ class NNAPIDelegateKernel { } else { return nullptr; } + case kTfLiteBuiltinL2Normalization: { + auto builtin = + reinterpret_cast(node->builtin_data); + if (builtin->activation != kTfLiteActNone) { + // NNAPI does not support activations + return nullptr; + } + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_L2_NORMALIZATION; + }; + } case kTfLiteBuiltinTranspose: // Transpose requires NNAPI1.1. Also note that the permutation input // tensor value dictates the output dimensions. diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc index ab2181e8ff..baf8046f9b 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc @@ -641,6 +641,41 @@ TEST(NNAPIDelegate, SqueezeWithAxisTest) { 17.0, 18.0, 19.0, 20.0, 21.0, 22.0, 23.0, 24.0})); } +class L2NormOpModel : public SingleOpModelWithNNAPI { + public: + L2NormOpModel(const TensorData& input, const TensorData& output, + ActivationFunctionType activation_type) { + input_ = AddInput(input); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_L2_NORMALIZATION, BuiltinOptions_L2NormOptions, + CreateL2NormOptions(builder_, activation_type).Union()); + BuildInterpreter({GetShape(input_)}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int new_shape_; + int output_; +}; + +TEST(NNAPIDelegate, L2NormSimpleTest) { + std::initializer_list data = {-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}; + L2NormOpModel m({TensorType_FLOAT32, {1, 1, 1, 6}}, + {TensorType_FLOAT32, {1, 1, 1, 6}}, + ActivationFunctionType_NONE); + m.SetInput(data); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 1, 1, 6})); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({-0.55, 0.3, 0.35, 0.6, -0.35, 0.05})); +} + class TransposeSimpleModel : public SingleOpModelWithNNAPI { public: TransposeSimpleModel(std::initializer_list input_shape, diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index 710ce1632e..659230e033 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -560,6 +560,14 @@ TfLiteStatus AddOpsAndParams( nnapi_version = 11; // require NNAPI 1.1 nn_op_type = ANEURALNETWORKS_TRANSPOSE; break; + case tflite::BuiltinOperator_L2_NORMALIZATION: + nn_op_type = ANEURALNETWORKS_L2_NORMALIZATION; + if (reinterpret_cast(node.builtin_data) + ->activation != kTfLiteActNone) { + FATAL( + "NNAPI does not support L2Normalization with fused activations"); + } + break; case tflite::BuiltinOperator_CONCAT_EMBEDDINGS: case tflite::BuiltinOperator_LSH_PROJECTION: case tflite::BuiltinOperator_HASHTABLE_LOOKUP: @@ -568,7 +576,6 @@ TfLiteStatus AddOpsAndParams( 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: case tflite::BuiltinOperator_PADV2: case tflite::BuiltinOperator_RESIZE_BILINEAR: -- GitLab From 68d48f52bc00a09e9d2458d0f267616030b27bf4 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Mon, 23 Jul 2018 08:31:00 -0700 Subject: [PATCH 255/519] [XLA:GPU] Make sure that buffers for tuple() have a unique top-level allocation There are edge cases where a top-level allocation exists but it's ambiguous. PiperOrigin-RevId: 205665320 --- .../xla/service/gpu/ir_emitter_unnested.cc | 5 +- tensorflow/compiler/xla/tests/tuple_test.cc | 47 +++++++++++++++++++ 2 files changed, 50 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index b3229303df..4844dc92db 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -1754,8 +1754,9 @@ Status IrEmitterUnnested::HandleReduce(HloInstruction* reduce) { Status IrEmitterUnnested::HandleTuple(HloInstruction* tuple) { bool all_tuple_elements_have_buffer = c_all_of(tuple->operands(), [&](HloInstruction* tuple_element) { - return ir_emitter_context_->buffer_assignment().HasTopLevelAllocation( - tuple_element); + return ir_emitter_context_->buffer_assignment() + .GetUniqueTopLevelSlice(tuple_element) + .ok(); }); // Tuples (especially tuples that are the final result of a computation) can // be so huge that if we were to emit a kernel that took each tuple element as diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc index bf86c5dfb6..a517007591 100644 --- a/tensorflow/compiler/xla/tests/tuple_test.cc +++ b/tensorflow/compiler/xla/tests/tuple_test.cc @@ -29,6 +29,7 @@ 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/status_test_util.h" #include "tensorflow/core/platform/test.h" namespace xla { @@ -545,5 +546,51 @@ XLA_TEST_F(TupleHloTest, DISABLED_ON_INTERPRETER(BitcastAfterGTE)) { *result)); } +// Disabled on interpreter due to lack of outfeed. +XLA_TEST_F(TupleHloTest, + DISABLED_ON_INTERPRETER(NonAmbiguousTopLevelAllocation)) { + const char* testcase = R"( + HloModule tuple + + ENTRY main { + a = f32[2] parameter(0) + b = f32[2] parameter(1) + c = f32[2] parameter(2) + d = f32[2] parameter(3) + cond = pred[] parameter(4) + + tup0 = (f32[2],f32[2]) tuple(a, b) + tup1 = (f32[2],f32[2]) tuple(c, d) + + s = (f32[2],f32[2]) tuple-select(cond, tup0, tup1) + gte = f32[2] get-tuple-element(s), index=0 + tuple = (f32[2]) tuple(gte) + token = token[] after-all() + ROOT outfeed = token[] outfeed(tuple, token) + } + )"; + auto module = + HloRunner::CreateModuleFromString(testcase, GetDebugOptionsForTest()) + .ValueOrDie(); + auto param0 = LiteralUtil::CreateR1({1, 2}); + auto param1 = LiteralUtil::CreateR1({2, 3}); + auto param4 = LiteralUtil::CreateR0(false); + // Put execution on a separate thread so we can block on outfeed. + std::unique_ptr thread( + tensorflow::Env::Default()->StartThread( + tensorflow::ThreadOptions(), "execute_thread", [&] { + TF_EXPECT_OK(Execute(std::move(module), + {param0.get(), param1.get(), param1.get(), + param0.get(), param4.get()}) + .status()); + })); + auto expected = + LiteralUtil::MakeTupleOwned(LiteralUtil::CreateR1({2, 3})); + auto literal = MakeUnique(); + TF_EXPECT_OK(backend().transfer_manager()->TransferLiteralFromOutfeed( + backend().default_stream_executor(), expected->shape(), literal.get())); + EXPECT_TRUE(LiteralTestUtil::Equal(*expected, *literal)); +} + } // namespace } // namespace xla -- GitLab From 482b056d3ba925f52ccad8e7166a81120f43a761 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Mon, 23 Jul 2018 08:40:11 -0700 Subject: [PATCH 256/519] Fix segment_test build dependency. --- tensorflow/contrib/tensorrt/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index e3248699dd..1ae3376a4f 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -282,6 +282,7 @@ tf_cc_test( "//tensorflow/cc:scope", "//tensorflow/core:core_cpu", "//tensorflow/core:lib", + "//tensorflow/core:ops", "//tensorflow/core:protos_all_cc", "//tensorflow/core:test", "//tensorflow/core:test_main", -- GitLab From efb8a6d2f89bdb307c23ea393df8701e61bedf9a Mon Sep 17 00:00:00 2001 From: Sergio Guadarrama Date: Mon, 23 Jul 2018 08:51:19 -0700 Subject: [PATCH 257/519] Default .inputs .outputs of Network should be [] otherwise uses_learning_phase fails. PiperOrigin-RevId: 205667812 --- tensorflow/python/keras/engine/network.py | 4 ++-- tensorflow/python/keras/engine/training_test.py | 16 ++++++++++++++++ 2 files changed, 18 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index a4d96de74f..752e9963ca 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -318,8 +318,8 @@ class Network(base_layer.Layer): else: self._expects_training_arg = False self._call_convention = self._determine_call_convention(call_argspec) - self.outputs = None - self.inputs = None + self.outputs = [] + self.inputs = [] self.built = False def _determine_call_convention(self, call_argspec): diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py index c621a88fb3..301a6ca866 100644 --- a/tensorflow/python/keras/engine/training_test.py +++ b/tensorflow/python/keras/engine/training_test.py @@ -767,6 +767,22 @@ class LossMaskingTest(test.TestCase): keras.backend.variable(weights), keras.backend.variable(mask))) +class LearningPhaseTest(test.TestCase): + + def test_empty_model_no_learning_phase(self): + with self.test_session(): + model = keras.models.Sequential() + self.assertFalse(model.uses_learning_phase) + + def test_dropout_has_learning_phase(self): + with self.test_session(): + model = keras.models.Sequential() + model.add(keras.layers.Dense(2, input_dim=3)) + model.add(keras.layers.Dropout(0.5)) + model.add(keras.layers.Dense(2)) + self.assertTrue(model.uses_learning_phase) + + class TestDynamicTrainability(test.TestCase): def test_trainable_warning(self): -- GitLab From 8048b3a53d5a919fef74874fcffbec9039e6acd1 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Mon, 23 Jul 2018 09:08:21 -0700 Subject: [PATCH 258/519] Destroy tf.Session when the session is closed. Previously session destruction was delayed until the destructor for the Python session object. If the session ends up requiring the Python cycle collector for deallocation, it could end up persisting for a long, non-deterministic period. This can tie up resources and lead to out of memory issues. This change introduces a SessionRef which causes session.close() to block until all outstanding run operations are finished and tears down the underlying session. PiperOrigin-RevId: 205670577 --- tensorflow/core/BUILD | 8 + tensorflow/core/common_runtime/session_ref.cc | 170 ++++++++++++++++++ tensorflow/core/common_runtime/session_ref.h | 86 +++++++++ tensorflow/python/BUILD | 1 + tensorflow/python/client/session.py | 2 +- tensorflow/python/client/tf_session.i | 1 + tensorflow/python/client/tf_session_helper.cc | 14 ++ tensorflow/python/client/tf_session_helper.h | 3 + 8 files changed, 284 insertions(+), 1 deletion(-) create mode 100644 tensorflow/core/common_runtime/session_ref.cc create mode 100644 tensorflow/core/common_runtime/session_ref.h diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 17e6ccda14..d51d9f0295 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -2921,6 +2921,14 @@ tf_cuda_library( ] + tf_additional_device_tracer_deps(), ) +cc_library( + name = "session_ref", + srcs = ["common_runtime/session_ref.cc"], + hdrs = ["common_runtime/session_ref.h"], + copts = tf_copts(), + deps = [":core_cpu_base"], +) + cc_library( name = "gpu_id", hdrs = [ diff --git a/tensorflow/core/common_runtime/session_ref.cc b/tensorflow/core/common_runtime/session_ref.cc new file mode 100644 index 0000000000..b931ef4229 --- /dev/null +++ b/tensorflow/core/common_runtime/session_ref.cc @@ -0,0 +1,170 @@ +/* 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/common_runtime/session_ref.h" + +#include + +namespace tensorflow { + +namespace { + +// Scope helper to track active calls and manage session lifetime. +struct RunCounter { + std::shared_ptr session; + uint64* value; + mutex* m; + condition_variable* cv; + + explicit RunCounter(std::shared_ptr s, uint64* v, mutex* m, + condition_variable* cv) + : session(std::move(s)), value(v), m(m), cv(cv) { + mutex_lock l(*m); + ++*value; + } + + ~RunCounter() { + mutex_lock l(*m); + if (--*value == 0) { + cv->notify_all(); + } + } +}; + +} // namespace + +Status SessionRef::CheckNotClosed() { + mutex_lock l(run_lock_); + if (session_ == nullptr) return errors::Cancelled("Session has been closed."); + return ::tensorflow::Status::OK(); +} + +Status SessionRef::Run(const RunOptions& run_options, + const std::vector >& inputs, + const std::vector& output_tensor_names, + const std::vector& target_node_names, + std::vector* outputs, + RunMetadata* run_metadata) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Run(run_options, inputs, output_tensor_names, + target_node_names, outputs, run_metadata); +} + +Status SessionRef::Create(const GraphDef& graph) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Create(graph); +} + +Status SessionRef::Create(const RunOptions& run_options, + const GraphDef& graph) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Create(run_options, graph); +} + +Status SessionRef::Extend(const RunOptions& run_options, + const GraphDef& graph) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Extend(run_options, graph); +} + +Status SessionRef::Extend(const GraphDef& graph) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Extend(graph); +} + +Status SessionRef::Close(const RunOptions& run_options) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + mutex_lock l(run_lock_); + Status status = session_->Close(run_options); + session_.reset(); + while (run_count_ > 0) { + run_finished_.wait(l); + } + return status; +} + +Status SessionRef::Close() { + TF_RETURN_IF_ERROR(CheckNotClosed()); + mutex_lock l(run_lock_); + Status status = session_->Close(); + session_.reset(); + while (run_count_ > 0) { + run_finished_.wait(l); + } + return status; +} + +Status SessionRef::Run(const std::vector >& inputs, + const std::vector& output_tensor_names, + const std::vector& target_node_names, + std::vector* outputs) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Run(inputs, output_tensor_names, target_node_names, + outputs); +} + +Status SessionRef::ListDevices(std::vector* response) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->ListDevices(response); +} + +Status SessionRef::PRunSetup(const std::vector& input_names, + const std::vector& output_names, + const std::vector& target_nodes, + string* handle) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->PRunSetup(input_names, output_names, target_nodes, handle); +} + +Status SessionRef::PRun(const string& handle, + const std::vector >& inputs, + const std::vector& output_names, + std::vector* outputs) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->PRun(handle, inputs, output_names, outputs); +} + +Status SessionRef::MakeCallable(const CallableOptions& callable_options, + CallableHandle* out_handle) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->MakeCallable(callable_options, out_handle); +} + +Status SessionRef::RunCallable(CallableHandle handle, + const std::vector& feed_tensors, + std::vector* fetch_tensors, + RunMetadata* run_metadata) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->RunCallable(handle, feed_tensors, fetch_tensors, + run_metadata); +} + +Status SessionRef::ReleaseCallable(CallableHandle handle) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->ReleaseCallable(handle); +} + +} // namespace tensorflow diff --git a/tensorflow/core/common_runtime/session_ref.h b/tensorflow/core/common_runtime/session_ref.h new file mode 100644 index 0000000000..6146933326 --- /dev/null +++ b/tensorflow/core/common_runtime/session_ref.h @@ -0,0 +1,86 @@ +/* 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_CORE_COMMON_RUNTIME_SESSION_REF_H_ +#define TENSORFLOW_CORE_COMMON_RUNTIME_SESSION_REF_H_ + +#include + +#include "tensorflow/core/platform/mutex.h" +#include "tensorflow/core/public/session.h" + +namespace tensorflow { + +// A `SessionRef` manages the lifetime of a wrapped `Session` pointer. +// +// SessionRef blocks the return of Close() until all pending operations have +// been completed or cancelled and underlying session has been freed. Any +// subsequent operations on the SessionRef object will return errors::Cancelled. +class SessionRef : public Session { + public: + SessionRef(Session* session) : session_(session) {} + virtual ~SessionRef() {} + + Status Create(const GraphDef& graph) override; + Status Extend(const GraphDef& graph) override; + Status Create(const RunOptions& run_options, const GraphDef& graph) override; + Status Extend(const RunOptions& run_options, const GraphDef& graph) override; + Status Run(const std::vector >& inputs, + const std::vector& output_tensor_names, + const std::vector& target_node_names, + std::vector* outputs) override; + + Status ListDevices(std::vector* response) override; + + Status Close() override; + Status Close(const RunOptions& run_options) override; + + Status Run(const RunOptions& run_options, + const std::vector >& inputs, + const std::vector& output_tensor_names, + const std::vector& target_node_names, + std::vector* outputs, RunMetadata* run_metadata) override; + + Status PRunSetup(const std::vector& input_names, + const std::vector& output_names, + const std::vector& target_nodes, + string* handle) override; + + Status PRun(const string& handle, + const std::vector >& inputs, + const std::vector& output_names, + std::vector* outputs) override; + + Status MakeCallable(const CallableOptions& callable_options, + CallableHandle* out_handle); + + Status RunCallable(CallableHandle handle, + const std::vector& feed_tensors, + std::vector* fetch_tensors, + RunMetadata* run_metadata); + + Status ReleaseCallable(CallableHandle handle); + + private: + mutex run_lock_; + condition_variable run_finished_; + uint64 run_count_ GUARDED_BY(run_lock_) = {0}; + std::shared_ptr session_; + + Status CheckNotClosed(); +}; + +} // namespace tensorflow + +#endif // TENSORFLOW_CORE_COMMON_RUNTIME_SESSION_REF_H_ diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 9c7f3b7b25..d45566c55e 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -3629,6 +3629,7 @@ tf_cuda_library( "//tensorflow/core:graph", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", + "//tensorflow/core:session_ref", "//third_party/py/numpy:headers", "//third_party/python_runtime:headers", ], diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index 8ede6ab54c..f3aa135fe4 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -629,7 +629,7 @@ class BaseSession(SessionInterface): opts = tf_session.TF_NewSessionOptions(target=self._target, config=config) try: # pylint: disable=protected-access - self._session = tf_session.TF_NewSession(self._graph._c_graph, opts) + self._session = tf_session.TF_NewSessionRef(self._graph._c_graph, opts) # pylint: enable=protected-access finally: tf_session.TF_DeleteSessionOptions(opts) diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i index 1cdd8e0b6a..39a2922ac0 100644 --- a/tensorflow/python/client/tf_session.i +++ b/tensorflow/python/client/tf_session.i @@ -777,6 +777,7 @@ def TF_Reset(target, containers=None, config=None): $1 = &types_local; } +%unignore TF_NewSessionRef; %unignore SetRequireShapeInferenceFns; %unignore TF_TryEvaluateConstant_wrapper; %noexception TF_TryEvaluateConstant_wrapper; diff --git a/tensorflow/python/client/tf_session_helper.cc b/tensorflow/python/client/tf_session_helper.cc index b6481e7e29..bcd4af2912 100644 --- a/tensorflow/python/client/tf_session_helper.cc +++ b/tensorflow/python/client/tf_session_helper.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/c/c_api.h" #include "tensorflow/c/c_api_internal.h" #include "tensorflow/c/tf_status_helper.h" +#include "tensorflow/core/common_runtime/session_ref.h" #include "tensorflow/core/framework/allocator.h" #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/framework/attr_value_util.h" @@ -42,6 +43,19 @@ static const char* kFeedDictErrorMsg = "feed_dict must be a dictionary mapping strings to NumPy arrays."; } // end namespace +TF_Session* TF_NewSessionRef(TF_Graph* graph, const TF_SessionOptions* opts, + TF_Status* status) { + TF_Session* tf_session = TF_NewSession(graph, opts, status); + if (tf_session == nullptr) { + return nullptr; + } + + Session* session = reinterpret_cast(tf_session->session); + SessionRef* session_ref = new SessionRef(session); + tf_session->session = session_ref; + return tf_session; +} + void TF_Run_wrapper_helper(TF_DeprecatedSession* session, const char* handle, const TF_Buffer* run_options, PyObject* feed_dict, const NameVector& output_names, diff --git a/tensorflow/python/client/tf_session_helper.h b/tensorflow/python/client/tf_session_helper.h index cfd27c2bee..dab7e71aac 100644 --- a/tensorflow/python/client/tf_session_helper.h +++ b/tensorflow/python/client/tf_session_helper.h @@ -40,6 +40,9 @@ typedef tensorflow::gtl::InlinedVector PyObjectVector; // A TF_TensorVector is a vector of borrowed pointers to TF_Tensors. typedef gtl::InlinedVector TF_TensorVector; +TF_Session* TF_NewSessionRef(TF_Graph* graph, const TF_SessionOptions* opts, + TF_Status* status); + // Run the graph associated with the session starting with the // supplied inputs[]. Regardless of success or failure, inputs[] are // stolen by the implementation (i.e. the implementation will -- GitLab From 7f2ebb2bc1cfc8f1d7713ec45a973d20b932c9aa Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Mon, 23 Jul 2018 09:20:05 -0700 Subject: [PATCH 259/519] [XLA] Don't call into the allocator for 0 byte allocations This makes it obvious that XLA does not depend on an allocator returning non-nullptr for zero sized allocations. PiperOrigin-RevId: 205672174 --- tensorflow/compiler/jit/xla_launch_util.cc | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/tensorflow/compiler/jit/xla_launch_util.cc b/tensorflow/compiler/jit/xla_launch_util.cc index 616c3ed2a2..6134b8c694 100644 --- a/tensorflow/compiler/jit/xla_launch_util.cc +++ b/tensorflow/compiler/jit/xla_launch_util.cc @@ -64,11 +64,13 @@ xla::StatusOr XlaAllocator::Allocate( int device_ordinal, uint64 size, bool retry_on_failure) { AllocationAttributes attrs; attrs.no_retry_on_failure = !retry_on_failure; - void* data = - wrapped_->AllocateRaw(Allocator::kAllocatorAlignment, size, attrs); - if (data == nullptr) { - return errors::ResourceExhausted("Out of memory while trying to allocate ", - size, " bytes."); + void* data = nullptr; + if (size != 0) { + data = wrapped_->AllocateRaw(Allocator::kAllocatorAlignment, size, attrs); + if (data == nullptr) { + return errors::ResourceExhausted( + "Out of memory while trying to allocate ", size, " bytes."); + } } return xla::OwningDeviceMemory(se::DeviceMemoryBase(data, size), device_ordinal, this); -- GitLab From 3795a79e1db750efb7af1b83834db5a4f0c03b41 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Mon, 23 Jul 2018 09:21:45 -0700 Subject: [PATCH 260/519] Use a error rendezvous wrapper to capture infeed/outfeed/training session errors. PiperOrigin-RevId: 205672414 --- tensorflow/contrib/tpu/BUILD | 1 + .../contrib/tpu/python/tpu/error_handling.py | 128 ++++++++++++++ .../contrib/tpu/python/tpu/tpu_estimator.py | 159 +++++++++--------- tensorflow/python/estimator/estimator.py | 4 + 4 files changed, 211 insertions(+), 81 deletions(-) create mode 100644 tensorflow/contrib/tpu/python/tpu/error_handling.py diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD index 14e4e9cc2b..643a7cc13a 100644 --- a/tensorflow/contrib/tpu/BUILD +++ b/tensorflow/contrib/tpu/BUILD @@ -36,6 +36,7 @@ cc_library( py_library( name = "tpu_estimator", srcs = [ + "python/tpu/error_handling.py", "python/tpu/tpu_config.py", "python/tpu/tpu_context.py", "python/tpu/tpu_estimator.py", diff --git a/tensorflow/contrib/tpu/python/tpu/error_handling.py b/tensorflow/contrib/tpu/python/tpu/error_handling.py new file mode 100644 index 0000000000..8d6d44b1a1 --- /dev/null +++ b/tensorflow/contrib/tpu/python/tpu/error_handling.py @@ -0,0 +1,128 @@ +# 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. +# =================================================================== +"""ErrorRendezvous handler for collecting errors from multiple threads.""" + +import contextlib +import threading +import time +import traceback + + +from tensorflow.python.framework import errors +from tensorflow.python.platform import tf_logging as logging + +_UNINTERESTING_ERRORS = (errors.CancelledError,) + + +class ErrorRendezvous(object): + """Resolve errors from multiple threads during TPU execution. + + TPU errors can occur on the infeed or outfeed threads as well as the main + training thread. + + Depending on which thread "wins" and receives the session error first, we may + end up showing users a confusing and non-actionable error message (session + cancelled) instead of a root cause (e.g. a bad filename). + + The rendezvous object provides a location to capture these errors until all + threads terminate. At that point we can choose the most informative error + to report. + """ + + def __init__(self, num_sources): + # string -> (message, traceback) + self._errors = {} + self._num_sources = num_sources + self._session_cancel_timer = None + + def record_error(self, source, exception, session=None): + """Report an exception from the given source. + + If a session is passed, a timer will be registered to close it after a few + seconds. This is necessary to ensure the main training loop does not hang + if an infeed/oufeed error occurs. We sleep a few seconds to allow a more + interesting error from another thread to propagate. + + Args: + source: string, source of the error + exception: Exception being thrown + session: Session to close after delay. + """ + logging.info('Error recorded from %s: %s', source, exception) + stack_trace = traceback.format_exc() + self._errors[source] = (exception, stack_trace) + + if session is not None and self._session_cancel_timer is None: + + def _cancel_session(): + time.sleep(5) + try: + session.close() + except: # pylint: disable=bare-except + pass + + self._session_cancel_timer = threading.Thread(target=_cancel_session,) + self._session_cancel_timer.daemon = True + self._session_cancel_timer.start() + + def record_done(self, source): + """Mark execution source `source` as done. + + If an error was originally reported from `source` it is left intact. + + Args: + source: `str`, source being recorded + """ + logging.info('%s marked as finished', source) + if source not in self._errors: + self._errors[source] = None + + @contextlib.contextmanager + def catch_errors(self, source, session=None): + """Context manager to report any errors within a block.""" + try: + yield + except Exception as e: # pylint: disable=broad-except + self.record_error(source, e, session) + + def raise_errors(self, timeout_sec=5): + """Wait for up to `timeout` seconds for all error sources to finish. + + Preferentially raise "interesting" errors (errors not in the + _UNINTERESTING_ERRORS) set. + + Args: + timeout_sec: Seconds to wait for other error sources. + """ + for _ in range(timeout_sec): + if len(self._errors) == self._num_sources: + break + time.sleep(1) + + kept_errors = [(k, v) for (k, v) in self._errors.items() if v is not None] + + if not kept_errors: + return + + # First check for any interesting errors, then fall back on the session + # cancelled errors etc. + for k, (exc, _) in kept_errors: + if isinstance(exc, _UNINTERESTING_ERRORS): + continue + else: + raise exc + + for k, (exc, _) in kept_errors: + raise exc diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 8ae0a31b6a..1208d557e7 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -24,7 +24,6 @@ import os import signal import threading import time -import traceback import numpy as np import six @@ -32,6 +31,7 @@ from six.moves import queue as Queue # pylint: disable=redefined-builtin from six.moves import xrange # pylint: disable=redefined-builtin from tensorflow.contrib.tpu.python.ops import tpu_ops +from tensorflow.contrib.tpu.python.tpu import error_handling from tensorflow.contrib.tpu.python.tpu import session_support from tensorflow.contrib.tpu.python.tpu import tpu from tensorflow.contrib.tpu.python.tpu import tpu_config @@ -365,17 +365,17 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): ctx, enqueue_ops, dequeue_ops, - run_infeed_loop_on_coordinator=True): + run_infeed_loop_on_coordinator=True, + rendezvous=None): self._master_job = ctx.master_job self._enqueue_ops = enqueue_ops self._dequeue_ops = dequeue_ops + self._rendezvous = rendezvous self._run_infeed_loop_on_coordinator = run_infeed_loop_on_coordinator self._initial_infeed_sleep_secs = ( ctx.config.tpu_config.initial_infeed_sleep_secs) - self._session_cancel_timer = None - self._feed_error = None self._finished = False @@ -392,62 +392,6 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): for op in summary_writer_init_ops: self._finalize_ops.append(contrib_summary.flush(writer=op.inputs[0])) - def _log_error(self, session, error): - """Log an infeed or outfeed error. - - This logs a short error message immediately, and schedules a timer to - emit the full stack trace and error message after a short period of time. - If the main session has terminated by the time the timer triggers, we - assume the real source of the error was from the main session and avoid - emitting a stack trace for the infeed. - - Args: - session: `tf.Session`, session to be terminated error: exception that - triggered logging. - error: the Exception to log. - """ - logging.warning( - '\n\n' - 'Error occurred during infeed/outfeed. This may be due to a compile ' - 'error in the main session. Waiting for a short time for the main ' - 'session to come back.\n\n%s', error) - - self._feed_error = traceback.format_exc() - - # If we've already encountered a feed error, don't schedule another - # cancellation op. - if self._session_cancel_timer: - return - - def _cancel_session(): - """Close the session to avoid the main thread from hanging. - - If input pipeline triggers any error, the infeed thread dies but the main - thread for TPU computation waits for the infeed enqueue forever. Close the - Session to cancel the main thread Session.run execution. - - We sleep for a few seconds before closing to give some time for the TPU - compilation error, if any, propagating, from TPU to CPU host. Compilation - errors should be reported by the main thread so that the program can be - interrupted and users can take action. Due to a race condition, the - infeed thread might see an error first. Closing the session here - immediately would result in a session cancellation exception in the main - thread, instead of the expected compile error. User code that depends on - having the proper exception type will therefore be confused. - """ - time.sleep(5) - - # If the main session is still running, the infeed/outfeed errors are - # legitimate, and should be logged. - if not self._finished and self._feed_error: - logging.error('Feed error: %s', self._feed_error) - logging.error('Closing session. A RuntimeError should follow.') - session.close() - - self._session_cancel_timer = threading.Thread(target=_cancel_session) - self._session_cancel_timer.daemon = True - self._session_cancel_timer.start() - def _run_infeed(self, queue_ctx, session): logging.info('Starting infeed thread controller.') if self._initial_infeed_sleep_secs: @@ -456,7 +400,7 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): time.sleep(self._initial_infeed_sleep_secs) logging.info('%s thread starting after sleep', self._name) - try: + with self._rendezvous.catch_errors(source='infeed', session=session): if self._run_infeed_loop_on_coordinator: for count, steps in enumerate(queue_ctx.read_iteration_counts()): for i in xrange(steps): @@ -466,19 +410,15 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): for _ in queue_ctx.read_iteration_counts(): session.run(self._enqueue_ops) logging.info('Infeed thread finished, shutting down.') - except Exception as e: # pylint: disable=broad-except - self._log_error(session, e) def _run_outfeed(self, queue_ctx, session): logging.info('Starting outfeed thread controller.') - try: + with self._rendezvous.catch_errors(source='outfeed', session=session): for count, steps in enumerate(queue_ctx.read_iteration_counts()): for i in xrange(steps): logging.debug('Outfeed dequeue for iteration (%d, %d)', count, i) session.run(self._dequeue_ops) logging.info('Outfeed thread finished, shutting down.') - except Exception as e: # pylint: disable=broad-except - self._log_error(session, e) def _create_infeed_controller(self, name, target, args): return _OpQueueContext(name=name, target=target, args=args) @@ -497,11 +437,6 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): def before_run(self, run_context): self._feed_error = None - # Wait for the cancellation timer to complete before continuing. - if self._session_cancel_timer: - self._session_cancel_timer.join() - self._session_cancel_timer = None - iterations = run_context.session.run(self._iterations_per_loop_var) logging.info('Enqueue next (%d) batch(es) of data to infeed.', iterations) @@ -512,16 +447,14 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): self._outfeed_controller.send_next_batch_signal(iterations) def end(self, session): - if self._session_cancel_timer: - logging.warning('Feed error occurred; waiting for message.') - self._session_cancel_timer.join() - self._finished = True logging.info('Stop infeed thread controller') self._infeed_controller.join() + self._rendezvous.record_done('infeed') logging.info('Stop output thread controller') self._outfeed_controller.join() + self._rendezvous.record_done('outfeed') logging.info('Shutdown TPU system.') session.run(self._finalize_ops) @@ -529,9 +462,10 @@ class TPUInfeedOutfeedSessionHook(session_run_hook.SessionRunHook): class TPUInfeedOutfeedSessionHookForPrediction(TPUInfeedOutfeedSessionHook): - def __init__(self, ctx, enqueue_ops, dequeue_ops): + def __init__(self, ctx, enqueue_ops, dequeue_ops, rendezvous=None): super(TPUInfeedOutfeedSessionHookForPrediction, self).__init__( - ctx, enqueue_ops, dequeue_ops, run_infeed_loop_on_coordinator=False) + ctx, enqueue_ops, dequeue_ops, run_infeed_loop_on_coordinator=False, + rendezvous=rendezvous) def _create_infeed_controller(self, name, target, args): return _OpSignalOnceQueueContext(name=name, target=target, args=args) @@ -2113,6 +2047,7 @@ class TPUEstimator(estimator_lib.Estimator): self._export_to_tpu = export_to_tpu self._is_input_fn_invoked = None + self._rendezvous = {} def _add_meta_graph_for_mode(self, builder, @@ -2356,6 +2291,65 @@ class TPUEstimator(estimator_lib.Estimator): """ pass + def train(self, + input_fn, + hooks=None, + steps=None, + max_steps=None, + saving_listeners=None): + rendezvous = error_handling.ErrorRendezvous(num_sources=3) + self._rendezvous[model_fn_lib.ModeKeys.TRAIN] = rendezvous + try: + return super(TPUEstimator, self).train( + input_fn=input_fn, hooks=hooks, steps=steps, max_steps=max_steps, + saving_listeners=saving_listeners + ) + except Exception as e: # pylint: disable=broad-except + rendezvous.record_error('training_loop', e) + finally: + rendezvous.record_done('training_loop') + rendezvous.raise_errors() + + def evaluate(self, input_fn, steps=None, hooks=None, checkpoint_path=None, + name=None): + rendezvous = error_handling.ErrorRendezvous(num_sources=3) + self._rendezvous[model_fn_lib.ModeKeys.EVAL] = rendezvous + try: + return super(TPUEstimator, self).evaluate( + input_fn, steps=steps, hooks=hooks, checkpoint_path=checkpoint_path, + name=name + ) + except Exception as e: # pylint: disable=broad-except + rendezvous.record_error('evaluation_loop', e) + finally: + rendezvous.record_done('evaluation_loop') + rendezvous.raise_errors() + + def predict(self, + input_fn, + predict_keys=None, + hooks=None, + checkpoint_path=None, + yield_single_examples=True): + rendezvous = error_handling.ErrorRendezvous(num_sources=3) + self._rendezvous[model_fn_lib.ModeKeys.PREDICT] = rendezvous + try: + for result in super(TPUEstimator, self).predict( + input_fn=input_fn, + predict_keys=predict_keys, + hooks=hooks, + checkpoint_path=checkpoint_path, + yield_single_examples=True): + yield result + except Exception as e: # pylint: disable=broad-except + rendezvous.record_error('prediction_loop', e) + finally: + rendezvous.record_done('prediction_loop') + rendezvous.raise_errors() + + rendezvous.record_done('prediction_loop') + rendezvous.raise_errors() + def _augment_model_fn(self, model_fn, batch_axis): """Returns a new model_fn, which wraps the TPU support.""" @@ -2450,7 +2444,9 @@ class TPUEstimator(estimator_lib.Estimator): enqueue_ops, host_ops, run_infeed_loop_on_coordinator=( - run_infeed_loop_on_coordinator)), + run_infeed_loop_on_coordinator), + rendezvous=self._rendezvous[mode], + ), InstallSignalHandlerHook(), training.LoggingTensorHook( { @@ -2533,7 +2529,8 @@ class TPUEstimator(estimator_lib.Estimator): enqueue_ops, eval_update_ops + host_ops, run_infeed_loop_on_coordinator=( - run_infeed_loop_on_coordinator)), + run_infeed_loop_on_coordinator), + rendezvous=self._rendezvous[mode]), ] + input_hooks return model_fn_lib.EstimatorSpec( @@ -2599,8 +2596,8 @@ class TPUEstimator(estimator_lib.Estimator): hooks = [ _StoppingPredictHook(scalar_stopping_signal), - TPUInfeedOutfeedSessionHookForPrediction(ctx, enqueue_ops, - host_ops), + TPUInfeedOutfeedSessionHookForPrediction( + ctx, enqueue_ops, host_ops, rendezvous=self._rendezvous[mode]), ] + input_hooks return model_fn_lib.EstimatorSpec( diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 2fd6f6fab9..915ceeb98b 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -567,6 +567,10 @@ class Estimator(object): def _assert_members_are_not_overridden(self): """Asserts members of `Estimator` are not overridden.""" + # TPUEstimator is special cased (owned by TF). + if self.__class__.__name__ == 'TPUEstimator': + return + allowed_overrides = set([ '_call_input_fn', '_create_global_step', '_convert_train_steps_to_hooks', '_convert_eval_steps_to_hooks', -- GitLab From b148d228886e53f71401c332b8d48d45467dad47 Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Mon, 23 Jul 2018 09:29:21 -0700 Subject: [PATCH 261/519] Specify minSdkVersion/targetSdkVersion in library manifests PiperOrigin-RevId: 205673447 --- tensorflow/contrib/android/cmake/src/main/AndroidManifest.xml | 4 ++++ tensorflow/contrib/lite/java/AndroidManifest.xml | 3 ++- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/android/cmake/src/main/AndroidManifest.xml b/tensorflow/contrib/android/cmake/src/main/AndroidManifest.xml index bced47e046..c17110a78b 100644 --- a/tensorflow/contrib/android/cmake/src/main/AndroidManifest.xml +++ b/tensorflow/contrib/android/cmake/src/main/AndroidManifest.xml @@ -1,6 +1,10 @@ + + diff --git a/tensorflow/contrib/lite/java/AndroidManifest.xml b/tensorflow/contrib/lite/java/AndroidManifest.xml index c3849e6868..b91c6d149a 100644 --- a/tensorflow/contrib/lite/java/AndroidManifest.xml +++ b/tensorflow/contrib/lite/java/AndroidManifest.xml @@ -7,5 +7,6 @@ android:targetSdkVersion="19" /> - + + -- GitLab From e2d19b5be97c231a00a895b87fd7ca756395bb35 Mon Sep 17 00:00:00 2001 From: James Keeling Date: Mon, 23 Jul 2018 09:31:29 -0700 Subject: [PATCH 262/519] Fix meaningless "const" on return type. Because this file is included in lots of places, this generates a large number of warnings during compilation. PiperOrigin-RevId: 205673744 --- tensorflow/core/framework/device_base.h | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/tensorflow/core/framework/device_base.h b/tensorflow/core/framework/device_base.h index 922d34fac9..b184fd91e1 100644 --- a/tensorflow/core/framework/device_base.h +++ b/tensorflow/core/framework/device_base.h @@ -184,9 +184,7 @@ class DeviceBase { virtual ScopedAllocatorMgr* GetScopedAllocatorMgr() const { return nullptr; } - const bool has_eigen_cpu_device() const { - return !eigen_cpu_devices_.empty(); - } + bool has_eigen_cpu_device() const { return !eigen_cpu_devices_.empty(); } virtual const Eigen::ThreadPoolDevice* eigen_cpu_device(); -- GitLab From bf62c1042cfc79ae9df00bd2ac77de772f350762 Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Mon, 23 Jul 2018 09:35:31 -0700 Subject: [PATCH 263/519] Allow scalar inputs for toco and TFLite zip tests PiperOrigin-RevId: 205674334 --- .../contrib/lite/testing/generate_examples.py | 41 ++++++++++++------- .../contrib/lite/toco/model_cmdline_flags.cc | 4 ++ tensorflow/contrib/lite/toco/tooling_util.cc | 5 --- .../contrib/lite/toco/tooling_util_test.cc | 17 ++++++++ 4 files changed, 48 insertions(+), 19 deletions(-) diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index a91ff8626a..b3ccc65e85 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -772,6 +772,11 @@ def make_binary_op_tests(zip_path, binary_operator): "input_shape_1": [[1, 3, 4, 3]], "input_shape_2": [[3]], "activation": [True] + }, { + "dtype": [tf.float32], + "input_shape_1": [[]], + "input_shape_2": [[]], + "activation": [False] }] def build_graph(parameters): @@ -821,7 +826,7 @@ def make_reduce_tests(reduce_op): "input_dtype": [tf.float32, tf.int32, tf.int64], "input_shape": [[3, 2, 4]], "axis": [ - None, 0, 1, 2, [0, 1], [0, 2], [1, 2], [0, 1, 2], [1, 0], [2, 0], + 0, 1, 2, [0, 1], [0, 2], [1, 2], [0, 1, 2], [1, 0], [2, 0], [2, 1], [2, 1, 0], [2, 0, 1], -1, -2, -3, [1, -1], [0, -1], [-1, 0], [-1, -2, -3], [0, 0, 0], [2, 2, 0], [1, 0, -3, -3] ], @@ -831,13 +836,19 @@ def make_reduce_tests(reduce_op): "input_dtype": [tf.float32], "input_shape": [[1, 8, 8, 3]], "axis": [ - None, 0, 1, 2, 3, [1, 2], [0, 3], [1, 2, 3], [0, 1, 2, 3], + 0, 1, 2, 3, [1, 2], [0, 3], [1, 2, 3], [0, 1, 2, 3], [3, 2, 1, 0], [3, 1, 0, 2], [2, 0], [3, 0], [3, 1], [1, 0], -1, -2, -3, -4, [0, -2], [2, 3, -1, 0], [3, 1, 2, -3], [3, -4], [2, 2, 2], [2, 2, 3], [-3, -3, -4], [-3, 2, 1] ], "const_axis": [True, False], "keepdims": [True, False], + }, { + "input_dtype": [tf.float32], + "input_shape": [[], [1, 8, 8, 3], [3, 2, 4]], + "axis": [None], + "const_axis": [True], + "keepdims": [True, False], }] def build_graph(parameters): @@ -855,7 +866,7 @@ def make_reduce_tests(reduce_op): if isinstance(parameters["axis"], list): shape = [len(parameters["axis"])] else: - shape = [0] # shape for None or integers. + shape = [] # shape for None or integers. axis = tf.placeholder(dtype=tf.int32, name="axis", shape=shape) input_tensors = [input_tensor, axis] @@ -866,10 +877,11 @@ def make_reduce_tests(reduce_op): def build_inputs(parameters, sess, inputs, outputs): values = [ create_tensor_data(parameters["input_dtype"], - parameters["input_shape"])] + parameters["input_shape"], + min_value=-10, + max_value=10)] if not parameters["const_axis"]: - if parameters["axis"]: - values.append(np.array(parameters["axis"])) + values.append(np.array(parameters["axis"])) return values, sess.run(outputs, feed_dict=dict(zip(inputs, values))) make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) @@ -902,7 +914,7 @@ def make_exp_tests(zip_path): test_parameters = [{ "input_dtype": [tf.float32], - "input_shape": [[3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], + "input_shape": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], }] def build_graph(parameters): @@ -961,8 +973,8 @@ def make_maximum_tests(zip_path): test_parameters = [{ "input_dtype": [tf.float32], - "input_shape_1": [[3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], - "input_shape_2": [[3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], + "input_shape_1": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], + "input_shape_2": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], }] def build_graph(parameters): @@ -996,8 +1008,8 @@ def make_minimum_tests(zip_path): test_parameters = [{ "input_dtype": [tf.float32], - "input_shape_1": [[3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], - "input_shape_2": [[3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], + "input_shape_1": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], + "input_shape_2": [[], [3], [1, 100], [4, 2, 3], [5, 224, 224, 3]], }] def build_graph(parameters): @@ -2252,7 +2264,7 @@ def make_arg_min_max_tests(zip_path): test_parameters = [{ "input_dtype": [tf.float32, tf.int32], - "input_shape": [[1, 1, 1, 3], [2, 3, 4, 5], [2, 3, 3], [5, 5], [10]], + "input_shape": [[], [1, 1, 1, 3], [2, 3, 4, 5], [2, 3, 3], [5, 5], [10]], "output_type": [tf.int32, tf.int64], "axis_is_last_dim": [True, False], "is_arg_max": [True], @@ -2288,7 +2300,8 @@ def make_equal_tests(zip_path): test_parameters = [{ "input_dtype": [tf.float32, tf.int32, tf.int64], - "input_shape_pair": [([1, 1, 1, 3], [1, 1, 1, 3]), + "input_shape_pair": [([], []), + ([1, 1, 1, 3], [1, 1, 1, 3]), ([2, 3, 4, 5], [2, 3, 4, 5]), ([2, 3, 3], [2, 3]), ([5, 5], [1]), ([10], [2, 4, 10])], }] @@ -2545,7 +2558,7 @@ def _make_elementwise_tests(op): """Actual function that generates examples.""" test_parameters = [{ "input_dtype": [tf.float32], - "input_shape": [[1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]], + "input_shape": [[], [1], [1, 2], [5, 6, 7, 8], [3, 4, 5, 6]], }] def build_graph(parameters): diff --git a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc index 06072d1fcb..d34da63e43 100644 --- a/tensorflow/contrib/lite/toco/model_cmdline_flags.cc +++ b/tensorflow/contrib/lite/toco/model_cmdline_flags.cc @@ -322,6 +322,10 @@ void ReadModelFlagsFromCommandLineFlags( for (int i = 0; i < input_shapes.size(); ++i) { auto* shape = model_flags->mutable_input_arrays(i)->mutable_shape(); shape->clear_dims(); + // Treat an empty input shape as a scalar. + if (input_shapes[i].empty()) { + continue; + } for (const auto& dim_str : absl::StrSplit(input_shapes[i], ',')) { int size; CHECK(absl::SimpleAtoi(dim_str, &size)) diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 52f8df45a2..98e416b76e 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -1585,11 +1585,6 @@ void ResolveModelFlags(const ModelFlags& model_flags, Model* model) { model); } - for (const auto& input_array : model->flags.input_arrays()) { - if (input_array.has_shape()) { - CHECK(input_array.shape().dims_size()); - } - } model->flags.set_change_concat_input_ranges( model_flags.change_concat_input_ranges()); model->flags.set_allow_nonascii_arrays(model_flags.allow_nonascii_arrays()); diff --git a/tensorflow/contrib/lite/toco/tooling_util_test.cc b/tensorflow/contrib/lite/toco/tooling_util_test.cc index 8609e5bedd..eb495646a2 100644 --- a/tensorflow/contrib/lite/toco/tooling_util_test.cc +++ b/tensorflow/contrib/lite/toco/tooling_util_test.cc @@ -39,6 +39,8 @@ std::vector CreateShapePairs() { {Shape({256, 256, 3}), Shape({256, 256, 3}), Agreement::kBroadcast}, {Shape({256, 256, 3}), Shape({3}), Agreement::kBroadcast}, {Shape({8, 1, 6, 1}), Shape({7, 1, 5}), Agreement::kBroadcast}, + {Shape({}), Shape({3}), Agreement::kBroadcast}, + {Shape({}), Shape({3, 1}), Agreement::kBroadcast}, // These extend (and therefore broadcast). {Shape({3}), Shape({3}), Agreement::kExtend}, @@ -54,6 +56,7 @@ std::vector CreateShapePairs() { {Shape({15, 3, 5}), Shape({15, 1, 5}), Agreement::kBroadcastNotExtend}, {Shape({15, 3, 5}), Shape({3, 5}), Agreement::kBroadcastNotExtend}, {Shape({15, 3, 5}), Shape({3, 1}), Agreement::kBroadcastNotExtend}, + {Shape({3, 1}), Shape({}), Agreement::kBroadcastNotExtend}, // These do not broadcast (and therefore also do not extend). {Shape({3}), Shape({4}), Agreement::kNeither}, @@ -175,6 +178,20 @@ TEST(NumElementsTest, UnsignedInt64) { EXPECT_EQ(status.error_message(), kLargeTensorMessage); } +TEST(NumElementsTest, Scalar) { + tensorflow::Status status = tensorflow::Status::OK(); + + int32_t count; + status = NumElements(std::vector{}, &count); + EXPECT_TRUE(status.ok()); + EXPECT_EQ(count, 1); + + uint64_t countu64; + status = NumElements(std::vector{}, &countu64); + EXPECT_TRUE(status.ok()); + EXPECT_EQ(countu64, 1ULL); +} + TEST(FusedActivationTest, DefaultsToUnfused) { EXPECT_TRUE(OperatorSupportsFusedActivation(OperatorType::kAdd)); EXPECT_FALSE(OperatorSupportsFusedActivation(OperatorType::kNone)); -- GitLab From 6795a8c3a3678fb805b6a8ba806af77ddfe61628 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 09:41:14 -0700 Subject: [PATCH 264/519] Automated rollback of commit aee128fb46f2721b07e5fa04ed6fac4b67963807. Revert #20746. PiperOrigin-RevId: 205675194 --- tensorflow/core/kernels/BUILD | 11 +- tensorflow/core/kernels/crop_and_resize_op.cc | 64 +-- .../core/kernels/crop_resize_bilinear_core.h | 464 ------------------ tensorflow/core/kernels/resize_bilinear_op.cc | 153 +++++- 4 files changed, 170 insertions(+), 522 deletions(-) delete mode 100644 tensorflow/core/kernels/crop_resize_bilinear_core.h diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 10cbcdecc8..23f84c46a9 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -557,12 +557,6 @@ cc_header_only_library( deps = [":image_resizer_state"], ) -cc_library( - name = "crop_resize_bilinear_core", - hdrs = ["crop_resize_bilinear_core.h"], - visibility = ["//visibility:private"], -) - # OpKernel libraries ---------------------------------------------------------- ARRAY_DEPS = [ @@ -2158,7 +2152,7 @@ tf_kernel_library( tf_kernel_library( name = "crop_and_resize_op", prefix = "crop_and_resize_op", - deps = IMAGE_DEPS + [":crop_resize_bilinear_core"], + deps = IMAGE_DEPS, ) tf_kernel_library( @@ -2224,7 +2218,7 @@ tf_kernel_library( tf_kernel_library( name = "resize_bilinear_op", prefix = "resize_bilinear_op", - deps = IMAGE_DEPS + [":crop_resize_bilinear_core"], + deps = IMAGE_DEPS, ) tf_kernel_library( @@ -4864,7 +4858,6 @@ filegroup( "concat_op.cc", "constant_op.cc", "constant_op.h", - "crop_resize_bilinear_core.h", "cwise_ops.h", "cwise_ops_common.cc", "cwise_ops_common.h", diff --git a/tensorflow/core/kernels/crop_and_resize_op.cc b/tensorflow/core/kernels/crop_and_resize_op.cc index 22524dc1c4..99d01b4db6 100644 --- a/tensorflow/core/kernels/crop_and_resize_op.cc +++ b/tensorflow/core/kernels/crop_and_resize_op.cc @@ -28,7 +28,6 @@ limitations under the License. #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/kernels/bounds_check.h" -#include "tensorflow/core/kernels/crop_resize_bilinear_core.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" @@ -43,10 +42,6 @@ limitations under the License. using stream_executor::cuda::ScopedActivateExecutorContext; #endif // GOOGLE_CUDA -using ::tensorflow::internal::CachedInterpolation; -using ::tensorflow::internal::compute_interpolation_weights; -using ::tensorflow::internal::crop_resize_single_image; - namespace tensorflow { namespace { @@ -254,34 +249,39 @@ struct CropAndResize { continue; } if (method_name == "bilinear") { - CachedInterpolation *interp_x = nullptr, *interp_y = nullptr; - int min_ix, max_ix, min_iy, max_iy; - compute_interpolation_weights(crop_width, image_width, x1, x2, - min_ix, max_ix, interp_x); - compute_interpolation_weights(crop_height, image_height, y1, y2, - min_iy, max_iy, interp_y); - - // multiply by depth to avoid multiplication in resize_single_image. - for (int i = min_ix; i <= max_ix; ++i) { - interp_x[i - min_ix].lower *= depth; - interp_x[i - min_ix].upper *= depth; - } + const int top_y_index = floorf(in_y); + const int bottom_y_index = ceilf(in_y); + const float y_lerp = in_y - top_y_index; + + for (int x = 0; x < crop_width; ++x) { + const float in_x = (crop_width > 1) + ? x1 * (image_width - 1) + x * width_scale + : 0.5 * (x1 + x2) * (image_width - 1); + if (in_x < 0 || in_x > image_width - 1) { + for (int d = 0; d < depth; ++d) { + crops(b, y, x, d) = extrapolation_value; + } + continue; + } + const int left_x_index = floorf(in_x); + const int right_x_index = ceilf(in_x); + const float x_lerp = in_x - left_x_index; - crop_resize_single_image( - image.data() + static_cast(b_in) * - static_cast(image_height) * - static_cast(image_width) * - static_cast(depth), - image_height, image_width, crop_height, crop_width, depth, - min_ix, max_ix, interp_x, min_iy, max_iy, interp_y, - extrapolation_value, false, false, - crops.data() + static_cast(b) * - static_cast(crop_height) * - static_cast(crop_width) * - static_cast(depth)); - - delete[] interp_y; - delete[] interp_x; + for (int d = 0; d < depth; ++d) { + const float top_left(static_cast( + image(b_in, top_y_index, left_x_index, d))); + const float top_right(static_cast( + image(b_in, top_y_index, right_x_index, d))); + const float bottom_left(static_cast( + image(b_in, bottom_y_index, left_x_index, d))); + const float bottom_right(static_cast( + image(b_in, bottom_y_index, right_x_index, d))); + const float top = top_left + (top_right - top_left) * x_lerp; + const float bottom = + bottom_left + (bottom_right - bottom_left) * x_lerp; + crops(b, y, x, d) = top + (bottom - top) * y_lerp; + } + } } else { // method == "nearest" for (int x = 0; x < crop_width; ++x) { const float in_x = (crop_width > 1) diff --git a/tensorflow/core/kernels/crop_resize_bilinear_core.h b/tensorflow/core/kernels/crop_resize_bilinear_core.h deleted file mode 100644 index 51327aca67..0000000000 --- a/tensorflow/core/kernels/crop_resize_bilinear_core.h +++ /dev/null @@ -1,464 +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. -==============================================================================*/ - -#ifndef TENSORFLOW_CORE_KERNELS_CROP_RESIZE_BILINEAR_CORE_H_ -#define TENSORFLOW_CORE_KERNELS_CROP_RESIZE_BILINEAR_CORE_H_ - -namespace tensorflow { -namespace internal { -// Compute the interpolation indices only once. -struct CachedInterpolation { - int lower; // Lower source index used in the interpolation - int upper; // Upper source index used in the interpolation - // 1-D linear iterpolation scale (see: - // https://en.wikipedia.org/wiki/Bilinear_interpolation) - float lerp; -}; - -inline bool compute_single_interpolation_weight( - const int in_size, const float out2in_scale, const float out2in_start, - const bool clip, const int i, int* lower, int* upper, float* lerp) { - const float in = i * out2in_scale + out2in_start; - *lower = (int)floor(in); - *upper = (int)ceil(in); - *lerp = (float)(in - (float)*lower); - if (clip) { - if (*lower < 0) - *lower = 0; - else if (*lower >= in_size) - *lower = in_size - 1; - if (*upper < 0) - *upper = 0; - else if (*upper >= in_size) - *upper = in_size - 1; - return true; - } else { - return (*lower >= 0 && *upper < in_size) ? true : false; - } -} -/** - * Compute interpolation values for output indexes in range - * [out_start,out_start+out_size-1]. - * Returns true if all output indexes have lower and upper (input) indexes - * within range [0,in_size-1]. - */ -inline bool compute_interpolation_weights(const int min_i, const int max_i, - const int in_size, - const float out2in_scale, - const float out2in_start, - const bool clip, - CachedInterpolation* interpolation) { - bool rval = true; - int num_i = max_i - min_i + 1; - for (int i = 0; i < num_i; ++i) { - if (!compute_single_interpolation_weight( - in_size, out2in_scale, out2in_start, clip, i + min_i, - &interpolation[i].lower, &interpolation[i].upper, - &interpolation[i].lerp)) { - rval = false; - } - } - return rval; -} -/** - * Compatibility method for resize_bilinear_op.cc - */ -inline void compute_interpolation_weights(const int out_size, const int in_size, - const float out2in_scale, - CachedInterpolation* interpolation) { - interpolation[out_size].lower = 0; - interpolation[out_size].upper = 0; - const bool clip = true; - if (!compute_interpolation_weights(0, out_size - 1, in_size, out2in_scale, - 0.0f, clip, interpolation)) { - // Should never happen, check for it anyway - printf( - "Warning! Interpolation values have lower,upper indexes outside of " - "range [0,in_size-1]\n"); - } -} -/** - * Compute minimum and maximum (output) i where both lower and upper (input) is - * in range [0,in_size-1] - * If no values of i satisfy condition, min_i = in_size, max_i = -1 and method - * returns false. - * Returns true if min_i >= max_i. - */ -inline bool compute_minmax_indexes(const int out_size, const int in_size, - const float out2in_scale, - const float out2in_start, int& min_i, - int& max_i) { - min_i = out_size; - max_i = -1; - int lower, upper; - float lerp; - for (int i = 0; i < out_size; ++i) { - if (compute_single_interpolation_weight(in_size, out2in_scale, out2in_start, - false, i, &lower, &upper, &lerp)) { - if (i < min_i) min_i = i; - if (i > max_i) max_i = i; - } - } - return (min_i <= max_i) ? true : false; -} -/** - * Compute interpolation weights for crop_and_resize_op.cc - * Also computes extrapolation areas. - * Returns true if at least one point requires interpolation, false otherwise. - */ -inline bool compute_interpolation_weights( - const int out_size, const int in_size, - const float x1, // lower bounding box, crop region starts at in_size*x1 - const float x2, // upper bounding box, crop region ends at in_size*x2 - int& min_i, int& max_i, CachedInterpolation*& interpolation) { - float out2in_start = out_size > 1 - ? (float)(in_size - 1) * (float)x1 - : (float)(in_size - 1) * (float)(x1 + x2) / 2.0f; - float out2in_scale = out_size > 1 ? (float)(x2 - x1) * (float)(in_size - 1) / - (float)(out_size - 1) - : 0.0f; - if (compute_minmax_indexes(out_size, in_size, out2in_scale, out2in_start, - min_i, max_i)) { - interpolation = new CachedInterpolation[max_i - min_i + 1]; - bool all_inputs_ok = - compute_interpolation_weights(min_i, max_i, in_size, out2in_scale, - out2in_start, false, interpolation); - if (!all_inputs_ok) { - // should never happen, purpose of compute_minmax_indexes is to ensure - // that all inputs are ok. - printf( - "Error! compute_interpolation_weights returned input indexes outside " - "valid range - SEGV will likely ensue.\n"); - } - return true; - } else { - interpolation = 0l; - return false; - } -} - -/** - * Cast float v to type U with range clamping. - * - * If vmax_val, - * return value is clamped to u_max_val. - */ -template -U cast_to(float v, float min_val, float max_val, U u_min_val, U u_max_val); -template -inline U cast_to(float v, float min_val, float max_val, U u_min_val, - U u_max_val) { - if (v < min_val) - return u_min_val; - else if (v > max_val) - return u_max_val; - else - return static_cast(v); -} -template <> -inline float cast_to(float v, float min_val, float max_val, - float u_min_val, float u_max_val) { - return v; -} - -inline float compute_lerp(const float top_left, const float top_right, - const float bottom_left, const float bottom_right, - const float x_lerp, const float y_lerp) { - const float top = top_left + (top_right - top_left) * x_lerp; - const float bottom = bottom_left + (bottom_right - bottom_left) * x_lerp; - return top + (bottom - top) * y_lerp; -} - -/** - * Computes the bilinear interpolation from the appropriate 4 float points - * and the linear interpolation weights. - * Accepts input tensors of type T and produces output tensors of type U. - * Optionally flips horizontal and/or vertical axis. - */ -template -void crop_resize_single_image(const T* image, const int64 in_height, - const int64 in_width, const int64 out_height, - const int64 out_width, const int channels, - const int min_ix, const int max_ix, - const CachedInterpolation* xs, const int min_iy, - const int max_iy, const CachedInterpolation* ys, - const float extrapolated_value, const bool flip_x, - const bool flip_y, - U* output) TF_ATTRIBUTE_NOINLINE; -template -void crop_resize_single_image(const T* image, const int64 in_height, - const int64 in_width, const int64 out_height, - const int64 out_width, const int channels, - const int min_ix, const int max_ix, - const CachedInterpolation* xs, const int min_iy, - const int max_iy, const CachedInterpolation* ys, - const float extrapolated_value, const bool flip_x, - const bool flip_y, U* output) { - const int64 in_row_size = in_width * channels; - const int64 out_row_size = out_width * channels; - U u_min_val = std::numeric_limits::min(); - U u_max_val = std::numeric_limits::max(); - float min_val = static_cast(u_min_val); - float max_val = static_cast(u_max_val); - U uEx = - cast_to(extrapolated_value, min_val, max_val, u_min_val, u_max_val); - // low y extrapolation zone - if (min_iy > 0) { - U* p = flip_y ? output + out_row_size * (out_height - min_iy) : output; - int64 nn = out_row_size * (int64)min_iy; - for (int64 i = 0; i < nn; ++i) p[i] = uEx; - } - // high y extrapolation zone - if (max_iy < out_height - 1) { - U* p = flip_y ? output : output + out_row_size * (max_iy + 1); - int64 nn = out_row_size * (int64)(out_height - 1 - max_iy); - for (int64 i = 0; i < nn; ++i) p[i] = uEx; - } - // low x extrapolation zone - if (min_ix > 0) { - for (int iy = min_iy; iy <= max_iy; ++iy) { - int xx0 = flip_x ? (out_width - min_ix) * channels : 0; - int nxx = min_ix * channels; - U* p = output + xx0 + - out_row_size * (int64)(flip_y ? out_height - 1 - iy : iy); - for (int ix = 0; ix < nxx; ++ix) { - p[ix] = uEx; - } - } - } - // high x extrapolation zone - if (max_ix < out_width - 1) { - for (int iy = min_iy; iy <= max_iy; ++iy) { - int xx0 = flip_x ? 0 : (max_ix + 1) * channels; - int nxx = (out_width - 1 - max_ix) * channels; - U* p = output + xx0 + - out_row_size * (int64)(flip_y ? out_height - 1 - iy : iy); - for (int ix = 0; ix < nxx; ++ix) { - p[ix] = uEx; - } - } - } - U* output_y_ptr = - output + - out_row_size * (int64)(flip_y ? out_height - 1 - min_iy : min_iy); - // interpolation zone - if (channels == 1) { - for (int y = min_iy; y <= max_iy; ++y) { - const int iy = y - min_iy; - const T* ys_input_lower_ptr = image + ys[iy].lower * in_row_size; - const T* ys_input_upper_ptr = image + ys[iy].upper * in_row_size; - const float ys_lerp = ys[iy].lerp; - const int x0 = flip_x ? out_width - 1 - max_ix : min_ix; - const int x1 = flip_x ? out_width - 1 - min_ix : max_ix; - for (int x = x0; x <= x1; ++x) { - const int ix = flip_x ? out_width - 1 - min_ix - x : x - min_ix; - const int64 xs_lower = xs[ix].lower; - const int64 xs_upper = xs[ix].upper; - const float xs_lerp = xs[ix].lerp; - - // Read channel 0. - const float top_left0(ys_input_lower_ptr[xs_lower]); - const float top_right0(ys_input_lower_ptr[xs_upper]); - const float bottom_left0(ys_input_upper_ptr[xs_lower]); - const float bottom_right0(ys_input_upper_ptr[xs_upper]); - - // Compute output. - float result0 = compute_lerp(top_left0, top_right0, bottom_left0, - bottom_right0, xs_lerp, ys_lerp); - output_y_ptr[x] = - cast_to(result0, min_val, max_val, u_min_val, u_max_val); - } - output_y_ptr = - flip_y ? output_y_ptr - out_row_size : output_y_ptr + out_row_size; - } - } else if (channels == 2) { - for (int y = min_iy; y <= max_iy; ++y) { - const int iy = y - min_iy; - const T* ys_input_lower_ptr = image + ys[iy].lower * in_row_size; - const T* ys_input_upper_ptr = image + ys[iy].upper * in_row_size; - const float ys_lerp = ys[iy].lerp; - const int x0 = flip_x ? out_width - 1 - max_ix : min_ix; - const int x1 = flip_x ? out_width - 1 - min_ix : max_ix; - for (int x = x0; x <= x1; ++x) { - const int ix = flip_x ? out_width - 1 - min_ix - x : x - min_ix; - const int64 xs_lower = xs[ix].lower; - const int64 xs_upper = xs[ix].upper; - const float xs_lerp = xs[ix].lerp; - - // Read channel 0. - const float top_left0(ys_input_lower_ptr[xs_lower + 0]); - const float top_right0(ys_input_lower_ptr[xs_upper + 0]); - const float bottom_left0(ys_input_upper_ptr[xs_lower + 0]); - const float bottom_right0(ys_input_upper_ptr[xs_upper + 0]); - - // Read channel 1. - const float top_left1(ys_input_lower_ptr[xs_lower + 1]); - const float top_right1(ys_input_lower_ptr[xs_upper + 1]); - const float bottom_left1(ys_input_upper_ptr[xs_lower + 1]); - const float bottom_right1(ys_input_upper_ptr[xs_upper + 1]); - - // Compute output. - float result0 = compute_lerp(top_left0, top_right0, bottom_left0, - bottom_right0, xs_lerp, ys_lerp); - float result1 = compute_lerp(top_left1, top_right1, bottom_left1, - bottom_right1, xs_lerp, ys_lerp); - output_y_ptr[x * 2 + 0] = - cast_to(result0, min_val, max_val, u_min_val, u_max_val); - output_y_ptr[x * 2 + 1] = - cast_to(result1, min_val, max_val, u_min_val, u_max_val); - } - output_y_ptr = - flip_y ? output_y_ptr - out_row_size : output_y_ptr + out_row_size; - } - } else if (channels == 3) { - for (int y = min_iy; y <= max_iy; ++y) { - const int iy = y - min_iy; - const T* ys_input_lower_ptr = image + ys[iy].lower * in_row_size; - const T* ys_input_upper_ptr = image + ys[iy].upper * in_row_size; - const float ys_lerp = ys[iy].lerp; - const int x0 = flip_x ? out_width - 1 - max_ix : min_ix; - const int x1 = flip_x ? out_width - 1 - min_ix : max_ix; - for (int x = x0; x <= x1; ++x) { - const int ix = flip_x ? out_width - 1 - min_ix - x : x - min_ix; - const int64 xs_lower = xs[ix].lower; - const int64 xs_upper = xs[ix].upper; - const float xs_lerp = xs[ix].lerp; - - // Read channel 0. - const float top_left0(ys_input_lower_ptr[xs_lower + 0]); - const float top_right0(ys_input_lower_ptr[xs_upper + 0]); - const float bottom_left0(ys_input_upper_ptr[xs_lower + 0]); - const float bottom_right0(ys_input_upper_ptr[xs_upper + 0]); - - // Read channel 1. - const float top_left1(ys_input_lower_ptr[xs_lower + 1]); - const float top_right1(ys_input_lower_ptr[xs_upper + 1]); - const float bottom_left1(ys_input_upper_ptr[xs_lower + 1]); - const float bottom_right1(ys_input_upper_ptr[xs_upper + 1]); - - // Read channel 2. - const float top_left2(ys_input_lower_ptr[xs_lower + 2]); - const float top_right2(ys_input_lower_ptr[xs_upper + 2]); - const float bottom_left2(ys_input_upper_ptr[xs_lower + 2]); - const float bottom_right2(ys_input_upper_ptr[xs_upper + 2]); - - // Compute output. - float result0 = compute_lerp(top_left0, top_right0, bottom_left0, - bottom_right0, xs_lerp, ys_lerp); - float result1 = compute_lerp(top_left1, top_right1, bottom_left1, - bottom_right1, xs_lerp, ys_lerp); - float result2 = compute_lerp(top_left2, top_right2, bottom_left2, - bottom_right2, xs_lerp, ys_lerp); - output_y_ptr[x * 3 + 0] = - cast_to(result0, min_val, max_val, u_min_val, u_max_val); - output_y_ptr[x * 3 + 1] = - cast_to(result1, min_val, max_val, u_min_val, u_max_val); - output_y_ptr[x * 3 + 2] = - cast_to(result2, min_val, max_val, u_min_val, u_max_val); - } - output_y_ptr = - flip_y ? output_y_ptr - out_row_size : output_y_ptr + out_row_size; - } - } else if (channels == 4) { - for (int y = min_iy; y <= max_iy; ++y) { - const int iy = y - min_iy; - const T* ys_input_lower_ptr = image + ys[iy].lower * in_row_size; - const T* ys_input_upper_ptr = image + ys[iy].upper * in_row_size; - const float ys_lerp = ys[iy].lerp; - const int x0 = flip_x ? out_width - 1 - max_ix : min_ix; - const int x1 = flip_x ? out_width - 1 - min_ix : max_ix; - for (int x = x0; x <= x1; ++x) { - const int ix = flip_x ? out_width - 1 - min_ix - x : x - min_ix; - const int64 xs_lower = xs[ix].lower; - const int64 xs_upper = xs[ix].upper; - const float xs_lerp = xs[ix].lerp; - - // Read channel 0. - const float top_left0(ys_input_lower_ptr[xs_lower + 0]); - const float top_right0(ys_input_lower_ptr[xs_upper + 0]); - const float bottom_left0(ys_input_upper_ptr[xs_lower + 0]); - const float bottom_right0(ys_input_upper_ptr[xs_upper + 0]); - - // Read channel 1. - const float top_left1(ys_input_lower_ptr[xs_lower + 1]); - const float top_right1(ys_input_lower_ptr[xs_upper + 1]); - const float bottom_left1(ys_input_upper_ptr[xs_lower + 1]); - const float bottom_right1(ys_input_upper_ptr[xs_upper + 1]); - - // Read channel 2. - const float top_left2(ys_input_lower_ptr[xs_lower + 2]); - const float top_right2(ys_input_lower_ptr[xs_upper + 2]); - const float bottom_left2(ys_input_upper_ptr[xs_lower + 2]); - const float bottom_right2(ys_input_upper_ptr[xs_upper + 2]); - - // Read channel 3. - const float top_left3(ys_input_lower_ptr[xs_lower + 3]); - const float top_right3(ys_input_lower_ptr[xs_upper + 3]); - const float bottom_left3(ys_input_upper_ptr[xs_lower + 3]); - const float bottom_right3(ys_input_upper_ptr[xs_upper + 3]); - - // Compute output. - float result0 = compute_lerp(top_left0, top_right0, bottom_left0, - bottom_right0, xs_lerp, ys_lerp); - float result1 = compute_lerp(top_left1, top_right1, bottom_left1, - bottom_right1, xs_lerp, ys_lerp); - float result2 = compute_lerp(top_left2, top_right2, bottom_left2, - bottom_right2, xs_lerp, ys_lerp); - float result3 = compute_lerp(top_left3, top_right3, bottom_left3, - bottom_right3, xs_lerp, ys_lerp); - output_y_ptr[x * 4 + 0] = - cast_to(result0, min_val, max_val, u_min_val, u_max_val); - output_y_ptr[x * 4 + 1] = - cast_to(result1, min_val, max_val, u_min_val, u_max_val); - output_y_ptr[x * 4 + 2] = - cast_to(result2, min_val, max_val, u_min_val, u_max_val); - output_y_ptr[x * 4 + 3] = - cast_to(result3, min_val, max_val, u_min_val, u_max_val); - } - output_y_ptr = - flip_y ? output_y_ptr - out_row_size : output_y_ptr + out_row_size; - } - } else { - for (int y = min_iy; y <= max_iy; ++y) { - const int iy = y - min_iy; - const T* ys_input_lower_ptr = image + ys[iy].lower * in_row_size; - const T* ys_input_upper_ptr = image + ys[iy].upper * in_row_size; - const float ys_lerp = ys[iy].lerp; - const int x0 = flip_x ? out_width - 1 - max_ix : min_ix; - const int x1 = flip_x ? out_width - 1 - min_ix : max_ix; - for (int x = x0; x <= x1; ++x) { - const int ix = flip_x ? out_width - 1 - min_ix - x : x - min_ix; - const int64 xs_lower = xs[ix].lower; - const int64 xs_upper = xs[ix].upper; - const float xs_lerp = xs[ix].lerp; - for (int ichan = 0; ichan < channels; ++ichan) { - const float top_left0(ys_input_lower_ptr[xs_lower + ichan]); - const float top_right0(ys_input_lower_ptr[xs_upper + ichan]); - const float bottom_left0(ys_input_upper_ptr[xs_lower + ichan]); - const float bottom_right0(ys_input_upper_ptr[xs_upper + ichan]); - float result0 = compute_lerp(top_left0, top_right0, bottom_left0, - bottom_right0, xs_lerp, ys_lerp); - output_y_ptr[x * channels + ichan] = - cast_to(result0, min_val, max_val, u_min_val, u_max_val); - } - } - output_y_ptr = - flip_y ? output_y_ptr - out_row_size : output_y_ptr + out_row_size; - } - } -} -} // namespace internal -} // namespace tensorflow -#endif // TENSORFLOW_CORE_KERNELS_CROP_RESIZE_BILINEAR_CORE_H_ diff --git a/tensorflow/core/kernels/resize_bilinear_op.cc b/tensorflow/core/kernels/resize_bilinear_op.cc index bf9e93ef3f..dde59e8e74 100644 --- a/tensorflow/core/kernels/resize_bilinear_op.cc +++ b/tensorflow/core/kernels/resize_bilinear_op.cc @@ -25,15 +25,10 @@ limitations under the License. #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/types.h" -#include "tensorflow/core/kernels/crop_resize_bilinear_core.h" #include "tensorflow/core/kernels/image_resizer_state.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/platform/logging.h" -using ::tensorflow::internal::CachedInterpolation; -using ::tensorflow::internal::compute_interpolation_weights; -using ::tensorflow::internal::crop_resize_single_image; - namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; @@ -68,6 +63,140 @@ class ResizeBilinearOp : public OpKernel { bool align_corners_; }; +namespace { +// Compute the interpolation indices only once. +struct CachedInterpolation { + int64 lower; // Lower source index used in the interpolation + int64 upper; // Upper source index used in the interpolation + // 1-D linear iterpolation scale (see: + // https://en.wikipedia.org/wiki/Bilinear_interpolation) + float lerp; +}; + +inline void compute_interpolation_weights(const int64 out_size, + const int64 in_size, + const float scale, + CachedInterpolation* interpolation) { + interpolation[out_size].lower = 0; + interpolation[out_size].upper = 0; + for (int64 i = out_size - 1; i >= 0; --i) { + const float in = i * scale; + interpolation[i].lower = static_cast(in); + interpolation[i].upper = std::min(interpolation[i].lower + 1, in_size - 1); + interpolation[i].lerp = in - interpolation[i].lower; + } +} + +/** + * Computes the bilinear interpolation from the appropriate 4 float points + * and the linear interpolation weights. + */ +inline float compute_lerp(const float top_left, const float top_right, + const float bottom_left, const float bottom_right, + const float x_lerp, const float y_lerp) { + const float top = top_left + (top_right - top_left) * x_lerp; + const float bottom = bottom_left + (bottom_right - bottom_left) * x_lerp; + return top + (bottom - top) * y_lerp; +} + +template +void resize_image( + typename TTypes::ConstTensor images, const int batch_size, + const int64 in_height, const int64 in_width, const int64 out_height, + const int64 out_width, const int channels, + const std::vector& xs, + const std::vector& ys, + typename TTypes::Tensor output) TF_ATTRIBUTE_NOINLINE; +template +void resize_image(typename TTypes::ConstTensor images, + const int batch_size, const int64 in_height, + const int64 in_width, const int64 out_height, + const int64 out_width, const int channels, + const std::vector& xs_vec, + const std::vector& ys, + typename TTypes::Tensor output) { + const int64 in_row_size = in_width * channels; + const int64 in_batch_num_values = in_height * in_row_size; + const int64 out_row_size = out_width * channels; + + const T* input_b_ptr = images.data(); + const CachedInterpolation* xs = xs_vec.data(); + + if (channels == 3) { + float* output_y_ptr = output.data(); + for (int b = 0; b < batch_size; ++b) { + for (int64 y = 0; y < out_height; ++y) { + const T* ys_input_lower_ptr = input_b_ptr + ys[y].lower * in_row_size; + const T* ys_input_upper_ptr = input_b_ptr + ys[y].upper * in_row_size; + const float ys_lerp = ys[y].lerp; + for (int64 x = 0; x < out_width; ++x) { + const int64 xs_lower = xs[x].lower; + const int64 xs_upper = xs[x].upper; + const float xs_lerp = xs[x].lerp; + + // Read channel 0. + const float top_left0(ys_input_lower_ptr[xs_lower + 0]); + const float top_right0(ys_input_lower_ptr[xs_upper + 0]); + const float bottom_left0(ys_input_upper_ptr[xs_lower + 0]); + const float bottom_right0(ys_input_upper_ptr[xs_upper + 0]); + + // Read channel 1. + const float top_left1(ys_input_lower_ptr[xs_lower + 1]); + const float top_right1(ys_input_lower_ptr[xs_upper + 1]); + const float bottom_left1(ys_input_upper_ptr[xs_lower + 1]); + const float bottom_right1(ys_input_upper_ptr[xs_upper + 1]); + + // Read channel 2. + const float top_left2(ys_input_lower_ptr[xs_lower + 2]); + const float top_right2(ys_input_lower_ptr[xs_upper + 2]); + const float bottom_left2(ys_input_upper_ptr[xs_lower + 2]); + const float bottom_right2(ys_input_upper_ptr[xs_upper + 2]); + + // Compute output. + output_y_ptr[x * channels + 0] = + compute_lerp(top_left0, top_right0, bottom_left0, bottom_right0, + xs_lerp, ys_lerp); + output_y_ptr[x * channels + 1] = + compute_lerp(top_left1, top_right1, bottom_left1, bottom_right1, + xs_lerp, ys_lerp); + output_y_ptr[x * channels + 2] = + compute_lerp(top_left2, top_right2, bottom_left2, bottom_right2, + xs_lerp, ys_lerp); + } + output_y_ptr += out_row_size; + } + input_b_ptr += in_batch_num_values; + } + } else { + float* output_y_ptr = output.data(); + for (int b = 0; b < batch_size; ++b) { + for (int64 y = 0; y < out_height; ++y) { + const T* ys_input_lower_ptr = input_b_ptr + ys[y].lower * in_row_size; + const T* ys_input_upper_ptr = input_b_ptr + ys[y].upper * in_row_size; + const float ys_lerp = ys[y].lerp; + for (int64 x = 0; x < out_width; ++x) { + auto xs_lower = xs[x].lower; + auto xs_upper = xs[x].upper; + auto xs_lerp = xs[x].lerp; + for (int c = 0; c < channels; ++c) { + const float top_left(ys_input_lower_ptr[xs_lower + c]); + const float top_right(ys_input_lower_ptr[xs_upper + c]); + const float bottom_left(ys_input_upper_ptr[xs_lower + c]); + const float bottom_right(ys_input_upper_ptr[xs_upper + c]); + output_y_ptr[x * channels + c] = + compute_lerp(top_left, top_right, bottom_left, bottom_right, + xs_lerp, ys_lerp); + } + } + output_y_ptr += out_row_size; + } + input_b_ptr += in_batch_num_values; + } + } +} + +} // namespace + // Partial specialization of ResizeBilinear functor for a CPUDevice. namespace functor { template @@ -83,11 +212,6 @@ struct ResizeBilinear { const int64 out_height = output.dimension(1); const int64 out_width = output.dimension(2); - const int64 in_row_size = in_width * channels; - const int64 in_batch_num_values = in_height * in_row_size; - const int64 out_row_size = out_width * channels; - const int64 out_batch_num_values = out_row_size * out_height; - // Handle no-op resizes efficiently. if (out_height == in_height && out_width == in_width) { output = images.template cast(); @@ -108,13 +232,8 @@ struct ResizeBilinear { xs[i].upper *= channels; } - for (int b = 0; b < batch_size; ++b) { - crop_resize_single_image( - images.data() + static_cast(b) * in_batch_num_values, - in_height, in_width, out_height, out_width, channels, 0, - out_width - 1, xs.data(), 0, out_height - 1, ys.data(), 0.0f, false, - false, output.data() + static_cast(b) * out_batch_num_values); - } + resize_image(images, batch_size, in_height, in_width, out_height, + out_width, channels, xs, ys, output); } }; } // namespace functor -- GitLab From bbc2c3f1c82fb3987134019e11dbd055a623d395 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Mon, 23 Jul 2018 09:42:36 -0700 Subject: [PATCH 265/519] Refactor imagenet configurations. PiperOrigin-RevId: 205675401 --- .../eager/python/examples/revnet/config.py | 34 ++++++++++++++----- .../python/examples/revnet/revnet_test.py | 4 --- 2 files changed, 26 insertions(+), 12 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/revnet/config.py b/tensorflow/contrib/eager/python/examples/revnet/config.py index 1532c7b67b..e108686b66 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/config.py +++ b/tensorflow/contrib/eager/python/examples/revnet/config.py @@ -45,7 +45,7 @@ def get_hparams_cifar_38(): config.add_hparam("bottleneck", False) config.add_hparam("fused", True) config.add_hparam("init_max_pool", False) - if tf.test.is_gpu_available() > 0: + if tf.test.is_gpu_available(): config.add_hparam("input_shape", (3, 32, 32)) config.add_hparam("data_format", "channels_first") else: @@ -74,7 +74,7 @@ def get_hparams_cifar_38(): # TPU architecture specifics # Suggested batch sizes to reduce overhead from excessive tensor padding # https://cloud.google.com/tpu/docs/troubleshooting - config.add_hparam("tpu_batch_size", 128) + config.add_hparam("tpu_batch_size", 1024) config.add_hparam("tpu_eval_batch_size", 1024) config.add_hparam("tpu_iters_per_epoch", 50000 // config.tpu_batch_size) config.add_hparam("tpu_epochs", @@ -107,6 +107,8 @@ def get_hparams_imagenet_56(): """RevNet-56 configurations for ImageNet.""" config = tf.contrib.training.HParams() + config.add_hparam("n_classes", 1000) + config.add_hparam("dataset", "ImageNet") config.add_hparam("init_filters", 128) config.add_hparam("init_kernel", 7) config.add_hparam("init_stride", 2) @@ -114,7 +116,7 @@ def get_hparams_imagenet_56(): config.add_hparam("n_res", [2, 2, 2, 2]) config.add_hparam("filters", [128, 256, 512, 832]) config.add_hparam("strides", [1, 2, 2, 2]) - config.add_hparam("batch_size", 16) + config.add_hparam("batch_size", 256) config.add_hparam("bottleneck", True) config.add_hparam("fused", True) config.add_hparam("init_max_pool", True) @@ -133,16 +135,32 @@ def get_hparams_imagenet_56(): config.add_hparam("max_train_iter", 600000) config.add_hparam("seed", 1234) config.add_hparam("shuffle", True) - config.add_hparam("log_every", 50) - config.add_hparam("save_every", 50) + config.add_hparam("log_every", 500) + config.add_hparam("save_every", 500) config.add_hparam("dtype", tf.float32) - config.add_hparam("eval_batch_size", 1000) + config.add_hparam("eval_batch_size", 256) config.add_hparam("div255", True) - # TODO(lxuechen): Update this according to ImageNet data - config.add_hparam("iters_per_epoch", 50000 // config.batch_size) + config.add_hparam("iters_per_epoch", 1281167 // config.batch_size) config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) # Due to bottleneck residual blocks filters = [f * 4 for f in config.filters] config.filters = filters + # Customized TPU hyperparameters due to differing batch size caused by + # TPU architecture specifics + # Suggested batch sizes to reduce overhead from excessive tensor padding + # https://cloud.google.com/tpu/docs/troubleshooting + config.add_hparam("tpu_batch_size", 1024) + config.add_hparam("tpu_eval_batch_size", 1024) + config.add_hparam("tpu_iters_per_epoch", 1281167 // config.tpu_batch_size) + config.add_hparam("tpu_epochs", + config.max_train_iter // config.tpu_iters_per_epoch) + + return config + + +def get_hparams_imagenet_104(): + config = get_hparams_imagenet_56() + config.n_res = [2, 2, 11, 2] + return config diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py index 2dc7b9fd70..26b0847523 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py @@ -223,8 +223,6 @@ class RevNetBenchmark(tf.test.Benchmark): execution_mode=None, compiled=False): config = config_.get_hparams_imagenet_56() - config.add_hparam("n_classes", 1000) - config.add_hparam("dataset", "ImageNet") with tfe.execution_mode(execution_mode): device, data_format = device_and_format model = revnet.RevNet(config=config) @@ -270,8 +268,6 @@ class RevNetBenchmark(tf.test.Benchmark): execution_mode=None, compiled=False): config = config_.get_hparams_imagenet_56() - config.add_hparam("n_classes", 1000) - config.add_hparam("dataset", "ImageNet") with tfe.execution_mode(execution_mode): device, data_format = device_and_format for batch_size in self._train_batch_sizes(): -- GitLab From b8a9d163d9cbb4b581c044d9c4b1b256c801a9c4 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Mon, 23 Jul 2018 10:05:16 -0700 Subject: [PATCH 266/519] Automated rollback of commit 8048b3a53d5a919fef74874fcffbec9039e6acd1 PiperOrigin-RevId: 205679162 --- tensorflow/core/BUILD | 8 - tensorflow/core/common_runtime/session_ref.cc | 170 ------------------ tensorflow/core/common_runtime/session_ref.h | 86 --------- tensorflow/python/BUILD | 1 - tensorflow/python/client/session.py | 2 +- tensorflow/python/client/tf_session.i | 1 - tensorflow/python/client/tf_session_helper.cc | 14 -- tensorflow/python/client/tf_session_helper.h | 3 - 8 files changed, 1 insertion(+), 284 deletions(-) delete mode 100644 tensorflow/core/common_runtime/session_ref.cc delete mode 100644 tensorflow/core/common_runtime/session_ref.h diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index d51d9f0295..17e6ccda14 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -2921,14 +2921,6 @@ tf_cuda_library( ] + tf_additional_device_tracer_deps(), ) -cc_library( - name = "session_ref", - srcs = ["common_runtime/session_ref.cc"], - hdrs = ["common_runtime/session_ref.h"], - copts = tf_copts(), - deps = [":core_cpu_base"], -) - cc_library( name = "gpu_id", hdrs = [ diff --git a/tensorflow/core/common_runtime/session_ref.cc b/tensorflow/core/common_runtime/session_ref.cc deleted file mode 100644 index b931ef4229..0000000000 --- a/tensorflow/core/common_runtime/session_ref.cc +++ /dev/null @@ -1,170 +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. -==============================================================================*/ -#include "tensorflow/core/common_runtime/session_ref.h" - -#include - -namespace tensorflow { - -namespace { - -// Scope helper to track active calls and manage session lifetime. -struct RunCounter { - std::shared_ptr session; - uint64* value; - mutex* m; - condition_variable* cv; - - explicit RunCounter(std::shared_ptr s, uint64* v, mutex* m, - condition_variable* cv) - : session(std::move(s)), value(v), m(m), cv(cv) { - mutex_lock l(*m); - ++*value; - } - - ~RunCounter() { - mutex_lock l(*m); - if (--*value == 0) { - cv->notify_all(); - } - } -}; - -} // namespace - -Status SessionRef::CheckNotClosed() { - mutex_lock l(run_lock_); - if (session_ == nullptr) return errors::Cancelled("Session has been closed."); - return ::tensorflow::Status::OK(); -} - -Status SessionRef::Run(const RunOptions& run_options, - const std::vector >& inputs, - const std::vector& output_tensor_names, - const std::vector& target_node_names, - std::vector* outputs, - RunMetadata* run_metadata) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->Run(run_options, inputs, output_tensor_names, - target_node_names, outputs, run_metadata); -} - -Status SessionRef::Create(const GraphDef& graph) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->Create(graph); -} - -Status SessionRef::Create(const RunOptions& run_options, - const GraphDef& graph) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->Create(run_options, graph); -} - -Status SessionRef::Extend(const RunOptions& run_options, - const GraphDef& graph) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->Extend(run_options, graph); -} - -Status SessionRef::Extend(const GraphDef& graph) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->Extend(graph); -} - -Status SessionRef::Close(const RunOptions& run_options) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - mutex_lock l(run_lock_); - Status status = session_->Close(run_options); - session_.reset(); - while (run_count_ > 0) { - run_finished_.wait(l); - } - return status; -} - -Status SessionRef::Close() { - TF_RETURN_IF_ERROR(CheckNotClosed()); - mutex_lock l(run_lock_); - Status status = session_->Close(); - session_.reset(); - while (run_count_ > 0) { - run_finished_.wait(l); - } - return status; -} - -Status SessionRef::Run(const std::vector >& inputs, - const std::vector& output_tensor_names, - const std::vector& target_node_names, - std::vector* outputs) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->Run(inputs, output_tensor_names, target_node_names, - outputs); -} - -Status SessionRef::ListDevices(std::vector* response) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->ListDevices(response); -} - -Status SessionRef::PRunSetup(const std::vector& input_names, - const std::vector& output_names, - const std::vector& target_nodes, - string* handle) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->PRunSetup(input_names, output_names, target_nodes, handle); -} - -Status SessionRef::PRun(const string& handle, - const std::vector >& inputs, - const std::vector& output_names, - std::vector* outputs) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->PRun(handle, inputs, output_names, outputs); -} - -Status SessionRef::MakeCallable(const CallableOptions& callable_options, - CallableHandle* out_handle) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->MakeCallable(callable_options, out_handle); -} - -Status SessionRef::RunCallable(CallableHandle handle, - const std::vector& feed_tensors, - std::vector* fetch_tensors, - RunMetadata* run_metadata) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->RunCallable(handle, feed_tensors, fetch_tensors, - run_metadata); -} - -Status SessionRef::ReleaseCallable(CallableHandle handle) { - TF_RETURN_IF_ERROR(CheckNotClosed()); - RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); - return rc.session->ReleaseCallable(handle); -} - -} // namespace tensorflow diff --git a/tensorflow/core/common_runtime/session_ref.h b/tensorflow/core/common_runtime/session_ref.h deleted file mode 100644 index 6146933326..0000000000 --- a/tensorflow/core/common_runtime/session_ref.h +++ /dev/null @@ -1,86 +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. -==============================================================================*/ -#ifndef TENSORFLOW_CORE_COMMON_RUNTIME_SESSION_REF_H_ -#define TENSORFLOW_CORE_COMMON_RUNTIME_SESSION_REF_H_ - -#include - -#include "tensorflow/core/platform/mutex.h" -#include "tensorflow/core/public/session.h" - -namespace tensorflow { - -// A `SessionRef` manages the lifetime of a wrapped `Session` pointer. -// -// SessionRef blocks the return of Close() until all pending operations have -// been completed or cancelled and underlying session has been freed. Any -// subsequent operations on the SessionRef object will return errors::Cancelled. -class SessionRef : public Session { - public: - SessionRef(Session* session) : session_(session) {} - virtual ~SessionRef() {} - - Status Create(const GraphDef& graph) override; - Status Extend(const GraphDef& graph) override; - Status Create(const RunOptions& run_options, const GraphDef& graph) override; - Status Extend(const RunOptions& run_options, const GraphDef& graph) override; - Status Run(const std::vector >& inputs, - const std::vector& output_tensor_names, - const std::vector& target_node_names, - std::vector* outputs) override; - - Status ListDevices(std::vector* response) override; - - Status Close() override; - Status Close(const RunOptions& run_options) override; - - Status Run(const RunOptions& run_options, - const std::vector >& inputs, - const std::vector& output_tensor_names, - const std::vector& target_node_names, - std::vector* outputs, RunMetadata* run_metadata) override; - - Status PRunSetup(const std::vector& input_names, - const std::vector& output_names, - const std::vector& target_nodes, - string* handle) override; - - Status PRun(const string& handle, - const std::vector >& inputs, - const std::vector& output_names, - std::vector* outputs) override; - - Status MakeCallable(const CallableOptions& callable_options, - CallableHandle* out_handle); - - Status RunCallable(CallableHandle handle, - const std::vector& feed_tensors, - std::vector* fetch_tensors, - RunMetadata* run_metadata); - - Status ReleaseCallable(CallableHandle handle); - - private: - mutex run_lock_; - condition_variable run_finished_; - uint64 run_count_ GUARDED_BY(run_lock_) = {0}; - std::shared_ptr session_; - - Status CheckNotClosed(); -}; - -} // namespace tensorflow - -#endif // TENSORFLOW_CORE_COMMON_RUNTIME_SESSION_REF_H_ diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index d45566c55e..9c7f3b7b25 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -3629,7 +3629,6 @@ tf_cuda_library( "//tensorflow/core:graph", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", - "//tensorflow/core:session_ref", "//third_party/py/numpy:headers", "//third_party/python_runtime:headers", ], diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index f3aa135fe4..8ede6ab54c 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -629,7 +629,7 @@ class BaseSession(SessionInterface): opts = tf_session.TF_NewSessionOptions(target=self._target, config=config) try: # pylint: disable=protected-access - self._session = tf_session.TF_NewSessionRef(self._graph._c_graph, opts) + self._session = tf_session.TF_NewSession(self._graph._c_graph, opts) # pylint: enable=protected-access finally: tf_session.TF_DeleteSessionOptions(opts) diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i index 39a2922ac0..1cdd8e0b6a 100644 --- a/tensorflow/python/client/tf_session.i +++ b/tensorflow/python/client/tf_session.i @@ -777,7 +777,6 @@ def TF_Reset(target, containers=None, config=None): $1 = &types_local; } -%unignore TF_NewSessionRef; %unignore SetRequireShapeInferenceFns; %unignore TF_TryEvaluateConstant_wrapper; %noexception TF_TryEvaluateConstant_wrapper; diff --git a/tensorflow/python/client/tf_session_helper.cc b/tensorflow/python/client/tf_session_helper.cc index bcd4af2912..b6481e7e29 100644 --- a/tensorflow/python/client/tf_session_helper.cc +++ b/tensorflow/python/client/tf_session_helper.cc @@ -20,7 +20,6 @@ limitations under the License. #include "tensorflow/c/c_api.h" #include "tensorflow/c/c_api_internal.h" #include "tensorflow/c/tf_status_helper.h" -#include "tensorflow/core/common_runtime/session_ref.h" #include "tensorflow/core/framework/allocator.h" #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/framework/attr_value_util.h" @@ -43,19 +42,6 @@ static const char* kFeedDictErrorMsg = "feed_dict must be a dictionary mapping strings to NumPy arrays."; } // end namespace -TF_Session* TF_NewSessionRef(TF_Graph* graph, const TF_SessionOptions* opts, - TF_Status* status) { - TF_Session* tf_session = TF_NewSession(graph, opts, status); - if (tf_session == nullptr) { - return nullptr; - } - - Session* session = reinterpret_cast(tf_session->session); - SessionRef* session_ref = new SessionRef(session); - tf_session->session = session_ref; - return tf_session; -} - void TF_Run_wrapper_helper(TF_DeprecatedSession* session, const char* handle, const TF_Buffer* run_options, PyObject* feed_dict, const NameVector& output_names, diff --git a/tensorflow/python/client/tf_session_helper.h b/tensorflow/python/client/tf_session_helper.h index dab7e71aac..cfd27c2bee 100644 --- a/tensorflow/python/client/tf_session_helper.h +++ b/tensorflow/python/client/tf_session_helper.h @@ -40,9 +40,6 @@ typedef tensorflow::gtl::InlinedVector PyObjectVector; // A TF_TensorVector is a vector of borrowed pointers to TF_Tensors. typedef gtl::InlinedVector TF_TensorVector; -TF_Session* TF_NewSessionRef(TF_Graph* graph, const TF_SessionOptions* opts, - TF_Status* status); - // Run the graph associated with the session starting with the // supplied inputs[]. Regardless of success or failure, inputs[] are // stolen by the implementation (i.e. the implementation will -- GitLab From 97a0da7cd265d25a1b2caf2e6e344694bb795a1c Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Mon, 23 Jul 2018 10:19:12 -0700 Subject: [PATCH 267/519] Allow clustering ControlTrigger nodes With b/111092066 fixed, we should be able to cluster ControlTrigger since all inputs to the XLA computation (and thus all inputs to every internal node) should be live and in that case ControlTrigger is a no-op. PRESUBMIT=passed FIXED=111570009 CC=brain-reviews DELTA=9 (0 added, 9 deleted, 0 changed) DELTA_BY_EXTENSION=cc=0 PiperOrigin-RevId: 205681775 --- .../compiler/jit/mark_for_compilation_pass.cc | 9 ----- .../jit/mark_for_compilation_pass_test.cc | 33 +++++++++++++++++++ 2 files changed, 33 insertions(+), 9 deletions(-) diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass.cc b/tensorflow/compiler/jit/mark_for_compilation_pass.cc index a3949bc14b..38eb6d830f 100644 --- a/tensorflow/compiler/jit/mark_for_compilation_pass.cc +++ b/tensorflow/compiler/jit/mark_for_compilation_pass.cc @@ -477,15 +477,6 @@ Status MarkForCompilationPass::Run( return false; } - // TODO(b/111570009): This bailout for ControlTrigger is probably not - // needed. - // - // Don't compile control trigger nodes. We won't preserve their deadness - // semantics correctly, so it's safest not to compile them. - if (node->IsControlTrigger()) { - return false; - } - // If this device requires a JIT, we must say yes. if (registration->requires_compilation) return true; diff --git a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc index 772c92d369..2c5f4fb774 100644 --- a/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc +++ b/tensorflow/compiler/jit/mark_for_compilation_pass_test.cc @@ -19,6 +19,7 @@ limitations under the License. #include "tensorflow/cc/ops/array_ops.h" #include "tensorflow/cc/ops/control_flow_ops_internal.h" #include "tensorflow/cc/ops/function_ops.h" +#include "tensorflow/cc/ops/sendrecv_ops.h" #include "tensorflow/cc/ops/standard_ops.h" #include "tensorflow/compiler/jit/defs.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" @@ -680,5 +681,37 @@ TEST(XlaCompilationTest, ClusterIdentityWithNonRefInput) { EXPECT_EQ(clusters, expected_clusters); } +TEST(XlaCompilationTest, ClusterControlTrigger) { + Scope root = Scope::NewRootScope().ExitOnError(); + + Output recv_a = ops::_Recv(root.WithOpName("recv_a"), DT_BOOL, "tensor_a", + "sender", 0, "receiver"); + Output recv_b = ops::_Recv(root.WithOpName("recv_b"), DT_BOOL, "tensor_b", + "sender", 0, "receiver"); + Output const_a = ops::Const(root.WithOpName("const_a"), 42); + + ops::ControlTrigger ctrl_trigger_a(root.WithOpName("ctrl_trigger_a")); + ops::ControlTrigger ctrl_trigger_b(root.WithOpName("ctrl_trigger_b")); + root.graph()->AddControlEdge(recv_a.node(), ctrl_trigger_a.operation.node()); + root.graph()->AddControlEdge(recv_b.node(), ctrl_trigger_a.operation.node()); + root.graph()->AddControlEdge(ctrl_trigger_b.operation.node(), const_a.node()); + + std::unique_ptr graph(new Graph(OpRegistry::Global())); + + TF_ASSERT_OK(root.ToGraph(graph.get())); + TF_ASSERT_OK(MarkForCompilation(&graph)); + + std::unordered_map clusters = GetClusters(*graph); + + ASSERT_FALSE(clusters.empty()); + string cluster_name = clusters.begin()->second; + + // ctrl_trigger_a has inputs with mismatching deadness so it won't be + // clustered. ctrl_trigger_b is okay to cluster. + std::unordered_map expected_clusters( + {{"const_a", cluster_name}, {"ctrl_trigger_b", cluster_name}}); + EXPECT_EQ(clusters, expected_clusters); +} + } // namespace } // namespace tensorflow -- GitLab From 5f4ebb000057d2c0c76ef856526d374648958d86 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Mon, 23 Jul 2018 11:35:07 -0700 Subject: [PATCH 268/519] Fix improperly forwarded argument. PiperOrigin-RevId: 205695945 --- tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 1208d557e7..09eeb6a7f5 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -2339,7 +2339,7 @@ class TPUEstimator(estimator_lib.Estimator): predict_keys=predict_keys, hooks=hooks, checkpoint_path=checkpoint_path, - yield_single_examples=True): + yield_single_examples=yield_single_examples): yield result except Exception as e: # pylint: disable=broad-except rendezvous.record_error('prediction_loop', e) -- GitLab From 989819a78d59387aea728eb1807fb59f9d101411 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 12:14:01 -0700 Subject: [PATCH 269/519] Broaden dropped exception set in error handler in `transformer.Base.visit`. Test with an example that breaks error reporting in said `transformer.Base.visit` without this change. (Admittedly, by directly calling `visit` and committing a type error in the API.) PiperOrigin-RevId: 205702348 --- .../contrib/autograph/pyct/transformer.py | 17 +++- .../autograph/pyct/transformer_test.py | 82 +++++++++++++++++++ 2 files changed, 98 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/autograph/pyct/transformer.py b/tensorflow/contrib/autograph/pyct/transformer.py index bbdfefc50a..3e8906823e 100644 --- a/tensorflow/contrib/autograph/pyct/transformer.py +++ b/tensorflow/contrib/autograph/pyct/transformer.py @@ -398,10 +398,25 @@ class Base(gast.NodeTransformer): try: source, _ = compiler.ast_to_source(node) return source - except AssertionError: + # pylint: disable=broad-except + # This function is used for error reporting. If an exception occurs here, + # it should be suppressed, in favor of emitting as informative a message + # about the original error as possible. + except Exception: return '' def visit(self, node): + if not isinstance(node, gast.AST): + # This is not that uncommon a mistake: various node bodies are lists, for + # example, posing a land mine for transformers that need to recursively + # call `visit`. The error needs to be raised before the exception handler + # below is installed, because said handler will mess up if `node` is not, + # in fact, a node. + msg = ( + 'invalid value for "node": expected "ast.AST", got "{}"; to' + ' visit lists of nodes, use "visit_block" instead').format(type(node)) + raise ValueError(msg) + source_code = self.entity_info.source_code source_file = self.entity_info.source_file did_enter_function = False diff --git a/tensorflow/contrib/autograph/pyct/transformer_test.py b/tensorflow/contrib/autograph/pyct/transformer_test.py index 19b80b09ac..a37e922a1d 100644 --- a/tensorflow/contrib/autograph/pyct/transformer_test.py +++ b/tensorflow/contrib/autograph/pyct/transformer_test.py @@ -282,6 +282,88 @@ class TransformerTest(test.TestCase): self.assertTrue(isinstance(node.body[1].body[0], gast.Assign)) self.assertTrue(isinstance(node.body[1].body[1], gast.Return)) + def test_robust_error_on_list_visit(self): + + class BrokenTransformer(transformer.Base): + + def visit_If(self, node): + # This is broken because visit expects a single node, not a list, and + # the body of an if is a list. + # Importantly, the default error handling in visit also expects a single + # node. Therefore, mistakes like this need to trigger a type error + # before the visit called here installs its error handler. + # That type error can then be caught by the enclosing call to visit, + # and correctly blame the If node. + self.visit(node.body) + return node + + def test_function(x): + if x > 0: + return x + + tr = BrokenTransformer(self._simple_source_info()) + + node, _ = parser.parse_entity(test_function) + with self.assertRaises(transformer.AutographParseError) as cm: + node = tr.visit(node) + obtained_message = str(cm.exception) + expected_message = r'expected "ast.AST", got "\<(type|class) \'list\'\>"' + self.assertRegexpMatches(obtained_message, expected_message) + # The exception should point at the if statement, not any place else. Could + # also check the stack trace. + self.assertTrue( + 'Occurred at node:\nIf' in obtained_message, obtained_message) + self.assertTrue( + 'Occurred at node:\nFunctionDef' not in obtained_message, + obtained_message) + self.assertTrue( + 'Occurred at node:\nReturn' not in obtained_message, obtained_message) + + def test_robust_error_on_ast_corruption(self): + # A child class should not be able to be so broken that it causes the error + # handling in `transformer.Base` to raise an exception. Why not? Because + # then the original error location is dropped, and an error handler higher + # up in the call stack gives misleading information. + + # Here we test that the error handling in `visit` completes, and blames the + # correct original exception, even if the AST gets corrupted. + + class NotANode(object): + pass + + class BrokenTransformer(transformer.Base): + + def visit_If(self, node): + node.body = NotANode() + raise ValueError('I blew up') + + def test_function(x): + if x > 0: + return x + + tr = BrokenTransformer(self._simple_source_info()) + + node, _ = parser.parse_entity(test_function) + with self.assertRaises(transformer.AutographParseError) as cm: + node = tr.visit(node) + obtained_message = str(cm.exception) + # The message should reference the exception actually raised, not anything + # from the exception handler. + expected_substring = 'I blew up' + self.assertTrue(expected_substring in obtained_message, obtained_message) + # Expect the exception to have failed to parse the corrupted AST + self.assertTrue( + '' in obtained_message, + obtained_message) + # The exception should point at the if statement, not any place else. Could + # also check the stack trace. + self.assertTrue( + 'Occurred at node:\nIf' in obtained_message, obtained_message) + self.assertTrue( + 'Occurred at node:\nFunctionDef' not in obtained_message, + obtained_message) + self.assertTrue( + 'Occurred at node:\nReturn' not in obtained_message, obtained_message) if __name__ == '__main__': test.main() -- GitLab From c26b95e707ed2304e2e50f51f6751c9b9cb87f1c Mon Sep 17 00:00:00 2001 From: Wesley Qian Date: Mon, 23 Jul 2018 12:15:03 -0700 Subject: [PATCH 270/519] Change partial reconstruction loss in cyclegan_loss to the standard abs_diff. PiperOrigin-RevId: 205702476 --- .../gan/python/losses/python/losses_impl.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/gan/python/losses/python/losses_impl.py b/tensorflow/contrib/gan/python/losses/python/losses_impl.py index 1ba3a64167..d389748374 100644 --- a/tensorflow/contrib/gan/python/losses/python/losses_impl.py +++ b/tensorflow/contrib/gan/python/losses/python/losses_impl.py @@ -949,6 +949,11 @@ def cycle_consistency_loss(data_x, * loss = (loss_x2x + loss_y2y) / 2 where `loss` is the final result. + For the L1-norm, we follow the original implementation: + https://github.com/junyanz/CycleGAN/blob/master/models/cycle_gan_model.lua + we use L1-norm of pixel-wise error normalized by data size such that + `cycle_loss_weight` can be specified independent of image size. + See https://arxiv.org/abs/1703.10593 for more details. Args: @@ -965,19 +970,12 @@ def cycle_consistency_loss(data_x, A scalar `Tensor` of cycle consistency loss. """ - def _partial_cycle_consistency_loss(data, reconstructed_data): - # Following the original implementation - # https://github.com/junyanz/CycleGAN/blob/master/models/cycle_gan_model.lua - # use L1-norm of pixel-wise error normalized by data size so that - # `cycle_loss_weight` can be specified independent of image size. - return math_ops.reduce_mean(math_ops.abs(data - reconstructed_data)) - with ops.name_scope( scope, 'cycle_consistency_loss', values=[data_x, reconstructed_data_x, data_y, reconstructed_data_y]): - loss_x2x = _partial_cycle_consistency_loss(data_x, reconstructed_data_x) - loss_y2y = _partial_cycle_consistency_loss(data_y, reconstructed_data_y) + loss_x2x = losses.absolute_difference(data_x, reconstructed_data_x) + loss_y2y = losses.absolute_difference(data_y, reconstructed_data_y) loss = (loss_x2x + loss_y2y) / 2.0 if add_summaries: summary.scalar('cycle_consistency_loss_x2x', loss_x2x) -- GitLab From 049fc23966eeef02a0945ddb80ae5f40592b90c1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 12:19:01 -0700 Subject: [PATCH 271/519] Extend random pool to work with arbitrarily nested tensor structures. PiperOrigin-RevId: 205703156 --- tensorflow/contrib/gan/BUILD | 3 ++ .../python/random_tensor_pool_impl.py | 37 ++++++++----------- .../python/random_tensor_pool_test.py | 19 ++++++++++ 3 files changed, 38 insertions(+), 21 deletions(-) diff --git a/tensorflow/contrib/gan/BUILD b/tensorflow/contrib/gan/BUILD index 781e4ae4d7..7e6cb72485 100644 --- a/tensorflow/contrib/gan/BUILD +++ b/tensorflow/contrib/gan/BUILD @@ -257,12 +257,15 @@ py_library( py_test( name = "random_tensor_pool_test", srcs = ["python/features/python/random_tensor_pool_test.py"], + shard_count = 6, srcs_version = "PY2AND3", deps = [ ":random_tensor_pool", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", "//tensorflow/python:dtypes", + "//tensorflow/python:framework_ops", "//third_party/py/numpy", ], ) diff --git a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py index 9e4ec59e70..ca2d724b49 100644 --- a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py +++ b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_impl.py @@ -36,16 +36,15 @@ 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 random_ops +from tensorflow.python.util import nest __all__ = [ 'tensor_pool', ] -def _to_tuple(x): - if isinstance(x, (list, tuple)): - return tuple(x) - return (x,) +def _to_list(x): + return [x] if isinstance(x, ops.Tensor) else list(x) def tensor_pool(input_values, @@ -63,8 +62,8 @@ def tensor_pool(input_values, `pool_size` = 0 or `pooling_probability` = 0. Args: - input_values: A `Tensor`, or a list or tuple of `Tensor`s from which to read - values to be pooled. + input_values: An arbitrarily nested structure of `tf.Tensors`, from which to + read values to be pooled. pool_size: An integer specifying the maximum size of the pool. Defaults to 50. pooling_probability: A float `Tensor` specifying the probability of getting @@ -72,9 +71,10 @@ def tensor_pool(input_values, name: A string prefix for the name scope for all tensorflow ops. Returns: - A `Tensor`, or a list or tuple of `Tensor`s (according to the type ofx - `input_values`) which is with given probability either the `input_values` or - a randomly chosen sample that was previously inserted in the pool. + A nested structure of `Tensor` objects with the same structure as + `input_values`. With the given probability, the Tensor values are either the + same as in `input_values` or a randomly chosen sample that was previously + inserted in the pool. Raises: ValueError: If `pool_size` is negative. @@ -86,11 +86,10 @@ def tensor_pool(input_values, return input_values original_input_values = input_values - input_values = _to_tuple(input_values) + input_values = nest.flatten(input_values) - with ops.name_scope( - '{}_pool_queue'.format(name), - values=input_values + (pooling_probability,)): + with ops.name_scope('{}_pool_queue'.format(name), + values=input_values + [pooling_probability]): pool_queue = data_flow_ops.RandomShuffleQueue( capacity=pool_size, min_after_dequeue=0, @@ -112,10 +111,10 @@ def tensor_pool(input_values, def _get_input_value_pooled(): enqueue_op = pool_queue.enqueue(input_values) with ops.control_dependencies([enqueue_op]): - return tuple(array_ops.identity(v) for v in input_values) + return [array_ops.identity(v) for v in input_values] def _get_random_pool_value_and_enqueue_input(): - dequeue_values = _to_tuple(pool_queue.dequeue()) + dequeue_values = _to_list(pool_queue.dequeue()) with ops.control_dependencies(dequeue_values): enqueue_op = pool_queue.enqueue(input_values) with ops.control_dependencies([enqueue_op]): @@ -124,7 +123,7 @@ def tensor_pool(input_values, return control_flow_ops.cond(prob, lambda: dequeue_values, lambda: input_values) - output_values = _to_tuple(control_flow_ops.cond( + output_values = _to_list(control_flow_ops.cond( pool_queue.size() < pool_size, _get_input_value_pooled, _get_random_pool_value_and_enqueue_input)) @@ -132,8 +131,4 @@ def tensor_pool(input_values, for input_value, output_value in zip(input_values, output_values): output_value.set_shape(input_value.shape) - if isinstance(original_input_values, list): - return list(output_values) - elif isinstance(original_input_values, tuple): - return output_values - return output_values[0] + return nest.pack_sequence_as(original_input_values, output_values) diff --git a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_test.py b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_test.py index d8cf549cf7..08584dcd65 100644 --- a/tensorflow/contrib/gan/python/features/python/random_tensor_pool_test.py +++ b/tensorflow/contrib/gan/python/features/python/random_tensor_pool_test.py @@ -21,7 +21,9 @@ from __future__ import print_function import numpy as np from tensorflow.contrib.gan.python.features.python.random_tensor_pool_impl import tensor_pool +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.platform import test @@ -111,6 +113,23 @@ class TensorPoolTest(test.TestCase): self.assertEqual(len(outs), len(input_values)) self.assertEqual(outs[1] - outs[0], 1) + def test_pool_preserves_shape(self): + t = constant_op.constant(1) + input_values = [[t, t, t], (t, t), t] + output_values = tensor_pool(input_values, pool_size=5) + print('stuff: ', output_values) + # Overall shape. + self.assertIsInstance(output_values, list) + self.assertEqual(3, len(output_values)) + # Shape of first element. + self.assertIsInstance(output_values[0], list) + self.assertEqual(3, len(output_values[0])) + # Shape of second element. + self.assertIsInstance(output_values[1], tuple) + self.assertEqual(2, len(output_values[1])) + # Shape of third element. + self.assertIsInstance(output_values[2], ops.Tensor) + if __name__ == '__main__': test.main() -- GitLab From 65b611d3cc67d0f12007ba0eb87e2b3d2a074ff3 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Mon, 23 Jul 2018 12:19:47 -0700 Subject: [PATCH 272/519] Fix sanity. Adding future imports. PiperOrigin-RevId: 205703315 --- tensorflow/contrib/tpu/python/tpu/error_handling.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tensorflow/contrib/tpu/python/tpu/error_handling.py b/tensorflow/contrib/tpu/python/tpu/error_handling.py index 8d6d44b1a1..182cac6f0f 100644 --- a/tensorflow/contrib/tpu/python/tpu/error_handling.py +++ b/tensorflow/contrib/tpu/python/tpu/error_handling.py @@ -14,6 +14,10 @@ # =================================================================== """ErrorRendezvous handler for collecting errors from multiple threads.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + import contextlib import threading import time -- GitLab From 54870b3de345a748dc5189f4c7dc759d6ffd6084 Mon Sep 17 00:00:00 2001 From: Dan Ringwalt Date: Mon, 23 Jul 2018 12:22:38 -0700 Subject: [PATCH 273/519] Use code blocks in the swap_ts and reroute_ts docstrings. This makes the diagrams readable in the HTML documentation. PiperOrigin-RevId: 205703761 --- tensorflow/contrib/graph_editor/reroute.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/graph_editor/reroute.py b/tensorflow/contrib/graph_editor/reroute.py index 95c02a64d4..d42e0c01f4 100644 --- a/tensorflow/contrib/graph_editor/reroute.py +++ b/tensorflow/contrib/graph_editor/reroute.py @@ -208,9 +208,9 @@ def _reroute_ts(ts0, ts1, mode, can_modify=None, cannot_modify=None): def swap_ts(ts0, ts1, can_modify=None, cannot_modify=None): """For each tensor's pair, swap the end of (t0,t1). - B0 B1 B0 B1 - | | => X - A0 A1 A0 A1 + B0 B1 B0 B1 + | | => X + A0 A1 A0 A1 Args: ts0: an object convertible to a list of `tf.Tensor`. @@ -233,9 +233,9 @@ def swap_ts(ts0, ts1, can_modify=None, cannot_modify=None): def reroute_ts(ts0, ts1, can_modify=None, cannot_modify=None): """For each tensor's pair, replace the end of t1 by the end of t0. - B0 B1 B0 B1 - | | => |/ - A0 A1 A0 A1 + B0 B1 B0 B1 + | | => |/ + A0 A1 A0 A1 The end of the tensors in ts1 are left dangling. -- GitLab From 4c9e4ba5d305fdedf7e0bbcd6fd6b84e29dfa5a2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 12:39:07 -0700 Subject: [PATCH 274/519] Fix ShapeUtil::CompatibleIgnoringElementType for opaque types Previously we had an assymetric comparision when comparing an array type with an opaque type returning false for array vs opaque while true for opaque vs array. PiperOrigin-RevId: 205706477 --- tensorflow/compiler/xla/shape_util.cc | 4 ++-- tensorflow/compiler/xla/shape_util_test.cc | 11 +++++++++++ 2 files changed, 13 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/shape_util.cc b/tensorflow/compiler/xla/shape_util.cc index 6480148336..ec901af1e2 100644 --- a/tensorflow/compiler/xla/shape_util.cc +++ b/tensorflow/compiler/xla/shape_util.cc @@ -682,7 +682,7 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { CompatibleIgnoringElementType); } else { // Opaque, token, etc types are vacuously compatible. - return true; + return lhs.element_type() == rhs.element_type(); } } @@ -697,7 +697,7 @@ StatusOr ParseShapeStringInternal(tensorflow::StringPiece* s) { CompatibleIgnoringFpPrecision); } else { // Opaque, token, etc types are vacuously compatible. - return true; + return lhs.element_type() == rhs.element_type(); } } diff --git a/tensorflow/compiler/xla/shape_util_test.cc b/tensorflow/compiler/xla/shape_util_test.cc index ed2d16c0e9..e5dd62ae9a 100644 --- a/tensorflow/compiler/xla/shape_util_test.cc +++ b/tensorflow/compiler/xla/shape_util_test.cc @@ -334,6 +334,17 @@ TEST(ShapeUtilTest, IncompatibleScalarVsTuple) { EXPECT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(shape2, shape1)); } +TEST(ShapeUtilTest, OpaqueVsArray) { + Shape shape1 = ShapeUtil::MakeShape(F32, {5, 7}); + Shape shape2 = ShapeUtil::MakeOpaqueShape(); + EXPECT_FALSE(ShapeUtil::Compatible(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::Compatible(shape2, shape1)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringFpPrecision(shape2, shape1)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringElementType(shape1, shape2)); + EXPECT_FALSE(ShapeUtil::CompatibleIgnoringElementType(shape2, shape1)); +} + TEST(ShapeUtilTest, CompareShapesWithPaddedDimensionsMismatch) { Shape shape1 = ShapeUtil::MakeShape(F32, {20, 30}); shape1.mutable_layout()->add_padded_dimensions(10); -- GitLab From f6548bffe577202381fa5893ad7aa452ae4e4931 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Mon, 23 Jul 2018 12:46:01 -0700 Subject: [PATCH 275/519] Add main script training sampler on 2D energy landscapes. PiperOrigin-RevId: 205707483 --- .../eager/python/examples/l2hmc/l2hmc.py | 143 ++++++----- .../eager/python/examples/l2hmc/l2hmc_test.py | 97 ++------ .../eager/python/examples/l2hmc/main.py | 235 ++++++++++++++++++ 3 files changed, 346 insertions(+), 129 deletions(-) create mode 100644 tensorflow/contrib/eager/python/examples/l2hmc/main.py diff --git a/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc.py b/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc.py index 275aee5130..14b8324e48 100644 --- a/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc.py +++ b/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc.py @@ -32,20 +32,28 @@ from tensorflow.contrib.eager.python.examples.l2hmc import neural_nets class Dynamics(tf.keras.Model): - """Dynamics engine of naive L2HMC sampler. - - Args: - x_dim: dimensionality of observed data - loglikelihood_fn: log-likelihood function of conditional probability - n_steps: number of leapfrog steps within each transition - eps: initial value learnable scale of step size - """ - - def __init__(self, x_dim, loglikelihood_fn, n_steps=25, eps=.1): + """Dynamics engine of naive L2HMC sampler.""" + + def __init__(self, + x_dim, + minus_loglikelihood_fn, + n_steps=25, + eps=.1, + np_seed=1): + """Initialization. + + Args: + x_dim: dimensionality of observed data + minus_loglikelihood_fn: log-likelihood function of conditional probability + n_steps: number of leapfrog steps within each transition + eps: initial value learnable scale of step size + np_seed: Random seed for numpy; used to control sampled masks. + """ super(Dynamics, self).__init__() + npr.seed(np_seed) self.x_dim = x_dim - self.potential = loglikelihood_fn + self.potential = minus_loglikelihood_fn self.n_steps = n_steps self._construct_time() @@ -68,8 +76,8 @@ class Dynamics(tf.keras.Model): position, forward=False) # Decide direction uniformly - forward_mask = tf.cast( - tf.random_uniform(shape=[tf.shape(position)[0]]) > .5, tf.float32) + batch_size = tf.shape(position)[0] + forward_mask = tf.cast(tf.random_uniform((batch_size,)) > .5, tf.float32) backward_mask = 1. - forward_mask # Obtain proposed states @@ -108,7 +116,6 @@ class Dynamics(tf.keras.Model): position_post, momentum_post, logdet = lf_fn(position_post, momentum_post, i) sumlogdet += logdet - accept_prob = self._compute_accept_prob(position, momentum, position_post, momentum_post, sumlogdet) @@ -125,17 +132,17 @@ class Dynamics(tf.keras.Model): sumlogdet += logdet position, logdet = self._update_position_forward(position, momentum, t, - mask) + mask, mask_inv) sumlogdet += logdet position, logdet = self._update_position_forward(position, momentum, t, - mask_inv) + mask_inv, mask) sumlogdet += logdet momentum, logdet = self._update_momentum_forward(position, momentum, t) sumlogdet += logdet - return position, momentum, tf.reduce_sum(sumlogdet, axis=1) + return position, momentum, sumlogdet def _backward_lf(self, position, momentum, i): """One backward augmented leapfrog step. See Appendix A in paper.""" @@ -149,17 +156,17 @@ class Dynamics(tf.keras.Model): sumlogdet += logdet position, logdet = self._update_position_backward(position, momentum, t, - mask) + mask_inv, mask) sumlogdet += logdet position, logdet = self._update_position_backward(position, momentum, t, - mask_inv) + mask, mask_inv) sumlogdet += logdet momentum, logdet = self._update_momentum_backward(position, momentum, t) sumlogdet += logdet - return position, momentum, tf.reduce_sum(sumlogdet, axis=1) + return position, momentum, sumlogdet def _update_momentum_forward(self, position, momentum, t): """Update v in the forward leapfrog step.""" @@ -172,12 +179,11 @@ class Dynamics(tf.keras.Model): momentum * tf.exp(scale) - .5 * self.eps * (tf.exp(transformed) * grad - translation)) - return momentum, scale + return momentum, tf.reduce_sum(scale, axis=1) - def _update_position_forward(self, position, momentum, t, mask): + def _update_position_forward(self, position, momentum, t, mask, mask_inv): """Update x in the forward leapfrog step.""" - mask_inv = 1. - mask scale, translation, transformed = self.position_fn( [momentum, mask * position, t]) scale *= self.eps @@ -186,8 +192,7 @@ class Dynamics(tf.keras.Model): mask * position + mask_inv * (position * tf.exp(scale) + self.eps * (tf.exp(transformed) * momentum + translation))) - - return position, mask_inv * scale + return position, tf.reduce_sum(mask_inv * scale, axis=1) def _update_momentum_backward(self, position, momentum, t): """Update v in the backward leapfrog step. Inverting the forward update.""" @@ -200,21 +205,20 @@ class Dynamics(tf.keras.Model): tf.exp(scale) * (momentum + .5 * self.eps * (tf.exp(transformed) * grad - translation))) - return momentum, scale + return momentum, tf.reduce_sum(scale, axis=1) - def _update_position_backward(self, position, momentum, t, mask): + def _update_position_backward(self, position, momentum, t, mask, mask_inv): """Update x in the backward leapfrog step. Inverting the forward update.""" - mask_inv = 1. - mask scale, translation, transformed = self.position_fn( - [momentum, mask_inv * position, t]) + [momentum, mask * position, t]) scale *= -self.eps transformed *= self.eps position = ( - mask_inv * position + mask * tf.exp(scale) * - (position - self.eps * tf.exp(transformed) * momentum + translation)) + mask * position + mask_inv * tf.exp(scale) * + (position - self.eps * (tf.exp(transformed) * momentum + translation))) - return position, mask * scale + return position, tf.reduce_sum(mask_inv * scale, axis=1) def _compute_accept_prob(self, position, momentum, position_post, momentum_post, sumlogdet): @@ -222,8 +226,10 @@ class Dynamics(tf.keras.Model): old_hamil = self.hamiltonian(position, momentum) new_hamil = self.hamiltonian(position_post, momentum_post) + prob = tf.exp(tf.minimum(old_hamil - new_hamil + sumlogdet, 0.)) - return tf.exp(tf.minimum(old_hamil - new_hamil + sumlogdet, 0.)) + # Ensure numerical stability as well as correct gradients + return tf.where(tf.is_finite(prob), prob, tf.zeros_like(prob)) def _construct_time(self): """Convert leapfrog step index into sinusoidal time.""" @@ -248,6 +254,8 @@ class Dynamics(tf.keras.Model): self.masks = [] for _ in range(self.n_steps): + # Need to use npr here because tf would generated different random + # values across different `sess.run` idx = npr.permutation(np.arange(self.x_dim))[:self.x_dim // 2] mask = np.zeros((self.x_dim,)) mask[idx] = 1. @@ -273,19 +281,15 @@ class Dynamics(tf.keras.Model): def grad_potential(self, position, check_numerics=True): """Get gradient of potential function at current location.""" - if not tf.executing_eagerly(): - # TODO(lxuechen): Change this to tfe.gradients_function when it works - grad = tf.gradients(self.potential(position), position)[0] - else: + if tf.executing_eagerly(): grad = tfe.gradients_function(self.potential)(position)[0] - - if check_numerics: - return tf.check_numerics(grad, message="gradient of potential") + else: + grad = tf.gradients(self.potential(position), position)[0] return grad -# Examples of unnormalized log density/probabilities +# Examples of unnormalized log densities def get_scg_energy_fn(): """Get energy function for 2d strongly correlated Gaussian.""" @@ -295,32 +299,53 @@ def get_scg_energy_fn(): sigma_inv = tf.matrix_inverse(sigma) def energy(x): - """Unnormalized log density/energy of 2d strongly correlated Gaussian.""" + """Unnormalized minus log density of 2d strongly correlated Gaussian.""" xmmu = x - mu return .5 * tf.diag_part( tf.matmul(tf.matmul(xmmu, sigma_inv), tf.transpose(xmmu))) - return energy + return energy, mu, sigma -def get_multivariate_gaussian_energy_fn(x_dim=2): - """Get energy function for 2d strongly correlated Gaussian.""" - - mu = tf.random_normal(shape=[x_dim]) - # Lower triangularize and positive diagonal - l = tf.sigmoid( - tf.matrix_band_part(tf.random_normal(shape=[x_dim, x_dim]), -1, 0)) - # Exploit Cholesky decomposition - sigma = tf.matmul(l, tf.transpose(l)) - sigma *= 100. # Small covariance causes extreme numerical instability - sigma_inv = tf.matrix_inverse(sigma) +def get_rw_energy_fn(): + """Get energy function for rough well distribution.""" + # For small eta, the density underlying the rough-well energy is very close to + # a unit Gaussian; however, the gradient is greatly affected by the small + # cosine perturbations + eta = 1e-2 + mu = tf.constant([0., 0.]) + sigma = tf.constant([[1., 0.], [0., 1.]]) def energy(x): - """Unnormalized log density/energy of 2d strongly correlated Gaussian.""" + ip = tf.reduce_sum(x**2., axis=1) + return .5 * ip + eta * tf.reduce_sum(tf.cos(x / eta), axis=1) - xmmu = x - mu - return .5 * tf.diag_part( - tf.matmul(tf.matmul(xmmu, sigma_inv), tf.transpose(xmmu))) + return energy, mu, sigma + + +# Loss function +def compute_loss(dynamics, x, scale=.1, eps=1e-4): + """Compute loss defined in equation (8).""" + + z = tf.random_normal(tf.shape(x)) # Auxiliary variable + x_, _, x_accept_prob, x_out = dynamics.apply_transition(x) + z_, _, z_accept_prob, _ = dynamics.apply_transition(z) + + # Add eps for numerical stability; following released impl + x_loss = tf.reduce_sum((x - x_)**2, axis=1) * x_accept_prob + eps + z_loss = tf.reduce_sum((z - z_)**2, axis=1) * z_accept_prob + eps + + loss = tf.reduce_mean( + (1. / x_loss + 1. / z_loss) * scale - (x_loss + z_loss) / scale, axis=0) + + return loss, x_out, x_accept_prob + + +def loss_and_grads(dynamics, x, loss_fn=compute_loss): + """Obtain loss value and gradients.""" + with tf.GradientTape() as tape: + loss_val, out, accept_prob = loss_fn(dynamics, x) + grads = tape.gradient(loss_val, dynamics.trainable_variables) - return energy + return loss_val, grads, out, accept_prob diff --git a/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc_test.py b/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc_test.py index e33b4cae4c..9557479885 100644 --- a/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc_test.py +++ b/tensorflow/contrib/eager/python/examples/l2hmc/l2hmc_test.py @@ -37,63 +37,37 @@ def get_default_hparams(): n_warmup_iters=3) -# Relevant functions for benchmarking -def compute_loss(dynamics, x, scale=.1, eps=1e-4): - """Compute loss defined in equation (8).""" - - z = tf.random_normal(tf.shape(x)) - x_, _, x_accept_prob, x_out = dynamics.apply_transition(x) - z_, _, z_accept_prob, _ = dynamics.apply_transition(z) - - # Add eps for numerical stability; following released impl - x_loss = tf.reduce_sum((x - x_)**2, axis=1) * x_accept_prob + eps - z_loss = tf.reduce_sum((z - z_)**2, axis=1) * z_accept_prob + eps - - loss = tf.reduce_mean( - (1. / x_loss + 1. / z_loss) * scale - (x_loss + z_loss) / scale, axis=0) - - return loss, x_out - - -def loss_and_grads(dynamics, x, loss_fn=compute_loss): - """Obtain loss value and gradients.""" - - with tf.GradientTape() as tape: - loss_val, x_out = loss_fn(dynamics, x) - grads = tape.gradient(loss_val, dynamics.variables) - - return loss_val, grads, x_out - - -def warmup(dynamics, optimizer, n_iters=1, n_samples=200, loss_fn=compute_loss): +def warmup(dynamics, + optimizer, + n_iters=1, + n_samples=200, + loss_fn=l2hmc.compute_loss): """Warmup optimization to reduce overhead.""" samples = tf.random_normal( shape=[n_samples, dynamics.x_dim], dtype=tf.float32) for _ in range(n_iters): - _, grads, samples = loss_and_grads(dynamics, samples, loss_fn=loss_fn) + _, grads, samples, _ = l2hmc.loss_and_grads( + dynamics, samples, loss_fn=loss_fn) optimizer.apply_gradients(zip(grads, dynamics.variables)) def fit(dynamics, samples, optimizer, - loss_fn=compute_loss, + loss_fn=l2hmc.compute_loss, n_iters=5000, verbose=True, - logdir=None, - decay_lr=True): + logdir=None): """Fit L2HMC sampler with given log-likelihood function.""" if logdir: summary_writer = tf.contrib.summary.create_file_writer(logdir) for i in range(n_iters): - loss, grads, samples = loss_and_grads(dynamics, samples, loss_fn=loss_fn) - # TODO(lxuechen): Proper learning rate decay - if decay_lr: - grads = [grad * .96**(i // 1000) for grad in grads] + loss, grads, samples, _ = l2hmc.loss_and_grads( + dynamics, samples, loss_fn=loss_fn) optimizer.apply_gradients(zip(grads, dynamics.variables)) if verbose: print("Iteration %d: loss %.4f" % (i, loss)) @@ -112,9 +86,10 @@ class L2hmcTest(tf.test.TestCase): # Eager mode testing hparams = get_default_hparams() + energy_fn, _, _ = l2hmc.get_scg_energy_fn() dynamics = l2hmc.Dynamics( x_dim=hparams.x_dim, - loglikelihood_fn=l2hmc.get_scg_energy_fn(), + minus_loglikelihood_fn=energy_fn, n_steps=hparams.n_steps, eps=hparams.eps) samples = tf.random_normal(shape=[hparams.n_samples, hparams.x_dim]) @@ -127,9 +102,10 @@ class L2hmcTest(tf.test.TestCase): # Graph mode testing with tf.Graph().as_default(): + energy_fn, _, _ = l2hmc.get_scg_energy_fn() dynamics = l2hmc.Dynamics( x_dim=hparams.x_dim, - loglikelihood_fn=l2hmc.get_scg_energy_fn(), + minus_loglikelihood_fn=energy_fn, n_steps=hparams.n_steps, eps=hparams.eps) x = tf.placeholder(tf.float32, shape=[None, hparams.x_dim]) @@ -150,32 +126,20 @@ class L2hmcTest(tf.test.TestCase): class L2hmcBenchmark(tf.test.Benchmark): """Eager and graph benchmarks for l2hmc.""" - def _get_energy_fn(self): - """Get specific energy function according to FLAGS.""" - - if FLAGS.energy_fn == "scg": - energy_fn = l2hmc.get_scg_energy_fn() - elif FLAGS.energy_fn == "multivariate_gaussian": - energy_fn = l2hmc.get_multivariate_gaussian_energy_fn(x_dim=FLAGS.x_dim) - else: - raise ValueError("No such energy function %s" % FLAGS.energy_fn) - - return energy_fn - def benchmark_graph(self): """Benchmark Graph performance.""" hparams = get_default_hparams() tf.reset_default_graph() with tf.Graph().as_default(): - energy_fn = self._get_energy_fn() + energy_fn, _, _ = l2hmc.get_scg_energy_fn() dynamics = l2hmc.Dynamics( x_dim=hparams.x_dim, - loglikelihood_fn=energy_fn, + minus_loglikelihood_fn=energy_fn, n_steps=hparams.n_steps, eps=hparams.eps) x = tf.placeholder(tf.float32, shape=[None, hparams.x_dim]) - loss, x_out = compute_loss(dynamics, x) + loss, x_out, _ = l2hmc.compute_loss(dynamics, x) global_step = tf.Variable(0., name="global_step", trainable=False) learning_rate = tf.train.exponential_decay( @@ -183,7 +147,11 @@ class L2hmcBenchmark(tf.test.Benchmark): optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) train_op = optimizer.minimize(loss, global_step=global_step) - with tf.Session() as sess: + # Single thread; fairer comparison against eager + session_conf = tf.ConfigProto( + intra_op_parallelism_threads=1, inter_op_parallelism_threads=1) + + with tf.Session(config=session_conf) as sess: sess.run(tf.global_variables_initializer()) # Warmup to reduce initialization effect when timing @@ -218,14 +186,14 @@ class L2hmcBenchmark(tf.test.Benchmark): """Benchmark Eager performance.""" hparams = get_default_hparams() - energy_fn = self._get_energy_fn() + energy_fn, _, _ = l2hmc.get_scg_energy_fn() dynamics = l2hmc.Dynamics( x_dim=hparams.x_dim, - loglikelihood_fn=energy_fn, + minus_loglikelihood_fn=energy_fn, n_steps=hparams.n_steps, eps=hparams.eps) optimizer = tf.train.AdamOptimizer(learning_rate=hparams.learning_rate) - loss_fn = tfe.defun(compute_loss) if defun else compute_loss + loss_fn = tfe.defun(l2hmc.compute_loss) if defun else l2hmc.compute_loss # Warmup to reduce initialization effect when timing warmup(dynamics, optimizer, n_iters=hparams.n_warmup_iters, loss_fn=loss_fn) @@ -234,12 +202,7 @@ class L2hmcBenchmark(tf.test.Benchmark): samples = tf.random_normal( shape=[hparams.n_samples, hparams.x_dim], dtype=tf.float32) start_time = time.time() - fit(dynamics, - samples, - optimizer, - loss_fn=loss_fn, - n_iters=hparams.n_iters, - decay_lr=True) + fit(dynamics, samples, optimizer, loss_fn=loss_fn, n_iters=hparams.n_iters) wall_time = time.time() - start_time examples_per_sec = hparams.n_samples / wall_time @@ -251,14 +214,8 @@ class L2hmcBenchmark(tf.test.Benchmark): wall_time=wall_time) del dynamics - del loss_fn if __name__ == "__main__": - tf.flags.DEFINE_string("energy_fn", "scg", - ("The energy function/unnormalized log-probability. " - "Either be `scg` or `multivariate_gaussian`")) - tf.flags.DEFINE_integer("x_dim", 2, "Dimensionality of observation space.") - FLAGS = tf.flags.FLAGS tf.enable_eager_execution() tf.test.main() diff --git a/tensorflow/contrib/eager/python/examples/l2hmc/main.py b/tensorflow/contrib/eager/python/examples/l2hmc/main.py new file mode 100644 index 0000000000..45e1f98429 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/l2hmc/main.py @@ -0,0 +1,235 @@ +# 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. +# ============================================================================== +"""L2HMC on simple Gaussian mixture model with TensorFlow eager.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import sys + +from absl import flags +import numpy as np +import tensorflow as tf +from tensorflow.contrib.eager.python.examples.l2hmc import l2hmc +try: + import matplotlib.pyplot as plt # pylint: disable=g-import-not-at-top + HAS_MATPLOTLIB = True +except ImportError: + HAS_MATPLOTLIB = False +tfe = tf.contrib.eager + + +def main(_): + tf.enable_eager_execution() + global_step = tf.train.get_or_create_global_step() + global_step.assign(1) + + energy_fn, mean, covar = { + "scg": l2hmc.get_scg_energy_fn(), + "rw": l2hmc.get_rw_energy_fn() + }[FLAGS.energy_fn] + + x_dim = 2 + train_iters = 5000 + eval_iters = 2000 + eps = 0.1 + n_steps = 10 # Chain length + n_samples = 200 + record_loss_every = 100 + + dynamics = l2hmc.Dynamics( + x_dim=x_dim, minus_loglikelihood_fn=energy_fn, n_steps=n_steps, eps=eps) + learning_rate = tf.train.exponential_decay( + 1e-3, global_step, 1000, 0.96, staircase=True) + optimizer = tf.train.AdamOptimizer(learning_rate) + checkpointer = tf.train.Checkpoint( + optimizer=optimizer, dynamics=dynamics, global_step=global_step) + + if FLAGS.train_dir: + summary_writer = tf.contrib.summary.create_file_writer(FLAGS.train_dir) + if FLAGS.restore: + latest_path = tf.train.latest_checkpoint(FLAGS.train_dir) + checkpointer.restore(latest_path) + print("Restored latest checkpoint at path:\"{}\" ".format(latest_path)) + sys.stdout.flush() + + if not FLAGS.restore: + # Training + if FLAGS.use_defun: + # Use `tfe.deun` to boost performance when there are lots of small ops + loss_fn = tfe.defun(l2hmc.compute_loss) + else: + loss_fn = l2hmc.compute_loss + + samples = tf.random_normal(shape=[n_samples, x_dim]) + for i in range(1, train_iters + 1): + loss, samples, accept_prob = train_one_iter( + dynamics, + samples, + optimizer, + loss_fn=loss_fn, + global_step=global_step) + + if i % record_loss_every == 0: + print("Iteration {}, loss {:.4f}, x_accept_prob {:.4f}".format( + i, loss.numpy(), + accept_prob.numpy().mean())) + if FLAGS.train_dir: + with summary_writer.as_default(): + with tf.contrib.summary.always_record_summaries(): + tf.contrib.summary.scalar("Training loss", loss, step=global_step) + print("Training complete.") + sys.stdout.flush() + + if FLAGS.train_dir: + saved_path = checkpointer.save( + file_prefix=os.path.join(FLAGS.train_dir, "ckpt")) + print("Saved checkpoint at path: \"{}\" ".format(saved_path)) + sys.stdout.flush() + + # Evaluation + if FLAGS.use_defun: + # Use tfe.deun to boost performance when there are lots of small ops + apply_transition = tfe.defun(dynamics.apply_transition) + else: + apply_transition = dynamics.apply_transition + + samples = tf.random_normal(shape=[n_samples, x_dim]) + samples_history = [] + for i in range(eval_iters): + samples_history.append(samples.numpy()) + _, _, _, samples = apply_transition(samples) + samples_history = np.array(samples_history) + print("Sampling complete.") + sys.stdout.flush() + + # Mean and covariance of target distribution + mean = mean.numpy() + covar = covar.numpy() + ac_spectrum = compute_ac_spectrum(samples_history, mean, covar) + print("First 25 entries of the auto-correlation spectrum: {}".format( + ac_spectrum[:25])) + ess = compute_ess(ac_spectrum) + print("Effective sample size per Metropolis-Hastings step: {}".format(ess)) + sys.stdout.flush() + + if FLAGS.train_dir: + # Plot autocorrelation spectrum in tensorboard + plot_step = tfe.Variable(1, trainable=False, dtype=tf.int64) + + for ac in ac_spectrum: + with summary_writer.as_default(): + with tf.contrib.summary.always_record_summaries(): + tf.contrib.summary.scalar("Autocorrelation", ac, step=plot_step) + plot_step.assign(plot_step + n_steps) + + if HAS_MATPLOTLIB: + # Choose a single chain and plot the trajectory + single_chain = samples_history[:, 0, :] + xs = single_chain[:100, 0] + ys = single_chain[:100, 1] + plt.figure() + plt.plot(xs, ys, color="orange", marker="o", alpha=0.6) # Trained chain + plt.savefig(os.path.join(FLAGS.train_dir, "single_chain.png")) + + +def train_one_iter(dynamics, + x, + optimizer, + loss_fn=l2hmc.compute_loss, + global_step=None): + """Train the sampler for one iteration.""" + loss, grads, out, accept_prob = l2hmc.loss_and_grads( + dynamics, x, loss_fn=loss_fn) + optimizer.apply_gradients( + zip(grads, dynamics.trainable_variables), global_step=global_step) + + return loss, out, accept_prob + + +def compute_ac_spectrum(samples_history, target_mean, target_covar): + """Compute autocorrelation spectrum. + + Follows equation 15 from the L2HMC paper. + + Args: + samples_history: Numpy array of shape [T, B, D], where T is the total + number of time steps, B is the batch size, and D is the dimensionality + of sample space. + target_mean: 1D Numpy array of the mean of target(true) distribution. + target_covar: 2D Numpy array representing a symmetric matrix for variance. + Returns: + Autocorrelation spectrum, Numpy array of shape [T-1]. + """ + + # Using numpy here since eager is a bit slow due to the loop + time_steps = samples_history.shape[0] + trace = np.trace(target_covar) + + rhos = [] + for t in range(time_steps - 1): + rho_t = 0. + for tau in range(time_steps - t): + v_tau = samples_history[tau, :, :] - target_mean + v_tau_plus_t = samples_history[tau + t, :, :] - target_mean + # Take dot product over observation dims and take mean over batch dims + rho_t += np.mean(np.sum(v_tau * v_tau_plus_t, axis=1)) + + rho_t /= trace * (time_steps - t) + rhos.append(rho_t) + + return np.array(rhos) + + +def compute_ess(ac_spectrum): + """Compute the effective sample size based on autocorrelation spectrum. + + This follows equation 16 from the L2HMC paper. + + Args: + ac_spectrum: Autocorrelation spectrum + Returns: + The effective sample size + """ + # Cutoff from the first value less than 0.05 + cutoff = np.argmax(ac_spectrum[1:] < .05) + if cutoff == 0: + cutoff = len(ac_spectrum) + ess = 1. / (1. + 2. * np.sum(ac_spectrum[1:cutoff])) + return ess + + +if __name__ == "__main__": + flags.DEFINE_string( + "train_dir", + default=None, + help="[Optional] Directory to store the training information") + flags.DEFINE_boolean( + "restore", + default=False, + help="[Optional] Restore the latest checkpoint from `train_dir` if True") + flags.DEFINE_boolean( + "use_defun", + default=False, + help="[Optional] Use `tfe.defun` to boost performance") + flags.DEFINE_string( + "energy_fn", + default="scg", + help="[Optional] The energy function used for experimentation" + "Other options include `rw`") + FLAGS = flags.FLAGS + tf.app.run(main) -- GitLab From bd515dc93105a8f66437a2c2c70847eaa689b2ef Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Mon, 23 Jul 2018 12:57:56 -0700 Subject: [PATCH 276/519] Fix file paths. PiperOrigin-RevId: 205709394 --- .../notebooks/dev_summit_2018_demo.ipynb | 24 ++++++++----------- 1 file changed, 10 insertions(+), 14 deletions(-) diff --git a/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb b/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb index 86e38c3490..a3109fa5db 100644 --- a/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb +++ b/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 0, "metadata": { "colab": { "autoexec": { @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 0, "metadata": { "colab": { "autoexec": { @@ -275,7 +275,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 0, "metadata": { "colab": { "autoexec": { @@ -362,7 +362,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 0, "metadata": { "colab": { "autoexec": { @@ -467,7 +467,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 0, "metadata": { "colab": { "autoexec": { @@ -531,7 +531,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 0, "metadata": { "cellView": "code", "colab": { @@ -896,7 +896,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 0, "metadata": { "colab": { "autoexec": { @@ -1070,8 +1070,8 @@ " return dataset\n", "\n", "\n", - "train_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/extras/colorbot/data/train.csv\"\n", - "test_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/extras/colorbot/data/test.csv\"\n", + "train_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/archive/extras/colorbot/data/train.csv\"\n", + "test_url = \"https://raw.githubusercontent.com/random-forests/tensorflow-workshop/master/archive/extras/colorbot/data/test.csv\"\n", "data_dir = \"tmp/rnn/data\"" ] }, @@ -1304,7 +1304,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 0, "metadata": { "colab": { "autoexec": { @@ -1905,10 +1905,6 @@ "colab": { "collapsed_sections": [], "default_view": {}, - "last_runtime": { - "build_target": "", - "kind": "local" - }, "name": "Dev Summit 2018 - Autograph", "provenance": [ { -- GitLab From 0720cced3baa17a5930f5d965cfd9b693954bc04 Mon Sep 17 00:00:00 2001 From: Timon Van Overveldt Date: Mon, 23 Jul 2018 13:03:44 -0700 Subject: [PATCH 277/519] Add explicit "-lz" linkopt for Android selective registration targets. tensorflow/core/lib/io/zlib_inputstream.cc uses zlib, and hence, if a binary is compiled that uses any of the symbols in zlib_inputstream.cc, then the binary needs zlib's symbols to be available. One way to do that is to specify "-lz" as a linkopt, making the binary use the system's zlib library. PiperOrigin-RevId: 205710390 --- tensorflow/core/BUILD | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 17e6ccda14..b6a990ac7d 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -1655,6 +1655,7 @@ cc_library( copts = tf_copts(android_optimization_level_override = None) + [ "-DSUPPORT_SELECTIVE_REGISTRATION", ], + linkopts = if_android(["-lz"]), tags = [ "manual", "notap", @@ -1678,6 +1679,7 @@ cc_library( copts = tf_copts(android_optimization_level_override = None) + tf_opts_nortti_if_android() + [ "-DSUPPORT_SELECTIVE_REGISTRATION", ], + linkopts = if_android(["-lz"]), tags = [ "manual", "notap", -- GitLab From 31c3d55ec5b509142df00268611438ff52f3794a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 13:16:37 -0700 Subject: [PATCH 278/519] Internal change PiperOrigin-RevId: 205712557 --- .../core/common_runtime/direct_session.cc | 25 +++- .../common_runtime/direct_session_test.cc | 139 +++++++++++++++++- tensorflow/core/protobuf/config.proto | 5 + 3 files changed, 161 insertions(+), 8 deletions(-) diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc index 4c670820be..44291b0b20 100644 --- a/tensorflow/core/common_runtime/direct_session.cc +++ b/tensorflow/core/common_runtime/direct_session.cc @@ -235,7 +235,11 @@ void DirectSession::SchedClosure(thread::ThreadPool* pool, // safe given the reasoning above. c(); #else - pool->Schedule(std::move(c)); + if (pool != nullptr) { + pool->Schedule(std::move(c)); + } else { + c(); + } #endif // __ANDROID__ } @@ -522,8 +526,9 @@ Status DirectSession::RunInternal(int64 step_id, const RunOptions& run_options, } } - if (run_options.inter_op_thread_pool() < 0 || - run_options.inter_op_thread_pool() >= thread_pools_.size()) { + if (run_options.inter_op_thread_pool() < -1 || + run_options.inter_op_thread_pool() >= + static_cast(thread_pools_.size())) { run_state.executors_done.Notify(); delete barrier; return errors::InvalidArgument("Invalid inter_op_thread_pool: ", @@ -548,7 +553,19 @@ Status DirectSession::RunInternal(int64 step_id, const RunOptions& run_options, } thread::ThreadPool* pool = - thread_pools_[run_options.inter_op_thread_pool()].first; + run_options.inter_op_thread_pool() >= 0 + ? thread_pools_[run_options.inter_op_thread_pool()].first + : nullptr; + + if (pool == nullptr) { + // We allow using the caller thread only when having a single executor + // specified. + if (executors_and_keys->items.size() > 1) { + pool = thread_pools_[0].first; + } else { + VLOG(1) << "Executing Session::Run() synchronously!"; + } + } Executor::Args::Runner default_runner = [this, pool](Executor::Args::Closure c) { diff --git a/tensorflow/core/common_runtime/direct_session_test.cc b/tensorflow/core/common_runtime/direct_session_test.cc index 142d613129..4b51b20bb1 100644 --- a/tensorflow/core/common_runtime/direct_session_test.cc +++ b/tensorflow/core/common_runtime/direct_session_test.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include #include +#include #include #include @@ -896,6 +897,125 @@ TEST(DirectSessionTest, FetchMultipleTimes) { } } +TEST(DirectSessionTest, MultipleFeedTestSomeSyncRun) { + GraphDef def; + Graph g(OpRegistry::Global()); + RunOptions run_options; + run_options.set_inter_op_thread_pool(-1); + + Tensor first_value(DT_FLOAT, TensorShape({})); + first_value.scalar()() = 1.0; + Node* first_const = test::graph::Constant(&g, first_value); + Node* first_identity = test::graph::Identity(&g, first_const); + + Tensor second_value(DT_FLOAT, TensorShape({})); + second_value.scalar()() = 2.0; + Node* second_const = test::graph::Constant(&g, second_value); + Node* second_identity = test::graph::Identity(&g, second_const); + + test::graph::ToGraphDef(&g, &def); + + auto session = CreateSession(); + ASSERT_TRUE(session != nullptr); + TF_ASSERT_OK(session->Create(def)); + + std::vector outputs; + + // Fetch without feeding. + Status s = session->Run( + run_options, {}, + {first_identity->name() + ":0", second_identity->name() + ":0"}, {}, + &outputs, nullptr); + TF_ASSERT_OK(s); + ASSERT_EQ(2, outputs.size()); + ASSERT_EQ(1.0, outputs[0].flat()(0)); + ASSERT_EQ(2.0, outputs[1].flat()(0)); + + s = session->Run( + {}, {second_identity->name() + ":0", first_identity->name() + ":0"}, {}, + &outputs); + TF_ASSERT_OK(s); + ASSERT_EQ(2, outputs.size()); + ASSERT_EQ(2.0, outputs[0].flat()(0)); + ASSERT_EQ(1.0, outputs[1].flat()(0)); + + Tensor value_11(DT_FLOAT, TensorShape({})); + value_11.scalar()() = 11.0; + Tensor value_22(DT_FLOAT, TensorShape({})); + value_22.scalar()() = 22.0; + + // Feed [first_const, second_const] + s = session->Run( + {{first_const->name(), value_11}, {second_const->name(), value_22}}, + {first_identity->name() + ":0", second_identity->name() + ":0"}, {}, + &outputs); + TF_ASSERT_OK(s); + ASSERT_EQ(2, outputs.size()); + ASSERT_EQ(11.0, outputs[0].flat()(0)); + ASSERT_EQ(22.0, outputs[1].flat()(0)); + + // Feed [second_const, first_const] + s = session->Run( + {{second_const->name(), value_22}, {first_const->name(), value_11}}, + {first_identity->name() + ":0", second_identity->name() + ":0"}, {}, + &outputs); + TF_ASSERT_OK(s); + ASSERT_EQ(2, outputs.size()); + ASSERT_EQ(11.0, outputs[0].flat()(0)); + ASSERT_EQ(22.0, outputs[1].flat()(0)); + + // Feed [first_const, first_const] + s = session->Run( + run_options, + {{first_const->name(), value_11}, {first_const->name(), value_22}}, + {first_identity->name() + ":0", second_identity->name() + ":0"}, {}, + &outputs, nullptr); + EXPECT_TRUE(errors::IsInvalidArgument(s)); + EXPECT_TRUE(str_util::StrContains(s.error_message(), "fed more than once")); +} + +REGISTER_OP("ThreadID").Input("x: int64").Output("y: int64").Doc(R"doc( +ThreadID returns the thread ID that called compute. + +x: int64 +y: int64 +)doc"); + +// The ThreadID kernel returns the thread ID that executed Compute. +class ThreadIDOp : public OpKernel { + public: + explicit ThreadIDOp(OpKernelConstruction* ctx) : OpKernel(ctx) {} + void Compute(OpKernelContext* ctx) override { + Tensor* out_tensor = nullptr; + OP_REQUIRES_OK(ctx, + ctx->allocate_output("y", TensorShape({}), &out_tensor)); + std::hash hasher; + out_tensor->scalar()() = + static_cast(hasher(std::this_thread::get_id())); + } +}; +REGISTER_KERNEL_BUILDER(Name("ThreadID").Device(DEVICE_CPU), ThreadIDOp); + +TEST(DirectSessionTest, SessionSyncRun) { + Graph g(OpRegistry::Global()); + Tensor vx(DT_INT64, TensorShape({})); + vx.scalar()() = 17; + Node* x = test::graph::Constant(&g, vx); + Node* y = test::graph::Unary(&g, "ThreadID", x); + GraphDef def; + test::graph::ToGraphDef(&g, &def); + auto sess = CreateSession(); + TF_ASSERT_OK(sess->Create(def)); + std::vector outputs; + RunOptions run_opts; + run_opts.set_inter_op_thread_pool(-1); + auto s = sess->Run(run_opts, {}, {y->name() + ":0"}, {}, &outputs, nullptr); + + std::hash hasher; + EXPECT_EQ(static_cast(hasher(std::this_thread::get_id())), + static_cast(outputs[0].scalar()())); +} + REGISTER_OP("Darth").Input("x: float").Output("y: float").Doc(R"doc( Darth promises one return value. @@ -1400,6 +1520,7 @@ static void TestSessionInterOpThreadsImpl(bool use_function_lib, p = options.config.add_session_inter_op_thread_pool(); if (use_global_pools) p->set_global_name("small pool"); p->set_num_threads(1); + const int kSyncPool = -1; const int kLargePool = 0; const int kSmallPool = 1; @@ -1442,7 +1563,11 @@ static void TestSessionInterOpThreadsImpl(bool use_function_lib, EXPECT_FLOAT_EQ(1.2, flat(0)); num_done.fetch_add(1); }; - tp->Schedule(fn); + if (tp != nullptr) { + tp->Schedule(fn); + } else { + fn(); + } }; // For blocking states: @@ -1463,9 +1588,10 @@ static void TestSessionInterOpThreadsImpl(bool use_function_lib, tp1 = new thread::ThreadPool(Env::Default(), "tp1", 5); - // Launch 2 session run calls. Neither will finish until the blocking op is + // Launch a session run call. It will not finish until the blocking op is // unblocked, because it is using all threads in the small pool. add_session_run_call(tp1, y, kSmallPool); + blocking_op_state->AwaitState(1); // Wait for the blocking op to Compute. // These will block on . @@ -1484,10 +1610,15 @@ static void TestSessionInterOpThreadsImpl(bool use_function_lib, delete tp2; EXPECT_EQ(kUnblockedThreads, num_done.load()); + // Launch a session call using this thread. This will finish as it runs + // synchronously in this thread. + add_session_run_call(nullptr, x, kSyncPool); + // Unblock the blocked op and wait for the blocked functions to finish. blocking_op_state->MoveToState(1, 2); delete tp1; - EXPECT_EQ(kUnblockedThreads + kBlockedThreads + 1, num_done.load()); + + EXPECT_EQ(kUnblockedThreads + kBlockedThreads + 1 + 1, num_done.load()); delete blocking_op_state; blocking_op_state = nullptr; } @@ -1532,7 +1663,7 @@ TEST(DirectSessionTest, TestSessionInterOpThreadsInvalidOptions) { { std::unique_ptr session(NewSession(options)); TF_ASSERT_OK(session->Create(def)); - for (int pool_num = -1; pool_num <= 1; pool_num += 2) { + for (int pool_num = -2; pool_num <= 1; pool_num += 3) { RunOptions run_options; run_options.set_inter_op_thread_pool(pool_num); std::vector outputs; diff --git a/tensorflow/core/protobuf/config.proto b/tensorflow/core/protobuf/config.proto index 22a2691dcc..d701ce8e12 100644 --- a/tensorflow/core/protobuf/config.proto +++ b/tensorflow/core/protobuf/config.proto @@ -416,6 +416,11 @@ message RunOptions { int64 timeout_in_ms = 2; // The thread pool to use, if session_inter_op_thread_pool is configured. + // To use the caller thread set this to -1 - this uses the caller thread + // to execute Session::Run() and thus avoids a context switch. Using the + // caller thread to execute Session::Run() should be done ONLY for simple + // graphs, where the overhead of an additional context switch is + // comparable with the overhead of Session::Run(). int32 inter_op_thread_pool = 3; // Whether the partition graph(s) executed by the executor(s) should be -- GitLab From f1df683d04f828e6e677116bc8632c4c4f3bc4a0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 13:26:43 -0700 Subject: [PATCH 279/519] Update description of TPUEstimator. PiperOrigin-RevId: 205714060 --- 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 09eeb6a7f5..42406db88a 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -1724,6 +1724,9 @@ class InstallSignalHandlerHook(session_run_hook.SessionRunHook): class TPUEstimator(estimator_lib.Estimator): """Estimator with TPU support. + TPUEstimator also supports training on CPU and GPU. You don't need to define + a separate `tf.estimator.Estimator`. + TPUEstimator handles many of the details of running on TPU devices, such as replicating inputs and models for each core, and returning to host periodically to run hooks. -- GitLab From 5e7807be1c709f55f5643e7993bba04d2ba72ea6 Mon Sep 17 00:00:00 2001 From: Timon Van Overveldt Date: Mon, 23 Jul 2018 13:52:14 -0700 Subject: [PATCH 280/519] Split textual headers off of ":android_all_ops" into ":android_all_ops_textual_hdrs". Also add some missing includes. PiperOrigin-RevId: 205718290 --- tensorflow/core/kernels/BUILD | 22 +++++++++++++++++++++- 1 file changed, 21 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 23f84c46a9..2cb54bd973 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -5225,6 +5225,16 @@ filegroup( visibility = ["//visibility:public"], ) +ANDROID_TEXTUAL_HDRS = [ + "gather_nd_op_cpu_impl.h", + "gemm_functors.h", + "mirror_pad_op_cpu_impl.h", + "scatter_nd_op_cpu_impl.h", + "slice_op_cpu_impl.h", + "strided_slice_op_impl.h", + "tile_ops_cpu_impl.h", +] + # A file group which contains nearly all available operators which # may work on Android. This is intended to be used with selective # registration. @@ -5286,10 +5296,20 @@ filegroup( "batch_kernels.*", "regex_full_match_op.cc", "regex_replace_op.cc", - ], + # Ops that are inherently incompatible with Android (e.g. tied to x86 platform). + "mkl_*", + "xsmm_*", + "cwise_ops_sycl_common.h", + ] + ANDROID_TEXTUAL_HDRS, ), visibility = ["//visibility:public"], ) + +filegroup( + name = "android_all_ops_textual_hdrs", + srcs = ANDROID_TEXTUAL_HDRS, + visibility = ["//visibility:public"], +) # LINT.ThenChange(//tensorflow/contrib/makefile/tf_op_files.txt) cc_library( -- GitLab From 69f229a56652f076454ce9f3cb99bba285604ebe Mon Sep 17 00:00:00 2001 From: Goutham Bhat Date: Mon, 23 Jul 2018 14:30:36 -0700 Subject: [PATCH 281/519] Work around gfile.Glob's divergent behavior in different environments. PiperOrigin-RevId: 205725301 --- .../estimator/python/estimator/early_stopping.py | 9 +++++---- .../python/estimator/early_stopping_test.py | 13 +++++++++++++ 2 files changed, 18 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/estimator/python/estimator/early_stopping.py b/tensorflow/contrib/estimator/python/estimator/early_stopping.py index af4855e91e..3eab21d5ac 100644 --- a/tensorflow/contrib/estimator/python/estimator/early_stopping.py +++ b/tensorflow/contrib/estimator/python/estimator/early_stopping.py @@ -394,10 +394,11 @@ def _summaries(eval_dir): Yields: `tensorflow.Event` object read from the event files. """ - for event_file in gfile.Glob( - os.path.join(eval_dir, _EVENT_FILE_GLOB_PATTERN)): - for event in summary_iterator.summary_iterator(event_file): - yield event + if gfile.Exists(eval_dir): + for event_file in gfile.Glob( + os.path.join(eval_dir, _EVENT_FILE_GLOB_PATTERN)): + for event in summary_iterator.summary_iterator(event_file): + yield event def _get_or_create_stop_var(): diff --git a/tensorflow/contrib/estimator/python/estimator/early_stopping_test.py b/tensorflow/contrib/estimator/python/estimator/early_stopping_test.py index b5eee818fa..e4bfd4b446 100644 --- a/tensorflow/contrib/estimator/python/estimator/early_stopping_test.py +++ b/tensorflow/contrib/estimator/python/estimator/early_stopping_test.py @@ -92,6 +92,19 @@ class ReadEvalMetricsTest(test.TestCase): }, }, early_stopping.read_eval_metrics(eval_dir)) + def test_read_eval_metrics_when_no_events(self): + eval_dir = tempfile.mkdtemp() + self.assertTrue(os.path.exists(eval_dir)) + + # No error should be raised when eval directory exists with no event files. + self.assertEqual({}, early_stopping.read_eval_metrics(eval_dir)) + + os.rmdir(eval_dir) + self.assertFalse(os.path.exists(eval_dir)) + + # No error should be raised when eval directory does not exist. + self.assertEqual({}, early_stopping.read_eval_metrics(eval_dir)) + class EarlyStoppingHooksTest(test.TestCase, parameterized.TestCase): -- GitLab From cf94a46c34f8568608d78b77e9a1c4369ebcafa2 Mon Sep 17 00:00:00 2001 From: Karmel Allison Date: Mon, 23 Jul 2018 14:47:30 -0700 Subject: [PATCH 282/519] The SavedModel legacy_init_op and main_op are functionally equivalent. Here, we remove duplicated code paths by mapping legacy_init_op into main_op in the SavedModelBuilder, and we deprecate the legacy_init_op arg. Note that the loader will still look for both, so old SavedModels will still load without trouble. PiperOrigin-RevId: 205728344 --- tensorflow/cc/saved_model/loader.cc | 39 ++-------- tensorflow/python/saved_model/builder_impl.py | 76 ++++++++++--------- tensorflow/python/saved_model/loader_impl.py | 42 +++------- .../python/saved_model/saved_model_test.py | 28 +++++-- 4 files changed, 79 insertions(+), 106 deletions(-) diff --git a/tensorflow/cc/saved_model/loader.cc b/tensorflow/cc/saved_model/loader.cc index 07807ed2f3..d47b025743 100644 --- a/tensorflow/cc/saved_model/loader.cc +++ b/tensorflow/cc/saved_model/loader.cc @@ -86,10 +86,11 @@ bool HasMainOp(const MetaGraphDef& meta_graph_def) { Status RunMainOp(const RunOptions& run_options, const string& export_dir, const MetaGraphDef& meta_graph_def, const std::vector& asset_file_defs, - Session* session) { - LOG(INFO) << "Running MainOp on SavedModel bundle."; + Session* session, const string& main_op_key) { + LOG(INFO) << "Running MainOp with key " << main_op_key + << " on SavedModel bundle."; const auto& collection_def_map = meta_graph_def.collection_def(); - const auto main_op_it = collection_def_map.find(kSavedModelMainOpKey); + const auto main_op_it = collection_def_map.find(main_op_key); if (main_op_it != collection_def_map.end()) { if (main_op_it->second.node_list().value_size() != 1) { return errors::FailedPrecondition( @@ -141,30 +142,6 @@ Status RunRestore(const RunOptions& run_options, const string& export_dir, nullptr /* outputs */, &run_metadata); } -Status RunLegacyInitOp(const RunOptions& run_options, const string& export_dir, - const MetaGraphDef& meta_graph_def, - const std::vector& asset_file_defs, - Session* session) { - LOG(INFO) << "Running LegacyInitOp on SavedModel bundle."; - const auto& collection_def_map = meta_graph_def.collection_def(); - const auto init_op_it = collection_def_map.find(kSavedModelLegacyInitOpKey); - if (init_op_it != collection_def_map.end()) { - if (init_op_it->second.node_list().value_size() != 1) { - return errors::FailedPrecondition(strings::StrCat( - "Expected exactly one serving init op in : ", export_dir)); - } - std::vector> inputs; - AddAssetsTensorsToInputs(export_dir, asset_file_defs, &inputs); - RunMetadata run_metadata; - const StringPiece legacy_init_op_name = - init_op_it->second.node_list().value(0); - return session->Run(run_options, inputs, {}, - {legacy_init_op_name.ToString()}, nullptr /* outputs */, - &run_metadata); - } - return Status::OK(); -} - Status GetAssetFileDefs(const MetaGraphDef& meta_graph_def, std::vector* asset_file_defs) { const auto& collection_def_map = meta_graph_def.collection_def(); @@ -204,11 +181,11 @@ Status LoadSavedModelInternal(const SessionOptions& session_options, if (HasMainOp(bundle->meta_graph_def)) { TF_RETURN_IF_ERROR(RunMainOp(run_options, export_dir, bundle->meta_graph_def, asset_file_defs, - bundle->session.get())); + bundle->session.get(), kSavedModelMainOpKey)); } else { - TF_RETURN_IF_ERROR(RunLegacyInitOp(run_options, export_dir, - bundle->meta_graph_def, asset_file_defs, - bundle->session.get())); + TF_RETURN_IF_ERROR(RunMainOp( + run_options, export_dir, bundle->meta_graph_def, asset_file_defs, + bundle->session.get(), kSavedModelLegacyInitOpKey)); } return Status::OK(); } diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py index e58be804c2..8c985a7c2f 100644 --- a/tensorflow/python/saved_model/builder_impl.py +++ b/tensorflow/python/saved_model/builder_impl.py @@ -34,6 +34,7 @@ from tensorflow.python.platform import tf_logging from tensorflow.python.saved_model import constants from tensorflow.python.training import saver as tf_saver from tensorflow.python.util import compat +from tensorflow.python.util.deprecation import deprecated_args from tensorflow.python.util.tf_export import tf_export @@ -133,39 +134,32 @@ class SavedModelBuilder(object): tf_logging.info("Assets written to: %s", compat.as_text(assets_destination_dir)) - def _maybe_add_legacy_init_op(self, legacy_init_op=None): - """Add legacy init op to the SavedModel. + def _maybe_add_main_op(self, main_op): + """Adds main op to the SavedModel. Args: - legacy_init_op: Optional legacy init op to support backward compatibility. + main_op: Main op to run as part of graph initialization. If None, no + main op will be added to the graph. Raises: - TypeError if legacy init op is not of type `Operation`. - AssertionError if the graph already contains one or more legacy init ops. + TypeError: if main op is provided but is not of type `Operation`. + ValueError: if the Graph already contains an init op. """ - if legacy_init_op is not None: - if not isinstance(legacy_init_op, ops.Operation): - raise TypeError("legacy_init_op needs to be an Operation: %r" % - legacy_init_op) - if ops.get_collection(constants.LEGACY_INIT_OP_KEY): - raise AssertionError( - "graph already contains one or more legacy init ops under the " - "collection {}.".format(constants.LEGACY_INIT_OP_KEY)) - ops.add_to_collection(constants.LEGACY_INIT_OP_KEY, legacy_init_op) - - def _add_main_op(self, main_op): - """Add main op to the SavedModel. + if main_op is None: + return - Args: - main_op: Main op to run as part of graph initialization. + if not isinstance(main_op, ops.Operation): + raise TypeError("main_op needs to be an Operation: %r" % main_op) - Raises: - TypeError if main op is not of type `Operation`. - """ - if main_op is not None: - if not isinstance(main_op, ops.Operation): - raise TypeError("main_op needs to be an Operation: %r" % main_op) - ops.add_to_collection(constants.MAIN_OP_KEY, main_op) + # Validate that no other init ops have been added to this graph already. + # We check main_op and legacy_init_op for thoroughness and explicitness. + for init_op_key in (constants.MAIN_OP_KEY, constants.LEGACY_INIT_OP_KEY): + if ops.get_collection(init_op_key): + raise ValueError( + "Graph already contains one or more main ops under the " + "collection {}.".format(init_op_key)) + + ops.add_to_collection(constants.MAIN_OP_KEY, main_op) def _add_train_op(self, train_op): """Add train op to the SavedModel. @@ -257,16 +251,12 @@ class SavedModelBuilder(object): self._validate_tensor_info(outputs[outputs_key]) def _add_collections( - self, assets_collection, legacy_init_op, main_op, train_op): + self, assets_collection, main_op, train_op): """Add asset and op collections to be saved.""" # Save asset files and write them to disk, if any. self._save_and_write_assets(assets_collection) - if main_op is None: - # Add legacy init op to the SavedModel. - self._maybe_add_legacy_init_op(legacy_init_op) - else: - self._add_main_op(main_op) + self._maybe_add_main_op(main_op) self._add_train_op(train_op) @@ -282,6 +272,9 @@ class SavedModelBuilder(object): allow_empty=True) return saver + @deprecated_args(None, + "Pass your op to the equivalent parameter main_op instead.", + "legacy_init_op") def add_meta_graph(self, tags, signature_def_map=None, @@ -306,7 +299,7 @@ class SavedModelBuilder(object): that this collection should be a subset of the assets saved as part of the first meta graph in the SavedModel. legacy_init_op: Legacy support for op or group of ops to execute after the - restore op upon a load. + restore op upon a load. Deprecated; please use main_op instead. clear_devices: Set to true if the device info on the default graph should be cleared. main_op: Op or group of ops to execute when the graph is loaded. Note @@ -333,8 +326,12 @@ class SavedModelBuilder(object): # properly populated. self._validate_signature_def_map(signature_def_map) + # legacy_init_op is deprecated, and going away in TF 2.0. + # Re-mapping to main_op, as treatment is identical regardless. + main_op = main_op or legacy_init_op + # Add assets and ops - self._add_collections(assets_collection, legacy_init_op, main_op, None) + self._add_collections(assets_collection, main_op, None) saver = self._maybe_create_saver(saver) @@ -351,6 +348,9 @@ class SavedModelBuilder(object): # Tag the meta graph def and add it to the SavedModel. self._tag_and_add_meta_graph(meta_graph_def, tags, signature_def_map) + @deprecated_args(None, + "Pass your op to the equivalent parameter main_op instead.", + "legacy_init_op") def add_meta_graph_and_variables(self, sess, tags, @@ -378,7 +378,7 @@ class SavedModelBuilder(object): def. assets_collection: Assets collection to be saved with SavedModel. legacy_init_op: Legacy support for op or group of ops to execute after the - restore op upon a load. + restore op upon a load. Deprecated; please use main_op instead. clear_devices: Set to true if the device info on the default graph should be cleared. main_op: Op or group of ops to execute when the graph is loaded. Note @@ -402,8 +402,12 @@ class SavedModelBuilder(object): # properly populated. self._validate_signature_def_map(signature_def_map) + # legacy_init_op is deprecated, and going away in TF 2.0. + # Re-mapping to main_op, as treatment is identical regardless. + main_op = main_op or legacy_init_op + # Add assets and ops - self._add_collections(assets_collection, legacy_init_op, main_op, None) + self._add_collections(assets_collection, main_op, None) # Create the variables sub-directory, if it does not exist. variables_dir = os.path.join( diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index e5f649fdab..fb70c91c29 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -116,11 +116,14 @@ def _get_asset_tensors(export_dir, meta_graph_def_to_load, import_scope=None): return asset_tensor_dict -def _get_main_op_tensor(meta_graph_def_to_load): +def _get_main_op_tensor( + meta_graph_def_to_load, init_op_key=constants.MAIN_OP_KEY): """Gets the main op tensor, if one exists. Args: meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded. + init_op_key: name of collection to check; should be one of MAIN_OP_KEY + or the deprecated LEGACY_INIT_OP_KEY Returns: The main op tensor, if it exists and `None` otherwise. @@ -131,38 +134,15 @@ def _get_main_op_tensor(meta_graph_def_to_load): """ collection_def = meta_graph_def_to_load.collection_def main_op_tensor = None - if constants.MAIN_OP_KEY in collection_def: - main_ops = collection_def[constants.MAIN_OP_KEY].node_list.value + if init_op_key in collection_def: + main_ops = collection_def[init_op_key].node_list.value if len(main_ops) != 1: - raise RuntimeError("Expected exactly one SavedModel main op.") - main_op_tensor = ops.get_collection(constants.MAIN_OP_KEY)[0] + raise RuntimeError("Expected exactly one SavedModel main op. " + "Found: {}".format(main_ops)) + main_op_tensor = ops.get_collection(init_op_key)[0] return main_op_tensor -def _get_legacy_init_op_tensor(meta_graph_def_to_load): - """Gets the legacy init op tensor, if one exists. - - Args: - meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded. - - Returns: - The legacy init op tensor, if it exists and `None` otherwise. - - Raises: - RuntimeError: If the collection def corresponding to the legacy init op key - has other than exactly one tensor. - """ - collection_def = meta_graph_def_to_load.collection_def - legacy_init_op_tensor = None - if constants.LEGACY_INIT_OP_KEY in collection_def: - legacy_init_ops = collection_def[ - constants.LEGACY_INIT_OP_KEY].node_list.value - if len(legacy_init_ops) != 1: - raise RuntimeError("Expected exactly one legacy serving init op.") - legacy_init_op_tensor = ops.get_collection(constants.LEGACY_INIT_OP_KEY)[0] - return legacy_init_op_tensor - - @tf_export("saved_model.loader.maybe_saved_model_directory") def maybe_saved_model_directory(export_dir): """Checks whether the provided export directory could contain a SavedModel. @@ -340,8 +320,8 @@ class SavedModelLoader(object): self._export_dir, meta_graph_def, import_scope=import_scope) main_op_tensor = ( - _get_main_op_tensor(meta_graph_def) or - (_get_legacy_init_op_tensor(meta_graph_def))) + _get_main_op_tensor(meta_graph_def, constants.MAIN_OP_KEY) or + _get_main_op_tensor(meta_graph_def, constants.LEGACY_INIT_OP_KEY)) if main_op_tensor is not None: sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py index fb4732aca2..00b669fc97 100644 --- a/tensorflow/python/saved_model/saved_model_test.py +++ b/tensorflow/python/saved_model/saved_model_test.py @@ -846,9 +846,19 @@ class SavedModelTest(test.TestCase): def testLegacyInitOpWithNonEmptyCollection(self): export_dir = self._get_export_dir( "test_legacy_init_op_with_non_empty_collection") + self._testInitOpsWithNonEmptyCollection( + export_dir, constants.LEGACY_INIT_OP_KEY) + + def testMainOpWithNonEmptyCollection(self): + export_dir = self._get_export_dir( + "test_main_op_with_non_empty_collection") + self._testInitOpsWithNonEmptyCollection(export_dir, constants.MAIN_OP_KEY) + + def _testInitOpsWithNonEmptyCollection(self, export_dir, key): builder = saved_model_builder.SavedModelBuilder(export_dir) - with self.test_session(graph=ops.Graph()) as sess: + g = ops.Graph() + with self.test_session(graph=g) as sess: # Initialize variable `v1` to 1. v1 = variables.Variable(1, name="v1") ops.add_to_collection("v", v1) @@ -857,19 +867,21 @@ class SavedModelTest(test.TestCase): v2 = variables.Variable(42, name="v2", trainable=False, collections=[]) ops.add_to_collection("v", v2) - # Set up an assignment op to be run as part of the legacy_init_op. + # Set up an assignment op to be run as part of the init op. assign_v2 = state_ops.assign(v2, v1) - legacy_init_op = control_flow_ops.group(assign_v2, name="legacy_init_op") + init_op = control_flow_ops.group(assign_v2, name="init_op") sess.run(variables.global_variables_initializer()) - ops.add_to_collection(constants.LEGACY_INIT_OP_KEY, - control_flow_ops.no_op()) - # AssertionError should be raised since the LEGACY_INIT_OP_KEY collection + ops.add_to_collection(key, control_flow_ops.no_op()) + # ValueError should be raised since the LEGACY_INIT_OP_KEY collection # is not empty and we don't support multiple init ops. - with self.assertRaises(AssertionError): + with self.assertRaisesRegexp(ValueError, "Graph already contains"): builder.add_meta_graph_and_variables( - sess, ["foo"], legacy_init_op=legacy_init_op) + sess, ["foo"], legacy_init_op=init_op) + # We shouldn't be able to add as MAIN_OP, either. + with self.assertRaisesRegexp(ValueError, "Graph already contains"): + builder.add_meta_graph_and_variables(sess, ["foo"], main_op=init_op) def testTrainOp(self): export_dir = self._get_export_dir("test_train_op") -- GitLab From 218bd6facc481da1ed199a8c680427051cb1c6cb Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Mon, 23 Jul 2018 14:49:59 -0700 Subject: [PATCH 283/519] Add initial experimental C API for TFLite PiperOrigin-RevId: 205728711 --- tensorflow/contrib/lite/build_def.bzl | 15 ++ tensorflow/contrib/lite/experimental/c/BUILD | 63 ++++++++ .../contrib/lite/experimental/c/c_api.cc | 118 ++++++++++++++ .../contrib/lite/experimental/c/c_api.h | 149 ++++++++++++++++++ .../contrib/lite/experimental/c/c_api_test.cc | 84 ++++++++++ .../lite/experimental/c/exported_symbols.lds | 1 + .../lite/experimental/c/version_script.lds | 9 ++ tensorflow/contrib/lite/testdata/add.bin | Bin 0 -> 476 bytes 8 files changed, 439 insertions(+) create mode 100644 tensorflow/contrib/lite/experimental/c/BUILD create mode 100644 tensorflow/contrib/lite/experimental/c/c_api.cc create mode 100644 tensorflow/contrib/lite/experimental/c/c_api.h create mode 100644 tensorflow/contrib/lite/experimental/c/c_api_test.cc create mode 100644 tensorflow/contrib/lite/experimental/c/exported_symbols.lds create mode 100644 tensorflow/contrib/lite/experimental/c/version_script.lds create mode 100644 tensorflow/contrib/lite/testdata/add.bin diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index bed862454e..79f7455ad8 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -2,6 +2,7 @@ load( "//tensorflow:tensorflow.bzl", "tf_cc_test", + "tf_cc_shared_object", ) def tflite_copts(): @@ -118,6 +119,20 @@ def tflite_jni_binary(name, deps= deps + [linkscript], linkopts=linkopts) +def tflite_cc_shared_object(name, + copts=tflite_copts(), + linkopts=[], + linkstatic=1, + deps=[]): + """Builds a shared object for TFLite.""" + tf_cc_shared_object( + name=name, + copts=copts, + linkstatic=linkstatic, + linkopts=linkopts + tflite_jni_linkopts(), + framework_so=[], + deps=deps) + def tf_to_tflite(name, src, options, out): """Convert a frozen tensorflow graphdef to TF Lite's flatbuffer. diff --git a/tensorflow/contrib/lite/experimental/c/BUILD b/tensorflow/contrib/lite/experimental/c/BUILD new file mode 100644 index 0000000000..b09bb9ea10 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/BUILD @@ -0,0 +1,63 @@ +package(default_visibility = ["//visibility:private"]) + +licenses(["notice"]) # Apache 2.0 + +load( + "//tensorflow/contrib/lite:build_def.bzl", + "tflite_cc_shared_object", + "tflite_jni_binary", +) + +tflite_cc_shared_object( + name = "libtensorflowlite_c.so", + linkopts = select({ + "//tensorflow:darwin": [ + "-Wl,-exported_symbols_list", # This line must be directly followed by the exported_symbols.lds file + "$(location //tensorflow/contrib/lite/experimental/c:exported_symbols.lds)", + "-Wl,-install_name,@rpath/libtensorflowlite_c.so", + ], + "//tensorflow:windows": [], + "//conditions:default": [ + "-z defs", + "-Wl,--version-script", # This line must be directly followed by the version_script.lds file + "$(location //tensorflow/contrib/lite/experimental/c:version_script.lds)", + ], + }), + deps = [ + ":c_api", + ":exported_symbols.lds", + ":version_script.lds", + ], +) + +tflite_jni_binary( + name = "libtensorflowlite_c_jni.so", + linkscript = ":version_script.lds", + deps = [":c_api"], +) + +cc_library( + name = "c_api", + srcs = ["c_api.cc"], + hdrs = ["c_api.h"], + deps = [ + "//tensorflow/contrib/lite:context", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:schema_fbs_version", + "//tensorflow/contrib/lite/kernels:builtin_ops", + ], +) + +cc_test( + name = "c_api_test", + size = "small", + srcs = ["c_api_test.cc"], + data = ["//tensorflow/contrib/lite:testdata/add.bin"], + deps = [ + ":c_api", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:kernel_api", + "//tensorflow/contrib/lite/testing:util", + "@com_google_googletest//:gtest", + ], +) diff --git a/tensorflow/contrib/lite/experimental/c/c_api.cc b/tensorflow/contrib/lite/experimental/c/c_api.cc new file mode 100644 index 0000000000..add4c6813d --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/c_api.cc @@ -0,0 +1,118 @@ +/* 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/lite/experimental/c/c_api.h" + +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/interpreter.h" +#include "tensorflow/contrib/lite/kernels/register.h" +#include "tensorflow/contrib/lite/model.h" + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +struct _TFL_Interpreter { + std::unique_ptr impl; +}; + +TFL_Interpreter* TFL_NewInterpreter(const void* model_data, + int32_t model_size) { + auto model = tflite::FlatBufferModel::BuildFromBuffer( + static_cast(model_data), static_cast(model_size)); + if (!model) { + return nullptr; + } + + tflite::ops::builtin::BuiltinOpResolver resolver; + tflite::InterpreterBuilder builder(*model, resolver); + std::unique_ptr interpreter_impl; + if (builder(&interpreter_impl) != kTfLiteOk) { + return nullptr; + } + + return new TFL_Interpreter{std::move(interpreter_impl)}; +} + +void TFL_DeleteInterpreter(TFL_Interpreter* interpreter) { delete interpreter; } + +int32_t TFL_InterpreterGetInputTensorCount(const TFL_Interpreter* interpreter) { + return static_cast(interpreter->impl->inputs().size()); +} + +TFL_Tensor* TFL_InterpreterGetInputTensor(const TFL_Interpreter* interpreter, + int32_t input_index) { + return interpreter->impl->tensor(interpreter->impl->inputs()[input_index]); +} + +TFL_Status TFL_InterpreterResizeInputTensor(TFL_Interpreter* interpreter, + int32_t input_index, + const int* input_dims, + int32_t input_dims_size) { + std::vector dims{input_dims, input_dims + input_dims_size}; + return interpreter->impl->ResizeInputTensor( + interpreter->impl->inputs()[input_index], dims); +} + +TFL_Status TFL_InterpreterAllocateTensors(TFL_Interpreter* interpreter) { + return interpreter->impl->AllocateTensors(); +} + +TFL_Status TFL_InterpreterInvoke(TFL_Interpreter* interpreter) { + return interpreter->impl->Invoke(); +} + +int32_t TFL_InterpreterGetOutputTensorCount( + const TFL_Interpreter* interpreter) { + return static_cast(interpreter->impl->outputs().size()); +} + +const TFL_Tensor* TFL_InterpreterGetOutputTensor( + const TFL_Interpreter* interpreter, int32_t output_index) { + return interpreter->impl->tensor(interpreter->impl->outputs()[output_index]); +} + +TFL_Type TFL_TensorType(const TFL_Tensor* tensor) { return tensor->type; } + +int32_t TFL_TensorNumDims(const TFL_Tensor* tensor) { + return tensor->dims->size; +} + +int32_t TFL_TensorDim(const TFL_Tensor* tensor, int32_t dim_index) { + return tensor->dims->data[dim_index]; +} + +size_t TFL_TensorByteSize(const TFL_Tensor* tensor) { return tensor->bytes; } + +TFL_Status TFL_TensorCopyFromBuffer(TFL_Tensor* tensor, const void* input_data, + int32_t input_data_size) { + if (tensor->bytes != static_cast(input_data_size)) { + return kTfLiteError; + } + memcpy(tensor->data.raw, input_data, input_data_size); + return kTfLiteOk; +} + +TFL_Status TFL_TensorCopyToBuffer(const TFL_Tensor* tensor, void* output_data, + int32_t output_data_size) { + if (tensor->bytes != static_cast(output_data_size)) { + return kTfLiteError; + } + memcpy(output_data, tensor->data.raw, output_data_size); + return kTfLiteOk; +} + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus diff --git a/tensorflow/contrib/lite/experimental/c/c_api.h b/tensorflow/contrib/lite/experimental/c/c_api.h new file mode 100644 index 0000000000..070f1add13 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/c_api.h @@ -0,0 +1,149 @@ +/* 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_LITE_EXPERIMENTAL_C_C_API_H_ +#define TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_C_C_API_H_ + +#include + +// Eventually the various C APIs defined in context.h will be migrated into +// the appropriate /c/c_api*.h header. For now, we pull in existing definitions +// for convenience. +#include "tensorflow/contrib/lite/context.h" + +// -------------------------------------------------------------------------- +// Experimental C API for TensorFlowLite. +// +// The API leans towards simplicity and uniformity instead of convenience, as +// most usage will be by language-specific wrappers. +// +// Conventions: +// * We use the prefix TFL_ for everything in the API. + +#ifdef SWIG +#define TFL_CAPI_EXPORT +#else +#if defined(_WIN32) +#ifdef TF_COMPILE_LIBRARY +#define TFL_CAPI_EXPORT __declspec(dllexport) +#else +#define TFL_CAPI_EXPORT __declspec(dllimport) +#endif // TF_COMPILE_LIBRARY +#else +#define TFL_CAPI_EXPORT __attribute__((visibility("default"))) +#endif // _WIN32 +#endif // SWIG + +#ifdef __cplusplus +extern "C" { +#endif // __cplusplus + +typedef TfLiteTensor TFL_Tensor; +typedef TfLiteStatus TFL_Status; +typedef TfLiteType TFL_Type; + +// -------------------------------------------------------------------------- +// TFL_Interpreter provides inference from a provided model. +typedef struct _TFL_Interpreter TFL_Interpreter; + +// Returns an interpreter for the provided model, or null on failure. +// +// NOTE: The client *must* explicitly allocate tensors before attempting to +// access input tensor data or invoke the interpreter. +TFL_CAPI_EXPORT extern TFL_Interpreter* TFL_NewInterpreter( + const void* model_data, int32_t model_size); + +// Destroys the interpreter. +TFL_CAPI_EXPORT extern void TFL_DeleteInterpreter(TFL_Interpreter* interpreter); + +// Returns the number of input tensors associated with the model. +TFL_CAPI_EXPORT extern int TFL_InterpreterGetInputTensorCount( + const TFL_Interpreter* interpreter); + +// Returns the tensor associated with the input index. +// REQUIRES: 0 <= input_index < TFL_InterpreterGetInputTensorCount(tensor) +TFL_CAPI_EXPORT extern TFL_Tensor* TFL_InterpreterGetInputTensor( + const TFL_Interpreter* interpreter, int32_t input_index); + +// Attempts to resize the specified input tensor. +// NOTE: After a resize, the client *must* explicitly allocate tensors before +// attempting to access the resized tensor data or invoke the interpreter. +// REQUIRES: 0 <= input_index < TFL_InterpreterGetInputTensorCount(tensor) +TFL_CAPI_EXPORT extern TFL_Status TFL_InterpreterResizeInputTensor( + TFL_Interpreter* interpreter, int32_t input_index, const int* input_dims, + int32_t input_dims_size); + +// Updates allocations for all tensors, resizing dependent tensors using the +// specified input tensor dimensionality. +// +// This is a relatively expensive operation, and need only be called after +// creating the graph and/or resizing any inputs. +TFL_CAPI_EXPORT extern TFL_Status TFL_InterpreterAllocateTensors( + TFL_Interpreter* interpreter); + +// Runs inference for the loaded graph. +// +// NOTE: It is possible that the interpreter is not in a ready state to +// evaluate (e.g., if a ResizeInputTensor() has been performed without a call to +// AllocateTensors()). +TFL_CAPI_EXPORT extern TFL_Status TFL_InterpreterInvoke( + TFL_Interpreter* interpreter); + +// Returns the number of output tensors associated with the model. +TFL_CAPI_EXPORT extern int32_t TFL_InterpreterGetOutputTensorCount( + const TFL_Interpreter* interpreter); + +// Returns the tensor associated with the output index. +// REQUIRES: 0 <= input_index < TFL_InterpreterGetOutputTensorCount(tensor) +TFL_CAPI_EXPORT extern const TFL_Tensor* TFL_InterpreterGetOutputTensor( + const TFL_Interpreter* interpreter, int32_t output_index); + +// -------------------------------------------------------------------------- +// TFL_Tensor wraps data associated with a graph tensor. +// +// Note that, while the TFL_Tensor struct is not currently opaque, and its +// fields can be accessed directly, these methods are still convenient for +// language bindings. In the future the tensor struct will likely be made opaque +// in the public API. + +// Returns the type of a tensor element. +TFL_CAPI_EXPORT extern TFL_Type TFL_TensorType(const TFL_Tensor* tensor); + +// Returns the number of dimensions that the tensor has. +TFL_CAPI_EXPORT extern int32_t TFL_TensorNumDims(const TFL_Tensor* tensor); + +// Returns the length of the tensor in the "dim_index" dimension. +// REQUIRES: 0 <= dim_index < TFLiteTensorNumDims(tensor) +TFL_CAPI_EXPORT extern int32_t TFL_TensorDim(const TFL_Tensor* tensor, + int32_t dim_index); + +// Returns the size of the underlying data in bytes. +TFL_CAPI_EXPORT extern size_t TFL_TensorByteSize(const TFL_Tensor* tensor); + +// Copies from the provided input buffer into the tensor's buffer. +// REQUIRES: input_data_size == TFL_TensorByteSize(tensor) +TFL_CAPI_EXPORT extern TFL_Status TFL_TensorCopyFromBuffer( + TFL_Tensor* tensor, const void* input_data, int32_t input_data_size); + +// Copies to the provided output buffer from the tensor's buffer. +// REQUIRES: output_data_size == TFL_TensorByteSize(tensor) +TFL_CAPI_EXPORT extern TFL_Status TFL_TensorCopyToBuffer( + const TFL_Tensor* output_tensor, void* output_data, + int32_t output_data_size); + +#ifdef __cplusplus +} // extern "C" +#endif // __cplusplus + +#endif // TENSORFLOW_CONTRIB_LITE_EXPERIMENTAL_C_C_API_H_ diff --git a/tensorflow/contrib/lite/experimental/c/c_api_test.cc b/tensorflow/contrib/lite/experimental/c/c_api_test.cc new file mode 100644 index 0000000000..bc925e00a6 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/c_api_test.cc @@ -0,0 +1,84 @@ +/* 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 + +#include "tensorflow/contrib/lite/experimental/c/c_api.h" + +#include +#include "tensorflow/contrib/lite/allocation.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/testing/util.h" + +namespace { + +TEST(CApiSimple, Smoke) { + tflite::FileCopyAllocation model_file( + "tensorflow/contrib/lite/testdata/add.bin", + tflite::DefaultErrorReporter()); + + TFL_Interpreter* interpreter = + TFL_NewInterpreter(model_file.base(), model_file.bytes()); + ASSERT_NE(interpreter, nullptr); + ASSERT_EQ(TFL_InterpreterAllocateTensors(interpreter), kTfLiteOk); + + ASSERT_EQ(TFL_InterpreterGetInputTensorCount(interpreter), 1); + ASSERT_EQ(TFL_InterpreterGetOutputTensorCount(interpreter), 1); + + std::array input_dims = {2}; + ASSERT_EQ(TFL_InterpreterResizeInputTensor(interpreter, 0, input_dims.data(), + input_dims.size()), + kTfLiteOk); + ASSERT_EQ(TFL_InterpreterAllocateTensors(interpreter), kTfLiteOk); + + TFL_Tensor* input_tensor = TFL_InterpreterGetInputTensor(interpreter, 0); + ASSERT_NE(input_tensor, nullptr); + EXPECT_EQ(TFL_TensorType(input_tensor), kTfLiteFloat32); + EXPECT_EQ(TFL_TensorNumDims(input_tensor), 1); + EXPECT_EQ(TFL_TensorDim(input_tensor, 0), 2); + EXPECT_EQ(TFL_TensorByteSize(input_tensor), sizeof(float) * 2); + + std::array input = {1.f, 3.f}; + ASSERT_EQ(TFL_TensorCopyFromBuffer(input_tensor, input.data(), + input.size() * sizeof(float)), + kTfLiteOk); + + ASSERT_EQ(TFL_InterpreterInvoke(interpreter), kTfLiteOk); + + const TFL_Tensor* output_tensor = + TFL_InterpreterGetOutputTensor(interpreter, 0); + ASSERT_NE(output_tensor, nullptr); + EXPECT_EQ(TFL_TensorType(output_tensor), kTfLiteFloat32); + EXPECT_EQ(TFL_TensorNumDims(output_tensor), 1); + EXPECT_EQ(TFL_TensorDim(output_tensor, 0), 2); + EXPECT_EQ(TFL_TensorByteSize(output_tensor), sizeof(float) * 2); + + std::array output; + ASSERT_EQ(TFL_TensorCopyToBuffer(output_tensor, output.data(), + output.size() * sizeof(float)), + kTfLiteOk); + EXPECT_EQ(output[0], 3.f); + EXPECT_EQ(output[1], 9.f); + + TFL_DeleteInterpreter(interpreter); +} + +} // namespace + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} diff --git a/tensorflow/contrib/lite/experimental/c/exported_symbols.lds b/tensorflow/contrib/lite/experimental/c/exported_symbols.lds new file mode 100644 index 0000000000..a3ddc6bc8d --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/exported_symbols.lds @@ -0,0 +1 @@ +_TFL_* diff --git a/tensorflow/contrib/lite/experimental/c/version_script.lds b/tensorflow/contrib/lite/experimental/c/version_script.lds new file mode 100644 index 0000000000..c0c8a2bca1 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/c/version_script.lds @@ -0,0 +1,9 @@ +VERS_1.0 { + # Export symbols in c_api.h. + global: + *TFL_*; + + # Hide everything else. + local: + *; +}; diff --git a/tensorflow/contrib/lite/testdata/add.bin b/tensorflow/contrib/lite/testdata/add.bin new file mode 100644 index 0000000000000000000000000000000000000000..aef0fe3d82c9d92dc444076d3b46e05af1923f46 GIT binary patch literal 476 zcmb1OU|n29Q2%dO&Ukxfo;y2!reZnajWcaVrBuy)!EV z14CvW$Vfg00kCU8{^0@p52PMsHZC`%{Qv(S=Xc!~w2e||0evp0;8-(Bd|NkFE!!XFN ZEDRtB@h8Y13=C`x91I|vAYl%&8vuH^CRP9d literal 0 HcmV?d00001 -- GitLab From 3baa7b63edf7890b5489cf2085a79598f13af2c6 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Mon, 23 Jul 2018 14:50:48 -0700 Subject: [PATCH 284/519] Further simplify backward pass. PiperOrigin-RevId: 205728836 --- .../eager/python/examples/revnet/blocks.py | 28 ++++++++----------- 1 file changed, 12 insertions(+), 16 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py index 639bb06a34..8a530b0d71 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -202,20 +202,18 @@ class _Residual(tf.keras.Model): with tf.GradientTape(persistent=True) as tape: tape.watch(y) y1, y2 = tf.split(y, num_or_size_splits=2, axis=self.axis) - z1 = y1 - gz1 = self.g(z1, training=training) - x2 = y2 - gz1 + gy1 = self.g(y1, training=training) + x2 = y2 - gy1 fx2 = self.f(x2, training=training) - x1 = z1 - fx2 + x1 = y1 - fx2 grads_combined = tape.gradient( - gz1, [z1] + self.g.trainable_variables, output_gradients=dy2) - dz1 = dy1 + grads_combined[0] + gy1, [y1] + self.g.trainable_variables, output_gradients=dy2) dg = grads_combined[1:] - dx1 = dz1 + dx1 = dy1 + grads_combined[0] grads_combined = tape.gradient( - fx2, [x2] + self.f.trainable_variables, output_gradients=dz1) + fx2, [x2] + self.f.trainable_variables, output_gradients=dx1) dx2 = dy2 + grads_combined[0] df = grads_combined[1:] @@ -263,7 +261,6 @@ class _BottleneckResidualInner(tf.keras.Model): if batch_norm_first: self.batch_norm_0 = tf.keras.layers.BatchNormalization( axis=axis, input_shape=input_shape, fused=fused, dtype=dtype) - self.conv2d_1 = tf.keras.layers.Conv2D( filters=filters // 4, kernel_size=1, @@ -273,9 +270,9 @@ class _BottleneckResidualInner(tf.keras.Model): use_bias=False, padding="SAME", dtype=dtype) + self.batch_norm_1 = tf.keras.layers.BatchNormalization( axis=axis, fused=fused, dtype=dtype) - self.conv2d_2 = tf.keras.layers.Conv2D( filters=filters // 4, kernel_size=3, @@ -303,15 +300,14 @@ class _BottleneckResidualInner(tf.keras.Model): if self.batch_norm_first: net = self.batch_norm_0(net, training=training) net = tf.nn.relu(net) - net = self.conv2d_1(net) + net = self.batch_norm_1(net, training=training) net = tf.nn.relu(net) - net = self.conv2d_2(net) + net = self.batch_norm_2(net, training=training) net = tf.nn.relu(net) - net = self.conv2d_3(net) return net @@ -356,9 +352,9 @@ class _ResidualInner(tf.keras.Model): use_bias=False, padding="SAME", dtype=dtype) + self.batch_norm_1 = tf.keras.layers.BatchNormalization( axis=axis, fused=fused, dtype=dtype) - self.conv2d_2 = tf.keras.layers.Conv2D( filters=filters, kernel_size=3, @@ -375,10 +371,10 @@ class _ResidualInner(tf.keras.Model): if self.batch_norm_first: net = self.batch_norm_0(net, training=training) net = tf.nn.relu(net) - net = self.conv2d_1(net) - net = self.batch_norm_1(net, training=training) + net = self.batch_norm_1(net, training=training) + net = tf.nn.relu(net) net = self.conv2d_2(net) return net -- GitLab From 32d121be1b105ef44fd5d4b421b78eb74dc94870 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 14:51:53 -0700 Subject: [PATCH 285/519] Adding core interface to a contrib version PiperOrigin-RevId: 205728990 --- .../estimator_batch/estimator.py | 43 +++++++++++ .../estimator_batch/estimator_test.py | 29 +++++++- .../boosted_trees/estimator_batch/model.py | 74 +++++++++++++------ 3 files changed, 124 insertions(+), 22 deletions(-) diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py b/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py index 59a78515c6..38fa8c3834 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py @@ -22,6 +22,7 @@ from tensorflow.contrib.boosted_trees.estimator_batch import model from tensorflow.contrib.boosted_trees.python.utils import losses from tensorflow.contrib.learn.python.learn.estimators import estimator from tensorflow.contrib.learn.python.learn.estimators import head as head_lib +from tensorflow.python.estimator import estimator as core_estimator from tensorflow.python.ops import math_ops @@ -354,3 +355,45 @@ class GradientBoostedDecisionTreeRanker(estimator.Estimator): model_dir=model_dir, config=config, feature_engineering_fn=feature_engineering_fn) + + +class CoreGradientBoostedDecisionTreeEstimator(core_estimator.Estimator): + """An estimator using gradient boosted decision trees.""" + + def __init__(self, + learner_config, + examples_per_layer, + head, + num_trees=None, + feature_columns=None, + weight_column_name=None, + model_dir=None, + config=None, + label_keys=None, + feature_engineering_fn=None, + logits_modifier_function=None, + center_bias=True, + output_leaf_index=False): + + def _model_fn(features, labels, mode, config): + return model.model_builder( + features=features, + labels=labels, + mode=mode, + config=config, + params={ + 'head': head, + 'feature_columns': feature_columns, + 'learner_config': learner_config, + 'num_trees': num_trees, + 'weight_column_name': weight_column_name, + 'examples_per_layer': examples_per_layer, + 'center_bias': center_bias, + 'logits_modifier_function': logits_modifier_function, + 'use_core_libs': True, + 'output_leaf_index': output_leaf_index, + }, + output_type=model.ModelBuilderOutputType.ESTIMATOR_SPEC) + + super(CoreGradientBoostedDecisionTreeEstimator, self).__init__( + model_fn=_model_fn, model_dir=model_dir, config=config) diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py b/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py index 2c2dcb039d..f787d3cdb8 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py @@ -182,7 +182,7 @@ class BoostedTreeEstimatorTest(test_util.TensorFlowTestCase): config = run_config.RunConfig() head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( - loss_reduction=losses.Reduction.SUM_OVER_BATCH_SIZE) + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) model = estimator.GradientBoostedDecisionTreeRanker( head=head_fn, @@ -203,5 +203,32 @@ class BoostedTreeEstimatorTest(test_util.TensorFlowTestCase): model.predict(input_fn=_infer_ranking_train_input_fn) +class CoreGradientBoostedDecisionTreeEstimator(test_util.TensorFlowTestCase): + + def testTrainEvaluateInferDoesNotThrowError(self): + head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + learner_config = learner_pb2.LearnerConfig() + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 1 + model_dir = tempfile.mkdtemp() + config = run_config.RunConfig() + + est = estimator.CoreGradientBoostedDecisionTreeEstimator( + head=head_fn, + learner_config=learner_config, + num_trees=1, + examples_per_layer=3, + model_dir=model_dir, + config=config, + feature_columns=[core_feature_column.numeric_column("x")]) + + # Train for a few steps. + est.train(input_fn=_train_input_fn, steps=1000) + est.evaluate(input_fn=_eval_input_fn, steps=1) + est.predict(input_fn=_eval_input_fn) + + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/model.py b/tensorflow/contrib/boosted_trees/estimator_batch/model.py index 0e8a56e6e9..2fbe72951a 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/model.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/model.py @@ -29,7 +29,17 @@ from tensorflow.python.framework import ops from tensorflow.python.ops import state_ops from tensorflow.python.training import training_util -def model_builder(features, labels, mode, params, config): +class ModelBuilderOutputType(object): + MODEL_FN_OPS = 0 + ESTIMATOR_SPEC = 1 + + +def model_builder(features, + labels, + mode, + params, + config, + output_type=ModelBuilderOutputType.MODEL_FN_OPS): """Multi-machine batch gradient descent tree model. Args: @@ -115,31 +125,53 @@ def model_builder(features, labels, mode, params, config): return update_op create_estimator_spec_op = getattr(head, "create_estimator_spec", None) - if use_core_libs and callable(create_estimator_spec_op): - model_fn_ops = head.create_estimator_spec( - features=features, - mode=mode, - labels=labels, - train_op_fn=_train_op_fn, - logits=logits) - model_fn_ops = estimator_utils.estimator_spec_to_model_fn_ops(model_fn_ops) - else: - model_fn_ops = head.create_model_fn_ops( - features=features, - mode=mode, - labels=labels, - train_op_fn=_train_op_fn, - logits=logits) - if output_leaf_index and gbdt_batch.LEAF_INDEX in predictions_dict: - model_fn_ops.predictions[gbdt_batch.LEAF_INDEX] = predictions_dict[ - gbdt_batch.LEAF_INDEX] + if num_trees: if center_bias: num_trees += 1 finalized_trees, attempted_trees = gbdt_model.get_number_of_trees_tensor() - model_fn_ops.training_hooks.append( + training_hooks = [ trainer_hooks.StopAfterNTrees(num_trees, attempted_trees, - finalized_trees)) + finalized_trees) + ] + + if output_type == ModelBuilderOutputType.MODEL_FN_OPS: + if use_core_libs and callable(create_estimator_spec_op): + model_fn_ops = head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + model_fn_ops = estimator_utils.estimator_spec_to_model_fn_ops( + model_fn_ops) + else: + model_fn_ops = head.create_model_fn_ops( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + + if output_leaf_index and gbdt_batch.LEAF_INDEX in predictions_dict: + model_fn_ops.predictions[gbdt_batch.LEAF_INDEX] = predictions_dict[ + gbdt_batch.LEAF_INDEX] + + model_fn_ops.training_hooks.extend(training_hooks) + return model_fn_ops + elif output_type == ModelBuilderOutputType.ESTIMATOR_SPEC: + assert callable(create_estimator_spec_op) + estimator_spec = head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + + estimator_spec = estimator_spec._replace( + training_hooks=training_hooks + list(estimator_spec.training_hooks)) + return estimator_spec + return model_fn_ops -- GitLab From f8e8c0c6f7746d3f2b5820e76c9e382149090034 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 14:53:58 -0700 Subject: [PATCH 286/519] Fix for conditional attributes crashing in static analysis PiperOrigin-RevId: 205729321 --- .../examples/integration_tests/keras_test.py | 25 +++++++++++++++++++ .../pyct/static_analysis/live_values.py | 16 +++++++++--- 2 files changed, 37 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/autograph/examples/integration_tests/keras_test.py b/tensorflow/contrib/autograph/examples/integration_tests/keras_test.py index a2fc7c550e..73125eb452 100644 --- a/tensorflow/contrib/autograph/examples/integration_tests/keras_test.py +++ b/tensorflow/contrib/autograph/examples/integration_tests/keras_test.py @@ -20,6 +20,8 @@ from __future__ import print_function import tensorflow as tf +from tensorflow.contrib import autograph + class MinimalKeras(tf.keras.Model): @@ -27,11 +29,34 @@ class MinimalKeras(tf.keras.Model): return x * 3 +class ModelWithStaticConditional(object): + + def __init__(self, initial): + self.initial = initial + if self.initial: + self.h = 15 + + @autograph.convert() + def call(self): + x = 10 + if self.initial: + x += self.h + return x + + class KerasTest(tf.test.TestCase): def test_basic(self): MinimalKeras() + def test_conditional_attributes_False(self): + model = ModelWithStaticConditional(False) + self.assertEqual(model.call(), 10) + + def test_conditional_attributes_True(self): + model = ModelWithStaticConditional(True) + self.assertEqual(model.call(), 25) + if __name__ == '__main__': tf.test.main() diff --git a/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py b/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py index 32802069ba..2d8f922a45 100644 --- a/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py +++ b/tensorflow/contrib/autograph/pyct/static_analysis/live_values.py @@ -91,12 +91,20 @@ class LiveValueResolver(transformer.Base): if anno.hasanno(node.value, 'live_val'): assert anno.hasanno(node.value, 'fqn') parent_object = anno.getanno(node.value, 'live_val') - 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,)) + if hasattr(parent_object, node.attr): + # This can happen when the attribute's creation and use depend on the + # same static condition, for example: + # + # if cond: + # foo.bar = baz + # if cond: + # x = foo.bar + # + anno.setanno(node, 'live_val', getattr(parent_object, node.attr)) + # TODO(mdan): Investigate the role built-in annotations can play here. elif anno.hasanno(node.value, 'type'): parent_type = anno.getanno(node.value, 'type') -- GitLab From 09c4c387913c86247121589caa7fb2e85351fa58 Mon Sep 17 00:00:00 2001 From: Reed Wanderman-Milne Date: Mon, 23 Jul 2018 15:01:42 -0700 Subject: [PATCH 287/519] Add check at GPU initialization to see if GPU kernels can be run. PiperOrigin-RevId: 205730535 --- tensorflow/core/BUILD | 11 ++++ .../core/common_runtime/gpu/gpu_device.cc | 51 ++++++++++++++++++- .../core/common_runtime/gpu/gpu_device.h | 11 ++++ .../gpu/gpu_device_kernel_check.cu.cc | 37 ++++++++++++++ .../gpu/gpu_device_kernel_check.h | 32 ++++++++++++ 5 files changed, 141 insertions(+), 1 deletion(-) create mode 100644 tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.cu.cc create mode 100644 tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index b6a990ac7d..13e1b643d1 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -90,6 +90,7 @@ load( "tf_genrule_cmd_append_to_srcs", "tf_opts_nortti_if_android", "tf_features_nomodules_if_android", + "tf_gpu_kernel_library", ) load("//tensorflow:tensorflow.bzl", "tf_cc_test_mkl") load("//tensorflow:tensorflow.bzl", "tf_cc_test_gpu") @@ -2948,6 +2949,15 @@ cc_library( ], ) +tf_gpu_kernel_library( + name = "gpu_device_kernel_check", + srcs = ["common_runtime/gpu/gpu_device_kernel_check.cu.cc"], + hdrs = ["common_runtime/gpu/gpu_device_kernel_check.h"], + deps = [ + "//tensorflow/core:stream_executor", + ], +) + GPU_RUNTIME_HEADERS = [ "common_runtime/gpu/cuda_host_allocator.h", "common_runtime/gpu/gpu_bfc_allocator.h", @@ -2986,6 +2996,7 @@ tf_cuda_library( ":core_cpu_lib", ":framework", ":framework_internal", + ":gpu_device_kernel_check", ":gpu_id_impl", ":gpu_init_impl", ":gpu_lib", diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc index 3292ef2f62..fbe158c777 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_device.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc @@ -31,6 +31,7 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/common_runtime/device_factory.h" +#include "tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h" #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" #include "tensorflow/core/common_runtime/gpu/gpu_id.h" #include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h" @@ -377,7 +378,7 @@ Status BaseGPUDevice::Init(const SessionOptions& options) { } } - return Status::OK(); + return CheckGPU(); } bool BaseGPUDevice::RequiresRecordingAccessedTensors() const { @@ -894,6 +895,54 @@ Allocator* BaseGPUDevice::GetScopedAllocator(AllocatorAttributes attr, return gpu_allocator_; } +Status BaseGPUDevice::CheckGPU() { + se::Stream* stream = tensorflow_gpu_device_info()->stream; + TF_RETURN_IF_ERROR(stream->BlockHostUntilDone()); + Tensor device_tensor(gpu_allocator_, DT_FLOAT, {}); + if (!device_tensor.IsInitialized()) { + return errors::ResourceExhausted("Failed to allocate ", sizeof(float), + " bytes on the GPU for initialization " + "checks"); + } + float* val_dev = device_tensor.scalar().data(); + const cudaStream_t cu_stream = *reinterpret_cast( + stream->implementation()->GpuStreamMemberHack()); + { + se::cuda::ScopedActivateExecutorContext scoped_activation{stream->parent()}; + run_test_kernel(val_dev, cu_stream); + // We have to use the CUDA runtime function cudaPeekAtLastError here, + // because 'stream' does not provide a way to check if a kernel launch + // succeeds. Calling 'stream->BlockHostUntilDone()', which internally calls + // 'cuCtxSynchronize()', does not catch all kernel launch errors. + cudaError_t cuda_error = cudaPeekAtLastError(); + if (cuda_error == cudaSuccess) { + cuda_error = cudaDeviceSynchronize(); + } + TF_RETURN_IF_ERROR(CudaErrorToStatus(cuda_error, *stream)); + } + + float val_host = 0.; + stream->ThenMemcpy(&val_host, se::DeviceMemoryBase(val_dev, sizeof(float)), + sizeof(float)); + TF_RETURN_IF_ERROR(stream->BlockHostUntilDone()); + if (val_host != 12345.) { + return errors::Internal( + "GPU kernel for initialization returned wrong value: ", val_host); + } + return Status::OK(); +} + +Status BaseGPUDevice::CudaErrorToStatus(cudaError_t cuda_error, + const se::Stream& stream) { + if (cuda_error != cudaSuccess) { + return errors::Internal( + "Failed to run GPU kernel for the initialization check. Received " + "error ", + cudaGetErrorName(cuda_error), " after running GPU kernel."); + } + return Status::OK(); +} + const int BaseGPUDeviceFactory::InterconnectMap::kSameDeviceStrength = 1000; const int BaseGPUDeviceFactory::InterconnectMap::kStreamExecutorStrength = 1; diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.h b/tensorflow/core/common_runtime/gpu/gpu_device.h index 56d03d7a8c..d02901a7ae 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_device.h +++ b/tensorflow/core/common_runtime/gpu/gpu_device.h @@ -26,6 +26,7 @@ limitations under the License. #include #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" +#include "cuda/include/cuda_runtime_api.h" #include "tensorflow/core/common_runtime/device_factory.h" #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" #include "tensorflow/core/common_runtime/gpu/gpu_id.h" @@ -115,6 +116,12 @@ class BaseGPUDevice : public LocalDevice { se::StreamExecutor* executor_; // not owned std::unique_ptr scoped_allocator_mgr_; + // Returns a Status corresponding to a cudaError_t. The CUDA error must have + // been obtained from a CUDA kernel launch used to check if the GPU is + // initialized properly. + virtual Status CudaErrorToStatus(cudaError_t cuda_error, + const se::Stream& stream); + private: struct StreamGroup { se::Stream* compute = nullptr; @@ -151,6 +158,10 @@ class BaseGPUDevice : public LocalDevice { Status MaybeCopyTensorToGPU(const AllocatorAttributes& alloc_attrs, const Tensor& from, Tensor* to, StatusCallback done); + + // Checks that the GPU is capable of doing work, by running a test kernel on + // it. + Status CheckGPU(); }; class BaseGPUDeviceFactory : public DeviceFactory { diff --git a/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.cu.cc b/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.cu.cc new file mode 100644 index 0000000000..017565195b --- /dev/null +++ b/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.cu.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. +==============================================================================*/ + +#if GOOGLE_CUDA + +#include "tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h" +#include "tensorflow/stream_executor/cuda/cuda_activation.h" + +namespace { +__global__ void test_kernel(float* val) { + if (blockIdx.x == 0 && threadIdx.x == 0) { + (*val) = 12345.; + } +} +} // namespace + +namespace tensorflow { + +void run_test_kernel(float* val, cudaStream_t cu_stream) { + test_kernel<<<1, 1, 0, cu_stream>>>(val); +} + +} // namespace tensorflow + +#endif // GOOGLE_CUDA diff --git a/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h b/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h new file mode 100644 index 0000000000..064fb7a49f --- /dev/null +++ b/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h @@ -0,0 +1,32 @@ +/* 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_CORE_COMMON_RUNTIME_GPU_GPU_DEVICE_KERNEL_CHECK_H_ +#define TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_DEVICE_KERNEL_CHECK_H_ + +#if GOOGLE_CUDA + +#include "tensorflow/core/platform/stream_executor.h" + +namespace tensorflow { + +// Runs a GPU kernel to test that it functions correctly. Sets 'val' to 12345. +void run_test_kernel(float* val, cudaStream_t cu_stream); + +} // namespace tensorflow + +#endif // GOOGLE_CUDA + +#endif // TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_DEVICE_KERNEL_CHECK_H_ -- GitLab From 931a3054d2c13c3438fc58978b3463a0bd268aee Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 15:05:15 -0700 Subject: [PATCH 288/519] [tfgan] Issue #18041: Make pooling consistent in `gan_loss`. PiperOrigin-RevId: 205731279 --- tensorflow/contrib/gan/python/train.py | 56 ++++++++++------ tensorflow/contrib/gan/python/train_test.py | 71 ++++++++------------- 2 files changed, 61 insertions(+), 66 deletions(-) diff --git a/tensorflow/contrib/gan/python/train.py b/tensorflow/contrib/gan/python/train.py index 49d9327333..df603d1f18 100644 --- a/tensorflow/contrib/gan/python/train.py +++ b/tensorflow/contrib/gan/python/train.py @@ -514,33 +514,42 @@ def _tensor_pool_adjusted_model(model, tensor_pool_fn): Raises: ValueError: If tensor pool does not support the `model`. """ - if tensor_pool_fn is None: - return model - - pooled_generated_data, pooled_generator_inputs = tensor_pool_fn( - (model.generated_data, model.generator_inputs)) - if isinstance(model, namedtuples.GANModel): + pooled_generator_inputs, pooled_generated_data = tensor_pool_fn( + (model.generator_inputs, model.generated_data)) with variable_scope.variable_scope(model.discriminator_scope, reuse=True): dis_gen_outputs = model.discriminator_fn(pooled_generated_data, pooled_generator_inputs) - return model._replace(discriminator_gen_outputs=dis_gen_outputs) + return model._replace( + generator_inputs=pooled_generator_inputs, + generated_data=pooled_generated_data, + discriminator_gen_outputs=dis_gen_outputs) elif isinstance(model, namedtuples.ACGANModel): + pooled_generator_inputs, pooled_generated_data = tensor_pool_fn( + (model.generator_inputs, model.generated_data)) with variable_scope.variable_scope(model.discriminator_scope, reuse=True): - (dis_pooled_gen_outputs, - dis_pooled_gen_classification_logits) = model.discriminator_fn( + (pooled_discriminator_gen_outputs, + pooled_discriminator_gen_classification_logits) = model.discriminator_fn( pooled_generated_data, pooled_generator_inputs) return model._replace( - discriminator_gen_outputs=dis_pooled_gen_outputs, + generator_inputs=pooled_generator_inputs, + generated_data=pooled_generated_data, + discriminator_gen_outputs=pooled_discriminator_gen_outputs, discriminator_gen_classification_logits= - dis_pooled_gen_classification_logits) + pooled_discriminator_gen_classification_logits) elif isinstance(model, namedtuples.InfoGANModel): + pooled_generator_inputs, pooled_generated_data, pooled_structured_input = ( + tensor_pool_fn((model.generator_inputs, model.generated_data, + model.structured_generator_inputs))) with variable_scope.variable_scope(model.discriminator_scope, reuse=True): - (dis_pooled_gen_outputs, + (pooled_discriminator_gen_outputs, pooled_predicted_distributions) = model.discriminator_and_aux_fn( pooled_generated_data, pooled_generator_inputs) return model._replace( - discriminator_gen_outputs=dis_pooled_gen_outputs, + generator_inputs=pooled_generator_inputs, + generated_data=pooled_generated_data, + structured_generator_inputs=pooled_structured_input, + discriminator_gen_outputs=pooled_discriminator_gen_outputs, predicted_distributions=pooled_predicted_distributions) else: raise ValueError('Tensor pool does not support `model`: %s.' % type(model)) @@ -632,33 +641,38 @@ def gan_loss( 'is provided, `model` must be an `ACGANModel`. Instead, was %s.' % type(model)) + # Optionally create pooled model. + pooled_model = (_tensor_pool_adjusted_model(model, tensor_pool_fn) if + tensor_pool_fn else model) + # Create standard losses. gen_loss = generator_loss_fn(model, add_summaries=add_summaries) - dis_loss = discriminator_loss_fn( - _tensor_pool_adjusted_model(model, tensor_pool_fn), - add_summaries=add_summaries) + dis_loss = discriminator_loss_fn(pooled_model, add_summaries=add_summaries) # Add optional extra losses. if _use_aux_loss(gradient_penalty_weight): gp_loss = tfgan_losses.wasserstein_gradient_penalty( - model, + pooled_model, epsilon=gradient_penalty_epsilon, target=gradient_penalty_target, one_sided=gradient_penalty_one_sided, add_summaries=add_summaries) dis_loss += gradient_penalty_weight * gp_loss if _use_aux_loss(mutual_information_penalty_weight): - info_loss = tfgan_losses.mutual_information_penalty( + gen_info_loss = tfgan_losses.mutual_information_penalty( model, add_summaries=add_summaries) - dis_loss += mutual_information_penalty_weight * info_loss - gen_loss += mutual_information_penalty_weight * info_loss + dis_info_loss = (gen_info_loss if tensor_pool_fn is None else + tfgan_losses.mutual_information_penalty( + pooled_model, add_summaries=add_summaries)) + gen_loss += mutual_information_penalty_weight * gen_info_loss + dis_loss += mutual_information_penalty_weight * dis_info_loss if _use_aux_loss(aux_cond_generator_weight): ac_gen_loss = tfgan_losses.acgan_generator_loss( model, add_summaries=add_summaries) gen_loss += aux_cond_generator_weight * ac_gen_loss if _use_aux_loss(aux_cond_discriminator_weight): ac_disc_loss = tfgan_losses.acgan_discriminator_loss( - model, add_summaries=add_summaries) + pooled_model, add_summaries=add_summaries) dis_loss += aux_cond_discriminator_weight * ac_disc_loss # Gathers auxiliary losses. if model.generator_scope: diff --git a/tensorflow/contrib/gan/python/train_test.py b/tensorflow/contrib/gan/python/train_test.py index cd99a33c03..fa52e9cca1 100644 --- a/tensorflow/contrib/gan/python/train_test.py +++ b/tensorflow/contrib/gan/python/train_test.py @@ -278,25 +278,6 @@ def get_sync_optimizer(): replicas_to_aggregate=1) -def get_tensor_pool_fn(pool_size): - - def tensor_pool_fn_impl(input_values): - return random_tensor_pool.tensor_pool(input_values, pool_size=pool_size) - - return tensor_pool_fn_impl - - -def get_tensor_pool_fn_for_infogan(pool_size): - - def tensor_pool_fn_impl(input_values): - generated_data, generator_inputs = input_values - output_values = random_tensor_pool.tensor_pool( - [generated_data] + generator_inputs, pool_size=pool_size) - return output_values[0], output_values[1:] - - return tensor_pool_fn_impl - - class GANModelTest(test.TestCase, parameterized.TestCase): """Tests for `gan_model`.""" @@ -344,7 +325,6 @@ class StarGANModelTest(test.TestCase): @staticmethod def create_input_and_label_tensor(batch_size, img_size, c_size, num_domains): - input_tensor_list = [] label_tensor_list = [] for _ in range(num_domains): @@ -356,7 +336,6 @@ class StarGANModelTest(test.TestCase): return input_tensor_list, label_tensor_list def test_generate_stargan_random_domain_target(self): - batch_size = 8 domain_numbers = 3 @@ -371,7 +350,6 @@ class StarGANModelTest(test.TestCase): self.assertEqual(1, np.max(target)) def test_stargan_model_output_type(self): - batch_size = 2 img_size = 16 c_size = 3 @@ -395,7 +373,6 @@ class StarGANModelTest(test.TestCase): self.assertTrue(callable(model.generator_fn)) def test_stargan_model_generator_output(self): - batch_size = 2 img_size = 16 c_size = 3 @@ -426,7 +403,6 @@ class StarGANModelTest(test.TestCase): reconstructed_data.shape) def test_stargan_model_discriminator_output(self): - batch_size = 2 img_size = 16 c_size = 3 @@ -643,10 +619,7 @@ class GANLossTest(test.TestCase, parameterized.TestCase): def test_tensor_pool(self, create_gan_model_fn): """Test tensor pool option.""" model = create_gan_model_fn() - if isinstance(model, namedtuples.InfoGANModel): - tensor_pool_fn = get_tensor_pool_fn_for_infogan(pool_size=5) - else: - tensor_pool_fn = get_tensor_pool_fn(pool_size=5) + tensor_pool_fn = lambda x: random_tensor_pool.tensor_pool(x, pool_size=5) loss = train.gan_loss(model, tensor_pool_fn=tensor_pool_fn) self.assertIsInstance(loss, namedtuples.GANLoss) @@ -656,6 +629,25 @@ class GANLossTest(test.TestCase, parameterized.TestCase): for _ in range(10): sess.run([loss.generator_loss, loss.discriminator_loss]) + def test_discriminator_only_sees_pool(self): + """Checks that discriminator only sees pooled values.""" + def checker_gen_fn(_): + return constant_op.constant(0.0) + model = train.gan_model( + checker_gen_fn, + discriminator_model, + real_data=array_ops.zeros([]), + generator_inputs=random_ops.random_normal([])) + def tensor_pool_fn(_): + return (random_ops.random_uniform([]), random_ops.random_uniform([])) + def checker_dis_fn(inputs, _): + """Discriminator that checks that it only sees pooled Tensors.""" + self.assertFalse(constant_op.is_constant(inputs)) + return inputs + model = model._replace( + discriminator_fn=checker_dis_fn) + train.gan_loss(model, tensor_pool_fn=tensor_pool_fn) + def test_doesnt_crash_when_in_nested_scope(self): with variable_scope.variable_scope('outer_scope'): gan_model = train.gan_model( @@ -673,8 +665,8 @@ class GANLossTest(test.TestCase, parameterized.TestCase): class TensorPoolAdjusteModelTest(test.TestCase): - def _check_tensor_pool_adjusted_model_outputs(self, tensor1, tensor2, - pool_size): + def _check_tensor_pool_adjusted_model_outputs( + self, tensor1, tensor2, pool_size): history_values = [] with self.test_session(use_gpu=True) as sess: variables.global_variables_initializer().run() @@ -691,10 +683,9 @@ class TensorPoolAdjusteModelTest(test.TestCase): # pool). self.assertTrue(any([(v == t2).all() for v in history_values])) - def _make_new_model_and_check(self, model, pool_size, - pool_fn=get_tensor_pool_fn): - new_model = train._tensor_pool_adjusted_model( - model, pool_fn(pool_size=pool_size)) + def _make_new_model_and_check(self, model, pool_size): + pool_fn = lambda x: random_tensor_pool.tensor_pool(x, pool_size=pool_size) + new_model = train._tensor_pool_adjusted_model(model, pool_fn) # 'Generator/dummy_g:0' and 'Discriminator/dummy_d:0' self.assertEqual(2, len(ops.get_collection(ops.GraphKeys.VARIABLES))) self.assertIsNot(new_model.discriminator_gen_outputs, @@ -702,15 +693,6 @@ class TensorPoolAdjusteModelTest(test.TestCase): return new_model - def test_tensor_pool_adjusted_model_no_pool(self): - """Test `_tensor_pool_adjusted_model` for no pool size.""" - model = create_gan_model() - new_model = train._tensor_pool_adjusted_model(model, None) - - # Check values. - self.assertIs(new_model.discriminator_gen_outputs, - model.discriminator_gen_outputs) - def test_tensor_pool_adjusted_model_gan(self): """Test `_tensor_pool_adjusted_model` for gan model.""" pool_size = 5 @@ -726,8 +708,7 @@ class TensorPoolAdjusteModelTest(test.TestCase): """Test _tensor_pool_adjusted_model for infogan model.""" pool_size = 5 model = create_infogan_model() - new_model = self._make_new_model_and_check( - model, pool_size, pool_fn=get_tensor_pool_fn_for_infogan) + new_model = self._make_new_model_and_check(model, pool_size) # Check values. self.assertIsNot(new_model.predicted_distributions, -- GitLab From f85d825500357603afb7a02d2c88ad306ee43006 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Mon, 23 Jul 2018 15:11:22 -0700 Subject: [PATCH 289/519] Allow differentiating tfe.defun functions which contain conds. PiperOrigin-RevId: 205732423 --- .../core/common_runtime/direct_session.cc | 3 ++- tensorflow/core/common_runtime/function.cc | 19 +++++++++++-------- tensorflow/core/framework/function.cc | 5 ++++- tensorflow/core/framework/function.h | 8 +++++++- .../core/kernels/partitioned_function_ops.cc | 1 + tensorflow/python/eager/function_test.py | 13 +++++++++++++ 6 files changed, 38 insertions(+), 11 deletions(-) diff --git a/tensorflow/core/common_runtime/direct_session.cc b/tensorflow/core/common_runtime/direct_session.cc index 44291b0b20..d1fd930d25 100644 --- a/tensorflow/core/common_runtime/direct_session.cc +++ b/tensorflow/core/common_runtime/direct_session.cc @@ -717,7 +717,8 @@ Status DirectSession::Run(const RunOptions& run_options, // Receive outputs. if (outputs) { std::vector sorted_outputs; - const Status s = call_frame.ConsumeRetvals(&sorted_outputs); + const Status s = call_frame.ConsumeRetvals( + &sorted_outputs, /* allow_dead_tensors = */ false); if (errors::IsInternal(s)) { return errors::InvalidArgument(s.error_message()); } else if (!s.ok()) { diff --git a/tensorflow/core/common_runtime/function.cc b/tensorflow/core/common_runtime/function.cc index a93cfa2ec5..54bbe84b57 100644 --- a/tensorflow/core/common_runtime/function.cc +++ b/tensorflow/core/common_runtime/function.cc @@ -746,6 +746,8 @@ void FunctionLibraryRuntimeImpl::RunRemote(const Options& opts, Handle handle, rets_alloc_attrs.push_back(ret_alloc_attrs); } + bool allow_dead_tensors = opts.allow_dead_tensors; + // The ProcFLR sends the arguments to the function from the source_device to // the target_device. So here we receive those arguments. Similarly, when the // computation is done and stored in *rets, we send the return values back @@ -756,7 +758,7 @@ void FunctionLibraryRuntimeImpl::RunRemote(const Options& opts, Handle handle, device_context, args_alloc_attrs, rendezvous, remote_args, [frame, remote_args, item, source_device, target_device, target_incarnation, rendezvous, device_context, rets, done, exec_args, - rets_alloc_attrs](const Status& status) { + rets_alloc_attrs, allow_dead_tensors](const Status& status) { Status s = status; if (s.ok()) { s = frame->SetArgs(*remote_args); @@ -769,13 +771,13 @@ void FunctionLibraryRuntimeImpl::RunRemote(const Options& opts, Handle handle, return; } item->exec->RunAsync( - *exec_args, - [frame, rets, done, source_device, target_device, - target_incarnation, rendezvous, device_context, remote_args, - exec_args, rets_alloc_attrs](const Status& status) { + *exec_args, [frame, rets, done, source_device, target_device, + target_incarnation, rendezvous, device_context, + remote_args, exec_args, rets_alloc_attrs, + allow_dead_tensors](const Status& status) { Status s = status; if (s.ok()) { - s = frame->ConsumeRetvals(rets); + s = frame->ConsumeRetvals(rets, allow_dead_tensors); } delete frame; if (!s.ok()) { @@ -859,14 +861,15 @@ void FunctionLibraryRuntimeImpl::Run(const Options& opts, Handle handle, return; } + bool allow_dead_tensors = opts.allow_dead_tensors; item->exec->RunAsync( // Executor args *exec_args, // Done callback. - [frame, rets, done, exec_args](const Status& status) { + [frame, rets, done, exec_args, allow_dead_tensors](const Status& status) { Status s = status; if (s.ok()) { - s = frame->ConsumeRetvals(rets); + s = frame->ConsumeRetvals(rets, allow_dead_tensors); } delete frame; delete exec_args; diff --git a/tensorflow/core/framework/function.cc b/tensorflow/core/framework/function.cc index 88d9d65f5a..57bcc0f513 100644 --- a/tensorflow/core/framework/function.cc +++ b/tensorflow/core/framework/function.cc @@ -865,12 +865,15 @@ Status FunctionCallFrame::GetRetvals(std::vector* rets) const { return Status::OK(); } -Status FunctionCallFrame::ConsumeRetvals(std::vector* rets) { +Status FunctionCallFrame::ConsumeRetvals(std::vector* rets, + bool allow_dead_tensors) { rets->clear(); rets->reserve(rets_.size()); for (size_t i = 0; i < rets_.size(); ++i) { if (rets_[i].has_val) { rets->emplace_back(std::move(rets_[i].val)); + } else if (allow_dead_tensors) { + rets->emplace_back(); } else { return errors::Internal("Retval[", i, "] does not have value"); } diff --git a/tensorflow/core/framework/function.h b/tensorflow/core/framework/function.h index 8e607b927c..5da9af7db3 100644 --- a/tensorflow/core/framework/function.h +++ b/tensorflow/core/framework/function.h @@ -261,7 +261,10 @@ class FunctionCallFrame : public CallFrameInterface { // Caller methods. Status SetArgs(gtl::ArraySlice args); Status GetRetvals(std::vector* rets) const; - Status ConsumeRetvals(std::vector* rets); + + // Moves the return values from the frame to rets. If allow_dead_tensors is + // false it will fail if any of the retvals do not have a value. + Status ConsumeRetvals(std::vector* rets, bool allow_dead_tensors); size_t num_args() const override { return arg_types_.size(); } size_t num_retvals() const override { return ret_types_.size(); } @@ -510,6 +513,9 @@ class FunctionLibraryRuntime { // If true, we create a new IntraProcessRendezvous, else use the existing // one. bool create_rendezvous = false; + + // If True, allow returning dead tensors. + bool allow_dead_tensors = false; }; typedef std::function DoneCallback; virtual void Run(const Options& opts, Handle handle, diff --git a/tensorflow/core/kernels/partitioned_function_ops.cc b/tensorflow/core/kernels/partitioned_function_ops.cc index b5c6ba1da3..a7a9609c21 100644 --- a/tensorflow/core/kernels/partitioned_function_ops.cc +++ b/tensorflow/core/kernels/partitioned_function_ops.cc @@ -330,6 +330,7 @@ class PartitionedCallOp : public AsyncOpKernel { // using device-specific threadpools when available. opts.runner = ctx->runner(); opts.source_device = local_device_name_; + opts.allow_dead_tensors = true; // TODO(akshayka): Accommodate the multiple-worker scenario by adding the // constructed rendezvous to a rendezvous manager. Rendezvous* rendez = new IntraProcessRendezvous(lib->device_mgr()); diff --git a/tensorflow/python/eager/function_test.py b/tensorflow/python/eager/function_test.py index e6592b2e37..2e86563a7d 100644 --- a/tensorflow/python/eager/function_test.py +++ b/tensorflow/python/eager/function_test.py @@ -213,6 +213,19 @@ class FunctionTest(test.TestCase): self.assertEqual(fn_op.output_shapes, None) self.assertAllEqual(fn_op(x, x), None) + @test_util.run_in_graph_and_eager_modes() + def testDefunCondGradient(self): + + @function.defun + def f(x): + return control_flow_ops.cond(x > 0.5, lambda: 2 * x, lambda: 3 * x) + + with backprop.GradientTape() as t: + x = constant_op.constant(1.0) + t.watch(x) + y = f(x) + self.assertAllEqual(self.evaluate(t.gradient(y, x)), 2.0) + def testDefunCapturedInt32(self): x = constant_op.constant(1, dtype=dtypes.int32) -- GitLab From ed0f543bb03e684cf8f1fa8da3666876fcb43674 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Mon, 23 Jul 2018 15:15:07 -0700 Subject: [PATCH 290/519] Add defun option. PiperOrigin-RevId: 205732970 --- .../contrib/eager/python/examples/revnet/main.py | 13 ++++++++++--- 1 file changed, 10 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main.py b/tensorflow/contrib/eager/python/examples/revnet/main.py index 1a4fd45c8b..dcd4e1697f 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/main.py +++ b/tensorflow/contrib/eager/python/examples/revnet/main.py @@ -46,6 +46,9 @@ def main(_): checkpointer = tf.train.Checkpoint( optimizer=optimizer, model=model, optimizer_step=global_step) + if FLAGS.use_defun: + model.call = tfe.defun(model.call) + if FLAGS.train_dir: summary_writer = tf.contrib.summary.create_file_writer(FLAGS.train_dir) if FLAGS.restore: @@ -69,7 +72,7 @@ def main(_): acc_validation, loss_validation = evaluate(model, it_validation) print("Iter {}, " "training set accuracy {:.4f}, loss {:.4f}; " - "validation set accuracy {:.4f}, loss {:4.f}" + "validation set accuracy {:.4f}, loss {:.4f}; " "test accuracy {:.4f}, loss {:.4f}".format( global_step.numpy(), acc_train, loss_train, acc_validation, loss_validation, acc_test, loss_test)) @@ -81,11 +84,11 @@ def main(_): if FLAGS.train_dir: with summary_writer.as_default(): with tf.contrib.summary.always_record_summaries(): - tf.contrib.summary.scalar("Training accuracy", acc_train) tf.contrib.summary.scalar("Test accuracy", acc_test) - tf.contrib.summary.scalar("Training loss", loss_train) tf.contrib.summary.scalar("Test loss", loss_test) if FLAGS.validate: + tf.contrib.summary.scalar("Training accuracy", acc_train) + tf.contrib.summary.scalar("Training loss", loss_train) tf.contrib.summary.scalar("Validation accuracy", acc_validation) tf.contrib.summary.scalar("Validation loss", loss_validation) @@ -240,5 +243,9 @@ if __name__ == "__main__": default="revnet-38", help="[Optional] Architecture of network. " "Other options include `revnet-110` and `revnet-164`") + flags.DEFINE_boolean( + "use_defun", + default=False, + help="[Optional] Use `tfe.defun` to boost performance.") FLAGS = flags.FLAGS tf.app.run(main) -- GitLab From 9affd713580f5f6cf81511c82e1d94bcd3c84e32 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Mon, 23 Jul 2018 15:17:14 -0700 Subject: [PATCH 291/519] Add README for l2hmc. PiperOrigin-RevId: 205733306 --- .../eager/python/examples/l2hmc/README.md | 54 +++++++++++++++++++ 1 file changed, 54 insertions(+) create mode 100644 tensorflow/contrib/eager/python/examples/l2hmc/README.md diff --git a/tensorflow/contrib/eager/python/examples/l2hmc/README.md b/tensorflow/contrib/eager/python/examples/l2hmc/README.md new file mode 100644 index 0000000000..d6a2ff7558 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/l2hmc/README.md @@ -0,0 +1,54 @@ +# L2HMC with TensorFlow eager execution + +This folder contains an implementation of [L2HMC](https://arxiv.org/pdf/1711.09268.pdf) adapted from the released implementation by the authors. The presented implementation runs in both eager and graph mode. +With eager execution enabled, longer sample chains can be handled compared to graph mode, since no graph is explicitly stored. Moreover, with eager execution enabled, there is no need to use a `tf.while_loop`. + +## What is L2HMC? +L2HMC is an algorithm that learns a non-volume preserving transformation +for an HMC-like sampling algorithm. More specifically, the non-volume preserving +transformation is learned with neural nets instantiated within Normalizing Flows +(more precisely, real-NVPs). + +## Content + +- `l2hmc.py`: Dynamics definitions and example energy functions, +including the 2D strongly correlated Gaussian, the rough well energy function, +and a Gaussian mixture model. +- `l2hmc_test.py`: Unit tests and benchmarks for training a sampler on the energy functions in both eager and graph mode. +- `neural_nets.py`: The neural net for learning the kernel on the 2D strongly correlated example. +- `main.py`: Run to train a samplers on 2D energy landscapes. + +## To run +- Make sure you have installed TensorFlow 1.9+ or the latest `tf-nightly` or `tf-nightly-gpu` pip package. +- Execute the command + +```bash +python main.py --train_dir ${PWD}/dump --use_defun +``` + +Specifying the optional argument `train_dir` will store event files for +tensorboard and a plot of sampled chain from the trained sampler. + +Specifying the optional argument `use_defun` will let the program use compiled +graphs when running specific sections and improve the overall speed. + +## Boosting Performance with `defun` +Currently, some models may experience increased overhead with eager execution enabled. +To improve performance, we could wrap certain functions with the decorator `@tfe.defun`. +For example, we could wrap the function that does the sampling step: + +```python +@tfe.defun +def apply_transition(old_sample): + new_sample = ... + return new_sample +``` + +We could also explicitly wrap the desired function with `tfe.defun`: + +```python +apply_transition = tfe.defun(apply_transition) +``` + +## Reference +Generalizing Hamiltonian Monte Carlo with Neural Networks. Levy, Daniel, Hoffman, Matthew D, and Sohl-Dickstein, Jascha. International Conference on Learning Representations (ICLR), 2018. -- GitLab From 9225bbbe0aaaa14b69176576097bb67bae98e6c5 Mon Sep 17 00:00:00 2001 From: Blake Hechtman Date: Mon, 23 Jul 2018 16:02:19 -0700 Subject: [PATCH 292/519] [XLA] Simplify slice(slice()) to a single slice and simplify Reduce(Concat()) to Accumulate(Reudce(),Reduce(),...Reduce()) PiperOrigin-RevId: 205740411 --- .../xla/service/algebraic_simplifier.cc | 39 +++++++++ .../xla/service/algebraic_simplifier_test.cc | 82 +++++++++++++++++++ 2 files changed, 121 insertions(+) diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc index 2205a7ec18..26a8a67601 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc @@ -1744,6 +1744,25 @@ Status AlgebraicSimplifierVisitor::HandleSlice(HloInstruction* slice) { if (ReplaceInstructionIfSameShape(slice, slice->mutable_operand(0))) { return Status::OK(); } + + auto is_unstrided_slice = [](const HloInstruction* hlo) { + return c_all_of(hlo->slice_strides(), + [](int64 stride) { return stride == 1; }); + }; + if (slice->operand(0)->opcode() == HloOpcode::kSlice && + is_unstrided_slice(slice) && is_unstrided_slice(slice->operand(0))) { + HloInstruction* operand_slice = slice->mutable_operand(0); + std::vector new_slice_starts = slice->slice_starts(); + std::vector new_slice_limits = slice->slice_limits(); + for (int64 i = 0; i < new_slice_starts.size(); ++i) { + new_slice_starts[i] += operand_slice->slice_starts(i); + new_slice_limits[i] += operand_slice->slice_starts(i); + } + return ReplaceWithNewInstruction( + slice, HloInstruction::CreateSlice( + slice->shape(), operand_slice->mutable_operand(0), + new_slice_starts, new_slice_limits, slice->slice_strides())); + } return Status::OK(); } @@ -1904,6 +1923,26 @@ Status AlgebraicSimplifierVisitor::HandleReduce(HloInstruction* reduce) { new_reduce_dimensions, function)); } } + // Convert Reduce(concat({a,b,...})) to + // map(reduce(a),map(reduce(b),...,)) + // + // This should make fusion easier or use less memory bandwidth in the unfused + // case. + if (arg->opcode() == HloOpcode::kConcatenate && + c_linear_search(reduce->dimensions(), arg->concatenate_dimension())) { + HloInstruction* old_reduce = nullptr; + for (HloInstruction* operand : arg->operands()) { + HloInstruction* new_reduce = computation_->AddInstruction( + HloInstruction::CreateReduce(reduce->shape(), operand, init_value, + reduce->dimensions(), function)); + if (old_reduce != nullptr) { + new_reduce = computation_->AddInstruction(HloInstruction::CreateMap( + reduce->shape(), {old_reduce, new_reduce}, function)); + } + old_reduce = new_reduce; + } + return ReplaceInstruction(reduce, old_reduce); + } return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc index 3f0f2afadd..ddf0a513c0 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc @@ -1250,6 +1250,55 @@ TEST_F(AlgebraicSimplifierTest, RemoveEmptyConcatenateOperands) { op::Concatenate(param0, param0, param1)); } +// Test that reduce of concat is simplified. +TEST_F(AlgebraicSimplifierTest, SimplifyReduceOfConcat) { + const int kParamLength = 100; + Shape r3f32 = + ShapeUtil::MakeShape(F32, {kParamLength, kParamLength, kParamLength}); + HloComputation::Builder builder(TestName()); + HloInstruction* param0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, r3f32, "param0")); + HloInstruction* param1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, r3f32, "param1")); + HloInstruction* param2 = builder.AddInstruction( + HloInstruction::CreateParameter(2, r3f32, "param2")); + Shape concat_shape = + ShapeUtil::MakeShape(F32, {kParamLength, 3 * kParamLength, kParamLength}); + HloInstruction* Concatenate = + builder.AddInstruction(HloInstruction::CreateConcatenate( + concat_shape, {param0, param1, param2}, 1)); + HloComputation* add_computation = nullptr; + { + HloComputation::Builder builder(TestName() + ".add"); + const Shape scalar_shape = ShapeUtil::MakeShape(F32, {}); + HloInstruction* p0 = builder.AddInstruction( + HloInstruction::CreateParameter(0, scalar_shape, "p0")); + HloInstruction* p1 = builder.AddInstruction( + HloInstruction::CreateParameter(1, scalar_shape, "p1")); + builder.AddInstruction( + HloInstruction::CreateBinary(scalar_shape, HloOpcode::kAdd, p0, p1)); + add_computation = module().AddEmbeddedComputation(builder.Build()); + } + Shape r4f32 = ShapeUtil::MakeShape(F32, {4, 5, 6, 7}); + Shape reduce_shape = ShapeUtil::MakeShape(F32, {kParamLength}); + + HloInstruction* zero = builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))); + builder.AddInstruction(HloInstruction::CreateReduce( + reduce_shape, Concatenate, zero, {1, 2}, add_computation)); + + auto 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(), + op::Map(op::Map(op::Reduce(param0, zero), op::Reduce(param1, zero)), + op::Reduce(param2, zero))); +} + // Test a concatenate with only empty operands is removed. TEST_F(AlgebraicSimplifierTest, OnlyEmptyConcatenateOperands) { const int kParamLength = 100; @@ -1859,6 +1908,39 @@ TEST_F(AlgebraicSimplifierTest, RemoveNoopSlice) { EXPECT_THAT(computation->root_instruction(), param); } +TEST_F(AlgebraicSimplifierTest, SliceOfSliceToSlice) { + HloComputation::Builder builder(TestName()); + const int64 dim0 = 11; + const int64 dim1 = 12; + HloInstruction* param = + builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(F32, {dim0, dim1}), "param")); + HloInstruction* original_slice = + builder.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {dim0 - 2, dim1 - 4}), param, + /*start_indices=*/{1, 2}, + /*limit_indices=*/{dim0 - 1, dim1 - 2}, /*strides=*/{1, 1})); + + builder.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {dim0 - 5, dim1 - 9}), original_slice, + /*start_indices=*/{2, 3}, + /*limit_indices=*/{dim0 - 3, dim1 - 6}, /*strides=*/{1, 1})); + auto module = CreateNewModule(); + HloComputation* computation = module->AddEntryComputation(builder.Build()); + + EXPECT_THAT(computation->root_instruction(), op::Slice(op::Slice(param))); + + AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, + non_bitcasting_callback()); + ASSERT_TRUE(simplifier.Run(module).ValueOrDie()); + + EXPECT_THAT(computation->root_instruction(), op::Slice(param)); + EXPECT_EQ(computation->root_instruction()->slice_starts(0), 3); + EXPECT_EQ(computation->root_instruction()->slice_starts(1), 5); + EXPECT_EQ(computation->root_instruction()->slice_limits(0), dim0 - 2); + EXPECT_EQ(computation->root_instruction()->slice_limits(1), dim1 - 4); +} + TEST_F(AlgebraicSimplifierTest, ConvertConvToMatmul) { struct ConvTestOptions { int in_batch = 10; -- GitLab From 632e48c27e09b53ab52523149e759f9bc1711e71 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Mon, 23 Jul 2018 16:17:12 -0700 Subject: [PATCH 293/519] Teach StreamExecutor to load modules and resolve symbols in them This will be used in a future CL. PiperOrigin-RevId: 205742731 --- .../stream_executor/cuda/cuda_gpu_executor.cc | 179 ++++++++++++------ .../stream_executor/cuda/cuda_gpu_executor.h | 16 +- tensorflow/stream_executor/module_spec.h | 65 +++++++ .../stream_executor_internal.h | 32 +++- .../stream_executor/stream_executor_pimpl.cc | 38 +++- .../stream_executor/stream_executor_pimpl.h | 76 ++++++-- 6 files changed, 331 insertions(+), 75 deletions(-) create mode 100644 tensorflow/stream_executor/module_spec.h diff --git a/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc b/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc index 259c813c57..73f05b94db 100644 --- a/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc +++ b/tensorflow/stream_executor/cuda/cuda_gpu_executor.cc @@ -206,6 +206,48 @@ static string GetBinaryDir(bool strip_exe) { return exe_path; } +bool CUDAExecutor::LoadModuleFromCuBin(const char *cubin, CUmodule *module) { + uint64_t module_refcount; + std::tie(*module, module_refcount) = gpu_binary_to_module_[cubin]; + + if (*module == nullptr) { + auto load_status = CUDADriver::LoadCubin(context_, cubin, module); + if (!load_status.ok()) { + LOG(ERROR) << "failed to load CUBIN: " << load_status; + return false; + } + module_refcount = 1; + VLOG(3) << "Loaded CUBIN " << static_cast(cubin) + << " as module " << *module; + } else { + ++module_refcount; + VLOG(3) << "CUBIN " << static_cast(cubin) + << " is already loaded as module " << *module; + } + gpu_binary_to_module_[cubin] = {*module, module_refcount}; + return true; +} + +bool CUDAExecutor::LoadModuleFromPtx(const char *ptx, CUmodule *module) { + uint64_t module_refcount; + std::tie(*module, module_refcount) = gpu_binary_to_module_[ptx]; + + if (*module == nullptr) { + if (!CUDADriver::LoadPtx(context_, ptx, module)) { + return false; + } + VLOG(3) << "Loaded PTX " << static_cast(ptx) << " as module " + << *module; + module_refcount = 1; + } else { + ++module_refcount; + VLOG(3) << "PTX " << static_cast(ptx) + << " is already loaded as module " << module; + } + gpu_binary_to_module_[ptx] = {*module, module_refcount}; + return true; +} + bool CUDAExecutor::GetKernel(const MultiKernelLoaderSpec &spec, KernelBase *kernel) { CUDAKernel *cuda_kernel = AsCUDAKernel(kernel); @@ -215,28 +257,13 @@ bool CUDAExecutor::GetKernel(const MultiKernelLoaderSpec &spec, VLOG(3) << "GetKernel on kernel " << kernel << " : " << kernel->name(); if (spec.has_cuda_cubin_in_memory()) { + mutex_lock lock{in_memory_modules_mu_}; kernelname = &spec.cuda_cubin_in_memory().kernelname(); const char *cubin = spec.cuda_cubin_in_memory().bytes(); - mutex_lock lock{in_memory_modules_mu_}; - uint64_t module_refcount; - std::tie(module, module_refcount) = gpu_binary_to_module_[cubin]; - - if (module == nullptr) { - auto load_status = CUDADriver::LoadCubin(context_, cubin, &module); - if (!load_status.ok()) { - LOG(ERROR) << "failed to load CUBIN: " << load_status; - return false; - } - module_refcount = 1; - VLOG(3) << "Loaded CUBIN " << static_cast(cubin) - << " as module " << module; - } else { - ++module_refcount; - VLOG(3) << "CUBIN " << static_cast(cubin) - << " is already loaded as module " << module; + if (!LoadModuleFromCuBin(cubin, &module)) { + return false; } kernel_to_gpu_binary_[kernel] = cubin; - gpu_binary_to_module_[cubin] = {module, module_refcount}; } else if (spec.has_cuda_ptx_in_memory()) { kernelname = &spec.cuda_ptx_in_memory().kernelname(); @@ -254,24 +281,10 @@ bool CUDAExecutor::GetKernel(const MultiKernelLoaderSpec &spec, } mutex_lock lock{in_memory_modules_mu_}; - uint64_t module_refcount; - std::tie(module, module_refcount) = gpu_binary_to_module_[ptx]; - - if (module == nullptr) { - if (!CUDADriver::LoadPtx(context_, ptx, &module)) { - LOG(ERROR) << "failed to load PTX for kernel " << *kernelname; - return false; - } - VLOG(3) << "Loaded PTX " << static_cast(ptx) - << " as module " << module; - module_refcount = 1; - } else { - ++module_refcount; - VLOG(3) << "PTX " << static_cast(ptx) - << " is already loaded as module " << module; + if (!LoadModuleFromPtx(ptx, &module)) { + return false; } kernel_to_gpu_binary_[kernel] = ptx; - gpu_binary_to_module_[ptx] = {module, module_refcount}; } else { LOG(WARNING) << "no method of loading CUDA kernel provided"; return false; @@ -295,6 +308,23 @@ bool CUDAExecutor::GetKernel(const MultiKernelLoaderSpec &spec, return true; } +bool CUDAExecutor::UnloadGpuBinary(const void *gpu_binary) { + auto module_it = gpu_binary_to_module_.find(gpu_binary); + if (gpu_binary_to_module_.end() == module_it) { + VLOG(3) << "No loaded CUDA module for " << gpu_binary; + return false; + } + auto &module = module_it->second.first; + auto &refcount = module_it->second.second; + VLOG(3) << "Found CUDA module " << module << " with refcount " << refcount; + if (--refcount == 0) { + VLOG(3) << "Unloading CUDA module " << module; + CUDADriver::UnloadModule(context_, module); + gpu_binary_to_module_.erase(module_it); + } + return true; +} + void CUDAExecutor::UnloadKernel(const KernelBase *kernel) { VLOG(3) << "Unloading kernel " << kernel << " : " << kernel->name(); @@ -307,25 +337,52 @@ void CUDAExecutor::UnloadKernel(const KernelBase *kernel) { } VLOG(3) << "Kernel " << kernel << " : " << kernel->name() << " has loaded GPU code " << gpu_binary_it->second; - auto module_it = gpu_binary_to_module_.find(gpu_binary_it->second); - if (gpu_binary_to_module_.end() == module_it) { - VLOG(3) << "Kernel " << kernel << " : " << kernel->name() - << " has no loaded CUDA module."; - return; // This kernel never loaded any modules - } - auto &module = module_it->second.first; - auto &refcount = module_it->second.second; - VLOG(3) << "Kernel " << kernel << " : " << kernel->name() - << " has loaded GPU code " << gpu_binary_it->second - << " into CUDA module " << module << " with refcount " << refcount; - if (--refcount == 0) { - VLOG(3) << "Unloading CUDA module " << module; - CUDADriver::UnloadModule(context_, module); - gpu_binary_to_module_.erase(module_it); - } + UnloadGpuBinary(gpu_binary_it->second); kernel_to_gpu_binary_.erase(gpu_binary_it); } +bool CUDAExecutor::LoadModule(const MultiModuleLoaderSpec &spec, + ModuleHandle *module_handle) { + // In CUDAExecutor we store the pointer to the GPU binary (PTX or CUBIN) as + // ModuleHandle::id(). + CUmodule cu_module; + if (spec.has_cuda_cubin_in_memory()) { + mutex_lock lock{in_memory_modules_mu_}; + if (!LoadModuleFromCuBin( + reinterpret_cast(spec.cuda_cubin_in_memory().data()), + &cu_module)) { + return false; + } + *module_handle = ModuleHandle(const_cast( + static_cast(spec.cuda_cubin_in_memory().data()))); + return true; + } else if (spec.has_cuda_ptx_in_memory()) { + if (cc_major_ == 0 && cc_minor_ == 0) { + return false; + } + + if (!spec.cuda_ptx_in_memory()) { + return false; + } + + mutex_lock lock{in_memory_modules_mu_}; + if (!LoadModuleFromPtx(spec.cuda_ptx_in_memory(), &cu_module)) { + return false; + } + *module_handle = ModuleHandle(const_cast( + static_cast(spec.cuda_ptx_in_memory()))); + return true; + } + LOG(WARNING) << "no method of loading CUDA module provided"; + return false; +} + +bool CUDAExecutor::UnloadModule(ModuleHandle module_handle) { + const char *gpu_binary = reinterpret_cast(module_handle.id()); + mutex_lock lock{in_memory_modules_mu_}; + return UnloadGpuBinary(gpu_binary); +} + bool CUDAExecutor::GetKernelMetadata(CUDAKernel *cuda_kernel, KernelMetadata *kernel_metadata) { int value; @@ -783,16 +840,26 @@ bool CUDAExecutor::DeviceMemoryUsage(int64 *free, int64 *total) const { return CUDADriver::GetDeviceMemoryInfo(context_, free, total); } -bool CUDAExecutor::GetSymbol(const string& symbol_name, void **mem, +bool CUDAExecutor::GetSymbol(const string &symbol_name, + ModuleHandle module_handle, void **mem, size_t *bytes) { + auto lookup_in_module = [&](CUmodule module) { + CHECK(module != nullptr); + return CUDADriver::GetModuleSymbol(context_, module, symbol_name.c_str(), + reinterpret_cast(mem), + bytes); + }; + { // give limited scope to mutex_lock mutex_lock lock{in_memory_modules_mu_}; + if (static_cast(module_handle)) { + auto it = gpu_binary_to_module_.find(module_handle.id()); + CHECK(it != gpu_binary_to_module_.end()); + return lookup_in_module(it->second.first); + } + for (auto &it : gpu_binary_to_module_) { - CUmodule module = it.second.first; - CHECK(module != nullptr); - if (CUDADriver::GetModuleSymbol(context_, module, symbol_name.c_str(), - reinterpret_cast(mem), - bytes)) { + if (lookup_in_module(it.second.first)) { return true; } } diff --git a/tensorflow/stream_executor/cuda/cuda_gpu_executor.h b/tensorflow/stream_executor/cuda/cuda_gpu_executor.h index f7c341c857..8a954d5461 100644 --- a/tensorflow/stream_executor/cuda/cuda_gpu_executor.h +++ b/tensorflow/stream_executor/cuda/cuda_gpu_executor.h @@ -62,6 +62,9 @@ class CUDAExecutor : public internal::StreamExecutorInterface { bool GetKernel(const MultiKernelLoaderSpec &spec, KernelBase *kernel) override; void UnloadKernel(const KernelBase *kernel) override; + bool LoadModule(const MultiModuleLoaderSpec &spec, + ModuleHandle *module_handle) override; + bool UnloadModule(ModuleHandle module_handle) override; bool Launch(Stream *stream, const ThreadDim &thread_dims, const BlockDim &block_dims, const KernelBase &k, @@ -175,7 +178,8 @@ class CUDAExecutor : public internal::StreamExecutorInterface { // Search for the symbol and returns a device pointer and size. // Returns false if symbol does not exist. - bool GetSymbol(const string& symbol_name, void **mem, size_t *bytes) override; + bool GetSymbol(const string &symbol_name, ModuleHandle module_handle, + void **mem, size_t *bytes) override; DeviceDescription *PopulateDeviceDescription() const override; @@ -239,6 +243,16 @@ class CUDAExecutor : public internal::StreamExecutorInterface { void VlogOccupancyInfo(const KernelBase &kernel, const ThreadDim &thread_dims, const BlockDim &block_dims); + bool LoadModuleFromCuBin(const char *cubin, CUmodule *module) + EXCLUSIVE_LOCKS_REQUIRED(in_memory_modules_mu_); + + // Loads the PTX text `ptx` as a CUDA module. `ptx` must be null terminated. + bool LoadModuleFromPtx(const char *ptx, CUmodule *module) + EXCLUSIVE_LOCKS_REQUIRED(in_memory_modules_mu_); + + bool UnloadGpuBinary(const void *gpu_binary) + EXCLUSIVE_LOCKS_REQUIRED(in_memory_modules_mu_); + // Guards the in-memory-module mapping. mutex in_memory_modules_mu_; diff --git a/tensorflow/stream_executor/module_spec.h b/tensorflow/stream_executor/module_spec.h new file mode 100644 index 0000000000..212ae7ba9c --- /dev/null +++ b/tensorflow/stream_executor/module_spec.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_STREAM_EXECUTOR_MODULE_SPEC_H_ +#define TENSORFLOW_STREAM_EXECUTOR_MODULE_SPEC_H_ + +#include "tensorflow/stream_executor/lib/array_slice.h" +#include "tensorflow/stream_executor/lib/stringpiece.h" +#include "tensorflow/stream_executor/platform/logging.h" +#include "tensorflow/stream_executor/platform/port.h" + +namespace stream_executor { + +// Describes how to load a module on a target platform. +// +// The exact meaning of a "module" may differ from platform to platform but +// loosely speaking a module a collection of kernels and global variables. It +// corresponds to CUmodule when running on CUDA. +class MultiModuleLoaderSpec { + public: + bool has_cuda_cubin_in_memory() const { return has_cuda_cubin_in_memory_; } + port::ArraySlice cuda_cubin_in_memory() const { + CHECK(has_cuda_cubin_in_memory()); + return {cuda_cubin_in_memory_.data(), cuda_cubin_in_memory_.size()}; + } + + bool has_cuda_ptx_in_memory() const { return has_cuda_ptx_in_memory_; } + const char* cuda_ptx_in_memory() const { + CHECK(has_cuda_ptx_in_memory()); + return cuda_ptx_in_memory_; + } + + void AddCudaCubinInMemory(port::ArraySlice cubin_bytes) { + has_cuda_cubin_in_memory_ = true; + cuda_cubin_in_memory_ = cubin_bytes; + } + + void AddCudaPtxInMemory(const char* ptx) { + has_cuda_ptx_in_memory_ = true; + // The CUDA driver does not like getting an empty string as PTX. + cuda_ptx_in_memory_ = *ptx ? ptx : nullptr; + } + + private: + port::ArraySlice cuda_cubin_in_memory_; + bool has_cuda_cubin_in_memory_ = false; + const char* cuda_ptx_in_memory_; + bool has_cuda_ptx_in_memory_ = false; +}; + +} // namespace stream_executor + +#endif // TENSORFLOW_STREAM_EXECUTOR_MODULE_SPEC_H_ diff --git a/tensorflow/stream_executor/stream_executor_internal.h b/tensorflow/stream_executor/stream_executor_internal.h index fb1b92cb84..f34b1fc083 100644 --- a/tensorflow/stream_executor/stream_executor_internal.h +++ b/tensorflow/stream_executor/stream_executor_internal.h @@ -36,20 +36,38 @@ limitations under the License. #include "tensorflow/stream_executor/kernel_cache_config.h" #include "tensorflow/stream_executor/kernel_spec.h" #include "tensorflow/stream_executor/launch_dim.h" +#include "tensorflow/stream_executor/lib/inlined_vector.h" #include "tensorflow/stream_executor/lib/status.h" #include "tensorflow/stream_executor/lib/statusor.h" +#include "tensorflow/stream_executor/module_spec.h" #include "tensorflow/stream_executor/platform.h" #include "tensorflow/stream_executor/platform/port.h" #include "tensorflow/stream_executor/plugin_registry.h" #include "tensorflow/stream_executor/shared_memory_config.h" #include "tensorflow/stream_executor/trace_listener.h" -#include "tensorflow/stream_executor/lib/inlined_vector.h" namespace stream_executor { class Stream; class Timer; +// An opaque handle to a loaded module. +// +// An instance of this is returned from StreamExecutor::GetModule. +class ModuleHandle { + public: + /*implicit*/ ModuleHandle(void *id = nullptr) : id_(id) {} + + // A ModuleHandle with id() == nullptr is an invalid module handle, akin to a + // null pointer. + void *id() const { return id_; } + + explicit operator bool() const { return id() != nullptr; } + + private: + void *id_; +}; + namespace internal { // Platform-dependent interface class for the generic Events interface, in @@ -164,6 +182,11 @@ class StreamExecutorInterface { KernelBase *kernel) { return false; } + virtual bool LoadModule(const MultiModuleLoaderSpec &spec, + ModuleHandle *module_handle) { + return false; + } + virtual bool UnloadModule(ModuleHandle module_handle) { return false; } virtual bool Launch(Stream *stream, const ThreadDim &thread_dims, const BlockDim &block_dims, const KernelBase &k, const KernelArgsArrayBase &args) { @@ -247,7 +270,12 @@ class StreamExecutorInterface { // null, however, both of them cannot be null at the same time. To use // constant memory in CUDA, GetSymbol has to be used. Returns true if symbol // is found. - virtual bool GetSymbol(const string& symbol_name, void **mem, size_t *bytes) { + // + // If ModuleHandle is set then we search for `symbol_name` only within the + // module corresponding to `module_handle`. Otherwise all loaded modules are + // searched. + virtual bool GetSymbol(const string &symbol_name, ModuleHandle module_handle, + void **mem, size_t *bytes) { return false; } diff --git a/tensorflow/stream_executor/stream_executor_pimpl.cc b/tensorflow/stream_executor/stream_executor_pimpl.cc index 000795ff00..2e0137a485 100644 --- a/tensorflow/stream_executor/stream_executor_pimpl.cc +++ b/tensorflow/stream_executor/stream_executor_pimpl.cc @@ -220,6 +220,15 @@ void StreamExecutor::UnloadKernel(const KernelBase *kernel) { implementation_->UnloadKernel(kernel); } +bool StreamExecutor::LoadModule(const MultiModuleLoaderSpec &spec, + ModuleHandle *module_handle) { + return implementation_->LoadModule(spec, module_handle); +} + +bool StreamExecutor::UnloadModule(ModuleHandle module_handle) { + return implementation_->UnloadModule(module_handle); +} + void StreamExecutor::Deallocate(DeviceMemoryBase *mem) { VLOG(1) << "Called StreamExecutor::Deallocate(mem=" << mem->opaque() << ") mem->size()=" << mem->size() << StackTraceIfVLOG10(); @@ -459,9 +468,34 @@ void *StreamExecutor::Allocate(uint64 size) { return buf; } -bool StreamExecutor::GetSymbol(const string &symbol_name, void **mem, +port::StatusOr StreamExecutor::GetUntypedSymbol( + const string &symbol_name, ModuleHandle module_handle) { + // If failed to get the symbol, opaque/bytes are unchanged. Initialize them to + // be nullptr/0 for consistency with DeviceMemory semantics. + void *opaque = nullptr; + size_t bytes = 0; + if (GetSymbol(symbol_name, module_handle, &opaque, &bytes)) { + return DeviceMemoryBase(opaque, bytes); + } + + if (static_cast(module_handle)) { + return port::Status( + port::error::NOT_FOUND, + port::StrCat("Check if module containing symbol ", symbol_name, + " is loaded (module_handle = ", + reinterpret_cast(module_handle.id()), ")")); + } else { + return port::Status( + port::error::NOT_FOUND, + port::StrCat("Check if kernel using the symbol is loaded: ", + symbol_name)); + } +} + +bool StreamExecutor::GetSymbol(const string &symbol_name, + ModuleHandle module_handle, void **mem, size_t *bytes) { - return implementation_->GetSymbol(symbol_name, mem, bytes); + return implementation_->GetSymbol(symbol_name, module_handle, mem, bytes); } void *StreamExecutor::UnifiedMemoryAllocate(uint64 bytes) { diff --git a/tensorflow/stream_executor/stream_executor_pimpl.h b/tensorflow/stream_executor/stream_executor_pimpl.h index ad80a1ba25..47b3a2b030 100644 --- a/tensorflow/stream_executor/stream_executor_pimpl.h +++ b/tensorflow/stream_executor/stream_executor_pimpl.h @@ -106,6 +106,16 @@ class StreamExecutor { // Releases any state associated with the previously loaded kernel. void UnloadKernel(const KernelBase *kernel); + // Loads a module for the platform this StreamExecutor is acting upon. + // + // `spec` describes the module to be loaded. On success writes the handle for + // the loaded module to `module_handle` and returns true. Else returns false. + bool LoadModule(const MultiModuleLoaderSpec &spec, + ModuleHandle *module_handle); + + // Unloads the module with handle `module_handle`. + bool UnloadModule(ModuleHandle module_handle); + // Synchronously allocates an array on the device of type T with element_count // elements. template @@ -169,8 +179,16 @@ class StreamExecutor { // type of symbol and T match. // - Note: symbol_name should include its namespace as well. For example, // pass "nms0::symbol" if referring to nms0::symbol. + // + // If `module_handle` is set then searches only within the module + // corresponding to `module_handle`. template - port::StatusOr> GetSymbol(const string &symbol_name); + port::StatusOr> GetSymbol(const string &symbol_name, + ModuleHandle module_handle = {}); + + // An untyped version of GetSymbol. + port::StatusOr GetUntypedSymbol( + const string &symbol_name, ModuleHandle module_handle = {}); // Deallocate the DeviceMemory previously allocated via this interface. // Deallocation of a nullptr-representative value is permitted. @@ -507,7 +525,8 @@ class StreamExecutor { // Finds and retrieves device memory for the symbol on the underlying // platform. - bool GetSymbol(const string& symbol_name, void **mem, size_t *bytes); + bool GetSymbol(const string &symbol_name, ModuleHandle module_handle, + void **mem, size_t *bytes); // Entrains a memcpy operation onto stream, with a host destination location // host_dst and a device memory source, with target size size. @@ -678,6 +697,41 @@ class StreamExecutor { SE_DISALLOW_COPY_AND_ASSIGN(StreamExecutor); }; +// A wrapper around ModuleHandle that uses RAII to manage its lifetime. +class ScopedModuleHandle { + public: + explicit ScopedModuleHandle(StreamExecutor *executor, + ModuleHandle module_handle) + : executor_(executor), module_handle_(module_handle) {} + + ScopedModuleHandle(ScopedModuleHandle &&other) { + executor_ = other.executor_; + module_handle_ = other.module_handle_; + other.executor_ = nullptr; + other.module_handle_ = ModuleHandle(); + } + + ScopedModuleHandle &operator=(ScopedModuleHandle &&other) { + executor_ = other.executor_; + module_handle_ = other.module_handle_; + other.executor_ = nullptr; + other.module_handle_ = ModuleHandle(); + return *this; + } + + ~ScopedModuleHandle() { + if (static_cast(module_handle_)) { + CHECK(executor_->UnloadModule(module_handle_)); + } + } + + private: + StreamExecutor *executor_; + ModuleHandle module_handle_; + + TF_DISALLOW_COPY_AND_ASSIGN(ScopedModuleHandle); +}; + //////////// // Inlines @@ -690,19 +744,13 @@ inline DeviceMemory StreamExecutor::AllocateArray(uint64 element_count) { template inline port::StatusOr> StreamExecutor::GetSymbol( - const string &symbol_name) { - // If failed to get the symbol, opaque/bytes are unchanged. Initialize them to - // be nullptr/0 for consistency with DeviceMemory semantics. - void *opaque = nullptr; - size_t bytes = 0; - if (GetSymbol(symbol_name, &opaque, &bytes)) { - CHECK_EQ(bytes % sizeof(T), 0); - return DeviceMemory::MakeFromByteSize(opaque, bytes); + const string &symbol_name, ModuleHandle module_handle) { + port::StatusOr untyped_symbol = + GetUntypedSymbol(symbol_name, module_handle); + if (!untyped_symbol.ok()) { + return untyped_symbol.status(); } - return port::Status( - port::error::NOT_FOUND, - port::StrCat("Check if kernel using the symbol is loaded: ", - symbol_name)); + return DeviceMemory(untyped_symbol.ValueOrDie()); } template -- GitLab From 806105c2f5c43cee58ab997b1822286bc3f15ad7 Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Mon, 23 Jul 2018 16:29:59 -0700 Subject: [PATCH 294/519] Remove unnecessary includes PiperOrigin-RevId: 205744600 --- tensorflow/contrib/lite/kernels/activations.cc | 1 - tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc | 1 - tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc | 1 - tensorflow/contrib/lite/kernels/concatenation.cc | 1 - tensorflow/contrib/lite/kernels/conv.cc | 1 - tensorflow/contrib/lite/kernels/depthwise_conv.cc | 1 - tensorflow/contrib/lite/kernels/embedding_lookup.cc | 1 - tensorflow/contrib/lite/kernels/fully_connected.cc | 1 - tensorflow/contrib/lite/kernels/hashtable_lookup.cc | 1 - tensorflow/contrib/lite/kernels/lsh_projection.cc | 1 - tensorflow/contrib/lite/kernels/lstm.cc | 1 - tensorflow/contrib/lite/kernels/pooling.cc | 1 - tensorflow/contrib/lite/kernels/sparse_to_dense.cc | 1 - tensorflow/contrib/lite/kernels/svdf.cc | 1 - tensorflow/contrib/lite/kernels/transpose_conv.cc | 1 - tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc | 1 - tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc | 1 - tensorflow/contrib/lite/model.cc | 1 - tensorflow/contrib/lite/model_test.cc | 1 - 19 files changed, 19 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/activations.cc b/tensorflow/contrib/lite/kernels/activations.cc index d5ac2a7814..6e13b8c667 100644 --- a/tensorflow/contrib/lite/kernels/activations.cc +++ b/tensorflow/contrib/lite/kernels/activations.cc @@ -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. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc index 14a19aeef3..a11a59aa05 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_lstm.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc index aa24c1f34c..517309a226 100644 --- a/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc +++ b/tensorflow/contrib/lite/kernels/bidirectional_sequence_rnn.cc @@ -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. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/concatenation.cc b/tensorflow/contrib/lite/kernels/concatenation.cc index 45ea8d0049..ad211e9c67 100644 --- a/tensorflow/contrib/lite/kernels/concatenation.cc +++ b/tensorflow/contrib/lite/kernels/concatenation.cc @@ -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. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/conv.cc b/tensorflow/contrib/lite/kernels/conv.cc index a4fe9e5550..6f174763df 100644 --- a/tensorflow/contrib/lite/kernels/conv.cc +++ b/tensorflow/contrib/lite/kernels/conv.cc @@ -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. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/depthwise_conv.cc b/tensorflow/contrib/lite/kernels/depthwise_conv.cc index 16e5f1d065..21518156b8 100644 --- a/tensorflow/contrib/lite/kernels/depthwise_conv.cc +++ b/tensorflow/contrib/lite/kernels/depthwise_conv.cc @@ -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. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/embedding_lookup.cc b/tensorflow/contrib/lite/kernels/embedding_lookup.cc index f550339d03..b2dff87e62 100644 --- a/tensorflow/contrib/lite/kernels/embedding_lookup.cc +++ b/tensorflow/contrib/lite/kernels/embedding_lookup.cc @@ -29,7 +29,6 @@ limitations under the License. // When indices are out of bound, the ops will not succeed. // -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/fully_connected.cc b/tensorflow/contrib/lite/kernels/fully_connected.cc index d6e297a66a..bc370608c0 100644 --- a/tensorflow/contrib/lite/kernels/fully_connected.cc +++ b/tensorflow/contrib/lite/kernels/fully_connected.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/hashtable_lookup.cc b/tensorflow/contrib/lite/kernels/hashtable_lookup.cc index 41211d41aa..f37c66acb3 100644 --- a/tensorflow/contrib/lite/kernels/hashtable_lookup.cc +++ b/tensorflow/contrib/lite/kernels/hashtable_lookup.cc @@ -31,7 +31,6 @@ limitations under the License. // Each item indicates whether the corresponding lookup has a returned value. // 0 for missing key, 1 for found key. -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/lsh_projection.cc b/tensorflow/contrib/lite/kernels/lsh_projection.cc index 25d2dc2cdd..69523b02cc 100644 --- a/tensorflow/contrib/lite/kernels/lsh_projection.cc +++ b/tensorflow/contrib/lite/kernels/lsh_projection.cc @@ -50,7 +50,6 @@ limitations under the License. // Output.Dim == { Tensor[0].Dim[0] * Tensor[0].Dim[1] } // A flattened tensor represents projected bit vectors. -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/lstm.cc b/tensorflow/contrib/lite/kernels/lstm.cc index 50487a8d59..ba251c451e 100644 --- a/tensorflow/contrib/lite/kernels/lstm.cc +++ b/tensorflow/contrib/lite/kernels/lstm.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/pooling.cc b/tensorflow/contrib/lite/kernels/pooling.cc index 9b0487ae16..29a5be0683 100644 --- a/tensorflow/contrib/lite/kernels/pooling.cc +++ b/tensorflow/contrib/lite/kernels/pooling.cc @@ -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. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/sparse_to_dense.cc b/tensorflow/contrib/lite/kernels/sparse_to_dense.cc index 404c32ad9c..7be5e66c16 100644 --- a/tensorflow/contrib/lite/kernels/sparse_to_dense.cc +++ b/tensorflow/contrib/lite/kernels/sparse_to_dense.cc @@ -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. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/svdf.cc b/tensorflow/contrib/lite/kernels/svdf.cc index 179c2dc266..6d4912ce3a 100644 --- a/tensorflow/contrib/lite/kernels/svdf.cc +++ b/tensorflow/contrib/lite/kernels/svdf.cc @@ -16,7 +16,6 @@ limitations under the License. // SVDF op that compresses a fully connected op via low-rank matrix // factorization. See https://research.google.com/pubs/archive/43813.pdf for // details. -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/transpose_conv.cc b/tensorflow/contrib/lite/kernels/transpose_conv.cc index 8b9deeed20..a9baa5c698 100644 --- a/tensorflow/contrib/lite/kernels/transpose_conv.cc +++ b/tensorflow/contrib/lite/kernels/transpose_conv.cc @@ -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. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc index c48b470f92..0acd705950 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_lstm.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc index 164a0cbd08..0d6d29a171 100644 --- a/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc +++ b/tensorflow/contrib/lite/kernels/unidirectional_sequence_rnn.cc @@ -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. ==============================================================================*/ -#include #include #include #include diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index 5e6106a87e..d318591b49 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -19,7 +19,6 @@ limitations under the License. #include #include #include -#include #include "tensorflow/contrib/lite/allocation.h" #include "tensorflow/contrib/lite/builtin_op_data.h" diff --git a/tensorflow/contrib/lite/model_test.cc b/tensorflow/contrib/lite/model_test.cc index 15bae21a41..edfdec9315 100644 --- a/tensorflow/contrib/lite/model_test.cc +++ b/tensorflow/contrib/lite/model_test.cc @@ -19,7 +19,6 @@ limitations under the License. #include #include #include -#include #include "tensorflow/contrib/lite/model.h" -- GitLab From 7087243b8594faa92b274b92d586cbb2d3b24bfe Mon Sep 17 00:00:00 2001 From: Karmel Allison Date: Mon, 23 Jul 2018 16:40:25 -0700 Subject: [PATCH 295/519] Automated rollback of commit cf94a46c34f8568608d78b77e9a1c4369ebcafa2 PiperOrigin-RevId: 205746329 --- tensorflow/cc/saved_model/loader.cc | 39 ++++++++-- tensorflow/python/saved_model/builder_impl.py | 76 +++++++++---------- tensorflow/python/saved_model/loader_impl.py | 42 +++++++--- .../python/saved_model/saved_model_test.py | 28 ++----- 4 files changed, 106 insertions(+), 79 deletions(-) diff --git a/tensorflow/cc/saved_model/loader.cc b/tensorflow/cc/saved_model/loader.cc index d47b025743..07807ed2f3 100644 --- a/tensorflow/cc/saved_model/loader.cc +++ b/tensorflow/cc/saved_model/loader.cc @@ -86,11 +86,10 @@ bool HasMainOp(const MetaGraphDef& meta_graph_def) { Status RunMainOp(const RunOptions& run_options, const string& export_dir, const MetaGraphDef& meta_graph_def, const std::vector& asset_file_defs, - Session* session, const string& main_op_key) { - LOG(INFO) << "Running MainOp with key " << main_op_key - << " on SavedModel bundle."; + Session* session) { + LOG(INFO) << "Running MainOp on SavedModel bundle."; const auto& collection_def_map = meta_graph_def.collection_def(); - const auto main_op_it = collection_def_map.find(main_op_key); + const auto main_op_it = collection_def_map.find(kSavedModelMainOpKey); if (main_op_it != collection_def_map.end()) { if (main_op_it->second.node_list().value_size() != 1) { return errors::FailedPrecondition( @@ -142,6 +141,30 @@ Status RunRestore(const RunOptions& run_options, const string& export_dir, nullptr /* outputs */, &run_metadata); } +Status RunLegacyInitOp(const RunOptions& run_options, const string& export_dir, + const MetaGraphDef& meta_graph_def, + const std::vector& asset_file_defs, + Session* session) { + LOG(INFO) << "Running LegacyInitOp on SavedModel bundle."; + const auto& collection_def_map = meta_graph_def.collection_def(); + const auto init_op_it = collection_def_map.find(kSavedModelLegacyInitOpKey); + if (init_op_it != collection_def_map.end()) { + if (init_op_it->second.node_list().value_size() != 1) { + return errors::FailedPrecondition(strings::StrCat( + "Expected exactly one serving init op in : ", export_dir)); + } + std::vector> inputs; + AddAssetsTensorsToInputs(export_dir, asset_file_defs, &inputs); + RunMetadata run_metadata; + const StringPiece legacy_init_op_name = + init_op_it->second.node_list().value(0); + return session->Run(run_options, inputs, {}, + {legacy_init_op_name.ToString()}, nullptr /* outputs */, + &run_metadata); + } + return Status::OK(); +} + Status GetAssetFileDefs(const MetaGraphDef& meta_graph_def, std::vector* asset_file_defs) { const auto& collection_def_map = meta_graph_def.collection_def(); @@ -181,11 +204,11 @@ Status LoadSavedModelInternal(const SessionOptions& session_options, if (HasMainOp(bundle->meta_graph_def)) { TF_RETURN_IF_ERROR(RunMainOp(run_options, export_dir, bundle->meta_graph_def, asset_file_defs, - bundle->session.get(), kSavedModelMainOpKey)); + bundle->session.get())); } else { - TF_RETURN_IF_ERROR(RunMainOp( - run_options, export_dir, bundle->meta_graph_def, asset_file_defs, - bundle->session.get(), kSavedModelLegacyInitOpKey)); + TF_RETURN_IF_ERROR(RunLegacyInitOp(run_options, export_dir, + bundle->meta_graph_def, asset_file_defs, + bundle->session.get())); } return Status::OK(); } diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py index 8c985a7c2f..e58be804c2 100644 --- a/tensorflow/python/saved_model/builder_impl.py +++ b/tensorflow/python/saved_model/builder_impl.py @@ -34,7 +34,6 @@ from tensorflow.python.platform import tf_logging from tensorflow.python.saved_model import constants from tensorflow.python.training import saver as tf_saver from tensorflow.python.util import compat -from tensorflow.python.util.deprecation import deprecated_args from tensorflow.python.util.tf_export import tf_export @@ -134,32 +133,39 @@ class SavedModelBuilder(object): tf_logging.info("Assets written to: %s", compat.as_text(assets_destination_dir)) - def _maybe_add_main_op(self, main_op): - """Adds main op to the SavedModel. + def _maybe_add_legacy_init_op(self, legacy_init_op=None): + """Add legacy init op to the SavedModel. Args: - main_op: Main op to run as part of graph initialization. If None, no - main op will be added to the graph. + legacy_init_op: Optional legacy init op to support backward compatibility. Raises: - TypeError: if main op is provided but is not of type `Operation`. - ValueError: if the Graph already contains an init op. + TypeError if legacy init op is not of type `Operation`. + AssertionError if the graph already contains one or more legacy init ops. """ - if main_op is None: - return - - if not isinstance(main_op, ops.Operation): - raise TypeError("main_op needs to be an Operation: %r" % main_op) + if legacy_init_op is not None: + if not isinstance(legacy_init_op, ops.Operation): + raise TypeError("legacy_init_op needs to be an Operation: %r" % + legacy_init_op) + if ops.get_collection(constants.LEGACY_INIT_OP_KEY): + raise AssertionError( + "graph already contains one or more legacy init ops under the " + "collection {}.".format(constants.LEGACY_INIT_OP_KEY)) + ops.add_to_collection(constants.LEGACY_INIT_OP_KEY, legacy_init_op) + + def _add_main_op(self, main_op): + """Add main op to the SavedModel. - # Validate that no other init ops have been added to this graph already. - # We check main_op and legacy_init_op for thoroughness and explicitness. - for init_op_key in (constants.MAIN_OP_KEY, constants.LEGACY_INIT_OP_KEY): - if ops.get_collection(init_op_key): - raise ValueError( - "Graph already contains one or more main ops under the " - "collection {}.".format(init_op_key)) + Args: + main_op: Main op to run as part of graph initialization. - ops.add_to_collection(constants.MAIN_OP_KEY, main_op) + Raises: + TypeError if main op is not of type `Operation`. + """ + if main_op is not None: + if not isinstance(main_op, ops.Operation): + raise TypeError("main_op needs to be an Operation: %r" % main_op) + ops.add_to_collection(constants.MAIN_OP_KEY, main_op) def _add_train_op(self, train_op): """Add train op to the SavedModel. @@ -251,12 +257,16 @@ class SavedModelBuilder(object): self._validate_tensor_info(outputs[outputs_key]) def _add_collections( - self, assets_collection, main_op, train_op): + self, assets_collection, legacy_init_op, main_op, train_op): """Add asset and op collections to be saved.""" # Save asset files and write them to disk, if any. self._save_and_write_assets(assets_collection) - self._maybe_add_main_op(main_op) + if main_op is None: + # Add legacy init op to the SavedModel. + self._maybe_add_legacy_init_op(legacy_init_op) + else: + self._add_main_op(main_op) self._add_train_op(train_op) @@ -272,9 +282,6 @@ class SavedModelBuilder(object): allow_empty=True) return saver - @deprecated_args(None, - "Pass your op to the equivalent parameter main_op instead.", - "legacy_init_op") def add_meta_graph(self, tags, signature_def_map=None, @@ -299,7 +306,7 @@ class SavedModelBuilder(object): that this collection should be a subset of the assets saved as part of the first meta graph in the SavedModel. legacy_init_op: Legacy support for op or group of ops to execute after the - restore op upon a load. Deprecated; please use main_op instead. + restore op upon a load. clear_devices: Set to true if the device info on the default graph should be cleared. main_op: Op or group of ops to execute when the graph is loaded. Note @@ -326,12 +333,8 @@ class SavedModelBuilder(object): # properly populated. self._validate_signature_def_map(signature_def_map) - # legacy_init_op is deprecated, and going away in TF 2.0. - # Re-mapping to main_op, as treatment is identical regardless. - main_op = main_op or legacy_init_op - # Add assets and ops - self._add_collections(assets_collection, main_op, None) + self._add_collections(assets_collection, legacy_init_op, main_op, None) saver = self._maybe_create_saver(saver) @@ -348,9 +351,6 @@ class SavedModelBuilder(object): # Tag the meta graph def and add it to the SavedModel. self._tag_and_add_meta_graph(meta_graph_def, tags, signature_def_map) - @deprecated_args(None, - "Pass your op to the equivalent parameter main_op instead.", - "legacy_init_op") def add_meta_graph_and_variables(self, sess, tags, @@ -378,7 +378,7 @@ class SavedModelBuilder(object): def. assets_collection: Assets collection to be saved with SavedModel. legacy_init_op: Legacy support for op or group of ops to execute after the - restore op upon a load. Deprecated; please use main_op instead. + restore op upon a load. clear_devices: Set to true if the device info on the default graph should be cleared. main_op: Op or group of ops to execute when the graph is loaded. Note @@ -402,12 +402,8 @@ class SavedModelBuilder(object): # properly populated. self._validate_signature_def_map(signature_def_map) - # legacy_init_op is deprecated, and going away in TF 2.0. - # Re-mapping to main_op, as treatment is identical regardless. - main_op = main_op or legacy_init_op - # Add assets and ops - self._add_collections(assets_collection, main_op, None) + self._add_collections(assets_collection, legacy_init_op, main_op, None) # Create the variables sub-directory, if it does not exist. variables_dir = os.path.join( diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index fb70c91c29..e5f649fdab 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -116,14 +116,11 @@ def _get_asset_tensors(export_dir, meta_graph_def_to_load, import_scope=None): return asset_tensor_dict -def _get_main_op_tensor( - meta_graph_def_to_load, init_op_key=constants.MAIN_OP_KEY): +def _get_main_op_tensor(meta_graph_def_to_load): """Gets the main op tensor, if one exists. Args: meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded. - init_op_key: name of collection to check; should be one of MAIN_OP_KEY - or the deprecated LEGACY_INIT_OP_KEY Returns: The main op tensor, if it exists and `None` otherwise. @@ -134,15 +131,38 @@ def _get_main_op_tensor( """ collection_def = meta_graph_def_to_load.collection_def main_op_tensor = None - if init_op_key in collection_def: - main_ops = collection_def[init_op_key].node_list.value + if constants.MAIN_OP_KEY in collection_def: + main_ops = collection_def[constants.MAIN_OP_KEY].node_list.value if len(main_ops) != 1: - raise RuntimeError("Expected exactly one SavedModel main op. " - "Found: {}".format(main_ops)) - main_op_tensor = ops.get_collection(init_op_key)[0] + raise RuntimeError("Expected exactly one SavedModel main op.") + main_op_tensor = ops.get_collection(constants.MAIN_OP_KEY)[0] return main_op_tensor +def _get_legacy_init_op_tensor(meta_graph_def_to_load): + """Gets the legacy init op tensor, if one exists. + + Args: + meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded. + + Returns: + The legacy init op tensor, if it exists and `None` otherwise. + + Raises: + RuntimeError: If the collection def corresponding to the legacy init op key + has other than exactly one tensor. + """ + collection_def = meta_graph_def_to_load.collection_def + legacy_init_op_tensor = None + if constants.LEGACY_INIT_OP_KEY in collection_def: + legacy_init_ops = collection_def[ + constants.LEGACY_INIT_OP_KEY].node_list.value + if len(legacy_init_ops) != 1: + raise RuntimeError("Expected exactly one legacy serving init op.") + legacy_init_op_tensor = ops.get_collection(constants.LEGACY_INIT_OP_KEY)[0] + return legacy_init_op_tensor + + @tf_export("saved_model.loader.maybe_saved_model_directory") def maybe_saved_model_directory(export_dir): """Checks whether the provided export directory could contain a SavedModel. @@ -320,8 +340,8 @@ class SavedModelLoader(object): self._export_dir, meta_graph_def, import_scope=import_scope) main_op_tensor = ( - _get_main_op_tensor(meta_graph_def, constants.MAIN_OP_KEY) or - _get_main_op_tensor(meta_graph_def, constants.LEGACY_INIT_OP_KEY)) + _get_main_op_tensor(meta_graph_def) or + (_get_legacy_init_op_tensor(meta_graph_def))) if main_op_tensor is not None: sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py index 00b669fc97..fb4732aca2 100644 --- a/tensorflow/python/saved_model/saved_model_test.py +++ b/tensorflow/python/saved_model/saved_model_test.py @@ -846,19 +846,9 @@ class SavedModelTest(test.TestCase): def testLegacyInitOpWithNonEmptyCollection(self): export_dir = self._get_export_dir( "test_legacy_init_op_with_non_empty_collection") - self._testInitOpsWithNonEmptyCollection( - export_dir, constants.LEGACY_INIT_OP_KEY) - - def testMainOpWithNonEmptyCollection(self): - export_dir = self._get_export_dir( - "test_main_op_with_non_empty_collection") - self._testInitOpsWithNonEmptyCollection(export_dir, constants.MAIN_OP_KEY) - - def _testInitOpsWithNonEmptyCollection(self, export_dir, key): builder = saved_model_builder.SavedModelBuilder(export_dir) - g = ops.Graph() - with self.test_session(graph=g) as sess: + with self.test_session(graph=ops.Graph()) as sess: # Initialize variable `v1` to 1. v1 = variables.Variable(1, name="v1") ops.add_to_collection("v", v1) @@ -867,21 +857,19 @@ class SavedModelTest(test.TestCase): v2 = variables.Variable(42, name="v2", trainable=False, collections=[]) ops.add_to_collection("v", v2) - # Set up an assignment op to be run as part of the init op. + # Set up an assignment op to be run as part of the legacy_init_op. assign_v2 = state_ops.assign(v2, v1) - init_op = control_flow_ops.group(assign_v2, name="init_op") + legacy_init_op = control_flow_ops.group(assign_v2, name="legacy_init_op") sess.run(variables.global_variables_initializer()) - ops.add_to_collection(key, control_flow_ops.no_op()) - # ValueError should be raised since the LEGACY_INIT_OP_KEY collection + ops.add_to_collection(constants.LEGACY_INIT_OP_KEY, + control_flow_ops.no_op()) + # AssertionError should be raised since the LEGACY_INIT_OP_KEY collection # is not empty and we don't support multiple init ops. - with self.assertRaisesRegexp(ValueError, "Graph already contains"): + with self.assertRaises(AssertionError): builder.add_meta_graph_and_variables( - sess, ["foo"], legacy_init_op=init_op) - # We shouldn't be able to add as MAIN_OP, either. - with self.assertRaisesRegexp(ValueError, "Graph already contains"): - builder.add_meta_graph_and_variables(sess, ["foo"], main_op=init_op) + sess, ["foo"], legacy_init_op=legacy_init_op) def testTrainOp(self): export_dir = self._get_export_dir("test_train_op") -- GitLab From 808db1f8d49618e64170f174998bf1e0db49701f Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Mon, 23 Jul 2018 16:47:57 -0700 Subject: [PATCH 296/519] Allow ResourceVariable to be written to TensorArray. This is similar to how graph behaves. PiperOrigin-RevId: 205747377 --- tensorflow/python/ops/tensor_array_ops.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/ops/tensor_array_ops.py b/tensorflow/python/ops/tensor_array_ops.py index cc92da4fd7..f86dfb3527 100644 --- a/tensorflow/python/ops/tensor_array_ops.py +++ b/tensorflow/python/ops/tensor_array_ops.py @@ -554,7 +554,7 @@ class _EagerTensorArray(object): self._tensor_array.extend([None for _ in range(index - size + 1)]) if not isinstance(value, ops.EagerTensor): - value = constant_op.constant(value) + value = ops.convert_to_tensor(value) if self._infer_shape: if self._element_shape is None: @@ -633,8 +633,8 @@ class _EagerTensorArray(object): def split(self, value, lengths, name=None): """See TensorArray.""" # error checking to match graph-mode errors - value = constant_op.constant(value) - lengths = constant_op.constant(lengths) + value = ops.convert_to_tensor(value) + lengths = ops.convert_to_tensor(lengths) sum_lengths = math_ops.reduce_sum(lengths) if lengths.shape.ndims != 1: raise errors_impl.InvalidArgumentError( -- GitLab From 340cbbee4ee1b268378ee342bbc19bd52f30e8da Mon Sep 17 00:00:00 2001 From: Toby Boyd Date: Mon, 23 Jul 2018 16:51:22 -0700 Subject: [PATCH 297/519] Remove linking NCCL license file. PiperOrigin-RevId: 205747892 --- tensorflow/tools/docker/Dockerfile.devel-gpu | 4 ---- 1 file changed, 4 deletions(-) diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index 44120bf274..a5560e459c 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -40,10 +40,6 @@ RUN mkdir /usr/local/cuda-9.0/lib && \ ln -s /usr/lib/x86_64-linux-gnu/libnccl.so.2 /usr/local/cuda/lib/libnccl.so.2 && \ ln -s /usr/include/nccl.h /usr/local/cuda/include/nccl.h -# TODO(tobyboyd): Remove after license is excluded from BUILD file. -RUN gunzip /usr/share/doc/libnccl2/NCCL-SLA.txt.gz && \ - cp /usr/share/doc/libnccl2/NCCL-SLA.txt /usr/local/cuda/ - RUN curl -fSsL -O https://bootstrap.pypa.io/get-pip.py && \ python get-pip.py && \ rm get-pip.py -- GitLab From 6218750b0616e6b7cf14196b04549b2842f3dd99 Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Mon, 23 Jul 2018 16:51:49 -0700 Subject: [PATCH 298/519] op->Device can be a nullptr, so don't dereference directly. PiperOrigin-RevId: 205747965 --- tensorflow/core/common_runtime/eager/execute.cc | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/tensorflow/core/common_runtime/eager/execute.cc b/tensorflow/core/common_runtime/eager/execute.cc index 5ea814ed4e..27d0cd611f 100644 --- a/tensorflow/core/common_runtime/eager/execute.cc +++ b/tensorflow/core/common_runtime/eager/execute.cc @@ -88,6 +88,8 @@ Status MaybeCopyInputToExpectedDevice(EagerOperation* op, int i, TF_RETURN_IF_ERROR((*handle)->Device(&handle_device)); const Device* actual_device = handle_device == nullptr ? ctx->HostCPU() : handle_device; + const Device* op_device = + op->Device() == nullptr ? ctx->HostCPU() : op->Device(); if (expected_device != actual_device) { switch (ctx->GetDevicePlacementPolicy()) { @@ -106,8 +108,8 @@ Status MaybeCopyInputToExpectedDevice(EagerOperation* op, int i, " cannot compute ", op->Name(), " as input #", i, " was expected to be on ", expected_device->name(), " but is actually on ", - actual_device->name(), " (operation running on ", - op->Device()->name(), ")", + actual_device->name(), " (operation running on ", op_device->name(), + ")", " Tensors can be copied explicitly using .gpu() or .cpu() " "methods," " or transparently copied by using tf.enable_eager_execution(" @@ -118,7 +120,7 @@ Status MaybeCopyInputToExpectedDevice(EagerOperation* op, int i, LOG(WARNING) << "before computing " << op->Name() << " input #" << i << " was expected to be on " << expected_device->name() << " but is actually on " << actual_device->name() - << " (operation running on " << op->Device()->name() + << " (operation running on " << op_device->name() << "). This triggers a copy which can be a performance " "bottleneck."; break; -- GitLab From 04c6e7de8494835aa2df295899e66e9f5ae7c3a0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 17:21:38 -0700 Subject: [PATCH 299/519] Improve the documentation for the Android app for TensorFlow Lite. PiperOrigin-RevId: 205752218 --- tensorflow/contrib/lite/examples/android/app/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/lite/examples/android/app/README.md b/tensorflow/contrib/lite/examples/android/app/README.md index 8e12bd04dd..cbdeeac879 100644 --- a/tensorflow/contrib/lite/examples/android/app/README.md +++ b/tensorflow/contrib/lite/examples/android/app/README.md @@ -2,9 +2,9 @@ ## Building from Source with Bazel -1. Follow the [Bazel steps for the TF Demo App](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/examples/android#bazel). +1. Install [Bazel](https://docs.bazel.build/versions/master/install.html), the Android NDK and SDK. The recommended versions are specified on this [webpage](https://www.tensorflow.org/mobile/tflite/demo_android#build_tensorflow_lite_and_the_demo_app_from_source). -2. Build the app with Bazel. The demo needs C++11. We configure the fat_apk_cpu flag to package support for 4 hardware variants. You may replace it with --config=android_arm64 on a 64-bit device and --config=android_arm for 32-bit device: +2. Build this demo app with Bazel. The demo needs C++11. We configure the fat_apk_cpu flag to package support for 4 hardware variants. You may replace it with --config=android_arm64 on a 64-bit device and --config=android_arm for 32-bit device: ```shell bazel build -c opt --cxxopt='--std=c++11' --fat_apk_cpu=x86,x86_64,arm64-v8a,armeabi-v7a \ -- GitLab From 90dbbba70e1c617a36b1d71b650410d6d4d3cb02 Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Mon, 23 Jul 2018 17:29:42 -0700 Subject: [PATCH 300/519] [Java]: Release 1.10.0-rc0 PiperOrigin-RevId: 205753168 --- tensorflow/java/maven/hadoop/pom.xml | 2 +- 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/spark-connector/pom.xml | 2 +- tensorflow/java/maven/tensorflow/pom.xml | 2 +- 8 files changed, 8 insertions(+), 8 deletions(-) diff --git a/tensorflow/java/maven/hadoop/pom.xml b/tensorflow/java/maven/hadoop/pom.xml index 7391dfb965..2c2c4106cb 100644 --- a/tensorflow/java/maven/hadoop/pom.xml +++ b/tensorflow/java/maven/hadoop/pom.xml @@ -5,7 +5,7 @@ org.tensorflow hadoop jar - 1.9.0 + 1.10.0-rc0 tensorflow-hadoop https://www.tensorflow.org TensorFlow TFRecord InputFormat/OutputFormat for Apache Hadoop diff --git a/tensorflow/java/maven/libtensorflow/pom.xml b/tensorflow/java/maven/libtensorflow/pom.xml index d44bdf8f81..5d4e04ecd3 100644 --- a/tensorflow/java/maven/libtensorflow/pom.xml +++ b/tensorflow/java/maven/libtensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0 + 1.10.0-rc0 ../ libtensorflow diff --git a/tensorflow/java/maven/libtensorflow_jni/pom.xml b/tensorflow/java/maven/libtensorflow_jni/pom.xml index e8925c6fb1..e107904f7d 100644 --- a/tensorflow/java/maven/libtensorflow_jni/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0 + 1.10.0-rc0 ../ libtensorflow_jni diff --git a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml index 3bf4a2590c..b3c525233f 100644 --- a/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml +++ b/tensorflow/java/maven/libtensorflow_jni_gpu/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0 + 1.10.0-rc0 ../ libtensorflow_jni_gpu diff --git a/tensorflow/java/maven/pom.xml b/tensorflow/java/maven/pom.xml index b96dcf2888..a2943a3172 100644 --- a/tensorflow/java/maven/pom.xml +++ b/tensorflow/java/maven/pom.xml @@ -6,7 +6,7 @@ 4.0.0 org.tensorflow parentpom - 1.9.0 + 1.10.0-rc0 pom https://www.tensorflow.org diff --git a/tensorflow/java/maven/proto/pom.xml b/tensorflow/java/maven/proto/pom.xml index 5581d864d7..7080d81b7d 100644 --- a/tensorflow/java/maven/proto/pom.xml +++ b/tensorflow/java/maven/proto/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0 + 1.10.0-rc0 ../ proto diff --git a/tensorflow/java/maven/spark-connector/pom.xml b/tensorflow/java/maven/spark-connector/pom.xml index 64956be02c..003d09a0b7 100644 --- a/tensorflow/java/maven/spark-connector/pom.xml +++ b/tensorflow/java/maven/spark-connector/pom.xml @@ -6,7 +6,7 @@ org.tensorflow spark-connector_2.11 jar - 1.9.0 + 1.10.0-rc0 spark-tensorflow-connector https://www.tensorflow.org TensorFlow TFRecord connector for Apache Spark DataFrames diff --git a/tensorflow/java/maven/tensorflow/pom.xml b/tensorflow/java/maven/tensorflow/pom.xml index 92e15aa2c7..b9affbf699 100644 --- a/tensorflow/java/maven/tensorflow/pom.xml +++ b/tensorflow/java/maven/tensorflow/pom.xml @@ -6,7 +6,7 @@ org.tensorflow parentpom - 1.9.0 + 1.10.0-rc0 ../ tensorflow -- GitLab From 77455f98a956e0f1e381136856564d5c8773b4e7 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 17:37:35 -0700 Subject: [PATCH 301/519] Add shape information to the "not a matrix" error message. PiperOrigin-RevId: 205754132 --- tensorflow/compiler/tf2xla/kernels/matmul_op.cc | 12 ++++++++---- tensorflow/core/kernels/matmul_op.cc | 12 ++++++++---- 2 files changed, 16 insertions(+), 8 deletions(-) diff --git a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc index 844080b8cf..aa45b02551 100644 --- a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc @@ -54,10 +54,14 @@ class MatMulOp : public XlaOpKernel { const TensorShape b_shape = ctx->InputShape(1); // Check that the dimensions of the two matrices are valid. - OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a_shape), - errors::InvalidArgument("In[0] is not a matrix")); - OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(b_shape), - errors::InvalidArgument("In[1] is not a matrix")); + OP_REQUIRES( + ctx, TensorShapeUtils::IsMatrix(a_shape), + errors::InvalidArgument("In[0] is not a matrix. Instead it has shape ", + a_shape.DebugString())); + OP_REQUIRES( + ctx, TensorShapeUtils::IsMatrix(b_shape), + errors::InvalidArgument("In[1] is not a matrix. Instead it has shape ", + b_shape.DebugString())); int first_index = transpose_a_ ? 0 : 1; int second_index = transpose_b_ ? 1 : 0; diff --git a/tensorflow/core/kernels/matmul_op.cc b/tensorflow/core/kernels/matmul_op.cc index b596dbc782..80376c61aa 100644 --- a/tensorflow/core/kernels/matmul_op.cc +++ b/tensorflow/core/kernels/matmul_op.cc @@ -453,10 +453,14 @@ class MatMulOp : public OpKernel { const Tensor& b = ctx->input(1); // Check that the dimensions of the two matrices are valid. - OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(a.shape()), - errors::InvalidArgument("In[0] is not a matrix")); - OP_REQUIRES(ctx, TensorShapeUtils::IsMatrix(b.shape()), - errors::InvalidArgument("In[1] is not a matrix")); + OP_REQUIRES( + ctx, TensorShapeUtils::IsMatrix(a.shape()), + errors::InvalidArgument("In[0] is not a matrix. Instead it has shape ", + a.shape().DebugString())); + OP_REQUIRES( + ctx, TensorShapeUtils::IsMatrix(b.shape()), + errors::InvalidArgument("In[1] is not a matrix. Instead it has shape ", + b.shape().DebugString())); Eigen::array, 1> dim_pair; dim_pair[0].first = transpose_a_ ? 0 : 1; dim_pair[0].second = transpose_b_ ? 1 : 0; -- GitLab From 86f63c717a354f342d1b714420a04c85434bb282 Mon Sep 17 00:00:00 2001 From: Smit Hinsu Date: Mon, 23 Jul 2018 17:42:19 -0700 Subject: [PATCH 302/519] Update example execution of configure script for installation from sources PiperOrigin-RevId: 205754672 --- .../docs_src/install/install_sources.md | 96 ++++++++++++++----- 1 file changed, 70 insertions(+), 26 deletions(-) diff --git a/tensorflow/docs_src/install/install_sources.md b/tensorflow/docs_src/install/install_sources.md index 4c09ba1a8b..31dcad64d4 100644 --- a/tensorflow/docs_src/install/install_sources.md +++ b/tensorflow/docs_src/install/install_sources.md @@ -223,46 +223,90 @@ will likely differ from our sample input: 
 $ cd tensorflow  # cd to the top-level directory created
 $ ./configure
+You have bazel 0.15.0 installed.
 Please specify the location of python. [Default is /usr/bin/python]: /usr/bin/python2.7
+
+
 Found possible Python library paths:
   /usr/local/lib/python2.7/dist-packages
   /usr/lib/python2.7/dist-packages
 Please input the desired Python library path to use.  Default is [/usr/lib/python2.7/dist-packages]
 
-Using python library path: /usr/local/lib/python2.7/dist-packages
-Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:
-Do you wish to use jemalloc as the malloc implementation? [Y/n]
-jemalloc enabled
-Do you wish to build TensorFlow with Google Cloud Platform support? [y/N]
-No Google Cloud Platform support will be enabled for TensorFlow
-Do you wish to build TensorFlow with Hadoop File System support? [y/N]
-No Hadoop File System support will be enabled for TensorFlow
-Do you wish to build TensorFlow with the XLA just-in-time compiler (experimental)? [y/N]
-No XLA support will be enabled for TensorFlow
-Do you wish to build TensorFlow with VERBS support? [y/N]
-No VERBS support will be enabled for TensorFlow
-Do you wish to build TensorFlow with OpenCL support? [y/N]
-No OpenCL support will be enabled for TensorFlow
-Do you wish to build TensorFlow with CUDA support? [y/N] Y
-CUDA support will be enabled for TensorFlow
-Do you want to use clang as CUDA compiler? [y/N]
-nvcc will be used as CUDA compiler
+Do you wish to build TensorFlow with jemalloc as malloc support? [Y/n]:
+jemalloc as malloc support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with Google Cloud Platform support? [Y/n]:
+Google Cloud Platform support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with Hadoop File System support? [Y/n]:
+Hadoop File System support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with Amazon AWS Platform support? [Y/n]:
+Amazon AWS Platform support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with Apache Kafka Platform support? [Y/n]:
+Apache Kafka Platform support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with XLA JIT support? [y/N]:
+No XLA JIT support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with GDR support? [y/N]:
+No GDR support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with VERBS support? [y/N]:
+No VERBS support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with OpenCL SYCL support? [y/N]:
+No OpenCL SYCL support will be enabled for TensorFlow.
+
+Do you wish to build TensorFlow with CUDA support? [y/N]: Y
+CUDA support will be enabled for TensorFlow.
+
 Please specify the CUDA SDK version you want to use. [Leave empty to default to CUDA 9.0]: 9.0
+
+
 Please specify the location where CUDA 9.0 toolkit is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
-Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
-Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7
+
+
+Please specify the cuDNN version you want to use. [Leave empty to default to cuDNN 7.0]: 7.0
+
+
 Please specify the location where cuDNN 7 library is installed. Refer to README.md for more details. [Default is /usr/local/cuda]:
-Please specify a list of comma-separated CUDA compute capabilities you want to build with.
+
+
+Do you wish to build TensorFlow with TensorRT support? [y/N]:
+No TensorRT support will be enabled for TensorFlow.
+
+Please specify the NCCL version you want to use. If NCLL 2.2 is not installed, then you can use version 1.3 that can be fetched automatically but it may have worse performance with multiple GPUs. [Default is 2.2]: 1.3
+
+
+Please specify a list of comma-separated Cuda compute capabilities you want to build with.
 You can find the compute capability of your device at: https://developer.nvidia.com/cuda-gpus.
-Please note that each additional compute capability significantly increases your build time and binary size.
+Please note that each additional compute capability significantly increases your
+build time and binary size. [Default is: 3.5,7.0] 6.1
+
+
+Do you want to use clang as CUDA compiler? [y/N]:
+nvcc will be used as CUDA compiler.
 
-Do you wish to build TensorFlow with MPI support? [y/N]
-MPI support will not be enabled for TensorFlow
+Please specify which gcc should be used by nvcc as the host compiler. [Default is /usr/bin/gcc]:
+
+
+Do you wish to build TensorFlow with MPI support? [y/N]:
+No MPI support will be enabled for TensorFlow.
+
+Please specify optimization flags to use during compilation when bazel option "--config=opt" is specified [Default is -march=native]:
+
+
+Would you like to interactively configure ./WORKSPACE for Android builds? [y/N]:
+Not configuring the WORKSPACE for Android builds.
+
+Preconfigured Bazel build configs. You can use any of the below by adding "--config=<>" to your build command. See tools/bazel.rc for more details.
+    --config=mkl            # Build with MKL support.
+    --config=monolithic     # Config for mostly static monolithic build.
 Configuration finished
-[Default is: "3.5,7.0"]: 6.0,7.0 - If you told `configure` to build for GPU support, then `configure` will create a canonical set of symbolic links to the CUDA libraries on your system. Therefore, every time you change the CUDA library paths, you must rerun the `configure` -- GitLab From b553a232a7537ca23efb36d19b4d6f5198ff46d1 Mon Sep 17 00:00:00 2001 From: Smit Hinsu Date: Mon, 23 Jul 2018 17:44:16 -0700 Subject: [PATCH 303/519] Add larger inputs to conv2d benchmark for better coverage PiperOrigin-RevId: 205754951 --- tensorflow/python/ops/conv2d_benchmark.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/ops/conv2d_benchmark.py b/tensorflow/python/ops/conv2d_benchmark.py index aacdaa7ad0..28111c2730 100644 --- a/tensorflow/python/ops/conv2d_benchmark.py +++ b/tensorflow/python/ops/conv2d_benchmark.py @@ -175,7 +175,8 @@ class Conv2DBenchmark(test.Benchmark): data_types = [dtypes.float32, dtypes.float16] data_formats = ["NHWC", "NCHW"] - in_channels = list(range(3, 16)) + in_channels = list(range(1, 10)) + list(range(10, 20, 2)) + list( + range(20, 33, 4)) out_channels = [4, 16, 32] hw_strides = [[2, 2]] paddings = ["VALID", "SAME"] -- GitLab From 6f6161a0110d99b2655efc9d933b753dadadbc38 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 17:45:23 -0700 Subject: [PATCH 304/519] Best Practices for writing custom operators PiperOrigin-RevId: 205755115 --- .../contrib/lite/g3doc/custom_operators.md | 44 +++++++++++++++++++ 1 file changed, 44 insertions(+) diff --git a/tensorflow/contrib/lite/g3doc/custom_operators.md b/tensorflow/contrib/lite/g3doc/custom_operators.md index 972e57f73e..f2fbcf64cf 100644 --- a/tensorflow/contrib/lite/g3doc/custom_operators.md +++ b/tensorflow/contrib/lite/g3doc/custom_operators.md @@ -89,3 +89,47 @@ builtins.AddCustom("Sin", Register_SIN()); Note that a similar process as above can be followed for supporting for a set of operations instead of a single operator. + +## Best Practices for writing custom operators + +1. Optimize memory allocations and de-allocations cautiously. It is more + efficient to allocate memory in Prepare() instead of Invoke(), and allocate + memory before a loop instead of in every iteration. Use temporary tensors + data rather than mallocing yourself (see item 2). Use pointers/references + instead of copying as much as possible. + +2. If a data structure will persist during the entire operation, we advise + pre-allocating the memory using temporary tensors. You may need to use + OpData struct to reference the tensor indices in other functions. See + example in the + [kernel for convolution](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/lite/kernels/conv.cc). + A sample code snippet is below + + ``` + auto* op_data = reinterpret_cast(node->user_data); + TfLiteIntArrayFree(node->temporaries); + node->temporaries = TfLiteIntArrayCreate(1); + node->temporaries->data[0] = op_data->temp_tensor_index; + TfLiteTensor* temp_tensor = &context->tensors[op_data->temp_tensor_index]; + temp_tensor->type = kTfLiteFloat32; + temp_tensor->allocation_type = kTfLiteArenaRw; + ``` + +3. If it doesn't cost too much wasted memory, prefer using a static fixed size + array (or in Resize() pre-allocated std::vector) rather than using a + dynamically allocating std::vector every iteration of execution. + +4. Avoid instantiating standard library container templates that don't already + exist, because they affect binary size. For example, if you need a std::map + in your operation that doesn't exist in other kernels, using a std::vector + with direct indexing mapping could work while keeping the binary size small. + See what other kernels use to gain insight (or ask). + +5. Check the pointer to the memory returned by malloc. If this pointer is + nullptr, no operations should be performed using that pointer. If you + malloc() in a function and have an error exit, deallocate memory before you + exit. + +6. Use TF_LITE_ENSURE(context, condition) to check for a specific condition. + Your code must not leave memory hanging when TF_LITE_ENSURE is done, i.e., + these should be done before any resources are allocated that will leak. -- GitLab From dcf568a4e297bb70a74ed5665b924077b9cb650a Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Mon, 23 Jul 2018 17:49:04 -0700 Subject: [PATCH 305/519] Open source XLA GPU tests PiperOrigin-RevId: 205755610 --- .../compiler/xla/service/gpu/tests/BUILD | 204 ++++++++++++++++++ .../xla/service/gpu/tests/gpu_codegen_test.cc | 50 +++++ .../xla/service/gpu/tests/gpu_codegen_test.h | 42 ++++ .../xla/service/gpu/tests/gpu_copy_test.cc | 59 +++++ .../xla/service/gpu/tests/gpu_ftz_test.cc | 119 ++++++++++ .../xla/service/gpu/tests/gpu_fusion_test.cc | 59 +++++ .../xla/service/gpu/tests/gpu_index_test.cc | 147 +++++++++++++ .../gpu/tests/gpu_kernel_tiling_test.cc | 177 +++++++++++++++ .../xla/service/gpu/tests/gpu_ldg_test.cc | 141 ++++++++++++ .../xla/service/gpu/tests/gpu_noalias_test.cc | 68 ++++++ .../service/gpu/tests/gpu_unrolling_test.cc | 185 ++++++++++++++++ .../xla/service/gpu/tests/infeed_test.cc | 121 +++++++++++ 12 files changed, 1372 insertions(+) create mode 100644 tensorflow/compiler/xla/service/gpu/tests/BUILD create mode 100644 tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.cc create mode 100644 tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h create mode 100644 tensorflow/compiler/xla/service/gpu/tests/gpu_copy_test.cc create mode 100644 tensorflow/compiler/xla/service/gpu/tests/gpu_ftz_test.cc create mode 100644 tensorflow/compiler/xla/service/gpu/tests/gpu_fusion_test.cc create mode 100644 tensorflow/compiler/xla/service/gpu/tests/gpu_index_test.cc create mode 100644 tensorflow/compiler/xla/service/gpu/tests/gpu_kernel_tiling_test.cc create mode 100644 tensorflow/compiler/xla/service/gpu/tests/gpu_ldg_test.cc create mode 100644 tensorflow/compiler/xla/service/gpu/tests/gpu_noalias_test.cc create mode 100644 tensorflow/compiler/xla/service/gpu/tests/gpu_unrolling_test.cc create mode 100644 tensorflow/compiler/xla/service/gpu/tests/infeed_test.cc diff --git a/tensorflow/compiler/xla/service/gpu/tests/BUILD b/tensorflow/compiler/xla/service/gpu/tests/BUILD new file mode 100644 index 0000000000..926262e2ad --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/BUILD @@ -0,0 +1,204 @@ +# Description: GPU-specific XLA tests. For example, codegen tests that +# verify the IR emitted. +# +# TODO(jlebar): None of these tests actually use the GPU, so they should not +# need to run on machines with GPUs present. + +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = [":friends"]) + +package_group( + name = "friends", + includes = [ + "//tensorflow/compiler/xla:friends", + ], +) + +# Filegroup used to collect source files for dependency checking. +filegroup( + name = "c_srcs", + data = glob([ + "**/*.cc", + "**/*.h", + ]), +) + +load("//tensorflow:tensorflow.bzl", "tf_cc_test") + +cc_library( + name = "gpu_codegen_test", + testonly = True, + srcs = ["gpu_codegen_test.cc"], + hdrs = ["gpu_codegen_test.h"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", + "//tensorflow/compiler/xla/service:gpu_plugin", + "//tensorflow/compiler/xla/service/gpu:gpu_executable", + "//tensorflow/compiler/xla/tests:filecheck", + "//tensorflow/compiler/xla/tests:llvm_irgen_test_base", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "gpu_copy_test", + srcs = ["gpu_copy_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_ftz_test", + srcs = ["gpu_ftz_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_index_test", + srcs = ["gpu_index_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla:xla_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_module_config", + "//tensorflow/compiler/xla/service:hlo_parser", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_infeed_test", + srcs = ["infeed_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:global_data", + "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client/lib:arithmetic", + "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/tests:client_library_test_base", + "//tensorflow/compiler/xla/tests:literal_test_util", + "//tensorflow/core:lib", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_kernel_tiling_test", + srcs = ["gpu_kernel_tiling_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_module_config", + "//tensorflow/compiler/xla/service:hlo_parser", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_ldg_test", + srcs = ["gpu_ldg_test.cc"], + tags = ["requires-gpu-sm35"], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_noalias_test", + srcs = ["gpu_noalias_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_fusion_test", + srcs = ["gpu_fusion_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla/service:hlo_module_config", + "//tensorflow/compiler/xla/service:hlo_parser", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) + +tf_cc_test( + name = "gpu_unrolling_test", + srcs = ["gpu_unrolling_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla/service:hlo_module_config", + "//tensorflow/compiler/xla/service:hlo_parser", + "//tensorflow/compiler/xla/tests:hlo_test_base", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.cc new file mode 100644 index 0000000000..4b8415fe91 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.cc @@ -0,0 +1,50 @@ +/* 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/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/gpu_executable.h" +#include "tensorflow/compiler/xla/tests/filecheck.h" +#include "tensorflow/core/platform/logging.h" + +namespace xla { +namespace gpu { + +std::unique_ptr GpuCodegenTest::CreateNewModuleWithFTZ(bool ftz) { + HloModuleConfig config; + auto debug_options = legacy_flags::GetDebugOptionsFromFlags(); + debug_options.set_xla_gpu_ftz(ftz); + debug_options.set_xla_gpu_max_kernel_unroll_factor(1); + // TODO(b/38354253): Change tests to use Parameters instead of Constants. + debug_options.add_xla_disable_hlo_passes("constant_folding"); + config.set_debug_options(debug_options); + + return MakeUnique(TestName(), config); +} + +void GpuCodegenTest::CompileAndVerifyPtx(std::unique_ptr hlo_module, + const string& pattern) { + std::unique_ptr executable = + std::move(CompileToExecutable(std::move(hlo_module)).ValueOrDie()); + string ptx_str = + std::string(static_cast(executable.get())->ptx()); + StatusOr filecheck_result = RunFileCheck(ptx_str, pattern); + ASSERT_TRUE(filecheck_result.ok()); + EXPECT_TRUE(filecheck_result.ValueOrDie()); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h b/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h new file mode 100644 index 0000000000..e4a3573bab --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.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_COMPILER_XLA_SERVICE_GPU_TESTS_GPU_CODEGEN_TEST_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_TESTS_GPU_CODEGEN_TEST_H_ + +#include + +#include "tensorflow/compiler/xla/tests/llvm_irgen_test_base.h" + +namespace xla { +namespace gpu { + +// Tests that verify IR or PTX emitted by the GPU backend is as expected. +class GpuCodegenTest : public LlvmIrGenTestBase { + protected: + // Like HloTestBase::CreateNewModule(), with a flag for configuring the ftz + // option. + std::unique_ptr CreateNewModuleWithFTZ(bool ftz); + + // Compiles the given HLO module to PTX and verifies the PTX matches the given + // FileCheck pattern. (See http://llvm.org/docs/CommandGuide/FileCheck.html). + void CompileAndVerifyPtx(std::unique_ptr hlo_module, + const string& pattern); +}; + +} // namespace gpu +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_GPU_TESTS_GPU_CODEGEN_TEST_H_ diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_copy_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_copy_test.cc new file mode 100644 index 0000000000..ce69e058e6 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_copy_test.cc @@ -0,0 +1,59 @@ +/* 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 +#include + +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.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/xla_data.pb.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { + +class GpuCopyTest : public GpuCodegenTest {}; + +// The GPU backend should not emit a copy kernel for the kCopy instruction in +// this test. Instead, it should generate a CopyThunk which invokes cuMemcpy at +// runtime. +TEST_F(GpuCopyTest, UseMemcpy) { + HloComputation::Builder builder(TestName()); + + std::unique_ptr literal = + LiteralUtil::CreateR2({{1.0, 2.0}, {3.0, 4.0}}); + HloInstruction* constant = builder.AddInstruction( + HloInstruction::CreateConstant(std::move(literal))); + builder.AddInstruction(HloInstruction::CreateUnary( + constant->shape(), HloOpcode::kCopy, constant)); + + std::unique_ptr computation = builder.Build(); + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(std::move(computation)); + + // There should not be any kernel prefixed "copy". + CompileAndVerifyIr(std::move(hlo_module), "; CHECK-NOT: define void @_copy", + /*match_optimized_ir=*/false); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_ftz_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_ftz_test.cc new file mode 100644 index 0000000000..177b94934c --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_ftz_test.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/compiler/xla/service/gpu/tests/gpu_codegen_test.h" + +// Check that the ftz (flush denormals to zero) flag is reflected in PTX as +// expected. + +namespace xla { +namespace gpu { +namespace { + +class GpuFtzTest : public GpuCodegenTest { + public: + explicit GpuFtzTest(bool ftz) : ftz_(ftz) {} + + // Creates an HLO module that performs the given binary operation on some + // data. + std::unique_ptr CreateBinaryOpModule(HloOpcode op) { + HloComputation::Builder builder(TestName()); + + Shape param_shape = ShapeUtil::MakeShapeWithLayout( + F32, /*dimensions=*/{100, 100}, /*minor_to_major=*/{1, 0}); + HloInstruction* x = builder.AddInstruction(HloInstruction::CreateParameter( + /* parameter_number=*/0, param_shape, "x")); + HloInstruction* y = builder.AddInstruction(HloInstruction::CreateParameter( + /* parameter_number=*/1, param_shape, "y")); + builder.AddInstruction(HloInstruction::CreateBinary(param_shape, op, x, y)); + + auto hlo_module = CreateNewModuleWithFTZ(ftz_); + hlo_module->AddEntryComputation(builder.Build()); + return hlo_module; + } + + // Creates an HLO module that performs the given unary operation on some data. + std::unique_ptr CreateUnaryOpModule(HloOpcode op) { + HloComputation::Builder builder(TestName()); + + Shape param_shape = ShapeUtil::MakeShapeWithLayout( + F32, /*dimensions=*/{100, 100}, /*minor_to_major=*/{1, 0}); + HloInstruction* x = builder.AddInstruction(HloInstruction::CreateParameter( + /* parameter_number=*/0, param_shape, "x")); + builder.AddInstruction(HloInstruction::CreateUnary(param_shape, op, x)); + + auto hlo_module = CreateNewModuleWithFTZ(ftz_); + hlo_module->AddEntryComputation(builder.Build()); + return hlo_module; + } + + bool ftz_; +}; + +class GpuFtzEnabledTest : public GpuFtzTest { + public: + GpuFtzEnabledTest() : GpuFtzTest(/*ftz=*/true) {} +}; + +class GpuFtzDisabledTest : public GpuFtzTest { + public: + GpuFtzDisabledTest() : GpuFtzTest(/*ftz=*/false) {} +}; + +// Check that we emit mul.ftz.f32 when in ftz mode, and plain mul.f32 otherwise. +TEST_F(GpuFtzEnabledTest, MultiplyFtz) { + CompileAndVerifyPtx(CreateBinaryOpModule(HloOpcode::kMultiply), R"( + CHECK-NOT: mul.f32 + CHECK: mul.ftz.f32 + CHECK-NOT: mul.f32 + )"); +} +TEST_F(GpuFtzDisabledTest, MultiplyFtz) { + CompileAndVerifyPtx(CreateBinaryOpModule(HloOpcode::kMultiply), R"( + CHECK-NOT: mul.ftz.f32 + CHECK: mul.f32 + CHECK-NOT: mul.ftz.f32 + )"); +} + +// In NVPTX, exp(float) is implemented in libdevice, and consults __nvvm_reflect +// to determine whether or not ftz is enabled. The implementation uses two +// calls to ex2.approx. When ftz is on, we get two calls to the ftz version; +// when ftz is off, we get one call to the ftz version and one call to the +// regular version. +TEST_F(GpuFtzEnabledTest, ExpFtz) { + CompileAndVerifyPtx(CreateUnaryOpModule(HloOpcode::kExp), R"( + CHECK-NOT: ex2.approx.f32 + CHECK: ex2.approx.ftz.f32 + CHECK-NOT: ex2.approx.f32 + CHECK: ex2.approx.ftz.f32 + CHECK-NOT: ex2.approx.f32 + CHECK-NOT: ex2.approx.ftz.f32 + )"); +} + +TEST_F(GpuFtzDisabledTest, ExpFtz) { + CompileAndVerifyPtx(CreateUnaryOpModule(HloOpcode::kExp), R"( + CHECK-NOT: ex2.approx.f32 + CHECK-DAG: ex2.approx.ftz.f32 + CHECK-DAG: ex2.approx.f32 + CHECK-NOT: ex2.approx.f32 + CHECK-NOT: ex2.approx.ftz.f32 + )"); +} + +} // namespace +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_fusion_test.cc new file mode 100644 index 0000000000..674b436a8e --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_fusion_test.cc @@ -0,0 +1,59 @@ +/* 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 + +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_module_config.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { +namespace { + +class GpuFusionTest : public GpuCodegenTest {}; + +TEST_F(GpuFusionTest, FusedReshape) { + const char* hlo_text = R"( + HloModule test_module + + fused_computation { + p0.param_0 = f32[4,1,1]{2,1,0} parameter(0) + p1.param_1 = f32[4,1]{1,0} parameter(1) + reshape = f32[4,1]{1,0} reshape(p0.param_0) + ROOT add = f32[4,1] add(reshape, p1.param_1) + } + + ENTRY BroadcastIntoAdd { + p0 = f32[4,1,1]{2,1,0} parameter(0) + p1 = f32[4,1]{1,0} parameter(1) + ROOT fusion = f32[4,1]{1,0} fusion(p0, p1), kind=kLoop, + calls=fused_computation + } +)"; + + CompileAndVerifyIr(hlo_text, + R"( +; CHECK-LABEL: @fusion +; CHECK: fadd +; CHECK: } + )"); +} + +} // namespace +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_index_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_index_test.cc new file mode 100644 index 0000000000..e5958165ef --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_index_test.cc @@ -0,0 +1,147 @@ +/* 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 +#include + +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.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_module_config.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/shape_util.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/compiler/xla/xla.pb.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { + +// This file tests the index expressions used to reference source tensors. When +// the destination tensor and source tensor have compatible shapes, the linear +// index is used to access the source tensor. Otherwise, dimensional indices +// computed from the linear index are used to access the source tensor. + +class GpuIndexTest : public GpuCodegenTest {}; + +TEST_F(GpuIndexTest, CompatibleUseLinearIndex) { + HloComputation::Builder builder(TestName()); + + auto param_shape = ShapeUtil::MakeShape(F32, {5, 7, 2}); + HloInstruction* param_x = builder.AddInstruction( + HloInstruction::CreateParameter(0, param_shape, "x")); + HloInstruction* param_y = builder.AddInstruction( + HloInstruction::CreateParameter(1, param_shape, "y")); + builder.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {5, 7, 2}), HloOpcode::kGe, param_x, param_y)); + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(builder.Build()); + + // Check the optimized IR as the unoptimized IR contains dead udiv and urem. + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-NOT: udiv +; CHECK-NOT: urem + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuIndexTest, CompatibleUseLinearIndexWithReshape) { + HloModuleConfig config; + config.set_debug_options(HloTestBase::GetDebugOptionsForTest()); + auto module = ParseHloString(R"( + HloModule test_module + + ENTRY CompatibleUseLinearIndexWithReshape { + x = f32[5,7,2]{2,1,0} parameter(0) + y = f32[5,14]{1,0} parameter(1) + reshape = f32[5,7,2]{2,1,0} reshape(y) + ROOT gte = pred[5,7,2]{2,1,0} greater-than-or-equal-to(x, reshape) + })", + config) + .ValueOrDie(); + + // Check the optimized IR as the unoptimized IR contains dead udiv and urem. + CompileAndVerifyIr(std::move(module), + R"( +; CHECK-NOT: udiv +; CHECK-NOT: urem + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuIndexTest, CompatibleUseLinearIndexWithReshapeAndBroadcast) { + HloModuleConfig config; + config.set_debug_options(HloTestBase::GetDebugOptionsForTest()); + auto module = ParseHloString(R"( + HloModule test_module + + ENTRY CompatibleUseLinearIndexWithReshape { + x = f32[5,7,2]{2,1,0} parameter(0) + y = f32[14]{0} parameter(1) + reshape = f32[7,2]{1,0} reshape(y) + broadcast = f32[5,7,2]{2,1,0} broadcast(reshape), dimensions={1,2} + ROOT gte = pred[5,7,2]{2,1,0} greater-than-or-equal-to(x, broadcast) + })", + config) + .ValueOrDie(); + + // Check the optimized IR reuses the linear index by calculating modulo 14. + CompileAndVerifyIr(std::move(module), + R"( +; CHECK: %[[urem1:.*]] = urem i{{[0-9]*}} %[[linear_index:.*]], 14 +; CHECK: %[[bitcast:.*]] = bitcast i8 addrspace(1)* %[[alloc:.*]] to float addrspace(1)* +; CHECK: %[[idx1:.*]] = zext i{{[0-9]*}} %[[urem1]] to i64 +; CHECK: getelementptr inbounds float, float addrspace(1)* %[[bitcast]], i64 %[[idx1]] + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuIndexTest, CompatibleUseLinearIndexWithSizeOneDimensions) { + HloModuleConfig config; + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + debug_options.set_xla_gpu_max_kernel_unroll_factor(1); + config.set_debug_options(debug_options); + + auto module = ParseHloString(R"( + HloModule test_module + + ENTRY CompatibleUseLinearIndexWithSizeOneDimensions { + x = f32[1,1024,1,256]{3,2,1,0} parameter(0) + ROOT y = f16[1,1024,1,256]{2,3,1,0} convert(x) + })", + config) + .ValueOrDie(); + + // Check that the unoptimized IR reuses the linear index. + CompileAndVerifyIr(std::move(module), + R"( +; CHECK-LABEL: @fusion +; CHECK: udiv i32 %[[linear_index:.*]], 262144 +; CHECK: %[[ld_addr:.*]] = getelementptr inbounds float, float* {{.*}}, i32 %[[linear_index]] +; CHECK: load float, float* %[[ld_addr]] +; CHECK: %[[st_addr:.*]] = getelementptr inbounds half, half* {{.*}}, i32 %[[linear_index]] +; CHECK: store half {{.*}}, half* %[[st_addr]] + )", + /*match_optimized_ir=*/false); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_kernel_tiling_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_kernel_tiling_test.cc new file mode 100644 index 0000000000..cca35316f0 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_kernel_tiling_test.cc @@ -0,0 +1,177 @@ +/* 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 + +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_module_config.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { +namespace { + +class GpuKernelTilingTest : public GpuCodegenTest { + protected: + GpuKernelTilingTest() { + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + config_.set_debug_options(debug_options); + // Disable layout_assignment to use the preassigned layouts. + debug_options.add_xla_disable_hlo_passes("layout_assignment"); + } + HloModuleConfig config_; +}; + +TEST_F(GpuKernelTilingTest, UnnestedTransposeWithProperDimensionsTiled) { + const char *const kHloString = R"( + HloModule unnested_transpose_1 + + ENTRY unnested_transpose_1 { + para0 = f16[32,3,64]{2,1,0} parameter(0) + ROOT copy1 = f16[32,3,64]{1,0,2} copy(para0) + })"; + + // Check that a call to llvm.nvvm.barrier0 is generated. + auto hlo_module = ParseHloString(kHloString, config_).ValueOrDie(); + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: define void @copy +; CHECK: tail call void @llvm.nvvm.barrier0() +; CHECK: } +)", + /*match_optimized_ir=*/true); + + // Check that the kernel runs correctly. + EXPECT_TRUE(RunAndCompareNoHloPasses(kHloString, ErrorSpec{0.0})); +} + +TEST_F(GpuKernelTilingTest, UnnestedTransposeWithSmallDimensionsNotTiled) { + const char *const kHloString = R"( + HloModule unnested_transpose_2 + + ENTRY unnested_transpose_2 { + para0 = f16[2,3,64]{2,1,0} parameter(0) + ROOT copy1 = f16[2,3,64]{1,0,2} copy(para0) + })"; + + // Check that a call to llvm.nvvm.barrier0 is not generated. + auto hlo_module = ParseHloString(kHloString, config_).ValueOrDie(); + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: define void @copy +; CHECK-NOT: tail call void @llvm.nvvm.barrier0() +; CHECK: } +)", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuKernelTilingTest, SimpleFusionWithTransposeTiled) { + const char *const kHloString = R"( + HloModule multiple_output_fusion_1 + fused_computation.1 { + param0 = f32[4,5,6,7,8]{4,3,2,1,0} parameter(0) + copy = f32[4,5,6,7,8]{2,1,4,3,0} copy(param0) + ROOT convert = f16[4,5,6,7,8]{2,1,4,3,0} convert(copy) + } + + ENTRY copy_in_fusion_run_without_hlo_passes { + para0 = f32[4,5,6,7,8]{4,3,2,1,0} parameter(0) + ROOT fusion.1 = f16[4,5,6,7,8]{2,1,4,3,0} fusion(para0), kind=kLoop, + calls=fused_computation.1 + })"; + + // Check that a call to llvm.nvvm.barrier0 is generated. + auto hlo_module = ParseHloString(kHloString, config_).ValueOrDie(); + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: define void @fusion +; CHECK: tail call void @llvm.nvvm.barrier0() +; CHECK: } +)", + /*match_optimized_ir=*/true); + + // Check that the kernel runs correctly. + EXPECT_TRUE(RunAndCompareNoHloPasses(kHloString, ErrorSpec{0.0})); +} + +TEST_F(GpuKernelTilingTest, MultipleOutputFusionWithOnePossibleTransposeTiled) { + const char *const kHloString = R"( + HloModule multiple_output_fusion_1 + fused_computation.1 { + param0 = f16[8,31,31,65]{3,2,1,0} parameter(0) + param1 = f16[8,31,31,65]{3,2,1,0} parameter(1) + copy0 = f16[8,31,31,65]{2,1,3,0} copy(param0) + copy1 = f16[8,31,31,65]{2,1,3,0} copy(param1) + ROOT tuple1 = (f16[8,31,31,65]{2,1,3,0}, f16[8,31,31,65]{2,1,3,0}) + tuple(copy0, copy1) + } + + ENTRY multiple_output_fusion_1 { + para0 = f16[8,31,31,65]{3,2,1,0} parameter(0) + para1 = f16[8,31,31,65]{3,2,1,0} parameter(1) + ROOT fusion.1 = (f16[8,31,31,65]{2,1,3,0}, f16[8,31,31,65]{2,1,3,0}) + fusion(para0,para1), kind=kLoop, calls=fused_computation.1 + })"; + + // Check that a call to llvm.nvvm.barrier0 is generated. + auto hlo_module = ParseHloString(kHloString, config_).ValueOrDie(); + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: define void @fusion +; CHECK: tail call void @llvm.nvvm.barrier0() +; CHECK: } +)", + /*match_optimized_ir=*/true); + + // Check that the kernel runs correctly. + EXPECT_TRUE(RunAndCompareNoHloPasses(kHloString, ErrorSpec{0.0})); +} + +TEST_F(GpuKernelTilingTest, + MultipleOutputFusionWithTwoPossibleTransposesNotTiled) { + const char *const kHloString = R"( + HloModule multiple_output_fusion_2 + fused_computation.1 { + param0 = f16[8,31,31,65]{3,2,1,0} parameter(0) + param1 = f16[8,31,31,65]{1,3,2,0} parameter(1) + copy2 = f16[8,31,31,65]{2,1,3,0} copy(param0) + copy3 = f16[8,31,31,65]{2,1,3,0} copy(param1) + ROOT tuple1 = (f16[8,31,31,65]{2,1,3,0}, f16[8,31,31,65]{2,1,3,0}) + tuple(copy2, copy3) + } + + ENTRY multiple_output_fusion_2 { + para0 = f16[8,31,31,65]{3,2,1,0} parameter(0) + para1 = f16[8,31,31,65]{1,3,2,0} parameter(1) + ROOT fusion1 = (f16[8,31,31,65]{2,1,3,0}, f16[8,31,31,65]{2,1,3,0}) + fusion(para0,para1), kind=kLoop, calls=fused_computation.1 + })"; + + // Check that a call to llvm.nvvm.barrier0 is not generated. + auto hlo_module = ParseHloString(kHloString, config_).ValueOrDie(); + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: define void @fusion +; CHECK-NOT: tail call void @llvm.nvvm.barrier0() +; CHECK: } +)", + /*match_optimized_ir=*/true); +} + +} // namespace +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_ldg_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_ldg_test.cc new file mode 100644 index 0000000000..6c9ae7bada --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_ldg_test.cc @@ -0,0 +1,141 @@ +/* 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 that we emit ld.global.nc (the PTX instruction corresponding to CUDA's +// __ldg builtin) for reads of buffers that don't change during a kernel's +// execution. + +#include +#include + +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.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_util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { + +class GpuLdgTest : public GpuCodegenTest {}; + +// Parameters are never overwritten, so parameter reads should get ld.global.nc +// reads. +TEST_F(GpuLdgTest, LdgForParamRead) { + HloComputation::Builder builder(TestName()); + + auto shape = ShapeUtil::MakeShape(F32, {2, 2}); + HloInstruction* param = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "x")); + builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param)); + std::unique_ptr computation = builder.Build(); + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(std::move(computation)); + + CompileAndVerifyPtx(std::move(hlo_module), R"( + CHECK-NOT: ld.global.f32 + CHECK: ld.global.nc.f32 + )"); +} + +// Check that reading a buffer produced by a non-parameter HLO also results in +// ld.global.nc, if that buffer isn't modified within the instruction that reads +// it. +TEST_F(GpuLdgTest, LdgForNonParamRead) { + HloComputation::Builder builder(TestName()); + + auto shape = ShapeUtil::MakeShape(F32, {2, 2}); + HloInstruction* param = + builder.AddInstruction(HloInstruction::CreateParameter(0, shape, "x")); + HloInstruction* add = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param, param)); + HloInstruction* square = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kMultiply, add, add)); + builder.AddInstruction(HloInstruction::CreateTuple({add, square})); + std::unique_ptr computation = builder.Build(); + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(std::move(computation)); + + CompileAndVerifyPtx(std::move(hlo_module), R"( + CHECK: { + CHECK-NOT: ld.global.f32 + CHECK: ld.global.nc.f32 + CHECK: } + )"); +} + +// Check that reading a buffer that's modified in-place does not produce +// ld.global.nc. +// +// We do this by creating a reduce that feeds into a sin. We don't currently +// fuse sin into reduce, and the sin is elementwise, so it reuses its input +// buffer as its output. +// +// It seems like a fair bet that we won't start fusing sin into the output of +// reduce in the foreseeable future. But if that turns out to be wrong, I give +// you, future reader, permission to delete this test. +TEST_F(GpuLdgTest, NoLdgWhenSharingBuffer) { + auto hlo_module = CreateNewModule(); + HloComputation::Builder builder(TestName()); + + HloComputation* reduce_computation; + { + auto embedded_builder = HloComputation::Builder("add"); + auto lhs = embedded_builder.AddInstruction(HloInstruction::CreateParameter( + 0, ShapeUtil::MakeShape(F32, {}), "lhs")); + auto rhs = embedded_builder.AddInstruction(HloInstruction::CreateParameter( + 1, ShapeUtil::MakeShape(F32, {}), "rhs")); + embedded_builder.AddInstruction( + HloInstruction::CreateBinary(lhs->shape(), HloOpcode::kAdd, lhs, rhs)); + reduce_computation = + hlo_module->AddEmbeddedComputation(embedded_builder.Build()); + } + + auto param_shape = ShapeUtil::MakeShape(F32, {2, 2}); + auto reduce_shape = ShapeUtil::MakeShape(F32, {2}); + HloInstruction* param = builder.AddInstruction( + HloInstruction::CreateParameter(0, param_shape, "x")); + HloInstruction* reduce = builder.AddInstruction(HloInstruction::CreateReduce( + reduce_shape, + builder.AddInstruction(HloInstruction::CreateBinary( + param_shape, HloOpcode::kAdd, param, param)), + builder.AddInstruction( + HloInstruction::CreateConstant(LiteralUtil::CreateR0(0))), + {0}, reduce_computation)); + builder.AddInstruction( + HloInstruction::CreateUnary(reduce_shape, HloOpcode::kSin, reduce)); + + std::unique_ptr computation = builder.Build(); + hlo_module->AddEntryComputation(std::move(computation)); + + CompileAndVerifyPtx(std::move(hlo_module), R"( + CHECK-LABEL: .entry sin + CHECK: { + CHECK-NOT: ld.global.nc.f32 + CHECK: ld.global.f32 + CHECK: } + )"); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_noalias_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_noalias_test.cc new file mode 100644 index 0000000000..c42e5704a4 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_noalias_test.cc @@ -0,0 +1,68 @@ +/* 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 +#include + +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/ptr_util.h" +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.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_util.h" +#include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { + +class GpuNoAliasTest : public GpuCodegenTest {}; + +TEST_F(GpuNoAliasTest, Concat) { + HloComputation::Builder builder(TestName()); + + auto param_shape = ShapeUtil::MakeShape(F32, {2, 2}); + HloInstruction* param_x = builder.AddInstruction( + HloInstruction::CreateParameter(0, param_shape, "x")); + HloInstruction* param_y = builder.AddInstruction( + HloInstruction::CreateParameter(1, param_shape, "y")); + HloInstruction* concat = + builder.AddInstruction(HloInstruction::CreateConcatenate( + ShapeUtil::MakeShape(F32, {2, 4}), {param_x, param_y}, 1)); + builder.AddInstruction(HloInstruction::CreateConcatenate( + ShapeUtil::MakeShape(F32, {2, 6}), {concat, param_x}, 1)); + + std::unique_ptr computation = builder.Build(); + + auto hlo_module = CreateNewModule(); + hlo_module->AddEntryComputation(std::move(computation)); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK: %[[x_gep:.*]] = getelementptr inbounds [2 x [2 x float]], [2 x [2 x float]]* %x{{.*}}, i32 0 +; CHECK: load float, float* %[[x_gep]], {{.*}}, !noalias ![[param_noalias:.*]] +; CHECK: %[[y_gep:.*]] = getelementptr inbounds [2 x [2 x float]], [2 x [2 x float]]* %y{{.*}}, i32 0 +; CHECK: load float, float* %[[y_gep]], {{.*}}, !noalias ![[param_noalias]] +; CHECK: %[[result_ptr:.*]] = bitcast [2 x [6 x float]]* %fusion{{.*}} to float* +; CHECK: %[[result_gep:.*]] = getelementptr inbounds float, float* %[[result_ptr]] +; CHECK: store float {{.*}}, float* %[[result_gep]], !alias.scope ![[param_noalias]] +; CHECK: ![[param_noalias]] = !{![[retval_buffer:.*]]} + )", + /*match_optimized_ir=*/false); +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_unrolling_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_unrolling_test.cc new file mode 100644 index 0000000000..9622936306 --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_unrolling_test.cc @@ -0,0 +1,185 @@ +/* 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 + +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/hlo_module_config.h" +#include "tensorflow/compiler/xla/service/hlo_parser.h" +#include "tensorflow/compiler/xla/tests/hlo_test_base.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { +namespace { + +class GpuUnrollingTest : public GpuCodegenTest {}; + +const char *const kAddModule = R"( + HloModule test_module + + fused_computation { + p0.param_0 = f32[2,2]{1,0} parameter(0) + p1.param_1 = f32[2,2]{1,0} parameter(1) + ROOT add = f32[2,2] add(p0.param_0, p1.param_1) + } + + ENTRY BroadcastIntoAdd { + p0 = f32[2,2]{1,0} parameter(0) + p1 = f32[2,2]{1,0} parameter(1) + ROOT fusion = f32[2,2]{1,0} fusion(p0, p1), kind=kLoop, + calls=fused_computation + })"; + +TEST_F(GpuUnrollingTest, DoNotUnroll) { + HloModuleConfig config; + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + debug_options.set_xla_gpu_max_kernel_unroll_factor(1); + config.set_debug_options(debug_options); + auto hlo_module = ParseHloString(kAddModule, config).ValueOrDie(); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: @fusion +; CHECK: fadd +; CHECK-NOT: fadd +; CHECK: } + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuUnrollingTest, UnrollFourTimes) { + HloModuleConfig config; + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + // We request a factor of 8, but the computation works on 4 elements, limiting + // the maximum unroll factor. + debug_options.set_xla_gpu_max_kernel_unroll_factor(8); + config.set_debug_options(debug_options); + auto hlo_module = ParseHloString(kAddModule, config).ValueOrDie(); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: @fusion +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK-NOT: fadd +; CHECK: } + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuUnrollingTest, UnrollDefaultTimes) { + // The default unrolling factor is 4. + HloModuleConfig config; + config.set_debug_options(legacy_flags::GetDebugOptionsFromFlags()); + auto hlo_module = ParseHloString(kAddModule, config).ValueOrDie(); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: @fusion +; CHECK: load <4 x float> +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK-NOT: fadd +; CHECK: store <4 x float> +; CHECK: } + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuUnrollingTest, UnrollUnfusedAdd) { + HloModuleConfig config; + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + debug_options.set_xla_gpu_max_kernel_unroll_factor(4); + config.set_debug_options(debug_options); + + const char *const kUnfusedAddModule = R"( + HloModule test_module + + ENTRY AddFunc { + p0 = f32[2,2]{1,0} parameter(0) + p1 = f32[2,2]{1,0} parameter(1) + ROOT add = f32[2,2]{1,0} add(p0, p1) + })"; + auto hlo_module = ParseHloString(kUnfusedAddModule, config).ValueOrDie(); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: @add +; CHECK: load <4 x float> +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK: fadd +; CHECK-NOT: fadd +; CHECK: store <4 x float> +; CHECK: } + )", + /*match_optimized_ir=*/true); +} + +TEST_F(GpuUnrollingTest, UnrollMultiOutputFusion) { + HloModuleConfig config; + auto debug_options = HloTestBase::GetDebugOptionsForTest(); + debug_options.set_xla_gpu_max_kernel_unroll_factor(2); + config.set_debug_options(debug_options); + + const char *const kMultiOutputFusionModule = R"( + HloModule test_module + + fused_computation { + p0.param_0 = f32[2,2]{1,0} parameter(0) + p1.param_1 = f32[2,2]{1,0} parameter(1) + add = f32[2,2]{1,0} add(p0.param_0, p1.param_1) + mul = f32[2,2]{1,0} multiply(p0.param_0, p1.param_1) + ROOT tuple = (f32[2,2]{1,0}, f32[2,2]{1,0}) tuple(add, mul) + } + + ENTRY BroadcastIntoAdd { + p0 = f32[2,2]{1,0} parameter(0) + p1 = f32[2,2]{1,0} parameter(1) + ROOT fusion = (f32[2,2]{1,0}, f32[2,2]{1,0}) fusion(p0, p1), kind=kLoop, + calls=fused_computation + })"; + auto hlo_module = + ParseHloString(kMultiOutputFusionModule, config).ValueOrDie(); + + CompileAndVerifyIr(std::move(hlo_module), + R"( +; CHECK-LABEL: @fusion +; CHECK: load <2 x float> +; CHECK: load <2 x float> +; CHECK-NOT: load <2 x float> +; CHECK: fadd +; CHECK: fmul +; CHECK: fadd +; CHECK: fmul +; CHECK: store <2 x float> +; CHECK: store <2 x float> +; CHECK-NOT: store <2 x float> +; CHECK-NOT: fadd +; CHECK-NOT: fmul +; CHECK: } + )", + /*match_optimized_ir=*/true); +} + +} // namespace +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/tests/infeed_test.cc b/tensorflow/compiler/xla/service/gpu/tests/infeed_test.cc new file mode 100644 index 0000000000..ba5cd2d84d --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/infeed_test.cc @@ -0,0 +1,121 @@ +/* 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 +#include + +#include "tensorflow/compiler/xla/client/global_data.h" +#include "tensorflow/compiler/xla/client/lib/arithmetic.h" +#include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/shape_util.h" +#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/xla_data.pb.h" +#include "tensorflow/core/lib/math/math_util.h" +#include "tensorflow/core/platform/env.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { +namespace { + +class InfeedTest : public ClientLibraryTestBase { + protected: + // Transfers the given literal to the infeed interface of the device, and + // check if the returned data from Infeed HLO is same as the literal. + void TestInfeedRoundTrip(const Literal& literal) { + // TODO(b/30481585) Explicitly reset the Infeed state so that the + // test is not affected by the state from the previous tests. + ASSERT_IS_OK(client_->TransferToInfeed(literal)); + XlaBuilder builder(TestName()); + Infeed(&builder, literal.shape()); + if (ShapeUtil::IsTuple(literal.shape())) { + // TODO(b/30609564): Use ComputeAndCompareLiteral instead. + ComputeAndCompareTuple(&builder, literal, {}); + } else { + ComputeAndCompareLiteral(&builder, literal, {}); + } + } +}; + +TEST_F(InfeedTest, SingleInfeedR0Bool) { + TestInfeedRoundTrip(*LiteralUtil::CreateR0(true)); +} + +TEST_F(InfeedTest, SingleInfeedR1U32) { + TestInfeedRoundTrip(*LiteralUtil::CreateR1({1, 2, 3})); +} + +TEST_F(InfeedTest, SingleInfeedR2F32) { + TestInfeedRoundTrip(*LiteralUtil::CreateR2F32Linspace(0.0, 1.0, 128, 64)); +} + +TEST_F(InfeedTest, SingleInfeedR3F32) { + TestInfeedRoundTrip( + *LiteralUtil::CreateR3({{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}})); +} + +TEST_F(InfeedTest, SingleInfeedR3F32DifferentLayout) { + const Layout r3_dim0minor = LayoutUtil::MakeLayout({0, 1, 2}); + const Layout r3_dim0major = LayoutUtil::MakeLayout({2, 1, 0}); + + TestInfeedRoundTrip(*LiteralUtil::CreateR3WithLayout( + {{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}, + r3_dim0minor)); + + TestInfeedRoundTrip(*LiteralUtil::CreateR3WithLayout( + {{{1.0f, 2.0f, 3.0f}, {4.0f, 5.0f, 6.0f}}, + {{1.1f, 2.1f, 3.1f}, {6.1f, 3.5f, 2.8f}}}, + r3_dim0major)); +} + +TEST_F(InfeedTest, SingleInfeedR4S32) { + TestInfeedRoundTrip(*LiteralUtil::CreateR4( + {{{{1, -2}, {-4, 5}, {6, 7}}, {{8, 9}, {10, 11}, {12, 13}}}, + {{{10, 3}, {7, -2}, {3, 6}}, {{2, 5}, {-11, 5}, {-2, -5}}}})); +} + +// Tests that a large infeed can be handled. +TEST_F(InfeedTest, LargeInfeed) { + Array4D array(80, 100, 8, 128); + array.FillIota(1.0f); + TestInfeedRoundTrip(*LiteralUtil::CreateR4FromArray4D(array)); +} + +TEST_F(InfeedTest, SingleInfeedTuple) { + TestInfeedRoundTrip( + *LiteralUtil::MakeTuple({LiteralUtil::CreateR1({1, 2, 3}).get(), + LiteralUtil::CreateR0(false).get()})); +} + +TEST_F(InfeedTest, SingleInfeedEmptyTuple) { + TestInfeedRoundTrip(*LiteralUtil::MakeTuple({})); +} + +// Tests that a large tuple infeed can be handled. +TEST_F(InfeedTest, SingleInfeedLargeTuple) { + Array4D array(40, 100, 8, 128); + array.FillIota(1.0f); + TestInfeedRoundTrip(*LiteralUtil::MakeTuple( + {LiteralUtil::CreateR4FromArray4D(array).get(), + LiteralUtil::CreateR0(5).get()})); +} + +} // namespace +} // namespace xla -- GitLab From d58d099edb59ba22e35067d5538edd91fae00e74 Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Mon, 23 Jul 2018 18:02:53 -0700 Subject: [PATCH 306/519] Remove unnecessary thread pool and use the worker env's compute pool directly. PiperOrigin-RevId: 205756865 --- .../distributed_runtime/rpc/eager/grpc_eager_service_impl.cc | 4 +--- .../distributed_runtime/rpc/eager/grpc_eager_service_impl.h | 5 ++--- 2 files changed, 3 insertions(+), 6 deletions(-) diff --git a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc index 52e06c263d..44e880de04 100644 --- a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc +++ b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc @@ -27,9 +27,7 @@ namespace eager { GrpcEagerServiceImpl::GrpcEagerServiceImpl( const WorkerEnv* env, ::grpc::ServerBuilder* server_builder) - : local_impl_(env) { - request_handler_threadpool_ = - MakeUnique(env->env, "EagerServiceRequestHandler", 4); + : env_(env), local_impl_(env) { server_builder->RegisterService(&service_); cq_ = server_builder->AddCompletionQueue(); } diff --git a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h index 9a94026342..502f3ef529 100644 --- a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h +++ b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h @@ -45,7 +45,7 @@ class GrpcEagerServiceImpl : public AsyncServiceInterface { private: #define HANDLER(method) \ void method##Handler(EagerCall* call) { \ - request_handler_threadpool_->Schedule([this, call]() { \ + env_->compute_pool->Schedule([this, call]() { \ call->SendResponse( \ ToGrpcStatus(local_impl_.method(&call->request, &call->response))); \ }); \ @@ -64,6 +64,7 @@ class GrpcEagerServiceImpl : public AsyncServiceInterface { HANDLER(RegisterFunction); #undef HANDLER + const WorkerEnv* const env_; // Not owned. EagerServiceImpl local_impl_; std::unique_ptr<::grpc::Alarm> shutdown_alarm_; @@ -71,8 +72,6 @@ class GrpcEagerServiceImpl : public AsyncServiceInterface { std::unique_ptr<::grpc::ServerCompletionQueue> cq_; tensorflow::eager::grpc::EagerService::AsyncService service_; - std::unique_ptr request_handler_threadpool_; - TF_DISALLOW_COPY_AND_ASSIGN(GrpcEagerServiceImpl); }; -- GitLab From efe370fcb367efd069c8166120858492dffa9a33 Mon Sep 17 00:00:00 2001 From: Pavithra Vijay Date: Mon, 23 Jul 2018 18:51:53 -0700 Subject: [PATCH 307/519] Fix error when symbolic tensors are passed as input to Sequential model (when no input shape is specified.) PiperOrigin-RevId: 205761788 --- tensorflow/python/keras/engine/sequential.py | 28 +++++++++-- .../python/keras/engine/sequential_test.py | 46 +++++++++++++++++-- tensorflow/python/keras/engine/training.py | 8 +++- .../golden/tensorflow.keras.-sequential.pbtxt | 4 ++ .../tensorflow.keras.models.-sequential.pbtxt | 4 ++ 5 files changed, 79 insertions(+), 11 deletions(-) diff --git a/tensorflow/python/keras/engine/sequential.py b/tensorflow/python/keras/engine/sequential.py index 371504a503..41cdfda660 100644 --- a/tensorflow/python/keras/engine/sequential.py +++ b/tensorflow/python/keras/engine/sequential.py @@ -213,13 +213,31 @@ class Sequential(Model): self.outputs = [self.layers[-1].output] self.build() - @checkpointable.no_automatic_dependency_tracking def build(self, input_shape=None): - if input_shape and not self.inputs: - batch_shape = tuple(input_shape) + self._set_inputs_and_outputs(input_shape=input_shape) + + def symbolic_set_inputs(self, inputs): + self._set_inputs_and_outputs(tensor=inputs) + + @checkpointable.no_automatic_dependency_tracking + def _set_inputs_and_outputs(self, input_shape=None, tensor=None): + """Set model's input and output specs based on the input received. + + If `tensor` is provided, `input_shape` is not required. + + Args: + input_shape: Optional shape of input. + tensor: Optional existing tensor to wrap into the `Input` layer. + """ + if not self.inputs: dtype = K.floatx() - x = Input( - batch_shape=batch_shape, dtype=dtype, name=self.name + '_input') + if tensor is not None: + batch_shape = (None,) + tuple(tensor.get_shape().as_list()[1:]) + x = Input(dtype=dtype, name=self.name + '_input', tensor=tensor) + elif input_shape is not None: + batch_shape = tuple(input_shape) + x = Input( + batch_shape=batch_shape, dtype=dtype, name=self.name + '_input') self.inputs = [x] for layer in self._layers: x = layer(x) diff --git a/tensorflow/python/keras/engine/sequential_test.py b/tensorflow/python/keras/engine/sequential_test.py index 0f54e29cee..4f4adca333 100644 --- a/tensorflow/python/keras/engine/sequential_test.py +++ b/tensorflow/python/keras/engine/sequential_test.py @@ -22,7 +22,6 @@ import numpy as np from tensorflow.python import keras from tensorflow.python.data.ops import dataset_ops -from tensorflow.python.eager import context from tensorflow.python.framework import test_util as tf_test_util from tensorflow.python.ops import array_ops from tensorflow.python.platform import test @@ -104,9 +103,6 @@ class TestSequential(test.TestCase): @tf_test_util.run_in_graph_and_eager_modes def test_sequential_deferred_build_with_dataset_iterators(self): - if not context.executing_eagerly(): - # TODO(psv/fchollet): Add support for this use case in graph mode. - return num_hidden = 5 input_dim = 3 num_classes = 2 @@ -136,6 +132,48 @@ class TestSequential(test.TestCase): [None, num_classes]) self.assertEqual(len(model.weights), 2 * 2) + def test_training_and_eval_methods_on_symbolic_tensors(self): + with self.test_session(): + + def create_model(): + model = keras.Sequential() + model.add(keras.layers.Dense(10, activation='relu')) + model.add(keras.layers.Dense(4, activation='softmax')) + + model.compile( + optimizer=rmsprop.RMSPropOptimizer(1e-3), + loss='categorical_crossentropy', + metrics=['accuracy']) + return model + + inputs = keras.backend.zeros(shape=(10, 3)) + targets = keras.backend.zeros(shape=(10, 4)) + + model = create_model() + model.fit(inputs, targets, epochs=10, steps_per_epoch=30) + + model = create_model() + model.evaluate(inputs, targets, steps=2, verbose=0) + + model = create_model() + model.predict(inputs, steps=2) + + model = create_model() + model.train_on_batch(inputs, targets) + + model = create_model() + model.test_on_batch(inputs, targets) + + model = create_model() + model.fit( + inputs, + targets, + epochs=1, + steps_per_epoch=2, + verbose=0, + validation_data=(inputs, targets), + validation_steps=2) + @tf_test_util.run_in_graph_and_eager_modes def test_invalid_use_cases(self): # Added objects must be layer instances diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index fbc2a11eda..1d4ab1fe37 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -991,10 +991,14 @@ class Model(Network): inputs = inputs[0] if tensor_util.is_tensor(inputs): - input_shape = (None,) + tuple(inputs.get_shape().as_list()[1:]) + if context.executing_eagerly(): + input_shape = (None,) + tuple(inputs.get_shape().as_list()[1:]) + self.build(input_shape=input_shape) + else: + self.symbolic_set_inputs(inputs) else: input_shape = (None,) + inputs.shape[1:] - self.build(input_shape=input_shape) + self.build(input_shape=input_shape) elif context.executing_eagerly(): self._eager_set_inputs(inputs) else: diff --git a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt index 8295905975..65cfad77d1 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.-sequential.pbtxt @@ -266,6 +266,10 @@ tf_class { name: "summary" argspec: "args=[\'self\', \'line_length\', \'positions\', \'print_fn\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " } + member_method { + name: "symbolic_set_inputs" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "test_on_batch" argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " diff --git a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt index 5211657414..6a83129f7d 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.models.-sequential.pbtxt @@ -266,6 +266,10 @@ tf_class { name: "summary" argspec: "args=[\'self\', \'line_length\', \'positions\', \'print_fn\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\'], " } + member_method { + name: "symbolic_set_inputs" + argspec: "args=[\'self\', \'inputs\'], varargs=None, keywords=None, defaults=None" + } member_method { name: "test_on_batch" argspec: "args=[\'self\', \'x\', \'y\', \'sample_weight\'], varargs=None, keywords=None, defaults=[\'None\', \'None\'], " -- GitLab From df7344f1933d932f03f472402068ff1883f0c011 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 19:08:40 -0700 Subject: [PATCH 308/519] Implementation of stack. PiperOrigin-RevId: 205763219 --- tensorflow/contrib/lite/build_def.bzl | 408 +++++++++--------- tensorflow/contrib/lite/builtin_op_data.h | 5 + .../lite/g3doc/tf_ops_compatibility.md | 12 + tensorflow/contrib/lite/kernels/BUILD | 15 + .../internal/reference/reference_ops.h | 20 + tensorflow/contrib/lite/kernels/pack.cc | 131 ++++++ tensorflow/contrib/lite/kernels/pack_test.cc | 119 +++++ tensorflow/contrib/lite/kernels/register.cc | 2 + tensorflow/contrib/lite/model.cc | 10 +- .../contrib/lite/testing/generate_examples.py | 38 ++ .../propagate_array_data_types.cc | 8 + .../contrib/lite/toco/import_tensorflow.cc | 1 + tensorflow/contrib/lite/toco/model.h | 1 + .../contrib/lite/toco/tflite/operator.cc | 22 + .../contrib/lite/toco/tflite/operator_test.cc | 10 + 15 files changed, 599 insertions(+), 203 deletions(-) create mode 100644 tensorflow/contrib/lite/kernels/pack.cc create mode 100644 tensorflow/contrib/lite/kernels/pack_test.cc diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 79f7455ad8..7c13f9011e 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -1,4 +1,5 @@ """Generate Flatbuffer binary from json.""" + load( "//tensorflow:tensorflow.bzl", "tf_cc_test", @@ -6,118 +7,120 @@ load( ) def tflite_copts(): - """Defines compile time flags.""" - copts = [ - "-DFARMHASH_NO_CXX_STRING", - ] + select({ - str(Label("//tensorflow:android_arm64")): [ - "-std=c++11", - "-O3", - ], - str(Label("//tensorflow:android_arm")): [ - "-mfpu=neon", - "-mfloat-abi=softfp", - "-std=c++11", - "-O3", - ], - str(Label("//tensorflow:android_x86")): [ - "-DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK", - ], - str(Label("//tensorflow:ios_x86_64")): [ - "-msse4.1", - ], - "//conditions:default": [], - }) + select({ - str(Label("//tensorflow:with_default_optimizations")): [], - "//conditions:default": ["-DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK"], - }) + """Defines compile time flags.""" + copts = [ + "-DFARMHASH_NO_CXX_STRING", + ] + select({ + str(Label("//tensorflow:android_arm64")): [ + "-std=c++11", + "-O3", + ], + str(Label("//tensorflow:android_arm")): [ + "-mfpu=neon", + "-mfloat-abi=softfp", + "-std=c++11", + "-O3", + ], + str(Label("//tensorflow:android_x86")): [ + "-DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK", + ], + str(Label("//tensorflow:ios_x86_64")): [ + "-msse4.1", + ], + "//conditions:default": [], + }) + select({ + str(Label("//tensorflow:with_default_optimizations")): [], + "//conditions:default": ["-DGEMMLOWP_ALLOW_SLOW_SCALAR_FALLBACK"], + }) - return copts + return copts LINKER_SCRIPT = "//tensorflow/contrib/lite/java/src/main/native:version_script.lds" def tflite_linkopts_unstripped(): - """Defines linker flags to reduce size of TFLite binary. + """Defines linker flags to reduce size of TFLite binary. - These are useful when trying to investigate the relative size of the - symbols in TFLite. + These are useful when trying to investigate the relative size of the + symbols in TFLite. - Returns: - a select object with proper linkopts - """ - return select({ - "//tensorflow:android": [ - "-Wl,--no-export-dynamic", # Only inc syms referenced by dynamic obj. - "-Wl,--exclude-libs,ALL", # Exclude syms in all libs from auto export. - "-Wl,--gc-sections", # Eliminate unused code and data. - "-Wl,--as-needed", # Don't link unused libs. - ], - "//tensorflow/contrib/lite:mips": [], - "//tensorflow/contrib/lite:mips64": [], - "//conditions:default": [ - "-Wl,--icf=all", # Identical code folding. - ], - }) + Returns: + a select object with proper linkopts + """ + return select({ + "//tensorflow:android": [ + "-Wl,--no-export-dynamic", # Only inc syms referenced by dynamic obj. + "-Wl,--exclude-libs,ALL", # Exclude syms in all libs from auto export. + "-Wl,--gc-sections", # Eliminate unused code and data. + "-Wl,--as-needed", # Don't link unused libs. + ], + "//tensorflow/contrib/lite:mips": [], + "//tensorflow/contrib/lite:mips64": [], + "//conditions:default": [ + "-Wl,--icf=all", # Identical code folding. + ], + }) def tflite_jni_linkopts_unstripped(): - """Defines linker flags to reduce size of TFLite binary with JNI. + """Defines linker flags to reduce size of TFLite binary with JNI. - These are useful when trying to investigate the relative size of the - symbols in TFLite. + These are useful when trying to investigate the relative size of the + symbols in TFLite. - Returns: - a select object with proper linkopts - """ - return select({ - "//tensorflow:android": [ - "-Wl,--gc-sections", # Eliminate unused code and data. - "-Wl,--as-needed", # Don't link unused libs. - ], - "//tensorflow/contrib/lite:mips": [], - "//tensorflow/contrib/lite:mips64": [], - "//conditions:default": [ - "-Wl,--icf=all", # Identical code folding. - ], - }) + Returns: + a select object with proper linkopts + """ + return select({ + "//tensorflow:android": [ + "-Wl,--gc-sections", # Eliminate unused code and data. + "-Wl,--as-needed", # Don't link unused libs. + ], + "//tensorflow/contrib/lite:mips": [], + "//tensorflow/contrib/lite:mips64": [], + "//conditions:default": [ + "-Wl,--icf=all", # Identical code folding. + ], + }) def tflite_linkopts(): - """Defines linker flags to reduce size of TFLite binary.""" - return tflite_linkopts_unstripped() + select({ - "//tensorflow:android": [ - "-s", # Omit symbol table. - ], - "//conditions:default": [], - }) + """Defines linker flags to reduce size of TFLite binary.""" + return tflite_linkopts_unstripped() + select({ + "//tensorflow:android": [ + "-s", # Omit symbol table. + ], + "//conditions:default": [], + }) def tflite_jni_linkopts(): - """Defines linker flags to reduce size of TFLite binary with JNI.""" - return tflite_jni_linkopts_unstripped() + select({ - "//tensorflow:android": [ - "-s", # Omit symbol table. - "-latomic", # Required for some uses of ISO C++11 in x86. - ], - "//conditions:default": [], - }) + """Defines linker flags to reduce size of TFLite binary with JNI.""" + return tflite_jni_linkopts_unstripped() + select({ + "//tensorflow:android": [ + "-s", # Omit symbol table. + "-latomic", # Required for some uses of ISO C++11 in x86. + ], + "//conditions:default": [], + }) -def tflite_jni_binary(name, - copts=tflite_copts(), - linkopts=tflite_jni_linkopts(), - linkscript=LINKER_SCRIPT, - linkshared=1, - linkstatic=1, - deps=[]): - """Builds a jni binary for TFLite.""" - linkopts = linkopts + [ - "-Wl,--version-script", # Export only jni functions & classes. - "$(location {})".format(linkscript), - ] - native.cc_binary( - name=name, - copts=copts, - linkshared=linkshared, - linkstatic=linkstatic, - deps= deps + [linkscript], - linkopts=linkopts) +def tflite_jni_binary( + name, + copts = tflite_copts(), + linkopts = tflite_jni_linkopts(), + linkscript = LINKER_SCRIPT, + linkshared = 1, + linkstatic = 1, + deps = []): + """Builds a jni binary for TFLite.""" + linkopts = linkopts + [ + "-Wl,--version-script", # Export only jni functions & classes. + "$(location {})".format(linkscript), + ] + native.cc_binary( + name = name, + copts = copts, + linkshared = linkshared, + linkstatic = linkstatic, + deps = deps + [linkscript], + linkopts = linkopts, + ) def tflite_cc_shared_object(name, copts=tflite_copts(), @@ -134,75 +137,75 @@ def tflite_cc_shared_object(name, deps=deps) def tf_to_tflite(name, src, options, out): - """Convert a frozen tensorflow graphdef to TF Lite's flatbuffer. + """Convert a frozen tensorflow graphdef to TF Lite's flatbuffer. - Args: - name: Name of rule. - src: name of the input graphdef file. - options: options passed to TOCO. - out: name of the output flatbuffer file. - """ + Args: + name: Name of rule. + src: name of the input graphdef file. + options: options passed to TOCO. + out: name of the output flatbuffer file. + """ - toco_cmdline = " ".join([ - "//tensorflow/contrib/lite/toco:toco", - "--input_format=TENSORFLOW_GRAPHDEF", - "--output_format=TFLITE", - ("--input_file=$(location %s)" % src), - ("--output_file=$(location %s)" % out), - ] + options ) - native.genrule( - name = name, - srcs=[src], - outs=[out], - cmd = toco_cmdline, - tools= ["//tensorflow/contrib/lite/toco:toco"], - ) + toco_cmdline = " ".join([ + "//tensorflow/contrib/lite/toco:toco", + "--input_format=TENSORFLOW_GRAPHDEF", + "--output_format=TFLITE", + ("--input_file=$(location %s)" % src), + ("--output_file=$(location %s)" % out), + ] + options) + native.genrule( + name = name, + srcs = [src], + outs = [out], + cmd = toco_cmdline, + tools = ["//tensorflow/contrib/lite/toco:toco"], + ) def tflite_to_json(name, src, out): - """Convert a TF Lite flatbuffer to JSON. + """Convert a TF Lite flatbuffer to JSON. - Args: - name: Name of rule. - src: name of the input flatbuffer file. - out: name of the output JSON file. - """ + Args: + name: Name of rule. + src: name of the input flatbuffer file. + out: name of the output JSON file. + """ - flatc = "@flatbuffers//:flatc" - schema = "//tensorflow/contrib/lite/schema:schema.fbs" - native.genrule( - name = name, - srcs = [schema, src], - outs = [out], - cmd = ("TMP=`mktemp`; cp $(location %s) $${TMP}.bin &&" + - "$(location %s) --raw-binary --strict-json -t" + - " -o /tmp $(location %s) -- $${TMP}.bin &&" + - "cp $${TMP}.json $(location %s)") - % (src, flatc, schema, out), - tools = [flatc], - ) + flatc = "@flatbuffers//:flatc" + schema = "//tensorflow/contrib/lite/schema:schema.fbs" + native.genrule( + name = name, + srcs = [schema, src], + outs = [out], + cmd = ("TMP=`mktemp`; cp $(location %s) $${TMP}.bin &&" + + "$(location %s) --raw-binary --strict-json -t" + + " -o /tmp $(location %s) -- $${TMP}.bin &&" + + "cp $${TMP}.json $(location %s)") % + (src, flatc, schema, out), + tools = [flatc], + ) def json_to_tflite(name, src, out): - """Convert a JSON file to TF Lite's flatbuffer. + """Convert a JSON file to TF Lite's flatbuffer. - Args: - name: Name of rule. - src: name of the input JSON file. - out: name of the output flatbuffer file. - """ + Args: + name: Name of rule. + src: name of the input JSON file. + out: name of the output flatbuffer file. + """ - flatc = "@flatbuffers//:flatc" - schema = "//tensorflow/contrib/lite/schema:schema_fbs" - native.genrule( - name = name, - srcs = [schema, src], - outs = [out], - cmd = ("TMP=`mktemp`; cp $(location %s) $${TMP}.json &&" + - "$(location %s) --raw-binary --unknown-json --allow-non-utf8 -b" + - " -o /tmp $(location %s) $${TMP}.json &&" + - "cp $${TMP}.bin $(location %s)") - % (src, flatc, schema, out), - tools = [flatc], - ) + flatc = "@flatbuffers//:flatc" + schema = "//tensorflow/contrib/lite/schema:schema_fbs" + native.genrule( + name = name, + srcs = [schema, src], + outs = [out], + cmd = ("TMP=`mktemp`; cp $(location %s) $${TMP}.json &&" + + "$(location %s) --raw-binary --unknown-json --allow-non-utf8 -b" + + " -o /tmp $(location %s) $${TMP}.json &&" + + "cp $${TMP}.bin $(location %s)") % + (src, flatc, schema, out), + tools = [flatc], + ) # This is the master list of generated examples that will be made into tests. A # function called make_XXX_tests() must also appear in generate_examples.py. @@ -245,6 +248,7 @@ def generated_test_models(): "mul", "neg", "not_equal", + "pack", "pad", "padv2", "prelu", @@ -279,58 +283,58 @@ def generated_test_models(): ] def gen_zip_test(name, test_name, **kwargs): - """Generate a zipped-example test and its dependent zip files. + """Generate a zipped-example test and its dependent zip files. - Args: - name: Resulting cc_test target name - test_name: Test targets this model. Comes from the list above. - **kwargs: tf_cc_test kwargs. - """ - gen_zipped_test_file( - name = "zip_%s" % test_name, - file = "%s.zip" % test_name, - ) - tf_cc_test(name, **kwargs) + Args: + name: Resulting cc_test target name + test_name: Test targets this model. Comes from the list above. + **kwargs: tf_cc_test kwargs. + """ + gen_zipped_test_file( + name = "zip_%s" % test_name, + file = "%s.zip" % test_name, + ) + tf_cc_test(name, **kwargs) def gen_zipped_test_file(name, file): - """Generate a zip file of tests by using :generate_examples. + """Generate a zip file of tests by using :generate_examples. - Args: - name: Name of output. We will produce "`file`.files" as a target. - file: The name of one of the generated_examples targets, e.g. "transpose" - """ - toco = "//tensorflow/contrib/lite/toco:toco" - native.genrule( - name = file + ".files", - cmd = ("$(locations :generate_examples) --toco $(locations %s) " % toco - + " --zip_to_output " + file + " $(@D)"), - outs = [file], - tools = [ - ":generate_examples", - toco, - ], - ) + Args: + name: Name of output. We will produce "`file`.files" as a target. + file: The name of one of the generated_examples targets, e.g. "transpose" + """ + toco = "//tensorflow/contrib/lite/toco:toco" + native.genrule( + name = file + ".files", + cmd = ("$(locations :generate_examples) --toco $(locations %s) " % toco + + " --zip_to_output " + file + " $(@D)"), + outs = [file], + tools = [ + ":generate_examples", + toco, + ], + ) - native.filegroup( - name = name, - srcs = [file], - ) + native.filegroup( + name = name, + srcs = [file], + ) def gen_selected_ops(name, model): - """Generate the library that includes only used ops. + """Generate the library that includes only used ops. - Args: - name: Name of the generated library. - model: TFLite model to interpret. - """ - out = name + "_registration.cc" - tool = "//tensorflow/contrib/lite/tools:generate_op_registrations" - tflite_path = "//tensorflow/contrib/lite" - native.genrule( - name = name, - srcs = [model], - outs = [out], - cmd = ("$(location %s) --input_model=$(location %s) --output_registration=$(location %s) --tflite_path=%s") - % (tool, model, out, tflite_path[2:]), - tools = [tool], - ) + Args: + name: Name of the generated library. + model: TFLite model to interpret. + """ + out = name + "_registration.cc" + tool = "//tensorflow/contrib/lite/tools:generate_op_registrations" + tflite_path = "//tensorflow/contrib/lite" + native.genrule( + name = name, + srcs = [model], + outs = [out], + cmd = ("$(location %s) --input_model=$(location %s) --output_registration=$(location %s) --tflite_path=%s") % + (tool, model, out, tflite_path[2:]), + tools = [tool], + ) diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index a24aaad7dd..fd16aa1063 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -277,6 +277,11 @@ typedef struct { bool narrow_range; } TfLiteFakeQuantParams; +typedef struct { + int values_count; + int axis; +} TfLitePackParams; + #ifdef __cplusplus } // extern "C" #endif // __cplusplus diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index 49d00a66ba..967259b7a6 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -815,6 +815,18 @@ Outputs { } ``` +**PACK** + +``` +Inputs { + 0: a list of tensors. + 1: an integer. +} +Outputs { + 0: A tensor of stacked tensors. +} +``` + And these are TensorFlow Lite operations that are present but not ready for custom models yet: diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index 9549b4445d..c224132cae 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -176,6 +176,7 @@ cc_library( "mfcc.cc", "mul.cc", "neg.cc", + "pack.cc", "pad.cc", "pooling.cc", "pow.cc", @@ -1156,6 +1157,20 @@ tf_cc_test( ], ) +tf_cc_test( + name = "pack_test", + size = "small", + srcs = ["pack_test.cc"], + tags = ["tflite_not_portable_ios"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:builtin_op_data", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index ef39be3f91..31a54c2b62 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -1860,6 +1860,26 @@ void Concatenation(int concat_dim, const Scalar* const* input_data, } } +template +void Pack(int dim, const Scalar* const* input_data, + const Dims<4>* const* input_dims, int inputs_count, + Scalar* output_data, const Dims<4>& output_dims) { + TFLITE_DCHECK(IsPackedWithoutStrides(output_dims)); + int outer_size = 1; + for (int i = dim + 1; i < 4; i++) { + outer_size *= output_dims.sizes[i]; + } + Scalar* output_ptr = output_data; + const int copy_size = FlatSize(**input_dims) / outer_size; + for (int k = 0; k < outer_size; k++) { + for (int i = 0; i < inputs_count; ++i) { + memcpy(output_ptr, input_data[i] + k * copy_size, + copy_size * sizeof(Scalar)); + output_ptr += copy_size; + } + } +} + // TODO(prabhumk): This is the same as the optimized implementation. // TODO(prabhumk): The quantized implementation of concatentation isn't fully // quantized as it takes scale as a floating point value. This should be fixed diff --git a/tensorflow/contrib/lite/kernels/pack.cc b/tensorflow/contrib/lite/kernels/pack.cc new file mode 100644 index 0000000000..bb3416f6a6 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/pack.cc @@ -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. +==============================================================================*/ + +#include "tensorflow/contrib/lite/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace pack { +namespace { + +constexpr int kOutputTensor = 0; + +// Op data for pack op. +struct OpData { + int values_count; + int axis; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* data = new OpData; + data->axis = 0; + return data; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + const OpData* data = reinterpret_cast(node->builtin_data); + + TF_LITE_ENSURE_EQ(context, NumInputs(node), data->values_count); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + const TfLiteTensor* input0 = GetInput(context, node, 0); + TF_LITE_ENSURE(context, NumDimensions(input0) < 4); + TF_LITE_ENSURE(context, NumDimensions(input0) >= data->axis); + // TODO(renjieliu): Support negative axis. + TF_LITE_ENSURE(context, data->axis >= 0); + if (input0->type != kTfLiteInt32 && input0->type != kTfLiteFloat32) { + context->ReportError(context, + "Currently pack only supports int32 and float32."); + return kTfLiteError; + } + // Make sure all inputs have the same shape and type. + for (int i = 1; i < data->values_count; ++i) { + const TfLiteTensor* input = GetInput(context, node, i); + TF_LITE_ENSURE(context, HaveSameShapes(input0, input)); + TF_LITE_ENSURE_EQ(context, input0->type, input->type); + } + + // Resize output. rank R will become rank R + 1 + const int dimension_size = NumDimensions(input0) + 1; + const TfLiteIntArray* input_shape = input0->dims; + TfLiteIntArray* output_shape = TfLiteIntArrayCreate(dimension_size); + int i = 0; + for (int index = 0; index < dimension_size; ++index) { + if (index == data->axis) { + output_shape->data[index] = data->values_count; + } else { + output_shape->data[index] = input_shape->data[i++]; + } + } + + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + TF_LITE_ENSURE_EQ(context, output->type, input0->type); + + return context->ResizeTensor(context, output, output_shape); +} + +template +void PackImpl(TfLiteContext* context, TfLiteNode* node, TfLiteTensor* output, + int values_count, int axis) { + VectorOfTensors all_inputs(*context, *node->inputs); + reference_ops::Pack(RemapDim(NumDimensions(output), axis), + all_inputs.data(), all_inputs.dims(), values_count, + GetTensorData(output), GetTensorDims(output)); +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + const OpData* data = reinterpret_cast(node->builtin_data); + + TfLiteTensor* output = GetOutput(context, node, kOutputTensor); + switch (output->type) { + case kTfLiteFloat32: { + PackImpl(context, node, output, data->values_count, data->axis); + break; + } + case kTfLiteInt32: { + PackImpl(context, node, output, data->values_count, data->axis); + break; + } + default: { + context->ReportError(context, + "Currently pack only supports int32 and float32."); + return kTfLiteError; + } + } + + return kTfLiteOk; +} + +} // namespace +} // namespace pack + +TfLiteRegistration* Register_PACK() { + static TfLiteRegistration r = {pack::Init, pack::Free, pack::Prepare, + pack::Eval}; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/pack_test.cc b/tensorflow/contrib/lite/kernels/pack_test.cc new file mode 100644 index 0000000000..cb9fed69b1 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/pack_test.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 +#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::ElementsAre; + +template +class PackOpModel : public SingleOpModel { + public: + PackOpModel(const TensorData& input_template, int axis, int values_count) { + std::vector> all_input_shapes; + for (int i = 0; i < values_count; ++i) { + all_input_shapes.push_back(input_template.shape); + AddInput(input_template); + } + output_ = AddOutput({input_template.type, /*shape=*/{}, input_template.min, + input_template.max}); + SetBuiltinOp(BuiltinOperator_PACK, BuiltinOptions_PackOptions, + CreatePackOptions(builder_, values_count, axis).Union()); + BuildInterpreter(all_input_shapes); + } + + void SetInput(int index, std::initializer_list data) { + PopulateTensor(index, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int output_; +}; + +TEST(PackOpTest, FloatThreeInputs) { + PackOpModel model({TensorType_FLOAT32, {2}}, 0, 3); + model.SetInput(0, {1, 4}); + model.SetInput(1, {2, 5}); + model.SetInput(2, {3, 6}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2)); + EXPECT_THAT(model.GetOutput(), ElementsAre(1, 4, 2, 5, 3, 6)); +} + +TEST(PackOpTest, FloatThreeInputsDifferentAxis) { + PackOpModel model({TensorType_FLOAT32, {2}}, 1, 3); + model.SetInput(0, {1, 4}); + model.SetInput(1, {2, 5}); + model.SetInput(2, {3, 6}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3)); + EXPECT_THAT(model.GetOutput(), ElementsAre(1, 2, 3, 4, 5, 6)); +} + +TEST(PackOpTest, FloatMultilDimensions) { + PackOpModel model({TensorType_FLOAT32, {2, 3}}, 1, 2); + model.SetInput(0, {1, 2, 3, 4, 5, 6}); + model.SetInput(1, {7, 8, 9, 10, 11, 12}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 3)); + EXPECT_THAT(model.GetOutput(), + ElementsAre(1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12)); +} + +TEST(PackOpTest, IntThreeInputs) { + PackOpModel model({TensorType_INT32, {2}}, 0, 3); + model.SetInput(0, {1, 4}); + model.SetInput(1, {2, 5}); + model.SetInput(2, {3, 6}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2)); + EXPECT_THAT(model.GetOutput(), ElementsAre(1, 4, 2, 5, 3, 6)); +} + +TEST(PackOpTest, IntThreeInputsDifferentAxis) { + PackOpModel model({TensorType_INT32, {2}}, 1, 3); + model.SetInput(0, {1, 4}); + model.SetInput(1, {2, 5}); + model.SetInput(2, {3, 6}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3)); + EXPECT_THAT(model.GetOutput(), ElementsAre(1, 2, 3, 4, 5, 6)); +} + +TEST(PackOpTest, IntMultilDimensions) { + PackOpModel model({TensorType_INT32, {2, 3}}, 1, 2); + model.SetInput(0, {1, 2, 3, 4, 5, 6}); + model.SetInput(1, {7, 8, 9, 10, 11, 12}); + model.Invoke(); + EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 3)); + EXPECT_THAT(model.GetOutput(), + ElementsAre(1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12)); +} +} // 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 f0f2757277..0b70bed308 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -106,6 +106,7 @@ TfLiteRegistration* Register_RSQRT(); TfLiteRegistration* Register_SHAPE(); TfLiteRegistration* Register_POW(); TfLiteRegistration* Register_FAKE_QUANT(); +TfLiteRegistration* Register_PACK(); BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_RELU, Register_RELU()); @@ -195,6 +196,7 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_SHAPE, Register_SHAPE()); AddBuiltin(BuiltinOperator_POW, Register_POW()); AddBuiltin(BuiltinOperator_FAKE_QUANT, Register_FAKE_QUANT(), 1, 2); + AddBuiltin(BuiltinOperator_PACK, Register_PACK()); // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that // custom ops aren't always included by default. diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index d318591b49..ad9a7de39c 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -705,6 +705,15 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = static_cast(params); break; } + case BuiltinOperator_PACK: { + TfLitePackParams* params = MallocPOD(); + if (auto* pack_params = op->builtin_options_as_PackOptions()) { + params->values_count = pack_params->values_count(); + params->axis = pack_params->axis(); + } + *builtin_data = reinterpret_cast(params); + break; + } case BuiltinOperator_DELEGATE: { // TODO(ycling): Revisit when supporting saving delegated models. error_reporter->Report("DELEGATE op shouldn't exist in model."); @@ -763,7 +772,6 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, case BuiltinOperator_TOPK_V2: case BuiltinOperator_TRANSPOSE: case BuiltinOperator_POW: - case BuiltinOperator_PACK: break; } return kTfLiteOk; diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index b3ccc65e85..41ece94237 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -2880,6 +2880,44 @@ def make_sparse_to_dense_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def make_pack_tests(zip_path): + """Make a set of tests to do stack.""" + + test_parameters = [{ + "base_shape": [[3, 4, 3], [3, 4], [5]], + "num_tensors": [1, 2, 3, 4, 5, 6], + "axis": [0, 1, 2, 3], + "additional_shape": [1, 2, 3], + }] + + def get_shape(parameters): + """Return a tweaked version of 'base_shape'.""" + axis = parameters["axis"] + shape = parameters["base_shape"][:] + if axis < len(shape): + shape[axis] += parameters["additional_shape"] + return shape + + def build_graph(parameters): + all_tensors = [] + for n in range(0, parameters["num_tensors"]): + input_tensor = tf.placeholder( + dtype=tf.float32, name=("input%d" % n), shape=get_shape(parameters)) + all_tensors.append(input_tensor) + out = tf.stack(all_tensors, parameters["axis"]) + return all_tensors, [out] + + def build_inputs(parameters, sess, inputs, outputs): + all_values = [] + for _ in range(0, parameters["num_tensors"]): + input_values = create_tensor_data(np.float32, get_shape(parameters)) + all_values.append(input_values) + return all_values, sess.run( + outputs, feed_dict=dict(zip(inputs, all_values))) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + # Toco binary path provided by the generate rule. bin_path = None 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 3dda536ef7..9848d55c83 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 @@ -193,6 +193,14 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { SetDataTypeForAllOutputs(model, op, data_type); break; } + case OperatorType::kPack: { + const ArrayDataType data_type = model->GetArray(op->inputs[0]).data_type; + for (const auto& input : op->inputs) { + CHECK(data_type == model->GetArray(input).data_type); + } + SetDataTypeForAllOutputs(model, op, data_type); + break; + } default: { // These operators produce outputs with the same type as their 1st input CHECK_GT(op->inputs.size(), 0); diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index 8bb797fe0f..032c863945 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -1529,6 +1529,7 @@ tensorflow::Status ConvertPackOperator( for (int i = 0; i < num_inputs; ++i) { op->inputs.push_back(node.input(i)); } + op->values_count = HasAttr(node, "N") ? GetIntAttr(node, "N") : num_inputs; op->axis = HasAttr(node, "axis") ? GetIntAttr(node, "axis") : 0; op->dtype = ConvertDataType(toco::GetDataTypeAttr(node, "T")); op->outputs.push_back(node.name()); diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 6fe194516d..d629787939 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -1164,6 +1164,7 @@ struct TensorFlowRsqrtOperator : Operator { // TensorFlow equivalent: Pack struct PackOperator : Operator { PackOperator() : Operator(OperatorType::kPack) {} + int values_count; int axis = 0; ArrayDataType dtype = ArrayDataType::kNone; }; diff --git a/tensorflow/contrib/lite/toco/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 1a1c4b8944..4b2ef756cc 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -1013,6 +1013,26 @@ class ExpandDims int GetVersion(const Operator& op) const override { return 1; } }; +class Pack : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreatePackOptions(*builder, op.values_count, op.axis); + } + + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->values_count = options.values_count(); + op->axis = options.axis(); + } + + int GetVersion(const Operator& op) const override { return 1; } +}; + class Shape : public BuiltinOperator { @@ -1256,6 +1276,8 @@ std::vector> BuildOperatorList() { new Shape(::tflite::BuiltinOperator_SHAPE, OperatorType::kShape)); ops.emplace_back(new FakeQuant(::tflite::BuiltinOperator_FAKE_QUANT, OperatorType::kFakeQuant)); + ops.emplace_back( + new Pack(::tflite::BuiltinOperator_PACK, OperatorType::kPack)); // Custom Operators. ops.emplace_back( diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index ff2d35b1f5..44de6fbf64 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -452,6 +452,16 @@ TEST_F(OperatorTest, BuiltinSparseToDense) { EXPECT_EQ(op.validate_indices, output_toco_op->validate_indices); } +TEST_F(OperatorTest, BuiltinPack) { + PackOperator op; + op.values_count = 3; + op.axis = 1; + std::unique_ptr output_toco_op = + SerializeAndDeserialize(GetOperator("PACK", OperatorType::kPack), op); + EXPECT_EQ(op.values_count, output_toco_op->values_count); + EXPECT_EQ(op.axis, output_toco_op->axis); +} + TEST_F(OperatorTest, TensorFlowUnsupported) { TensorFlowUnsupportedOperator op; op.tensorflow_op = "MyCustomUnsupportedOp"; -- GitLab From 438eae1515d6b49e6d0578874657fb80459ef8e8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 19:48:48 -0700 Subject: [PATCH 309/519] Add Logical_or to schema. PiperOrigin-RevId: 205765869 --- tensorflow/contrib/lite/builtin_ops.h | 1 + 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 | 124 +++++++++++++++++- 5 files changed, 126 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h index 558e547121..1ae73b9738 100644 --- a/tensorflow/contrib/lite/builtin_ops.h +++ b/tensorflow/contrib/lite/builtin_ops.h @@ -109,6 +109,7 @@ typedef enum { kTfLiteBuiltinReduceProd = 81, kTfLiteBuiltinReduceMax = 82, kTfLiteBuiltinPack = 83, + kTfLiteBuiltinLogicalOr = 84, } TfLiteBuiltinOperator; #ifdef __cplusplus diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index ad9a7de39c..c6869feb16 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -772,6 +772,7 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, case BuiltinOperator_TOPK_V2: case BuiltinOperator_TRANSPOSE: case BuiltinOperator_POW: + case BuiltinOperator_LOGICAL_OR: break; } return kTfLiteOk; diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index 659230e033..551e8ed320 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -622,6 +622,7 @@ TfLiteStatus AddOpsAndParams( case tflite::BuiltinOperator_POW: case tflite::BuiltinOperator_FAKE_QUANT: case tflite::BuiltinOperator_PACK: + case tflite::BuiltinOperator_LOGICAL_OR: logError("Op code %d is currently not delegated to NNAPI", builtin); return kTfLiteError; break; diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index 0434199a08..a285bf9919 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -165,6 +165,7 @@ enum BuiltinOperator : byte { REDUCE_PROD = 81, REDUCE_MAX = 82, PACK = 83, + LOGICAL_OR = 84, } // Options for the builtin operators. @@ -228,6 +229,7 @@ union BuiltinOptions { ArgMinOptions, FakeQuantOptions, PackOptions, + LogicalOrOptions, } enum Padding : byte { SAME, VALID } @@ -544,6 +546,9 @@ table PackOptions { axis:int; } +table LogicalOrOptions { +} + // 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 9b84030938..8c1d6d6a36 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -208,6 +208,9 @@ struct FakeQuantOptionsT; struct PackOptions; struct PackOptionsT; +struct LogicalOrOptions; +struct LogicalOrOptionsT; + struct OperatorCode; struct OperatorCodeT; @@ -357,11 +360,12 @@ enum BuiltinOperator { BuiltinOperator_REDUCE_PROD = 81, BuiltinOperator_REDUCE_MAX = 82, BuiltinOperator_PACK = 83, + BuiltinOperator_LOGICAL_OR = 84, BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_PACK + BuiltinOperator_MAX = BuiltinOperator_LOGICAL_OR }; -inline BuiltinOperator (&EnumValuesBuiltinOperator())[83] { +inline BuiltinOperator (&EnumValuesBuiltinOperator())[84] { static BuiltinOperator values[] = { BuiltinOperator_ADD, BuiltinOperator_AVERAGE_POOL_2D, @@ -445,7 +449,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[83] { BuiltinOperator_FAKE_QUANT, BuiltinOperator_REDUCE_PROD, BuiltinOperator_REDUCE_MAX, - BuiltinOperator_PACK + BuiltinOperator_PACK, + BuiltinOperator_LOGICAL_OR }; return values; } @@ -536,6 +541,7 @@ inline const char **EnumNamesBuiltinOperator() { "REDUCE_PROD", "REDUCE_MAX", "PACK", + "LOGICAL_OR", nullptr }; return names; @@ -607,11 +613,12 @@ enum BuiltinOptions { BuiltinOptions_ArgMinOptions = 57, BuiltinOptions_FakeQuantOptions = 58, BuiltinOptions_PackOptions = 59, + BuiltinOptions_LogicalOrOptions = 60, BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_PackOptions + BuiltinOptions_MAX = BuiltinOptions_LogicalOrOptions }; -inline BuiltinOptions (&EnumValuesBuiltinOptions())[60] { +inline BuiltinOptions (&EnumValuesBuiltinOptions())[61] { static BuiltinOptions values[] = { BuiltinOptions_NONE, BuiltinOptions_Conv2DOptions, @@ -672,7 +679,8 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[60] { BuiltinOptions_PowOptions, BuiltinOptions_ArgMinOptions, BuiltinOptions_FakeQuantOptions, - BuiltinOptions_PackOptions + BuiltinOptions_PackOptions, + BuiltinOptions_LogicalOrOptions }; return values; } @@ -739,6 +747,7 @@ inline const char **EnumNamesBuiltinOptions() { "ArgMinOptions", "FakeQuantOptions", "PackOptions", + "LogicalOrOptions", nullptr }; return names; @@ -989,6 +998,10 @@ template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_PackOptions; }; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions; +}; + struct BuiltinOptionsUnion { BuiltinOptions type; void *value; @@ -1492,6 +1505,14 @@ struct BuiltinOptionsUnion { return type == BuiltinOptions_PackOptions ? reinterpret_cast(value) : nullptr; } + LogicalOrOptionsT *AsLogicalOrOptions() { + return type == BuiltinOptions_LogicalOrOptions ? + reinterpret_cast(value) : nullptr; + } + const LogicalOrOptionsT *AsLogicalOrOptions() const { + return type == BuiltinOptions_LogicalOrOptions ? + reinterpret_cast(value) : nullptr; + } }; bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); @@ -5391,6 +5412,46 @@ inline flatbuffers::Offset CreatePackOptions( flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBufferBuilder &_fbb, const PackOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +struct LogicalOrOptionsT : public flatbuffers::NativeTable { + typedef LogicalOrOptions TableType; + LogicalOrOptionsT() { + } +}; + +struct LogicalOrOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef LogicalOrOptionsT NativeTableType; + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + verifier.EndTable(); + } + LogicalOrOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct LogicalOrOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + explicit LogicalOrOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + LogicalOrOptionsBuilder &operator=(const LogicalOrOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateLogicalOrOptions( + flatbuffers::FlatBufferBuilder &_fbb) { + LogicalOrOptionsBuilder builder_(_fbb); + return builder_.Finish(); +} + +flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + struct OperatorCodeT : public flatbuffers::NativeTable { typedef OperatorCode TableType; BuiltinOperator builtin_code; @@ -5701,6 +5762,9 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const PackOptions *builtin_options_as_PackOptions() const { return builtin_options_type() == BuiltinOptions_PackOptions ? static_cast(builtin_options()) : nullptr; } + const LogicalOrOptions *builtin_options_as_LogicalOrOptions() const { + return builtin_options_type() == BuiltinOptions_LogicalOrOptions ? static_cast(builtin_options()) : nullptr; + } const flatbuffers::Vector *custom_options() const { return GetPointer *>(VT_CUSTOM_OPTIONS); } @@ -5968,6 +6032,10 @@ template<> inline const PackOptions *Operator::builtin_options_as() return builtin_options_as_PackOptions(); } +template<> inline const LogicalOrOptions *Operator::builtin_options_as() const { + return builtin_options_as_LogicalOrOptions(); +} + struct OperatorBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; @@ -8060,6 +8128,29 @@ inline flatbuffers::Offset CreatePackOptions(flatbuffers::FlatBuffe _axis); } +inline LogicalOrOptionsT *LogicalOrOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new LogicalOrOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void LogicalOrOptions::UnPackTo(LogicalOrOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; +} + +inline flatbuffers::Offset LogicalOrOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateLogicalOrOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const LogicalOrOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + return tflite::CreateLogicalOrOptions( + _fbb); +} + inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new OperatorCodeT(); UnPackTo(_o, _resolver); @@ -8485,6 +8576,10 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } default: return false; } } @@ -8739,6 +8834,10 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } default: return nullptr; } } @@ -8981,6 +9080,10 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff auto ptr = reinterpret_cast(value); return CreatePackOptions(_fbb, ptr, _rehasher).Union(); } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(value); + return CreateLogicalOrOptions(_fbb, ptr, _rehasher).Union(); + } default: return 0; } } @@ -9223,6 +9326,10 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL value = new PackOptionsT(*reinterpret_cast(u.value)); break; } + case BuiltinOptions_LogicalOrOptions: { + value = new LogicalOrOptionsT(*reinterpret_cast(u.value)); + break; + } default: break; } @@ -9525,6 +9632,11 @@ inline void BuiltinOptionsUnion::Reset() { delete ptr; break; } + case BuiltinOptions_LogicalOrOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } default: break; } value = nullptr; -- GitLab From 7fe6d775bb4a3f1dcc6484cab3dae1563dac6b42 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Mon, 23 Jul 2018 20:56:42 -0700 Subject: [PATCH 310/519] Default nonempty list initializers to Python lists, because the idiom is widely used in lieu of a tuple, and almost never as an actual initialized list that is about to be added even more elements. This removes the possibility to create an initialized tensor list, and to cover for that this CL also introduces a special function for that purpose. PiperOrigin-RevId: 205771027 --- tensorflow/contrib/autograph/__init__.py | 12 ++- .../autograph/converters/lists_test.py | 18 ++-- .../examples/integration_tests/BUILD | 12 ++- .../integration_tests/list_literals_test.py | 41 +++++++++ .../autograph/lang/special_functions.py | 37 ++++++++ .../autograph/lang/special_functions_test.py | 18 +++- .../autograph/operators/data_structures.py | 91 +++++++++++++++++-- .../operators/data_structures_test.py | 41 +++++++++ 8 files changed, 244 insertions(+), 26 deletions(-) create mode 100644 tensorflow/contrib/autograph/examples/integration_tests/list_literals_test.py diff --git a/tensorflow/contrib/autograph/__init__.py b/tensorflow/contrib/autograph/__init__.py index 7821c98f1c..26e7a4a4d3 100644 --- a/tensorflow/contrib/autograph/__init__.py +++ b/tensorflow/contrib/autograph/__init__.py @@ -22,20 +22,21 @@ from __future__ import division from __future__ import print_function # TODO(mdan): Bring only the relevant symbols to the top level. -from tensorflow.contrib.autograph import utils from tensorflow.contrib.autograph import operators +from tensorflow.contrib.autograph import utils +from tensorflow.contrib.autograph.core.errors import GraphConstructionError +from tensorflow.contrib.autograph.core.errors import TfRuntimeError +from tensorflow.contrib.autograph.core.errors import improved_errors +from tensorflow.contrib.autograph.impl.api import RunMode from tensorflow.contrib.autograph.impl.api import convert from tensorflow.contrib.autograph.impl.api import converted_call from tensorflow.contrib.autograph.impl.api import do_not_convert -from tensorflow.contrib.autograph.impl.api import RunMode from tensorflow.contrib.autograph.impl.api import to_code -from tensorflow.contrib.autograph.core.errors import improved_errors -from tensorflow.contrib.autograph.core.errors import GraphConstructionError -from tensorflow.contrib.autograph.core.errors import TfRuntimeError from tensorflow.contrib.autograph.impl.api import to_graph from tensorflow.contrib.autograph.lang.directives import set_element_type from tensorflow.contrib.autograph.lang.directives import set_loop_options from tensorflow.contrib.autograph.lang.special_functions import stack +from tensorflow.contrib.autograph.lang.special_functions import tensor_list from tensorflow.contrib.autograph.pyct.transformer import AutographParseError from tensorflow.python.util.all_util import remove_undocumented @@ -57,6 +58,7 @@ _allowed_symbols = [ 'set_element_type', 'set_loop_options', 'stack', + 'tensor_list', # Exceptions 'AutographParseError', # Utilities: to be removed diff --git a/tensorflow/contrib/autograph/converters/lists_test.py b/tensorflow/contrib/autograph/converters/lists_test.py index 447a88bbe2..f906918ac0 100644 --- a/tensorflow/contrib/autograph/converters/lists_test.py +++ b/tensorflow/contrib/autograph/converters/lists_test.py @@ -21,6 +21,7 @@ from __future__ import print_function from tensorflow.contrib.autograph.converters import lists from tensorflow.contrib.autograph.core import converter_testing from tensorflow.contrib.autograph.lang import directives +from tensorflow.contrib.autograph.lang import special_functions from tensorflow.contrib.autograph.pyct import anno from tensorflow.contrib.autograph.pyct import parser from tensorflow.python.framework import dtypes @@ -52,20 +53,18 @@ class ListTest(converter_testing.TestCase): return [1, 2, 3] with self.converted(test_fn, lists, {}) as result: - with self.test_session() as sess: - tl = result.test_fn() - r = list_ops.tensor_list_stack(tl, dtypes.int32) - self.assertAllEqual(sess.run(r), [1, 2, 3]) + self.assertAllEqual(result.test_fn(), [1, 2, 3]) def test_list_append(self): def test_fn(): - l = [1] + l = special_functions.tensor_list([1]) l.append(2) l.append(3) return l - with self.converted(test_fn, lists, {}) as result: + ns = {'special_functions': special_functions} + with self.converted(test_fn, lists, ns) as result: with self.test_session() as sess: tl = result.test_fn() r = list_ops.tensor_list_stack(tl, dtypes.int32) @@ -74,11 +73,12 @@ class ListTest(converter_testing.TestCase): def test_list_pop(self): def test_fn(): - l = [1, 2, 3] + l = special_functions.tensor_list([1, 2, 3]) s = l.pop() return s, l - node, ctx = self.prepare(test_fn, {}) + ns = {'special_functions': special_functions} + node, ctx = self.prepare(test_fn, ns) def_, = anno.getanno(node.body[0].body[0].targets[0], anno.Static.ORIG_DEFINITIONS) def_.directives[directives.set_element_type] = { @@ -87,7 +87,7 @@ class ListTest(converter_testing.TestCase): } node = lists.transform(node, ctx) - with self.compiled(node, {}, dtypes.int32) as result: + with self.compiled(node, ns, dtypes.int32) as result: with self.test_session() as sess: ts, tl = result.test_fn() r = list_ops.tensor_list_stack(tl, dtypes.int32) diff --git a/tensorflow/contrib/autograph/examples/integration_tests/BUILD b/tensorflow/contrib/autograph/examples/integration_tests/BUILD index 1368ce244c..2a4a0f75e7 100644 --- a/tensorflow/contrib/autograph/examples/integration_tests/BUILD +++ b/tensorflow/contrib/autograph/examples/integration_tests/BUILD @@ -22,7 +22,17 @@ py_test( "keras_test.py", ], srcs_version = "PY2AND3", - visibility = ["//visibility:public"], + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_test( + name = "list_literals_test", + srcs = [ + "list_literals_test.py", + ], + srcs_version = "PY2AND3", deps = [ "//tensorflow:tensorflow_py", ], diff --git a/tensorflow/contrib/autograph/examples/integration_tests/list_literals_test.py b/tensorflow/contrib/autograph/examples/integration_tests/list_literals_test.py new file mode 100644 index 0000000000..680b6dbaf0 --- /dev/null +++ b/tensorflow/contrib/autograph/examples/integration_tests/list_literals_test.py @@ -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. +# ============================================================================== +"""Tests of functions that use list literals.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +from tensorflow.contrib import autograph as ag + + +def list_used_as_tuple(): + return tf.constant([1, 2, 3]) + + +class ListLiteralsTest(tf.test.TestCase): + + def test_basic(self): + converted = ag.to_graph(list_used_as_tuple) + result = converted() + + with self.test_session() as sess: + self.assertAllEqual(sess.run(result), [1, 2, 3]) + + +if __name__ == '__main__': + tf.test.main() diff --git a/tensorflow/contrib/autograph/lang/special_functions.py b/tensorflow/contrib/autograph/lang/special_functions.py index 11135295a7..6149cbbd6c 100644 --- a/tensorflow/contrib/autograph/lang/special_functions.py +++ b/tensorflow/contrib/autograph/lang/special_functions.py @@ -26,6 +26,43 @@ from __future__ import print_function from tensorflow.contrib.autograph.operators import data_structures +def tensor_list(elements, + element_dtype=None, + element_shape=None, + use_tensor_array=False): + """Creates an tensor list and populates it with the given elements. + + This function provides a more uniform access to tensor lists and tensor + arrays, and allows optional initialization. + + Note: this function is a simplified wrapper. If you need greater control, + it is recommended to use the underlying implementation directly. + + Args: + elements: Iterable[tf.Tensor, ...], the elements to initially fill the list + with + element_dtype: Optional[tf.DType], data type for the elements in the list; + required if the list is empty + element_shape: Optional[tf.TensorShape], shape for the elements in the list; + required if the list is empty + use_tensor_array: bool, whether to use the more compatible but restrictive + tf.TensorArray implementation + Returns: + Union[tf.Tensor, tf.TensorArray], the new list. + Raises: + ValueError: for invalid arguments + """ + if not (elements or (element_dtype and element_shape)): + raise ValueError( + 'element_dtype and element_shape are required for empty lists') + if use_tensor_array: + return data_structures.tf_tensor_array_new(elements, element_dtype, + element_shape) + else: + return data_structures.tf_tensor_list_new(elements, element_dtype, + element_shape) + + def stack(list_or_tensor, element_dtype=None, strict=True): """Stacks the input, if it admits the notion of stacking. diff --git a/tensorflow/contrib/autograph/lang/special_functions_test.py b/tensorflow/contrib/autograph/lang/special_functions_test.py index a49cb64075..db492cc5c6 100644 --- a/tensorflow/contrib/autograph/lang/special_functions_test.py +++ b/tensorflow/contrib/autograph/lang/special_functions_test.py @@ -28,7 +28,23 @@ from tensorflow.python.platform import test class SpecialFunctionsTest(test.TestCase): - def test_basic(self): + def test_tensor_list_from_elements(self): + elements = [constant_op.constant([1, 2]), constant_op.constant([3, 4])] + + l = special_functions.tensor_list(elements) + sl = list_ops.tensor_list_stack(l, element_dtype=dtypes.int32) + with self.test_session() as sess: + self.assertAllEqual(sess.run(sl), [[1, 2], [3, 4]]) + + def test_tensor_list_array_from_elements(self): + elements = [constant_op.constant([1, 2]), constant_op.constant([3, 4])] + + l = special_functions.tensor_list(elements, use_tensor_array=True) + sl = l.stack() + with self.test_session() as sess: + self.assertAllEqual(sess.run(sl), [[1, 2], [3, 4]]) + + def test_stack(self): self.assertEqual(special_functions.stack(1, strict=False), 1) self.assertListEqual( special_functions.stack([1, 2, 3], strict=False), [1, 2, 3]) diff --git a/tensorflow/contrib/autograph/operators/data_structures.py b/tensorflow/contrib/autograph/operators/data_structures.py index 06d8727b0f..cc0a3c3544 100644 --- a/tensorflow/contrib/autograph/operators/data_structures.py +++ b/tensorflow/contrib/autograph/operators/data_structures.py @@ -28,7 +28,6 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import list_ops from tensorflow.python.ops import tensor_array_ops -from tensorflow.python.ops import variables # TODO(mdan): Once control flow supports objects, repackage as a class. @@ -48,29 +47,101 @@ def new_list(iterable=None): else: elements = () - # TODO(mdan): Extend these criteria. - if any(isinstance(el, variables.Variable) for el in elements): + if elements: + # When the list contains elements, it is assumed to be a "Python" lvalue + # list. return _py_list_new(elements) - return _tf_tensor_list_new(elements) + return tf_tensor_list_new(elements) -def _tf_tensor_list_new(elements): +def tf_tensor_array_new(elements, element_dtype=None, element_shape=None): """Overload of new_list that stages a Tensor list creation.""" elements = tuple(ops.convert_to_tensor(el) for el in elements) + + all_dtypes = set(el.dtype for el in elements) + if len(all_dtypes) == 1: + inferred_dtype, = tuple(all_dtypes) + if element_dtype is not None and element_dtype != inferred_dtype: + raise ValueError( + 'incompatible dtype; specified: {}, inferred from {}: {}'.format( + element_dtype, elements, inferred_dtype)) + elif len(all_dtypes) > 1: + raise ValueError( + 'TensorArray requires all elements to have the same dtype:' + ' {}'.format(elements)) + else: + if element_dtype is None: + raise ValueError('dtype is required to create an empty TensorArray') + + all_shapes = set(tuple(el.shape.as_list()) for el in elements) + if len(all_shapes) == 1: + inferred_shape, = tuple(all_shapes) + if element_shape is not None and element_shape != inferred_shape: + raise ValueError( + 'incompatible shape; specified: {}, inferred from {}: {}'.format( + element_shape, elements, inferred_shape)) + elif len(all_shapes) > 1: + raise ValueError( + 'TensorArray requires all elements to have the same shape:' + ' {}'.format(elements)) + # TODO(mdan): We may want to allow different shapes with infer_shape=False. + else: + inferred_shape = None + + if element_dtype is None: + element_dtype = inferred_dtype + if element_shape is None: + element_shape = inferred_shape + + l = tensor_array_ops.TensorArray( + dtype=element_dtype, + size=len(elements), + dynamic_size=True, + infer_shape=(element_shape is None), + element_shape=element_shape) + for i, el in enumerate(elements): + l = l.write(i, el) + return l + + +def tf_tensor_list_new(elements, element_dtype=None, element_shape=None): + """Overload of new_list that stages a Tensor list creation.""" + elements = tuple(ops.convert_to_tensor(el) for el in elements) + all_dtypes = set(el.dtype for el in elements) if len(all_dtypes) == 1: - element_dtype = tuple(all_dtypes)[0] + inferred_dtype = tuple(all_dtypes)[0] + if element_dtype is not None and element_dtype != inferred_dtype: + raise ValueError( + 'incompatible dtype; specified: {}, inferred from {}: {}'.format( + element_dtype, elements, inferred_dtype)) else: # Heterogeneous lists are ok. - element_dtype = dtypes.variant + if element_dtype is not None: + raise ValueError( + 'specified dtype {} is inconsistent with that of elements {}'.format( + element_dtype, elements)) + inferred_dtype = dtypes.variant - # TODO(mdan): This may fail for elements of variable shapes. all_shapes = set(tuple(el.shape.as_list()) for el in elements) if len(all_shapes) == 1: - element_shape = array_ops.shape(elements[0]) + inferred_shape = array_ops.shape(elements[0]) + if element_shape is not None and element_shape != inferred_shape: + raise ValueError( + 'incompatible shape; specified: {}, inferred from {}: {}'.format( + element_shape, elements, inferred_shape)) else: # Heterogeneous lists are ok. - element_shape = constant_op.constant(-1) # unknown shape, by convention + if element_shape is not None: + raise ValueError( + 'specified shape {} is inconsistent with that of elements {}'.format( + element_shape, elements)) + inferred_shape = constant_op.constant(-1) # unknown shape, by convention + + if element_dtype is None: + element_dtype = inferred_dtype + if element_shape is None: + element_shape = inferred_shape l = list_ops.empty_tensor_list( element_shape=element_shape, element_dtype=element_dtype) diff --git a/tensorflow/contrib/autograph/operators/data_structures_test.py b/tensorflow/contrib/autograph/operators/data_structures_test.py index 8bbb52d6c1..7ea11a839b 100644 --- a/tensorflow/contrib/autograph/operators/data_structures_test.py +++ b/tensorflow/contrib/autograph/operators/data_structures_test.py @@ -37,10 +37,51 @@ class ListTest(test.TestCase): def test_new_list_tensor(self): l = data_structures.new_list([3, 4, 5]) + self.assertAllEqual(l, [3, 4, 5]) + + def test_tf_tensor_list_new(self): + l = data_structures.tf_tensor_list_new([3, 4, 5]) t = list_ops.tensor_list_stack(l, element_dtype=dtypes.int32) with self.test_session() as sess: self.assertAllEqual(sess.run(t), [3, 4, 5]) + def test_tf_tensor_list_new_illegal_input(self): + with self.assertRaises(ValueError): + data_structures.tf_tensor_list_new([3, 4.0]) + # TODO(mdan): It might make more sense to type cast in this case. + with self.assertRaises(ValueError): + data_structures.tf_tensor_list_new([3, 4], element_dtype=dtypes.float32) + # Tensor lists do support heterogeneous lists. + self.assertIsNot(data_structures.tf_tensor_list_new([3, [4, 5]]), None) + with self.assertRaises(ValueError): + data_structures.tf_tensor_list_new([3, 4], element_shape=(2,)) + with self.assertRaises(ValueError): + data_structures.tf_tensor_list_new([], element_shape=(2,)) + with self.assertRaises(ValueError): + data_structures.tf_tensor_list_new([], element_dtype=dtypes.float32) + + def test_tf_tensor_array_new(self): + l = data_structures.tf_tensor_array_new([3, 4, 5]) + t = l.stack() + with self.test_session() as sess: + self.assertAllEqual(sess.run(t), [3, 4, 5]) + + def test_tf_tensor_array_new_illegal_input(self): + with self.assertRaises(ValueError): + data_structures.tf_tensor_array_new([3, 4.0]) + with self.assertRaises(ValueError): + data_structures.tf_tensor_array_new([3, 4], element_dtype=dtypes.float32) + with self.assertRaises(ValueError): + data_structures.tf_tensor_array_new([3, [4, 5]]) + with self.assertRaises(ValueError): + data_structures.tf_tensor_array_new([3, 4], element_shape=(2,)) + with self.assertRaises(ValueError): + data_structures.tf_tensor_array_new([], element_shape=(2,)) + # TAs can infer the shape. + self.assertIsNot( + data_structures.tf_tensor_array_new([], element_dtype=dtypes.float32), + None) + def test_append_tensor_list(self): l = data_structures.new_list() x = constant_op.constant([1, 2, 3]) -- GitLab From e8836f85a1dd14bd1e1113c7fe9fc7037ebdaa76 Mon Sep 17 00:00:00 2001 From: adoda Date: Tue, 24 Jul 2018 12:43:16 +0800 Subject: [PATCH 311/519] fix typo --- tensorflow/contrib/lite/toco/tflite/export_test.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/toco/tflite/export_test.cc b/tensorflow/contrib/lite/toco/tflite/export_test.cc index d1fdbcb8e9..a95937ba0f 100644 --- a/tensorflow/contrib/lite/toco/tflite/export_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/export_test.cc @@ -262,7 +262,7 @@ TEST_F(VersionedOpExportTest, Export) { EXPECT_EQ(1, (*operators)[1]->opcode_index()); } -// TODO(ahentz): tests for tensors, inputs, outpus, opcodes and operators. +// TODO(ahentz): tests for tensors, inputs, outputs, opcodes and operators. } // namespace } // namespace tflite -- GitLab From fada40a564f9f22cc4372b33c90ccce592035a58 Mon Sep 17 00:00:00 2001 From: Bixia Zheng Date: Mon, 23 Jul 2018 21:51:03 -0700 Subject: [PATCH 312/519] [XLA:GPU] Add an operator<< to Thunk::Kind. This allows the use of CHECK_EQ with Thunk::Kind values. PiperOrigin-RevId: 205775065 --- tensorflow/compiler/xla/service/gpu/BUILD | 1 + .../xla/service/gpu/ir_emitter_unnested.cc | 2 +- tensorflow/compiler/xla/service/gpu/thunk.cc | 59 +++++++++++++++++++ tensorflow/compiler/xla/service/gpu/thunk.h | 4 +- 4 files changed, 64 insertions(+), 2 deletions(-) create mode 100644 tensorflow/compiler/xla/service/gpu/thunk.cc diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index ca39797e81..06ff3d9bba 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -269,6 +269,7 @@ cc_library( "memset_thunk.cc", "outfeed_thunk.cc", "sequential_thunk.cc", + "thunk.cc", "thunk_schedule.cc", "tuple_thunk.cc", "while_thunk.cc", diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 4844dc92db..64a6baf66d 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -2778,7 +2778,7 @@ Status IrEmitterUnnested::EmitTargetElementLoopInThunk( Status IrEmitterUnnested::EmitTargetElementLoop( const HloInstruction& hlo, const llvm_ir::ElementGenerator& element_generator) { - CHECK(Thunk::Kind::kKernel == LastThunk()->kind()); + CHECK_EQ(Thunk::Kind::kKernel, LastThunk()->kind()); return EmitTargetElementLoopInThunk(hlo, element_generator, static_cast(LastThunk())); } diff --git a/tensorflow/compiler/xla/service/gpu/thunk.cc b/tensorflow/compiler/xla/service/gpu/thunk.cc new file mode 100644 index 0000000000..c78605cebb --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/thunk.cc @@ -0,0 +1,59 @@ +/* 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/gpu/thunk.h" + +namespace xla { +namespace gpu { + +std::ostream& operator<<(std::ostream& os, Thunk::Kind kind) { + switch (kind) { + case Thunk::kConditional: + return os << "kConditional"; + case Thunk::kConvolution: + return os << "kConvolution"; + case Thunk::kCopy: + return os << "kCopy"; + case Thunk::kCudnnBatchNormBackward: + return os << "kCudnnBatchNormBackward"; + case Thunk::kCudnnBatchNormForwardInference: + return os << "kCudnnBatchNormForwardInference"; + case Thunk::kCudnnBatchNormForwardTraining: + return os << "kCudnnBatchNormForwardTraining"; + case Thunk::kFft: + return os << "kFft"; + case Thunk::kGemm: + return os << "kGemm"; + case Thunk::kInfeed: + return os << "kInfeed"; + case Thunk::kKernel: + return os << "kKernel"; + case Thunk::kMemset32BitValue: + return os << "kMemset32BitValue"; + case Thunk::kMemzero: + return os << "kMemzero"; + case Thunk::kOutfeed: + return os << "kOutfeed"; + case Thunk::kSequential: + return os << "kSequential"; + case Thunk::kTuple: + return os << "kTuple"; + case Thunk::kWhile: + return os << "kWhile"; + } +} + +} // namespace gpu +} // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/thunk.h b/tensorflow/compiler/xla/service/gpu/thunk.h index 99a1a0eae9..4df0bb005b 100644 --- a/tensorflow/compiler/xla/service/gpu/thunk.h +++ b/tensorflow/compiler/xla/service/gpu/thunk.h @@ -41,7 +41,7 @@ class GpuExecutable; // This is thread-compatible. class Thunk { public: - enum class Kind { + enum Kind { kConditional, kConvolution, kCopy, @@ -111,6 +111,8 @@ class Thunk { // A sequence of thunks. using ThunkSequence = std::vector>; +std::ostream& operator<<(std::ostream& os, Thunk::Kind kind); + } // namespace gpu } // namespace xla -- GitLab From 12f51c2873354577dcea167823d5a4c4dee5dbce Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Mon, 23 Jul 2018 22:29:55 -0700 Subject: [PATCH 313/519] Add LinearOperatorZeros. PiperOrigin-RevId: 205777765 --- tensorflow/python/kernel_tests/linalg/BUILD | 18 + .../linalg/linear_operator_zeros_test.py | 192 ++++++++ tensorflow/python/ops/linalg/linalg.py | 1 + .../ops/linalg/linear_operator_zeros.py | 452 ++++++++++++++++++ ...-linear-operator-zeros.__metaclass__.pbtxt | 14 + ...orflow.linalg.-linear-operator-zeros.pbtxt | 130 +++++ .../tools/api/golden/tensorflow.linalg.pbtxt | 4 + 7 files changed, 811 insertions(+) create mode 100644 tensorflow/python/kernel_tests/linalg/linear_operator_zeros_test.py create mode 100644 tensorflow/python/ops/linalg/linear_operator_zeros.py create mode 100644 tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.__metaclass__.pbtxt create mode 100644 tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.pbtxt diff --git a/tensorflow/python/kernel_tests/linalg/BUILD b/tensorflow/python/kernel_tests/linalg/BUILD index 487418e694..f4ec3e3996 100644 --- a/tensorflow/python/kernel_tests/linalg/BUILD +++ b/tensorflow/python/kernel_tests/linalg/BUILD @@ -234,3 +234,21 @@ cuda_py_test( "optonly", ], ) + +cuda_py_test( + name = "linear_operator_zeros_test", + size = "medium", + srcs = ["linear_operator_zeros_test.py"], + additional_deps = [ + "//tensorflow/python/ops/linalg", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:linalg_ops", + "//tensorflow/python:platform_test", + "//tensorflow/python:random_ops", + ], + shard_count = 5, + tags = ["optonly"], # Test is flaky without optimization. +) diff --git a/tensorflow/python/kernel_tests/linalg/linear_operator_zeros_test.py b/tensorflow/python/kernel_tests/linalg/linear_operator_zeros_test.py new file mode 100644 index 0000000000..8f60b55e0a --- /dev/null +++ b/tensorflow/python/kernel_tests/linalg/linear_operator_zeros_test.py @@ -0,0 +1,192 @@ +# 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 + +import numpy as np + +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import random_seed +from tensorflow.python.ops import array_ops +from tensorflow.python.ops.linalg import linalg as linalg_lib +from tensorflow.python.ops.linalg import linear_operator_test_util +from tensorflow.python.platform import test + + +random_seed.set_random_seed(23) +rng = np.random.RandomState(2016) + + +class LinearOperatorZerosTest( + linear_operator_test_util.SquareLinearOperatorDerivedClassTest): + """Most tests done in the base class LinearOperatorDerivedClassTest.""" + + @property + def _tests_to_skip(self): + return ["log_abs_det", "solve", "solve_with_broadcast"] + + @property + def _operator_build_infos(self): + build_info = linear_operator_test_util.OperatorBuildInfo + return [ + build_info((1, 1)), + build_info((1, 3, 3)), + build_info((3, 4, 4)), + build_info((2, 1, 4, 4))] + + def _operator_and_matrix(self, build_info, dtype, use_placeholder): + del use_placeholder + shape = list(build_info.shape) + assert shape[-1] == shape[-2] + + batch_shape = shape[:-2] + num_rows = shape[-1] + + operator = linalg_lib.LinearOperatorZeros( + num_rows, batch_shape=batch_shape, dtype=dtype) + matrix = array_ops.zeros(shape=shape, dtype=dtype) + + return operator, matrix + + def test_assert_positive_definite(self): + operator = linalg_lib.LinearOperatorZeros(num_rows=2) + with self.assertRaisesOpError("non-positive definite"): + operator.assert_positive_definite() + + def test_assert_non_singular(self): + with self.assertRaisesOpError("non-invertible"): + operator = linalg_lib.LinearOperatorZeros(num_rows=2) + operator.assert_non_singular() + + def test_assert_self_adjoint(self): + with self.test_session(): + operator = linalg_lib.LinearOperatorZeros(num_rows=2) + operator.assert_self_adjoint().run() # Should not fail + + def test_non_scalar_num_rows_raises_static(self): + with self.assertRaisesRegexp(ValueError, "must be a 0-D Tensor"): + linalg_lib.LinearOperatorZeros(num_rows=[2]) + with self.assertRaisesRegexp(ValueError, "must be a 0-D Tensor"): + linalg_lib.LinearOperatorZeros(num_rows=2, num_columns=[2]) + + def test_non_integer_num_rows_raises_static(self): + with self.assertRaisesRegexp(TypeError, "must be integer"): + linalg_lib.LinearOperatorZeros(num_rows=2.) + with self.assertRaisesRegexp(TypeError, "must be integer"): + linalg_lib.LinearOperatorZeros(num_rows=2, num_columns=2.) + + def test_negative_num_rows_raises_static(self): + with self.assertRaisesRegexp(ValueError, "must be non-negative"): + linalg_lib.LinearOperatorZeros(num_rows=-2) + with self.assertRaisesRegexp(ValueError, "must be non-negative"): + linalg_lib.LinearOperatorZeros(num_rows=2, num_columns=-2) + + def test_non_1d_batch_shape_raises_static(self): + with self.assertRaisesRegexp(ValueError, "must be a 1-D"): + linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=2) + + def test_non_integer_batch_shape_raises_static(self): + with self.assertRaisesRegexp(TypeError, "must be integer"): + linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=[2.]) + + def test_negative_batch_shape_raises_static(self): + with self.assertRaisesRegexp(ValueError, "must be non-negative"): + linalg_lib.LinearOperatorZeros(num_rows=2, batch_shape=[-2]) + + def test_non_scalar_num_rows_raises_dynamic(self): + with self.test_session(): + num_rows = array_ops.placeholder(dtypes.int32) + operator = linalg_lib.LinearOperatorZeros( + num_rows, assert_proper_shapes=True) + with self.assertRaisesOpError("must be a 0-D Tensor"): + operator.to_dense().eval(feed_dict={num_rows: [2]}) + + def test_negative_num_rows_raises_dynamic(self): + with self.test_session(): + n = array_ops.placeholder(dtypes.int32) + operator = linalg_lib.LinearOperatorZeros( + num_rows=n, assert_proper_shapes=True) + with self.assertRaisesOpError("must be non-negative"): + operator.to_dense().eval(feed_dict={n: -2}) + + operator = linalg_lib.LinearOperatorZeros( + num_rows=2, num_columns=n, assert_proper_shapes=True) + with self.assertRaisesOpError("must be non-negative"): + operator.to_dense().eval(feed_dict={n: -2}) + + def test_non_1d_batch_shape_raises_dynamic(self): + with self.test_session(): + batch_shape = array_ops.placeholder(dtypes.int32) + operator = linalg_lib.LinearOperatorZeros( + num_rows=2, batch_shape=batch_shape, assert_proper_shapes=True) + with self.assertRaisesOpError("must be a 1-D"): + operator.to_dense().eval(feed_dict={batch_shape: 2}) + + def test_negative_batch_shape_raises_dynamic(self): + with self.test_session(): + batch_shape = array_ops.placeholder(dtypes.int32) + operator = linalg_lib.LinearOperatorZeros( + num_rows=2, batch_shape=batch_shape, assert_proper_shapes=True) + with self.assertRaisesOpError("must be non-negative"): + operator.to_dense().eval(feed_dict={batch_shape: [-2]}) + + def test_wrong_matrix_dimensions_raises_static(self): + operator = linalg_lib.LinearOperatorZeros(num_rows=2) + x = rng.randn(3, 3).astype(np.float32) + with self.assertRaisesRegexp(ValueError, "Dimensions.*not compatible"): + operator.matmul(x) + + def test_wrong_matrix_dimensions_raises_dynamic(self): + num_rows = array_ops.placeholder(dtypes.int32) + x = array_ops.placeholder(dtypes.float32) + + with self.test_session(): + operator = linalg_lib.LinearOperatorZeros( + num_rows, assert_proper_shapes=True) + y = operator.matmul(x) + with self.assertRaisesOpError("Incompatible.*dimensions"): + y.eval(feed_dict={num_rows: 2, x: rng.rand(3, 3)}) + + def test_is_x_flags(self): + # The is_x flags are by default all True. + operator = linalg_lib.LinearOperatorZeros(num_rows=2) + self.assertFalse(operator.is_positive_definite) + self.assertFalse(operator.is_non_singular) + self.assertTrue(operator.is_self_adjoint) + + +class LinearOperatorZerosNotSquareTest( + linear_operator_test_util.NonSquareLinearOperatorDerivedClassTest): + + def _operator_and_matrix(self, build_info, dtype, use_placeholder): + del use_placeholder + shape = list(build_info.shape) + + batch_shape = shape[:-2] + num_rows = shape[-2] + num_columns = shape[-1] + + operator = linalg_lib.LinearOperatorZeros( + num_rows, num_columns, is_square=False, is_self_adjoint=False, + batch_shape=batch_shape, dtype=dtype) + matrix = array_ops.zeros(shape=shape, dtype=dtype) + + return operator, matrix + + +if __name__ == "__main__": + test.main() diff --git a/tensorflow/python/ops/linalg/linalg.py b/tensorflow/python/ops/linalg/linalg.py index a7ba0bbe9c..c29b5033bb 100644 --- a/tensorflow/python/ops/linalg/linalg.py +++ b/tensorflow/python/ops/linalg/linalg.py @@ -31,6 +31,7 @@ from tensorflow.python.ops.linalg.linear_operator_identity import * from tensorflow.python.ops.linalg.linear_operator_kronecker import * from tensorflow.python.ops.linalg.linear_operator_low_rank_update import * from tensorflow.python.ops.linalg.linear_operator_lower_triangular import * +from tensorflow.python.ops.linalg.linear_operator_zeros import * # pylint: enable=wildcard-import # Seal API. diff --git a/tensorflow/python/ops/linalg/linear_operator_zeros.py b/tensorflow/python/ops/linalg/linear_operator_zeros.py new file mode 100644 index 0000000000..b8a79c065b --- /dev/null +++ b/tensorflow/python/ops/linalg/linear_operator_zeros.py @@ -0,0 +1,452 @@ +# 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. +# ============================================================================== +"""`LinearOperator` acting like a zero matrix.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import errors +from tensorflow.python.framework import ops +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 check_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops.linalg import linalg_impl as linalg +from tensorflow.python.ops.linalg import linear_operator +from tensorflow.python.ops.linalg import linear_operator_util +from tensorflow.python.util.tf_export import tf_export + +__all__ = [ + "LinearOperatorZeros", +] + + +@tf_export("linalg.LinearOperatorZeros") +class LinearOperatorZeros(linear_operator.LinearOperator): + """`LinearOperator` acting like a [batch] zero matrix. + + This operator acts like a [batch] zero matrix `A` with shape + `[B1,...,Bb, N, M]` for some `b >= 0`. The first `b` indices index a + batch member. For every batch index `(i1,...,ib)`, `A[i1,...,ib, : :]` is + an `N x M` matrix. This matrix `A` is not materialized, but for + purposes of broadcasting this shape will be relevant. + + `LinearOperatorZeros` is initialized with `num_rows`, and optionally + `num_columns, `batch_shape`, and `dtype` arguments. If `num_columns` is + `None`, then this operator will be initialized as a square matrix. If + `batch_shape` is `None`, this operator efficiently passes through all + arguments. If `batch_shape` is provided, broadcasting may occur, which will + require making copies. + + ```python + # Create a 2 x 2 zero matrix. + operator = LinearOperatorZero(num_rows=2, dtype=tf.float32) + + operator.to_dense() + ==> [[0., 0.] + [0., 0.]] + + operator.shape + ==> [2, 2] + + operator.determinant() + ==> 0. + + x = ... Shape [2, 4] Tensor + operator.matmul(x) + ==> Shape [2, 4] Tensor, same as x. + + # Create a 2-batch of 2x2 zero matrices + operator = LinearOperatorZeros(num_rows=2, batch_shape=[2]) + operator.to_dense() + ==> [[[0., 0.] + [0., 0.]], + [[0., 0.] + [0., 0.]]] + + # Here, even though the operator has a batch shape, the input is the same as + # the output, so x can be passed through without a copy. The operator is able + # to detect that no broadcast is necessary because both x and the operator + # have statically defined shape. + x = ... Shape [2, 2, 3] + operator.matmul(x) + ==> Shape [2, 2, 3] Tensor, same as tf.zeros_like(x) + + # Here the operator and x have different batch_shape, and are broadcast. + # This requires a copy, since the output is different size than the input. + x = ... Shape [1, 2, 3] + operator.matmul(x) + ==> Shape [2, 2, 3] Tensor, equal to tf.zeros_like([x, x]) + ``` + + ### Shape compatibility + + This operator acts on [batch] matrix with compatible shape. + `x` is a batch matrix with compatible shape for `matmul` and `solve` if + + ``` + operator.shape = [B1,...,Bb] + [N, M], with b >= 0 + x.shape = [C1,...,Cc] + [M, R], + and [C1,...,Cc] broadcasts with [B1,...,Bb] to [D1,...,Dd] + ``` + + #### Matrix property hints + + This `LinearOperator` is initialized with boolean flags of the form `is_X`, + for `X = non_singular, self_adjoint, positive_definite, square`. + These have the following meaning: + + * If `is_X == True`, callers should expect the operator to have the + property `X`. This is a promise that should be fulfilled, but is *not* a + runtime assert. For example, finite floating point precision may result + in these promises being violated. + * If `is_X == False`, callers should expect the operator to not have `X`. + * If `is_X == None` (the default), callers should have no expectation either + way. + """ + + def __init__(self, + num_rows, + num_columns=None, + batch_shape=None, + dtype=None, + is_non_singular=False, + is_self_adjoint=True, + is_positive_definite=False, + is_square=True, + assert_proper_shapes=False, + name="LinearOperatorZeros"): + r"""Initialize a `LinearOperatorZeros`. + + The `LinearOperatorZeros` is initialized with arguments defining `dtype` + and shape. + + This operator is able to broadcast the leading (batch) dimensions, which + sometimes requires copying data. If `batch_shape` is `None`, the operator + can take arguments of any batch shape without copying. See examples. + + Args: + num_rows: Scalar non-negative integer `Tensor`. Number of rows in the + corresponding zero matrix. + num_columns: Scalar non-negative integer `Tensor`. Number of columns in + the corresponding zero matrix. If `None`, defaults to the value of + `num_rows`. + batch_shape: Optional `1-D` integer `Tensor`. The shape of the leading + dimensions. If `None`, this operator has no leading dimensions. + dtype: Data type of the matrix that this operator represents. + is_non_singular: Expect that this operator is non-singular. + is_self_adjoint: Expect that this operator is equal to its hermitian + transpose. + is_positive_definite: Expect that this operator is positive definite, + meaning the quadratic form `x^H A x` has positive real part for all + nonzero `x`. Note that we do not require the operator to be + self-adjoint to be positive-definite. See: + https://en.wikipedia.org/wiki/Positive-definite_matrix#Extension_for_non-symmetric_matrices + is_square: Expect that this operator acts like square [batch] matrices. + assert_proper_shapes: Python `bool`. If `False`, only perform static + checks that initialization and method arguments have proper shape. + If `True`, and static checks are inconclusive, add asserts to the graph. + name: A name for this `LinearOperator` + + Raises: + ValueError: If `num_rows` is determined statically to be non-scalar, or + negative. + ValueError: If `num_columns` is determined statically to be non-scalar, + or negative. + ValueError: If `batch_shape` is determined statically to not be 1-D, or + negative. + ValueError: If any of the following is not `True`: + `{is_self_adjoint, is_non_singular, is_positive_definite}`. + """ + dtype = dtype or dtypes.float32 + self._assert_proper_shapes = assert_proper_shapes + + with ops.name_scope(name): + dtype = dtypes.as_dtype(dtype) + if not is_self_adjoint and is_square: + raise ValueError("A zero operator is always self adjoint.") + if is_non_singular: + raise ValueError("A zero operator is always singular.") + if is_positive_definite: + raise ValueError("A zero operator is always not positive-definite.") + + super(LinearOperatorZeros, self).__init__( + dtype=dtype, + is_non_singular=is_non_singular, + is_self_adjoint=is_self_adjoint, + is_positive_definite=is_positive_definite, + is_square=is_square, + name=name) + + self._num_rows = linear_operator_util.shape_tensor( + num_rows, name="num_rows") + self._num_rows_static = tensor_util.constant_value(self._num_rows) + + if num_columns is None: + num_columns = num_rows + + self._num_columns = linear_operator_util.shape_tensor( + num_columns, name="num_columns") + self._num_columns_static = tensor_util.constant_value(self._num_columns) + + self._check_domain_range_possibly_add_asserts() + + if (self._num_rows_static is not None and + self._num_columns_static is not None): + if is_square and self._num_rows_static != self._num_columns_static: + raise ValueError( + "LinearOperatorZeros initialized as is_square=True, but got " + "num_rows({}) != num_columns({})".format( + self._num_rows_static, + self._num_columns_static)) + + if batch_shape is None: + self._batch_shape_arg = None + else: + self._batch_shape_arg = linear_operator_util.shape_tensor( + batch_shape, name="batch_shape_arg") + self._batch_shape_static = tensor_util.constant_value( + self._batch_shape_arg) + self._check_batch_shape_possibly_add_asserts() + + def _shape(self): + matrix_shape = tensor_shape.TensorShape((self._num_rows_static, + self._num_columns_static)) + if self._batch_shape_arg is None: + return matrix_shape + + batch_shape = tensor_shape.TensorShape(self._batch_shape_static) + return batch_shape.concatenate(matrix_shape) + + def _shape_tensor(self): + matrix_shape = array_ops.stack((self._num_rows, self._num_columns), axis=0) + if self._batch_shape_arg is None: + return matrix_shape + + return array_ops.concat((self._batch_shape_arg, matrix_shape), 0) + + def _assert_non_singular(self): + raise errors.InvalidArgumentError( + node_def=None, op=None, message="Zero operators are always " + "non-invertible.") + + def _assert_positive_definite(self): + raise errors.InvalidArgumentError( + node_def=None, op=None, message="Zero operators are always " + "non-positive definite.") + + def _assert_self_adjoint(self): + return control_flow_ops.no_op("assert_self_adjoint") + + def _possibly_broadcast_batch_shape(self, x): + """Return 'x', possibly after broadcasting the leading dimensions.""" + # If we have no batch shape, our batch shape broadcasts with everything! + if self._batch_shape_arg is None: + return x + + # Static attempt: + # If we determine that no broadcast is necessary, pass x through + # If we need a broadcast, add to an array of zeros. + # + # special_shape is the shape that, when broadcast with x's shape, will give + # the correct broadcast_shape. Note that + # We have already verified the second to last dimension of self.shape + # matches x's shape in assert_compatible_matrix_dimensions. + # Also, the final dimension of 'x' can have any shape. + # Therefore, the final two dimensions of special_shape are 1's. + special_shape = self.batch_shape.concatenate([1, 1]) + bshape = array_ops.broadcast_static_shape(x.get_shape(), special_shape) + if special_shape.is_fully_defined(): + # bshape.is_fully_defined iff special_shape.is_fully_defined. + if bshape == x.get_shape(): + return x + # Use the built in broadcasting of addition. + zeros = array_ops.zeros(shape=special_shape, dtype=self.dtype) + return x + zeros + + # Dynamic broadcast: + # Always add to an array of zeros, rather than using a "cond", since a + # cond would require copying data from GPU --> CPU. + special_shape = array_ops.concat((self.batch_shape_tensor(), [1, 1]), 0) + zeros = array_ops.zeros(shape=special_shape, dtype=self.dtype) + return x + zeros + + def _matmul(self, x, adjoint=False, adjoint_arg=False): + if self._assert_proper_shapes: + x = linalg.adjoint(x) if adjoint_arg else x + aps = linear_operator_util.assert_compatible_matrix_dimensions(self, x) + x = control_flow_ops.with_dependencies([aps], x) + if self.is_square: + # Note that adjoint has no effect since this matrix is self-adjoint. + if adjoint_arg: + output_shape = array_ops.concat([ + array_ops.shape(x)[:-2], + [array_ops.shape(x)[-1], array_ops.shape(x)[-2]]], axis=0) + else: + output_shape = array_ops.shape(x) + + return self._possibly_broadcast_batch_shape( + array_ops.zeros(shape=output_shape, dtype=x.dtype)) + + x_shape = array_ops.shape(x) + n = self._num_columns if adjoint else self._num_rows + m = x_shape[-2] if adjoint_arg else x_shape[-1] + + output_shape = array_ops.concat([x_shape[:-2], [n, m]], axis=0) + + zeros = array_ops.zeros(shape=output_shape, dtype=x.dtype) + return self._possibly_broadcast_batch_shape(zeros) + + def _determinant(self): + if self.batch_shape.is_fully_defined(): + return array_ops.zeros(shape=self.batch_shape, dtype=self.dtype) + else: + return array_ops.zeros(shape=self.batch_shape_tensor(), dtype=self.dtype) + + def _trace(self): + # Get Tensor of all zeros of same shape as self.batch_shape. + if self.batch_shape.is_fully_defined(): + return array_ops.zeros(shape=self.batch_shape, dtype=self.dtype) + else: + return array_ops.zeros(shape=self.batch_shape_tensor(), dtype=self.dtype) + + def _diag_part(self): + return self._zeros_diag() + + def add_to_tensor(self, mat, name="add_to_tensor"): + """Add matrix represented by this operator to `mat`. Equiv to `I + mat`. + + Args: + mat: `Tensor` with same `dtype` and shape broadcastable to `self`. + name: A name to give this `Op`. + + Returns: + A `Tensor` with broadcast shape and same `dtype` as `self`. + """ + return self._possibly_broadcast_batch_shape(mat) + + def _check_domain_range_possibly_add_asserts(self): + """Static check of init arg `num_rows`, possibly add asserts.""" + # Possibly add asserts. + if self._assert_proper_shapes: + self._num_rows = control_flow_ops.with_dependencies([ + check_ops.assert_rank( + self._num_rows, + 0, + message="Argument num_rows must be a 0-D Tensor."), + check_ops.assert_non_negative( + self._num_rows, + message="Argument num_rows must be non-negative."), + ], self._num_rows) + self._num_columns = control_flow_ops.with_dependencies([ + check_ops.assert_rank( + self._num_columns, + 0, + message="Argument num_columns must be a 0-D Tensor."), + check_ops.assert_non_negative( + self._num_columns, + message="Argument num_columns must be non-negative."), + ], self._num_columns) + + # Static checks. + if not self._num_rows.dtype.is_integer: + raise TypeError("Argument num_rows must be integer type. Found:" + " %s" % self._num_rows) + + if not self._num_columns.dtype.is_integer: + raise TypeError("Argument num_columns must be integer type. Found:" + " %s" % self._num_columns) + + num_rows_static = self._num_rows_static + num_columns_static = self._num_columns_static + + if num_rows_static is not None: + if num_rows_static.ndim != 0: + raise ValueError("Argument num_rows must be a 0-D Tensor. Found:" + " %s" % num_rows_static) + + if num_rows_static < 0: + raise ValueError("Argument num_rows must be non-negative. Found:" + " %s" % num_rows_static) + if num_columns_static is not None: + if num_columns_static.ndim != 0: + raise ValueError("Argument num_columns must be a 0-D Tensor. Found:" + " %s" % num_columns_static) + + if num_columns_static < 0: + raise ValueError("Argument num_columns must be non-negative. Found:" + " %s" % num_columns_static) + + def _check_batch_shape_possibly_add_asserts(self): + """Static check of init arg `batch_shape`, possibly add asserts.""" + if self._batch_shape_arg is None: + return + + # Possibly add asserts + if self._assert_proper_shapes: + self._batch_shape_arg = control_flow_ops.with_dependencies([ + check_ops.assert_rank( + self._batch_shape_arg, + 1, + message="Argument batch_shape must be a 1-D Tensor."), + check_ops.assert_non_negative( + self._batch_shape_arg, + message="Argument batch_shape must be non-negative."), + ], self._batch_shape_arg) + + # Static checks + if not self._batch_shape_arg.dtype.is_integer: + raise TypeError("Argument batch_shape must be integer type. Found:" + " %s" % self._batch_shape_arg) + + if self._batch_shape_static is None: + return # Cannot do any other static checks. + + if self._batch_shape_static.ndim != 1: + raise ValueError("Argument batch_shape must be a 1-D Tensor. Found:" + " %s" % self._batch_shape_static) + + if np.any(self._batch_shape_static < 0): + raise ValueError("Argument batch_shape must be non-negative. Found:" + "%s" % self._batch_shape_static) + + def _min_matrix_dim(self): + """Minimum of domain/range dimension, if statically available, else None.""" + domain_dim = self.domain_dimension.value + range_dim = self.range_dimension.value + if domain_dim is None or range_dim is None: + return None + return min(domain_dim, range_dim) + + def _min_matrix_dim_tensor(self): + """Minimum of domain/range dimension, as a tensor.""" + return math_ops.reduce_min(self.shape_tensor()[-2:]) + + def _zeros_diag(self): + """Returns the diagonal of this operator as all zeros.""" + if self.shape.is_fully_defined(): + d_shape = self.batch_shape.concatenate([self._min_matrix_dim()]) + else: + d_shape = array_ops.concat( + [self.batch_shape_tensor(), + [self._min_matrix_dim_tensor()]], axis=0) + + return array_ops.zeros(shape=d_shape, dtype=self.dtype) diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.__metaclass__.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.__metaclass__.pbtxt new file mode 100644 index 0000000000..49ff85728f --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.__metaclass__.pbtxt @@ -0,0 +1,14 @@ +path: "tensorflow.linalg.LinearOperatorZeros.__metaclass__" +tf_class { + is_instance: "" + member_method { + name: "__init__" + } + member_method { + name: "mro" + } + member_method { + name: "register" + argspec: "args=[\'cls\', \'subclass\'], varargs=None, keywords=None, defaults=None" + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.pbtxt new file mode 100644 index 0000000000..a1b0e06b47 --- /dev/null +++ b/tensorflow/tools/api/golden/tensorflow.linalg.-linear-operator-zeros.pbtxt @@ -0,0 +1,130 @@ +path: "tensorflow.linalg.LinearOperatorZeros" +tf_class { + is_instance: "" + is_instance: "" + is_instance: "" + member { + name: "batch_shape" + mtype: "" + } + member { + name: "domain_dimension" + mtype: "" + } + member { + name: "dtype" + mtype: "" + } + member { + name: "graph_parents" + mtype: "" + } + member { + name: "is_non_singular" + mtype: "" + } + member { + name: "is_positive_definite" + mtype: "" + } + member { + name: "is_self_adjoint" + mtype: "" + } + member { + name: "is_square" + mtype: "" + } + member { + name: "name" + mtype: "" + } + member { + name: "range_dimension" + mtype: "" + } + member { + name: "shape" + mtype: "" + } + member { + name: "tensor_rank" + mtype: "" + } + member_method { + name: "__init__" + argspec: "args=[\'self\', \'num_rows\', \'num_columns\', \'batch_shape\', \'dtype\', \'is_non_singular\', \'is_self_adjoint\', \'is_positive_definite\', \'is_square\', \'assert_proper_shapes\', \'name\'], varargs=None, keywords=None, defaults=[\'None\', \'None\', \'None\', \'False\', \'True\', \'False\', \'True\', \'False\', \'LinearOperatorZeros\'], " + } + member_method { + name: "add_to_tensor" + argspec: "args=[\'self\', \'mat\', \'name\'], varargs=None, keywords=None, defaults=[\'add_to_tensor\'], " + } + member_method { + name: "assert_non_singular" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_non_singular\'], " + } + member_method { + name: "assert_positive_definite" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_positive_definite\'], " + } + member_method { + name: "assert_self_adjoint" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'assert_self_adjoint\'], " + } + member_method { + name: "batch_shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'batch_shape_tensor\'], " + } + member_method { + name: "determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'det\'], " + } + member_method { + name: "diag_part" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'diag_part\'], " + } + member_method { + name: "domain_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'domain_dimension_tensor\'], " + } + member_method { + name: "log_abs_determinant" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'log_abs_det\'], " + } + member_method { + name: "matmul" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'matmul\'], " + } + member_method { + name: "matvec" + argspec: "args=[\'self\', \'x\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'matvec\'], " + } + member_method { + name: "range_dimension_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'range_dimension_tensor\'], " + } + member_method { + name: "shape_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'shape_tensor\'], " + } + member_method { + name: "solve" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'adjoint_arg\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'False\', \'solve\'], " + } + member_method { + name: "solvevec" + argspec: "args=[\'self\', \'rhs\', \'adjoint\', \'name\'], varargs=None, keywords=None, defaults=[\'False\', \'solve\'], " + } + member_method { + name: "tensor_rank_tensor" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'tensor_rank_tensor\'], " + } + member_method { + name: "to_dense" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'to_dense\'], " + } + member_method { + name: "trace" + argspec: "args=[\'self\', \'name\'], varargs=None, keywords=None, defaults=[\'trace\'], " + } +} diff --git a/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt b/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt index 3b5845f99a..d979116887 100644 --- a/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.linalg.pbtxt @@ -52,6 +52,10 @@ tf_module { name: "LinearOperatorScaledIdentity" mtype: "" } + member { + name: "LinearOperatorZeros" + mtype: "" + } member_method { name: "adjoint" argspec: "args=[\'matrix\', \'name\'], varargs=None, keywords=None, defaults=[\'None\'], " -- GitLab From 37757856cded3a5608cfd218a0b25b41a148d995 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 24 Jul 2018 00:46:17 -0700 Subject: [PATCH 314/519] Expose proto serialization publicly, to avoid code duplication in tensorflow_serving. PiperOrigin-RevId: 205788702 --- tensorflow/core/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 13e1b643d1..a960736295 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -663,6 +663,7 @@ cc_library( "lib/random/random_distributions.h", "lib/random/simple_philox.h", "lib/strings/numbers.h", + "lib/strings/proto_serialization.h", "lib/strings/str_util.h", "lib/strings/strcat.h", "lib/strings/stringprintf.h", -- GitLab From fca1561b9d5932f940cf89e03128cf197547bed2 Mon Sep 17 00:00:00 2001 From: Suharsh Sivakumar Date: Tue, 24 Jul 2018 00:52:24 -0700 Subject: [PATCH 315/519] BatchToSpaceND support quantization, so make the transformation know that. PiperOrigin-RevId: 205789178 --- tensorflow/contrib/lite/toco/graph_transformations/quantize.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc index 5be2757479..f6ce3b3ecb 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/quantize.cc @@ -50,6 +50,7 @@ bool SupportsQuantization(const Operator& op) { type == OperatorType::kSqueeze || type == OperatorType::kPad || type == OperatorType::kPadV2 || type == OperatorType::kReshape || type == OperatorType::kTanh || type == OperatorType::kMul || + type == OperatorType::kBatchToSpaceND || type == OperatorType::kSpaceToBatchND || type == OperatorType::kSpaceToDepth || type == OperatorType::kStridedSlice || -- GitLab From 33035bb79b6ecb408ef83cee5fc3e52ce058f39f Mon Sep 17 00:00:00 2001 From: Adrian Kuegel Date: Tue, 24 Jul 2018 01:13:09 -0700 Subject: [PATCH 316/519] Parallelize BitonicSort on GPU. We now emit O(log^n) kernel thunks. Each thunk is responsible for looping over the other dimensions, and then doing a comparison loop through the dimension that should be sorted. PiperOrigin-RevId: 205791397 --- .../compiler/xla/service/gpu/ir_emitter.cc | 12 -- .../compiler/xla/service/gpu/ir_emitter.h | 1 - .../xla/service/gpu/ir_emitter_unnested.cc | 48 ++++++- tensorflow/compiler/xla/service/llvm_ir/BUILD | 3 + .../compiler/xla/service/llvm_ir/sort_util.cc | 120 ++++++------------ .../compiler/xla/service/llvm_ir/sort_util.h | 12 +- 6 files changed, 97 insertions(+), 99 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index 76180cf486..f95541cba4 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -37,7 +37,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h" -#include "tensorflow/compiler/xla/service/llvm_ir/sort_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h" #include "tensorflow/compiler/xla/service/name_uniquer.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -123,17 +122,6 @@ Status IrEmitter::HandleGetTupleElement(HloInstruction* get_tuple_element) { return Status::OK(); } -Status IrEmitter::HandleSort(HloInstruction* sort) { - auto values = sort->operand_count() > 1 ? sort->operand(1) : nullptr; - if (values != nullptr) { - // TODO(b/26783907): Also sort the values by their corresponding key. - return Unimplemented("Key/Value Sort is not implemented on GPU"); - } - int dimension_to_sort = sort->dimensions(0); - return llvm_ir::EmitSortInPlace(dimension_to_sort, GetIrArray(*sort, *sort), - IrName(sort), &b_); -} - Status IrEmitter::HandleSend(HloInstruction*) { return Unimplemented("Send is not implemented on GPU"); } diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.h b/tensorflow/compiler/xla/service/gpu/ir_emitter.h index 172d4a4e29..e89967a378 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.h @@ -79,7 +79,6 @@ class IrEmitter : public DfsHloVisitorWithDefault { Status HandleCrossReplicaSum(HloInstruction* crs) override; Status HandleInfeed(HloInstruction* infeed) override; Status HandleOutfeed(HloInstruction* outfeed) override; - Status HandleSort(HloInstruction* sort) override; Status HandleSend(HloInstruction* send) override; Status HandleSendDone(HloInstruction* send_done) override; Status HandleRecv(HloInstruction* recv) override; diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 64a6baf66d..b1038a3cc9 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -63,6 +63,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h" #include "tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" +#include "tensorflow/compiler/xla/service/llvm_ir/sort_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h" #include "tensorflow/compiler/xla/service/name_uniquer.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -71,6 +72,7 @@ limitations under the License. #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/window_util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" +#include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/platform/logging.h" @@ -2036,11 +2038,51 @@ Status IrEmitterUnnested::HandleSort(HloInstruction* sort) { /*mem_size=*/ShapeUtil::ByteSizeOf(sort->shape()), sort)); } - thunks.push_back( - BuildKernelThunk(sort, /*implements_whole_instruction=*/false)); + int64 dimension_to_sort = sort->dimensions(0); + int64 dimension_to_sort_bound = sort->shape().dimensions(dimension_to_sort); + int64 num_stages = tensorflow::Log2Ceiling(dimension_to_sort_bound); + auto index_type = b_.getInt64Ty(); + + // Naive C++ code for the outer loops: + // + // for (int64 stage = 0; stage < Log2Ceiling(dimension_to_sort_bound); + // ++stage) { + // int64 first_xor_mask = (1LL << (stage + 1)) - 1; + // SortInPlace(first_xor_mask); + // for (int64 mask = stage - 1; mask >= 0; --mask) { + // int64 later_xor_mask = 1LL << mask; + // SortInPlace(later_xor_mask); + // } + // } + // + // This follows the algorithm described on Wikipedia: + // https://en.wikipedia.org/wiki/Bitonic_sorter + + for (int64 stage = 0; stage < num_stages; ++stage) { + for (int64 mask = stage; mask >= 0; --mask) { + thunks.push_back( + BuildKernelThunk(sort, /*implements_whole_instruction=*/false)); + LaunchDimensions launch_dimensions = CalculateLaunchDimensions( + sort->shape(), ir_emitter_context_->device_description()); + UpdateLaunchDimensions(launch_dimensions, thunks.back().get(), + ir_emitter_context_->llvm_module()); + + llvm::Value* xor_mask; + if (mask == stage) { + xor_mask = llvm::ConstantInt::get(index_type, (1LL << (stage + 1)) - 1); + } else { + xor_mask = llvm::ConstantInt::get(index_type, 1LL << mask); + } + + TF_RETURN_IF_ERROR(llvm_ir::EmitSortInPlace( + dimension_to_sort, GetIrArray(*sort, *sort), IrName(sort), xor_mask, + &b_, &launch_dimensions)); + } + } + thunk_sequence_->emplace_back( MakeUnique(std::move(thunks), sort)); - return IrEmitter::HandleSort(sort); + return Status::OK(); } Status IrEmitterUnnested::HandleTupleSelect(HloInstruction* tuple_select) { diff --git a/tensorflow/compiler/xla/service/llvm_ir/BUILD b/tensorflow/compiler/xla/service/llvm_ir/BUILD index 462be543bc..0573304912 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/BUILD +++ b/tensorflow/compiler/xla/service/llvm_ir/BUILD @@ -188,7 +188,10 @@ cc_library( ":ir_array", ":llvm_loop", ":llvm_util", + ":loop_emitter", "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla/service/gpu:parallel_loop_emitter", + "//tensorflow/compiler/xla/service/gpu:partition_assignment", "//tensorflow/core:lib", "@llvm//:core", ], diff --git a/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc b/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc index 585364458a..6f261c32f4 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc @@ -19,12 +19,15 @@ limitations under the License. #include "llvm/IR/BasicBlock.h" #include "llvm/IR/Constants.h" #include "llvm/IR/Instructions.h" +#include "llvm/IR/Value.h" #include "tensorflow/compiler/xla/primitive_util.h" +#include "tensorflow/compiler/xla/service/gpu/parallel_loop_emitter.h" +#include "tensorflow/compiler/xla/service/gpu/partition_assignment.h" #include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" +#include "tensorflow/compiler/xla/service/llvm_ir/loop_emitter.h" #include "tensorflow/compiler/xla/shape_util.h" -#include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/platform/types.h" @@ -73,7 +76,9 @@ void EmitCompareLoop(int64 dimension_to_sort, } // namespace Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, - tensorflow::StringPiece name, llvm::IRBuilder<>* b) { + tensorflow::StringPiece name, llvm::Value* xor_mask, + llvm::IRBuilder<>* b, + const gpu::LaunchDimensions* launch_dimensions) { const Shape& keys_shape = keys_array.GetShape(); // TODO(b/26783907): This case can probably be avoided with the Algebraic @@ -83,11 +88,13 @@ Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, } // Create loop nests which loop through the operand dimensions. The sort - // dimension is handled in three separate innermost loops which perform the - // sorting. + // dimension is handled in the innermost loop which performs the sorting. ForLoopNest loop_nest(name, b); IrArray::Index keys_index = loop_nest.EmitOperandArrayLoopNest(keys_array, dimension_to_sort, "keys"); + if (loop_nest.GetInnerLoopBodyBasicBlock() != nullptr) { + SetToFirstInsertPoint(loop_nest.GetInnerLoopBodyBasicBlock(), b); + } // 'compare_keys_index' is the index of the element that 'keys_index' should // be compared to. @@ -100,89 +107,42 @@ Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, } } - // Create the sorting loops which do the sorting. - int64 dimension_to_sort_bound = keys_shape.dimensions(dimension_to_sort); - std::unique_ptr stages_loop = loop_nest.AddLoop( - /*start_index=*/0, - /*end_index=*/ - tensorflow::Log2Ceiling64(dimension_to_sort_bound), - /*suffix=*/"sort_stages"); - std::unique_ptr mask_loop = loop_nest.AddLoop( - /*suffix=*/"mask", - /*start_index=*/keys_index.GetConstantWithIndexType(0), - /*end_index=*/stages_loop->GetIndVarValue()); - std::unique_ptr compare_loop = loop_nest.AddLoop( - /*start_index=*/0, - /*end_index=*/dimension_to_sort_bound, - /*suffix=*/"compare"); - - // Naive C++ code for the inner loops (without parallelization): + // Naive C++ code for the inner compare loop: // - // for (int64 stage = 0; stage < Log2Ceiling(dimension_to_sort_bound); - // ++stage) { - // int64 first_xor_mask = (1LL << (stage + 1)) - 1; - // for (int64 i = 0; i < dimension_to_sort_bound; ++i) { - // int64 j = i ^ first_xor_mask; - // if (i < j && j < dimension_to_sort_bound) { - // int64 min_key = std::min(keys[i], keys[j]); - // keys[j] = std::max(keys[i], keys[j]); - // keys[i] = min_key; - // } - // } - // for (int64 mask = 0; mask < stage; ++mask) { - // int64 later_xor_mask = (1LL << (stage - (mask + 1)); - // for (int64 i = 0; i < dimension_to_sort_bound; ++i) { - // int64 j = i ^ later_xor_mask; - // if (i < j && j < dimension_to_sort_bound) { - // int64 min_key = std::min(keys[i], keys[j]); - // keys[j] = std::max(keys[i], keys[j]); - // keys[i] = min_key; - // } - // } + // for (int64 i = 0; i < dimension_to_sort_bound; ++i) { + // int64 j = i ^ xor_mask; + // if (i < j && j < dimension_to_sort_bound) { + // int64 min_key = std::min(keys[i], keys[j]); + // keys[j] = std::max(keys[i], keys[j]); + // keys[i] = min_key; // } // } // // This follows the algorithm described on Wikipedia: // https://en.wikipedia.org/wiki/Bitonic_sorter - SetToFirstInsertPoint(stages_loop->GetBodyBasicBlock(), b); - // The first xor mask of a stage is 2^(stage + 1) - 1. - auto first_xor_mask = b->CreateSub( - b->CreateShl(keys_index.GetConstantWithIndexType(1), - b->CreateAdd(stages_loop->GetIndVarValue(), - keys_index.GetConstantWithIndexType(1))), - keys_index.GetConstantWithIndexType(1)); - std::unique_ptr first_compare_loop = ForLoop::EmitForLoop( - /*prefix=*/"first_compare", - /*start_index=*/keys_index.GetConstantWithIndexType(0), - /*end_index=*/ - keys_index.GetConstantWithIndexType(dimension_to_sort_bound), - /*step=*/keys_index.GetConstantWithIndexType(1), - /*b=*/b); - - SetToFirstInsertPoint(first_compare_loop->GetBodyBasicBlock(), b); - // 'first_compare_loop' iterates through the 'dimension_to_sort'. - keys_index[dimension_to_sort] = first_compare_loop->GetIndVarValue(); - compare_keys_index[dimension_to_sort] = - b->CreateXor(first_compare_loop->GetIndVarValue(), first_xor_mask); - EmitCompareLoop(dimension_to_sort, keys_index, compare_keys_index, keys_array, - b); - - SetToFirstInsertPoint(compare_loop->GetPreheaderBasicBlock(), b); - // The later masks of a stage are 2^(stage - (mask_loop_ind_var + 1)). - auto later_xor_mask = b->CreateShl( - keys_index.GetConstantWithIndexType(1), - b->CreateSub(stages_loop->GetIndVarValue(), - b->CreateAdd(mask_loop->GetIndVarValue(), - keys_index.GetConstantWithIndexType(1)))); - - SetToFirstInsertPoint(compare_loop->GetBodyBasicBlock(), b); - // 'compare_loop' iterates through the 'dimension_to_sort'. - keys_index[dimension_to_sort] = compare_loop->GetIndVarValue(); - compare_keys_index[dimension_to_sort] = - b->CreateXor(compare_loop->GetIndVarValue(), later_xor_mask); - EmitCompareLoop(dimension_to_sort, keys_index, compare_keys_index, keys_array, - b); + int64 dimension_to_sort_bound = + keys_array.GetShape().dimensions(dimension_to_sort); + Shape compare_shape = ShapeUtil::MakeShape(keys_shape.element_type(), + {dimension_to_sort_bound}); + auto compare_loop_body_emitter = + [&](const IrArray::Index& compare_index) -> Status { + keys_index[dimension_to_sort] = compare_index[0]; + compare_keys_index[dimension_to_sort] = + b->CreateXor(compare_index[0], xor_mask); + EmitCompareLoop(dimension_to_sort, keys_index, compare_keys_index, + keys_array, b); + return Status::OK(); + }; + if (launch_dimensions != nullptr) { + TF_RETURN_IF_ERROR(gpu::ParallelLoopEmitter(compare_loop_body_emitter, + compare_shape, + *launch_dimensions, b) + .EmitLoop(name)); + } else { + TF_RETURN_IF_ERROR(LoopEmitter(compare_loop_body_emitter, compare_shape, b) + .EmitLoop(name)); + } // Set the IR builder insert point to the exit basic block of the outer most // loop. This ensures later instructions are inserted after this loop nest. diff --git a/tensorflow/compiler/xla/service/llvm_ir/sort_util.h b/tensorflow/compiler/xla/service/llvm_ir/sort_util.h index d0f185e70b..e75f9b08fb 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/sort_util.h +++ b/tensorflow/compiler/xla/service/llvm_ir/sort_util.h @@ -16,6 +16,8 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_SORT_UTIL_H_ #define TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_SORT_UTIL_H_ +#include "llvm/IR/Value.h" +#include "tensorflow/compiler/xla/service/gpu/partition_assignment.h" #include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/core/stringpiece.h" @@ -23,10 +25,14 @@ limitations under the License. namespace xla { namespace llvm_ir { -// Emits llvm IR to sort the 'dimension_to_sort' dimension of 'keys_array' into -// ascending order. +// Emits llvm IR to do pairwise comparisons/swaps in the 'dimension_to_sort' +// dimension of 'keys_array'. All other dimensions are kept as-is. This +// implements the inner loop of BitonicSort. If 'launch_dimensions' is nullptr, +// the inner compare loop will not be parallelized. Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, - tensorflow::StringPiece name, llvm::IRBuilder<>* b); + tensorflow::StringPiece name, llvm::Value* xor_mask, + llvm::IRBuilder<>* b, + const gpu::LaunchDimensions* launch_dimensions); } // namespace llvm_ir } // namespace xla -- GitLab From c21078f527023e3074b63109fb768413f82a8f8f Mon Sep 17 00:00:00 2001 From: James Keeling Date: Tue, 24 Jul 2018 03:09:47 -0700 Subject: [PATCH 317/519] Add Python API functions to query kernels This is part of the work to make available kernels easier to query at runtime. PiperOrigin-RevId: 205802663 --- tensorflow/python/BUILD | 28 +++++++++++++ tensorflow/python/framework/kernels.py | 46 +++++++++++++++++++++ tensorflow/python/framework/kernels_test.py | 41 ++++++++++++++++++ 3 files changed, 115 insertions(+) create mode 100644 tensorflow/python/framework/kernels.py create mode 100644 tensorflow/python/framework/kernels_test.py diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 9c7f3b7b25..814239533c 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -96,6 +96,7 @@ py_library( ":image_ops", ":initializers_ns", ":io_ops", + ":kernels", ":layers", ":lib", ":list_ops", @@ -789,6 +790,19 @@ py_library( ], ) +py_library( + name = "kernels", + srcs = [ + "framework/kernels.py", + ], + srcs_version = "PY2AND3", + deps = [ + ":pywrap_tensorflow", + ":util", + "//tensorflow/core:protos_all_py", + ], +) + py_library( name = "op_def_library", srcs = ["framework/op_def_library.py"], @@ -1482,6 +1496,20 @@ py_test( ], ) +py_test( + name = "framework_kernels_test", + size = "small", + srcs = ["framework/kernels_test.py"], + main = "framework/kernels_test.py", + srcs_version = "PY2AND3", + deps = [ + ":framework_test_lib", + ":kernels", + ":platform_test", + ":test_ops", + ], +) + tf_gen_op_wrapper_private_py( name = "array_ops_gen", visibility = [ diff --git a/tensorflow/python/framework/kernels.py b/tensorflow/python/framework/kernels.py new file mode 100644 index 0000000000..f7641f3442 --- /dev/null +++ b/tensorflow/python/framework/kernels.py @@ -0,0 +1,46 @@ +# 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. +# ============================================================================== +"""Functions for querying registered kernels.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.core.framework import kernel_def_pb2 +from tensorflow.python import pywrap_tensorflow as c_api +from tensorflow.python.util import compat + + +def get_all_registered_kernels(): + """Returns a KernelList proto of all registered kernels. + """ + buf = c_api.TF_GetAllRegisteredKernels() + data = c_api.TF_GetBuffer(buf) + kernel_list = kernel_def_pb2.KernelList() + kernel_list.ParseFromString(compat.as_bytes(data)) + return kernel_list + + +def get_registered_kernels_for_op(name): + """Returns a KernelList proto of registered kernels for a given op. + + Args: + name: A string representing the name of the op whose kernels to retrieve. + """ + buf = c_api.TF_GetRegisteredKernelsForOp(name) + data = c_api.TF_GetBuffer(buf) + kernel_list = kernel_def_pb2.KernelList() + kernel_list.ParseFromString(compat.as_bytes(data)) + return kernel_list diff --git a/tensorflow/python/framework/kernels_test.py b/tensorflow/python/framework/kernels_test.py new file mode 100644 index 0000000000..c53500be73 --- /dev/null +++ b/tensorflow/python/framework/kernels_test.py @@ -0,0 +1,41 @@ +# 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 querying registered kernels.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.python.framework import kernels +from tensorflow.python.framework import test_util +from tensorflow.python.platform import googletest + + +class GetAllRegisteredKernelsTest(test_util.TensorFlowTestCase): + + def testFindsAtLeastOneKernel(self): + kernel_list = kernels.get_all_registered_kernels() + self.assertGreater(len(kernel_list.kernel), 0) + + +class GetRegisteredKernelsForOp(test_util.TensorFlowTestCase): + + def testFindsAtLeastOneKernel(self): + kernel_list = kernels.get_registered_kernels_for_op("KernelLabel") + self.assertGreater(len(kernel_list.kernel), 0) + self.assertEqual(kernel_list.kernel[0].op, "KernelLabel") + + +if __name__ == "__main__": + googletest.main() -- GitLab From 892adf59c59f3cfe5949baf7bbc266b3de606110 Mon Sep 17 00:00:00 2001 From: Taehoon Lee Date: Tue, 24 Jul 2018 20:30:48 +0900 Subject: [PATCH 318/519] Replace `NWHC` with `NHWC` --- tensorflow/python/kernel_tests/depthwise_conv_op_test.py | 6 +++--- .../python/kernel_tests/neon_depthwise_conv_op_test.py | 4 ++-- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py index 7134e02c34..58845552db 100644 --- a/tensorflow/python/kernel_tests/depthwise_conv_op_test.py +++ b/tensorflow/python/kernel_tests/depthwise_conv_op_test.py @@ -90,7 +90,7 @@ def CheckGradConfigsToTest(): class DepthwiseConv2DTest(test.TestCase): # This is testing that depthwise_conv2d and depthwise_conv2d_native - # produce the same results. It also tests that NCHW and NWHC + # produce the same results. It also tests that NCHW and NHWC # formats agree, by comparing the depthwise_conv2d_native with # 'NCHW' format (with transposition) matches the 'NHWC' format using # the higher level interface. @@ -142,7 +142,7 @@ class DepthwiseConv2DTest(test.TestCase): native_t1 = t1 strides = [1, stride, stride, 1] if data_format == "NCHW": - # Transpose from NWHC input to NCHW + # Transpose from NHWC input to NCHW # Ex. [4, 5, 5, 48] to [4, 48, 5, 5] native_t1 = array_ops.transpose(t1, [0, 3, 1, 2]) strides = [1, 1, stride, stride] @@ -368,7 +368,7 @@ class DepthwiseConv2DTest(test.TestCase): native_input = input_tensor strides = [1, stride, stride, 1] if data_format == "NCHW": - # Transpose from NWHC input to NCHW + # Transpose from NHWC input to NCHW # Ex. [4, 5, 5, 48] to [4, 48, 5, 5] native_input = array_ops.transpose(input_tensor, [0, 3, 1, 2]) input_shape = [ 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 d8ce9fffbd..3cbbd48c8c 100644 --- a/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py +++ b/tensorflow/python/kernel_tests/neon_depthwise_conv_op_test.py @@ -82,7 +82,7 @@ def CheckGradConfigsToTest(): class DepthwiseConv2DTest(test.TestCase): # This is testing that depthwise_conv2d and depthwise_conv2d_native - # produce the same results. It also tests that NCHW and NWHC + # produce the same results. It also tests that NCHW and NHWC # formats agree, by comparing the depthwise_conv2d_native with # 'NCHW' format (with transposition) matches the 'NHWC' format using # the higher level interface. @@ -123,7 +123,7 @@ class DepthwiseConv2DTest(test.TestCase): native_t1 = t1 strides = [1, stride, stride, 1] if data_format == "NCHW": - # Transpose from NWHC input to NCHW + # Transpose from NHWC input to NCHW # Ex. [4, 5, 5, 48] to [4, 48, 5, 5] native_t1 = array_ops.transpose(t1, [0, 3, 1, 2]) strides = [1, 1, stride, stride] -- GitLab From 226831aab92a395a26824a08caa9d43f0c3d604e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 24 Jul 2018 07:40:12 -0700 Subject: [PATCH 319/519] Fix pack_test. PiperOrigin-RevId: 205826660 --- tensorflow/contrib/lite/kernels/pack_test.cc | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/pack_test.cc b/tensorflow/contrib/lite/kernels/pack_test.cc index cb9fed69b1..485a50ad3a 100644 --- a/tensorflow/contrib/lite/kernels/pack_test.cc +++ b/tensorflow/contrib/lite/kernels/pack_test.cc @@ -22,6 +22,7 @@ namespace tflite { namespace { using ::testing::ElementsAre; +using ::testing::ElementsAreArray; template class PackOpModel : public SingleOpModel { @@ -57,7 +58,7 @@ TEST(PackOpTest, FloatThreeInputs) { model.SetInput(2, {3, 6}); model.Invoke(); EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2)); - EXPECT_THAT(model.GetOutput(), ElementsAre(1, 4, 2, 5, 3, 6)); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 4, 2, 5, 3, 6})); } TEST(PackOpTest, FloatThreeInputsDifferentAxis) { @@ -67,7 +68,7 @@ TEST(PackOpTest, FloatThreeInputsDifferentAxis) { model.SetInput(2, {3, 6}); model.Invoke(); EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3)); - EXPECT_THAT(model.GetOutput(), ElementsAre(1, 2, 3, 4, 5, 6)); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6})); } TEST(PackOpTest, FloatMultilDimensions) { @@ -77,7 +78,7 @@ TEST(PackOpTest, FloatMultilDimensions) { model.Invoke(); EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 3)); EXPECT_THAT(model.GetOutput(), - ElementsAre(1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12)); + ElementsAreArray({1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12})); } TEST(PackOpTest, IntThreeInputs) { @@ -87,7 +88,7 @@ TEST(PackOpTest, IntThreeInputs) { model.SetInput(2, {3, 6}); model.Invoke(); EXPECT_THAT(model.GetOutputShape(), ElementsAre(3, 2)); - EXPECT_THAT(model.GetOutput(), ElementsAre(1, 4, 2, 5, 3, 6)); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 4, 2, 5, 3, 6})); } TEST(PackOpTest, IntThreeInputsDifferentAxis) { @@ -97,7 +98,7 @@ TEST(PackOpTest, IntThreeInputsDifferentAxis) { model.SetInput(2, {3, 6}); model.Invoke(); EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 3)); - EXPECT_THAT(model.GetOutput(), ElementsAre(1, 2, 3, 4, 5, 6)); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 2, 3, 4, 5, 6})); } TEST(PackOpTest, IntMultilDimensions) { @@ -107,7 +108,7 @@ TEST(PackOpTest, IntMultilDimensions) { model.Invoke(); EXPECT_THAT(model.GetOutputShape(), ElementsAre(2, 2, 3)); EXPECT_THAT(model.GetOutput(), - ElementsAre(1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12)); + ElementsAreArray({1, 2, 3, 7, 8, 9, 4, 5, 6, 10, 11, 12})); } } // namespace } // namespace tflite -- GitLab From 1b33df1814e35015953c7cba392ba2a7387ce875 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Tue, 24 Jul 2018 08:29:15 -0700 Subject: [PATCH 320/519] [XLA:GPU] Don't lie about buffer alignment to LLVM PiperOrigin-RevId: 205832336 --- tensorflow/compiler/xla/service/gpu/BUILD | 1 + .../xla/service/gpu/buffer_allocations.cc | 8 +-- .../compiler/xla/service/gpu/gpu_constants.cc | 13 ++++- .../compiler/xla/service/gpu/gpu_constants.h | 9 ++-- .../xla/service/gpu/ir_emitter_unnested.cc | 4 +- .../xla/service/gpu/nvptx_compiler.cc | 2 +- .../compiler/xla/service/gpu/tests/BUILD | 19 +++++++ .../service/gpu/tests/gpu_alignment_test.cc | 54 +++++++++++++++++++ 8 files changed, 99 insertions(+), 11 deletions(-) create mode 100644 tensorflow/compiler/xla/service/gpu/tests/gpu_alignment_test.cc diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 06ff3d9bba..72aff197fc 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -36,6 +36,7 @@ cc_library( hdrs = ["gpu_constants.h"], deps = [ "//tensorflow/compiler/xla:types", + "//tensorflow/core:framework", ], ) diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc index ab5149dcdb..b095d4cd73 100644 --- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc +++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc @@ -49,12 +49,12 @@ StatusOr> BufferAllocations::Builder::Build( if (registered_buffers_.count(i)) { se::DeviceMemoryBase address = FindOrDie(registered_buffers_, i); if (reinterpret_cast(address.opaque()) % - kCudaMallocAlignBytes != + kEntryParameterAlignBytes != 0) { return InternalError( "Address of registered buffer %lld must be a multiple of %llx, but " "was %p", - i, kCudaMallocAlignBytes, address.opaque()); + i, kEntryParameterAlignBytes, address.opaque()); } buffer_allocations->SetBuffer(i, FindOrDie(registered_buffers_, i)); continue; @@ -71,12 +71,12 @@ StatusOr> BufferAllocations::Builder::Build( TF_ASSIGN_OR_RETURN( buffer, memory_allocator->Allocate(device_ordinal, buffer_size)); if (reinterpret_cast(buffer.opaque()) % - kCudaMallocAlignBytes != + kXlaAllocatedBufferAlignBytes != 0) { return InternalError( "Address returned by memory_allocator->Allocate must be a " "multiple of %llx, but was %p", - kCudaMallocAlignBytes, buffer.opaque()); + kXlaAllocatedBufferAlignBytes, buffer.opaque()); } // We do manual memory management within BufferAllocations. Be sure not // to do a TF_RETURN_IF_ERROR between this line and the diff --git a/tensorflow/compiler/xla/service/gpu/gpu_constants.cc b/tensorflow/compiler/xla/service/gpu/gpu_constants.cc index aa360c7f73..e6ddea6d25 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_constants.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_constants.cc @@ -14,12 +14,21 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/service/gpu/gpu_constants.h" +#include "tensorflow/core/framework/allocator.h" namespace xla { namespace gpu { -// http://docs.nvidia.com/cuda/cuda-c-programming-guide/#device-memory-accesses -const int64 kCudaMallocAlignBytes = 256; +// kEntryParameterAlignBytes is equal to EIGEN_MAX_ALIGN_BYTES, though including +// Eigen headers here to get that symbol may not be a good idea. +// EIGEN_MAX_ALIGN_BYTES may differ between CUDA-enabled builds vs CUDA-disabled +// builds and we don't want the IR generated by XLA:GPU to depend on that. +// +// TODO(b/111767313): Consider raising EIGEN_MAX_ALIGN_BYTES if it helps. +const int64 kEntryParameterAlignBytes = 16; + +const int64 kXlaAllocatedBufferAlignBytes = + tensorflow::Allocator::kAllocatorAlignment; } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/gpu_constants.h b/tensorflow/compiler/xla/service/gpu/gpu_constants.h index eb1ca4c6c9..925e6927b6 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_constants.h +++ b/tensorflow/compiler/xla/service/gpu/gpu_constants.h @@ -21,9 +21,12 @@ limitations under the License. namespace xla { namespace gpu { -// Minimum alignment of cudaMalloc. We require that buffers created by our -// DeviceMemoryAllocator, and all input/output buffers, have this alignment. -extern const int64 kCudaMallocAlignBytes; +// Minimum alignment for buffers passed as incoming arguments by TensorFlow. +extern const int64 kEntryParameterAlignBytes; + +// Minimum alignment for buffers allocated by XLA: the temp buffers and the live +// out (result) buffers. +extern const int64 kXlaAllocatedBufferAlignBytes; } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index b1038a3cc9..1f31a7f36b 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -232,7 +232,9 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype( kernel->addDereferenceableAttr(arg_no + 1, alloc->size()); kernel->addParamAttr( arg_no, llvm::Attribute::get(context, llvm::Attribute::Alignment, - kCudaMallocAlignBytes)); + alloc->is_entry_computation_parameter() + ? kEntryParameterAlignBytes + : kXlaAllocatedBufferAlignBytes)); if (alloc->IsPreallocatedTempBuffer()) { fn_arg->setName("temp_buf"); diff --git a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc index ad29862d83..2eefadebcd 100644 --- a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc @@ -543,7 +543,7 @@ StatusOr> NVPTXCompiler::RunBackend( BufferAssigner::Run(module.get(), hlo_schedule->ConsumeHloOrdering(), BufferSizeBytesFunction(), /*color_alignment=*/[](LogicalBuffer::Color) { - return kCudaMallocAlignBytes; + return kXlaAllocatedBufferAlignBytes; })); // BufferAssignment::Stats::ToString() and BufferAssignment::ToString() // include headers, so no need for us to print them ourselves. diff --git a/tensorflow/compiler/xla/service/gpu/tests/BUILD b/tensorflow/compiler/xla/service/gpu/tests/BUILD index 926262e2ad..686c3c16c9 100644 --- a/tensorflow/compiler/xla/service/gpu/tests/BUILD +++ b/tensorflow/compiler/xla/service/gpu/tests/BUILD @@ -202,3 +202,22 @@ tf_cc_test( "//tensorflow/core:test_main", ], ) + +tf_cc_test( + name = "gpu_alignment_test", + testonly = True, + srcs = ["gpu_alignment_test.cc"], + tags = [ + "requires-gpu-sm35", + ], + deps = [ + ":gpu_codegen_test", + "//tensorflow/compiler/xla/service:gpu_plugin", + "//tensorflow/compiler/xla/service/cpu:custom_call_target_registry", + "//tensorflow/compiler/xla/service/llvm_ir:alias_analysis", + "//tensorflow/compiler/xla/tests:filecheck", + "//tensorflow/compiler/xla/tests:llvm_irgen_test_base", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + ], +) diff --git a/tensorflow/compiler/xla/service/gpu/tests/gpu_alignment_test.cc b/tensorflow/compiler/xla/service/gpu/tests/gpu_alignment_test.cc new file mode 100644 index 0000000000..672c68e59b --- /dev/null +++ b/tensorflow/compiler/xla/service/gpu/tests/gpu_alignment_test.cc @@ -0,0 +1,54 @@ +/* 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 +#include + +#include "tensorflow/compiler/xla/service/cpu/custom_call_target_registry.h" +#include "tensorflow/compiler/xla/service/gpu/tests/gpu_codegen_test.h" +#include "tensorflow/compiler/xla/service/llvm_ir/alias_analysis.h" +#include "tensorflow/compiler/xla/tests/filecheck.h" +#include "tensorflow/core/platform/test.h" + +namespace xla { +namespace gpu { +namespace { + +class GpuAlignmentTest : public GpuCodegenTest {}; + +TEST_F(GpuAlignmentTest, Test) { + const char* hlo_string = R"( +HloModule GpuAlignmentTest + +ENTRY main { + zero = f32[] constant(0) + tok = token[] after-all() + a = f32[100] parameter(0) + b_tup = (f32[200], token[]) infeed(tok) + b = f32[200] get-tuple-element(b_tup), index=0 + a_padded = f32[150] pad(a, zero), padding=0_50 + b_sliced = f32[150] slice(b), slice={[0:150]} + ROOT c = f32[150] add(a_padded, b_sliced) +} +)"; + + CompileAndVerifyIr(hlo_string, R"( +CHECK: @fusion(i8* align 64 dereferenceable(600) %alloc0, i8* align 16 dereferenceable(400) %alloc1, i8* align 64 dereferenceable(864) %temp_buf) +)"); +} + +} // namespace +} // namespace gpu +} // namespace xla -- GitLab From 00f9eb9c589ad3c5ce5b4dde84763553c56ad0ee Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Tue, 24 Jul 2018 09:35:59 -0700 Subject: [PATCH 321/519] Improvement resource variable documentation. Removes stale mentions of how to construct them from docstrings and adds a new section to the programmer's guide explaining resource handles and resource variable semantics. PiperOrigin-RevId: 205842012 --- tensorflow/contrib/eager/python/saver.py | 4 +- .../api_def_ResourceScatterNdAdd.pbtxt | 2 +- tensorflow/python/eager/backprop.py | 8 ++-- tensorflow/python/eager/function.py | 27 ++++++------ .../python/ops/resource_variable_ops.py | 17 ++++---- tensorflow/python/ops/variables.py | 41 ++++++++----------- 6 files changed, 45 insertions(+), 54 deletions(-) diff --git a/tensorflow/contrib/eager/python/saver.py b/tensorflow/contrib/eager/python/saver.py index fdaca90fd1..d709308647 100644 --- a/tensorflow/contrib/eager/python/saver.py +++ b/tensorflow/contrib/eager/python/saver.py @@ -125,8 +125,8 @@ class Saver(object): Args: var_list: The list of variables that will be saved and restored. Either a - list of `tfe.Variable` objects, or a dictionary mapping names to - `tfe.Variable` objects. + list of `tf.Variable` objects, or a dictionary mapping names to + `tf.Variable` objects. Raises: RuntimeError: if invoked when eager execution has not been enabled. diff --git a/tensorflow/core/api_def/base_api/api_def_ResourceScatterNdAdd.pbtxt b/tensorflow/core/api_def/base_api/api_def_ResourceScatterNdAdd.pbtxt index 3b3a274df5..2b58969da2 100644 --- a/tensorflow/core/api_def/base_api/api_def_ResourceScatterNdAdd.pbtxt +++ b/tensorflow/core/api_def/base_api/api_def_ResourceScatterNdAdd.pbtxt @@ -51,7 +51,7 @@ For example, say we want to update 4 scattered elements to a rank-1 tensor to 8 elements. In Python, that update would look like this: ```python - ref = tfe.Variable([1, 2, 3, 4, 5, 6, 7, 8]) + ref = tf.Variable([1, 2, 3, 4, 5, 6, 7, 8], use_resource=True) indices = tf.constant([[4], [3], [1] ,[7]]) updates = tf.constant([9, 10, 11, 12]) update = tf.scatter_nd_add(ref, indices, updates) diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index 9e0bbce4a1..da8b93dba8 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -643,10 +643,10 @@ class GradientTape(object): Operations are recorded if they are executed within this context manager and at least one of their inputs is being "watched". - Trainable variables (created by `tf.contrib.eager.Variable` or - @{tf.get_variable}, trainable=True is default in both cases) are automatically - watched. Tensors can be manually watched by invoking the `watch` method on - this context manager. + Trainable variables (created by `tf.Variable` or @{tf.get_variable}, + trainable=True is default in both cases) are automatically watched. Tensors + can be manually watched by invoking the `watch` method on this context + manager. For example, consider the function `y = x * x`. The gradient at `x = 3.0` can be computed as: diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index a6906f9efd..d283a85532 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -1077,7 +1077,7 @@ def defun(func=None, compiled=False): tf.enable_eager_execution() def fn(): - x = tf.contrib.eager.Variable(0.0) + x = tf.Variable(0.0) x.assign_add(1.0) return x.read_value() @@ -1094,19 +1094,18 @@ def defun(func=None, compiled=False): ``` Finally, because each input signature is bound to a unique graph, if your - Python function constructs `tf.contrib.eager.Variable` objects, then each - graph constructed for that Python function will reference a unique set of - variables. To circumvent this problem, we recommend against compiling Python - functions that create `tf.contrib.eager.Variable` objects. Instead, Python - functions should either lexically close over `tf.contrib.eager.Variable` - objects or accept them as arguments, preferably encapsulated in an - object-oriented container. If you must create variables inside your Python - function and you want each graph generated for it to reference the same set of - variables, add logic to your Python function that ensures that variables are - only created the first time it is called and are reused for every subsequent - invocation; note that this is precisely what @{tf.keras.layers.Layer} objects - do, so we recommend using them to represent variable-bearing computations - whenever possible. + Python function constructs `tf.Variable` objects, then each graph constructed + for that Python function will reference a unique set of variables. To + circumvent this problem, we recommend against compiling Python functions that + create `tf.Variable` objects. Instead, Python functions should either + lexically close over `tf.Variable` objects or accept them as arguments, + preferably encapsulated in an object-oriented container. If you must create + variables inside your Python function and you want each graph generated for it + to reference the same set of variables, add logic to your Python function that + ensures that variables are only created the first time it is called and are + reused for every subsequent invocation; note that this is precisely what + @{tf.keras.layers.Layer} objects do, so we recommend using them to represent + variable-bearing computations whenever possible. Args: func: function to be compiled. If `func` is None, returns a diff --git a/tensorflow/python/ops/resource_variable_ops.py b/tensorflow/python/ops/resource_variable_ops.py index db071e3974..8b259b6b6b 100644 --- a/tensorflow/python/ops/resource_variable_ops.py +++ b/tensorflow/python/ops/resource_variable_ops.py @@ -196,15 +196,16 @@ class ResourceVariable(variables.RefVariable): the variable are fixed. The value can be changed using one of the assign methods. - Just like any `Tensor`, variables created with `ResourceVariable()` can be - used as inputs for other Ops in the graph. Additionally, all the operators - overloaded for the `Tensor` class are carried over to variables, so you can - also add nodes to the graph by just doing arithmetic on variables. + Just like any `Tensor`, variables created with + `tf.Variable(use_resource=True)` can be used as inputs for other Ops in the + graph. Additionally, all the operators overloaded for the `Tensor` class are + carried over to variables, so you can also add nodes to the graph by just + doing arithmetic on variables. - Unlike tf.Variable, a tf.ResourceVariable has well-defined semantics. Each + Unlike ref-based variable, a ResourceVariable has well-defined semantics. Each usage of a ResourceVariable in a TensorFlow graph adds a read_value operation - to the graph. The Tensors returned by a read_value operation are guaranteed - to see all modifications to the value of the variable which happen in any + to the graph. The Tensors returned by a read_value operation are guaranteed to + see all modifications to the value of the variable which happen in any operation on which the read_value depends on (either directly, indirectly, or via a control dependency) and guaranteed to not see any modification to the value of the variable from operations that depend on the read_value operation. @@ -218,7 +219,7 @@ class ResourceVariable(variables.RefVariable): can cause tf.Variable and tf.ResourceVariable to behave differently: ```python - a = tf.ResourceVariable(1.0) + a = tf.Variable(1.0, use_resource=True) a.initializer.run() assign = a.assign(2.0) diff --git a/tensorflow/python/ops/variables.py b/tensorflow/python/ops/variables.py index d03d93beeb..fc00ce68ae 100644 --- a/tensorflow/python/ops/variables.py +++ b/tensorflow/python/ops/variables.py @@ -220,27 +220,31 @@ class Variable(six.with_metaclass(VariableMetaclass, various `Optimizer` classes use this collection as the default list of variables to optimize. - WARNING: tf.Variable objects have a non-intuitive memory model. A Variable is - represented internally as a mutable Tensor which can non-deterministically - alias other Tensors in a graph. The set of operations which consume a Variable - and can lead to aliasing is undetermined and can change across TensorFlow - versions. Avoid writing code which relies on the value of a Variable either - changing or not changing as other operations happen. For example, using - Variable objects or simple functions thereof as predicates in a `tf.cond` is - dangerous and error-prone: + WARNING: tf.Variable objects by default have a non-intuitive memory model. A + Variable is represented internally as a mutable Tensor which can + non-deterministically alias other Tensors in a graph. The set of operations + which consume a Variable and can lead to aliasing is undetermined and can + change across TensorFlow versions. Avoid writing code which relies on the + value of a Variable either changing or not changing as other operations + happen. For example, using Variable objects or simple functions thereof as + predicates in a `tf.cond` is dangerous and error-prone: ``` v = tf.Variable(True) tf.cond(v, lambda: v.assign(False), my_false_fn) # Note: this is broken. ``` - Here replacing tf.Variable with tf.contrib.eager.Variable will fix any - nondeterminism issues. + Here replacing adding `use_resource=True` when constructing the variable will + fix any nondeterminism issues: + ``` + v = tf.Variable(True, use_resource=True) + tf.cond(v, lambda: v.assign(False), my_false_fn) + ``` To use the replacement for variables which does not have these issues: - * Replace `tf.Variable` with `tf.contrib.eager.Variable`; + * Add `use_resource=True` when constructing `tf.Variable`; * Call `tf.get_variable_scope().set_use_resource(True)` inside a `tf.variable_scope` before the `tf.get_variable()` call. """ @@ -869,19 +873,7 @@ class RefVariable(Variable): ValueError: If the initial value is not specified, or does not have a shape and `validate_shape` is `True`. RuntimeError: If eager execution is enabled. - - @compatibility(eager) - `tf.Variable` is not compatible with eager execution. Use - `tfe.Variable` instead which is compatible with both eager execution - and graph construction. See [the TensorFlow Eager Execution - guide](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/g3doc/guide.md#variables-and-optimizers) - for details on how variables work in eager execution. - @end_compatibility """ - if context.executing_eagerly(): - raise RuntimeError( - "tf.Variable not supported when eager execution is enabled. " - "Please use tf.contrib.eager.Variable instead") self._in_graph_mode = True if variable_def: # If variable_def is provided, recreates the variable from its fields. @@ -992,8 +984,7 @@ class RefVariable(Variable): # Ensure that we weren't lifted into the eager context. if context.executing_eagerly(): raise RuntimeError( - "tf.Variable not supported when eager execution is enabled. " - "Please use tf.contrib.eager.Variable instead") + "RefVariable not supported when eager execution is enabled. ") with ops.name_scope(name, "Variable", [] if init_from_fn else [initial_value]) as name: -- GitLab From f8bbd3ceb7e86b7595ba74a9a03cfc7c1be252a8 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 24 Jul 2018 09:38:26 -0700 Subject: [PATCH 322/519] A subsequent improvement to the creation of a config option to not link LGPL, including fix for the Android/Apple version of code (C++ macros-es fix) PiperOrigin-RevId: 205842327 --- tensorflow/tensorflow.bzl | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 26970c8cb0..340d3f393c 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -244,6 +244,7 @@ def tf_copts(android_optimization_level_override="-O2", is_external=False): clean_dep("//tensorflow:windows"): get_win_copts(is_external), clean_dep("//tensorflow:windows_msvc"): get_win_copts(is_external), clean_dep("//tensorflow:ios"): ["-std=c++11"], + clean_dep("//tensorflow:no_lgpl_deps"): ["-D__TENSORFLOW_NO_LGPL_DEPS__", "-pthread"], "//conditions:default": ["-pthread"] })) -- GitLab From 568727eed199dba04e37f500265b50f96fed455e Mon Sep 17 00:00:00 2001 From: Nick Felt Date: Tue, 24 Jul 2018 09:49:47 -0700 Subject: [PATCH 323/519] Add v2 summary support to Estimator.train() and MonitoredSession hooks This change makes Estimator.train() support v2 summaries (tf.contrib.summary.*) out-of-the-box, to match the support for v1 summaries. Estimator.train() will now handle the boilerplate necessary to initialize a file writer and enable summary writing every N steps, and will ensure that its own automatically exported summaries (for loss and global_step/sec) get written to the same underlying events file. As part of this change, tf.train.SummarySaverHook, tf.train.CheckpointSaverHook, tf.train.StepCounterHook, and tf.train.ProfilerHook have also been adapted to write summaries using the v2 summary system (via a compatibility layer), instead of using FileWriterCache. A couple additional smaller changes are: - the 'session' parameter to FileWriter() can now be a callable returning a tf.Session instance. - the introduction of tf.contrib.summary.record_summaries_if() which takes a boolean tensor for direct control of tf.contrib.summary.should_record_summaries(). - EstimatorSpec.train_op, besides a tf.Operation, is now allowed to be any Tensor-equivalent object rather than just a tf.Tensor. PiperOrigin-RevId: 205843986 --- .../contrib/summary/summary_ops_graph_test.py | 20 + .../contrib/tpu/python/tpu/tpu_estimator.py | 17 +- tensorflow/core/kernels/summary_kernels.cc | 2 + tensorflow/python/BUILD | 1 + tensorflow/python/estimator/estimator.py | 24 +- tensorflow/python/estimator/estimator_test.py | 260 +++++++++- tensorflow/python/estimator/model_fn.py | 3 +- tensorflow/python/estimator/training_test.py | 10 +- tensorflow/python/ops/summary_ops_v2.py | 68 ++- tensorflow/python/saved_model/builder_impl.py | 5 +- .../summary/writer/event_file_writer_v2.py | 71 ++- tensorflow/python/summary/writer/writer.py | 8 +- .../python/summary/writer/writer_test.py | 54 +- .../training/basic_session_run_hooks.py | 182 +++++-- .../training/basic_session_run_hooks_test.py | 476 ++++++++++++------ .../python/training/monitored_session.py | 11 +- tensorflow/python/training/optimizer.py | 6 +- 17 files changed, 939 insertions(+), 279 deletions(-) diff --git a/tensorflow/contrib/summary/summary_ops_graph_test.py b/tensorflow/contrib/summary/summary_ops_graph_test.py index ae8336daaf..409fdf4583 100644 --- a/tensorflow/contrib/summary/summary_ops_graph_test.py +++ b/tensorflow/contrib/summary/summary_ops_graph_test.py @@ -228,6 +228,26 @@ class GraphFileTest(test_util.TensorFlowTestCase): sess.run(writer.flush()) self.assertEqual(2, get_total()) + def testSummaryOpsCollector(self): + summary_ops.scalar('x', 1.0, step=1) + with summary_ops.create_file_writer(self.get_temp_dir()).as_default(): + s2 = summary_ops.scalar('x', 1.0, step=1) + collector1 = summary_ops._SummaryOpsCollector() + collector2 = summary_ops._SummaryOpsCollector() + with collector1.capture(): + s3 = summary_ops.scalar('x', 1.0, step=1) + with collector2.capture(): + s4 = summary_ops.scalar('x', 1.0, step=1) + s5 = summary_ops.scalar('x', 1.0, step=1) + s6 = summary_ops.scalar('x', 1.0, step=1) + summary_ops.scalar('six', 1.0, step=1) + + # Ops defined outside summary writer context are ignored; ops defined inside + # SummaryOpsCollector capture context are stored to innermost such context. + self.assertItemsEqual([s2, s6], summary_ops.all_summary_ops()) + self.assertItemsEqual([s3, s5], collector1.collected_ops) + self.assertItemsEqual([s4], collector2.collected_ops) + class GraphDbTest(summary_test_util.SummaryDbTest): diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 42406db88a..1eb43ac7f7 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -1506,13 +1506,17 @@ class _OutfeedHostCall(object): _OutfeedHostCall.validate(host_calls) ret = {} for name, host_call in host_calls.items(): + # Isolate host call summary ops from main graph. + summary_collector = contrib_summary._SummaryOpsCollector() # pylint: disable=protected-access host_fn, tensors = host_call if isinstance(tensors, (tuple, list)): - ret[name] = host_fn(*tensors) + with summary_collector.capture(): + ret[name] = host_fn(*tensors) else: # Must be dict. try: - ret[name] = host_fn(**tensors) + with summary_collector.capture(): + ret[name] = host_fn(**tensors) except TypeError as e: logging.warning( 'Exception while calling %s: %s. It is likely the tensors ' @@ -1627,11 +1631,14 @@ class _OutfeedHostCall(object): # dimension. dequeue_ops[i] = array_ops.concat(dequeue_ops[i], axis=0) + # Isolate host call summary ops from main graph. + summary_collector = contrib_summary._SummaryOpsCollector() # pylint: disable=protected-access if self._tensor_keys[name] is not None: # The user-provided eval_metrics[1] is a dict. dequeue_ops = dict(zip(self._tensor_keys[name], dequeue_ops)) try: - ret[name] = self._host_fns[name](**dequeue_ops) + with summary_collector.capture(): + ret[name] = self._host_fns[name](**dequeue_ops) except TypeError as e: logging.warning( 'Exception while calling %s: %s. It is likely the tensors ' @@ -1639,8 +1646,8 @@ class _OutfeedHostCall(object): 'function\'s arguments', name, e, name) raise e else: - ret[name] = self._host_fns[name](*dequeue_ops) - + with summary_collector.capture(): + ret[name] = self._host_fns[name](*dequeue_ops) return ret diff --git a/tensorflow/core/kernels/summary_kernels.cc b/tensorflow/core/kernels/summary_kernels.cc index b287f0cc2f..b518c3cbf4 100644 --- a/tensorflow/core/kernels/summary_kernels.cc +++ b/tensorflow/core/kernels/summary_kernels.cc @@ -53,6 +53,7 @@ class CreateSummaryFileWriterOp : public OpKernel { max_queue, flush_millis, logdir, filename_suffix, ctx->env(), s); })); + core::ScopedUnref unref(s); } }; REGISTER_KERNEL_BUILDER(Name("CreateSummaryFileWriter").Device(DEVICE_CPU), @@ -89,6 +90,7 @@ class CreateSummaryDbWriterOp : public OpKernel { db, experiment_name, run_name, user_name, ctx->env(), s)); return Status::OK(); })); + core::ScopedUnref unref(s); } }; REGISTER_KERNEL_BUILDER(Name("CreateSummaryDbWriter").Device(DEVICE_CPU), diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index 814239533c..b5a0051c28 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -2822,6 +2822,7 @@ py_library( ":framework_ops", ":math_ops", ":resource_variable_ops", + ":resources", ":smart_cond", ":summary_op_util", ":summary_ops_gen", diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 915ceeb98b..b7185e8966 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -46,6 +46,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import resources +from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging @@ -65,6 +66,7 @@ from tensorflow.python.util import compat from tensorflow.python.util import compat_internal from tensorflow.python.util import function_utils from tensorflow.python.util import nest +from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import estimator_export @@ -1156,7 +1158,8 @@ class Estimator(object): Loss from training """ worker_hooks = [] - with ops.Graph().as_default() as g, g.device(self._device_fn): + with ops.Graph().as_default() as g, g.device( + self._device_fn), self._summary_writing_context(): random_seed.set_random_seed(self._config.tf_random_seed) global_step_tensor = self._create_and_assert_global_step(g) training_util._get_or_create_global_step_read() # pylint: disable=protected-access @@ -1190,7 +1193,7 @@ class Estimator(object): is_tpu_strategy = self._distribution.__class__.__name__ == 'TPUStrategy' worker_hooks = [] - with ops.Graph().as_default() as g: + with ops.Graph().as_default() as g, self._summary_writing_context(): with self._distribution.scope(): random_seed.set_random_seed(self._config.tf_random_seed) @@ -1519,6 +1522,23 @@ class Estimator(object): (self._warm_start_settings,)) warm_starting_util.warm_start(*self._warm_start_settings) + @tf_contextlib.contextmanager + def _summary_writing_context(self): + """Context manager for enabling V2 summary writing.""" + # Avoid creating a file writer at all if no summary writing was requested. + if self._config.save_summary_steps <= 0: + yield + return + file_writer = summary_ops_v2.create_file_writer( + logdir=self._model_dir, filename_suffix='') + with file_writer.as_default(): + # Create a boolean placeholder, default False, that SummarySaverHook can + # use to enable/disable V2 summary writing according to its own logic. + placeholder = array_ops.placeholder_with_default(False, shape=[]) + training.SummarySaverHook._set_placeholder(placeholder) # pylint: disable=protected-access + with summary_ops_v2.record_summaries_if(placeholder): + yield + def create_per_tower_ready_op(scaffold): """Create a Scaffold.ready_op inside a tower.""" diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py index 8bc410ba0b..1dd45a07c2 100644 --- a/tensorflow/python/estimator/estimator_test.py +++ b/tensorflow/python/estimator/estimator_test.py @@ -22,6 +22,7 @@ import functools import glob import os import tempfile +import time import numpy as np import six @@ -29,6 +30,7 @@ import six from google.protobuf import text_format from tensorflow.core.protobuf import rewriter_config_pb2 +from tensorflow.core.util.event_pb2 import SessionLog from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.estimator import estimator @@ -40,6 +42,7 @@ from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors +from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.framework import test_util @@ -55,6 +58,7 @@ from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import string_ops +from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.ops.losses import losses @@ -85,13 +89,32 @@ def dummy_model_fn(features, labels, params): _, _, _ = features, labels, params -def summaries_with_matching_keyword(keyword, dir_): - """Yields summary protos matching given keyword from event file.""" - +def load_eventfile_contents(directory_path): + """Returns the contents of the singular event file in the given directory.""" writer_cache.FileWriterCache.clear() - event_paths = glob.glob(os.path.join(dir_, 'events*')) - for event in summary_iterator.summary_iterator(event_paths[-1]): + # Get last Event written. + event_paths = glob.glob(os.path.join(directory_path, '*tfevent*')) + if len(event_paths) != 1: + raise AssertionError('Expected one eventfile, got %s' % str(event_paths)) + return list(summary_iterator.summary_iterator(event_paths[0])) + + +def make_summary_steps(eventlist): + """Returns dict of tags in eventlist mapped to steps where they're logged.""" + tag_to_steps = {} + for event in eventlist: + if event.summary is not None: + for value in event.summary.value: + if value.tag not in tag_to_steps: + tag_to_steps[value.tag] = [] + tag_to_steps[value.tag].append(event.step) + return tag_to_steps + + +def summaries_with_matching_keyword(keyword, dir_): + """Yields summary protos matching given keyword from event file.""" + for event in load_eventfile_contents(dir_): if event.summary is not None: for value in event.summary.value: if keyword in value.tag: @@ -366,13 +389,51 @@ def dummy_input_fn(): constant_op.constant([[1], [1]])) +class StableGlobalStepEstimator(estimator.Estimator): + """Estimator subclass using a ResourceVariable global_step for testing.""" + # TODO(nickfelt): remove after standard global_step is a ResourceVariable. + + def _create_global_step(self, graph): + """Creates a stable ResourceVariable-based global step suitable for tests. + + Args: + graph: The graph in which to create the global step. + + Returns: + A global step `Tensor`. + """ + with graph.as_default(), graph.name_scope(None): + return variable_scope.get_variable( + ops.GraphKeys.GLOBAL_STEP, + shape=[], + dtype=dtypes.int64, + initializer=init_ops.zeros_initializer(), + trainable=False, + collections=[ + ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP + ], + # Use a ResourceVariable and set caching_device to make the read + # behavior deterministic and well-defined. + caching_device='cpu:0', + use_resource=True) + + def model_fn_global_step_incrementer(features, labels, mode): _, _ = features, labels - global_step = training.get_global_step() return model_fn_lib.EstimatorSpec( mode, loss=constant_op.constant(1.), - train_op=state_ops.assign_add(global_step, 1)) + train_op=training.get_global_step().assign_add(1)) + + +def model_fn_with_v1_and_v2_summaries(features, labels, mode): + del features, labels + summary.scalar('foo-v1', 1.0) + summary_ops_v2.scalar('foo-v2', 2.0) + return model_fn_lib.EstimatorSpec( + mode, + loss=constant_op.constant(1.), + train_op=training.get_global_step().assign_add(1)) def assert_features_op(expected_features, actual_features): @@ -408,6 +469,25 @@ def _make_input_fn(features, labels): return _input_fn +class RaiseOnceAtStepHook(session_run_hook.SessionRunHook): + """Hook that raises an Exception the first time it reaches step N.""" + + def __init__(self, n, ex): + self.n = n + self.ex = ex + self.raised = False + + def before_run(self, run_context): + # Raise the first time we reach step N. + self.n -= 1 + if 0 == self.n and not self.raised: + # Wait 1 sec so that event file names have different UNIX timestamps. + time.sleep(1.2) + self.raised = True + raise self.ex + return None + + class EstimatorTrainTest(test.TestCase): def test_callable_model_fn(self): @@ -617,17 +697,171 @@ class EstimatorTrainTest(test.TestCase): self.assertEqual( 5, estimator._load_global_step_from_checkpoint_dir(est.model_dir)) - def test_loss_summary(self): + def test_summary_loss(self): est = estimator.Estimator(model_fn=model_fn_global_step_incrementer, config=run_config.RunConfig(save_summary_steps=1)) est.train(dummy_input_fn, steps=1) + events = load_eventfile_contents(est.model_dir) + self.assertEqual({'loss': [1]}, make_summary_steps(events)) - # Make sure nothing is stuck in limbo. - writer_cache.FileWriterCache.clear() + def test_summary_user_defined_v1_and_v2(self): + est = StableGlobalStepEstimator( + model_fn=model_fn_with_v1_and_v2_summaries, + config=run_config.RunConfig(save_summary_steps=1)) + est.train(dummy_input_fn, steps=1) + events = load_eventfile_contents(est.model_dir) + self.assertEqual( + {'foo-v1': [1], 'foo-v2': [0], 'loss': [1]}, + make_summary_steps(events)) - if check_eventfile_for_keyword('loss', est.model_dir): - return - self.fail('{} should be part of reported summaries.'.format('loss')) + def test_summary_writing_disabled(self): + est = StableGlobalStepEstimator( + model_fn=model_fn_with_v1_and_v2_summaries, + config=run_config.RunConfig(save_summary_steps=0)) + est.train(dummy_input_fn, steps=1) + events = load_eventfile_contents(est.model_dir) + self.assertEqual({}, make_summary_steps(events)) + + def test_summary_saving_steps(self): + est = StableGlobalStepEstimator( + model_fn=model_fn_with_v1_and_v2_summaries, + config=run_config.RunConfig(save_summary_steps=2)) + est.train(dummy_input_fn, steps=5) + events = load_eventfile_contents(est.model_dir) + self.assertEqual( + {'foo-v1': [1, 3, 5], 'foo-v2': [0, 2, 4], 'loss': [1, 3, 5]}, + make_summary_steps(events)) + + def test_summary_additional_hook(self): + def model_fn_extra_summary_hook(features, labels, mode, config): + del features, labels + v1_op = summary.scalar('foo-v1', 1.0) + v2_op = summary_ops_v2.scalar('foo-v2', 2.0) + extra_hook = basic_session_run_hooks.SummarySaverHook( + output_dir=os.path.join(config.model_dir, 'extra'), + save_steps=3, + summary_op=control_flow_ops.with_dependencies([v2_op], v1_op)) + return model_fn_lib.EstimatorSpec( + mode, + loss=constant_op.constant(1.), + train_op=training.get_global_step().assign_add(1), + training_hooks=[extra_hook]) + est = StableGlobalStepEstimator( + model_fn=model_fn_extra_summary_hook, + config=run_config.RunConfig(save_summary_steps=2)) + est.train(dummy_input_fn, steps=7) + + events = load_eventfile_contents(est.model_dir) + self.assertEqual( + {'foo-v1': [1, 3, 5, 7], 'foo-v2': [0, 2, 4, 6], 'loss': [1, 3, 5, 7]}, + make_summary_steps(events)) + extra_dir = os.path.join(est.model_dir, 'extra') + extra_events = load_eventfile_contents(extra_dir) + self.assertEqual({'foo-v1': [1, 4, 7]}, make_summary_steps(extra_events)) + + def test_summary_user_defined_in_input_fn(self): + def input_fn_custom_summaries(): + summary.scalar('foo-v1', 1.0) + summary_ops_v2.scalar('foo-v2', 2.0) + return ({'x': constant_op.constant([[1], [1]])}, + constant_op.constant([[1], [1]])) + est = StableGlobalStepEstimator( + model_fn=model_fn_global_step_incrementer, + config=run_config.RunConfig(save_summary_steps=1)) + est.train(input_fn_custom_summaries, steps=1) + events = load_eventfile_contents(est.model_dir) + self.assertEqual( + {'foo-v1': [1], 'foo-v2': [0], 'loss': [1]}, + make_summary_steps(events)) + + def test_summary_with_warm_start(self): + est = StableGlobalStepEstimator( + model_fn=model_fn_with_v1_and_v2_summaries, + config=run_config.RunConfig(save_summary_steps=1)) + est.train(dummy_input_fn, steps=5) + warm_started_est = StableGlobalStepEstimator( + model_fn=model_fn_with_v1_and_v2_summaries, + config=run_config.RunConfig(save_summary_steps=1), + warm_start_from=est.model_dir) + warm_started_est.train(dummy_input_fn, steps=3) + events = load_eventfile_contents(warm_started_est.model_dir) + self.assertEqual( + {'foo-v1': [1, 2, 3], 'foo-v2': [0, 1, 2], 'loss': [1, 2, 3]}, + make_summary_steps(events)) + + def test_summary_with_error_and_auto_restart(self): + est = StableGlobalStepEstimator( + model_fn=model_fn_with_v1_and_v2_summaries, + config=run_config.RunConfig( + save_summary_steps=2, save_checkpoints_steps=5)) + abort_hook = RaiseOnceAtStepHook( + 7, errors_impl.AbortedError(None, None, 'Abort')) + est.train(dummy_input_fn, steps=10, hooks=[abort_hook]) + + # We expect two event files: one for the aborted run, and one post-restart. + event_paths = sorted(glob.glob(os.path.join(est.model_dir, '*tfevent*'))) + self.assertEqual(2, len(event_paths)) + + # First file should have summaries up to the last checkpoint. + first_events = list(summary_iterator.summary_iterator(event_paths[0])) + first_summaries = make_summary_steps(first_events) + self.assertEqual([0, 2, 4], first_summaries['foo-v2']) + # The V1 summaries may or may not include step 5 (depending on the flush() + # sequence) so just check that at least 1 and 3 are there. + # TODO(nickfelt): ensure summaries *at* checkpoint step get flushed too. + self.assertEqual([1, 3], first_summaries['foo-v1'][:2]) + self.assertEqual([1, 3], first_summaries['loss'][:2]) + + # Second file should pick up from global_step=5. Note that the 2 step save + # interval will reset at this step as well, so summaries logged at steps + # 2 and 4 continue not with 6, 8, ... but at steps 5, 7, ... instead. + second_events = list(summary_iterator.summary_iterator(event_paths[1])) + self.assertEqual( + {'foo-v1': [6, 8, 10], 'foo-v2': [5, 7, 9], 'loss': [6, 8, 10]}, + make_summary_steps(second_events)) + # Second file should contain a session START event at resumed global_step. + session_start_event = next(event for event in second_events + if event.session_log.status == SessionLog.START) + self.assertEqual(5, session_start_event.step) + + def test_summary_with_error_and_explicit_restart(self): + est = StableGlobalStepEstimator( + model_fn=model_fn_with_v1_and_v2_summaries, + config=run_config.RunConfig( + save_summary_steps=2, save_checkpoints_steps=5)) + abort_hook = RaiseOnceAtStepHook( + 7, errors_impl.UnknownError(None, None, 'Unknown failure')) + self.assertRaises( + errors_impl.UnknownError, + lambda: est.train(dummy_input_fn, max_steps=10, hooks=[abort_hook])) + # Explicitly retry after the error. + est.train(dummy_input_fn, max_steps=10, hooks=[abort_hook]) + + # We expect two event files: one for the failed run, and one post-restart. + event_paths = sorted(glob.glob(os.path.join(est.model_dir, '*tfevent*'))) + self.assertEqual(2, len(event_paths)) + + # First file should have summaries up to the last checkpoint. + first_events = list(summary_iterator.summary_iterator(event_paths[0])) + first_summaries = make_summary_steps(first_events) + self.assertEqual([0, 2, 4], first_summaries['foo-v2']) + # The V1 summaries may or may not include step 5 (depending on the flush() + # sequence) so just check that at least 1 and 3 are there. + # TODO(nickfelt): ensure summaries *at* checkpoint step get flushed too. + self.assertEqual([1, 3], first_summaries['foo-v1'][:2]) + self.assertEqual([1, 3], first_summaries['loss'][:2]) + + # Second file should pick up from global_step=5. Note that the 2 step save + # interval will reset at this step as well, so summaries logged at steps + # 2 and 4 continue not with 6, 8, ... but at steps 5, 7, ... instead. + second_events = list(summary_iterator.summary_iterator(event_paths[1])) + self.assertEqual( + {'foo-v1': [6, 8, 10], 'foo-v2': [5, 7, 9], 'loss': [6, 8, 10]}, + make_summary_steps(second_events)) + # Second file should contain a session START event at resumed global_step. + session_start_event = next(event for event in second_events + if event.session_log.status == SessionLog.START) + self.assertEqual(5, session_start_event.step) def test_latest_checkpoint(self): est = estimator.Estimator(model_fn=model_fn_global_step_incrementer) diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py index a9fd8f8e1a..b1b2f65edf 100644 --- a/tensorflow/python/estimator/model_fn.py +++ b/tensorflow/python/estimator/model_fn.py @@ -26,6 +26,7 @@ import six from tensorflow.python.estimator.export import export_output as export_output_lib from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants @@ -432,7 +433,7 @@ class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [ def _check_is_tensor_or_operation(x, name): - if not (isinstance(x, ops.Operation) or isinstance(x, ops.Tensor)): + if not (isinstance(x, ops.Operation) or tensor_util.is_tensor(x)): raise TypeError('{} must be Operation or Tensor, given: {}'.format(name, x)) diff --git a/tensorflow/python/estimator/training_test.py b/tensorflow/python/estimator/training_test.py index dc106c7d3b..121439a2cd 100644 --- a/tensorflow/python/estimator/training_test.py +++ b/tensorflow/python/estimator/training_test.py @@ -2059,7 +2059,7 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): def _extract_loss_and_global_step(self, event_folder): """Returns the loss and global step in last event.""" - event_paths = glob.glob(os.path.join(event_folder, 'events*')) + event_paths = sorted(glob.glob(os.path.join(event_folder, 'events*'))) loss = None global_step_count = None @@ -2139,10 +2139,12 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): # Make sure nothing is stuck in limbo. writer_cache.FileWriterCache.clear() - # Examine the training events. Use a range to check global step to avoid - # flakyness due to global step race condition. - training_loss, _ = self._extract_loss_and_global_step(est.model_dir) + # Examine the training events. + training_loss, training_global_step = self._extract_loss_and_global_step( + est.model_dir) self.assertIsNotNone(training_loss) + # Training summaries are logged for steps 1 and 10, so we see final step. + self.assertEqual(max_steps, training_global_step) # Examine the eval events. The global step should be accurate. eval_loss, eval_global_step = self._extract_loss_and_global_step( diff --git a/tensorflow/python/ops/summary_ops_v2.py b/tensorflow/python/ops/summary_ops_v2.py index 00150fe688..669358d9db 100644 --- a/tensorflow/python/ops/summary_ops_v2.py +++ b/tensorflow/python/ops/summary_ops_v2.py @@ -37,6 +37,7 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_summary_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.ops import resources from tensorflow.python.ops import summary_op_util from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import training_util @@ -66,41 +67,39 @@ def should_record_summaries(): return should_record_collection[0] +@tf_contextlib.contextmanager +def always_record_summaries(): + """Sets the should_record_summaries Tensor to always true.""" + with record_summaries_if(True): + yield + + +@tf_contextlib.contextmanager +def never_record_summaries(): + """Sets the should_record_summaries Tensor to always false.""" + with record_summaries_if(False): + yield + + # TODO(apassos) consider how to handle local step here. @tf_contextlib.contextmanager def record_summaries_every_n_global_steps(n, global_step=None): """Sets the should_record_summaries Tensor to true if global_step % n == 0.""" if global_step is None: global_step = training_util.get_or_create_global_step() - collection_ref = ops.get_collection_ref(_SHOULD_RECORD_SUMMARIES_NAME) - old = collection_ref[:] - try: - with ops.device("cpu:0"): - collection_ref[:] = [math_ops.equal(global_step % n, 0)] - yield - finally: - collection_ref[:] = old - - -@tf_contextlib.contextmanager -def always_record_summaries(): - """Sets the should_record_summaries Tensor to always true.""" - collection_ref = ops.get_collection_ref(_SHOULD_RECORD_SUMMARIES_NAME) - old = collection_ref[:] - try: - collection_ref[:] = [True] + with ops.device("cpu:0"): + on_nth_global_step = math_ops.equal(global_step % n, 0) + with record_summaries_if(on_nth_global_step): yield - finally: - collection_ref[:] = old @tf_contextlib.contextmanager -def never_record_summaries(): - """Sets the should_record_summaries Tensor to always false.""" +def record_summaries_if(bool_value): + """Sets the should_record_summaries Tensor to the given boolean value.""" collection_ref = ops.get_collection_ref(_SHOULD_RECORD_SUMMARIES_NAME) old = collection_ref[:] try: - collection_ref[:] = [False] + collection_ref[:] = [bool_value] yield finally: collection_ref[:] = old @@ -143,7 +142,6 @@ class SummaryWriter(object): finally: context.context().summary_writer_resource = old - def init(self): """Operation to initialize the summary writer resource.""" if self._resource is not None: @@ -311,6 +309,9 @@ def _make_summary_writer(name, factory, **kwargs): # TODO(apassos): Consider doing this instead. # ops.get_default_session().run(init_op) ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME, init_op) + # TODO(nickfelt): expose an actual op for this + is_initialized_op = constant_op.constant(True) + resources.register_resource(resource, init_op, is_initialized_op) return SummaryWriter(resource, init_op_fn) @@ -325,6 +326,27 @@ def _nothing(): return constant_op.constant(False) +class _SummaryOpsCollector(object): + """Defines a context manager for isolating out a subset of summary ops. + + Summary ops defined within this context will be accumulated within this + collector instead of being added to the graph-wide summary ops collection that + is returned by {@tf.contrib.summary.all_summary_ops}. + """ + + def __init__(self): + self.collected_ops = [] + + @tf_contextlib.contextmanager + def capture(self): + collection_ref = ops.get_collection_ref(ops.GraphKeys._SUMMARY_COLLECTION) # pylint: disable=protected-access + original_ops = collection_ref[:] + collection_ref[:] = [] + yield + self.collected_ops = collection_ref[:] + collection_ref[:] = original_ops + + def all_summary_ops(): """Graph-mode only. Returns all summary ops. diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py index e58be804c2..b67d0f2362 100644 --- a/tensorflow/python/saved_model/builder_impl.py +++ b/tensorflow/python/saved_model/builder_impl.py @@ -28,6 +28,7 @@ from tensorflow.core.protobuf import saved_model_pb2 from tensorflow.core.protobuf import saver_pb2 from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_util from tensorflow.python.lib.io import file_io from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging @@ -178,10 +179,10 @@ class SavedModelBuilder(object): stored as a collection with key TRAIN_OP_KEY, but not executed. Raises: - TypeError if Train op is not of type `Operation`. + TypeError if Train op is not of type `Operation` or a Tensor. """ if train_op is not None: - if (not isinstance(train_op, ops.Tensor) and + if (not tensor_util.is_tensor(train_op) and not isinstance(train_op, ops.Operation)): raise TypeError("train_op needs to be a Tensor or Op: %r" % train_op) ops.add_to_collection(constants.TRAIN_OP_KEY, train_op) diff --git a/tensorflow/python/summary/writer/event_file_writer_v2.py b/tensorflow/python/summary/writer/event_file_writer_v2.py index 5c66c0f7a8..262182d3b8 100644 --- a/tensorflow/python/summary/writer/event_file_writer_v2.py +++ b/tensorflow/python/summary/writer/event_file_writer_v2.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.client import session as tf_session from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -43,11 +44,11 @@ class EventFileWriterV2(object): """Creates an `EventFileWriterV2` and an event file to write to. On construction, this calls `tf.contrib.summary.create_file_writer` within - the graph from `session.graph` to look up a shared summary writer resource - for `logdir` if one exists, and create one if not. Creating the summary + the default graph, which finds and returns a shared summary writer resource + for `logdir` if one exists, and creates one if not. Creating the summary writer resource in turn creates a new event file in `logdir` to be filled with `Event` protocol buffers passed to `add_event`. Graph ops to control - this writer resource are added to `session.graph` during this init call; + this writer resource are added to the default graph during this init call; stateful methods on this class will call `session.run()` on these ops. Note that because the underlying resource is shared, it is possible that @@ -61,38 +62,50 @@ class EventFileWriterV2(object): no effect. See `tf.contrib.summary.create_file_writer` for details. Args: - session: A `tf.Session`. Session that will hold shared writer resource. - The writer ops will be added to session.graph during this init call. + session: A `tf.Session`, or a callable that provides one which will be + called on-demand. The session will hold the shared writer resource. logdir: A string. Directory where event file will be written. max_queue: Integer. Size of the queue for pending events and summaries. flush_secs: Number. How often, in seconds, to flush the pending events and summaries to disk. filename_suffix: A string. Every event file's name is suffixed with `filename_suffix`. + + Raises: + ValueError: if `session` is not a `tf.Session` or a callable """ - self._session = session + if isinstance(session, tf_session.SessionInterface): + self._session = lambda: session + elif callable(session): + self._session = session + else: + raise ValueError('session must be tf.Session or callable') self._logdir = logdir + self._initialized = False self._closed = False if not gfile.IsDirectory(self._logdir): gfile.MakeDirs(self._logdir) - with self._session.graph.as_default(): - with ops.name_scope('filewriter'): - file_writer = summary_ops_v2.create_file_writer( - logdir=self._logdir, - max_queue=max_queue, - flush_millis=flush_secs * 1000, - filename_suffix=filename_suffix) - with summary_ops_v2.always_record_summaries(), file_writer.as_default(): - self._event_placeholder = array_ops.placeholder_with_default( - constant_op.constant('unused', dtypes.string), - shape=[]) - self._add_event_op = summary_ops_v2.import_event( - self._event_placeholder) - self._init_op = file_writer.init() - self._flush_op = file_writer.flush() - self._close_op = file_writer.close() - self._session.run(self._init_op) + with ops.name_scope('filewriter'): + file_writer = summary_ops_v2.create_file_writer( + logdir=self._logdir, + max_queue=max_queue, + flush_millis=flush_secs * 1000, + filename_suffix=filename_suffix) + with summary_ops_v2.always_record_summaries(), file_writer.as_default(): + self._event_placeholder = array_ops.placeholder_with_default( + constant_op.constant('unused', dtypes.string), + shape=[]) + self._add_event_op = summary_ops_v2.import_event( + self._event_placeholder) + self._init_op = file_writer.init() + self._flush_op = file_writer.flush() + self._close_op = file_writer.close() + + def _init_if_needed(self): + if not self._initialized: + self._session().run(self._init_op) + self._initialized = True def get_logdir(self): """Returns the directory where event file will be written.""" @@ -108,7 +121,6 @@ class EventFileWriterV2(object): """ if self._closed: self._closed = False - self._session.run(self._init_op) def add_event(self, event): """Adds an event to the event file. @@ -117,8 +129,9 @@ class EventFileWriterV2(object): event: An `Event` protocol buffer. """ if not self._closed: + self._init_if_needed() event_pb = event.SerializeToString() - self._session.run( + self._session().run( self._add_event_op, feed_dict={self._event_placeholder: event_pb}) def flush(self): @@ -127,7 +140,9 @@ class EventFileWriterV2(object): Call this method to make sure that all pending events have been written to disk. """ - self._session.run(self._flush_op) + if not self._closed: + self._init_if_needed() + self._session().run(self._flush_op) def close(self): """Flushes the event file to disk and close the file. @@ -135,6 +150,8 @@ class EventFileWriterV2(object): Call this method when you do not need the summary writer anymore. """ if not self._closed: + self._init_if_needed() self.flush() - self._session.run(self._close_op) + self._session().run(self._close_op) self._closed = True + self._initialized = False diff --git a/tensorflow/python/summary/writer/writer.py b/tensorflow/python/summary/writer/writer.py index aca084fc91..2a967ae3a5 100644 --- a/tensorflow/python/summary/writer/writer.py +++ b/tensorflow/python/summary/writer/writer.py @@ -332,8 +332,11 @@ class FileWriter(SummaryToEventTransformer): the same shared resource name (which by default scoped to the logdir). If no such resource exists, one will be created using the remaining arguments to this constructor, but if one already exists those arguments are ignored. - In either case, ops will be added to `session.graph` to control the + In either case, ops will be added to the default graph to control the underlying file writer resource. See `tf.contrib.summary` for more details. + Instead of an actual `tf.Session`, this argument may also be a callable that + provides a `tf.Session` when invoked (e.g. `tf.get_default_session`), which + will be called on-demand when a session is needed. Args: logdir: A string. Directory where event file will be written. @@ -344,7 +347,8 @@ class FileWriter(SummaryToEventTransformer): graph_def: DEPRECATED: Use the `graph` argument instead. filename_suffix: A string. Every event file's name is suffixed with `suffix`. - session: A `tf.Session` object. See details above. + session: A `tf.Session` object or a callable that provides `tf.Session` + objects. See details above. Raises: RuntimeError: If called with eager execution enabled. diff --git a/tensorflow/python/summary/writer/writer_test.py b/tensorflow/python/summary/writer/writer_test.py index dc990c2602..3380dea317 100644 --- a/tensorflow/python/summary/writer/writer_test.py +++ b/tensorflow/python/summary/writer/writer_test.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for training_coordinator.py.""" +"""Tests for writer.py.""" from __future__ import absolute_import from __future__ import division @@ -574,6 +574,58 @@ class SessionBasedFileWriterTestCase(FileWriterTestCase): # No more files self.assertRaises(StopIteration, lambda: next(event_paths)) + def testSesssionArgument_callableProvider(self): + logdir = self.get_temp_dir() + setup_writer = summary_ops_v2.create_file_writer(logdir=logdir) + with summary_ops_v2.always_record_summaries(), setup_writer.as_default(): + summary1 = summary_ops_v2.scalar("one", 0.0, step=0) + summary2 = summary_ops_v2.scalar("two", 0.0, step=0) + sess1 = session.Session() + sess1.run(setup_writer.init()) + sess1.run(summary1) + sess1.run(setup_writer.flush()) + time.sleep(1.1) # Ensure filename has a different timestamp + sess2 = session.Session() + sess2.run(setup_writer.init()) + sess2.run(summary2) + sess2.run(setup_writer.flush()) + + # Using get_default_session as session provider should make this FileWriter + # send its summaries to the current default session's shared summary writer + # resource (initializing it as needed). + test_writer = writer.FileWriter( + session=ops.get_default_session, logdir=logdir) + with sess1.as_default(): + test_writer.add_summary(self._createTaggedSummary("won"), 1) + test_writer.flush() + with sess2.as_default(): + test_writer.add_summary(self._createTaggedSummary("too"), 1) + test_writer.flush() + + event_paths = iter(sorted(glob.glob(os.path.join(logdir, "event*")))) + + # First file should have tags "one", "won" + events = summary_iterator.summary_iterator(next(event_paths)) + self.assertEqual("brain.Event:2", next(events).file_version) + self.assertEqual("one", next(events).summary.value[0].tag) + self.assertEqual("won", next(events).summary.value[0].tag) + self.assertRaises(StopIteration, lambda: next(events)) + + # Second file should have tags "two", "too" + events = summary_iterator.summary_iterator(next(event_paths)) + self.assertEqual("brain.Event:2", next(events).file_version) + self.assertEqual("two", next(events).summary.value[0].tag) + self.assertEqual("too", next(events).summary.value[0].tag) + self.assertRaises(StopIteration, lambda: next(events)) + + # No more files + self.assertRaises(StopIteration, lambda: next(event_paths)) + + def testSessionArgument_notSessionOrCallable(self): + logdir = self.get_temp_dir() + self.assertRaises( + ValueError, lambda: writer.FileWriter(session=[], logdir=logdir)) + class FileWriterCacheTest(test.TestCase): """FileWriterCache tests.""" diff --git a/tensorflow/python/training/basic_session_run_hooks.py b/tensorflow/python/training/basic_session_run_hooks.py index b0dd188db1..b8df7fe51b 100644 --- a/tensorflow/python/training/basic_session_run_hooks.py +++ b/tensorflow/python/training/basic_session_run_hooks.py @@ -31,12 +31,13 @@ from tensorflow.python.client import timeline from tensorflow.python.framework import errors from tensorflow.python.framework import meta_graph from tensorflow.python.framework import ops +from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.summary.writer import writer from tensorflow.python.training import session_run_hook from tensorflow.python.training import training_util from tensorflow.python.training.session_run_hook import SessionRunArgs -from tensorflow.python.training.summary_io import SummaryWriterCache from tensorflow.python.util.tf_export import tf_export @@ -422,7 +423,9 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): self._steps_per_run = steps_per_run def begin(self): - self._summary_writer = SummaryWriterCache.get(self._checkpoint_dir) + self._summary_writer = writer.FileWriter( + self._checkpoint_dir, session=ops.get_default_session, + filename_suffix="") self._global_step_tensor = training_util._get_or_create_global_step_read() # pylint: disable=protected-access if self._global_step_tensor is None: raise RuntimeError( @@ -431,10 +434,12 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): l.begin() def after_create_session(self, session, coord): + del coord + # Ensure summary writer resource has been initialized. + session.run(summary_ops_v2.summary_writer_initializer_op()) global_step = session.run(self._global_step_tensor) - # We do write graph and saver_def at the first call of before_run. - # We cannot do this in begin, since we let other hooks to change graph and - # add variables in begin. Graph is finalized after all begin calls. + # Write graph and saver_def once graph is finalized, which isn't true yet + # in begin() since later hooks can still change the graph. training_util.write_graph( ops.get_default_graph().as_graph_def(add_shapes=True), self._checkpoint_dir, @@ -444,8 +449,9 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): meta_graph_def = meta_graph.create_meta_graph_def( graph_def=graph.as_graph_def(add_shapes=True), saver_def=saver_def) - self._summary_writer.add_graph(graph) - self._summary_writer.add_meta_graph(meta_graph_def) + with ops.default_session(session): + self._summary_writer.add_graph(graph) + self._summary_writer.add_meta_graph(meta_graph_def) # The checkpoint saved here is the state at step "global_step". self._save(session, global_step) self._timer.update_last_triggered_step(global_step) @@ -470,6 +476,8 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): self._save(session, last_step) for l in self._listeners: l.end(session, last_step) + with ops.default_session(session): + self._summary_writer.flush() def _save(self, session, step): """Saves the latest checkpoint, returns should_stop.""" @@ -479,10 +487,12 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): l.before_save(session, step) self._get_saver().save(session, self._save_path, global_step=step) - self._summary_writer.add_session_log( - SessionLog( - status=SessionLog.CHECKPOINT, checkpoint_path=self._save_path), - step) + with ops.default_session(session): + self._summary_writer.add_session_log( + SessionLog( + status=SessionLog.CHECKPOINT, checkpoint_path=self._save_path), + step) + self._summary_writer.flush() should_stop = False for l in self._listeners: @@ -543,13 +553,23 @@ class StepCounterHook(session_run_hook.SessionRunHook): def begin(self): if self._summary_writer is None and self._output_dir: - self._summary_writer = SummaryWriterCache.get(self._output_dir) + self._summary_writer = writer.FileWriter( + self._output_dir, session=ops.get_default_session, + filename_suffix="") self._global_step_tensor = training_util._get_or_create_global_step_read() # pylint: disable=protected-access if self._global_step_tensor is None: raise RuntimeError( "Global step should be created to use StepCounterHook.") self._summary_tag = training_util.get_global_step().op.name + "/sec" + def after_create_session(self, session, coord): + del coord + # Reset any stale state in case we're recovering from a previous error. + session.run(summary_ops_v2.summary_writer_initializer_op()) + self._last_global_step = None + self._global_step_check_count = 0 + self._timer.reset() + def before_run(self, run_context): # pylint: disable=unused-argument return SessionRunArgs(self._global_step_tensor) @@ -562,8 +582,6 @@ class StepCounterHook(session_run_hook.SessionRunHook): logging.info("%s: %g", self._summary_tag, steps_per_sec) def after_run(self, run_context, run_values): - _ = run_context - stale_global_step = run_values.results if self._timer.should_trigger_for_step( stale_global_step + self._steps_per_run): @@ -573,7 +591,8 @@ class StepCounterHook(session_run_hook.SessionRunHook): elapsed_time, elapsed_steps = self._timer.update_last_triggered_step( global_step) if elapsed_time is not None: - self._log_and_record(elapsed_steps, elapsed_time, global_step) + with ops.default_session(run_context.session): + self._log_and_record(elapsed_steps, elapsed_time, global_step) # Check whether the global step has been increased. Here, we do not use the # timer.last_triggered_step as the timer might record a different global @@ -599,6 +618,11 @@ class StepCounterHook(session_run_hook.SessionRunHook): self._last_global_step = stale_global_step + def end(self, session): + if self._summary_writer is not None: + with ops.default_session(session): + self._summary_writer.flush() + @tf_export("train.NanLossDuringTrainingError") class NanLossDuringTrainingError(RuntimeError): @@ -643,6 +667,25 @@ class NanTensorHook(session_run_hook.SessionRunHook): class SummarySaverHook(session_run_hook.SessionRunHook): """Saves summaries every N steps.""" + _SUMMARY_PLACEHOLDER_COLLECTION = "_SUMMARY_SAVER_PLACEHOLDER" + + @classmethod + def _set_placeholder(cls, placeholder): + """Sets a `tf.placeholder` to be fed by the first SummarySaverHook. + + If a placeholder is provided, the first instance of SummarySaverHook in use + will feed it a boolean indicating whether summaries should be written, + according to the `save_steps` and `save_secs` parameters of that hook. This + makes the placeholder usable with `tf.contrib.summary.record_summaries_if` + to control `tf.contrib.summary` summary writing using the same schedule as + the `tf.summary` summary writing (which the hook controls directly). + + Args: + placeholder: `tf.placeholder` for the first SummarySaverHook to feed + """ + collection = ops.get_collection_ref(cls._SUMMARY_PLACEHOLDER_COLLECTION) + collection[:] = [placeholder] + def __init__(self, save_steps=None, save_secs=None, @@ -680,53 +723,82 @@ class SummarySaverHook(session_run_hook.SessionRunHook): self._scaffold = scaffold self._timer = SecondOrStepTimer(every_secs=save_secs, every_steps=save_steps) + self._placeholder = None # TODO(mdan): Throw an error if output_dir and summary_writer are None. def begin(self): if self._summary_writer is None and self._output_dir: - self._summary_writer = SummaryWriterCache.get(self._output_dir) - self._next_step = None - self._global_step_tensor = training_util._get_or_create_global_step_read() # pylint: disable=protected-access + self._summary_writer = writer.FileWriter( + self._output_dir, filename_suffix="", session=ops.get_default_session) + # Designate the first SummarySaverHook to call begin() as the "primary" + # hook; it will control writing of v2 summaries via a placeholder bool. + collection = ops.get_collection_ref(self._SUMMARY_PLACEHOLDER_COLLECTION) + if collection: + self._placeholder = collection[0] + collection[:] = [] + self._current_step = None + self._global_step_tensor = training_util.get_or_create_global_step() if self._global_step_tensor is None: raise RuntimeError( "Global step should be created to use SummarySaverHook.") - def before_run(self, run_context): # pylint: disable=unused-argument - self._request_summary = ( - self._next_step is None or - self._timer.should_trigger_for_step(self._next_step)) + def after_create_session(self, session, coord): + del coord + # Reset any stale state in case we're recovering from a previous error. + session.run(summary_ops_v2.summary_writer_initializer_op()) + self._current_step = None + self._timer.reset() + + def before_run(self, run_context): + # For the first run, record a SessionLog.START at the pre-run global step. + if self._current_step is None: + self._current_step = run_context.session.run(self._global_step_tensor) + with ops.default_session(run_context.session): + self._summary_writer.add_session_log( + SessionLog(status=SessionLog.START), self._current_step) requests = {"global_step": self._global_step_tensor} + self._request_summary = self._timer.should_trigger_for_step( + self._current_step) if self._request_summary: + self._timer.update_last_triggered_step(self._current_step) if self._get_summary_op() is not None: requests["summary"] = self._get_summary_op() - - return SessionRunArgs(requests) + feeds = {} + if self._placeholder is not None and self._request_summary: + feeds[self._placeholder] = self._request_summary + args = SessionRunArgs(fetches=requests, feed_dict=feeds) + return args def after_run(self, run_context, run_values): - _ = run_context - if not self._summary_writer: - return - + # Collect any legacy v1 summaries to emit. + summaries_to_emit = [] + if self._summary_writer and self._request_summary: + for summary in run_values.results.get("summary", []): + # Skip None results corresponding to V2 summary operations. + if summary is not None: + summaries_to_emit.append(summary) + # Heuristically estimate current step as possibly-stale value plus one. stale_global_step = run_values.results["global_step"] - global_step = stale_global_step + 1 - if self._next_step is None or self._request_summary: - global_step = run_context.session.run(self._global_step_tensor) - - if self._next_step is None: - self._summary_writer.add_session_log( - SessionLog(status=SessionLog.START), global_step) - - if self._request_summary: - self._timer.update_last_triggered_step(global_step) - if "summary" in run_values.results: - for summary in run_values.results["summary"]: - self._summary_writer.add_summary(summary, global_step) - - self._next_step = global_step + 1 + self._current_step = stale_global_step + 1 + # Read the actual post-run global step if we need better accuracy because + # 1) we will request summaries on the next run (based on estimate now) and + # must ensure we record an accurate "last triggered step" value, or + # 2) we have legacy v1 summaries to emit using the post-run step value. + # Note: we could have dealt with (1) separately in before_run() but by doing + # it here we can consolidate the reads in case both (1) and (2) apply. + near_next_trigger = self._timer.should_trigger_for_step(self._current_step) + if near_next_trigger or summaries_to_emit: + self._current_step = run_context.session.run(self._global_step_tensor) + # Emit any legacy v1 summaries. + if summaries_to_emit: + with ops.default_session(run_context.session): + for summary in summaries_to_emit: + self._summary_writer.add_summary(summary, self._current_step) def end(self, session=None): - if self._summary_writer: - self._summary_writer.flush() + if self._summary_writer and session: + with ops.default_session(session): + self._summary_writer.flush() def _get_summary_op(self): """Fetches the summary op either from self._summary_op or self._scaffold. @@ -893,19 +965,27 @@ class ProfilerHook(session_run_hook.SessionRunHook): show_memory: `bool`, if True, add object snapshot events to the trace showing the sizes and lifetimes of tensors. """ + self._output_dir = output_dir self._output_file = os.path.join(output_dir, "timeline-{}.json") - self._file_writer = SummaryWriterCache.get(output_dir) self._show_dataflow = show_dataflow self._show_memory = show_memory self._timer = SecondOrStepTimer( every_secs=save_secs, every_steps=save_steps) def begin(self): + self._file_writer = writer.FileWriter( + self._output_dir, filename_suffix="", session=ops.get_default_session) self._next_step = None self._global_step_tensor = training_util._get_or_create_global_step_read() # pylint: disable=protected-access if self._global_step_tensor is None: raise RuntimeError("Global step should be created to use ProfilerHook.") + def after_create_session(self, session, coord): + del coord + # Reset any stale state in case we're recovering from a previous error. + session.run(summary_ops_v2.summary_writer_initializer_op()) + self._timer.reset() + def before_run(self, run_context): self._request_summary = ( self._next_step is None or @@ -925,8 +1005,10 @@ class ProfilerHook(session_run_hook.SessionRunHook): self._save(global_step, self._output_file.format(global_step), run_values.run_metadata.step_stats) - self._file_writer.add_run_metadata(run_values.run_metadata, - "step_%d" % global_step) + with ops.default_session(run_context.session): + self._file_writer.add_run_metadata(run_values.run_metadata, + "step_%d" % global_step, + global_step=global_step) self._next_step = global_step + 1 @@ -938,6 +1020,10 @@ class ProfilerHook(session_run_hook.SessionRunHook): trace.generate_chrome_trace_format( show_dataflow=self._show_dataflow, show_memory=self._show_memory)) + def end(self, session): + with ops.default_session(session): + self._file_writer.flush() + def _as_graph_element(obj): """Retrieves Graph element.""" diff --git a/tensorflow/python/training/basic_session_run_hooks_test.py b/tensorflow/python/training/basic_session_run_hooks_test.py index b49a871a56..b89167f3c1 100644 --- a/tensorflow/python/training/basic_session_run_hooks_test.py +++ b/tensorflow/python/training/basic_session_run_hooks_test.py @@ -19,8 +19,10 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import glob import os.path import shutil +import sys import tempfile import threading import time @@ -28,6 +30,9 @@ import time from tensorflow.contrib.framework.python.framework import checkpoint_utils from tensorflow.contrib.framework.python.ops import variables from tensorflow.contrib.testing.python.framework import fake_summary_writer +from tensorflow.core.framework import graph_pb2 +from tensorflow.core.protobuf import meta_graph_pb2 +from tensorflow.core.util.event_pb2 import SessionLog from tensorflow.python.client import session as session_lib from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op @@ -35,9 +40,12 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import meta_graph 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 control_flow_ops +from tensorflow.python.ops import init_ops from tensorflow.python.ops import state_ops +from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables as variables_lib import tensorflow.python.ops.nn_grad # pylint: disable=unused-import @@ -45,13 +53,27 @@ from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging from tensorflow.python.summary import summary as summary_lib +from tensorflow.python.summary import summary_iterator from tensorflow.python.summary.writer import writer_cache from tensorflow.python.training import basic_session_run_hooks from tensorflow.python.training import monitored_session +from tensorflow.python.training import saver from tensorflow.python.training import session_run_hook from tensorflow.python.training import training_util +def load_eventfile_contents(directory_path): + """Returns the contents of the singular event file in the given directory.""" + writer_cache.FileWriterCache.clear() + + # Get last Event written. + event_paths = glob.glob(os.path.join(directory_path, '*tfevent*')) + if len(event_paths) != 1: + raise AssertionError('Expected one eventfile, got %s' % str(event_paths)) + result = list(summary_iterator.summary_iterator(event_paths[0])) + return result + + class MockCheckpointSaverListener( basic_session_run_hooks.CheckpointSaverListener): @@ -717,11 +739,12 @@ class CheckpointSaverHookTest(test.TestCase): checkpoint_utils.load_variable(self.model_dir, self.global_step.name)) - def test_summary_writer_defs(self): - fake_summary_writer.FakeSummaryWriter.install() - writer_cache.FileWriterCache.clear() - summary_writer = writer_cache.FileWriterCache.get(self.model_dir) + def _assertCheckpointEvent(self, event, step, checkpoint_path): + self.assertEqual(step, event.step) + self.assertEqual(SessionLog.CHECKPOINT, event.session_log.status) + self.assertEqual(checkpoint_path, event.session_log.checkpoint_path) + def test_summary_writer_defs(self): with self.graph.as_default(): hook = basic_session_run_hooks.CheckpointSaverHook( self.model_dir, save_steps=2, scaffold=self.scaffold) @@ -730,18 +753,40 @@ class CheckpointSaverHookTest(test.TestCase): with session_lib.Session() as sess: sess.run(self.scaffold.init_op) mon_sess = monitored_session._HookedSession(sess, [hook]) - hook.after_create_session(sess, None) - mon_sess.run(self.train_op) - summary_writer.assert_summaries( - test_case=self, - expected_logdir=self.model_dir, - expected_added_meta_graphs=[ - meta_graph.create_meta_graph_def( - graph_def=self.graph.as_graph_def(add_shapes=True), - saver_def=self.scaffold.saver.saver_def) - ]) - - fake_summary_writer.FakeSummaryWriter.uninstall() + hook.after_create_session(sess, None) # Checkpoint saved at step 0. + expected_graph_def = self.graph.as_graph_def(add_shapes=True) + expected_meta_graph_def = meta_graph.create_meta_graph_def( + graph_def=expected_graph_def, + saver_def=self.scaffold.saver.saver_def) + mon_sess.run(self.train_op) # No checkpoint saved at step 1. + mon_sess.run(self.train_op) # Checkpoint saved at step 2. + mon_sess.run(self.train_op) # No checkpoint saved at step 3. + hook.end(sess) # Checkpoint saved at the last step (3) + events = iter(load_eventfile_contents(self.model_dir)) + next(events) # Skip version event that's always there. + + # Graph. + event = next(events) + self.assertEqual(0, event.step) + actual_graph_def = graph_pb2.GraphDef() + actual_graph_def.ParseFromString(event.graph_def) + test_util.assert_equal_graph_def(actual_graph_def, expected_graph_def) + + # Metagraph. + event = next(events) + self.assertEqual(0, event.step) + actual_meta_graph_def = meta_graph_pb2.MetaGraphDef() + actual_meta_graph_def.ParseFromString(event.meta_graph_def) + test_util.assert_meta_graph_protos_equal( + self, expected_meta_graph_def, actual_meta_graph_def) + + # Checkpoints. + # Strip the "-step#" suffix off the latest checkpoint to get base path. + checkpoint_path = saver.latest_checkpoint(self.model_dir).rsplit('-', 1)[0] + self._assertCheckpointEvent(next(events), 0, checkpoint_path) + self._assertCheckpointEvent(next(events), 2, checkpoint_path) + self._assertCheckpointEvent(next(events), 3, checkpoint_path) + self.assertRaises(StopIteration, lambda: next(events)) # No more events. def test_save_checkpoint_before_first_train_step(self): with self.graph.as_default(): @@ -1102,167 +1147,305 @@ class StepCounterHookTest(test.TestCase): self.assertEqual('global_step/sec', summary_value.tag) self.assertGreater(summary_value.simple_value, 0) + def test_summary_writer(self): + with ops.Graph().as_default(), session_lib.Session() as sess: + variables.get_or_create_global_step() + train_op = training_util._increment_global_step(1) + hook = basic_session_run_hooks.StepCounterHook( + output_dir=self.log_dir, every_n_steps=10) + hook.begin() + sess.run(variables_lib.global_variables_initializer()) + mon_sess = monitored_session._HookedSession(sess, [hook]) + for _ in range(30): + mon_sess.run(train_op) + hook.end(sess) + events = iter(load_eventfile_contents(self.log_dir)) + next(events) # Skip version event that's always there. + + event = next(events) + self.assertEqual(11, event.step) + self.assertEqual('global_step/sec', event.summary.value[0].tag) + self.assertLess(0, event.summary.value[0].simple_value) -class SummarySaverHookTest(test.TestCase): + event = next(events) + self.assertEqual(21, event.step) + self.assertEqual('global_step/sec', event.summary.value[0].tag) + self.assertLess(0, event.summary.value[0].simple_value) - def setUp(self): - test.TestCase.setUp(self) + self.assertRaises(StopIteration, lambda: next(events)) # No more events. - self.log_dir = 'log/dir' - self.summary_writer = fake_summary_writer.FakeSummaryWriter(self.log_dir) - var = variables_lib.Variable(0.0) - tensor = state_ops.assign_add(var, 1.0) - tensor2 = tensor * 2 - self.summary_op = summary_lib.scalar('my_summary', tensor) - self.summary_op2 = summary_lib.scalar('my_summary2', tensor2) +class SummarySaverHookTest(test.TestCase): - variables.get_or_create_global_step() - self.train_op = training_util._increment_global_step(1) + def setUp(self): + test.TestCase.setUp(self) + self.logdir = self.get_temp_dir() + self._create_stable_global_step() + + def _create_stable_global_step(self): + """Returns a new ResourceVariable global_step for deterministic tests.""" + # TODO(nickfelt): remove after standard global_step is a ResourceVariable. + with ops.get_default_graph().name_scope(None): + return variable_scope.get_variable( + ops.GraphKeys.GLOBAL_STEP, + shape=[], + dtype=dtypes.int64, + initializer=init_ops.zeros_initializer(), + trainable=False, + collections=[ + ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP + ], + # Use a ResourceVariable and set caching_device to make the read + # behavior deterministic and well-defined. + caching_device='cpu:0', + use_resource=True) def test_raise_when_scaffold_and_summary_op_both_missing(self): with self.assertRaises(ValueError): basic_session_run_hooks.SummarySaverHook() def test_raise_when_scaffold_and_summary_op_both_present(self): + summary_op = summary_lib.merge_all() with self.assertRaises(ValueError): basic_session_run_hooks.SummarySaverHook( - scaffold=monitored_session.Scaffold(), summary_op=self.summary_op) + scaffold=monitored_session.Scaffold(), summary_op=summary_op) - def test_raise_in_both_secs_and_steps(self): + def test_raise_when_secs_and_steps_both_missing(self): with self.assertRaises(ValueError): basic_session_run_hooks.SummarySaverHook( - save_secs=10, save_steps=20, summary_writer=self.summary_writer) + save_secs=None, save_steps=None, output_dir=self.logdir) - def test_raise_in_none_secs_and_steps(self): + def test_raise_when_secs_and_steps_both_present(self): with self.assertRaises(ValueError): basic_session_run_hooks.SummarySaverHook( - save_secs=None, save_steps=None, summary_writer=self.summary_writer) + save_secs=10, save_steps=20, output_dir=self.logdir) - def test_save_steps(self): - hook = basic_session_run_hooks.SummarySaverHook( - save_steps=8, - summary_writer=self.summary_writer, - summary_op=self.summary_op) + def _makeHook(self, **kwargs): + kwargs['output_dir'] = self.logdir + kwargs['scaffold'] = monitored_session.Scaffold() + return basic_session_run_hooks.SummarySaverHook(**kwargs) + def _runForSteps(self, hook, steps, loop_body_fn=None): + train_op = training_util.get_global_step().assign_add(1) with self.test_session() as sess: hook.begin() sess.run(variables_lib.global_variables_initializer()) + scaffold = hook._scaffold # pylint: disable=protected-access + if scaffold is not None: + scaffold.finalize() + sess.run(scaffold.init_op) mon_sess = monitored_session._HookedSession(sess, [hook]) - for _ in range(30): - mon_sess.run(self.train_op) + for _ in range(steps): + mon_sess.run(train_op) + if loop_body_fn is not None: + loop_body_fn() hook.end(sess) - self.summary_writer.assert_summaries( - test_case=self, - expected_logdir=self.log_dir, - expected_summaries={ - 1: { - 'my_summary': 1.0 - }, - 9: { - 'my_summary': 2.0 - }, - 17: { - 'my_summary': 3.0 - }, - 25: { - 'my_summary': 4.0 - }, - }) + def _assertSessionEvent(self, event, step, session_status): + self.assertEqual(step, event.step) + self.assertEqual(session_status, event.session_log.status) + + def _assertSummaryEvent(self, event, step, tag_value_list): + self.assertEqual(step, event.step) + tag_value_actual_list = [ + (value.tag, value.simple_value) for value in event.summary.value + ] + self.assertItemsEqual(tag_value_list, tag_value_actual_list) + + def test_no_summaries(self): + hook = self._makeHook(save_steps=1) + self._runForSteps(hook, 3) + events = iter(load_eventfile_contents(self.logdir)) + next(events) # Skip version event that's always there. + self._assertSessionEvent(next(events), 0, SessionLog.START) + self.assertRaises(StopIteration, lambda: next(events)) + + def test_basic_summaries(self): + summary_lib.scalar('foo-v1', 1.0) + with summary_ops_v2.create_file_writer(self.logdir).as_default(): + with summary_ops_v2.always_record_summaries(): + summary_ops_v2.scalar('foo-v2', 2.0) + hook = self._makeHook(save_steps=1) + self._runForSteps(hook, 3) + events = iter(load_eventfile_contents(self.logdir)) + next(events) # Skip version event that's always there. + self._assertSessionEvent(next(events), 0, SessionLog.START) + + self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 1, [('foo-v1', 1.0)]) + + self._assertSummaryEvent(next(events), 1, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 2, [('foo-v1', 1.0)]) + + self._assertSummaryEvent(next(events), 2, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 3, [('foo-v1', 1.0)]) + self.assertRaises(StopIteration, lambda: next(events)) def test_multiple_summaries(self): - hook = basic_session_run_hooks.SummarySaverHook( - save_steps=8, - summary_writer=self.summary_writer, - summary_op=[self.summary_op, self.summary_op2]) - + summary_lib.scalar('foo-v1', 1.0) + summary_lib.scalar('bar-v1', 10.0) + with summary_ops_v2.create_file_writer(self.logdir).as_default(): + with summary_ops_v2.always_record_summaries(): + foo = summary_ops_v2.scalar('foo-v2', 2.0) + # Ensure deterministic write order + with ops.control_dependencies([foo]): + summary_ops_v2.scalar('bar-v2', 20.0) + hook = self._makeHook(save_steps=1) + self._runForSteps(hook, 1) + events = iter(load_eventfile_contents(self.logdir)) + next(events) # Skip version event that's always there. + self._assertSessionEvent(next(events), 0, SessionLog.START) + self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 0, [('bar-v2', 20.0)]) + self._assertSummaryEvent( + next(events), 1, [('foo-v1', 1.0), ('bar-v1', 10.0)]) + self.assertRaises(StopIteration, lambda: next(events)) + + def test_v2_summaries_only(self): + with summary_ops_v2.create_file_writer(self.logdir).as_default(): + with summary_ops_v2.always_record_summaries(): + summary_ops_v2.scalar('foo-v2', 2.0) + hook = self._makeHook(save_steps=1) + self._runForSteps(hook, 1) + events = iter(load_eventfile_contents(self.logdir)) + next(events) # Skip version event that's always there. + self._assertSessionEvent(next(events), 0, SessionLog.START) + self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) + self.assertRaises(StopIteration, lambda: next(events)) + + def test_v2_summaries_custom_file_writer(self): + other_dir = os.path.join(self.logdir, 'other') + other_writer = summary_ops_v2.create_file_writer(other_dir) + # SummarySaverHook only flushes the writer for logdir; this one needs to be + # manually flushed. + flush_op = other_writer.flush() + with summary_ops_v2.always_record_summaries(): + with summary_ops_v2.create_file_writer(self.logdir).as_default(): + summary_ops_v2.scalar('foo-v2', 2.0) + with other_writer.as_default(): + summary_ops_v2.scalar('other-v2', 3.0) + hook = self._makeHook(save_steps=1) + self._runForSteps(hook, 1) with self.test_session() as sess: - hook.begin() - sess.run(variables_lib.global_variables_initializer()) - mon_sess = monitored_session._HookedSession(sess, [hook]) - for _ in range(10): - mon_sess.run(self.train_op) - hook.end(sess) + sess.run(flush_op) - self.summary_writer.assert_summaries( - test_case=self, - expected_logdir=self.log_dir, - expected_summaries={ - 1: { - 'my_summary': 1.0, - 'my_summary2': 2.0 - }, - 9: { - 'my_summary': 2.0, - 'my_summary2': 4.0 - }, - }) + events = iter(load_eventfile_contents(self.logdir)) + next(events) # Skip version event that's always there. + self._assertSessionEvent(next(events), 0, SessionLog.START) + self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) + self.assertRaises(StopIteration, lambda: next(events)) - def test_save_secs_saving_once_every_step(self): - hook = basic_session_run_hooks.SummarySaverHook( - save_secs=0.5, - summary_writer=self.summary_writer, - summary_op=self.summary_op) + events = iter(load_eventfile_contents(other_dir)) + next(events) # Skip version event that's always there. + self._assertSummaryEvent(next(events), 0, [('other-v2', 3.0)]) + self.assertRaises(StopIteration, lambda: next(events)) - with self.test_session() as sess: - hook.begin() - sess.run(variables_lib.global_variables_initializer()) - mon_sess = monitored_session._HookedSession(sess, [hook]) - for _ in range(4): - mon_sess.run(self.train_op) - time.sleep(0.5) - hook.end(sess) + def test_save_steps(self): + summary_lib.scalar('foo-v1', 1.0) + placeholder = array_ops.placeholder_with_default(False, shape=[]) + with summary_ops_v2.create_file_writer(self.logdir).as_default(): + with summary_ops_v2.record_summaries_if(placeholder): + summary_ops_v2.scalar('foo-v2', 2.0) - self.summary_writer.assert_summaries( - test_case=self, - expected_logdir=self.log_dir, - expected_summaries={ - 1: { - 'my_summary': 1.0 - }, - 2: { - 'my_summary': 2.0 - }, - 3: { - 'my_summary': 3.0 - }, - 4: { - 'my_summary': 4.0 - }, - }) + basic_session_run_hooks.SummarySaverHook._set_placeholder(placeholder) + hook = self._makeHook(save_steps=8) + self._runForSteps(hook, 30) + + events = load_eventfile_contents(self.logdir) + print('TEST SAVE STEPS EVENTS', str(events), file=sys.stderr) + events = iter(events) + next(events) # Skip version event that's always there. + self._assertSessionEvent(next(events), 0, SessionLog.START) + + self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 1, [('foo-v1', 1.0)]) + + self._assertSummaryEvent(next(events), 8, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 9, [('foo-v1', 1.0)]) + + self._assertSummaryEvent(next(events), 16, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 17, [('foo-v1', 1.0)]) + + self._assertSummaryEvent(next(events), 24, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 25, [('foo-v1', 1.0)]) + self.assertRaises(StopIteration, lambda: next(events)) @test.mock.patch.object(time, 'time') - def test_save_secs_saving_once_every_three_steps(self, mock_time): - mock_time.return_value = 1484695987.209386 - hook = basic_session_run_hooks.SummarySaverHook( - save_secs=9., - summary_writer=self.summary_writer, - summary_op=self.summary_op) + def test_save_secs_saving_once_every_step(self, mock_time): + mock_time.return_value = 1000.0 + summary_lib.scalar('foo-v1', 1.0) + placeholder = array_ops.placeholder_with_default(False, shape=[]) + with summary_ops_v2.create_file_writer(self.logdir).as_default(): + with summary_ops_v2.record_summaries_if(placeholder): + summary_ops_v2.scalar('foo-v2', 2.0) - with self.test_session() as sess: - hook.begin() - sess.run(variables_lib.global_variables_initializer()) - mon_sess = monitored_session._HookedSession(sess, [hook]) - for _ in range(8): - mon_sess.run(self.train_op) - mock_time.return_value += 3.1 - hook.end(sess) + basic_session_run_hooks.SummarySaverHook._set_placeholder(placeholder) + hook = self._makeHook(save_secs=0.5) + def fake_sleep(): + mock_time.return_value += 0.5 + self._runForSteps(hook, 4, fake_sleep) + + events = iter(load_eventfile_contents(self.logdir)) + next(events) # Skip version event that's always there. + self._assertSessionEvent(next(events), 0, SessionLog.START) + + self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 1, [('foo-v1', 1.0)]) + + self._assertSummaryEvent(next(events), 1, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 2, [('foo-v1', 1.0)]) + + self._assertSummaryEvent(next(events), 2, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 3, [('foo-v1', 1.0)]) + + self._assertSummaryEvent(next(events), 3, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 4, [('foo-v1', 1.0)]) + self.assertRaises(StopIteration, lambda: next(events)) + + @test.mock.patch.object(time, 'time') + def test_save_secs_saving_once_every_three_steps(self, mock_time): + mock_time.return_value = 1000.0 + summary_lib.scalar('foo-v1', 1.0) + placeholder = array_ops.placeholder_with_default(False, shape=[]) + with summary_ops_v2.create_file_writer(self.logdir).as_default(): + with summary_ops_v2.record_summaries_if(placeholder): + summary_ops_v2.scalar('foo-v2', 2.0) + + basic_session_run_hooks.SummarySaverHook._set_placeholder(placeholder) + hook = self._makeHook(save_secs=9) + def fake_sleep(): + mock_time.return_value += 3.1 + self._runForSteps(hook, 8, fake_sleep) + + events = iter(load_eventfile_contents(self.logdir)) + next(events) # Skip version event that's always there. + self._assertSessionEvent(next(events), 0, SessionLog.START) # 24.8 seconds passed (3.1*8), it saves every 9 seconds starting from first: - self.summary_writer.assert_summaries( + self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 1, [('foo-v1', 1.0)]) + + self._assertSummaryEvent(next(events), 3, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 4, [('foo-v1', 1.0)]) + + self._assertSummaryEvent(next(events), 6, [('foo-v2', 2.0)]) + self._assertSummaryEvent(next(events), 7, [('foo-v1', 1.0)]) + self.assertRaises(StopIteration, lambda: next(events)) + + def test_explicit_summary_writer_and_op(self): + summary_writer = fake_summary_writer.FakeSummaryWriter(self.logdir) + hook = basic_session_run_hooks.SummarySaverHook( + save_steps=1, + summary_writer=summary_writer, + summary_op=summary_lib.scalar('foo-v1', 1.0)) + self._runForSteps(hook, 3) + summary_writer.assert_summaries( test_case=self, - expected_logdir=self.log_dir, + expected_logdir=self.logdir, expected_summaries={ - 1: { - 'my_summary': 1.0 - }, - 4: { - 'my_summary': 2.0 - }, - 7: { - 'my_summary': 3.0 - }, + 1: {'foo-v1': 1.0}, + 2: {'foo-v1': 1.0}, + 3: {'foo-v1': 1.0}, }) @@ -1518,18 +1701,23 @@ class ProfilerHookTest(test.TestCase): sess.run(self.train_op) # Saved. self.assertEqual(3, self._count_timeline_files()) - def test_run_metadata_saves_in_first_step(self): - writer_cache.FileWriterCache.clear() - fake_summary_writer.FakeSummaryWriter.install() - fake_writer = writer_cache.FileWriterCache.get(self.output_dir) + def test_run_metadata_summary_saving(self): with self.graph.as_default(): hook = basic_session_run_hooks.ProfilerHook( - save_secs=2, output_dir=self.output_dir) + save_steps=2, output_dir=self.output_dir) with monitored_session.SingularMonitoredSession(hooks=[hook]) as sess: sess.run(self.train_op) # Saved. - self.assertEqual( - list(fake_writer._added_run_metadata.keys()), ['step_1']) - fake_summary_writer.FakeSummaryWriter.uninstall() + sess.run(self.train_op) # Not saved. + sess.run(self.train_op) # Saved. + events = iter(load_eventfile_contents(self.output_dir)) + next(events) # Skip version event that's always there. + event = next(events) + self.assertEqual(1, event.step) + self.assertEqual('step_1', event.tagged_run_metadata.tag) + event = next(events) + self.assertEqual(3, event.step) + self.assertEqual('step_3', event.tagged_run_metadata.tag) + self.assertRaises(StopIteration, lambda: next(events)) # No more events. if __name__ == '__main__': diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py index 7b06bffa4b..8a4ca04b1e 100644 --- a/tensorflow/python/training/monitored_session.py +++ b/tensorflow/python/training/monitored_session.py @@ -31,6 +31,7 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import resources +from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary import summary @@ -204,13 +205,17 @@ class Scaffold(object): 'local_init_op', ops.GraphKeys.LOCAL_INIT_OP, Scaffold.default_local_init_op) if self._summary_op is None: + def default_summary_op(): + v1_op = summary.merge_all() + v2_ops = summary_ops_v2.all_summary_ops() or [] + if v1_op is not None: + return control_flow_ops.with_dependencies(v2_ops, v1_op) + return control_flow_ops.group(v2_ops) if v2_ops else None self._summary_op = Scaffold.get_or_default('summary_op', ops.GraphKeys.SUMMARY_OP, - summary.merge_all) - # pylint: disable=g-long-lambda + default_summary_op) if self._saver is None: self._saver = training_saver._get_saver_or_default() # pylint: disable=protected-access - # pylint: enable=g-long-lambda self._saver.build() ops.get_default_graph().finalize() diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index f75db08059..b9d42b034e 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -611,10 +611,8 @@ class Optimizer( if isinstance(global_step, resource_variable_ops.ResourceVariable): # TODO(apassos): the implicit read in assign_add is slow; consider # making it less so. - apply_updates = resource_variable_ops.assign_add_variable_op( - global_step.handle, - ops.convert_to_tensor(1, dtype=global_step.dtype), - name=name) + apply_updates = global_step.assign_add( + 1, name=name, read_value=False) else: apply_updates = state_ops.assign_add(global_step, 1, name=name) -- GitLab From a45ffbd9b5c7d8fdaae6e41432f916639bdbe305 Mon Sep 17 00:00:00 2001 From: James Keeling Date: Tue, 24 Jul 2018 09:51:13 -0700 Subject: [PATCH 324/519] Allow TF_Delete* functions to accept nullptr The TF_Delete* functions in TensorFlow's C API now safely do nothing when asked to delete a null pointer. This mirrors the behaviour of free in C and delete in C++. PiperOrigin-RevId: 205844191 --- tensorflow/c/c_api.cc | 5 +++++ tensorflow/c/c_api.h | 1 + tensorflow/c/c_api_test.cc | 23 +++++++++++++++++++++++ 3 files changed, 29 insertions(+) diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc index 1b937883c8..f516ce4f18 100644 --- a/tensorflow/c/c_api.cc +++ b/tensorflow/c/c_api.cc @@ -328,6 +328,7 @@ TF_Buffer* TF_NewBufferFromString(const void* proto, size_t proto_len) { } void TF_DeleteBuffer(TF_Buffer* buffer) { + if (buffer == nullptr) return; if (buffer->data_deallocator != nullptr) { (*buffer->data_deallocator)(const_cast(buffer->data), buffer->length); @@ -357,6 +358,7 @@ void TF_CloseDeprecatedSession(TF_DeprecatedSession* s, TF_Status* status) { void TF_DeleteDeprecatedSession(TF_DeprecatedSession* s, TF_Status* status) { status->status = Status::OK(); + if (s == nullptr) return; delete s->session; delete s; } @@ -907,6 +909,7 @@ TF_Library* TF_LoadLibrary(const char* library_filename, TF_Status* status) { TF_Buffer TF_GetOpList(TF_Library* lib_handle) { return lib_handle->op_list; } void TF_DeleteLibraryHandle(TF_Library* lib_handle) { + if (lib_handle == nullptr) return; tensorflow::port::Free(const_cast(lib_handle->op_list.data)); delete lib_handle; } @@ -1854,6 +1857,7 @@ TF_Graph::TF_Graph() TF_Graph* TF_NewGraph() { return new TF_Graph; } void TF_DeleteGraph(TF_Graph* g) { + if (g == nullptr) return; g->mu.lock(); g->delete_requested = true; const bool del = g->sessions.empty(); @@ -2529,6 +2533,7 @@ void TF_CloseSession(TF_Session* s, TF_Status* status) { void TF_DeleteSession(TF_Session* s, TF_Status* status) { status->status = Status::OK(); + if (s == nullptr) return; TF_Graph* const graph = s->graph; if (graph != nullptr) { graph->mu.lock(); diff --git a/tensorflow/c/c_api.h b/tensorflow/c/c_api.h index c5035e0e41..c8ae6f2dd1 100644 --- a/tensorflow/c/c_api.h +++ b/tensorflow/c/c_api.h @@ -44,6 +44,7 @@ limitations under the License. // * size_t is used to represent byte sizes of objects that are // materialized in the address space of the calling process. // * int is used as an index into arrays. +// * Deletion functions are safe to call on nullptr. // // Questions left to address: // * Might at some point need a way for callers to provide their own Env. diff --git a/tensorflow/c/c_api_test.cc b/tensorflow/c/c_api_test.cc index c470ab5649..e674b1623c 100644 --- a/tensorflow/c/c_api_test.cc +++ b/tensorflow/c/c_api_test.cc @@ -1426,6 +1426,29 @@ TEST(CAPI, SavedModelNullArgsAreValid) { TF_DeleteStatus(s); } +TEST(CAPI, DeletingNullPointerIsSafe) { + TF_Status* status = TF_NewStatus(); + + TF_DeleteStatus(nullptr); + TF_DeleteBuffer(nullptr); + TF_DeleteTensor(nullptr); + TF_DeleteSessionOptions(nullptr); + TF_DeleteGraph(nullptr); + TF_DeleteImportGraphDefOptions(nullptr); + TF_DeleteImportGraphDefResults(nullptr); + TF_DeleteFunction(nullptr); + TF_DeleteSession(nullptr, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TF_DeletePRunHandle(nullptr); + TF_DeleteDeprecatedSession(nullptr, status); + EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); + TF_DeleteDeviceList(nullptr); + TF_DeleteLibraryHandle(nullptr); + TF_DeleteApiDefMap(nullptr); + + TF_DeleteStatus(status); +} + REGISTER_OP("TestOpWithNoGradient") .Input("x: T") .Output("y: T") -- GitLab From 4883a912780ab5783db654bebc6fc3ac25b63d74 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Tue, 24 Jul 2018 11:09:33 -0700 Subject: [PATCH 325/519] Add support for custom mapping types to util.nest Analagous to the existing support for custom collections.Sequence types. They need to be constructable with the same arguments as the base type for pack_sequence_as to work. Leaves PyDict_* calls for dict subclasses, but adds more general (and likely much slower) fallbacks for instances of collections.Mapping which are not dict subclasses. My hope is that this support will be enough so I can use a wrapper around dicts which does not inherit from dict in __setattr__ tracking (some tests failed without it). Inheriting from dict and properly shadowing a real dict seems impossible with CPython (since to shadow without synchronization issues, the wrapper needs to respond to updates to the original dict, but to work with e.g. {}.update(dict_subclass) the wrapper's C storage needs to also be updated). PiperOrigin-RevId: 205858082 --- tensorflow/python/BUILD | 1 + tensorflow/python/util/nest.py | 11 +- tensorflow/python/util/nest_test.py | 68 +++++-- tensorflow/python/util/util.cc | 299 ++++++++++++++++++++-------- tensorflow/python/util/util.h | 4 +- tensorflow/python/util/util.i | 3 + 6 files changed, 284 insertions(+), 102 deletions(-) diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index b5a0051c28..a7c60f5450 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -3408,6 +3408,7 @@ py_test( ":math_ops", ":util", "//third_party/py/numpy", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/python/util/nest.py b/tensorflow/python/util/nest.py index d63f59a8c8..5aac559b9b 100644 --- a/tensorflow/python/util/nest.py +++ b/tensorflow/python/util/nest.py @@ -73,7 +73,7 @@ def _sequence_like(instance, args): Returns: `args` with the type of `instance`. """ - if isinstance(instance, dict): + if isinstance(instance, (dict, _collections.Mapping)): # Pack dictionaries in a deterministic order by sorting the keys. # Notice this means that we ignore the original order of `OrderedDict` # instances. This is intentional, to avoid potential bugs caused by mixing @@ -89,7 +89,7 @@ def _sequence_like(instance, args): def _yield_value(iterable): - if isinstance(iterable, dict): + if isinstance(iterable, (dict, _collections.Mapping)): # Iterate through dictionaries in a deterministic order by sorting the # keys. Notice this means that we ignore the original order of `OrderedDict` # instances. This is intentional, to avoid potential bugs caused by mixing @@ -215,7 +215,7 @@ def flatten_dict_items(dictionary): ValueError: If any key and value have not the same structure, or if keys are not unique. """ - if not isinstance(dictionary, dict): + if not isinstance(dictionary, (dict, _collections.Mapping)): raise TypeError("input must be a dictionary") flat_dictionary = {} for i, v in _six.iteritems(dictionary): @@ -455,7 +455,7 @@ def assert_shallow_structure(shallow_tree, input_tree, check_types=True): "structure has length %s, while shallow structure has length %s." % (len(input_tree), len(shallow_tree))) - if check_types and isinstance(shallow_tree, dict): + if check_types and isinstance(shallow_tree, (dict, _collections.Mapping)): if set(input_tree) != set(shallow_tree): raise ValueError( "The two structures don't have the same keys. Input " @@ -716,7 +716,7 @@ def yield_flat_paths(nest): # The _maybe_add_final_path_element function is used below in order to avoid # adding trailing slashes when the sub-element recursed into is a leaf. - if isinstance(nest, dict): + if isinstance(nest, (dict, _collections.Mapping)): for key in _sorted(nest): value = nest[key] for sub_path in yield_flat_paths(value): @@ -760,3 +760,4 @@ def flatten_with_joined_string_paths(structure, separator="/"): _pywrap_tensorflow.RegisterSequenceClass(_collections.Sequence) +_pywrap_tensorflow.RegisterMappingClass(_collections.Mapping) diff --git a/tensorflow/python/util/nest_test.py b/tensorflow/python/util/nest_test.py index 2f12b25354..26c6ea4b01 100644 --- a/tensorflow/python/util/nest_test.py +++ b/tensorflow/python/util/nest_test.py @@ -21,6 +21,7 @@ from __future__ import print_function import collections import time +from absl.testing import parameterized import numpy as np from six.moves import xrange # pylint: disable=redefined-builtin @@ -33,7 +34,22 @@ from tensorflow.python.platform import test from tensorflow.python.util import nest -class NestTest(test.TestCase): +class _CustomMapping(collections.Mapping): + + def __init__(self, *args, **kwargs): + self._wrapped = dict(*args, **kwargs) + + def __getitem__(self, key): + return self._wrapped[key] + + def __iter__(self): + return iter(self._wrapped) + + def __len__(self): + return len(self._wrapped) + + +class NestTest(parameterized.TestCase, test.TestCase): PointXY = collections.namedtuple("Point", ["x", "y"]) # pylint: disable=invalid-name @@ -72,26 +88,32 @@ class NestTest(test.TestCase): with self.assertRaises(ValueError): nest.pack_sequence_as([5, 6, [7, 8]], ["a", "b", "c"]) + @parameterized.parameters({"mapping_type": collections.OrderedDict}, + {"mapping_type": _CustomMapping}) @test_util.assert_no_new_pyobjects_executing_eagerly - def testFlattenDictOrder(self): + def testFlattenDictOrder(self, mapping_type): """`flatten` orders dicts by key, including OrderedDicts.""" - ordered = collections.OrderedDict([("d", 3), ("b", 1), ("a", 0), ("c", 2)]) + ordered = mapping_type([("d", 3), ("b", 1), ("a", 0), ("c", 2)]) plain = {"d": 3, "b": 1, "a": 0, "c": 2} ordered_flat = nest.flatten(ordered) plain_flat = nest.flatten(plain) self.assertEqual([0, 1, 2, 3], ordered_flat) self.assertEqual([0, 1, 2, 3], plain_flat) - def testPackDictOrder(self): + @parameterized.parameters({"mapping_type": collections.OrderedDict}, + {"mapping_type": _CustomMapping}) + def testPackDictOrder(self, mapping_type): """Packing orders dicts by key, including OrderedDicts.""" - ordered = collections.OrderedDict([("d", 0), ("b", 0), ("a", 0), ("c", 0)]) + custom = mapping_type([("d", 0), ("b", 0), ("a", 0), ("c", 0)]) plain = {"d": 0, "b": 0, "a": 0, "c": 0} seq = [0, 1, 2, 3] - ordered_reconstruction = nest.pack_sequence_as(ordered, seq) + custom_reconstruction = nest.pack_sequence_as(custom, seq) plain_reconstruction = nest.pack_sequence_as(plain, seq) + self.assertIsInstance(custom_reconstruction, mapping_type) + self.assertIsInstance(plain_reconstruction, dict) self.assertEqual( - collections.OrderedDict([("d", 3), ("b", 1), ("a", 0), ("c", 2)]), - ordered_reconstruction) + mapping_type([("d", 3), ("b", 1), ("a", 0), ("c", 2)]), + custom_reconstruction) self.assertEqual({"d": 3, "b": 1, "a": 0, "c": 2}, plain_reconstruction) Abc = collections.namedtuple("A", ("b", "c")) # pylint: disable=invalid-name @@ -101,8 +123,10 @@ class NestTest(test.TestCase): # A nice messy mix of tuples, lists, dicts, and `OrderedDict`s. mess = [ "z", - NestTest.Abc(3, 4), - { + NestTest.Abc(3, 4), { + "d": _CustomMapping({ + 41: 4 + }), "c": [ 1, collections.OrderedDict([ @@ -111,17 +135,19 @@ class NestTest(test.TestCase): ]), ], "b": 5 - }, - 17 + }, 17 ] flattened = nest.flatten(mess) - self.assertEqual(flattened, ["z", 3, 4, 5, 1, 2, 3, 17]) + self.assertEqual(flattened, ["z", 3, 4, 5, 1, 2, 3, 4, 17]) structure_of_mess = [ 14, NestTest.Abc("a", True), { + "d": _CustomMapping({ + 41: 42 + }), "c": [ 0, collections.OrderedDict([ @@ -142,6 +168,10 @@ class NestTest(test.TestCase): self.assertIsInstance(unflattened_ordered_dict, collections.OrderedDict) self.assertEqual(list(unflattened_ordered_dict.keys()), ["b", "a"]) + unflattened_custom_mapping = unflattened[2]["d"] + self.assertIsInstance(unflattened_custom_mapping, _CustomMapping) + self.assertEqual(list(unflattened_custom_mapping.keys()), [41]) + def testFlatten_numpyIsNotFlattened(self): structure = np.array([1, 2, 3]) flattened = nest.flatten(structure) @@ -179,19 +209,23 @@ class NestTest(test.TestCase): self.assertFalse(nest.is_sequence(math_ops.tanh(ones))) self.assertFalse(nest.is_sequence(np.ones((4, 5)))) - def testFlattenDictItems(self): - dictionary = {(4, 5, (6, 8)): ("a", "b", ("c", "d"))} + @parameterized.parameters({"mapping_type": _CustomMapping}, + {"mapping_type": dict}) + def testFlattenDictItems(self, mapping_type): + dictionary = mapping_type({(4, 5, (6, 8)): ("a", "b", ("c", "d"))}) flat = {4: "a", 5: "b", 6: "c", 8: "d"} self.assertEqual(nest.flatten_dict_items(dictionary), flat) with self.assertRaises(TypeError): nest.flatten_dict_items(4) - bad_dictionary = {(4, 5, (4, 8)): ("a", "b", ("c", "d"))} + bad_dictionary = mapping_type({(4, 5, (4, 8)): ("a", "b", ("c", "d"))}) with self.assertRaisesRegexp(ValueError, "not unique"): nest.flatten_dict_items(bad_dictionary) - another_bad_dictionary = {(4, 5, (6, 8)): ("a", "b", ("c", ("d", "e")))} + another_bad_dictionary = mapping_type({ + (4, 5, (6, 8)): ("a", "b", ("c", ("d", "e"))) + }) with self.assertRaisesRegexp( ValueError, "Key had [0-9]* elements, but value had [0-9]* elements"): nest.flatten_dict_items(another_bad_dictionary) diff --git a/tensorflow/python/util/util.cc b/tensorflow/python/util/util.cc index 366f8a0deb..f9e0b7e4d2 100644 --- a/tensorflow/python/util/util.cc +++ b/tensorflow/python/util/util.cc @@ -31,6 +31,8 @@ namespace { // Type object for collections.Sequence. This is set by RegisterSequenceClass. PyObject* CollectionsSequenceType = nullptr; +// Type object for collections.Mapping, set by RegisterMappingClass. +PyObject* CollectionsMappingType = nullptr; PyTypeObject* SparseTensorValueType = nullptr; const int kMaxItemsInCache = 1024; @@ -45,6 +47,23 @@ bool IsString(PyObject* o) { PyUnicode_Check(o); } +// Work around a writable-strings warning with Python 2's PyMapping_Keys macro, +// and while we're at it give them consistent behavior by making sure the +// returned value is a list. +// +// As with PyMapping_Keys, returns a new reference. +PyObject* MappingKeys(PyObject* o) { +#if PY_MAJOR_VERSION >= 3 + return PyMapping_Keys(o); +#else + static char key_method_name[] = "keys"; + Safe_PyObjectPtr raw_result(PyObject_CallMethod(o, key_method_name, nullptr)); + return PySequence_Fast( + raw_result.get(), + "The '.keys()' method of a custom mapping returned a non-sequence."); +#endif +} + // Equivalent to Python's 'o.__class__.__name__' // Note that '__class__' attribute is set only in new-style classes. // A lot of tensorflow code uses __class__ without checks, so it seems like @@ -85,6 +104,119 @@ string PyObjectToString(PyObject* o) { } } +class CachedTypeCheck { + public: + explicit CachedTypeCheck(std::function ternary_predicate) + : ternary_predicate_(std::move(ternary_predicate)) {} + + ~CachedTypeCheck() { + mutex_lock l(type_to_sequence_map_mu_); + for (const auto& pair : type_to_sequence_map_) { + Py_DECREF(pair.first); + } + } + + // Caches successful executions of the one-argument (PyObject*) callable + // "ternary_predicate" based on the type of "o". -1 from the callable + // indicates an unsuccessful check (not cached), 0 indicates that "o"'s type + // does not match the predicate, and 1 indicates that it does. Used to avoid + // calling back into Python for expensive isinstance checks. + int CachedLookup(PyObject* o) { + // Try not to return to Python - see if the type has already been seen + // before. + + auto* type = Py_TYPE(o); + + { + mutex_lock l(type_to_sequence_map_mu_); + auto it = type_to_sequence_map_.find(type); + if (it != type_to_sequence_map_.end()) { + return it->second; + } + } + + int check_result = ternary_predicate_(o); + + if (check_result == -1) { + return -1; // Type check error, not cached. + } + + // NOTE: This is never decref'd as long as the object lives, which is likely + // forever, but we don't want the type to get deleted as long as it is in + // the map. This should not be too much of a leak, as there should only be a + // relatively small number of types in the map, and an even smaller number + // that are eligible for decref. As a precaution, we limit the size of the + // map to 1024. + { + mutex_lock l(type_to_sequence_map_mu_); + if (type_to_sequence_map_.size() < kMaxItemsInCache) { + Py_INCREF(type); + type_to_sequence_map_.insert({type, check_result}); + } + } + + return check_result; + } + + private: + std::function ternary_predicate_; + mutex type_to_sequence_map_mu_; + std::unordered_map type_to_sequence_map_ + GUARDED_BY(type_to_sequence_map_mu_); +}; + +// Returns 1 if `o` is considered a mapping for the purposes of Flatten(). +// Returns 0 otherwise. +// Returns -1 if an error occurred. +int IsMappingHelper(PyObject* o) { + static auto* const check_cache = new CachedTypeCheck([](PyObject* to_check) { + return PyObject_IsInstance(to_check, CollectionsMappingType); + }); + if (PyDict_Check(o)) return true; + if (TF_PREDICT_FALSE(CollectionsMappingType == nullptr)) { + PyErr_SetString( + PyExc_RuntimeError, + tensorflow::strings::StrCat( + "collections.Mapping type has not been set. " + "Please call RegisterMappingClass before using this module") + .c_str()); + return -1; + } + return check_cache->CachedLookup(o); +} + +// Returns 1 if `o` is considered a sequence for the purposes of Flatten(). +// Returns 0 otherwise. +// Returns -1 if an error occurred. +int IsSequenceHelper(PyObject* o) { + static auto* const check_cache = new CachedTypeCheck([](PyObject* to_check) { + int is_instance = PyObject_IsInstance(to_check, CollectionsSequenceType); + + // Don't cache a failed is_instance check. + if (is_instance == -1) return -1; + + return static_cast(is_instance != 0 && !IsString(to_check)); + }); + // We treat dicts and other mappings as special cases of sequences. + if (IsMappingHelper(o)) return true; + if (PySet_Check(o) && !WarnedThatSetIsNotSequence) { + LOG(WARNING) << "Sets are not currently considered sequences, " + "but this may change in the future, " + "so consider avoiding using them."; + WarnedThatSetIsNotSequence = true; + } + if (TF_PREDICT_FALSE(CollectionsSequenceType == nullptr)) { + PyErr_SetString( + PyExc_RuntimeError, + tensorflow::strings::StrCat( + "collections.Sequence type has not been set. " + "Please call RegisterSequenceClass before using this module") + .c_str()); + return -1; + } + return check_cache->CachedLookup(o); +} + // Implements the same idea as tensorflow.util.nest._yield_value // During construction we check if the iterable is a dictionary. // If so, we construct a sequence from its sorted keys that will be used @@ -96,7 +228,12 @@ string PyObjectToString(PyObject* o) { // 'iterable' must not be modified while ValIterator is used. class ValIterator { public: - explicit ValIterator(PyObject* iterable) : dict_(nullptr), index_(0) { + explicit ValIterator(PyObject* iterable) + : dict_(nullptr), + mapping_(nullptr), + last_mapping_element_(nullptr), + seq_(nullptr), + index_(0) { if (PyDict_Check(iterable)) { dict_ = iterable; // PyDict_Keys returns a list, which can be used with @@ -108,6 +245,10 @@ class ValIterator { // bugs caused by mixing ordered and plain dicts (e.g., flattening // a dict but using a corresponding `OrderedDict` to pack it back). PyList_Sort(seq_); + } else if (IsMappingHelper(iterable)) { + mapping_ = iterable; + seq_ = MappingKeys(iterable); + PyList_Sort(seq_); } else { seq_ = PySequence_Fast(iterable, ""); } @@ -122,7 +263,9 @@ class ValIterator { PyObject* element = nullptr; if (index_ < size_) { // Both PySequence_Fast_GET_ITEM and PyDict_GetItem return borrowed - // references. + // references. For general mappings, ValIterator keeps a reference to the + // last retrieved element (and decrefs it before producing the next + // element) to abstract away the borrowed/new difference. element = PySequence_Fast_GET_ITEM(seq_, index_); ++index_; if (dict_ != nullptr) { @@ -132,85 +275,32 @@ class ValIterator { "Dictionary was modified during iteration over it"); return nullptr; } + } else if (mapping_ != nullptr) { + element = PyObject_GetItem(mapping_, element); + if (element == nullptr) { + PyErr_SetString(PyExc_RuntimeError, + "Mapping was modified during iteration over it"); + return nullptr; + } + last_mapping_element_.reset(element); } } return element; } private: - PyObject* seq_; + // Special casing for things that pass PyDict_Check (faster, no Python calls) PyObject* dict_; + + // General mappings which have custom Python logic + PyObject* mapping_; + Safe_PyObjectPtr last_mapping_element_; + + PyObject* seq_; Py_ssize_t size_; Py_ssize_t index_; }; -mutex g_type_to_sequence_map(LINKER_INITIALIZED); -std::unordered_map* IsTypeSequenceMap() { - static auto* const m = new std::unordered_map; - return m; -} - -// Returns 1 if `o` is considered a sequence for the purposes of Flatten(). -// Returns 0 otherwise. -// Returns -1 if an error occurred. -int IsSequenceHelper(PyObject* o) { - if (PyDict_Check(o)) return true; - if (PySet_Check(o) && !WarnedThatSetIsNotSequence) { - LOG(WARNING) << "Sets are not currently considered sequences, " - "but this may change in the future, " - "so consider avoiding using them."; - WarnedThatSetIsNotSequence = true; - } - if (TF_PREDICT_FALSE(CollectionsSequenceType == nullptr)) { - PyErr_SetString( - PyExc_RuntimeError, - tensorflow::strings::StrCat( - "collections.Sequence type has not been set. " - "Please call RegisterSequenceClass before using this module") - .c_str()); - return -1; - } - - // Try not to return to Python - see if the type has already been seen - // before. - - auto* type_to_sequence_map = IsTypeSequenceMap(); - auto* type = Py_TYPE(o); - - { - mutex_lock l(g_type_to_sequence_map); - auto it = type_to_sequence_map->find(type); - if (it != type_to_sequence_map->end()) { - return it->second; - } - } - - // NOTE: We explicitly release the g_type_to_sequence_map mutex, - // because PyObject_IsInstance() may release the GIL, allowing another thread - // concurrent entry to this function. - int is_instance = PyObject_IsInstance(o, CollectionsSequenceType); - - // Don't cache a failed is_instance check. - if (is_instance == -1) return -1; - - bool is_sequence = static_cast(is_instance != 0 && !IsString(o)); - - // NOTE: This is never decref'd, but we don't want the type to get deleted - // as long as it is in the map. This should not be too much of a - // leak, as there should only be a relatively small number of types in the - // map, and an even smaller number that are eligible for decref. As a - // precaution, we limit the size of the map to 1024. - { - mutex_lock l(g_type_to_sequence_map); - if (type_to_sequence_map->size() < kMaxItemsInCache) { - Py_INCREF(type); - type_to_sequence_map->insert({type, is_sequence}); - } - } - - return is_sequence; -} - bool IsSparseTensorValueType(PyObject* o) { if (TF_PREDICT_FALSE(SparseTensorValueType == nullptr)) { return false; @@ -226,21 +316,35 @@ int IsSequenceForDataHelper(PyObject* o) { bool GetNextValuesForDict(PyObject* nested, std::vector* next_values) { - std::vector result; - - PyObject* keys = PyDict_Keys(nested); - if (PyList_Sort(keys) == -1) return false; - Py_ssize_t size = PyList_Size(keys); + Safe_PyObjectPtr keys(PyDict_Keys(nested)); + if (PyList_Sort(keys.get()) == -1) return false; + Py_ssize_t size = PyList_Size(keys.get()); for (Py_ssize_t i = 0; i < size; ++i) { // We know that key and item will not be deleted because nested owns // a reference to them and callers of flatten must not modify nested // while the method is running. - PyObject* key = PyList_GET_ITEM(keys, i); + PyObject* key = PyList_GET_ITEM(keys.get(), i); PyObject* item = PyDict_GetItem(nested, key); Py_INCREF(item); next_values->emplace_back(item); } - Py_DECREF(keys); + return true; +} + +bool GetNextValuesForMapping(PyObject* nested, + std::vector* next_values) { + Safe_PyObjectPtr keys(MappingKeys(nested)); + if (keys.get() == nullptr) { + return false; + } + if (PyList_Sort(keys.get()) == -1) return false; + Py_ssize_t size = PyList_Size(keys.get()); + for (Py_ssize_t i = 0; i < size; ++i) { + PyObject* key = PyList_GET_ITEM(keys.get(), i); + // Unlike PyDict_GetItem, PyObject_GetItem returns a new reference. + PyObject* item = PyObject_GetItem(nested, key); + next_values->emplace_back(item); + } return true; } @@ -265,6 +369,9 @@ bool GetNextValues(PyObject* nested, if (PyDict_Check(nested)) { // if nested is dictionary, sort it by key and recurse on each value return GetNextValuesForDict(nested, next_values); + } else if (IsMappingHelper(nested)) { + // same treatment as dictionaries, but for custom mapping types + return GetNextValuesForMapping(nested, next_values); } // iterate and recurse return GetNextValuesForIterable(nested, next_values); @@ -276,6 +383,9 @@ bool GetNextValuesForData(PyObject* nested, if (PyDict_Check(nested)) { // if nested is dictionary, sort it by key and recurse on each value return GetNextValuesForDict(nested, next_values); + } else if (IsMappingHelper(nested)) { + // same treatment as dictionaries, but for custom mapping types + return GetNextValuesForMapping(nested, next_values); } else if (IsSparseTensorValueType(nested)) { // if nested is a SparseTensorValue, just return itself as a single item Py_INCREF(nested); @@ -320,8 +430,8 @@ bool FlattenHelper( // 'dict1' and 'dict2' are assumed to be Python dictionaries. void SetDifferentKeysError(PyObject* dict1, PyObject* dict2, string* error_msg, bool* is_type_error) { - PyObject* k1 = PyDict_Keys(dict1); - PyObject* k2 = PyDict_Keys(dict2); + PyObject* k1 = MappingKeys(dict1); + PyObject* k2 = MappingKeys(dict2); *is_type_error = false; *error_msg = tensorflow::strings::StrCat( "The two dictionaries don't have the same set of keys. " @@ -423,6 +533,24 @@ bool AssertSameStructureHelper(PyObject* o1, PyObject* o2, bool check_types, return true; } } + } else if (IsMappingHelper(o1)) { + // Fallback for custom mapping types. Instead of using PyDict methods + // which stay in C, we call iter(o1). + if (PyMapping_Size(o1) != PyMapping_Size(o2)) { + SetDifferentKeysError(o1, o2, error_msg, is_type_error); + return true; + } + + Safe_PyObjectPtr iter(PyObject_GetIter(o1)); + PyObject* key; + while ((key = PyIter_Next(iter.get())) != nullptr) { + if (!PyMapping_HasKey(o2, key)) { + SetDifferentKeysError(o1, o2, error_msg, is_type_error); + Py_DECREF(key); + return true; + } + Py_DECREF(key); + } } } @@ -470,6 +598,19 @@ void RegisterSequenceClass(PyObject* sequence_class) { CollectionsSequenceType = sequence_class; } +void RegisterMappingClass(PyObject* mapping_class) { + if (!PyType_Check(mapping_class)) { + PyErr_SetString( + PyExc_TypeError, + tensorflow::strings::StrCat( + "Expecting a class definition for `collections.Mapping`. Got ", + Py_TYPE(mapping_class)->tp_name) + .c_str()); + return; + } + CollectionsMappingType = mapping_class; +} + void RegisterSparseTensorValueClass(PyObject* sparse_tensor_value_class) { if (!PyType_Check(sparse_tensor_value_class)) { PyErr_SetString( diff --git a/tensorflow/python/util/util.h b/tensorflow/python/util/util.h index 70efc10c9a..41dcc969f8 100644 --- a/tensorflow/python/util/util.h +++ b/tensorflow/python/util/util.h @@ -118,7 +118,9 @@ PyObject* Flatten(PyObject* nested); // the type from the module. This approach also requires some trigger from // Python so that we know that Python interpreter had been initialzied. void RegisterSequenceClass(PyObject* sequence_class); -// Similar to the above function, except for the +// Like RegisterSequenceClass, but for collections.Mapping. +void RegisterMappingClass(PyObject* mapping_class); +// Similar to the above functions, except for the // sparse_tensor.SparseTensorValue class. void RegisterSparseTensorValueClass(PyObject* sparse_tensor_value_class); diff --git a/tensorflow/python/util/util.i b/tensorflow/python/util/util.i index 9f3b11b982..6ad1484295 100644 --- a/tensorflow/python/util/util.i +++ b/tensorflow/python/util/util.i @@ -31,6 +31,9 @@ limitations under the License. %unignore tensorflow::swig::RegisterSequenceClass; %noexception tensorflow::swig::RegisterSequenceClass; +%unignore tensorflow::swig::RegisterMappingClass; +%noexception tensorflow::swig::RegisterMappingClass; + %unignore tensorflow::swig::RegisterSparseTensorValueClass; %noexception tensorflow::swig::RegisterSparseTensorValueClass; -- GitLab From 0527ba2f447fe0bc20152f393bcd672de0b59548 Mon Sep 17 00:00:00 2001 From: Zhenyu Tan Date: Tue, 24 Jul 2018 11:10:08 -0700 Subject: [PATCH 326/519] warm start estimator from keras model during model to estimator conversion. PiperOrigin-RevId: 205858208 --- tensorflow/python/estimator/keras.py | 112 +++++++++++----- tensorflow/python/estimator/keras_test.py | 155 ++++++++++++++++++++-- 2 files changed, 222 insertions(+), 45 deletions(-) diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py index 076359b503..682be8e7cc 100644 --- a/tensorflow/python/estimator/keras.py +++ b/tensorflow/python/estimator/keras.py @@ -21,11 +21,14 @@ from __future__ import print_function import os import re +import tempfile + from tensorflow.python.client import session from tensorflow.python.estimator import estimator as estimator_lib from tensorflow.python.estimator import export as export_lib from tensorflow.python.estimator import model_fn as model_fn_lib from tensorflow.python.estimator import run_config as run_config_lib +from tensorflow.python.estimator.run_config import RunConfig from tensorflow.python.framework import ops from tensorflow.python.framework import random_seed from tensorflow.python.framework import sparse_tensor as sparse_tensor_lib @@ -39,6 +42,7 @@ from tensorflow.python.keras.utils.generic_utils import CustomObjectScope from tensorflow.python.ops import check_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import metrics as metrics_module +from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging from tensorflow.python.saved_model import signature_constants from tensorflow.python.training import distribute as distribute_lib @@ -426,29 +430,34 @@ def _create_keras_model_fn(keras_model, custom_objects=None): return model_fn -def _save_first_checkpoint(keras_model, estimator, custom_objects, - keras_weights): +def _save_first_checkpoint(keras_model, custom_objects, config): """Save first checkpoint for the keras Estimator. Args: keras_model: an instance of compiled keras model. - estimator: keras estimator. custom_objects: Dictionary for custom objects. - keras_weights: A flat list of Numpy arrays for weights of given keras_model. + config: Estimator config. Returns: - The model_fn for a keras Estimator. + The path where keras model checkpoint is saved. """ + # save checkpoint into subdirectory to allow warm start + keras_model_dir = os.path.join(config.model_dir, 'keras') # Load weights and save to checkpoint if there is no checkpoint - latest_path = saver_lib.latest_checkpoint(estimator.model_dir) + latest_path = saver_lib.latest_checkpoint(keras_model_dir) if not latest_path: + keras_weights = None + if _any_weight_initialized(keras_model): + keras_weights = keras_model.get_weights() + if not gfile.IsDirectory(keras_model_dir): + gfile.MakeDirs(keras_model_dir) with ops.Graph().as_default(): - random_seed.set_random_seed(estimator.config.tf_random_seed) + random_seed.set_random_seed(config.tf_random_seed) training_util.create_global_step() model = _clone_and_build_model(model_fn_lib.ModeKeys.TRAIN, keras_model, custom_objects) # save to checkpoint - with session.Session(config=estimator._session_config) as sess: + with session.Session(config=config.session_config) as sess: if keras_weights: model.set_weights(keras_weights) # Make update ops and initialize all variables. @@ -458,7 +467,46 @@ def _save_first_checkpoint(keras_model, estimator, custom_objects, K._initialize_variables(sess) # pylint: enable=protected-access saver = saver_lib.Saver() - saver.save(sess, os.path.join(estimator.model_dir, 'keras_model.ckpt')) + latest_path = os.path.join(keras_model_dir, 'keras_model.ckpt') + saver.save(sess, latest_path) + return latest_path + + +def _maybe_overwrite_model_dir_and_session_config(config, model_dir): + """Overwrite estimator config by `model_dir` and `session_config` if needed. + + Args: + config: Original estimator config. + model_dir: Estimator model checkpoint directory. + + Returns: + Overwritten estimator config. + + Raises: + ValueError: Model directory inconsistent between `model_dir` and `config`. + """ + + default_session_config = run_config_lib.get_default_session_config() + if isinstance(config, dict): + config = RunConfig(**config) + elif config is None: + config = RunConfig(session_config=default_session_config) + if config.session_config is None: + config = RunConfig.replace(config, session_config=default_session_config) + + if model_dir is not None: + if (getattr(config, 'model_dir', None) is not None and + config.model_dir != model_dir): + raise ValueError( + "`model_dir` are set both in constructor and `RunConfig`, but with " + "different values. In constructor: '{}', in `RunConfig`: " + "'{}' ".format(model_dir, config.model_dir)) + config = RunConfig.replace(config, model_dir=model_dir) + elif getattr(config, 'model_dir', None) is None: + model_dir = tempfile.mkdtemp() + config = RunConfig.replace(config, model_dir=model_dir) + + return config def model_to_estimator(keras_model=None, @@ -517,45 +565,39 @@ def model_to_estimator(keras_model=None, 'Please compile the model with `model.compile()` ' 'before calling `model_to_estimator()`.') - if isinstance(config, dict): - config = run_config_lib.RunConfig(**config) + config = _maybe_overwrite_model_dir_and_session_config(config, model_dir) keras_model_fn = _create_keras_model_fn(keras_model, custom_objects) - estimator = estimator_lib.Estimator( - keras_model_fn, model_dir=model_dir, config=config) - - # Check if we need to call get_weights: if _any_weight_initialized(keras_model): - keras_weights = keras_model.get_weights() # Warn if config passed to estimator tries to update GPUOptions. If a # session has already been created, the GPUOptions passed to the first # session sticks. - if estimator._session_config.HasField('gpu_options'): + if config.session_config.HasField('gpu_options'): logging.warning( 'The Keras backend session has already been set. ' 'The _session_config passed to model_to_estimator will not be used.') else: # Pass the config into keras backend's default session. - sess = session.Session(config=estimator._session_config) + sess = session.Session(config=config.session_config) K.set_session(sess) - keras_weights = None + warm_start_path = None if keras_model._is_graph_network: - # TODO(yifeif): move checkpoint initialization to scaffold.init_fn - _save_first_checkpoint(keras_model, - estimator, - custom_objects, - keras_weights) + warm_start_path = _save_first_checkpoint(keras_model, custom_objects, + config) elif keras_model.built: - logging.warning('You are creating an Estimator from a Keras model ' - 'manually subclassed from `Model`, that was ' - 'already called on some inputs (and thus already had ' - 'weights). We are currently unable to preserve ' - 'the model\'s state (its weights) ' - 'as part of the estimator ' - 'in this case. Be warned that the estimator ' - 'has been created using ' - 'a freshly initialized version of your model.\n' - 'Note that this doesn\'t affect the state of the ' - 'model instance you passed as `keras_model` argument.') + logging.warning('You are creating an Estimator from a Keras model manually ' + 'subclassed from `Model`, that was already called on some ' + 'inputs (and thus already had weights). We are currently ' + 'unable to preserve the model\'s state (its weights) as ' + 'part of the estimator in this case. Be warned that the ' + 'estimator has been created using a freshly initialized ' + 'version of your model.\n' + 'Note that this doesn\'t affect the state of the model ' + 'instance you passed as `keras_model` argument.') + + estimator = estimator_lib.Estimator(keras_model_fn, + config=config, + warm_start_from=warm_start_path) + return estimator diff --git a/tensorflow/python/estimator/keras_test.py b/tensorflow/python/estimator/keras_test.py index 7a3c5a9bf1..cf4ec7f4da 100644 --- a/tensorflow/python/estimator/keras_test.py +++ b/tensorflow/python/estimator/keras_test.py @@ -33,11 +33,13 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import test_util from tensorflow.python.keras import testing_utils from tensorflow.python.keras.optimizers import SGD +from tensorflow.python.ops import variable_scope from tensorflow.python.ops.parsing_ops import gen_parsing_ops from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.summary.writer import writer_cache from tensorflow.python.training import rmsprop +from tensorflow.python.training import session_run_hook try: @@ -50,6 +52,8 @@ _TRAIN_SIZE = 200 _INPUT_SIZE = (10,) _NUM_CLASS = 2 +_TMP_DIR = '/tmp' + def simple_sequential_model(): model = keras.models.Sequential() @@ -167,6 +171,12 @@ def multi_inputs_multi_outputs_model(): return model +class MyHook(session_run_hook.SessionRunHook): + + def begin(self): + _ = variable_scope.get_variable('temp', [1]) + + class TestKerasEstimator(test_util.TensorFlowTestCase): def setUp(self): @@ -203,6 +213,54 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): writer_cache.FileWriterCache.clear() gfile.DeleteRecursively(self._config.model_dir) + # see b/109935364 + @test_util.run_in_graph_and_eager_modes + def test_train_with_hooks(self): + for model_type in ['sequential', 'functional']: + keras_model, (_, _), ( + _, _), train_input_fn, eval_input_fn = get_resource_for_simple_model( + model_type=model_type, is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=['mse', keras.metrics.categorical_accuracy]) + + my_hook = MyHook() + with self.test_session(): + est_keras = keras_lib.model_to_estimator( + keras_model=keras_model, config=self._config) + before_eval_results = est_keras.evaluate( + input_fn=eval_input_fn, steps=1) + est_keras.train(input_fn=train_input_fn, hooks=[my_hook], + steps=_TRAIN_SIZE / 16) + after_eval_results = est_keras.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(after_eval_results['loss'], before_eval_results['loss']) + + writer_cache.FileWriterCache.clear() + gfile.DeleteRecursively(self._config.model_dir) + + @test_util.run_in_graph_and_eager_modes + def test_train_with_model_fit_and_hooks(self): + keras_model, (x_train, y_train), _, \ + train_input_fn, eval_input_fn = get_resource_for_simple_model( + model_type='sequential', is_evaluate=True) + + keras_model.compile( + loss='categorical_crossentropy', + optimizer=rmsprop.RMSPropOptimizer(1e-3), + metrics=['mse', keras.metrics.categorical_accuracy]) + my_hook = MyHook() + with self.test_session(): + keras_model.fit(x_train, y_train, epochs=1) + + keras_est = keras_lib.model_to_estimator( + keras_model=keras_model, config=self._config) + before_eval_results = keras_est.evaluate(input_fn=eval_input_fn) + keras_est.train(input_fn=train_input_fn, hooks=[my_hook], + steps=_TRAIN_SIZE / 16) + after_eval_results = keras_est.evaluate(input_fn=eval_input_fn, steps=1) + self.assertLess(after_eval_results['loss'], before_eval_results['loss']) + @test_util.run_in_graph_and_eager_modes def test_train_with_tf_optimizer(self): for model_type in ['sequential', 'functional']: @@ -473,27 +531,43 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): est_keras.train(input_fn=invald_output_name_input_fn, steps=100) def test_custom_objects(self): - + def relu6(x): return keras.backend.relu(x, max_value=6) - + keras_model = simple_functional_model(activation=relu6) keras_model.compile(loss='categorical_crossentropy', optimizer='adam') custom_objects = { 'relu6': relu6 } + (x_train, y_train), _ = testing_utils.get_test_data( + train_samples=_TRAIN_SIZE, + test_samples=50, + input_shape=(10,), + num_classes=2) + y_train = keras.utils.to_categorical(y_train, 2) + input_name = keras_model.input_names[0] + output_name = keras_model.output_names[0] + train_input_fn = numpy_io.numpy_input_fn( + x=randomize_io_type(x_train, input_name), + y=randomize_io_type(y_train, output_name), + shuffle=False, + num_epochs=None, + batch_size=16) with self.assertRaisesRegexp(ValueError, 'relu6'): with self.test_session(): - keras_lib.model_to_estimator( + est = keras_lib.model_to_estimator( keras_model=keras_model, model_dir=tempfile.mkdtemp(dir=self._base_dir)) + est.train(input_fn=train_input_fn, steps=1) with self.test_session(): - keras_lib.model_to_estimator( + est = keras_lib.model_to_estimator( keras_model=keras_model, model_dir=tempfile.mkdtemp(dir=self._base_dir), custom_objects=custom_objects) + est.train(input_fn=train_input_fn, steps=1) def test_tf_config(self): keras_model, (_, _), (_, _), _, _ = get_resource_for_simple_model() @@ -530,12 +604,73 @@ class TestKerasEstimator(test_util.TensorFlowTestCase): gpu_options = config_pb2.GPUOptions(per_process_gpu_memory_fraction=0.3) sess_config = config_pb2.ConfigProto(gpu_options=gpu_options) self._config._session_config = sess_config - keras_lib.model_to_estimator( - keras_model=keras_model, config=self._config) - self.assertEqual( - keras.backend.get_session() - ._config.gpu_options.per_process_gpu_memory_fraction, - gpu_options.per_process_gpu_memory_fraction) + with self.test_session(): + keras_lib.model_to_estimator( + keras_model=keras_model, config=self._config) + self.assertEqual( + keras.backend.get_session() + ._config.gpu_options.per_process_gpu_memory_fraction, + gpu_options.per_process_gpu_memory_fraction) + + def test_with_empty_config(self): + keras_model, _, _, _, _ = get_resource_for_simple_model( + model_type='sequential', is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer='rmsprop', + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + est_keras = keras_lib.model_to_estimator( + keras_model=keras_model, model_dir=self._base_dir, + config=run_config_lib.RunConfig()) + self.assertEqual(run_config_lib.get_default_session_config(), + est_keras._session_config) + self.assertEqual(est_keras._session_config, + est_keras._config.session_config) + self.assertEqual(self._base_dir, est_keras._config.model_dir) + self.assertEqual(self._base_dir, est_keras._model_dir) + + with self.test_session(): + est_keras = keras_lib.model_to_estimator( + keras_model=keras_model, model_dir=self._base_dir, + config=None) + self.assertEqual(run_config_lib.get_default_session_config(), + est_keras._session_config) + self.assertEqual(est_keras._session_config, + est_keras._config.session_config) + self.assertEqual(self._base_dir, est_keras._config.model_dir) + self.assertEqual(self._base_dir, est_keras._model_dir) + + def test_with_empty_config_and_empty_model_dir(self): + keras_model, _, _, _, _ = get_resource_for_simple_model( + model_type='sequential', is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer='rmsprop', + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + with test.mock.patch.object(tempfile, 'mkdtemp', return_value=_TMP_DIR): + est_keras = keras_lib.model_to_estimator( + keras_model=keras_model, + config=run_config_lib.RunConfig()) + self.assertEqual(est_keras._model_dir, _TMP_DIR) + + def test_with_conflicting_model_dir_and_config(self): + keras_model, _, _, _, _ = get_resource_for_simple_model( + model_type='sequential', is_evaluate=True) + keras_model.compile( + loss='categorical_crossentropy', + optimizer='rmsprop', + metrics=['mse', keras.metrics.categorical_accuracy]) + + with self.test_session(): + with self.assertRaisesRegexp(ValueError, '`model_dir` are set both in ' + 'constructor and `RunConfig`'): + keras_lib.model_to_estimator( + keras_model=keras_model, model_dir=self._base_dir, + config=run_config_lib.RunConfig(model_dir=_TMP_DIR)) def test_pretrained_weights(self): keras_model, (_, _), (_, _), _, _ = get_resource_for_simple_model() -- GitLab From aef000ed3c2863a5cc7ccb5bf1fb46116e7f4f02 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Tue, 24 Jul 2018 11:10:46 -0700 Subject: [PATCH 327/519] Build more cuda compute capabilities in cmake build. Fixes #18652 PiperOrigin-RevId: 205858348 --- tensorflow/contrib/cmake/CMakeLists.txt | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt index 708618dcb0..6c93487e0d 100644 --- a/tensorflow/contrib/cmake/CMakeLists.txt +++ b/tensorflow/contrib/cmake/CMakeLists.txt @@ -394,16 +394,20 @@ if (tensorflow_ENABLE_GPU) # 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 - set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_30,code=\"sm_30,compute_30\";-gencode arch=compute_35,code=\"sm_35,compute_35\";-gencode arch=compute_52,code=\"sm_52,compute_52\") + set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_37,code=\"sm_37,compute_37\") + set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_52,code=\"sm_52,compute_52\") + set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_60,code=\"sm_60,compute_60\") + set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_61,code=\"sm_61,compute_61\") + set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-gencode arch=compute_70,code=\"sm_70,compute_70\") set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};--include-path ${PROJECT_BINARY_DIR}/$\{build_configuration\};--expt-relaxed-constexpr) set(CUDA_NVCC_FLAGS ${CUDA_NVCC_FLAGS};-ftz=true) # Flush denormals to zero set(CUDA_INCLUDE ${CUDA_TOOLKIT_TARGET_DIR} ${CUDA_TOOLKIT_TARGET_DIR}/extras/CUPTI/include) include_directories(${CUDA_INCLUDE}) if (WIN32) - add_definitions(-DGOOGLE_CUDA=1 -DTF_EXTRA_CUDA_CAPABILITIES=3.0,3.5,5.2) + add_definitions(-DGOOGLE_CUDA=1 -DTF_EXTRA_CUDA_CAPABILITIES=3.7,5.2,6.0,6.1,7.0) else (WIN32) - # Without these double quotes, cmake in Linux makes it "-DTF_EXTRA_CUDA_CAPABILITIES=3.0, -D3.5, -D5.2" for cc, which incurs build breaks - add_definitions(-DGOOGLE_CUDA=1 -D"TF_EXTRA_CUDA_CAPABILITIES=3.0,3.5,5.2") + # Without these double quotes, cmake in Linux makes it "-DTF_EXTRA_CUDA_CAPABILITIES=3.7, -D5.2, ..." for cc, which incurs build breaks + add_definitions(-DGOOGLE_CUDA=1 -D"TF_EXTRA_CUDA_CAPABILITIES=3.7,5.2,6.0,6.1,7.0") endif (WIN32) if (WIN32) @@ -452,7 +456,7 @@ if (tensorflow_ENABLE_GPU) FILE(WRITE ${tensorflow_source_dir}/third_party/gpus/cuda/cuda_config.h "#ifndef CUDA_CUDA_CONFIG_H_\n" "#define CUDA_CUDA_CONFIG_H_\n" - "#define TF_CUDA_CAPABILITIES CudaVersion(\"3.0\"),CudaVersion(\"3.5\"),CudaVersion(\"5.2\")\n" + "#define TF_CUDA_CAPABILITIES CudaVersion(\"3.7\"),CudaVersion(\"5.2\"),CudaVersion(\"6.0\"),CudaVersion(\"6.1\"),CudaVersion(\"7.0\")\n" "#define TF_CUDA_VERSION \"64_${short_CUDA_VER}\"\n" "#define TF_CUDNN_VERSION \"64_${tensorflow_CUDNN_VERSION}\"\n" "#define TF_CUDA_TOOLKIT_PATH \"${CUDA_TOOLKIT_ROOT_DIR}\"\n" -- GitLab From d53830cddfc74105e46a4bdb703cb1154a288f8f Mon Sep 17 00:00:00 2001 From: James Keeling Date: Tue, 24 Jul 2018 11:11:35 -0700 Subject: [PATCH 328/519] Update TF_ApiDefMapGet to return nullptr if there is an error. Previously it would return an allocated buffer, even if there was an error and the buffer was not usable. This could cause memory leaks if the caller did not manually delete the buffer. Because TF_DeleteBuffer has been updated to be safe to call on nullptr, it's still OK if callers attempt to delete this nullptr. PiperOrigin-RevId: 205858542 --- tensorflow/c/c_api.cc | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/tensorflow/c/c_api.cc b/tensorflow/c/c_api.cc index f516ce4f18..10bc8cdbee 100644 --- a/tensorflow/c/c_api.cc +++ b/tensorflow/c/c_api.cc @@ -2732,6 +2732,10 @@ TF_Buffer* TF_ApiDefMapGet(TF_ApiDefMap* api_def_map, const char* name, TF_Buffer* ret = TF_NewBuffer(); status->status = MessageToBuffer(*api_def, ret); + if (!status->status.ok()) { + TF_DeleteBuffer(ret); + return nullptr; + } return ret; #endif // __ANDROID__ } -- GitLab From 3acdbf8f904cf32e5d4d211934ee8d346aa48457 Mon Sep 17 00:00:00 2001 From: Michael Kuperstein Date: Tue, 24 Jul 2018 11:13:40 -0700 Subject: [PATCH 329/519] [XLA] Document DynamicSlice and DynamicUpdateSlice semantics. PiperOrigin-RevId: 205858924 --- .../performance/xla/operation_semantics.md | 26 ++++++++++++++++--- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md index d6fa8ab5f9..26a7b9e42c 100644 --- a/tensorflow/docs_src/performance/xla/operation_semantics.md +++ b/tensorflow/docs_src/performance/xla/operation_semantics.md @@ -791,8 +791,6 @@ DynamicSlice extracts a sub-array from the input array at dynamic `size_indices`, which specify the end point of exclusive slice intervals in each dimension: [start, start + size). The shape of `start_indices` must be rank == 1, with dimension size equal to the rank of `operand`. -Note: handling of out-of-bounds slice indices (generated by incorrect runtime -calculation of 'start_indices') is currently implementation-defined. `DynamicSlice(operand, start_indices, size_indices)` @@ -812,6 +810,17 @@ calculation of 'start_indices') is currently implementation-defined. : : : dimension to avoid wrapping modulo : : : : dimension size. : +The effective slice indices are computed by applying the following +transformation for each index `i` in `[1, N)` before performing the slice: + +``` +start_indices[i] = clamp(start_indices[i], 0, operand.dimension_size[i] - size_indices[i]) +``` + +This ensures that the extracted slice is always in-bounds with respect to the +operand array. If the slice is in-bounds before the transformation is applied, +the transformation has no effect. + 1-dimensional example: ``` @@ -847,8 +856,6 @@ The shape of `update` determines the shape of the sub-array of the result which is updated. The shape of `start_indices` must be rank == 1, with dimension size equal to the rank of `operand`. -Note: handling of out-of-bounds slice indices (generated by incorrect runtime -calculation of 'start_indices') is currently implementation-defined. `DynamicUpdateSlice(operand, update, start_indices)` @@ -866,6 +873,17 @@ calculation of 'start_indices') is currently implementation-defined. : : : dimension. Value must be greater than or equal : : : : to zero. : +The effective slice indices are computed by applying the following +transformation for each index `i` in `[1, N)` before performing the slice: + +``` +start_indices[i] = clamp(start_indices[i], 0, operand.dimension_size[i] - update.dimension_size[i]) +``` + +This ensures that the updated slice is always in-bounds with respect to the +operand array. If the slice is in-bounds before the transformation is applied, +the transformation has no effect. + 1-dimensional example: ``` -- GitLab From 7d266d6116438b8f22cd0f2c3b66115c2c05da7d Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Tue, 24 Jul 2018 11:17:25 -0700 Subject: [PATCH 330/519] [XLA:GPU] Limit the maximum number of parameters to a fusion. Very large fusions aren't usually useful on the GPU, and if they get large enough, they can fail to compile (or successfully compile but fail to launch!). PiperOrigin-RevId: 205859710 --- .../xla/service/gpu/instruction_fusion.cc | 49 ++++++++++++++++++- .../xla/service/gpu/instruction_fusion.h | 4 ++ .../service/gpu/instruction_fusion_test.cc | 30 ++++++++++++ 3 files changed, 81 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc index 64ed3d748f..8abae43a5a 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc @@ -183,8 +183,53 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, return true; } - return IsFusile(*producer) && IsFusile(*consumer) && - InstructionFusion::ShouldFuse(consumer, operand_index); + if (!IsFusile(*producer) || !IsFusile(*consumer) || + !InstructionFusion::ShouldFuse(consumer, operand_index)) { + return false; + } + + // Limit the maximum number of operands to a fusion. + // + // There's a limit to how many parameters we can pass to a CUDA kernel, but + // exactly what that limit is is hazy, as it depends on (among other things) + // how much GPU constant memory is in use for other purposes. + // + // Moreover, we don't even know at this point how many arguments the CUDA + // kernel for this fusion node will have: It depends on buffer assignment, + // where we will decide which of the fusion's operands live in XLA's big temp + // buffer versus in other allocations. + // + // As a heuristic, we simply cap the number of fusion operands at + // kMaxOperandsPerFusion. This puts an upper bound on the number of + // parameters to the kernel, working around the correctness problem. + // + // This limit is also often good for performance. In a fusion with many + // operands, each GPU thread likely has to do a lot of work, and so possibly + // uses a lot of registers, thus limiting occupancy. + // + // We put this check last because it's expensive to compute. + + // The new fusion will have no more operands than + // producer_operands + consumer_operands - 1 + // (minus one because we're fusing the producer->consumer edge). This fact + // may be enough to let us avoid having to compute the true total number of + // operands, taking into account the fact that producer and consumer may share + // operands. + if (producer->operand_count() + consumer->operand_count() - 1 > + kMaxOperandsPerFusion) { + tensorflow::gtl::FlatSet producer_operands( + producer->operands().begin(), producer->operands().end()); + int64 new_num_operands = + producer->operand_count() + + c_count_if(consumer->operands(), [&](const HloInstruction* operand) { + return operand != producer && !producer_operands.count(operand); + }); + if (new_num_operands > kMaxOperandsPerFusion) { + return false; + } + } + + return true; } bool GpuInstructionFusion::ShouldFuseIntoMultiOutput(HloInstruction* consumer, diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.h b/tensorflow/compiler/xla/service/gpu/instruction_fusion.h index f629d9ff2c..5ee1c004b6 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.h +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.h @@ -36,6 +36,10 @@ class GpuInstructionFusion : public InstructionFusion { HloInstruction::FusionKind ChooseKind( const HloInstruction* producer, const HloInstruction* consumer) override; + + // Maximum number of operands allowed on a single fusion node. Exposed + // publicly mainly for tests. + static constexpr int64 kMaxOperandsPerFusion = 64; }; } // namespace gpu diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc index 98ba162cd9..229eb23f12 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc @@ -606,5 +606,35 @@ TEST_F(InstructionFusionTest, FuseScalarConstant) { op::Parameter())); } +// Check that we limit the number of operands to fusions we create. +TEST_F(InstructionFusionTest, AvoidsLargeFusion) { + constexpr int64 kNumParams = 200; + ASSERT_GT(kNumParams, GpuInstructionFusion::kMaxOperandsPerFusion); + + // Compute p0 + p1 + ... + pN. + HloComputation::Builder b(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {10, 100}); + auto param0 = + b.AddInstruction(HloInstruction::CreateParameter(0, shape, "p")); + auto sum = param0; + for (int64 i = 1; i < kNumParams; ++i) { + auto param = + b.AddInstruction(HloInstruction::CreateParameter(i, shape, "p")); + sum = b.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, sum, param)); + } + auto module = CreateNewModule(); + auto computation = module->AddEntryComputation(b.Build()); + EXPECT_TRUE(GpuInstructionFusion(/*may_duplicate=*/true) + .Run(module.get()) + .ValueOrDie()); + SCOPED_TRACE(module->ToString()); + for (const HloInstruction* instr : computation->instructions()) { + EXPECT_LE(instr->operand_count(), + GpuInstructionFusion::kMaxOperandsPerFusion) + << instr->ToString(); + } +} + } // namespace gpu } // namespace xla -- GitLab From bd8ee2b6aba2f99d7ca9a7af12eda62db480f355 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Tue, 24 Jul 2018 11:17:33 -0700 Subject: [PATCH 331/519] Uses separate forward and backward graphs for tfe.defun backprop. PiperOrigin-RevId: 205859733 --- tensorflow/python/eager/function.py | 45 +++++++++++++++-------------- 1 file changed, 24 insertions(+), 21 deletions(-) diff --git a/tensorflow/python/eager/function.py b/tensorflow/python/eager/function.py index d283a85532..5e4f9e29da 100644 --- a/tensorflow/python/eager/function.py +++ b/tensorflow/python/eager/function.py @@ -470,37 +470,39 @@ class GraphModeFunction(object): def _construct_backprop_function(self): """Constructs the backprop function object for this function.""" - with self._graph.as_default(): - c_known_ops = set() - c_captured_tensors = set() - - existing_op_len = len(self._graph.get_operations()) - filtered_outputs = [x for x in self._python_returns if x is not None] + filtered_outputs = [x for x in self._python_returns if x is not None] + captures = {} + backwards_graph = CapturingGraph(captures) + backwards_graph._graph_key = self._graph._graph_key # pylint: disable=protected-access + for collection in self._graph.collections: + backwards_graph.get_collection_ref( + collection)[:] = self._graph.get_collection(collection) + backwards_graph.seed = self._graph.seed + with backwards_graph.as_default(): self._out_grad_placeholders = [ graph_placeholder(x.dtype, x.shape) for x in filtered_outputs] - in_gradients = gradients_impl.gradients( + in_gradients = gradients_impl._GradientsHelper( # pylint: disable=protected-access filtered_outputs, self._input_placeholders, - grad_ys=self._out_grad_placeholders) - for op in self._graph.get_operations()[existing_op_len:]: - if op.type in ["Variable", "VariableV2", "VarHandleOp"]: - raise ValueError("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) + grad_ys=self._out_grad_placeholders, + src_graph=self._graph) backward_outputs = tuple( 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)) + ids = list(sorted(captures.keys())) + if ids: + extra_inputs, extra_placeholders = zip(*[captures[x] for x in ids]) + else: + extra_inputs = [] + extra_placeholders = [] + forward_name = _forward_name(self._func_name) self._forward_fdef = _EagerDefinedFunction( forward_name, self._graph, self._ops, self._input_placeholders, - filtered_outputs + captures, self._attrs) - all_inputs = self._out_grad_placeholders + captures + filtered_outputs + list(extra_inputs), self._attrs) + all_inputs = self._out_grad_placeholders + list(extra_placeholders) # Excluding input ops from the body as we do not intend to execute these # operations when the function is executed. all_ignored_ops = frozenset(x.op for x in all_inputs) @@ -508,11 +510,12 @@ 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(backwards_graph.get_operations(), + key=lambda x: x.name) if x not in all_ignored_ops) bname = _backward_name(self._func_name) self._backward_function = GraphModeFunction( - bname, all_inputs, [], self._graph, function_def_ops, + bname, all_inputs, [], backwards_graph, function_def_ops, backward_outputs, in_gradients, output_shapes, attrs=self._attrs) def _backprop_call(self, args): -- GitLab From 9e2466b60cf81a92048d4a14237da198c8033dc4 Mon Sep 17 00:00:00 2001 From: Rohan Jain Date: Tue, 24 Jul 2018 11:25:43 -0700 Subject: [PATCH 332/519] Creating a MultiDeviceIterator that deterministically provides outputs to different iterators placed on devices. PiperOrigin-RevId: 205861287 --- .../data/kernels/prefetching_kernels.cc | 463 ++++++++++++++++++ tensorflow/contrib/data/ops/dataset_ops.cc | 74 +++ .../kernel_tests/prefetching_ops_test.py | 151 ++++++ .../data/python/ops/prefetching_ops.py | 172 +++++++ 4 files changed, 860 insertions(+) diff --git a/tensorflow/contrib/data/kernels/prefetching_kernels.cc b/tensorflow/contrib/data/kernels/prefetching_kernels.cc index b3d464d716..6edc61b2c2 100644 --- a/tensorflow/contrib/data/kernels/prefetching_kernels.cc +++ b/tensorflow/contrib/data/kernels/prefetching_kernels.cc @@ -15,6 +15,7 @@ limitations under the License. #include #include "tensorflow/core/common_runtime/process_function_library_runtime.h" +#include "tensorflow/core/framework/dataset.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/resource_op_kernel.h" @@ -23,6 +24,7 @@ limitations under the License. #include "tensorflow/core/util/device_name_utils.h" namespace tensorflow { +namespace { struct BufferElement { // The producer sets `status` if getting the input element fails. @@ -473,4 +475,465 @@ class IteratorGetDeviceOp : public OpKernel { REGISTER_KERNEL_BUILDER(Name("IteratorGetDevice").Device(DEVICE_CPU), IteratorGetDeviceOp); +Status VerifyTypesMatch(const DataTypeVector& expected, + const DataTypeVector& received) { + if (expected.size() != received.size()) { + return errors::InvalidArgument( + "Number of components does not match: expected ", expected.size(), + " types but got ", received.size(), "."); + } + for (size_t i = 0; i < expected.size(); ++i) { + if (expected[i] != received[i]) { + return errors::InvalidArgument("Data type mismatch at component ", i, + ": expected ", DataTypeString(expected[i]), + " but got ", DataTypeString(received[i]), + "."); + } + } + return Status::OK(); +} + +Status VerifyShapesCompatible(const std::vector& expected, + const std::vector& received) { + if (expected.size() != received.size()) { + return errors::InvalidArgument( + "Number of components does not match: expected ", expected.size(), + " shapes but got ", received.size(), "."); + } + for (size_t i = 0; i < expected.size(); ++i) { + if (!expected[i].IsCompatibleWith(received[i])) { + return errors::InvalidArgument("Incompatible shapes at component ", i, + ": expected ", expected[i].DebugString(), + " but got ", received[i].DebugString(), + "."); + } + } + + return Status::OK(); +} + +string SanitizeThreadSuffix(string suffix) { + string clean; + for (int i = 0; i < suffix.size(); ++i) { + const char ch = suffix[i]; + if ((ch >= 'a' && ch <= 'z') || (ch >= 'A' && ch <= 'Z') || + (ch >= '0' && ch <= '9') || ch == '_' || ch == '-') { + clean += ch; + } else { + clean += '_'; + } + } + return clean; +} + +class MultiDeviceIterator : public ResourceBase { + public: + MultiDeviceIterator(const DataTypeVector& output_types, + const std::vector& output_shapes, + const std::vector& devices, + std::unique_ptr flib_def, + std::unique_ptr pflr, + FunctionLibraryRuntime* lib) + : output_types_(output_types), + output_shapes_(output_shapes), + devices_(devices), + flib_def_(std::move(flib_def)), + pflr_(std::move(pflr)), + lib_(lib) { + buffer_.resize(devices_.size()); + } + + string DebugString() override { + return strings::StrCat("MultiDeviceIterator"); + } + + Status Init(std::unique_ptr iterator, int64* incarnation_id) { + mutex_lock l(mu_); + if (iterator) { + TF_RETURN_IF_ERROR( + VerifyTypesMatch(output_types_, iterator->output_dtypes())); + TF_RETURN_IF_ERROR( + VerifyShapesCompatible(output_shapes_, iterator->output_shapes())); + } + host_iterator_.reset(iterator.release()); + incarnation_id_++; + *incarnation_id = incarnation_id_; + max_buffer_size_ = 0; + num_elements_ = 0; + buffer_.clear(); + buffer_.resize(devices_.size()); + return Status::OK(); + } + + Status GetNextFromShard(IteratorContext* ctx, int shard_num, + int64 incarnation_id, + std::vector* out_tensors, + bool* end_of_sequence) { + // TODO(rohanj): This might potentially strand elements in other shards. + // Opportunity to do smarter locking semantics. + mutex_lock l(mu_); + // Make sure we're in the right incarnation. + if (incarnation_id != incarnation_id_) { + return errors::InvalidArgument( + "Current incarnation: ", incarnation_id_, + "; Supplied incarnation: ", incarnation_id); + } + // Then look it up in the buffer. + if (!buffer_[shard_num].empty()) { + const HostBufferElement& elem = buffer_[shard_num].front(); + *out_tensors = elem.value; + *end_of_sequence = elem.end_of_sequence; + Status s = elem.status; + buffer_[shard_num].pop_front(); + return s; + } + std::shared_ptr captured_iterator(host_iterator_); + if (captured_iterator) { + if (lib_ != nullptr) { + ctx->set_lib(lib_); + } + while (true) { + HostBufferElement elem; + elem.status = + captured_iterator->GetNext(ctx, &elem.value, &elem.end_of_sequence); + int buffer_index = num_elements_ % devices_.size(); + num_elements_++; + if (buffer_index == shard_num) { + out_tensors->swap(elem.value); + *end_of_sequence = elem.end_of_sequence; + return elem.status; + } else { + buffer_[buffer_index].push_back(std::move(elem)); + // TODO(rohanj): Put an upper bound to buffer size. + if (buffer_[buffer_index].size() > max_buffer_size_) { + max_buffer_size_ = buffer_[buffer_index].size(); + VLOG(1) << "MultiDeviceIterator: Max buffer size increased to: " + << max_buffer_size_; + } + } + } + } else { + return errors::FailedPrecondition("Iterator not initialized"); + } + return Status::OK(); + } + + const DataTypeVector& output_types() const { return output_types_; } + + const std::vector& output_shapes() const { + return output_shapes_; + } + + std::shared_ptr function_library() { + tf_shared_lock l(mu_); + return lib_def_; + } + + private: + struct HostBufferElement { + Status status; + bool end_of_sequence; + std::vector value; + }; + + mutex mu_; + const DataTypeVector output_types_; + const std::vector output_shapes_; + const std::vector devices_; + int64 num_elements_ GUARDED_BY(mu_) = 0; + int64 max_buffer_size_ GUARDED_BY(mu_) = 0; + int64 incarnation_id_ GUARDED_BY(mu_) = 0; + std::vector> buffer_ GUARDED_BY(mu_); + std::unique_ptr flib_def_; + std::unique_ptr pflr_; + FunctionLibraryRuntime* lib_ = nullptr; // not owned. + std::shared_ptr host_iterator_; + std::shared_ptr lib_def_ GUARDED_BY(mu_); +}; + +// Just creates a MultiDeviceIterator and returns it. +class MultiDeviceIteratorHandleOp : public OpKernel { + public: + explicit MultiDeviceIteratorHandleOp(OpKernelConstruction* ctx) + : OpKernel(ctx), graph_def_version_(ctx->graph_def_version()) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("shared_name", &name_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("container", &container_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("devices", &devices_)); + } + + // The resource is deleted from the resource manager only when it is private + // to kernel. + ~MultiDeviceIteratorHandleOp() override { + if (resource_ != nullptr) { + resource_->Unref(); + if (cinfo_.resource_is_private_to_kernel()) { + if (!cinfo_.resource_manager() + ->template Delete(cinfo_.container(), + cinfo_.name()) + .ok()) { + // Do nothing; the resource can have been deleted by session resets. + } + } + } + } + + void Compute(OpKernelContext* context) override LOCKS_EXCLUDED(mu_) { + { + mutex_lock l(mu_); + if (resource_ == nullptr) { + FunctionLibraryRuntime* lib; + std::unique_ptr flib_def(nullptr); + std::unique_ptr pflr(nullptr); + OP_REQUIRES_OK(context, context->function_library()->Clone( + &flib_def, &pflr, &lib)); + ResourceMgr* mgr = context->resource_manager(); + OP_REQUIRES_OK(context, cinfo_.Init(mgr, def())); + + MultiDeviceIterator* resource; + OP_REQUIRES_OK( + context, + mgr->LookupOrCreate( + cinfo_.container(), cinfo_.name(), &resource, + [this, lib, &flib_def, &pflr](MultiDeviceIterator** ret) + EXCLUSIVE_LOCKS_REQUIRED(mu_) { + *ret = new MultiDeviceIterator( + output_types_, output_shapes_, devices_, + std::move(flib_def), std::move(pflr), lib); + return Status::OK(); + })); + + Status s = VerifyResource(resource); + if (TF_PREDICT_FALSE(!s.ok())) { + resource->Unref(); + context->SetStatus(s); + return; + } + + resource_ = resource; + } + } + OP_REQUIRES_OK(context, MakeResourceHandleToOutput( + context, 0, cinfo_.container(), cinfo_.name(), + MakeTypeIndex())); + } + + private: + // During the first Compute(), resource is either created or looked up using + // shared_name. In the latter case, the resource found should be verified if + // it is compatible with this op's configuration. The verification may fail in + // cases such as two graphs asking queues of the same shared name to have + // inconsistent capacities. + Status VerifyResource(MultiDeviceIterator* resource) { + TF_RETURN_IF_ERROR( + VerifyTypesMatch(output_types_, resource->output_types())); + TF_RETURN_IF_ERROR( + VerifyShapesCompatible(output_shapes_, resource->output_shapes())); + return Status::OK(); + } + + mutex mu_; + ContainerInfo cinfo_; // Written once under mu_ then constant afterwards. + MultiDeviceIterator* resource_ GUARDED_BY(mu_) = nullptr; + DataTypeVector output_types_; + std::vector output_shapes_; + const int graph_def_version_; + string name_; + string container_; + std::vector devices_; +}; + +REGISTER_KERNEL_BUILDER(Name("MultiDeviceIterator").Device(DEVICE_CPU), + MultiDeviceIteratorHandleOp); + +// Calls init on the MultiDeviceIterator. +class MultiDeviceIteratorInitOp : public OpKernel { + public: + explicit MultiDeviceIteratorInitOp(OpKernelConstruction* ctx) + : OpKernel(ctx) {} + + void Compute(OpKernelContext* ctx) override { + DatasetBase* dataset; + OP_REQUIRES_OK(ctx, GetDatasetFromVariantTensor(ctx->input(0), &dataset)); + MultiDeviceIterator* resource; + OP_REQUIRES_OK(ctx, + LookupResource(ctx, HandleFromInput(ctx, 1), &resource)); + core::ScopedUnref unref(resource); + + IteratorContext iter_ctx = dataset::MakeIteratorContext(ctx); + std::unique_ptr iterator; + OP_REQUIRES_OK(ctx, + dataset->MakeIterator(&iter_ctx, "Iterator", &iterator)); + int64 incarnation_id; + OP_REQUIRES_OK(ctx, resource->Init(std::move(iterator), &incarnation_id)); + Tensor tensor_incarnation_id(DT_INT64, TensorShape({})); + tensor_incarnation_id.scalar()() = incarnation_id; + OP_REQUIRES_OK(ctx, + ctx->set_output("incarnation_id", tensor_incarnation_id)); + } +}; + +REGISTER_KERNEL_BUILDER(Name("MultiDeviceIteratorInit").Device(DEVICE_CPU), + MultiDeviceIteratorInitOp); + +// Calls GetNextFromShard(shard) and returns a vector of Tensors as output. +// TODO(rohanj): Implement using BackgroundWorker that Derek built? +class MultiDeviceIteratorGetNextFromShardOp : public AsyncOpKernel { + public: + explicit MultiDeviceIteratorGetNextFromShardOp(OpKernelConstruction* ctx) + : AsyncOpKernel(ctx), + thread_pool_(new thread::ThreadPool( + ctx->env(), ThreadOptions(), + strings::StrCat("multi_device_iterator_get_next_thread_", + SanitizeThreadSuffix(name())), + 1 /* num_threads */, false /* low_latency_hint */)) {} + + void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override { + const Tensor* tensor_shard_num; + OP_REQUIRES_OK(ctx, ctx->input("shard_num", &tensor_shard_num)); + int32 shard_num = tensor_shard_num->scalar()(); + + const Tensor* tensor_incarnation_id; + OP_REQUIRES_OK(ctx, ctx->input("incarnation_id", &tensor_incarnation_id)); + int64 incarnation_id = tensor_incarnation_id->scalar()(); + + MultiDeviceIterator* iterator; + OP_REQUIRES_OK(ctx, + LookupResource(ctx, HandleFromInput(ctx, 0), &iterator)); + thread_pool_->Schedule(std::bind( + [ctx, iterator, shard_num, incarnation_id](DoneCallback done) { + std::vector components; + bool end_of_sequence = false; + + IteratorContext::Params params; + params.env = ctx->env(); + params.runner = *(ctx->runner()); + params.function_library = iterator->function_library(); + DeviceBase* device = ctx->function_library()->device(); + params.allocator_getter = [device](AllocatorAttributes attrs) { + return device->GetAllocator(attrs); + }; + IteratorContext iter_ctx(std::move(params)); + + Status s = + iterator->GetNextFromShard(&iter_ctx, shard_num, incarnation_id, + &components, &end_of_sequence); + iterator->Unref(); + + if (!s.ok()) { + ctx->SetStatus(s); + } else if (end_of_sequence) { + ctx->SetStatus(errors::OutOfRange("End of sequence")); + } else { + for (int i = 0; i < components.size(); ++i) { + // TODO(mrry): Check that the shapes match the shape attrs. + ctx->set_output(i, components[i]); + } + } + done(); + }, + std::move(done))); + } + + private: + std::unique_ptr thread_pool_; +}; + +REGISTER_KERNEL_BUILDER( + Name("MultiDeviceIteratorGetNextFromShard").Device(DEVICE_CPU), + MultiDeviceIteratorGetNextFromShardOp); + +class MultiDeviceIteratorToStringHandleOp : public OpKernel { + public: + explicit MultiDeviceIteratorToStringHandleOp(OpKernelConstruction* ctx) + : OpKernel(ctx) {} + + void Compute(OpKernelContext* ctx) override { + const Tensor& resource_handle_t = ctx->input(0); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(resource_handle_t.shape()), + errors::InvalidArgument("resource_handle must be a scalar")); + + // Validate that the handle corresponds to a real resource, and + // that it is an MultiDeviceIterator. + MultiDeviceIterator* resource; + OP_REQUIRES_OK(ctx, + LookupResource(ctx, HandleFromInput(ctx, 0), &resource)); + resource->Unref(); + + Tensor* string_handle_t; + OP_REQUIRES_OK(ctx, + ctx->allocate_output(0, TensorShape({}), &string_handle_t)); + string_handle_t->scalar()() = + resource_handle_t.scalar()().SerializeAsString(); + } +}; + +REGISTER_KERNEL_BUILDER( + Name("MultiDeviceIteratorToStringHandle").Device(DEVICE_CPU), + MultiDeviceIteratorToStringHandleOp); + +class MultiDeviceIteratorFromStringHandleOp : public OpKernel { + public: + explicit MultiDeviceIteratorFromStringHandleOp(OpKernelConstruction* ctx) + : OpKernel(ctx) { + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_types", &output_types_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("output_shapes", &output_shapes_)); + OP_REQUIRES( + ctx, + output_types_.empty() || output_shapes_.empty() || + output_types_.size() == output_shapes_.size(), + errors::InvalidArgument("If both 'output_types' and 'output_shapes' " + "are set, they must have the same length.")); + } + + void Compute(OpKernelContext* ctx) override { + const Tensor& string_handle_t = ctx->input(0); + OP_REQUIRES(ctx, TensorShapeUtils::IsScalar(string_handle_t.shape()), + errors::InvalidArgument("string_handle must be a scalar")); + + ResourceHandle resource_handle; + OP_REQUIRES( + ctx, + resource_handle.ParseFromString(string_handle_t.scalar()()), + errors::InvalidArgument( + "Could not parse string_handle as a valid ResourceHandle")); + + OP_REQUIRES( + ctx, resource_handle.device() == ctx->device()->attributes().name(), + errors::InvalidArgument("Attempted create an iterator on device \"", + ctx->device()->attributes().name(), + "\" from handle defined on device \"", + resource_handle.device(), "\"")); + + // Validate that the handle corresponds to a real resource, and + // that it is an MultiDeviceIterator. + MultiDeviceIterator* resource; + OP_REQUIRES_OK(ctx, LookupResource(ctx, resource_handle, &resource)); + core::ScopedUnref unref_iterator(resource); + if (!output_types_.empty()) { + OP_REQUIRES_OK(ctx, + VerifyTypesMatch(output_types_, resource->output_types())); + } + if (!output_shapes_.empty()) { + OP_REQUIRES_OK(ctx, VerifyShapesCompatible(output_shapes_, + resource->output_shapes())); + } + + Tensor* resource_handle_t; + OP_REQUIRES_OK( + ctx, ctx->allocate_output(0, TensorShape({}), &resource_handle_t)); + resource_handle_t->scalar()() = resource_handle; + } + + private: + DataTypeVector output_types_; + std::vector output_shapes_; +}; + +REGISTER_KERNEL_BUILDER( + Name("MultiDeviceIteratorFromStringHandle").Device(DEVICE_CPU), + MultiDeviceIteratorFromStringHandleOp); + +} // anonymous namespace } // namespace tensorflow diff --git a/tensorflow/contrib/data/ops/dataset_ops.cc b/tensorflow/contrib/data/ops/dataset_ops.cc index b5c6f2e241..66a7c7fdcd 100644 --- a/tensorflow/contrib/data/ops/dataset_ops.cc +++ b/tensorflow/contrib/data/ops/dataset_ops.cc @@ -145,6 +145,80 @@ Resets the FunctionBufferingResource. function_buffer_resource: The FunctionBufferingResource handle. )doc"); +REGISTER_OP("MultiDeviceIterator") + .Output("handle: resource") + .Attr("devices: list(string) >= 1") + .Attr("shared_name: string") + .Attr("container: string") + .Attr("output_types: list(type) >= 1") + .Attr("output_shapes: list(shape) >= 1") + .Doc(R"doc( +Creates a MultiDeviceIterator resource. + +handle: Handle to the resource created. +devices: A list of devices the iterator works across. +shared_name: If non-empty, this resource will be shared under the given name + across multiple sessions. +container: If non-empty, this resource is placed in the given container. + Otherwise, a default container is used. +output_types: The type list for the return values. +output_shapes: The list of shapes being produced. +)doc"); + +REGISTER_OP("MultiDeviceIteratorInit") + .Input("dataset: variant") + .Input("multi_device_iterator: resource") + .Output("incarnation_id: int64") + .Doc(R"doc( +Initializes the multi device iterator with the given dataset. +incarnation_id: An int64 indicating which incarnation of the MultiDeviceIterator + is running. +dataset: Dataset to be iterated upon. +multi_device_iterator: A MultiDeviceIteratorResource. +)doc"); + +REGISTER_OP("MultiDeviceIteratorGetNextFromShard") + .Input("multi_device_iterator: resource") + .Input("shard_num: int32") + .Input("incarnation_id: int64") + .Output("components: output_types") + .Attr("output_types: list(type) >= 1") + .Attr("output_shapes: list(shape) >= 1") + .Doc(R"doc( +Gets next element for the provided shard number. + +multi_device_iterator: A MultiDeviceIterator resource. +shard_num: Integer representing which shard to fetch data for. +incarnation_id: Which incarnation of the MultiDeviceIterator is running. +components: Result of the get_next on the dataset. +output_types: The type list for the return values. +output_shapes: The list of shapes being produced. +)doc"); + +REGISTER_OP("MultiDeviceIteratorToStringHandle") + .Input("multi_device_iterator: resource") + .Output("string_handle: string") + .Doc(R"doc( +Produces a string handle for the given MultiDeviceIterator. + +multi_device_iterator: A MultiDeviceIterator resource. +string_handle: A string representing the resource. +)doc"); + +REGISTER_OP("MultiDeviceIteratorFromStringHandle") + .Input("string_handle: string") + .Output("multi_device_iterator: resource") + .Attr("output_types: list(type) >= 0 = []") + .Attr("output_shapes: list(shape) >= 0 = []") + .Doc(R"doc( +Generates a MultiDeviceIterator resource from its provided string handle. + +string_handle: String representing the resource. +multi_device_iterator: A MultiDeviceIterator resource. +output_types: The type list for the return values. +output_shapes: The list of shapes being produced. +)doc"); + REGISTER_OP("ThreadPoolDataset") .Input("input_dataset: variant") .Input("thread_pool: resource") diff --git a/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py b/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py index 82543b1039..2da6131e8e 100644 --- a/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/prefetching_ops_test.py @@ -31,6 +31,7 @@ from tensorflow.python.framework import function from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.framework import test_util +from tensorflow.python.ops import array_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.platform import test @@ -907,5 +908,155 @@ class CopyToDeviceTest(test.TestCase): sess.run(next_element) +class MultiDeviceIteratorTest(test.TestCase): + + def testBasic(self): + dataset = dataset_ops.Dataset.range(10) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/cpu:2"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 3}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 10, 2): + self.assertEqual(i, sess.run(elem_on_1)) + self.assertEqual(i + 1, sess.run(elem_on_2)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + def testOneOnSameDevice(self): + with ops.device("/cpu:0"): + dataset = dataset_ops.Dataset.range(10) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:0", "/cpu:1"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 2}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 10, 2): + self.assertEqual(i, sess.run(elem_on_1)) + self.assertEqual(i + 1, sess.run(elem_on_2)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + def testRepeatDevices(self): + with ops.device("/cpu:0"): + dataset = dataset_ops.Dataset.range(20) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/cpu:2", "/cpu:1", "/cpu:2"]) + elements = multi_device_iterator.get_next() + elem_on_1, elem_on_2, elem_on_3, elem_on_4 = elements + + config = config_pb2.ConfigProto(device_count={"CPU": 3}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 20, 4): + self.assertEqual(i, sess.run(elem_on_1)) + self.assertEqual(i + 1, sess.run(elem_on_2)) + self.assertEqual(i + 2, sess.run(elem_on_3)) + self.assertEqual(i + 3, sess.run(elem_on_4)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + sess.run(elem_on_3) + sess.run(elem_on_4) + + def testNotFullyDivisible(self): + dataset = dataset_ops.Dataset.range(9) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/cpu:2"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 3}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 8, 2): + self.assertEqual(i, sess.run(elem_on_1)) + self.assertEqual(i + 1, sess.run(elem_on_2)) + self.assertEqual(8, sess.run(elem_on_1)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + def testUneven(self): + dataset = dataset_ops.Dataset.range(10) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/cpu:2"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 3}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 10, 2): + self.assertEqual(i, sess.run(elem_on_1)) + for i in range(0, 10, 2): + self.assertEqual(i + 1, sess.run(elem_on_2)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + def testMultipleInitializations(self): + with ops.device("/cpu:0"): + epoch = array_ops.placeholder(dtypes.int64, shape=[]) + dataset1 = dataset_ops.Dataset.from_tensors(epoch).repeat(1000) + dataset2 = dataset_ops.Dataset.range(1000) + dataset = dataset_ops.Dataset.zip((dataset1, dataset2)) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/cpu:2"], prefetch_buffer_size=4) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + init_op = multi_device_iterator.initializer + + config = config_pb2.ConfigProto(device_count={"CPU": 3}) + with self.test_session(config=config) as sess: + for i in range(1000): + sess.run(init_op, feed_dict={epoch: i}) + self.assertEqual([(i, 0), (i, 1)], sess.run([elem_on_1, elem_on_2])) + + def testBasicGpu(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + with compat.forward_compatibility_horizon(2018, 8, 4): + dataset = dataset_ops.Dataset.range(10) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/gpu:0"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 2, "GPU": 1}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 10, 2): + self.assertEqual(i, sess.run(elem_on_1)) + self.assertEqual(i + 1, sess.run(elem_on_2)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + def testUnevenGpu(self): + if not test_util.is_gpu_available(): + self.skipTest("No GPU available") + + with compat.forward_compatibility_horizon(2018, 8, 4): + dataset = dataset_ops.Dataset.range(10) + multi_device_iterator = prefetching_ops.MultiDeviceIterator( + dataset, ["/cpu:1", "/gpu:0"]) + elem_on_1, elem_on_2 = multi_device_iterator.get_next() + + config = config_pb2.ConfigProto(device_count={"CPU": 2, "GPU": 1}) + with self.test_session(config=config) as sess: + sess.run(multi_device_iterator.initializer) + for i in range(0, 10, 2): + self.assertEqual(i, sess.run(elem_on_1)) + for i in range(0, 10, 2): + self.assertEqual(i + 1, sess.run(elem_on_2)) + with self.assertRaises(errors.OutOfRangeError): + sess.run(elem_on_1) + sess.run(elem_on_2) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/ops/prefetching_ops.py b/tensorflow/contrib/data/python/ops/prefetching_ops.py index 45abd6376c..0edd7c9fe9 100644 --- a/tensorflow/contrib/data/python/ops/prefetching_ops.py +++ b/tensorflow/contrib/data/python/ops/prefetching_ops.py @@ -31,6 +31,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import function 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 functional_ops from tensorflow.python.ops import gen_dataset_ops as core_gen_dataset_ops from tensorflow.python.ops import resource_variable_ops @@ -523,3 +524,174 @@ class _CopyToDeviceDataset(dataset_ops.Dataset): @property def output_classes(self): return self._input_dataset.output_classes + + +class _PerDeviceGenerator(dataset_ops.Dataset): + """A `dummy` generator dataset.""" + + def __init__(self, shard_num, multi_device_iterator_resource, incarnation_id, + source_device, target_device, output_shapes, output_types, + output_classes): + self._target_device = target_device + self._output_types = output_types + self._output_shapes = output_shapes + self._output_classes = output_classes + self._flat_output_shapes = nest.flatten( + sparse.as_dense_shapes(self._output_shapes, self._output_classes)) + self._flat_output_types = nest.flatten( + sparse.as_dense_types(self._output_types, self._output_classes)) + + multi_device_iterator_string_handle = ( + gen_dataset_ops.multi_device_iterator_to_string_handle( + multi_device_iterator_resource)) + + @function.Defun() + def _init_func(): + return multi_device_iterator_string_handle + + @function.Defun() + def _remote_init_func(): + return functional_ops.remote_call( + target=source_device, + args=_init_func.captured_inputs, + Tout=[dtypes.string], + f=_init_func) + + self._init_func = _remote_init_func + self._init_captured_args = _remote_init_func.captured_inputs + + @function.Defun(dtypes.string) + def _next_func(string_handle): + multi_device_iterator = ( + gen_dataset_ops.multi_device_iterator_from_string_handle( + string_handle=string_handle, + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes)) + return gen_dataset_ops.multi_device_iterator_get_next_from_shard( + multi_device_iterator=multi_device_iterator, + shard_num=shard_num, + incarnation_id=incarnation_id, + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes) + + @function.Defun(dtypes.string) + def _remote_next_func(string_handle): + return functional_ops.remote_call( + target=source_device, + args=[string_handle] + _next_func.captured_inputs, + Tout=self._flat_output_types, + f=_next_func) + + self._next_func = _remote_next_func + self._next_captured_args = _remote_next_func.captured_inputs + + @function.Defun(dtypes.string) + def _finalize_func(unused_string_handle): + return array_ops.constant(0, dtypes.int64) + + @function.Defun(dtypes.string) + def _remote_finalize_func(string_handle): + return functional_ops.remote_call( + target=source_device, + args=[string_handle] + _finalize_func.captured_inputs, + Tout=[dtypes.int64], + f=_finalize_func) + + self._finalize_func = _remote_finalize_func + self._finalize_captured_args = _remote_finalize_func.captured_inputs + + def _as_variant_tensor(self): + with ops.device(self._target_device): + return core_gen_dataset_ops.generator_dataset( + self._init_captured_args, + self._next_captured_args, + self._finalize_captured_args, + init_func=self._init_func, + next_func=self._next_func, + finalize_func=self._finalize_func, + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes) + + @property + def output_types(self): + return self._output_types + + @property + def output_shapes(self): + return self._output_shapes + + @property + def output_classes(self): + return self._output_classes + + +class MultiDeviceIterator(object): + """An iterator over multiple devices.""" + + def __init__(self, + dataset, + devices, + prefetch_buffer_size=1, + source_device="/cpu:0"): + self._dataset = dataset + self._devices = devices + self._source_device = source_device + self._source_device_tensor = ops.convert_to_tensor(source_device) + + self._flat_output_shapes = nest.flatten( + sparse.as_dense_shapes(self._dataset.output_shapes, + self._dataset.output_classes)) + self._flat_output_types = nest.flatten( + sparse.as_dense_types(self._dataset.output_types, + self._dataset.output_classes)) + + # Create the MultiDeviceIterator. + with ops.device(self._source_device): + self._multi_device_iterator_resource = ( + gen_dataset_ops.multi_device_iterator( + devices=self._devices, + shared_name="", + container="", + output_types=self._flat_output_types, + output_shapes=self._flat_output_shapes)) + + # The incarnation ID is used to ensure consistency between the per-device + # iterators and the multi-device iterator. + self._incarnation_id = gen_dataset_ops.multi_device_iterator_init( + self._dataset._as_variant_tensor(), # pylint: disable=protected-access + self._multi_device_iterator_resource) + + # TODO(rohanj): Explore the possibility of the MultiDeviceIterator to + # initialize the device side of the pipeline. This would allow the + # MultiDeviceIterator to choose, for example, to move some transformations + # into the device side from its input. It might be useful in rewriting. + # Create the per device iterators. + self._device_iterators = [] + i = 0 + for device in self._devices: + ds = _PerDeviceGenerator( + i, self._multi_device_iterator_resource, self._incarnation_id, + self._source_device_tensor, device, self._dataset.output_shapes, + self._dataset.output_types, self._dataset.output_classes) + ds = ds.prefetch(prefetch_buffer_size) + with ops.device(device): + self._device_iterators.append(ds.make_initializable_iterator()) + i += 1 + + device_iterator_initializers = [ + iterator.initializer for iterator in self._device_iterators + ] + self._initializer = control_flow_ops.group(*device_iterator_initializers) + + def get_next(self): + result = [] + i = 0 + for device in self._devices: + with ops.device(device): + result.append(self._device_iterators[i].get_next()) + i += 1 + return result + + @property + def initializer(self): + return self._initializer -- GitLab From 3f74665d12d5adbc5dfb17d3616b9904af45b93a Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 24 Jul 2018 11:31:56 -0700 Subject: [PATCH 333/519] Fix link PiperOrigin-RevId: 205862584 --- tensorflow/docs_src/guide/saved_model.md | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow/docs_src/guide/saved_model.md b/tensorflow/docs_src/guide/saved_model.md index acc3d3ca0b..717488e7cc 100644 --- a/tensorflow/docs_src/guide/saved_model.md +++ b/tensorflow/docs_src/guide/saved_model.md @@ -2,9 +2,8 @@ The @{tf.train.Saver} class provides methods to save and restore models. The @{tf.saved_model.simple_save} function is an easy way to build a -@{tf.saved_model$saved model} suitable for serving. -[Estimators](@{$guide/estimators}) automatically save and restore -variables in the `model_dir`. +@{tf.saved_model$saved model} suitable for serving. [Estimators](./estimators) +automatically save and restore variables in the `model_dir`. ## Save and restore variables -- GitLab From badf913c0a2f83ca933b8fe73a29f7dd5d2bc5ce Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 24 Jul 2018 11:45:30 -0700 Subject: [PATCH 334/519] New triangular solve algorithm. PiperOrigin-RevId: 205865103 --- .../compiler/tf2xla/lib/triangular_solve.cc | 828 +++++++----------- .../compiler/tf2xla/lib/triangular_solve.h | 6 - .../tf2xla/lib/triangular_solve_test.cc | 64 +- tensorflow/compiler/xla/client/lib/numeric.cc | 28 + tensorflow/compiler/xla/client/lib/numeric.h | 9 + .../compiler/xla/client/lib/numeric_test.cc | 14 + 6 files changed, 381 insertions(+), 568 deletions(-) diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc index a2dd5a0d57..75c0ad7f7e 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc @@ -29,9 +29,307 @@ limitations under the License. #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/errors.h" +#include "tensorflow/core/lib/math/math_util.h" namespace tensorflow { +// Get the diagonal blocks of the coefficient matrix +xla::XlaOp DiagonalBlocks(xla::XlaOp a, int64 block_size) { + xla::XlaBuilder* builder = a.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(a)); + int ndims = xla::ShapeUtil::Rank(shape); + int64 n = xla::ShapeUtil::GetDimension(shape, -1); + int64 num_blocks = n / block_size; + + xla::XlaOp diag_blocks; + + // If the coefficient matrix is exactly the block size, we just add a + // singleton dimension i.e. [..., n, n] -> [..., 1, n, n] + if (n == block_size) { + std::vector permutation(ndims); + std::iota(permutation.begin(), permutation.end(), 1); + permutation.insert(permutation.end() - 2, 0); + return Transpose(Broadcast(a, /*broadcast_sizes=*/{1}), permutation); + } + + // We can grab entire blocks using gather + if (n > block_size) { + // Construct the starting indices of the diagonal blocks + auto gather_indices = + Transpose(Broadcast(Mul(Iota(builder, xla::S32, num_blocks), + xla::ConstantR0(builder, block_size)), + /*broadcast_sizes=*/{2}), + /*permutation=*/{1, 0}); + + // Gather the diagonal blocks + xla::GatherDimensionNumbers dim_numbers; + dim_numbers.add_output_window_dims(ndims - 1); + dim_numbers.add_output_window_dims(ndims); + dim_numbers.add_gather_dims_to_operand_dims(ndims - 2); + dim_numbers.add_gather_dims_to_operand_dims(ndims - 1); + dim_numbers.set_index_vector_dim(1); + diag_blocks = Gather(a, gather_indices, dim_numbers, + /*window_bounds=*/{block_size, block_size}); + } + + // The last block might be smaller than the block size, + // so we will need to pad it + if (n % block_size != 0) { + // Pad with zeros + auto last_blocks = + SliceInMinorDims(a, {n - n % block_size, n - n % block_size}, {n, n}); + xla::PaddingConfig config = xla::MakeNoPaddingConfig(ndims); + int64 padding = block_size - n % block_size; + config.mutable_dimensions(ndims - 1)->set_edge_padding_high(padding); + config.mutable_dimensions(ndims - 2)->set_edge_padding_high(padding); + last_blocks = + Pad(last_blocks, Zero(builder, shape.element_type()), config); + + // Add a singleton dimension + // i.e. [..., block_size, block_size] -> [..., 1, block_size, block_size] + TF_ASSIGN_OR_RETURN(xla::Shape blocks_shape, + builder->GetShape(last_blocks)); + auto shape_dims = xla::AsInt64Slice(blocks_shape.dimensions()); + auto last_blocks_dims = std::vector(ndims); + std::copy(shape_dims.begin(), shape_dims.end(), last_blocks_dims.begin()); + last_blocks_dims.insert(last_blocks_dims.end() - 2, 1); + last_blocks = Reshape(last_blocks, last_blocks_dims); + + // Concatenate with the other blocks if necessary + if (n > block_size) { + diag_blocks = + xla::ConcatInDim(builder, {diag_blocks, last_blocks}, ndims - 2); + } else { + diag_blocks = last_blocks; + } + } + + return diag_blocks; + }); +} + +xla::XlaOp InvertDiagonalBlocks(xla::XlaOp diag_blocks, bool lower, + bool transpose_a, bool conjugate_a) { + xla::XlaBuilder* builder = diag_blocks.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + // Input is a batch of square lower triangular square matrices. Its shape is + // (..., size, size). We resize this to (num_blocks, size, size). + TF_ASSIGN_OR_RETURN(xla::Shape shape, builder->GetShape(diag_blocks)); + int64 block_size = xla::ShapeUtil::GetDimension(shape, -1); + int64 num_blocks = xla::ShapeUtil::ElementsIn(shape) / + tensorflow::MathUtil::IPow(block_size, 2); + diag_blocks = Reshape(diag_blocks, {num_blocks, block_size, block_size}); + + // The input must be triangular because we rely on that when doing + // multiplications later on + diag_blocks = Triangle(diag_blocks, /*lower=*/lower); + + // Rescale blocks to be unit triangular, but avoid dividing by + // zero (which can happen if the last block was padded) otherwise it will + // introduce nans which will propagate + auto diags = GetMatrixDiagonal(diag_blocks); + TF_ASSIGN_OR_RETURN(xla::Shape diags_shape, builder->GetShape(diags)); + auto one = ScalarLike(diags, 1); + auto ones = Broadcast(one, xla::AsInt64Slice(diags_shape.dimensions())); + diags = Select(Eq(diags, Zero(builder, shape.element_type())), ones, diags); + auto scaled_diag_blocks = Div(diag_blocks, diags, {0, 2}); + + // We can now use the fact that for an upper triangular matrix + // [[L11, 0], [L21, L22]], given the inverses L11' and L22', we have + // L22' = -L22' * L21 * L11'. In our case, L21 is a vector and our blocks + // have been rescaled to be unit triangular, so L22 = L22' = 1. + + // Initialize the output matrix with -1s on the diagonal. We use -1 instead + // of 1 because we cannot do matrix-vector multiplies with variable shapes + // inside of a loop, or do irregularly shaped in-place updates. Hence, + // L21 <- -L22 * L21 * L11 cannot be done naively. Instead, we update the + // entire row i.e. we calculate + // [L21 L22 0] <- -[L21 L22 0] @ diag_blocks([L11', -I, -I]) + // which means [L21 L22 0] <- [-L21 * L11', L22, 0]. + auto identity = + IdentityMatrix(builder, shape.element_type(), block_size, block_size); + auto neg_identity = -identity; + + // The first or last diagonal element should be set to 1 instead of -1 + // though, since we never update it + auto pos_one = Reshape(One(builder, shape.element_type()), {1, 1}); + auto start_index = (lower) ? 0 : block_size - 1; + auto output_block = DynamicUpdateSlice( + neg_identity, pos_one, + /*start_indices=*/xla::ConstantR1(builder, 2, start_index)); + + // Broadcast diag([1, -1, -1, ...]) to every block + xla::XlaOp output = Broadcast(output_block, + /*broadcast_sizes=*/{num_blocks}); + + // Now we construct a loop that performs matrix-vector multiplications + // inverting the blocks one row at a time + std::vector tuple_shapes = { + // The loop iteration counter is a scalar, incremented each iteration. + xla::ShapeUtil::MakeShape(xla::S32, {}), + // The output has the shape of A, with one row updated each iteration. + xla::ShapeUtil::MakeShape(shape.element_type(), + {num_blocks, block_size, block_size}), + // The input is a loop invariant. + xla::ShapeUtil::MakeShape(shape.element_type(), + {num_blocks, block_size, block_size})}; + xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); + + auto init_i = One(builder, xla::S32); + auto init = xla::Tuple(builder, {init_i, output, scaled_diag_blocks}); + + // Construct the loop condition function. + std::unique_ptr condb = + builder->CreateSubBuilder("InvertDiagCond"); + { + auto i = GetTupleElement( + Parameter(condb.get(), 0, tuple_shape, "InvertDiagCondTuple"), 0); + Lt(i, xla::ConstantR0(condb.get(), block_size)); + } + TF_ASSIGN_OR_RETURN(auto cond, condb->Build()); + + // Construct the loop body function. + std::unique_ptr bodyb = + builder->CreateSubBuilder("InvertDiagBody"); + { + auto input_tuple = + Parameter(bodyb.get(), 0, tuple_shape, "InvertDiagBodyTuple"); + + auto i = GetTupleElement(input_tuple, 0); + auto body_out = GetTupleElement(input_tuple, 1); + auto body_input = GetTupleElement(input_tuple, 2); + + auto zero = xla::ConstantR1(bodyb.get(), 1, 0); + auto j = (lower) ? i : ScalarLike(i, block_size - 1) - i; + auto start_indices = + xla::ConcatInDim(bodyb.get(), {zero, Reshape(j, {1}), zero}, 0); + auto input_row = + DynamicSlice(body_input, start_indices, + /*slice_sizes=*/{num_blocks, 1, block_size}); + + // We want -L21 L11^{-1} + xla::DotDimensionNumbers dnums; + dnums.add_lhs_batch_dimensions(0); + dnums.add_rhs_batch_dimensions(0); + dnums.add_lhs_contracting_dimensions(2); + dnums.add_rhs_contracting_dimensions(1); + auto update = -DotGeneral(input_row, body_out, dnums); + + body_out = DynamicUpdateSlice(body_out, update, start_indices); + + auto next_i = i + ScalarLike(i, 1); + xla::Tuple(bodyb.get(), {next_i, body_out, body_input}); + } + TF_ASSIGN_OR_RETURN(auto body, bodyb->Build()); + + // Construct the While loop and return the result, + // return while_loop(cond_fun, body_fun, init)[1] + auto invert_while = While(cond, body, init); + auto inv_diag_blocks = GetTupleElement(invert_while, 1); + + // Undo the scaling + inv_diag_blocks = Div(inv_diag_blocks, diags, + /*broadcast_dimensions=*/{0, 1}); + + // Reshape back to original batch major dimensions + return Reshape(inv_diag_blocks, xla::AsInt64Slice(shape.dimensions())); + }); +} + +xla::XlaOp SolveWithInvertedDiagonalBlocks(xla::XlaOp a, xla::XlaOp b, + xla::XlaOp inv_diag_blocks, + bool left_side, bool lower, + bool transpose_a, bool conjugate_a) { + xla::XlaBuilder* builder = a.builder(); + return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { + TF_ASSIGN_OR_RETURN(xla::Shape blocks_shape, + builder->GetShape(inv_diag_blocks)); + TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); + int64 block_size = xla::ShapeUtil::GetDimension(blocks_shape, -1); + + TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); + int64 ndims = xla::ShapeUtil::Rank(a_shape); + int64 n = xla::ShapeUtil::GetDimension(a_shape, -1); + int64 num_blocks = n / block_size + (n % block_size != 0); + int64 m_dim = (left_side) ? -1 : -2; + int64 m = xla::ShapeUtil::GetDimension(b_shape, m_dim); + + // Initialize the solution + auto x = ZerosLike(b); + + // This loop is unrolled for performance reasons, but it could be expressed + // rolled as well since the matrices are of the same size each iteration + for (int i = 0; i < num_blocks; i++) { + // High-level intuition: We have B[i] = L[i] @ X. Since L is upper + // triangular this means B[i] = L[i, :i + 1] @ X[:i + 1]. We can split + // this into two parts: B[i] = L[i, :i] @ X[:i] + L[i, i] @ X[i] which + // can be solved for X[i] as X[i] = inv(L[i, i]) @ B[i] - L[i, :i] @ X[:i] + + // Decide whether we go from first block to last or vice versa + auto j = (left_side ^ lower ^ transpose_a) ? num_blocks - 1 - i : i; + + // Get the size of the inverse blocks (the last one might be smaller) + int64 block = (n % block_size != 0 && j + 1 == num_blocks) + ? n % block_size + : block_size; + auto inv_block = + MaybeConjugate(Collapse(SliceInMinorDims(inv_diag_blocks, {j, 0, 0}, + {j + 1, block, block}), + /*dimensions=*/{ndims - 2, ndims - 1}), + conjugate_a); + + // Get the corresponding row of B + int64 k = std::min((j + 1) * block_size, n); + std::vector start = {j * block_size, 0}; + std::vector end = {k, m}; + if (!left_side) { + std::swap(start[0], start[1]); + std::swap(end[0], end[1]); + } + auto b_row = SliceInMinorDims(b, start, end); + + xla::XlaOp remainder; + if (i == 0) { + remainder = b_row; + } else { + // This matrix multiply involves a lot of multiplying with zero (namely, + // X[i * block_size:] = 0), but this is faster than slicing... + end = {k, n}; + if (!left_side) { + std::swap(end[0], end[1]); + } + if (transpose_a) { + std::swap(start[0], start[1]); + std::swap(end[0], end[1]); + } + auto a_row = + MaybeConjugate(SliceInMinorDims(a, start, end), conjugate_a); + if (left_side) { + remainder = b_row - BatchDot(a_row, x, transpose_a, false); + } else { + remainder = b_row - BatchDot(x, a_row, false, transpose_a); + } + } + + xla::XlaOp x_update; + auto zero = Zero(builder, xla::S32); + auto start_index = + xla::ConstantR0WithType(builder, xla::S32, j * block_size); + std::vector update_starts = {start_index, zero}; + if (left_side) { + x_update = BatchDot(inv_block, remainder, transpose_a, false); + } else { + x_update = BatchDot(remainder, inv_block, false, transpose_a); + std::swap(update_starts[0], update_starts[1]); + } + x = DynamicUpdateSliceInMinorDims(x, x_update, /*starts=*/update_starts); + } + + return x; + }); +} + xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, bool lower, bool transpose_a, bool conjugate_a, int64 block_size) { @@ -45,7 +343,7 @@ xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, xla::ShapeUtil::HumanString(a_shape), " vs. ", xla::ShapeUtil::HumanString(b_shape)); } - const int ndims = xla::ShapeUtil::Rank(a_shape); + const int64 ndims = xla::ShapeUtil::Rank(a_shape); if (ndims < 2) { return errors::InvalidArgument( "Arguments to TriangularSolve must have rank >= 2: ", ndims); @@ -85,528 +383,18 @@ xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, block_size); } - std::map base_computations; - auto get_base_triangular_solve = - [&](int k) -> xla::StatusOr { - xla::XlaComputation& computation = base_computations[k]; - if (computation.IsNull()) { - std::unique_ptr sub = builder->CreateSubBuilder( - tensorflow::strings::StrCat("trsm_base_", k)); - - auto a_param = xla::Parameter( - sub.get(), 0, - xla::ShapeUtil::MakeShape(b_shape.element_type(), - ConcatVectors(batch_dimensions, {k, k})), - "a"); - - std::array b_lastd; - if (left_side) { - b_lastd = {k, n}; - } else { - b_lastd = {m, k}; - } - auto b_param = xla::Parameter( - sub.get(), 1, - xla::ShapeUtil::MakeShape(b_shape.element_type(), - ConcatVectors(batch_dimensions, b_lastd)), - "b"); - - // We use a left-looking or right-looking subroutine on the block - // diagonal in the lower=true cases, while falling back to a recursive - // call in others. The left-looking and right-looking subroutines are - // written with a While loop and so yields much faster compile times. - // Moreover, they can give higher performance on smaller (sub)problems. - if (left_side && lower) { - TriangularSolveLeftLooking(a_param, b_param, transpose_a, - conjugate_a); - } else if (!left_side && lower) { - TriangularSolveRightLooking(a_param, b_param, transpose_a, - conjugate_a); - } else { - TriangularSolve(a_param, b_param, left_side, lower, transpose_a, - conjugate_a, - /*block_size=*/1); - } - - TF_ASSIGN_OR_RETURN(computation, sub->Build()); - } - return &computation; - }; - - xla::XlaOp output = xla::ZerosLike(b); - - // Right-looking blocked triangular solve. - // For an explanation of the algorithm, see the TRSM discussion in: - // Goto, Kazushige, and Robert Van De Geijn. "High-performance - // implementation of the level-3 BLAS." ACM Transactions on Mathematical - // Software (TOMS) 35.1 (2008): 4. - - // In the code comments below, T = lambda x: np.swapaxes(x, -1, -2) if - // conjugate_a is False, or T = lambda x: np.conj(np.swapaxes(x, -1, -2)) if - // conjugate_a is True. - - if (!left_side && lower == transpose_a) { - // for i in range(0, a.shape[-1], block_size): - for (int64 i = 0; i < n; i += block_size) { - int64 k = std::min(block_size, n - i); - - // output[..., :, i:i+k] = triangular_solve( - // a[..., i:i+k, i:i+k], - // b[..., :, i:i+k] - np.matmul(output[..., :, :i], - // a[..., :i, i:i+k]), - // ..., block_size=1) - auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); - auto b_slice = SliceInMinorDims(b, {0, i}, {m, i + k}); - - // Note that we multiply with the full output, since this is faster - // than slicing, and output[..., :, i:] = 0 - xla::XlaOp a_prev; - if (lower) { - a_prev = SliceInMinorDims(a, {i, 0}, {i + k, n}); - } else { - a_prev = SliceInMinorDims(a, {0, i}, {n, i + k}); - } - auto prev_contribution = BatchDot(output, a_prev, - /*transpose_x=*/false, - /*transpose_y=*/transpose_a, - /*conjugate_x=*/false, - /*conjugate_y=*/conjugate_a); - auto to_solve = b_slice - prev_contribution; - - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = xla::Call(builder, *solve, {a_slice, to_solve}); - } else { - auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); - update = to_solve / a_slice_conj; - } - output = UpdateSliceInMinorDims(output, update, {0, i}); - } - - } else if (left_side && lower != transpose_a) { - // for i in range(0, a.shape[-1], block_size): - for (int64 i = 0; i < m; i += block_size) { - int64 k = std::min(block_size, m - i); - - // output[..., i:i+k, :] = triangular_solve( - // a[..., i:i+k, i:i+k], - // b[..., i:i+k, :] - np.matmul(a[..., i:i+k, :i], - // output[..., :i, :]), - // ..., block_size=1) - auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); - auto b_slice = SliceInMinorDims(b, {i, 0}, {i + k, n}); - - xla::XlaOp a_prev; - if (lower) { - a_prev = SliceInMinorDims(a, {i, 0}, {i + k, m}); - } else { - a_prev = SliceInMinorDims(a, {0, i}, {m, i + k}); - } - auto prev_contribution = BatchDot(a_prev, output, - /*transpose_x=*/transpose_a, - /*transpose_y=*/false, - /*conjugate_x=*/conjugate_a, - /*conjugate_y=*/false); - auto to_solve = b_slice - prev_contribution; - - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = xla::Call(builder, *solve, {a_slice, to_solve}); - } else { - auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); - update = to_solve / a_slice_conj; - } - output = UpdateSliceInMinorDims(output, update, {i, 0}); - } - } else if (!left_side && lower != transpose_a) { - // for i in reversed(range(0, a.shape[-1], block_size)): - const int64 last_blk_ix = - xla::RoundUpToNearest(n, block_size) - block_size; - for (int64 i = last_blk_ix; i >= 0; i -= block_size) { - int64 k = std::min(block_size, n - i); - - // output[..., :, i:i+k] = triangular_solve( - // a[..., i:i+k, i:i+k], - // b[..., :, i:i+k] - np.matmul(output[..., :, :i], - // a[..., :i, i:i+k]),\ - // ..., block_size=1) - auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); - auto b_slice = SliceInMinorDims(b, {0, i}, {m, i + k}); - - xla::XlaOp a_prev; - if (lower) { - a_prev = SliceInMinorDims(a, {0, i}, {n, i + k}); - } else { - a_prev = SliceInMinorDims(a, {i, 0}, {i + k, n}); - } - auto prev_contribution = BatchDot(output, a_prev, - /*transpose_x=*/false, - /*transpose_y=*/transpose_a, - /*conjugate_x=*/false, - /*conjugate_y=*/conjugate_a); - auto to_solve = b_slice - prev_contribution; - - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = xla::Call(builder, *solve, {a_slice, to_solve}); - } else { - auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); - update = to_solve / a_slice_conj; - } - output = UpdateSliceInMinorDims(output, update, {0, i}); - } - } else { // left_side && lower == transpose_a - // for i in reversed(range(0, a.shape[-1], block_size)): - const int64 last_blk_ix = - xla::RoundUpToNearest(m, block_size) - block_size; - for (int64 i = last_blk_ix; i >= 0; i -= block_size) { - int64 k = std::min(block_size, m - i); - - // output[..., i:i+k, :] = triangular_solve( - // a[..., i:i+k, i:i+k], - // b[..., i:i+k, :] - np.matmul(a[..., i:i+k, :i], - // output[..., :i, :]), - // ..., block_size=1) - auto a_slice = SliceInMinorDims(a, {i, i}, {i + k, i + k}); - auto b_slice = SliceInMinorDims(b, {i, 0}, {i + k, n}); - - xla::XlaOp a_prev; - if (lower) { - a_prev = SliceInMinorDims(a, {0, i}, {m, i + k}); - } else { - a_prev = SliceInMinorDims(a, {i, 0}, {i + k, m}); - } - auto prev_contribution = BatchDot(a_prev, output, - /*transpose_x=*/transpose_a, - /*transpose_y=*/false, - /*conjugate_x=*/conjugate_a, - /*conjugate_y=*/false); - auto to_solve = b_slice - prev_contribution; - - xla::XlaOp update; - if (k > 1) { - TF_ASSIGN_OR_RETURN(xla::XlaComputation * solve, - get_base_triangular_solve(k)); - update = xla::Call(builder, *solve, {a_slice, to_solve}); - } else { - auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); - update = to_solve / a_slice_conj; - } - output = UpdateSliceInMinorDims(output, update, {i, 0}); - } - } - - return output; - }); -} - -xla::XlaOp TriangularSolveLeftLooking(xla::XlaOp a, xla::XlaOp b, - bool transpose_a, bool conjugate_a) { - xla::XlaBuilder* builder = a.builder(); - return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); - const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); - const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); - const int64 ndims = xla::ShapeUtil::Rank(a_shape); - - std::vector batch_dimensions; - int64 num_batches = 1; - for (int i = 0; i < ndims - 2; ++i) { - int64 a_size = a_shape.dimensions(i); - batch_dimensions.push_back(a_size); - num_batches = num_batches * a_size; - } - - // Rescale the input to be unit triangular - auto diag = xla::GetMatrixDiagonal(a); - xla::XlaOp scaled_a; - std::vector broadcast_dimensions(ndims - 1); - std::iota(broadcast_dimensions.begin(), broadcast_dimensions.end(), 0); - if (transpose_a) { - scaled_a = Div(a, diag, broadcast_dimensions); - } else { - // Broadcast over the rows - broadcast_dimensions[ndims - 2] = ndims - 1; - scaled_a = Div(a, diag, broadcast_dimensions); - } - - // The main computation is performed in a While loop. - - // Allocate the output and set its first or last row, - // output = np.zeros_like(b) - // if transpose_a: - // output[..., m-1:, :] = b[..., m-1:, :] / a[..., m-1:, m-1:] - // else: - // output[..., :1, :] = b[..., :1, :] / a[..., :1, :1] - xla::XlaOp output = xla::ZerosLike(b); - { - auto i = transpose_a ? m - 1 : 0; - auto a_slice = SliceInMinorDims(scaled_a, {i, i}, {i + 1, i + 1}); - auto b_slice = SliceInMinorDims(b, {i, 0}, {i + 1, n}); - auto a_slice_conj = MaybeConjugate(a_slice, conjugate_a); - auto update = b_slice / a_slice_conj; - output = UpdateSliceInMinorDims(output, update, {i, 0}); - } - - // Construct the initial loop carry tuple, - // if transpose_a: - // init = (m-2, output, a, b) - // else: - // init = (1, output, a, b) - std::vector tuple_shapes = { - // The loop iteration counter is a scalar, incremented each iteration. - xla::ShapeUtil::MakeShape(xla::S32, {}), - // The output has the shape of b, with one row updated each iteration. - b_shape, - // The coefficient matrix a is a loop invariant. - a_shape, - // The right-hand-side matrix b is a loop invariant. - b_shape}; - xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); - auto init_i = xla::ConstantR0(builder, transpose_a ? m - 2 : 1); - auto init = xla::Tuple(builder, {init_i, output, scaled_a, b}); - - // Construct the loop condition function, - // def cond_fun(loop_carry): - // i, output, a, b = loop_carry - // return i >= 0 if transpose_a else i < m - std::unique_ptr condb = - builder->CreateSubBuilder("TriangularSolveLeftLookingWhileCond"); - { - auto i = xla::GetTupleElement( - xla::Parameter(condb.get(), 0, tuple_shape, - "TriangularSolveLeftLookingWhileTuple"), - 0); - if (transpose_a) { - xla::Ge(i, xla::ConstantR0(condb.get(), 0)); - } else { - xla::Lt(i, xla::ConstantR0(condb.get(), m)); - } - } - TF_ASSIGN_OR_RETURN(auto cond, condb->Build()); - - // Construct the loop body function, - // def body_fun(loop_carry): - // i, output, a, b = loop_carry - // if transpose_a: - // a_row = np.swapaxes(a[..., i+1:, i:i+1], -1 -2) - // else: - // a_row = a[..., i:i+1, :i] - // result_row = b[..., i:i+1, :] - np.matmul(a_row, output[..., :, :]) - // output[..., i:i+1, :] = result_row / a[..., i:i+1, i:i+1] - // if transpose_a: - // return (i - 1, output, a, b) - // else: - // return (i + 1, output, a, b) - // We have to do some extra FLOPs propagating zeros in the matrix multiply - // because we can't have the size of its arguments depend on the loop - // counter. - std::unique_ptr bodyb = - builder->CreateSubBuilder("TriangularSolveLeftLookingWhileBody"); - { - auto input_tuple = xla::Parameter(bodyb.get(), 0, tuple_shape, - "TriangularSolveLeftLookingWhileTuple"); - - // i, output, a, b = loop_carry - auto i = xla::GetTupleElement(input_tuple, 0); - auto body_out = xla::GetTupleElement(input_tuple, 1); - auto body_a = xla::GetTupleElement(input_tuple, 2); - auto body_b = xla::GetTupleElement(input_tuple, 3); - auto zero = xla::ConstantR0(bodyb.get(), 0); - - // We'd like to implement this: - // if transpose_a: - // a_row = T(a[..., i+1:, i:i+1]) - // result_row = (b[..., i:i+1, :] - // - np.matmul(a_row, body_out[..., i+1:, :])) - // else: - // result_row = (b[..., i:i+1, :] - // - np.matmul(a[..., i:i+1, :i], body_out[..., :i, :])) - // But since we can't have intermediate array sizes depend on the loop - // counter, we instead exploit the fact that we initialized the output to - // all zeros and use that as zero-padding (doing unnecessary FLOPs). - xla::XlaOp a_row; - if (transpose_a) { - a_row = DynamicSliceInMinorDims(body_a, {zero, i}, {m, 1}); - } else { - a_row = DynamicSliceInMinorDims(body_a, {i, zero}, {1, m}); - } - auto b_update = BatchDot(a_row, body_out, - /*transpose_x=*/transpose_a, - /*transpose_y=*/false, - /*conjugate_x=*/conjugate_a, - /*conjugate_y=*/false); - auto result_row_slice = - DynamicSliceInMinorDims(body_b, {i, zero}, {1, n}); - auto result_row = result_row_slice - b_update; - - // body_out[..., i:i+1, :] = result_row - body_out = DynamicUpdateSliceInMinorDims(body_out, result_row, {i, zero}); - - // if transpose_a: - // return (i - 1, body_out, a, b) - // else: - // return (i + 1, body_out, a, b) - auto next_i = xla::Add( - i, xla::ConstantR0(bodyb.get(), transpose_a ? -1 : 1)); - xla::Tuple(bodyb.get(), {next_i, body_out, body_a, body_b}); - } - TF_ASSIGN_OR_RETURN(auto body, bodyb->Build()); - - // Construct the While loop and return the result, - // return while_loop(cond_fun, body_fun, init)[1] - auto triangular_solve_left_looking_while = xla::While(cond, body, init); - output = xla::GetTupleElement(triangular_solve_left_looking_while, 1); - auto scaling = MaybeConjugate(diag, conjugate_a); - // Broadcast over the columns - broadcast_dimensions[ndims - 2] = ndims - 2; - return Div(output, scaling, broadcast_dimensions); - }); -} - -xla::XlaOp TriangularSolveRightLooking(xla::XlaOp a, xla::XlaOp b, - bool transpose_a, bool conjugate_a) { - xla::XlaBuilder* builder = a.builder(); - return builder->ReportErrorOrReturn([&]() -> xla::StatusOr { - TF_ASSIGN_OR_RETURN(xla::Shape a_shape, builder->GetShape(a)); - TF_ASSIGN_OR_RETURN(xla::Shape b_shape, builder->GetShape(b)); - const int64 m = xla::ShapeUtil::GetDimension(b_shape, -2); - const int64 n = xla::ShapeUtil::GetDimension(b_shape, -1); - const int64 ndims = xla::ShapeUtil::Rank(a_shape); - - std::vector batch_dimensions; - int64 num_batches = 1; - for (int i = 0; i < ndims - 2; ++i) { - int64 a_size = a_shape.dimensions(i); - batch_dimensions.push_back(a_size); - num_batches = num_batches * a_size; - } + // We find the diagonal blocks of the coefficient matrix + auto diag_blocks = DiagonalBlocks(a, block_size); - // Rescale the input to be unit triangular - auto diag = xla::GetMatrixDiagonal(a); - xla::XlaOp scaled_a; - std::vector broadcast_dimensions(ndims - 1); - std::iota(broadcast_dimensions.begin(), broadcast_dimensions.end(), 0); - if (transpose_a) { - // Broadcast over the rows - broadcast_dimensions[ndims - 2] = ndims - 1; - scaled_a = Div(a, diag, broadcast_dimensions); - } else { - scaled_a = Div(a, diag, broadcast_dimensions); - } + // We invert these blocks in parallel using batched matrix-vector products + auto inv_diag_blocks = + InvertDiagonalBlocks(diag_blocks, lower, transpose_a, conjugate_a); - // The main computation is performed in a While loop. - xla::XlaOp output = xla::ZerosLike(b); + // We now find the solution using GEMMs + auto x = SolveWithInvertedDiagonalBlocks(a, b, inv_diag_blocks, left_side, + lower, transpose_a, conjugate_a); - // Construct the initial loop carry tuple, - // if transpose_a: - // init = (0, output, a, b) - // else: - // init = (n-1, output, a, b) - std::vector tuple_shapes = { - // The loop iteration counter is a scalar, incremented each iteration. - xla::ShapeUtil::MakeShape(xla::S32, {}), - // The output has the shape of b, with one row updated each iteration. - b_shape, - // The coefficient matrix a is a loop invariant. - a_shape, - // The right-hand-side matrix b is a loop invariant. - b_shape}; - xla::Shape tuple_shape = xla::ShapeUtil::MakeTupleShape(tuple_shapes); - auto init_i = xla::ConstantR0(builder, transpose_a ? 0 : n - 1); - auto init = xla::Tuple(builder, {init_i, output, scaled_a, b}); - - // Construct the loop condition function, - // def cond_fun(loop_carry): - // i, output, a, b = loop_carry - // return i < n if transpose_a else i >= 0 - std::unique_ptr condb = - builder->CreateSubBuilder("TriangularSolveRightLookingWhileCond"); - { - auto i = xla::GetTupleElement( - xla::Parameter(condb.get(), 0, tuple_shape, - "TriangularSolveRightLookingWhileTuple"), - 0); - if (transpose_a) { - xla::Lt(i, xla::ConstantR0(condb.get(), n)); - } else { - xla::Ge(i, xla::ConstantR0(condb.get(), 0)); - } - } - TF_ASSIGN_OR_RETURN(auto cond, condb->Build()); - - // Construct the loop body function, - // def body_fun(loop_carry): - // i, output, a, b = loop_carry - // if transpose_a: - // a_row = np.swapaxes(a[..., :, i:i+1], -1, -2) - // else: - // a_row = a[..., :, i:i+1] - // result_row = b[..., :, i:i+1] - np.matmul(output, a_row) - // output[..., :, i:i+1] = result_row / a[..., i:i+1, i:i+1] - // if transpose_a: - // return (i - 1, output, a, b) - // else: - // return (i + 1, output, a, b) - // We have to do some extra FLOPs propagating zeros in the matrix multiply - // because we can't have the size of its arguments depend on the loop - // counter. - std::unique_ptr bodyb = - builder->CreateSubBuilder("TriangularSolveRightLookingWhileBody"); - { - auto input_tuple = xla::Parameter( - bodyb.get(), 0, tuple_shape, "TriangularSolveRightLookingWhileTuple"); - - // i, output, a, b = loop_carry - auto i = xla::GetTupleElement(input_tuple, 0); - auto body_out = xla::GetTupleElement(input_tuple, 1); - auto body_a = xla::GetTupleElement(input_tuple, 2); - auto body_b = xla::GetTupleElement(input_tuple, 3); - auto zero = xla::ConstantR0(bodyb.get(), 0); - - // result = b - np.matmul(output, a) - // result_row = result[..., :, i:i+1] - auto body_b_slice = DynamicSliceInMinorDims(body_b, {zero, i}, {m, 1}); - xla::XlaOp a_slice; - if (transpose_a) { - a_slice = DynamicSliceInMinorDims(body_a, {i, zero}, {1, n}); - } else { - a_slice = DynamicSliceInMinorDims(body_a, {zero, i}, {n, 1}); - } - auto b_update = body_b_slice - BatchDot(body_out, a_slice, - /*transpose_x=*/false, - /*transpose_y=*/transpose_a, - /*conjugate_x=*/false, - /*conjugate_y=*/conjugate_a); - - // body_out[..., :, i:i+1] = b_update - body_out = DynamicUpdateSliceInMinorDims(body_out, b_update, {zero, i}); - - // if transpose_a: - // return (i + 1, body_out, a, b) - // else: - // return (i - 1, body_out, a, b) - auto next_i = xla::Add( - i, xla::ConstantR0(bodyb.get(), transpose_a ? 1 : -1)); - xla::Tuple(bodyb.get(), {next_i, body_out, body_a, body_b}); - } - TF_ASSIGN_OR_RETURN(auto body, bodyb->Build()); - - // Construct the While loop and return the result, - // return while_loop(cond_fun, body_fun, init)[1] - auto triangular_solve_left_looking_while = xla::While(cond, body, init); - output = xla::GetTupleElement(triangular_solve_left_looking_while, 1); - auto scaling = MaybeConjugate(diag, conjugate_a); - // Broadcast over the rows - broadcast_dimensions[ndims - 2] = ndims - 1; - return Div(output, scaling, broadcast_dimensions); + return x; }); } diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.h b/tensorflow/compiler/tf2xla/lib/triangular_solve.h index 7eb9238014..2dce620ba8 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.h +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.h @@ -61,12 +61,6 @@ xla::XlaOp TriangularSolve(xla::XlaOp a, xla::XlaOp b, bool left_side, bool lower, bool transpose_a, bool conjugate_a, int64 block_size = 128); -xla::XlaOp TriangularSolveLeftLooking(xla::XlaOp a, xla::XlaOp b, - bool transpose_a, bool conjugate_a); - -xla::XlaOp TriangularSolveRightLooking(xla::XlaOp a, xla::XlaOp b, - bool transpose_a, bool conjugate_a); - } // namespace tensorflow #endif // TENSORFLOW_COMPILER_TF2XLA_LIB_TRIANGULAR_SOLVE_H_ diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc index f1bff6037b..a29496dec4 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc @@ -207,6 +207,28 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerNotranspose) { xla::ErrorSpec(1e-2, 1e-2)); } +XLA_TEST_F(TriangularSolveTest, SimpleLeftLowerNotransposeIrregularblock) { + xla::XlaBuilder builder(TestName()); + + xla::XlaOp a, b; + auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); + auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); + TriangularSolve(a, b, + /*left_side=*/true, /*lower=*/true, + /*transpose_a=*/false, /*conjugate_a=*/false, + /*block_size=*/3); + + xla::Array2D expected({ + {0.5, 1.0, 1.5}, + {0.41666667, 0.33333333, 0.25}, + {0.23148148, 0.18518519, 0.13888889}, + {0.16835017, 0.13468013, 0.1010101}, + }); + + ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, + xla::ErrorSpec(1e-2, 1e-2)); +} + XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperTranspose) { xla::XlaBuilder builder(TestName()); @@ -307,47 +329,5 @@ XLA_TEST_F(TriangularSolveTest, SimpleLeftUpperTransposeNoconjugate) { xla::ErrorSpec(1e-2, 1e-2)); } -XLA_TEST_F(TriangularSolveLeftLookingTest, Simple) { - xla::XlaBuilder builder(TestName()); - - xla::XlaOp a, b; - auto a_data = CreateR2Parameter(AValsLower(), 0, "a", &builder, &a); - auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); - TriangularSolveLeftLooking(a, b, - /*transpose_a=*/false, - /*conjugate_a=*/false); - - xla::Array2D expected({ - {0.5, 1.0, 1.5}, - {0.41666667, 0.33333333, 0.25}, - {0.23148148, 0.18518519, 0.13888889}, - {0.16835017, 0.13468013, 0.1010101}, - }); - - ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, - xla::ErrorSpec(1e-2, 1e-2)); -} - -XLA_TEST_F(TriangularSolveLeftLookingTest, NonzeroUpperTriangle) { - xla::XlaBuilder builder(TestName()); - - xla::XlaOp a, b; - auto a_data = CreateR2Parameter(AValsFull(), 0, "a", &builder, &a); - auto b_data = CreateR2Parameter(BValsLeft(), 1, "b", &builder, &b); - TriangularSolveLeftLooking(a, b, - /*transpose_a=*/false, - /*conjugate_a=*/false); - - xla::Array2D expected({ - {0.5, 1.0, 1.5}, - {0.41666667, 0.33333333, 0.25}, - {0.23148148, 0.18518519, 0.13888889}, - {0.16835017, 0.13468013, 0.1010101}, - }); - - ComputeAndCompareR2(&builder, expected, {a_data.get(), b_data.get()}, - xla::ErrorSpec(1e-2, 1e-2)); -} - } // namespace } // namespace tensorflow diff --git a/tensorflow/compiler/xla/client/lib/numeric.cc b/tensorflow/compiler/xla/client/lib/numeric.cc index a6e460aa75..1c91237ae1 100644 --- a/tensorflow/compiler/xla/client/lib/numeric.cc +++ b/tensorflow/compiler/xla/client/lib/numeric.cc @@ -106,4 +106,32 @@ XlaOp GetMatrixDiagonal(XlaOp x) { }); } +XlaOp Triangle(XlaOp x, bool lower) { + XlaBuilder* builder = x.builder(); + return builder->ReportErrorOrReturn([&]() -> StatusOr { + TF_ASSIGN_OR_RETURN(Shape shape, builder->GetShape(x)); + const int64 n_dims = ShapeUtil::Rank(shape); + TF_RET_CHECK(n_dims >= 2); + const int64 m = shape.dimensions(n_dims - 2); + const int64 n = shape.dimensions(n_dims - 1); + tensorflow::gtl::ArraySlice major_dims( + AsInt64Slice(shape.dimensions()), /*pos=*/0, /*len=*/n_dims - 2); + auto a = Iota(builder, U32, n); + auto b = Iota(builder, U32, m); + xla::XlaOp indicator; + if (lower) { + indicator = Ge(b, Broadcast(a, {m}), /*broadcast_dimensions=*/{0}); + } else { + indicator = Le(b, Broadcast(a, {m}), /*broadcast_dimensions=*/{0}); + } + auto mask = Broadcast(indicator, major_dims); + + return Select(mask, x, Zeros(builder, shape)); + }); +} + +XlaOp UpperTriangle(XlaOp x) { return Triangle(x, false); } + +XlaOp LowerTriangle(XlaOp x) { return Triangle(x, true); } + } // namespace xla diff --git a/tensorflow/compiler/xla/client/lib/numeric.h b/tensorflow/compiler/xla/client/lib/numeric.h index e9037b722c..212f658313 100644 --- a/tensorflow/compiler/xla/client/lib/numeric.h +++ b/tensorflow/compiler/xla/client/lib/numeric.h @@ -34,6 +34,15 @@ XlaOp IdentityMatrix(XlaBuilder* builder, PrimitiveType type, int64 m, int64 n); // diagonal elements (i.e., with indices [..., i, i]). XlaOp GetMatrixDiagonal(XlaOp x); +// Get the upper or lower triangle part of the last two dimensions +XlaOp Triangle(XlaOp x, bool lower); + +// Get the upper triangle part of the last two dimensions +XlaOp UpperTriangle(XlaOp x); + +// Get the lower triangle part of the last two dimensions +XlaOp LowerTriangle(XlaOp x); + } // namespace xla #endif // TENSORFLOW_COMPILER_XLA_CLIENT_LIB_NUMERIC_H_ diff --git a/tensorflow/compiler/xla/client/lib/numeric_test.cc b/tensorflow/compiler/xla/client/lib/numeric_test.cc index 113d359197..f56cadc547 100644 --- a/tensorflow/compiler/xla/client/lib/numeric_test.cc +++ b/tensorflow/compiler/xla/client/lib/numeric_test.cc @@ -40,6 +40,20 @@ XLA_TEST_F(NumericTest, Iota) { ComputeAndCompareR1(&builder, {0, 1, 2, 3, 4, 5, 6, 7, 8, 9}, {}); } +XLA_TEST_F(NumericTest, Triangle) { + XlaBuilder builder(TestName()); + Array3D input(2, 3, 4); + input.FillIota(0); + + XlaOp a; + auto a_data = CreateR3Parameter(input, 0, "a", &builder, &a); + LowerTriangle(a); + Array3D expected({{{0, 0, 0, 0}, {4, 5, 0, 0}, {8, 9, 10, 0}}, + {{12, 0, 0, 0}, {16, 17, 0, 0}, {20, 21, 22, 0}}}); + + ComputeAndCompareR3(&builder, expected, {a_data.get()}); +} + template void NumericTest::TestMatrixDiagonal() { XlaBuilder builder("GetMatrixDiagonal"); -- GitLab From ff2aa1b59d4a111af094c0c7724e453eefe1f3b7 Mon Sep 17 00:00:00 2001 From: Billy Lamberta Date: Tue, 24 Jul 2018 11:52:23 -0700 Subject: [PATCH 335/519] Setup for TFLite subsite PiperOrigin-RevId: 205866236 --- tensorflow/contrib/lite/g3doc/README.md | 4 + tensorflow/contrib/lite/g3doc/_book.yaml | 58 ++++++ tensorflow/contrib/lite/g3doc/_index.yaml | 67 +++++++ tensorflow/contrib/lite/g3doc/_project.yaml | 10 + .../lite/g3doc/api_docs/python/_toc.yaml | 6 + .../lite/g3doc/api_docs/python/index.md | 10 + tensorflow/contrib/lite/g3doc/apis.md | 3 + tensorflow/contrib/lite/g3doc/benchmarks.md | 178 ------------------ .../contrib/lite/g3doc/custom_operators.md | 3 + .../lite/g3doc}/demo_android.md | 3 + .../tflite => contrib/lite/g3doc}/demo_ios.md | 3 + .../tflite => contrib/lite/g3doc}/devguide.md | 27 +-- tensorflow/contrib/lite/g3doc/ios.md | 3 + tensorflow/contrib/lite/g3doc/models.md | 3 + .../contrib/lite/g3doc/ops_versioning.md | 3 + .../lite/g3doc/overview.md} | 3 + .../lite/g3doc}/performance.md | 3 + tensorflow/contrib/lite/g3doc/rpi.md | 3 + .../lite/g3doc/tf_ops_compatibility.md | 3 + .../lite/g3doc/tfmobile}/android_build.md | 6 +- .../lite/g3doc/tfmobile/index.md} | 50 ++++- .../lite/g3doc/tfmobile}/ios_build.md | 3 + .../lite/g3doc/tfmobile}/linking_libs.md | 109 ++++++----- .../lite/g3doc/tfmobile}/optimizing.md | 11 +- .../lite/g3doc/tfmobile}/prepare_models.md | 7 +- tensorflow/docs_src/mobile/README.md | 3 + tensorflow/docs_src/mobile/index.md | 33 ---- tensorflow/docs_src/mobile/leftnav_files | 15 -- 28 files changed, 331 insertions(+), 299 deletions(-) create mode 100644 tensorflow/contrib/lite/g3doc/README.md create mode 100644 tensorflow/contrib/lite/g3doc/_book.yaml create mode 100644 tensorflow/contrib/lite/g3doc/_index.yaml create mode 100644 tensorflow/contrib/lite/g3doc/_project.yaml create mode 100644 tensorflow/contrib/lite/g3doc/api_docs/python/_toc.yaml create mode 100644 tensorflow/contrib/lite/g3doc/api_docs/python/index.md delete mode 100644 tensorflow/contrib/lite/g3doc/benchmarks.md rename tensorflow/{docs_src/mobile/tflite => contrib/lite/g3doc}/demo_android.md (98%) rename tensorflow/{docs_src/mobile/tflite => contrib/lite/g3doc}/demo_ios.md (97%) rename tensorflow/{docs_src/mobile/tflite => contrib/lite/g3doc}/devguide.md (91%) rename tensorflow/{docs_src/mobile/tflite/index.md => contrib/lite/g3doc/overview.md} (99%) rename tensorflow/{docs_src/mobile/tflite => contrib/lite/g3doc}/performance.md (98%) rename tensorflow/{docs_src/mobile => contrib/lite/g3doc/tfmobile}/android_build.md (97%) rename tensorflow/{docs_src/mobile/mobile_intro.md => contrib/lite/g3doc/tfmobile/index.md} (86%) rename tensorflow/{docs_src/mobile => contrib/lite/g3doc/tfmobile}/ios_build.md (98%) rename tensorflow/{docs_src/mobile => contrib/lite/g3doc/tfmobile}/linking_libs.md (83%) rename tensorflow/{docs_src/mobile => contrib/lite/g3doc/tfmobile}/optimizing.md (98%) rename tensorflow/{docs_src/mobile => contrib/lite/g3doc/tfmobile}/prepare_models.md (98%) create mode 100644 tensorflow/docs_src/mobile/README.md delete mode 100644 tensorflow/docs_src/mobile/index.md delete mode 100644 tensorflow/docs_src/mobile/leftnav_files diff --git a/tensorflow/contrib/lite/g3doc/README.md b/tensorflow/contrib/lite/g3doc/README.md new file mode 100644 index 0000000000..e3db478481 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/README.md @@ -0,0 +1,4 @@ +This is a *work-in-progress* TF Lite subsite for: +https://www.tensorflow.org/mobile + +DO NOT PUBLISH diff --git a/tensorflow/contrib/lite/g3doc/_book.yaml b/tensorflow/contrib/lite/g3doc/_book.yaml new file mode 100644 index 0000000000..98abd5743b --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/_book.yaml @@ -0,0 +1,58 @@ +upper_tabs: +# Tabs left of dropdown menu +- include: /_upper_tabs_left.yaml +# Dropdown menu +- name: Ecosystem + path: /ecosystem + is_default: True + menu: + - include: /ecosystem/_menu_toc.yaml + lower_tabs: + # Subsite tabs + other: + - name: Guide + contents: + - title: Overview + path: /mobile/overview + - title: Developer Guide + path: /mobile/devguide + - title: Android Demo App + path: /mobile/demo_android + - title: iOS Demo App + path: /mobile/demo_ios + - title: Performance + path: /mobile/performance + - break: True + - title: TensorFlow Lite APIs + path: /mobile/apis + - title: Custom operators + path: /mobile/custom_operators + - title: TensorFlow Lite Ops Versioning + path: /mobile/ops_versioning + - title: TensorFlow Lite Compatibility Guide + path: /mobile/tf_ops_compatibility + - title: List of Hosted Models + path: /mobile/models + - title: TensorFlow Lite for iOS + path: /mobile/ios + - title: TensorFlow Lite for Raspberry Pi + path: /mobile/rpi + + - heading: TF Mobile + status: deprecated + - title: Overview + path: /mobile/tfmobile/ + - title: Building TensorFlow on Android + path: /mobile/tfmobile/android_build + - title: Building TensorFlow on IOS + path: /mobile/tfmobile/ios_build + - title: Integrating TensorFlow libraries + path: /mobile/tfmobile/linking_libs + - title: Preparing models for mobile deployment + path: /mobile/tfmobile/prepare_models + - title: Optimizing for mobile + path: /mobile/tfmobile/optimizing + + - name: API + contents: + - include: /mobile/api_docs/python/_toc.yaml diff --git a/tensorflow/contrib/lite/g3doc/_index.yaml b/tensorflow/contrib/lite/g3doc/_index.yaml new file mode 100644 index 0000000000..9119e49117 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/_index.yaml @@ -0,0 +1,67 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml +description: +landing_page: + rows: + - heading: TensorFlow Lite is a lightweight solution for mobile and embedded devices. + items: + - description: > + TensorFlow Lite is TensorFlow’s lightweight solution for mobile and + embedded devices. It enables on-device machine learning inference with + low latency and a small binary size. TensorFlow Lite also supports + hardware acceleration with the + Android Neural Networks API. + list: + - heading: Key point 1 + description: > + [high-level overview] + icon: + icon_name: chevron_right + foreground: theme + background: grey + - heading: Key point 2 + description: > + [high-level overview] + icon: + icon_name: chevron_right + foreground: theme + background: grey + - heading: Key point 3 + description: > + [high-level overview] + icon: + icon_name: chevron_right + foreground: theme + background: grey + - code_block: | + 
+        $ 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 \
+               --input_type=FLOAT \
+               --input_arrays=input \
+               --output_arrays=MobilenetV1/Predictions/Reshape_1 \
+               --input_shapes=1,224,224,3
+
+ + - classname: devsite-landing-row-cards + items: + - heading: Using TensorFlow Lite on Android + image_path: /ecosystem/images/tf-logo-card-16x9.png + path: https://medium.com/tensorflow/using-tensorflow-lite-on-android-9bbc9cb7d69d + buttons: + - label: Read on TensorFlow blog + path: https://medium.com/tensorflow/using-tensorflow-lite-on-android-9bbc9cb7d69d + - heading: TensorFlow Lite at the Dev Summit + youtube_id: FAMfy7izB6A + buttons: + - label: Watch the video + path: https://www.youtube.com/watch?v=FAMfy7izB6A + - heading: TensorFlow Lite on GitHub + image_path: /ecosystem/images/github-card-16x9.png + path: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite + buttons: + - label: View on GitHub + path: https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite diff --git a/tensorflow/contrib/lite/g3doc/_project.yaml b/tensorflow/contrib/lite/g3doc/_project.yaml new file mode 100644 index 0000000000..b39666516b --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/_project.yaml @@ -0,0 +1,10 @@ +name: TensorFlow Lite +breadcrumb_name: Mobile +home_url: /mobile/ +parent_project_metadata_path: /_project.yaml +description: > + TensorFlow Lite is a lightweight solution for mobile and embedded devices. +use_site_branding: True +hide_from_products_list: True +content_license: cc3-apache2 +buganizer_id: 316308 diff --git a/tensorflow/contrib/lite/g3doc/api_docs/python/_toc.yaml b/tensorflow/contrib/lite/g3doc/api_docs/python/_toc.yaml new file mode 100644 index 0000000000..1e1c44c692 --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/api_docs/python/_toc.yaml @@ -0,0 +1,6 @@ +# Automatically generated file; please do not edit +toc: + - title: TensorFlow Lite + section: + - title: Overview + path: /mobile/api_docs/python/ diff --git a/tensorflow/contrib/lite/g3doc/api_docs/python/index.md b/tensorflow/contrib/lite/g3doc/api_docs/python/index.md new file mode 100644 index 0000000000..70031a3c3d --- /dev/null +++ b/tensorflow/contrib/lite/g3doc/api_docs/python/index.md @@ -0,0 +1,10 @@ +Project: /mobile/_project.yaml +Book: /mobile/_book.yaml +page_type: reference + + + + +# All symbols in TensorFlow Lite + +TEMP PAGE diff --git a/tensorflow/contrib/lite/g3doc/apis.md b/tensorflow/contrib/lite/g3doc/apis.md index e94a2cc44e..776803da8c 100644 --- a/tensorflow/contrib/lite/g3doc/apis.md +++ b/tensorflow/contrib/lite/g3doc/apis.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # TensorFlow Lite APIs TensorFlow Lite provides programming APIs in C++ and Java, and in both cases diff --git a/tensorflow/contrib/lite/g3doc/benchmarks.md b/tensorflow/contrib/lite/g3doc/benchmarks.md deleted file mode 100644 index 96536cba27..0000000000 --- a/tensorflow/contrib/lite/g3doc/benchmarks.md +++ /dev/null @@ -1,178 +0,0 @@ -# Performance Benchmark numbers - -This document contains the performance benchmark numbers for running a few well -known models on some Android and iOS devices. - -The benchmark numbers were generated by running the [TFLite benchmark -binary](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark) -on Android and running the [iOS benchmark -app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios) -on iOS. - -# Android benchmarks - -When running Android benchmarks, the CPU affinity is set to use big cores on the -device to reduce variance (see -[details](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark#reducing-variance-between-runs-on-android)). - -Models are assumed to have been downloaded from the link, unzipped and pushed to -`/data/local/tmp/tflite_models` folder. The benchmark binary is built according -to instructions listed -[here](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark#on-android) -and is assumed to have been pushed to `/data/local/tmp`. - -The following command was used to run the benchmark: - -``` -adb shell taskset ${CPU_MASK} /data/local/tmp/benchmark_model \ - --num_threads=1 \ - --graph=/data/local/tmp/tflite_models/${GRAPH} \ - --warmup_runs=1 \ - --num_runs=50 \ - --use_nnapi=false -``` - -where `${GRAPH}` is the name of model and `${CPU_MASK}` is the CPU affinity -chosen according to the following table: - -Device | CPU_MASK | --------| ---------- -Pixel 2 | f0 | -Pixel xl | 0c | - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Model NameDevice Mean inference time (std dev)
- Mobilenet_1.0_224(float) - Pixel 2 166.5 ms (2.6 ms)
Pixel xl 122.9 ms (1.8 ms)
- Mobilenet_1.0_224 (quant) - Pixel 2 69.5 ms (0.9 ms)
Pixel xl 78.9 ms (2.2 ms)
- NASNet mobile - Pixel 2 273.8 ms (3.5 ms)
Pixel xl 210.8 ms (4.2 ms)
- SqueezeNet - Pixel 2 234.0 ms (2.1 ms)
Pixel xl 158.0 ms (2.1 ms)
- Inception_ResNet_V2 - Pixel 2 2846.0 ms (15.0 ms)
Pixel xl 1973.0 ms (15.0 ms)
- Inception_V4 - Pixel 2 3180.0 ms (11.7 ms)
Pixel xl 2262.0 ms (21.0 ms)
- -# iOS benchmarks - -For running iOS benchmarks, the [benchmark -app](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/tools/benchmark/ios) -was modified to include the appropriate model and `benchmark_params.json` was -modified to set `num_threads` to 1. - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -
Model NameDevice Mean inference time (std dev)
- Mobilenet_1.0_224(float) - iPhone 8 32.2 ms (0.8 ms)
- Mobilenet_1.0_224 (quant) - iPhone 8 24.4 ms (0.8 ms)
- NASNet mobile - iPhone 8 60.3 ms (0.6 ms)
- SqueezeNet - iPhone 8 44.3 (0.7 ms)
- Inception_ResNet_V2 - iPhone 8562.4 ms (18.2 ms)
- Inception_V4 - iPhone 8 661.0 ms (29.2 ms)
diff --git a/tensorflow/contrib/lite/g3doc/custom_operators.md b/tensorflow/contrib/lite/g3doc/custom_operators.md index f2fbcf64cf..2296f5a064 100644 --- a/tensorflow/contrib/lite/g3doc/custom_operators.md +++ b/tensorflow/contrib/lite/g3doc/custom_operators.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # How to use custom operators TensorFlow Lite currently supports a subset of TensorFlow operators. However, it diff --git a/tensorflow/docs_src/mobile/tflite/demo_android.md b/tensorflow/contrib/lite/g3doc/demo_android.md similarity index 98% rename from tensorflow/docs_src/mobile/tflite/demo_android.md rename to tensorflow/contrib/lite/g3doc/demo_android.md index fdf0bcf3c1..d79a2696b4 100644 --- a/tensorflow/docs_src/mobile/tflite/demo_android.md +++ b/tensorflow/contrib/lite/g3doc/demo_android.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Android Demo App An example Android application using TensorFLow Lite is available diff --git a/tensorflow/docs_src/mobile/tflite/demo_ios.md b/tensorflow/contrib/lite/g3doc/demo_ios.md similarity index 97% rename from tensorflow/docs_src/mobile/tflite/demo_ios.md rename to tensorflow/contrib/lite/g3doc/demo_ios.md index 3be21da89f..a554898899 100644 --- a/tensorflow/docs_src/mobile/tflite/demo_ios.md +++ b/tensorflow/contrib/lite/g3doc/demo_ios.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # iOS Demo App The TensorFlow Lite demo is a camera app that continuously classifies whatever diff --git a/tensorflow/docs_src/mobile/tflite/devguide.md b/tensorflow/contrib/lite/g3doc/devguide.md similarity index 91% rename from tensorflow/docs_src/mobile/tflite/devguide.md rename to tensorflow/contrib/lite/g3doc/devguide.md index b168d6c183..dc9cc98c08 100644 --- a/tensorflow/docs_src/mobile/tflite/devguide.md +++ b/tensorflow/contrib/lite/g3doc/devguide.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Developer Guide Using a TensorFlow Lite model in your mobile app requires multiple @@ -56,7 +59,7 @@ both floating point and quantized inference. A developer may choose to train a custom model using Tensorflow (see the [TensorFlow tutorials](../../tutorials/) for examples of building and training models). If you have already written a model, the first step is to export this -to a @{tf.GraphDef} file. This is required because some formats do not store the +to a `tf.GraphDef` file. This is required because some formats do not store the model structure outside the code, and we must communicate with other parts of the framework. See [Exporting the Inference Graph](https://github.com/tensorflow/models/blob/master/research/slim/README.md) @@ -71,12 +74,12 @@ grow in future Tensorflow Lite releases. ## 2. Convert the model format The model generated (or downloaded) in the previous step is a *standard* -Tensorflow model and you should now have a .pb or .pbtxt @{tf.GraphDef} file. +Tensorflow model and you should now have a .pb or .pbtxt `tf.GraphDef` file. Models generated with transfer learning (re-training) or custom models must be converted—but, we must first freeze the graph to convert the model to the Tensorflow Lite format. This process uses several model formats: -* @{tf.GraphDef} (.pb) —A protobuf that represents the TensorFlow training or +* `tf.GraphDef` (.pb) —A protobuf that represents the TensorFlow training or computation graph. It contains operators, tensors, and variables definitions. * *CheckPoint* (.ckpt) —Serialized variables from a TensorFlow graph. Since this does not contain a graph structure, it cannot be interpreted by itself. @@ -143,11 +146,11 @@ containing the model architecture. The [frozen_graph.pb](https://storage.googlea file used here is available for download. `output_file` is where the TensorFlow Lite model will get generated. The `input_type` and `inference_type` arguments should be set to `FLOAT`, unless converting a -@{$performance/quantization$quantized model}. Setting the `input_array`, -`output_array`, and `input_shape` arguments are not as straightforward. The -easiest way to find these values is to explore the graph using Tensorboard. Reuse -the arguments for specifying the output nodes for inference in the -`freeze_graph` step. +quantized model. +Setting the `input_array`, `output_array`, and `input_shape` arguments are not as +straightforward. The easiest way to find these values is to explore the graph +using Tensorboard. Reuse the arguments for specifying the output nodes for +inference in the `freeze_graph` step. It is also possible to use the Tensorflow Optimizing Converter with protobufs from either Python or from the command line (see the @@ -204,16 +207,16 @@ The open source Android demo app uses the JNI interface and is available [on GitHub](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/java/demo/app). You can also download a [prebuilt APK](http://download.tensorflow.org/deps/tflite/TfLiteCameraDemo.apk). -See the @{$tflite/demo_android} guide for details. +See the Android demo guide for details. -The @{$mobile/android_build} guide has instructions for installing TensorFlow on -Android and setting up `bazel` and Android Studio. +The Android mobile guide has instructions for +installing TensorFlow on Android and setting up `bazel` and Android Studio. ### iOS To integrate a TensorFlow model in an iOS app, see the [TensorFlow Lite for iOS](https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/lite/g3doc/ios.md) -guide and @{$tflite/demo_ios} guide. +guide and iOS demo guide. #### Core ML support diff --git a/tensorflow/contrib/lite/g3doc/ios.md b/tensorflow/contrib/lite/g3doc/ios.md index e0358a444d..d78d373ccf 100644 --- a/tensorflow/contrib/lite/g3doc/ios.md +++ b/tensorflow/contrib/lite/g3doc/ios.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # TensorFlow Lite for iOS ## Building diff --git a/tensorflow/contrib/lite/g3doc/models.md b/tensorflow/contrib/lite/g3doc/models.md index 4e7d33a1b6..3292aece0e 100644 --- a/tensorflow/contrib/lite/g3doc/models.md +++ b/tensorflow/contrib/lite/g3doc/models.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # List of Hosted Models ## Image classification (Float Models) diff --git a/tensorflow/contrib/lite/g3doc/ops_versioning.md b/tensorflow/contrib/lite/g3doc/ops_versioning.md index bd2f797e6c..b06f4fd3b8 100644 --- a/tensorflow/contrib/lite/g3doc/ops_versioning.md +++ b/tensorflow/contrib/lite/g3doc/ops_versioning.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # TensorFlow Lite Ops Versioning This document describes TensorFlow Lite's op versioning schema. Op diff --git a/tensorflow/docs_src/mobile/tflite/index.md b/tensorflow/contrib/lite/g3doc/overview.md similarity index 99% rename from tensorflow/docs_src/mobile/tflite/index.md rename to tensorflow/contrib/lite/g3doc/overview.md index cc4af2a875..be60d7941a 100644 --- a/tensorflow/docs_src/mobile/tflite/index.md +++ b/tensorflow/contrib/lite/g3doc/overview.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Introduction to TensorFlow Lite TensorFlow Lite is TensorFlow’s lightweight solution for mobile and embedded diff --git a/tensorflow/docs_src/mobile/tflite/performance.md b/tensorflow/contrib/lite/g3doc/performance.md similarity index 98% rename from tensorflow/docs_src/mobile/tflite/performance.md rename to tensorflow/contrib/lite/g3doc/performance.md index 79bacaaa1b..613e9f97c3 100644 --- a/tensorflow/docs_src/mobile/tflite/performance.md +++ b/tensorflow/contrib/lite/g3doc/performance.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Performance This document lists TensorFlow Lite performance benchmarks when running well diff --git a/tensorflow/contrib/lite/g3doc/rpi.md b/tensorflow/contrib/lite/g3doc/rpi.md index ab50789307..cdc9172d87 100644 --- a/tensorflow/contrib/lite/g3doc/rpi.md +++ b/tensorflow/contrib/lite/g3doc/rpi.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # TensorFlow Lite for Raspberry Pi ## Cross compiling diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index 967259b7a6..0e8f4339fc 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # TensorFlow Lite & TensorFlow Compatibility Guide TensorFlow Lite supports a number of TensorFlow operations used in common diff --git a/tensorflow/docs_src/mobile/android_build.md b/tensorflow/contrib/lite/g3doc/tfmobile/android_build.md similarity index 97% rename from tensorflow/docs_src/mobile/android_build.md rename to tensorflow/contrib/lite/g3doc/tfmobile/android_build.md index f4b07db459..76e16fc9db 100644 --- a/tensorflow/docs_src/mobile/android_build.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/android_build.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Building TensorFlow on Android To get you started working with TensorFlow on Android, we'll walk through two @@ -91,7 +94,8 @@ using [ADB](https://developer.android.com/studio/command-line/adb.html). This requires some knowledge of build systems and Android developer tools, but we'll guide you through the basics here. -- First, follow our instructions for @{$install/install_sources$installing from sources}. +- First, follow our instructions for + installing from sources. This will also guide you through installing Bazel and cloning the TensorFlow code. diff --git a/tensorflow/docs_src/mobile/mobile_intro.md b/tensorflow/contrib/lite/g3doc/tfmobile/index.md similarity index 86% rename from tensorflow/docs_src/mobile/mobile_intro.md rename to tensorflow/contrib/lite/g3doc/tfmobile/index.md index baad443308..bd047bfcec 100644 --- a/tensorflow/docs_src/mobile/mobile_intro.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/index.md @@ -1,4 +1,45 @@ -# Introduction to TensorFlow Mobile +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + +# Overview + +TensorFlow was designed to be a good deep learning solution for mobile +platforms. Currently we have two solutions for deploying machine learning +applications on mobile and embedded devices: TensorFlow for Mobile and +TensorFlow Lite. + +## TensorFlow Lite versus TensorFlow Mobile + +Here are a few of the differences between the two: + +- TensorFlow Lite is an evolution of TensorFlow Mobile. In most cases, apps + developed with TensorFlow Lite will have a smaller binary size, fewer + dependencies, and better performance. + +- TensorFlow Lite is in developer preview, so not all use cases are covered yet. + We expect you to use TensorFlow Mobile to cover production cases. + +- TensorFlow Lite supports only a limited set of operators, so not all models + will work on it by default. TensorFlow for Mobile has a fuller set of + supported functionality. + +TensorFlow Lite provides better performance and a small binary size on mobile +platforms as well as the ability to leverage hardware acceleration if available +on their platforms. In addition, it has many fewer dependencies so it can be +built and hosted on simpler, more constrained device scenarios. TensorFlow Lite +also allows targeting accelerators through the [Neural Networks +API](https://developer.android.com/ndk/guides/neuralnetworks/index.html). + +TensorFlow Lite currently has coverage for a limited set of operators. While +TensorFlow for Mobile supports only a constrained set of ops by default, in +principle if you use an arbitrary operator in TensorFlow, it can be customized +to build that kernel. Thus use cases which are not currently supported by +TensorFlow Lite should continue to use TensorFlow for Mobile. As TensorFlow Lite +evolves, it will gain additional operators, and the decision will be easier to +make. + + +## Introduction to TensorFlow Mobile TensorFlow was designed from the ground up to be a good deep learning solution for mobile platforms like Android and iOS. This mobile guide should help you @@ -167,7 +208,7 @@ interesting products possible. TensorFlow runs on Ubuntu Linux, Windows 10, and OS X. For a list of all supported operating systems and instructions to install TensorFlow, see -@{$install$Installing Tensorflow}. +Installing Tensorflow. Note that some of the sample code we provide for mobile TensorFlow requires you to compile TensorFlow from source, so you’ll need more than just `pip install` @@ -241,8 +282,3 @@ results you’ll see. It’s common for an algorithm to get great training accur numbers but then fail to be useful within a real application because there’s a mismatch between the dataset and real usage. Prototype end-to-end usage as soon as possible to create a consistent user experience. - -## Next Steps - -We suggest you get started by building one of our demos for -@{$mobile/android_build$Android} or @{$mobile/ios_build$iOS}. diff --git a/tensorflow/docs_src/mobile/ios_build.md b/tensorflow/contrib/lite/g3doc/tfmobile/ios_build.md similarity index 98% rename from tensorflow/docs_src/mobile/ios_build.md rename to tensorflow/contrib/lite/g3doc/tfmobile/ios_build.md index 4c84a1214a..6223707892 100644 --- a/tensorflow/docs_src/mobile/ios_build.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/ios_build.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Building TensorFlow on iOS ## Using CocoaPods diff --git a/tensorflow/docs_src/mobile/linking_libs.md b/tensorflow/contrib/lite/g3doc/tfmobile/linking_libs.md similarity index 83% rename from tensorflow/docs_src/mobile/linking_libs.md rename to tensorflow/contrib/lite/g3doc/tfmobile/linking_libs.md index efef5dd0da..4c2071ed05 100644 --- a/tensorflow/docs_src/mobile/linking_libs.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/linking_libs.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Integrating TensorFlow libraries Once you have made some progress on a model that addresses the problem you’re @@ -14,11 +17,11 @@ TensorFlow mobile demo apps. After you've managed to build the examples, you'll probably want to call TensorFlow from one of your existing applications. The very easiest way to do -this is to use the Pod installation steps described -@{$mobile/ios_build#using_cocoapods$here}, but if you want to build TensorFlow -from source (for example to customize which operators are included) you'll need -to break out TensorFlow as a framework, include the right header files, and link -against the built libraries and dependencies. +this is to use the Pod installation steps described in +Building TensorFlow on iOS, but if you want to build +TensorFlow from source (for example to customize which operators are included) +you'll need to break out TensorFlow as a framework, include the right header +files, and link against the built libraries and dependencies. ### Android @@ -82,10 +85,12 @@ recompile of the core. To achieve this capability, TensorFlow uses a registration pattern in a lot of places. In the code, it looks like this: - class MulKernel : OpKernel { - Status Compute(OpKernelContext* context) { … } - }; - REGISTER_KERNEL(MulKernel, “Mul”); +``` +class MulKernel : OpKernel { + Status Compute(OpKernelContext* context) { … } +}; +REGISTER_KERNEL(MulKernel, “Mul”); +``` This would be in a standalone `.cc` file linked into your application, either as part of the main set of kernels or as a separate custom library. The magic @@ -101,15 +106,17 @@ doesn’t offer a good mechanism for doing this sort of registration, so we have to resort to some tricky code. Under the hood, the macro is implemented so that it produces something like this: - class RegisterMul { - public: - RegisterMul() { - global_kernel_registry()->Register(“Mul”, [](){ - return new MulKernel() - }); - } - }; - RegisterMul g_register_mul; +``` +class RegisterMul { + public: + RegisterMul() { + global_kernel_registry()->Register(“Mul”, [](){ + return new MulKernel() + }); + } +}; +RegisterMul g_register_mul; +``` This sets up a class `RegisterMul` with a constructor that tells the global kernel registry what function to call when somebody asks it how to create a @@ -176,8 +183,10 @@ have an experimental script at [rename_protobuf.sh](https://github.com/tensorflo You need to run this as part of the makefile build, after you’ve downloaded all the dependencies: - tensorflow/contrib/makefile/download_dependencies.sh - tensorflow/contrib/makefile/rename_protobuf.sh +``` +tensorflow/contrib/makefile/download_dependencies.sh +tensorflow/contrib/makefile/rename_protobuf.sh +``` ## Calling the TensorFlow API @@ -193,18 +202,20 @@ use case, while on iOS and Raspberry Pi you call directly into the C++ API. Here’s what a typical Inference Library sequence looks like on Android: - // Load the model from disk. - TensorFlowInferenceInterface inferenceInterface = - new TensorFlowInferenceInterface(assetManager, modelFilename); +``` +// Load the model from disk. +TensorFlowInferenceInterface inferenceInterface = +new TensorFlowInferenceInterface(assetManager, modelFilename); - // Copy the input data into TensorFlow. - inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3); +// Copy the input data into TensorFlow. +inferenceInterface.feed(inputName, floatValues, 1, inputSize, inputSize, 3); - // Run the inference call. - inferenceInterface.run(outputNames, logStats); +// Run the inference call. +inferenceInterface.run(outputNames, logStats); - // Copy the output Tensor back into the output array. - inferenceInterface.fetch(outputName, outputs); +// Copy the output Tensor back into the output array. +inferenceInterface.fetch(outputName, outputs); +``` You can find the source of this code in the [Android examples](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/examples/android/src/org/tensorflow/demo/TensorFlowImageClassifier.java#L107). @@ -212,27 +223,29 @@ You can find the source of this code in the [Android examples](https://github.co Here’s the equivalent code for iOS and Raspberry Pi: - // Load the model. - PortableReadFileToProto(file_path, &tensorflow_graph); - - // Create a session from the model. - tensorflow::Status s = session->Create(tensorflow_graph); - if (!s.ok()) { - LOG(FATAL) << "Could not create TensorFlow Graph: " << s; - } - - // Run the model. - std::string input_layer = "input"; - std::string output_layer = "output"; - std::vector outputs; - tensorflow::Status run_status = session->Run({{input_layer, image_tensor}}, +``` +// Load the model. +PortableReadFileToProto(file_path, &tensorflow_graph); + +// Create a session from the model. +tensorflow::Status s = session->Create(tensorflow_graph); +if (!s.ok()) { + LOG(FATAL) << "Could not create TensorFlow Graph: " << s; +} + +// Run the model. +std::string input_layer = "input"; +std::string output_layer = "output"; +std::vector outputs; +tensorflow::Status run_status = session->Run({\{input_layer, image_tensor}}, {output_layer}, {}, &outputs); - if (!run_status.ok()) { - LOG(FATAL) << "Running model failed: " << run_status; - } +if (!run_status.ok()) { + LOG(FATAL) << "Running model failed: " << run_status; +} - // Access the output data. - tensorflow::Tensor* output = &outputs[0]; +// Access the output data. +tensorflow::Tensor* output = &outputs[0]; +``` This is all based on the [iOS sample code](https://www.tensorflow.org/code/tensorflow/examples/ios/simple/RunModelViewController.mm), diff --git a/tensorflow/docs_src/mobile/optimizing.md b/tensorflow/contrib/lite/g3doc/tfmobile/optimizing.md similarity index 98% rename from tensorflow/docs_src/mobile/optimizing.md rename to tensorflow/contrib/lite/g3doc/tfmobile/optimizing.md index 778e4d3a62..a0192c3541 100644 --- a/tensorflow/docs_src/mobile/optimizing.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/optimizing.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Optimizing for mobile There are some special issues that you have to deal with when you’re trying to @@ -77,7 +80,7 @@ out of a mobile device's memory faster. To understand how large your network will be on disk, start by looking at the size on disk of your `GraphDef` file after you’ve run `freeze_graph` and -`strip_unused_nodes` on it (see @{$mobile/prepare_models$Preparing models} for +`strip_unused_nodes` on it (see Preparing models for more details on these tools), since then it should only contain inference-related nodes. To double-check that your results are as expected, run the `summarize_graph` tool to see how many parameters are in constants: @@ -103,7 +106,8 @@ you multiply the number of const parameters by four, you should get something that’s close to the size of the file on disk. You can often get away with only eight-bits per parameter with very little loss of accuracy in the final result, so if your file size is too large you can try using -@{$performance/quantization$quantize_weights} to transform the parameters down. +quantize_weights +to transform the parameters down. bazel build tensorflow/tools/graph_transforms:transform_graph && \ bazel-bin/tensorflow/tools/graph_transforms/transform_graph \ @@ -292,7 +296,8 @@ run it on a 64-bit ARM device: You can interpret the results in exactly the same way as the desktop version above. If you have any trouble figuring out what the right input and output -names and types are, take a look at the @{$mobile/prepare_models$Preparing models} +names and types are, take a look at the +Preparing models page for details about detecting these for your model, and look at the `summarize_graph` tool which may give you helpful information. diff --git a/tensorflow/docs_src/mobile/prepare_models.md b/tensorflow/contrib/lite/g3doc/tfmobile/prepare_models.md similarity index 98% rename from tensorflow/docs_src/mobile/prepare_models.md rename to tensorflow/contrib/lite/g3doc/tfmobile/prepare_models.md index 2b84dbb973..6b4e4a92bd 100644 --- a/tensorflow/docs_src/mobile/prepare_models.md +++ b/tensorflow/contrib/lite/g3doc/tfmobile/prepare_models.md @@ -1,3 +1,6 @@ +book_path: /mobile/_book.yaml +project_path: /mobile/_project.yaml + # Preparing models for mobile deployment The requirements for storing model information during training are very @@ -255,8 +258,8 @@ The criteria for including ops and types fall into several categories: These ops are trimmed by default to optimize for inference on mobile, but it is possible to alter some build files to change the default. After alternating the build files, you will need to recompile TensorFlow. See below for more details -on how to do this, and also see @{$mobile/optimizing#binary_size$Optimizing} for -more on reducing your binary size. +on how to do this, and also see optimizing binary size +for more on reducing your binary size. ### Locate the implementation diff --git a/tensorflow/docs_src/mobile/README.md b/tensorflow/docs_src/mobile/README.md new file mode 100644 index 0000000000..ecf4267265 --- /dev/null +++ b/tensorflow/docs_src/mobile/README.md @@ -0,0 +1,3 @@ +# TF Lite subsite + +This subsite directory lives in [tensorflow/contrib/lite/g3doc](../../contrib/lite/g3doc/). diff --git a/tensorflow/docs_src/mobile/index.md b/tensorflow/docs_src/mobile/index.md deleted file mode 100644 index 6032fcad02..0000000000 --- a/tensorflow/docs_src/mobile/index.md +++ /dev/null @@ -1,33 +0,0 @@ -# Overview - -TensorFlow was designed to be a good deep learning solution for mobile -platforms. Currently we have two solutions for deploying machine learning -applications on mobile and embedded devices: -@{$mobile/mobile_intro$TensorFlow for Mobile} and @{$mobile/tflite$TensorFlow Lite}. - -## TensorFlow Lite versus TensorFlow Mobile - -Here are a few of the differences between the two: - -- TensorFlow Lite is an evolution of TensorFlow Mobile. In most cases, apps - developed with TensorFlow Lite will have a smaller binary size, fewer - dependencies, and better performance. - -- TensorFlow Lite supports only a limited set of operators, so not all models - will work on it by default. TensorFlow for Mobile has a fuller set of - supported functionality. - -TensorFlow Lite provides better performance and a small binary size on mobile -platforms as well as the ability to leverage hardware acceleration if available -on their platforms. In addition, it has many fewer dependencies so it can be -built and hosted on simpler, more constrained device scenarios. TensorFlow Lite -also allows targeting accelerators through the [Neural Networks -API](https://developer.android.com/ndk/guides/neuralnetworks/index.html). - -TensorFlow Lite currently has coverage for a limited set of operators. While -TensorFlow for Mobile supports only a constrained set of ops by default, in -principle if you use an arbitrary operator in TensorFlow, it can be customized -to build that kernel. Thus use cases which are not currently supported by -TensorFlow Lite should continue to use TensorFlow for Mobile. As TensorFlow Lite -evolves, it will gain additional operators, and the decision will be easier to -make. diff --git a/tensorflow/docs_src/mobile/leftnav_files b/tensorflow/docs_src/mobile/leftnav_files deleted file mode 100644 index 97340ef7e1..0000000000 --- a/tensorflow/docs_src/mobile/leftnav_files +++ /dev/null @@ -1,15 +0,0 @@ -index.md -### TensorFlow Lite -tflite/index.md -tflite/devguide.md -tflite/demo_android.md -tflite/demo_ios.md -tflite/performance.md ->>> -### TensorFlow Mobile -mobile_intro.md -android_build.md -ios_build.md -linking_libs.md -prepare_models.md -optimizing.md -- GitLab From eb1ee5a245327b509dfb929d2dbbef90cd3299a5 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Tue, 24 Jul 2018 11:52:30 -0700 Subject: [PATCH 336/519] [TF:XLA] Bump open source llvm revision to r337814 PiperOrigin-RevId: 205866253 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 3b7674e397..062beb9348 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -485,11 +485,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/62b518b75a780a3bc75982cbe54b0e7bc262aa6e.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/62b518b75a780a3bc75982cbe54b0e7bc262aa6e.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/35ffbe6bcf3b755f30633d834534a892b4c5fb29.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/35ffbe6bcf3b755f30633d834534a892b4c5fb29.tar.gz", ], - sha256 = "51ab0edcf7dde0207f5cf141aec16b14fcac5290112cdf1ea671a2757f719f8b", - strip_prefix = "llvm-62b518b75a780a3bc75982cbe54b0e7bc262aa6e", + sha256 = "42b3924b56339bb953b587f3e55788c8fefa51068756e6ac2ee4aed9c187bbb8", + strip_prefix = "llvm-35ffbe6bcf3b755f30633d834534a892b4c5fb29", build_file = clean_dep("//third_party/llvm:llvm.autogenerated.BUILD"), ) -- GitLab From e571644c9fd3ede16ce870b317c1b5070a58628e Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Tue, 24 Jul 2018 12:23:50 -0700 Subject: [PATCH 337/519] Let TrtEngineOp fallback to native tf function call for most of the failure cases, and fix a bug where it sets the status of the context but still execute the op. --- .../contrib/tensorrt/kernels/trt_engine_op.cc | 149 +++++++++--------- .../contrib/tensorrt/kernels/trt_engine_op.h | 6 + 2 files changed, 80 insertions(+), 75 deletions(-) diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index 646d62483f..f45de29a14 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -45,11 +45,11 @@ using ::tensorflow::strings::StrCat; // Helps simultaneous execution of native and TRT engines. class AsyncHelper : public tensorflow::core::RefCounted { public: - AsyncHelper(tensorflow::AsyncOpKernel::DoneCallback done) { done_ = done; } + AsyncHelper(AsyncOpKernel::DoneCallback done) { done_ = done; } ~AsyncHelper() override { done_(); } private: - tensorflow::AsyncOpKernel::DoneCallback done_; + AsyncOpKernel::DoneCallback done_; }; #define TYPECASE(dt, X, Y) \ @@ -152,7 +152,7 @@ TRTEngineOp::TRTEngineOp(OpKernelConstruction* context) } } -void TRTEngineOp::ExecuteNativeSegment(tensorflow::OpKernelContext* ctx, +void TRTEngineOp::ExecuteNativeSegment(OpKernelContext* ctx, AsyncHelper* helper) { if (!calibration_mode_) { VLOG(1) << "Executing native engine"; @@ -193,7 +193,7 @@ void TRTEngineOp::ExecuteNativeSegment(tensorflow::OpKernelContext* ctx, }); } -void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, +void TRTEngineOp::ExecuteCalibration(OpKernelContext* ctx, AsyncHelper* helper) { helper->Ref(); tensorflow::core::ScopedUnref sc(helper); @@ -238,7 +238,7 @@ void TRTEngineOp::ExecuteCalibration(tensorflow::OpKernelContext* ctx, ExecuteNativeSegment(ctx, helper); } -int TRTEngineOp::GetEngineBatch(tensorflow::OpKernelContext* ctx) { +int TRTEngineOp::GetEngineBatch(OpKernelContext* ctx) { int num_batch = ctx->input(0).shape().dim_size(0); int smallest_engine = 0; for (const auto i : cached_engine_batches_) { @@ -254,21 +254,20 @@ int TRTEngineOp::GetEngineBatch(tensorflow::OpKernelContext* ctx) { cached_engine_batches_.push_back(num_batch); VLOG(1) << "Running with batch size " << num_batch; } else { - string s("Engine buffer is full. buffer limit= "); - StrAppend(&s, max_cached_engines_, ", current entries= "); - for (auto i : cached_engine_batches_) StrAppend(&s, i, ", "); - StrAppend(&s, "Requested batch= ", num_batch); - LOG(ERROR) << s; - ctx->SetStatus(tensorflow::errors::ResourceExhausted( - "Requested batch size is not available and engine cache is full")); + string msg = + StrCat("Engine buffer is full. buffer limit=", max_cached_engines_, + ", current entries="); + for (auto i : cached_engine_batches_) StrAppend(&msg, i, ","); + StrAppend(&msg, "Requested batch=", num_batch); + LOG(WARNING) << msg; return -1; } } return smallest_engine; } -void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, - tensorflow::AsyncOpKernel::DoneCallback done) { +void TRTEngineOp::ComputeAsync(OpKernelContext* ctx, + AsyncOpKernel::DoneCallback done) { auto helper = new AsyncHelper(done); tensorflow::core::ScopedUnref sc(helper); if (calibration_mode_) { @@ -276,32 +275,51 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, return; } const int smallest_engine = GetEngineBatch(ctx); - if (smallest_engine < 0) return; // GetEngineBatch already set the status. + if (smallest_engine < 0) { + LOG(WARNING) << "Failed to get engine batch, running native segment"; + ExecuteNativeSegment(ctx, helper); + return; + } const int num_batch = ctx->input(0).shape().dim_size(0); auto& engine_ctx_pair = GetEngine(smallest_engine, ctx); auto& trt_engine_ptr = engine_ctx_pair.first; if (!trt_engine_ptr) { LOG(WARNING) << "Engine retrieval for batch size " << num_batch - << " failed Running native segment"; + << " failed. Running native segment"; ExecuteNativeSegment(ctx, helper); return; } + const bool retry = ExecuteTrtEngine(ctx, num_batch, trt_engine_ptr.get(), + engine_ctx_pair.second.get()); + if (retry) { + ExecuteNativeSegment(ctx, helper); + return; + } +} +bool TRTEngineOp::ExecuteTrtEngine( + OpKernelContext* ctx, const int num_batch, + nvinfer1::ICudaEngine* trt_engine_ptr, + nvinfer1::IExecutionContext* trt_execution_context_ptr) { + const bool kRetry = true; const int num_binding = ctx->num_inputs() + ctx->num_outputs(); std::vector buffers(num_binding); for (int i = 0; i < ctx->num_inputs(); i++) { - const string inp_name = StrCat(kInputPHName, i); + const string input_name = StrCat(kInputPHName, i); const size_t binding_index = - trt_engine_ptr->getBindingIndex(inp_name.c_str()); + trt_engine_ptr->getBindingIndex(input_name.c_str()); + if (binding_index == -1) { + LOG(WARNING) << "Iutput node not found, at " << input_name; + return kRetry; + } const Tensor& input_tensor = ctx->input(i); const TensorShape& input_shape = input_tensor.shape(); if (num_batch != input_shape.dim_size(0)) { - LOG(ERROR) << "input data inconsistent batch size"; - ctx->SetStatus(tensorflow::errors::FailedPrecondition( - "Different batch sizes between input tensors")); - return; + LOG(WARNING) << "Input data has inconsistent batch size: " << num_batch + << " vs " << input_shape.dim_size(0); + return kRetry; } auto dtype = trt_engine_ptr->getBindingDataType(binding_index); switch (dtype) { @@ -309,25 +327,19 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, buffers[binding_index] = (void*)(input_tensor.flat().data()); break; case nvinfer1::DataType::kHALF: - LOG(ERROR) << "FP16 inputs are not supported yet!"; - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "FP16 inputs are not supported!")); - return; + LOG(WARNING) << "FP16 inputs are not supported yet!"; + return kRetry; case nvinfer1::DataType::kINT8: - LOG(ERROR) << "INT8 inputs are not supported yet!"; - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "INT8 inputs are not supported!")); - return; + LOG(WARNING) << "INT8 inputs are not supported yet!"; + return kRetry; #if NV_TENSORRT_MAJOR > 3 case nvinfer1::DataType::kINT32: buffers[binding_index] = (void*)(input_tensor.flat().data()); break; #endif default: - LOG(ERROR) << "Unknown TRT data type: " << int(dtype); - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "Unknown output TRT data type! ", static_cast(dtype))); - return; + LOG(WARNING) << "Unknown TRT data type: " << int(dtype); + return kRetry; } } @@ -344,20 +356,23 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, std::vector 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( - ctx, TensorShapeUtils::MakeShape(trt_shape.data(), trt_shape.size(), - &output_shape)); + auto status = TensorShapeUtils::MakeShape( + trt_shape.data(), trt_shape.size(), &output_shape); + if (!status.ok()) { + LOG(WARNING) << "Failed to get output shape: " << status; + return kRetry; + } } else { - LOG(ERROR) << "output node not found, at " << output_name; - ctx->SetStatus(tensorflow::errors::Internal("output ", output_name, - " couldn't be found!")); - return; + LOG(WARNING) << "Output node not found, at " << output_name; + return kRetry; } auto status = ctx->allocate_output(i, output_shape, &output_tensor); if (!status.ok()) { - LOG(ERROR) << "Allocating output failed with " << status; + LOG(WARNING) << "Allocating output failed with " << status; ctx->SetStatus(status); - return; + // Do not retry since we cannot allocate the same output twice. + // TODO(aaroey): ideally we should retry, fix this. + return !kRetry; } auto dtype = trt_engine_ptr->getBindingDataType(binding_index); switch (dtype) { @@ -366,15 +381,11 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, reinterpret_cast(output_tensor->flat().data()); break; case nvinfer1::DataType::kHALF: - LOG(ERROR) << "half size is not supported yet!"; - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "Half outputs are not supported!")); - return; + LOG(WARNING) << "half size is not supported yet!"; + return kRetry; case nvinfer1::DataType::kINT8: - LOG(ERROR) << "int8 is not supported yet!"; - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "INT8 outputs are not supported!")); - return; + LOG(WARNING) << "int8 is not supported yet!"; + return kRetry; #if NV_TENSORRT_MAJOR > 3 case nvinfer1::DataType::kINT32: buffers[binding_index] = @@ -382,13 +393,11 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, break; #endif default: - LOG(ERROR) << "Unknown TRT data type: " << static_cast(dtype); - ctx->SetStatus(tensorflow::errors::InvalidArgument( - "Unsupported output data type! ", static_cast(dtype))); - return; + LOG(WARNING) << "Unknown TRT data type: " << static_cast(dtype); + return kRetry; } } - // copied from cuda_kernel_helper since it seems only valid in *.cu.cc files + // Copied from cuda_kernel_helper since it seems only valid in *.cu.cc files const cudaStream_t* stream = CHECK_NOTNULL( reinterpret_cast(ctx->op_device_context() ->stream() @@ -396,15 +405,14 @@ void TRTEngineOp::ComputeAsync(tensorflow::OpKernelContext* ctx, ->GpuStreamMemberHack())); // TODO(jie): trt enqueue does not return error - auto& trt_execution_context_ptr = engine_ctx_pair.second; auto ret = trt_execution_context_ptr->enqueue(num_batch, &buffers[0], *stream, nullptr); if (!ret) { - LOG(ERROR) << "Failed to enqueue batch for TRT engine: " << name(); - ctx->SetStatus(tensorflow::errors::Internal( - "Failed to enqueue batch for TRT engine: ", name())); + LOG(WARNING) << "Failed to enqueue batch for TRT engine: " << name(); + return kRetry; } - // sync should be done by TF. + // Synchronization will be done by TF. + return !kRetry; } TRTEngineOp::~TRTEngineOp() { @@ -424,8 +432,6 @@ nvinfer1::IGpuAllocator* TRTEngineOp::GetAllocator(OpKernelContext* ctx) { if (!alloc) { LOG(ERROR) << "Can't find device allocator for gpu device " << device->name(); - ctx->SetStatus(tensorflow::errors::Internal( - "Can't get device allocator for device ", device->name())); return nullptr; } allocator_.reset(new TRTDeviceAllocator(alloc)); @@ -451,10 +457,7 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, TrtUniquePtrType infer(nvinfer1::createInferRuntime(logger)); #if NV_TENSORRT_MAJOR > 3 auto allocator = GetAllocator(ctx); - if (allocator == nullptr) { - // GetAllocator already set the Status. - return null_pair; - } + if (allocator == nullptr) return null_pair; infer->setGpuAllocator(allocator); #endif TrtUniquePtrType static_engine( @@ -480,10 +483,7 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, nvinfer1::IGpuAllocator* allocator = nullptr; #if NV_TENSORRT_MAJOR > 3 allocator = GetAllocator(ctx); - if (allocator == nullptr) { - // GetAllocator already set the Status. - return null_pair; - } + if (allocator == nullptr) return null_pair; #endif std::vector shapes; for (int i = 0; i < ctx->num_inputs(); ++i) { @@ -505,9 +505,8 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, // retry in the future. engine_map_[batch_size] = {nullptr, nullptr}; } - LOG(ERROR) << "Engine creation for batch size " << batch_size - << " failed " << status; - ctx->SetStatus(tensorflow::errors::Internal("Engine creation failed!")); + LOG(WARNING) << "Engine creation for batch size " << batch_size + << " failed " << status; return null_pair; } VLOG(1) << "Conversion is done"; @@ -519,7 +518,7 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, } tensorflow::Status TRTEngineOp::AllocateCalibrationResources( - tensorflow::OpKernelContext* ctx, TRTCalibrationResource** cr) { + OpKernelContext* ctx, TRTCalibrationResource** cr) { auto cres = new TRTCalibrationResource(); *cr = cres; // Get the allocator. diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h index 9265250605..59b744e6d3 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.h @@ -60,6 +60,12 @@ class TRTEngineOp : public AsyncOpKernel { // Execute replaced native segment as function Op. void ExecuteNativeSegment(OpKernelContext* ctx, AsyncHelper* helper); + // Execute the tensorrt engine. Returns whether we need to retry by running + // the native segment. + bool ExecuteTrtEngine(OpKernelContext* ctx, const int num_batch, + nvinfer1::ICudaEngine* trt_engine_ptr, + nvinfer1::IExecutionContext* trt_execution_context_ptr); + // Allocate necessary resources for calibration Status AllocateCalibrationResources(OpKernelContext* ctx, TRTCalibrationResource** cr); -- GitLab From a9489a0d05c89ab6b2cb94dda95e9ff911ad2058 Mon Sep 17 00:00:00 2001 From: Pavithra Vijay Date: Tue, 24 Jul 2018 12:47:50 -0700 Subject: [PATCH 338/519] Add binary accuracy metric class. Remove the need for decorators in metric subclasses. PiperOrigin-RevId: 205875214 --- tensorflow/python/keras/metrics.py | 175 +++++++++++++----- tensorflow/python/keras/metrics_test.py | 95 +++++++++- .../api/golden/tensorflow.keras.metrics.pbtxt | 2 +- 3 files changed, 223 insertions(+), 49 deletions(-) diff --git a/tensorflow/python/keras/metrics.py b/tensorflow/python/keras/metrics.py index 72e15763cb..7d8b1fec45 100644 --- a/tensorflow/python/keras/metrics.py +++ b/tensorflow/python/keras/metrics.py @@ -21,6 +21,8 @@ from __future__ import print_function from abc import ABCMeta from abc import abstractmethod + +import types import six from tensorflow.python.eager import context @@ -58,7 +60,14 @@ from tensorflow.python.util import tf_decorator from tensorflow.python.util.tf_export import tf_export -def update_state(update_state_fn): +def check_is_tensor_or_operation(x, name): + """Raises type error if the given input is not a tensor or operation.""" + if not (isinstance(x, ops.Tensor) or isinstance(x, ops.Operation)): + raise TypeError('{0} must be a Tensor or Operation, given: {1}'.format( + name, x)) + + +def update_state_wrapper(update_state_fn): """Decorator to wrap metric `update_state()` with `defun()`, `add_update()`. Args: @@ -70,7 +79,7 @@ def update_state(update_state_fn): executed to update the metric state with the given inputs. """ - def decorated(*args, **kwargs): + def decorated(metric_obj, *args, **kwargs): """Decorated function with `defun()` and `add_update()`.""" # Converting update_state_fn() into a graph function, so that @@ -79,14 +88,15 @@ def update_state(update_state_fn): defuned_update_state_fn = function.defun(update_state_fn) update_op = defuned_update_state_fn(*args, **kwargs) if update_op is not None: # update_op will be None in eager execution. - metric_obj = args[0] metric_obj.add_update(update_op, inputs=True) + check_is_tensor_or_operation( + update_op, 'Metric {0}\'s update'.format(metric_obj.name)) return update_op return tf_decorator.make_decorator(update_state_fn, decorated) -def result(result_fn): +def result_wrapper(result_fn): """Decorator to wrap metric `result()` function in `merge_call()`. Result computation is an idempotent operation that simply calculates the @@ -104,26 +114,29 @@ def result(result_fn): The metric result tensor. """ - def decorated(*args): + def decorated(metric_obj, *args): """Decorated function with merge_call.""" tower_context = distribute_lib.get_tower_context() if tower_context is None: # if in cross tower context already - return result_fn() - - # TODO(psv): Test distribution of metrics using different distribution - # strategies. - - # Creating a wrapper for merge_fn. merge_call invokes the given merge_fn - # with distribution object as the first parameter. We create a wrapper here - # so that the result function need not have that parameter. - def merge_fn_wrapper(distribution, merge_fn, *args): - # We will get `PerDevice` merge function. Taking the first one as all are - # identical copies of the function that we had passed below. - return distribution.unwrap(merge_fn)[0](*args) - - # Wrapping result in merge_call. merge_call is used when we want to leave - # tower mode and compute a value in cross tower mode. - return tower_context.merge_call(merge_fn_wrapper, result_fn, *args) + result_t = result_fn(*args) + else: + # TODO(psv): Test distribution of metrics using different distribution + # strategies. + + # Creating a wrapper for merge_fn. merge_call invokes the given merge_fn + # with distribution object as the first parameter. We create a wrapper + # here so that the result function need not have that parameter. + def merge_fn_wrapper(distribution, merge_fn, *args): + # We will get `PerDevice` merge function. Taking the first one as all + # are identical copies of the function that we had passed below. + return distribution.unwrap(merge_fn)[0](*args) + + # Wrapping result in merge_call. merge_call is used when we want to leave + # tower mode and compute a value in cross tower mode. + result_t = tower_context.merge_call(merge_fn_wrapper, result_fn, *args) + check_is_tensor_or_operation(result_t, + 'Metric {0}\'s result'.format(metric_obj.name)) + return result_t return tf_decorator.make_decorator(result_fn, decorated) @@ -246,24 +259,19 @@ class Metric(Layer): * `__init__()`: All state variables should be created in this method by calling `self.add_weight()` like: `self.var = self.add_weight(...)` * `update_state()`: Has all updates to the state variables like: - self.var.assign_add(...). Please decorate the function with: - @update_state: Converts `update_state()` into a graph function, so that - we can return a single op that performs all of the variable updates and - adds the update op to the metric layer. + self.var.assign_add(...). * `result()`: Computes and returns a value for the metric - from the state variables. Please decorate the function with: - @result: Wraps `result()` in a distribution strategy merge_call(). + from the state variables. Example subclass implementation: ``` class BinaryTruePositives(Metric): - def __init__(self, name='binary-true-positives', dtype=dtypes.float64): + def __init__(self, name='binary-true-positives', dtype=None): super(BinaryTruePositives, self).__init__(name=name, dtype=dtype) self.true_positives = self.add_weight( 'true_positives', initializer=init_ops.zeros_initializer) - @update_state def update_state(self, y_true, y_pred, sample_weight=None): y_true = math_ops.cast(y_true, dtypes.bool) y_pred = math_ops.cast(y_pred, dtypes.bool) @@ -278,17 +286,24 @@ class Metric(Layer): values = math_ops.multiply(values, sample_weight) state_ops.assign_add(self.true_positives, math_ops.reduce_sum(values)) - @result def result(self): return array_ops.identity(self.true_positives) ``` """ __metaclass__ = ABCMeta - def __init__(self, name=None, dtype=dtypes.float64): + def __init__(self, name=None, dtype=None): super(Metric, self).__init__(name=name, dtype=dtype) self.stateful = True # All metric layers are stateful. self.built = True + self._dtype = K.floatx() if dtype is None else dtypes.as_dtype(dtype).name + + def __new__(cls, *args, **kwargs): + obj = super(Metric, cls).__new__(cls, *args, **kwargs) + obj.update_state = types.MethodType( + update_state_wrapper(obj.update_state), obj) + obj.result = types.MethodType(result_wrapper(obj.result), obj) + return obj def __call__(self, *args, **kwargs): """Accumulates statistics and then computes metric result value. @@ -301,9 +316,9 @@ class Metric(Layer): Returns: The metric value tensor. """ - update_op = self.update_state(*args, **kwargs) + update_op = self.update_state(*args, **kwargs) # pylint: disable=not-callable with ops.control_dependencies([update_op]): - return self.result() + return self.result() # pylint: disable=not-callable def reset_states(self): """Resets all of the metric state variables. @@ -318,10 +333,8 @@ class Metric(Layer): def update_state(self, *args, **kwargs): """Accumulates statistics for the metric. - Please decorate the function with: - @update_state: Converts `update_state()` into a graph function, so that - we can return a single op that performs all of the variable updates - This means: + Note: This function is executed as a graph function in graph mode. + This means: a) Operations on the same resource are executed in textual order. This should make it easier to do things like add the updated value of a variable to another, for example. @@ -343,9 +356,6 @@ class Metric(Layer): Result computation is an idempotent operation that simply calculates the metric value using the state variables. - - Please decorate the function with: - @result: Wraps `result()` in a distribution strategy merge_call(). """ NotImplementedError('Must be implemented in subclasses.') @@ -380,7 +390,13 @@ class Mean(Metric): Use `sample_weight` of 0 to mask values. """ - def __init__(self, name='mean', dtype=dtypes.float64): + def __init__(self, name='mean', dtype=None): + """Creates a `Mean` instance. + + Args: + name: (Optional) string name of the metric instance. + dtype: (Optional) data type of the metric result. + """ super(Mean, self).__init__(name=name, dtype=dtype) # Create new state variables self.total = self.add_weight( @@ -388,7 +404,6 @@ class Mean(Metric): self.count = self.add_weight( 'count', initializer=init_ops.zeros_initializer) - @update_state def update_state(self, values, sample_weight=None): """Accumulates statistics for computing the mean. @@ -418,14 +433,84 @@ class Mean(Metric): state_ops.assign_add(self.total, values) state_ops.assign_add(self.count, num_values) - @result def result(self): return _safe_div(self.total, self.count) +class MeanMetricWrapper(Mean): + """Wraps a stateless metric function with the Mean metric.""" + + def __init__(self, fn, name=None, dtype=None, **kwargs): + """Creates a `MeanMetricWrapper` instance. + + Args: + fn: The metric function to wrap, with signature + `fn(y_true, y_pred, **kwargs)`. + name: (Optional) string name of the metric instance. + dtype: (Optional) data type of the metric result. + **kwargs: The keyword arguments that are passed on to `fn`. + """ + super(MeanMetricWrapper, self).__init__(name=name, dtype=dtype) + self._fn = fn + self._fn_kwargs = kwargs + + def update_state(self, y_true, y_pred, sample_weight=None): + """Accumulates metric statistics. + + `y_true` and `y_pred` should have the same shape. + + Args: + y_true: The ground truth values. + y_pred: The predicted values. + sample_weight: Optional weighting of each example. Defaults to 1. Can be + a `Tensor` whose rank is either 0, or the same rank as `y_true`, + and must be broadcastable to `y_true`. + """ + y_true = math_ops.cast(y_true, self._dtype) + y_pred = math_ops.cast(y_pred, self._dtype) + y_pred, y_true, sample_weight = _squeeze_or_expand_dimensions( + y_pred, y_true, sample_weight) + + matches = self._fn(y_true, y_pred, **self._fn_kwargs) + super(MeanMetricWrapper, self).update_state( + matches, sample_weight=sample_weight) + + def get_config(self): + config = self._fn_kwargs + base_config = super(MeanMetricWrapper, self).get_config() + return dict(list(base_config.items()) + list(config.items())) + + +class BinaryAccuracy(MeanMetricWrapper): + """Calculates how often predictions matches labels. + + This metric creates two local variables, `total` and `count` that are used to + compute the frequency with which `y_pred` matches `y_true`. This frequency is + ultimately returned as `binary accuracy`: an idempotent operation that simply + divides `total` by `count`. + + If `sample_weight` is `None`, weights default to 1. + Use `sample_weight` of 0 to mask values. + """ + + def __init__(self, name='binary-accuracy', dtype=None, threshold=0.5): + """Creates a `BinaryAccuracy` instance. + + Args: + name: (Optional) string name of the metric instance. + dtype: (Optional) data type of the metric result. + threshold: (Optional) Float representing the threshold for deciding + whether prediction values are 1 or 0. + """ + super(BinaryAccuracy, self).__init__( + binary_accuracy, name, dtype=dtype, threshold=threshold) + + @tf_export('keras.metrics.binary_accuracy') -def binary_accuracy(y_true, y_pred): - return K.mean(math_ops.equal(y_true, math_ops.round(y_pred)), axis=-1) +def binary_accuracy(y_true, y_pred, threshold=0.5): + threshold = math_ops.cast(threshold, y_pred.dtype) + y_pred = math_ops.cast(y_pred > threshold, y_pred.dtype) + return K.mean(math_ops.equal(y_true, y_pred), axis=-1) @tf_export('keras.metrics.categorical_accuracy') diff --git a/tensorflow/python/keras/metrics_test.py b/tensorflow/python/keras/metrics_test.py index 6d8269f34d..d583379708 100644 --- a/tensorflow/python/keras/metrics_test.py +++ b/tensorflow/python/keras/metrics_test.py @@ -196,7 +196,7 @@ class KerasMetricsTest(test.TestCase): # check config self.assertEqual(m.name, 'my_mean') self.assertTrue(m.stateful) - self.assertEqual(m.dtype, dtypes.float64) + self.assertEqual(m.dtype, dtypes.float32) self.assertEqual(len(m.variables), 2) self.evaluate(variables.global_variables_initializer()) @@ -212,7 +212,7 @@ class KerasMetricsTest(test.TestCase): # check update_state() and result() + state accumulation + tensor input update_op = m.update_state(ops.convert_n_to_tensor([1, 5])) self.evaluate(update_op) - self.assertEqual(self.evaluate(m.result()), 106 / 3) + self.assertAlmostEqual(self.evaluate(m.result()), 106 / 3, 2) self.assertEqual(self.evaluate(m.total), 106) # 100 + 1 + 5 self.assertEqual(self.evaluate(m.count), 3) @@ -223,7 +223,8 @@ class KerasMetricsTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def test_mean_with_sample_weight(self): - m = metrics.Mean() + m = metrics.Mean(dtype=dtypes.float64) + self.assertEqual(m.dtype, dtypes.float64) self.evaluate(variables.global_variables_initializer()) # check scalar weight @@ -308,6 +309,94 @@ class KerasMetricsTest(test.TestCase): self.assertEqual(200., self.evaluate(restore_mean.result())) self.assertEqual(3, self.evaluate(restore_mean.count)) + @test_util.run_in_graph_and_eager_modes + def test_binary_accuracy(self): + acc_obj = metrics.BinaryAccuracy(name='my acc') + + # check config + self.assertEqual(acc_obj.name, 'my acc') + self.assertTrue(acc_obj.stateful) + self.assertEqual(len(acc_obj.variables), 2) + self.assertEqual(acc_obj.dtype, dtypes.float32) + self.evaluate(variables.global_variables_initializer()) + + # verify that correct value is returned + update_op = acc_obj.update_state([[1], [0]], [[1], [0]]) + self.evaluate(update_op) + result = self.evaluate(acc_obj.result()) + self.assertEqual(result, 1) # 2/2 + + # check y_pred squeeze + update_op = acc_obj.update_state([[1], [1]], [[[1]], [[0]]]) + self.evaluate(update_op) + result = self.evaluate(acc_obj.result()) + self.assertAlmostEqual(result, 0.75, 2) # 3/4 + + # check y_true squeeze + result_t = acc_obj([[[1]], [[1]]], [[1], [0]]) + result = self.evaluate(result_t) + self.assertAlmostEqual(result, 0.67, 2) # 4/6 + + # check with sample_weight + result_t = acc_obj([[1], [1]], [[1], [0]], [[0.5], [0.2]]) + result = self.evaluate(result_t) + self.assertAlmostEqual(result, 0.67, 2) # 4.5/6.7 + + # check incompatible shapes + with self.assertRaisesRegexp(ValueError, + r'Shapes \(1,\) and \(2,\) are incompatible'): + acc_obj.update_state([1, 1], [1]) + + @test_util.run_in_graph_and_eager_modes + def test_binary_accuracy_threshold(self): + acc_obj = metrics.BinaryAccuracy(threshold=0.7) + self.evaluate(variables.global_variables_initializer()) + result_t = acc_obj([[1], [1], [0], [0]], [[0.9], [0.6], [0.4], [0.8]]) + result = self.evaluate(result_t) + self.assertAlmostEqual(result, 0.5, 2) + + @test_util.run_in_graph_and_eager_modes + def test_invalid_result(self): + + class InvalidResult(metrics.Metric): + + def __init__(self, name='invalid-result', dtype=dtypes.float64): + super(InvalidResult, self).__init__(name=name, dtype=dtype) + + def update_state(self, *args, **kwargs): + pass + + def result(self): + return 1 + + invalid_result_obj = InvalidResult() + with self.assertRaisesRegexp( + TypeError, + 'Metric invalid-result\'s result must be a Tensor or Operation, given:' + ): + invalid_result_obj.result() + + @test_util.run_in_graph_and_eager_modes + def test_invalid_update(self): + + class InvalidUpdate(metrics.Metric): + + def __init__(self, name='invalid-update', dtype=dtypes.float64): + super(InvalidUpdate, self).__init__(name=name, dtype=dtype) + + def update_state(self, *args, **kwargs): + return [1] + + def result(self): + pass + + invalid_update_obj = InvalidUpdate() + with self.assertRaisesRegexp( + TypeError, + 'Metric invalid-update\'s update must be a Tensor or Operation, given:' + ): + invalid_update_obj.update_state() + if __name__ == '__main__': test.main() diff --git a/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt index a97a9b5758..73b577da37 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.metrics.pbtxt @@ -22,7 +22,7 @@ tf_module { } member_method { name: "binary_accuracy" - argspec: "args=[\'y_true\', \'y_pred\'], varargs=None, keywords=None, defaults=None" + argspec: "args=[\'y_true\', \'y_pred\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0.5\'], " } member_method { name: "binary_crossentropy" -- GitLab From 26e531ee70f0b7efbb8f1452a40d5e926b7f38c0 Mon Sep 17 00:00:00 2001 From: Nick Felt Date: Tue, 24 Jul 2018 12:50:46 -0700 Subject: [PATCH 339/519] Automated rollback of commit 568727eed199dba04e37f500265b50f96fed455e PiperOrigin-RevId: 205875586 --- .../contrib/summary/summary_ops_graph_test.py | 20 - .../contrib/tpu/python/tpu/tpu_estimator.py | 17 +- tensorflow/core/kernels/summary_kernels.cc | 2 - tensorflow/python/BUILD | 1 - tensorflow/python/estimator/estimator.py | 24 +- tensorflow/python/estimator/estimator_test.py | 260 +--------- tensorflow/python/estimator/model_fn.py | 3 +- tensorflow/python/estimator/training_test.py | 10 +- tensorflow/python/ops/summary_ops_v2.py | 68 +-- tensorflow/python/saved_model/builder_impl.py | 5 +- .../summary/writer/event_file_writer_v2.py | 71 +-- tensorflow/python/summary/writer/writer.py | 8 +- .../python/summary/writer/writer_test.py | 54 +- .../training/basic_session_run_hooks.py | 182 ++----- .../training/basic_session_run_hooks_test.py | 476 ++++++------------ .../python/training/monitored_session.py | 11 +- tensorflow/python/training/optimizer.py | 6 +- 17 files changed, 279 insertions(+), 939 deletions(-) diff --git a/tensorflow/contrib/summary/summary_ops_graph_test.py b/tensorflow/contrib/summary/summary_ops_graph_test.py index 409fdf4583..ae8336daaf 100644 --- a/tensorflow/contrib/summary/summary_ops_graph_test.py +++ b/tensorflow/contrib/summary/summary_ops_graph_test.py @@ -228,26 +228,6 @@ class GraphFileTest(test_util.TensorFlowTestCase): sess.run(writer.flush()) self.assertEqual(2, get_total()) - def testSummaryOpsCollector(self): - summary_ops.scalar('x', 1.0, step=1) - with summary_ops.create_file_writer(self.get_temp_dir()).as_default(): - s2 = summary_ops.scalar('x', 1.0, step=1) - collector1 = summary_ops._SummaryOpsCollector() - collector2 = summary_ops._SummaryOpsCollector() - with collector1.capture(): - s3 = summary_ops.scalar('x', 1.0, step=1) - with collector2.capture(): - s4 = summary_ops.scalar('x', 1.0, step=1) - s5 = summary_ops.scalar('x', 1.0, step=1) - s6 = summary_ops.scalar('x', 1.0, step=1) - summary_ops.scalar('six', 1.0, step=1) - - # Ops defined outside summary writer context are ignored; ops defined inside - # SummaryOpsCollector capture context are stored to innermost such context. - self.assertItemsEqual([s2, s6], summary_ops.all_summary_ops()) - self.assertItemsEqual([s3, s5], collector1.collected_ops) - self.assertItemsEqual([s4], collector2.collected_ops) - class GraphDbTest(summary_test_util.SummaryDbTest): diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 1eb43ac7f7..42406db88a 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -1506,17 +1506,13 @@ class _OutfeedHostCall(object): _OutfeedHostCall.validate(host_calls) ret = {} for name, host_call in host_calls.items(): - # Isolate host call summary ops from main graph. - summary_collector = contrib_summary._SummaryOpsCollector() # pylint: disable=protected-access host_fn, tensors = host_call if isinstance(tensors, (tuple, list)): - with summary_collector.capture(): - ret[name] = host_fn(*tensors) + ret[name] = host_fn(*tensors) else: # Must be dict. try: - with summary_collector.capture(): - ret[name] = host_fn(**tensors) + ret[name] = host_fn(**tensors) except TypeError as e: logging.warning( 'Exception while calling %s: %s. It is likely the tensors ' @@ -1631,14 +1627,11 @@ class _OutfeedHostCall(object): # dimension. dequeue_ops[i] = array_ops.concat(dequeue_ops[i], axis=0) - # Isolate host call summary ops from main graph. - summary_collector = contrib_summary._SummaryOpsCollector() # pylint: disable=protected-access if self._tensor_keys[name] is not None: # The user-provided eval_metrics[1] is a dict. dequeue_ops = dict(zip(self._tensor_keys[name], dequeue_ops)) try: - with summary_collector.capture(): - ret[name] = self._host_fns[name](**dequeue_ops) + ret[name] = self._host_fns[name](**dequeue_ops) except TypeError as e: logging.warning( 'Exception while calling %s: %s. It is likely the tensors ' @@ -1646,8 +1639,8 @@ class _OutfeedHostCall(object): 'function\'s arguments', name, e, name) raise e else: - with summary_collector.capture(): - ret[name] = self._host_fns[name](*dequeue_ops) + ret[name] = self._host_fns[name](*dequeue_ops) + return ret diff --git a/tensorflow/core/kernels/summary_kernels.cc b/tensorflow/core/kernels/summary_kernels.cc index b518c3cbf4..b287f0cc2f 100644 --- a/tensorflow/core/kernels/summary_kernels.cc +++ b/tensorflow/core/kernels/summary_kernels.cc @@ -53,7 +53,6 @@ class CreateSummaryFileWriterOp : public OpKernel { max_queue, flush_millis, logdir, filename_suffix, ctx->env(), s); })); - core::ScopedUnref unref(s); } }; REGISTER_KERNEL_BUILDER(Name("CreateSummaryFileWriter").Device(DEVICE_CPU), @@ -90,7 +89,6 @@ class CreateSummaryDbWriterOp : public OpKernel { db, experiment_name, run_name, user_name, ctx->env(), s)); return Status::OK(); })); - core::ScopedUnref unref(s); } }; REGISTER_KERNEL_BUILDER(Name("CreateSummaryDbWriter").Device(DEVICE_CPU), diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index a7c60f5450..b5876c3457 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -2822,7 +2822,6 @@ py_library( ":framework_ops", ":math_ops", ":resource_variable_ops", - ":resources", ":smart_cond", ":summary_op_util", ":summary_ops_gen", diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index b7185e8966..915ceeb98b 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -46,7 +46,6 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import resources -from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.ops import variables from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging @@ -66,7 +65,6 @@ from tensorflow.python.util import compat from tensorflow.python.util import compat_internal from tensorflow.python.util import function_utils from tensorflow.python.util import nest -from tensorflow.python.util import tf_contextlib from tensorflow.python.util.tf_export import estimator_export @@ -1158,8 +1156,7 @@ class Estimator(object): Loss from training """ worker_hooks = [] - with ops.Graph().as_default() as g, g.device( - self._device_fn), self._summary_writing_context(): + with ops.Graph().as_default() as g, g.device(self._device_fn): random_seed.set_random_seed(self._config.tf_random_seed) global_step_tensor = self._create_and_assert_global_step(g) training_util._get_or_create_global_step_read() # pylint: disable=protected-access @@ -1193,7 +1190,7 @@ class Estimator(object): is_tpu_strategy = self._distribution.__class__.__name__ == 'TPUStrategy' worker_hooks = [] - with ops.Graph().as_default() as g, self._summary_writing_context(): + with ops.Graph().as_default() as g: with self._distribution.scope(): random_seed.set_random_seed(self._config.tf_random_seed) @@ -1522,23 +1519,6 @@ class Estimator(object): (self._warm_start_settings,)) warm_starting_util.warm_start(*self._warm_start_settings) - @tf_contextlib.contextmanager - def _summary_writing_context(self): - """Context manager for enabling V2 summary writing.""" - # Avoid creating a file writer at all if no summary writing was requested. - if self._config.save_summary_steps <= 0: - yield - return - file_writer = summary_ops_v2.create_file_writer( - logdir=self._model_dir, filename_suffix='') - with file_writer.as_default(): - # Create a boolean placeholder, default False, that SummarySaverHook can - # use to enable/disable V2 summary writing according to its own logic. - placeholder = array_ops.placeholder_with_default(False, shape=[]) - training.SummarySaverHook._set_placeholder(placeholder) # pylint: disable=protected-access - with summary_ops_v2.record_summaries_if(placeholder): - yield - def create_per_tower_ready_op(scaffold): """Create a Scaffold.ready_op inside a tower.""" diff --git a/tensorflow/python/estimator/estimator_test.py b/tensorflow/python/estimator/estimator_test.py index 1dd45a07c2..8bc410ba0b 100644 --- a/tensorflow/python/estimator/estimator_test.py +++ b/tensorflow/python/estimator/estimator_test.py @@ -22,7 +22,6 @@ import functools import glob import os import tempfile -import time import numpy as np import six @@ -30,7 +29,6 @@ import six from google.protobuf import text_format from tensorflow.core.protobuf import rewriter_config_pb2 -from tensorflow.core.util.event_pb2 import SessionLog from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops from tensorflow.python.estimator import estimator @@ -42,7 +40,6 @@ from tensorflow.python.estimator.inputs import numpy_io from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors -from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.framework import test_util @@ -58,7 +55,6 @@ from tensorflow.python.ops import metrics as metrics_lib from tensorflow.python.ops import parsing_ops from tensorflow.python.ops import state_ops from tensorflow.python.ops import string_ops -from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables from tensorflow.python.ops.losses import losses @@ -89,32 +85,13 @@ def dummy_model_fn(features, labels, params): _, _, _ = features, labels, params -def load_eventfile_contents(directory_path): - """Returns the contents of the singular event file in the given directory.""" - writer_cache.FileWriterCache.clear() - - # Get last Event written. - event_paths = glob.glob(os.path.join(directory_path, '*tfevent*')) - if len(event_paths) != 1: - raise AssertionError('Expected one eventfile, got %s' % str(event_paths)) - return list(summary_iterator.summary_iterator(event_paths[0])) - - -def make_summary_steps(eventlist): - """Returns dict of tags in eventlist mapped to steps where they're logged.""" - tag_to_steps = {} - for event in eventlist: - if event.summary is not None: - for value in event.summary.value: - if value.tag not in tag_to_steps: - tag_to_steps[value.tag] = [] - tag_to_steps[value.tag].append(event.step) - return tag_to_steps - - def summaries_with_matching_keyword(keyword, dir_): """Yields summary protos matching given keyword from event file.""" - for event in load_eventfile_contents(dir_): + + writer_cache.FileWriterCache.clear() + + event_paths = glob.glob(os.path.join(dir_, 'events*')) + for event in summary_iterator.summary_iterator(event_paths[-1]): if event.summary is not None: for value in event.summary.value: if keyword in value.tag: @@ -389,51 +366,13 @@ def dummy_input_fn(): constant_op.constant([[1], [1]])) -class StableGlobalStepEstimator(estimator.Estimator): - """Estimator subclass using a ResourceVariable global_step for testing.""" - # TODO(nickfelt): remove after standard global_step is a ResourceVariable. - - def _create_global_step(self, graph): - """Creates a stable ResourceVariable-based global step suitable for tests. - - Args: - graph: The graph in which to create the global step. - - Returns: - A global step `Tensor`. - """ - with graph.as_default(), graph.name_scope(None): - return variable_scope.get_variable( - ops.GraphKeys.GLOBAL_STEP, - shape=[], - dtype=dtypes.int64, - initializer=init_ops.zeros_initializer(), - trainable=False, - collections=[ - ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP - ], - # Use a ResourceVariable and set caching_device to make the read - # behavior deterministic and well-defined. - caching_device='cpu:0', - use_resource=True) - - def model_fn_global_step_incrementer(features, labels, mode): _, _ = features, labels + global_step = training.get_global_step() return model_fn_lib.EstimatorSpec( mode, loss=constant_op.constant(1.), - train_op=training.get_global_step().assign_add(1)) - - -def model_fn_with_v1_and_v2_summaries(features, labels, mode): - del features, labels - summary.scalar('foo-v1', 1.0) - summary_ops_v2.scalar('foo-v2', 2.0) - return model_fn_lib.EstimatorSpec( - mode, - loss=constant_op.constant(1.), - train_op=training.get_global_step().assign_add(1)) + train_op=state_ops.assign_add(global_step, 1)) def assert_features_op(expected_features, actual_features): @@ -469,25 +408,6 @@ def _make_input_fn(features, labels): return _input_fn -class RaiseOnceAtStepHook(session_run_hook.SessionRunHook): - """Hook that raises an Exception the first time it reaches step N.""" - - def __init__(self, n, ex): - self.n = n - self.ex = ex - self.raised = False - - def before_run(self, run_context): - # Raise the first time we reach step N. - self.n -= 1 - if 0 == self.n and not self.raised: - # Wait 1 sec so that event file names have different UNIX timestamps. - time.sleep(1.2) - self.raised = True - raise self.ex - return None - - class EstimatorTrainTest(test.TestCase): def test_callable_model_fn(self): @@ -697,171 +617,17 @@ class EstimatorTrainTest(test.TestCase): self.assertEqual( 5, estimator._load_global_step_from_checkpoint_dir(est.model_dir)) - def test_summary_loss(self): + def test_loss_summary(self): est = estimator.Estimator(model_fn=model_fn_global_step_incrementer, config=run_config.RunConfig(save_summary_steps=1)) est.train(dummy_input_fn, steps=1) - events = load_eventfile_contents(est.model_dir) - self.assertEqual({'loss': [1]}, make_summary_steps(events)) - def test_summary_user_defined_v1_and_v2(self): - est = StableGlobalStepEstimator( - model_fn=model_fn_with_v1_and_v2_summaries, - config=run_config.RunConfig(save_summary_steps=1)) - est.train(dummy_input_fn, steps=1) - events = load_eventfile_contents(est.model_dir) - self.assertEqual( - {'foo-v1': [1], 'foo-v2': [0], 'loss': [1]}, - make_summary_steps(events)) - - def test_summary_writing_disabled(self): - est = StableGlobalStepEstimator( - model_fn=model_fn_with_v1_and_v2_summaries, - config=run_config.RunConfig(save_summary_steps=0)) - est.train(dummy_input_fn, steps=1) - events = load_eventfile_contents(est.model_dir) - self.assertEqual({}, make_summary_steps(events)) - - def test_summary_saving_steps(self): - est = StableGlobalStepEstimator( - model_fn=model_fn_with_v1_and_v2_summaries, - config=run_config.RunConfig(save_summary_steps=2)) - est.train(dummy_input_fn, steps=5) - events = load_eventfile_contents(est.model_dir) - self.assertEqual( - {'foo-v1': [1, 3, 5], 'foo-v2': [0, 2, 4], 'loss': [1, 3, 5]}, - make_summary_steps(events)) - - def test_summary_additional_hook(self): - def model_fn_extra_summary_hook(features, labels, mode, config): - del features, labels - v1_op = summary.scalar('foo-v1', 1.0) - v2_op = summary_ops_v2.scalar('foo-v2', 2.0) - extra_hook = basic_session_run_hooks.SummarySaverHook( - output_dir=os.path.join(config.model_dir, 'extra'), - save_steps=3, - summary_op=control_flow_ops.with_dependencies([v2_op], v1_op)) - return model_fn_lib.EstimatorSpec( - mode, - loss=constant_op.constant(1.), - train_op=training.get_global_step().assign_add(1), - training_hooks=[extra_hook]) - est = StableGlobalStepEstimator( - model_fn=model_fn_extra_summary_hook, - config=run_config.RunConfig(save_summary_steps=2)) - est.train(dummy_input_fn, steps=7) - - events = load_eventfile_contents(est.model_dir) - self.assertEqual( - {'foo-v1': [1, 3, 5, 7], 'foo-v2': [0, 2, 4, 6], 'loss': [1, 3, 5, 7]}, - make_summary_steps(events)) - extra_dir = os.path.join(est.model_dir, 'extra') - extra_events = load_eventfile_contents(extra_dir) - self.assertEqual({'foo-v1': [1, 4, 7]}, make_summary_steps(extra_events)) - - def test_summary_user_defined_in_input_fn(self): - def input_fn_custom_summaries(): - summary.scalar('foo-v1', 1.0) - summary_ops_v2.scalar('foo-v2', 2.0) - return ({'x': constant_op.constant([[1], [1]])}, - constant_op.constant([[1], [1]])) - est = StableGlobalStepEstimator( - model_fn=model_fn_global_step_incrementer, - config=run_config.RunConfig(save_summary_steps=1)) - est.train(input_fn_custom_summaries, steps=1) - events = load_eventfile_contents(est.model_dir) - self.assertEqual( - {'foo-v1': [1], 'foo-v2': [0], 'loss': [1]}, - make_summary_steps(events)) + # Make sure nothing is stuck in limbo. + writer_cache.FileWriterCache.clear() - def test_summary_with_warm_start(self): - est = StableGlobalStepEstimator( - model_fn=model_fn_with_v1_and_v2_summaries, - config=run_config.RunConfig(save_summary_steps=1)) - est.train(dummy_input_fn, steps=5) - warm_started_est = StableGlobalStepEstimator( - model_fn=model_fn_with_v1_and_v2_summaries, - config=run_config.RunConfig(save_summary_steps=1), - warm_start_from=est.model_dir) - warm_started_est.train(dummy_input_fn, steps=3) - events = load_eventfile_contents(warm_started_est.model_dir) - self.assertEqual( - {'foo-v1': [1, 2, 3], 'foo-v2': [0, 1, 2], 'loss': [1, 2, 3]}, - make_summary_steps(events)) - - def test_summary_with_error_and_auto_restart(self): - est = StableGlobalStepEstimator( - model_fn=model_fn_with_v1_and_v2_summaries, - config=run_config.RunConfig( - save_summary_steps=2, save_checkpoints_steps=5)) - abort_hook = RaiseOnceAtStepHook( - 7, errors_impl.AbortedError(None, None, 'Abort')) - est.train(dummy_input_fn, steps=10, hooks=[abort_hook]) - - # We expect two event files: one for the aborted run, and one post-restart. - event_paths = sorted(glob.glob(os.path.join(est.model_dir, '*tfevent*'))) - self.assertEqual(2, len(event_paths)) - - # First file should have summaries up to the last checkpoint. - first_events = list(summary_iterator.summary_iterator(event_paths[0])) - first_summaries = make_summary_steps(first_events) - self.assertEqual([0, 2, 4], first_summaries['foo-v2']) - # The V1 summaries may or may not include step 5 (depending on the flush() - # sequence) so just check that at least 1 and 3 are there. - # TODO(nickfelt): ensure summaries *at* checkpoint step get flushed too. - self.assertEqual([1, 3], first_summaries['foo-v1'][:2]) - self.assertEqual([1, 3], first_summaries['loss'][:2]) - - # Second file should pick up from global_step=5. Note that the 2 step save - # interval will reset at this step as well, so summaries logged at steps - # 2 and 4 continue not with 6, 8, ... but at steps 5, 7, ... instead. - second_events = list(summary_iterator.summary_iterator(event_paths[1])) - self.assertEqual( - {'foo-v1': [6, 8, 10], 'foo-v2': [5, 7, 9], 'loss': [6, 8, 10]}, - make_summary_steps(second_events)) - # Second file should contain a session START event at resumed global_step. - session_start_event = next(event for event in second_events - if event.session_log.status == SessionLog.START) - self.assertEqual(5, session_start_event.step) - - def test_summary_with_error_and_explicit_restart(self): - est = StableGlobalStepEstimator( - model_fn=model_fn_with_v1_and_v2_summaries, - config=run_config.RunConfig( - save_summary_steps=2, save_checkpoints_steps=5)) - abort_hook = RaiseOnceAtStepHook( - 7, errors_impl.UnknownError(None, None, 'Unknown failure')) - self.assertRaises( - errors_impl.UnknownError, - lambda: est.train(dummy_input_fn, max_steps=10, hooks=[abort_hook])) - # Explicitly retry after the error. - est.train(dummy_input_fn, max_steps=10, hooks=[abort_hook]) - - # We expect two event files: one for the failed run, and one post-restart. - event_paths = sorted(glob.glob(os.path.join(est.model_dir, '*tfevent*'))) - self.assertEqual(2, len(event_paths)) - - # First file should have summaries up to the last checkpoint. - first_events = list(summary_iterator.summary_iterator(event_paths[0])) - first_summaries = make_summary_steps(first_events) - self.assertEqual([0, 2, 4], first_summaries['foo-v2']) - # The V1 summaries may or may not include step 5 (depending on the flush() - # sequence) so just check that at least 1 and 3 are there. - # TODO(nickfelt): ensure summaries *at* checkpoint step get flushed too. - self.assertEqual([1, 3], first_summaries['foo-v1'][:2]) - self.assertEqual([1, 3], first_summaries['loss'][:2]) - - # Second file should pick up from global_step=5. Note that the 2 step save - # interval will reset at this step as well, so summaries logged at steps - # 2 and 4 continue not with 6, 8, ... but at steps 5, 7, ... instead. - second_events = list(summary_iterator.summary_iterator(event_paths[1])) - self.assertEqual( - {'foo-v1': [6, 8, 10], 'foo-v2': [5, 7, 9], 'loss': [6, 8, 10]}, - make_summary_steps(second_events)) - # Second file should contain a session START event at resumed global_step. - session_start_event = next(event for event in second_events - if event.session_log.status == SessionLog.START) - self.assertEqual(5, session_start_event.step) + if check_eventfile_for_keyword('loss', est.model_dir): + return + self.fail('{} should be part of reported summaries.'.format('loss')) def test_latest_checkpoint(self): est = estimator.Estimator(model_fn=model_fn_global_step_incrementer) diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py index b1b2f65edf..a9fd8f8e1a 100644 --- a/tensorflow/python/estimator/model_fn.py +++ b/tensorflow/python/estimator/model_fn.py @@ -26,7 +26,6 @@ import six from tensorflow.python.estimator.export import export_output as export_output_lib from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape -from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops from tensorflow.python.saved_model import signature_constants from tensorflow.python.saved_model import tag_constants @@ -433,7 +432,7 @@ class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [ def _check_is_tensor_or_operation(x, name): - if not (isinstance(x, ops.Operation) or tensor_util.is_tensor(x)): + if not (isinstance(x, ops.Operation) or isinstance(x, ops.Tensor)): raise TypeError('{} must be Operation or Tensor, given: {}'.format(name, x)) diff --git a/tensorflow/python/estimator/training_test.py b/tensorflow/python/estimator/training_test.py index 121439a2cd..dc106c7d3b 100644 --- a/tensorflow/python/estimator/training_test.py +++ b/tensorflow/python/estimator/training_test.py @@ -2059,7 +2059,7 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): def _extract_loss_and_global_step(self, event_folder): """Returns the loss and global step in last event.""" - event_paths = sorted(glob.glob(os.path.join(event_folder, 'events*'))) + event_paths = glob.glob(os.path.join(event_folder, 'events*')) loss = None global_step_count = None @@ -2139,12 +2139,10 @@ class TrainAndEvaluateIntegrationTest(test.TestCase): # Make sure nothing is stuck in limbo. writer_cache.FileWriterCache.clear() - # Examine the training events. - training_loss, training_global_step = self._extract_loss_and_global_step( - est.model_dir) + # Examine the training events. Use a range to check global step to avoid + # flakyness due to global step race condition. + training_loss, _ = self._extract_loss_and_global_step(est.model_dir) self.assertIsNotNone(training_loss) - # Training summaries are logged for steps 1 and 10, so we see final step. - self.assertEqual(max_steps, training_global_step) # Examine the eval events. The global step should be accurate. eval_loss, eval_global_step = self._extract_loss_and_global_step( diff --git a/tensorflow/python/ops/summary_ops_v2.py b/tensorflow/python/ops/summary_ops_v2.py index 669358d9db..00150fe688 100644 --- a/tensorflow/python/ops/summary_ops_v2.py +++ b/tensorflow/python/ops/summary_ops_v2.py @@ -37,7 +37,6 @@ from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import gen_summary_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops -from tensorflow.python.ops import resources from tensorflow.python.ops import summary_op_util from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import training_util @@ -67,39 +66,41 @@ def should_record_summaries(): return should_record_collection[0] -@tf_contextlib.contextmanager -def always_record_summaries(): - """Sets the should_record_summaries Tensor to always true.""" - with record_summaries_if(True): - yield - - -@tf_contextlib.contextmanager -def never_record_summaries(): - """Sets the should_record_summaries Tensor to always false.""" - with record_summaries_if(False): - yield - - # TODO(apassos) consider how to handle local step here. @tf_contextlib.contextmanager def record_summaries_every_n_global_steps(n, global_step=None): """Sets the should_record_summaries Tensor to true if global_step % n == 0.""" if global_step is None: global_step = training_util.get_or_create_global_step() - with ops.device("cpu:0"): - on_nth_global_step = math_ops.equal(global_step % n, 0) - with record_summaries_if(on_nth_global_step): + collection_ref = ops.get_collection_ref(_SHOULD_RECORD_SUMMARIES_NAME) + old = collection_ref[:] + try: + with ops.device("cpu:0"): + collection_ref[:] = [math_ops.equal(global_step % n, 0)] + yield + finally: + collection_ref[:] = old + + +@tf_contextlib.contextmanager +def always_record_summaries(): + """Sets the should_record_summaries Tensor to always true.""" + collection_ref = ops.get_collection_ref(_SHOULD_RECORD_SUMMARIES_NAME) + old = collection_ref[:] + try: + collection_ref[:] = [True] yield + finally: + collection_ref[:] = old @tf_contextlib.contextmanager -def record_summaries_if(bool_value): - """Sets the should_record_summaries Tensor to the given boolean value.""" +def never_record_summaries(): + """Sets the should_record_summaries Tensor to always false.""" collection_ref = ops.get_collection_ref(_SHOULD_RECORD_SUMMARIES_NAME) old = collection_ref[:] try: - collection_ref[:] = [bool_value] + collection_ref[:] = [False] yield finally: collection_ref[:] = old @@ -142,6 +143,7 @@ class SummaryWriter(object): finally: context.context().summary_writer_resource = old + def init(self): """Operation to initialize the summary writer resource.""" if self._resource is not None: @@ -309,9 +311,6 @@ def _make_summary_writer(name, factory, **kwargs): # TODO(apassos): Consider doing this instead. # ops.get_default_session().run(init_op) ops.add_to_collection(_SUMMARY_WRITER_INIT_COLLECTION_NAME, init_op) - # TODO(nickfelt): expose an actual op for this - is_initialized_op = constant_op.constant(True) - resources.register_resource(resource, init_op, is_initialized_op) return SummaryWriter(resource, init_op_fn) @@ -326,27 +325,6 @@ def _nothing(): return constant_op.constant(False) -class _SummaryOpsCollector(object): - """Defines a context manager for isolating out a subset of summary ops. - - Summary ops defined within this context will be accumulated within this - collector instead of being added to the graph-wide summary ops collection that - is returned by {@tf.contrib.summary.all_summary_ops}. - """ - - def __init__(self): - self.collected_ops = [] - - @tf_contextlib.contextmanager - def capture(self): - collection_ref = ops.get_collection_ref(ops.GraphKeys._SUMMARY_COLLECTION) # pylint: disable=protected-access - original_ops = collection_ref[:] - collection_ref[:] = [] - yield - self.collected_ops = collection_ref[:] - collection_ref[:] = original_ops - - def all_summary_ops(): """Graph-mode only. Returns all summary ops. diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py index b67d0f2362..e58be804c2 100644 --- a/tensorflow/python/saved_model/builder_impl.py +++ b/tensorflow/python/saved_model/builder_impl.py @@ -28,7 +28,6 @@ from tensorflow.core.protobuf import saved_model_pb2 from tensorflow.core.protobuf import saver_pb2 from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_util from tensorflow.python.lib.io import file_io from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging @@ -179,10 +178,10 @@ class SavedModelBuilder(object): stored as a collection with key TRAIN_OP_KEY, but not executed. Raises: - TypeError if Train op is not of type `Operation` or a Tensor. + TypeError if Train op is not of type `Operation`. """ if train_op is not None: - if (not tensor_util.is_tensor(train_op) and + if (not isinstance(train_op, ops.Tensor) and not isinstance(train_op, ops.Operation)): raise TypeError("train_op needs to be a Tensor or Op: %r" % train_op) ops.add_to_collection(constants.TRAIN_OP_KEY, train_op) diff --git a/tensorflow/python/summary/writer/event_file_writer_v2.py b/tensorflow/python/summary/writer/event_file_writer_v2.py index 262182d3b8..5c66c0f7a8 100644 --- a/tensorflow/python/summary/writer/event_file_writer_v2.py +++ b/tensorflow/python/summary/writer/event_file_writer_v2.py @@ -18,7 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from tensorflow.python.client import session as tf_session from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops @@ -44,11 +43,11 @@ class EventFileWriterV2(object): """Creates an `EventFileWriterV2` and an event file to write to. On construction, this calls `tf.contrib.summary.create_file_writer` within - the default graph, which finds and returns a shared summary writer resource - for `logdir` if one exists, and creates one if not. Creating the summary + the graph from `session.graph` to look up a shared summary writer resource + for `logdir` if one exists, and create one if not. Creating the summary writer resource in turn creates a new event file in `logdir` to be filled with `Event` protocol buffers passed to `add_event`. Graph ops to control - this writer resource are added to the default graph during this init call; + this writer resource are added to `session.graph` during this init call; stateful methods on this class will call `session.run()` on these ops. Note that because the underlying resource is shared, it is possible that @@ -62,50 +61,38 @@ class EventFileWriterV2(object): no effect. See `tf.contrib.summary.create_file_writer` for details. Args: - session: A `tf.Session`, or a callable that provides one which will be - called on-demand. The session will hold the shared writer resource. + session: A `tf.Session`. Session that will hold shared writer resource. + The writer ops will be added to session.graph during this init call. logdir: A string. Directory where event file will be written. max_queue: Integer. Size of the queue for pending events and summaries. flush_secs: Number. How often, in seconds, to flush the pending events and summaries to disk. filename_suffix: A string. Every event file's name is suffixed with `filename_suffix`. - - Raises: - ValueError: if `session` is not a `tf.Session` or a callable """ - if isinstance(session, tf_session.SessionInterface): - self._session = lambda: session - elif callable(session): - self._session = session - else: - raise ValueError('session must be tf.Session or callable') + self._session = session self._logdir = logdir - self._initialized = False self._closed = False if not gfile.IsDirectory(self._logdir): gfile.MakeDirs(self._logdir) - with ops.name_scope('filewriter'): - file_writer = summary_ops_v2.create_file_writer( - logdir=self._logdir, - max_queue=max_queue, - flush_millis=flush_secs * 1000, - filename_suffix=filename_suffix) - with summary_ops_v2.always_record_summaries(), file_writer.as_default(): - self._event_placeholder = array_ops.placeholder_with_default( - constant_op.constant('unused', dtypes.string), - shape=[]) - self._add_event_op = summary_ops_v2.import_event( - self._event_placeholder) - self._init_op = file_writer.init() - self._flush_op = file_writer.flush() - self._close_op = file_writer.close() - - def _init_if_needed(self): - if not self._initialized: - self._session().run(self._init_op) - self._initialized = True + with self._session.graph.as_default(): + with ops.name_scope('filewriter'): + file_writer = summary_ops_v2.create_file_writer( + logdir=self._logdir, + max_queue=max_queue, + flush_millis=flush_secs * 1000, + filename_suffix=filename_suffix) + with summary_ops_v2.always_record_summaries(), file_writer.as_default(): + self._event_placeholder = array_ops.placeholder_with_default( + constant_op.constant('unused', dtypes.string), + shape=[]) + self._add_event_op = summary_ops_v2.import_event( + self._event_placeholder) + self._init_op = file_writer.init() + self._flush_op = file_writer.flush() + self._close_op = file_writer.close() + self._session.run(self._init_op) def get_logdir(self): """Returns the directory where event file will be written.""" @@ -121,6 +108,7 @@ class EventFileWriterV2(object): """ if self._closed: self._closed = False + self._session.run(self._init_op) def add_event(self, event): """Adds an event to the event file. @@ -129,9 +117,8 @@ class EventFileWriterV2(object): event: An `Event` protocol buffer. """ if not self._closed: - self._init_if_needed() event_pb = event.SerializeToString() - self._session().run( + self._session.run( self._add_event_op, feed_dict={self._event_placeholder: event_pb}) def flush(self): @@ -140,9 +127,7 @@ class EventFileWriterV2(object): Call this method to make sure that all pending events have been written to disk. """ - if not self._closed: - self._init_if_needed() - self._session().run(self._flush_op) + self._session.run(self._flush_op) def close(self): """Flushes the event file to disk and close the file. @@ -150,8 +135,6 @@ class EventFileWriterV2(object): Call this method when you do not need the summary writer anymore. """ if not self._closed: - self._init_if_needed() self.flush() - self._session().run(self._close_op) + self._session.run(self._close_op) self._closed = True - self._initialized = False diff --git a/tensorflow/python/summary/writer/writer.py b/tensorflow/python/summary/writer/writer.py index 2a967ae3a5..aca084fc91 100644 --- a/tensorflow/python/summary/writer/writer.py +++ b/tensorflow/python/summary/writer/writer.py @@ -332,11 +332,8 @@ class FileWriter(SummaryToEventTransformer): the same shared resource name (which by default scoped to the logdir). If no such resource exists, one will be created using the remaining arguments to this constructor, but if one already exists those arguments are ignored. - In either case, ops will be added to the default graph to control the + In either case, ops will be added to `session.graph` to control the underlying file writer resource. See `tf.contrib.summary` for more details. - Instead of an actual `tf.Session`, this argument may also be a callable that - provides a `tf.Session` when invoked (e.g. `tf.get_default_session`), which - will be called on-demand when a session is needed. Args: logdir: A string. Directory where event file will be written. @@ -347,8 +344,7 @@ class FileWriter(SummaryToEventTransformer): graph_def: DEPRECATED: Use the `graph` argument instead. filename_suffix: A string. Every event file's name is suffixed with `suffix`. - session: A `tf.Session` object or a callable that provides `tf.Session` - objects. See details above. + session: A `tf.Session` object. See details above. Raises: RuntimeError: If called with eager execution enabled. diff --git a/tensorflow/python/summary/writer/writer_test.py b/tensorflow/python/summary/writer/writer_test.py index 3380dea317..dc990c2602 100644 --- a/tensorflow/python/summary/writer/writer_test.py +++ b/tensorflow/python/summary/writer/writer_test.py @@ -12,7 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. # ============================================================================== -"""Tests for writer.py.""" +"""Tests for training_coordinator.py.""" from __future__ import absolute_import from __future__ import division @@ -574,58 +574,6 @@ class SessionBasedFileWriterTestCase(FileWriterTestCase): # No more files self.assertRaises(StopIteration, lambda: next(event_paths)) - def testSesssionArgument_callableProvider(self): - logdir = self.get_temp_dir() - setup_writer = summary_ops_v2.create_file_writer(logdir=logdir) - with summary_ops_v2.always_record_summaries(), setup_writer.as_default(): - summary1 = summary_ops_v2.scalar("one", 0.0, step=0) - summary2 = summary_ops_v2.scalar("two", 0.0, step=0) - sess1 = session.Session() - sess1.run(setup_writer.init()) - sess1.run(summary1) - sess1.run(setup_writer.flush()) - time.sleep(1.1) # Ensure filename has a different timestamp - sess2 = session.Session() - sess2.run(setup_writer.init()) - sess2.run(summary2) - sess2.run(setup_writer.flush()) - - # Using get_default_session as session provider should make this FileWriter - # send its summaries to the current default session's shared summary writer - # resource (initializing it as needed). - test_writer = writer.FileWriter( - session=ops.get_default_session, logdir=logdir) - with sess1.as_default(): - test_writer.add_summary(self._createTaggedSummary("won"), 1) - test_writer.flush() - with sess2.as_default(): - test_writer.add_summary(self._createTaggedSummary("too"), 1) - test_writer.flush() - - event_paths = iter(sorted(glob.glob(os.path.join(logdir, "event*")))) - - # First file should have tags "one", "won" - events = summary_iterator.summary_iterator(next(event_paths)) - self.assertEqual("brain.Event:2", next(events).file_version) - self.assertEqual("one", next(events).summary.value[0].tag) - self.assertEqual("won", next(events).summary.value[0].tag) - self.assertRaises(StopIteration, lambda: next(events)) - - # Second file should have tags "two", "too" - events = summary_iterator.summary_iterator(next(event_paths)) - self.assertEqual("brain.Event:2", next(events).file_version) - self.assertEqual("two", next(events).summary.value[0].tag) - self.assertEqual("too", next(events).summary.value[0].tag) - self.assertRaises(StopIteration, lambda: next(events)) - - # No more files - self.assertRaises(StopIteration, lambda: next(event_paths)) - - def testSessionArgument_notSessionOrCallable(self): - logdir = self.get_temp_dir() - self.assertRaises( - ValueError, lambda: writer.FileWriter(session=[], logdir=logdir)) - class FileWriterCacheTest(test.TestCase): """FileWriterCache tests.""" diff --git a/tensorflow/python/training/basic_session_run_hooks.py b/tensorflow/python/training/basic_session_run_hooks.py index b8df7fe51b..b0dd188db1 100644 --- a/tensorflow/python/training/basic_session_run_hooks.py +++ b/tensorflow/python/training/basic_session_run_hooks.py @@ -31,13 +31,12 @@ from tensorflow.python.client import timeline from tensorflow.python.framework import errors from tensorflow.python.framework import meta_graph from tensorflow.python.framework import ops -from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.platform import gfile from tensorflow.python.platform import tf_logging as logging -from tensorflow.python.summary.writer import writer from tensorflow.python.training import session_run_hook from tensorflow.python.training import training_util from tensorflow.python.training.session_run_hook import SessionRunArgs +from tensorflow.python.training.summary_io import SummaryWriterCache from tensorflow.python.util.tf_export import tf_export @@ -423,9 +422,7 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): self._steps_per_run = steps_per_run def begin(self): - self._summary_writer = writer.FileWriter( - self._checkpoint_dir, session=ops.get_default_session, - filename_suffix="") + self._summary_writer = SummaryWriterCache.get(self._checkpoint_dir) self._global_step_tensor = training_util._get_or_create_global_step_read() # pylint: disable=protected-access if self._global_step_tensor is None: raise RuntimeError( @@ -434,12 +431,10 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): l.begin() def after_create_session(self, session, coord): - del coord - # Ensure summary writer resource has been initialized. - session.run(summary_ops_v2.summary_writer_initializer_op()) global_step = session.run(self._global_step_tensor) - # Write graph and saver_def once graph is finalized, which isn't true yet - # in begin() since later hooks can still change the graph. + # We do write graph and saver_def at the first call of before_run. + # We cannot do this in begin, since we let other hooks to change graph and + # add variables in begin. Graph is finalized after all begin calls. training_util.write_graph( ops.get_default_graph().as_graph_def(add_shapes=True), self._checkpoint_dir, @@ -449,9 +444,8 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): meta_graph_def = meta_graph.create_meta_graph_def( graph_def=graph.as_graph_def(add_shapes=True), saver_def=saver_def) - with ops.default_session(session): - self._summary_writer.add_graph(graph) - self._summary_writer.add_meta_graph(meta_graph_def) + self._summary_writer.add_graph(graph) + self._summary_writer.add_meta_graph(meta_graph_def) # The checkpoint saved here is the state at step "global_step". self._save(session, global_step) self._timer.update_last_triggered_step(global_step) @@ -476,8 +470,6 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): self._save(session, last_step) for l in self._listeners: l.end(session, last_step) - with ops.default_session(session): - self._summary_writer.flush() def _save(self, session, step): """Saves the latest checkpoint, returns should_stop.""" @@ -487,12 +479,10 @@ class CheckpointSaverHook(session_run_hook.SessionRunHook): l.before_save(session, step) self._get_saver().save(session, self._save_path, global_step=step) - with ops.default_session(session): - self._summary_writer.add_session_log( - SessionLog( - status=SessionLog.CHECKPOINT, checkpoint_path=self._save_path), - step) - self._summary_writer.flush() + self._summary_writer.add_session_log( + SessionLog( + status=SessionLog.CHECKPOINT, checkpoint_path=self._save_path), + step) should_stop = False for l in self._listeners: @@ -553,23 +543,13 @@ class StepCounterHook(session_run_hook.SessionRunHook): def begin(self): if self._summary_writer is None and self._output_dir: - self._summary_writer = writer.FileWriter( - self._output_dir, session=ops.get_default_session, - filename_suffix="") + self._summary_writer = SummaryWriterCache.get(self._output_dir) self._global_step_tensor = training_util._get_or_create_global_step_read() # pylint: disable=protected-access if self._global_step_tensor is None: raise RuntimeError( "Global step should be created to use StepCounterHook.") self._summary_tag = training_util.get_global_step().op.name + "/sec" - def after_create_session(self, session, coord): - del coord - # Reset any stale state in case we're recovering from a previous error. - session.run(summary_ops_v2.summary_writer_initializer_op()) - self._last_global_step = None - self._global_step_check_count = 0 - self._timer.reset() - def before_run(self, run_context): # pylint: disable=unused-argument return SessionRunArgs(self._global_step_tensor) @@ -582,6 +562,8 @@ class StepCounterHook(session_run_hook.SessionRunHook): logging.info("%s: %g", self._summary_tag, steps_per_sec) def after_run(self, run_context, run_values): + _ = run_context + stale_global_step = run_values.results if self._timer.should_trigger_for_step( stale_global_step + self._steps_per_run): @@ -591,8 +573,7 @@ class StepCounterHook(session_run_hook.SessionRunHook): elapsed_time, elapsed_steps = self._timer.update_last_triggered_step( global_step) if elapsed_time is not None: - with ops.default_session(run_context.session): - self._log_and_record(elapsed_steps, elapsed_time, global_step) + self._log_and_record(elapsed_steps, elapsed_time, global_step) # Check whether the global step has been increased. Here, we do not use the # timer.last_triggered_step as the timer might record a different global @@ -618,11 +599,6 @@ class StepCounterHook(session_run_hook.SessionRunHook): self._last_global_step = stale_global_step - def end(self, session): - if self._summary_writer is not None: - with ops.default_session(session): - self._summary_writer.flush() - @tf_export("train.NanLossDuringTrainingError") class NanLossDuringTrainingError(RuntimeError): @@ -667,25 +643,6 @@ class NanTensorHook(session_run_hook.SessionRunHook): class SummarySaverHook(session_run_hook.SessionRunHook): """Saves summaries every N steps.""" - _SUMMARY_PLACEHOLDER_COLLECTION = "_SUMMARY_SAVER_PLACEHOLDER" - - @classmethod - def _set_placeholder(cls, placeholder): - """Sets a `tf.placeholder` to be fed by the first SummarySaverHook. - - If a placeholder is provided, the first instance of SummarySaverHook in use - will feed it a boolean indicating whether summaries should be written, - according to the `save_steps` and `save_secs` parameters of that hook. This - makes the placeholder usable with `tf.contrib.summary.record_summaries_if` - to control `tf.contrib.summary` summary writing using the same schedule as - the `tf.summary` summary writing (which the hook controls directly). - - Args: - placeholder: `tf.placeholder` for the first SummarySaverHook to feed - """ - collection = ops.get_collection_ref(cls._SUMMARY_PLACEHOLDER_COLLECTION) - collection[:] = [placeholder] - def __init__(self, save_steps=None, save_secs=None, @@ -723,82 +680,53 @@ class SummarySaverHook(session_run_hook.SessionRunHook): self._scaffold = scaffold self._timer = SecondOrStepTimer(every_secs=save_secs, every_steps=save_steps) - self._placeholder = None # TODO(mdan): Throw an error if output_dir and summary_writer are None. def begin(self): if self._summary_writer is None and self._output_dir: - self._summary_writer = writer.FileWriter( - self._output_dir, filename_suffix="", session=ops.get_default_session) - # Designate the first SummarySaverHook to call begin() as the "primary" - # hook; it will control writing of v2 summaries via a placeholder bool. - collection = ops.get_collection_ref(self._SUMMARY_PLACEHOLDER_COLLECTION) - if collection: - self._placeholder = collection[0] - collection[:] = [] - self._current_step = None - self._global_step_tensor = training_util.get_or_create_global_step() + self._summary_writer = SummaryWriterCache.get(self._output_dir) + self._next_step = None + self._global_step_tensor = training_util._get_or_create_global_step_read() # pylint: disable=protected-access if self._global_step_tensor is None: raise RuntimeError( "Global step should be created to use SummarySaverHook.") - def after_create_session(self, session, coord): - del coord - # Reset any stale state in case we're recovering from a previous error. - session.run(summary_ops_v2.summary_writer_initializer_op()) - self._current_step = None - self._timer.reset() - - def before_run(self, run_context): - # For the first run, record a SessionLog.START at the pre-run global step. - if self._current_step is None: - self._current_step = run_context.session.run(self._global_step_tensor) - with ops.default_session(run_context.session): - self._summary_writer.add_session_log( - SessionLog(status=SessionLog.START), self._current_step) + def before_run(self, run_context): # pylint: disable=unused-argument + self._request_summary = ( + self._next_step is None or + self._timer.should_trigger_for_step(self._next_step)) requests = {"global_step": self._global_step_tensor} - self._request_summary = self._timer.should_trigger_for_step( - self._current_step) if self._request_summary: - self._timer.update_last_triggered_step(self._current_step) if self._get_summary_op() is not None: requests["summary"] = self._get_summary_op() - feeds = {} - if self._placeholder is not None and self._request_summary: - feeds[self._placeholder] = self._request_summary - args = SessionRunArgs(fetches=requests, feed_dict=feeds) - return args + + return SessionRunArgs(requests) def after_run(self, run_context, run_values): - # Collect any legacy v1 summaries to emit. - summaries_to_emit = [] - if self._summary_writer and self._request_summary: - for summary in run_values.results.get("summary", []): - # Skip None results corresponding to V2 summary operations. - if summary is not None: - summaries_to_emit.append(summary) - # Heuristically estimate current step as possibly-stale value plus one. + _ = run_context + if not self._summary_writer: + return + stale_global_step = run_values.results["global_step"] - self._current_step = stale_global_step + 1 - # Read the actual post-run global step if we need better accuracy because - # 1) we will request summaries on the next run (based on estimate now) and - # must ensure we record an accurate "last triggered step" value, or - # 2) we have legacy v1 summaries to emit using the post-run step value. - # Note: we could have dealt with (1) separately in before_run() but by doing - # it here we can consolidate the reads in case both (1) and (2) apply. - near_next_trigger = self._timer.should_trigger_for_step(self._current_step) - if near_next_trigger or summaries_to_emit: - self._current_step = run_context.session.run(self._global_step_tensor) - # Emit any legacy v1 summaries. - if summaries_to_emit: - with ops.default_session(run_context.session): - for summary in summaries_to_emit: - self._summary_writer.add_summary(summary, self._current_step) + global_step = stale_global_step + 1 + if self._next_step is None or self._request_summary: + global_step = run_context.session.run(self._global_step_tensor) + + if self._next_step is None: + self._summary_writer.add_session_log( + SessionLog(status=SessionLog.START), global_step) + + if self._request_summary: + self._timer.update_last_triggered_step(global_step) + if "summary" in run_values.results: + for summary in run_values.results["summary"]: + self._summary_writer.add_summary(summary, global_step) + + self._next_step = global_step + 1 def end(self, session=None): - if self._summary_writer and session: - with ops.default_session(session): - self._summary_writer.flush() + if self._summary_writer: + self._summary_writer.flush() def _get_summary_op(self): """Fetches the summary op either from self._summary_op or self._scaffold. @@ -965,27 +893,19 @@ class ProfilerHook(session_run_hook.SessionRunHook): show_memory: `bool`, if True, add object snapshot events to the trace showing the sizes and lifetimes of tensors. """ - self._output_dir = output_dir self._output_file = os.path.join(output_dir, "timeline-{}.json") + self._file_writer = SummaryWriterCache.get(output_dir) self._show_dataflow = show_dataflow self._show_memory = show_memory self._timer = SecondOrStepTimer( every_secs=save_secs, every_steps=save_steps) def begin(self): - self._file_writer = writer.FileWriter( - self._output_dir, filename_suffix="", session=ops.get_default_session) self._next_step = None self._global_step_tensor = training_util._get_or_create_global_step_read() # pylint: disable=protected-access if self._global_step_tensor is None: raise RuntimeError("Global step should be created to use ProfilerHook.") - def after_create_session(self, session, coord): - del coord - # Reset any stale state in case we're recovering from a previous error. - session.run(summary_ops_v2.summary_writer_initializer_op()) - self._timer.reset() - def before_run(self, run_context): self._request_summary = ( self._next_step is None or @@ -1005,10 +925,8 @@ class ProfilerHook(session_run_hook.SessionRunHook): self._save(global_step, self._output_file.format(global_step), run_values.run_metadata.step_stats) - with ops.default_session(run_context.session): - self._file_writer.add_run_metadata(run_values.run_metadata, - "step_%d" % global_step, - global_step=global_step) + self._file_writer.add_run_metadata(run_values.run_metadata, + "step_%d" % global_step) self._next_step = global_step + 1 @@ -1020,10 +938,6 @@ class ProfilerHook(session_run_hook.SessionRunHook): trace.generate_chrome_trace_format( show_dataflow=self._show_dataflow, show_memory=self._show_memory)) - def end(self, session): - with ops.default_session(session): - self._file_writer.flush() - def _as_graph_element(obj): """Retrieves Graph element.""" diff --git a/tensorflow/python/training/basic_session_run_hooks_test.py b/tensorflow/python/training/basic_session_run_hooks_test.py index b89167f3c1..b49a871a56 100644 --- a/tensorflow/python/training/basic_session_run_hooks_test.py +++ b/tensorflow/python/training/basic_session_run_hooks_test.py @@ -19,10 +19,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import glob import os.path import shutil -import sys import tempfile import threading import time @@ -30,9 +28,6 @@ import time from tensorflow.contrib.framework.python.framework import checkpoint_utils from tensorflow.contrib.framework.python.ops import variables from tensorflow.contrib.testing.python.framework import fake_summary_writer -from tensorflow.core.framework import graph_pb2 -from tensorflow.core.protobuf import meta_graph_pb2 -from tensorflow.core.util.event_pb2 import SessionLog from tensorflow.python.client import session as session_lib from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import constant_op @@ -40,12 +35,9 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import errors from tensorflow.python.framework import meta_graph 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 control_flow_ops -from tensorflow.python.ops import init_ops from tensorflow.python.ops import state_ops -from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.ops import variable_scope from tensorflow.python.ops import variables as variables_lib import tensorflow.python.ops.nn_grad # pylint: disable=unused-import @@ -53,27 +45,13 @@ from tensorflow.python.platform import gfile from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging from tensorflow.python.summary import summary as summary_lib -from tensorflow.python.summary import summary_iterator from tensorflow.python.summary.writer import writer_cache from tensorflow.python.training import basic_session_run_hooks from tensorflow.python.training import monitored_session -from tensorflow.python.training import saver from tensorflow.python.training import session_run_hook from tensorflow.python.training import training_util -def load_eventfile_contents(directory_path): - """Returns the contents of the singular event file in the given directory.""" - writer_cache.FileWriterCache.clear() - - # Get last Event written. - event_paths = glob.glob(os.path.join(directory_path, '*tfevent*')) - if len(event_paths) != 1: - raise AssertionError('Expected one eventfile, got %s' % str(event_paths)) - result = list(summary_iterator.summary_iterator(event_paths[0])) - return result - - class MockCheckpointSaverListener( basic_session_run_hooks.CheckpointSaverListener): @@ -739,12 +717,11 @@ class CheckpointSaverHookTest(test.TestCase): checkpoint_utils.load_variable(self.model_dir, self.global_step.name)) - def _assertCheckpointEvent(self, event, step, checkpoint_path): - self.assertEqual(step, event.step) - self.assertEqual(SessionLog.CHECKPOINT, event.session_log.status) - self.assertEqual(checkpoint_path, event.session_log.checkpoint_path) - def test_summary_writer_defs(self): + fake_summary_writer.FakeSummaryWriter.install() + writer_cache.FileWriterCache.clear() + summary_writer = writer_cache.FileWriterCache.get(self.model_dir) + with self.graph.as_default(): hook = basic_session_run_hooks.CheckpointSaverHook( self.model_dir, save_steps=2, scaffold=self.scaffold) @@ -753,40 +730,18 @@ class CheckpointSaverHookTest(test.TestCase): with session_lib.Session() as sess: sess.run(self.scaffold.init_op) mon_sess = monitored_session._HookedSession(sess, [hook]) - hook.after_create_session(sess, None) # Checkpoint saved at step 0. - expected_graph_def = self.graph.as_graph_def(add_shapes=True) - expected_meta_graph_def = meta_graph.create_meta_graph_def( - graph_def=expected_graph_def, - saver_def=self.scaffold.saver.saver_def) - mon_sess.run(self.train_op) # No checkpoint saved at step 1. - mon_sess.run(self.train_op) # Checkpoint saved at step 2. - mon_sess.run(self.train_op) # No checkpoint saved at step 3. - hook.end(sess) # Checkpoint saved at the last step (3) - events = iter(load_eventfile_contents(self.model_dir)) - next(events) # Skip version event that's always there. - - # Graph. - event = next(events) - self.assertEqual(0, event.step) - actual_graph_def = graph_pb2.GraphDef() - actual_graph_def.ParseFromString(event.graph_def) - test_util.assert_equal_graph_def(actual_graph_def, expected_graph_def) - - # Metagraph. - event = next(events) - self.assertEqual(0, event.step) - actual_meta_graph_def = meta_graph_pb2.MetaGraphDef() - actual_meta_graph_def.ParseFromString(event.meta_graph_def) - test_util.assert_meta_graph_protos_equal( - self, expected_meta_graph_def, actual_meta_graph_def) - - # Checkpoints. - # Strip the "-step#" suffix off the latest checkpoint to get base path. - checkpoint_path = saver.latest_checkpoint(self.model_dir).rsplit('-', 1)[0] - self._assertCheckpointEvent(next(events), 0, checkpoint_path) - self._assertCheckpointEvent(next(events), 2, checkpoint_path) - self._assertCheckpointEvent(next(events), 3, checkpoint_path) - self.assertRaises(StopIteration, lambda: next(events)) # No more events. + hook.after_create_session(sess, None) + mon_sess.run(self.train_op) + summary_writer.assert_summaries( + test_case=self, + expected_logdir=self.model_dir, + expected_added_meta_graphs=[ + meta_graph.create_meta_graph_def( + graph_def=self.graph.as_graph_def(add_shapes=True), + saver_def=self.scaffold.saver.saver_def) + ]) + + fake_summary_writer.FakeSummaryWriter.uninstall() def test_save_checkpoint_before_first_train_step(self): with self.graph.as_default(): @@ -1147,305 +1102,167 @@ class StepCounterHookTest(test.TestCase): self.assertEqual('global_step/sec', summary_value.tag) self.assertGreater(summary_value.simple_value, 0) - def test_summary_writer(self): - with ops.Graph().as_default(), session_lib.Session() as sess: - variables.get_or_create_global_step() - train_op = training_util._increment_global_step(1) - hook = basic_session_run_hooks.StepCounterHook( - output_dir=self.log_dir, every_n_steps=10) - hook.begin() - sess.run(variables_lib.global_variables_initializer()) - mon_sess = monitored_session._HookedSession(sess, [hook]) - for _ in range(30): - mon_sess.run(train_op) - hook.end(sess) - events = iter(load_eventfile_contents(self.log_dir)) - next(events) # Skip version event that's always there. - - event = next(events) - self.assertEqual(11, event.step) - self.assertEqual('global_step/sec', event.summary.value[0].tag) - self.assertLess(0, event.summary.value[0].simple_value) - - event = next(events) - self.assertEqual(21, event.step) - self.assertEqual('global_step/sec', event.summary.value[0].tag) - self.assertLess(0, event.summary.value[0].simple_value) - - self.assertRaises(StopIteration, lambda: next(events)) # No more events. - class SummarySaverHookTest(test.TestCase): def setUp(self): test.TestCase.setUp(self) - self.logdir = self.get_temp_dir() - self._create_stable_global_step() - - def _create_stable_global_step(self): - """Returns a new ResourceVariable global_step for deterministic tests.""" - # TODO(nickfelt): remove after standard global_step is a ResourceVariable. - with ops.get_default_graph().name_scope(None): - return variable_scope.get_variable( - ops.GraphKeys.GLOBAL_STEP, - shape=[], - dtype=dtypes.int64, - initializer=init_ops.zeros_initializer(), - trainable=False, - collections=[ - ops.GraphKeys.GLOBAL_VARIABLES, ops.GraphKeys.GLOBAL_STEP - ], - # Use a ResourceVariable and set caching_device to make the read - # behavior deterministic and well-defined. - caching_device='cpu:0', - use_resource=True) + + self.log_dir = 'log/dir' + self.summary_writer = fake_summary_writer.FakeSummaryWriter(self.log_dir) + + var = variables_lib.Variable(0.0) + tensor = state_ops.assign_add(var, 1.0) + tensor2 = tensor * 2 + self.summary_op = summary_lib.scalar('my_summary', tensor) + self.summary_op2 = summary_lib.scalar('my_summary2', tensor2) + + variables.get_or_create_global_step() + self.train_op = training_util._increment_global_step(1) def test_raise_when_scaffold_and_summary_op_both_missing(self): with self.assertRaises(ValueError): basic_session_run_hooks.SummarySaverHook() def test_raise_when_scaffold_and_summary_op_both_present(self): - summary_op = summary_lib.merge_all() with self.assertRaises(ValueError): basic_session_run_hooks.SummarySaverHook( - scaffold=monitored_session.Scaffold(), summary_op=summary_op) + scaffold=monitored_session.Scaffold(), summary_op=self.summary_op) - def test_raise_when_secs_and_steps_both_missing(self): + def test_raise_in_both_secs_and_steps(self): with self.assertRaises(ValueError): basic_session_run_hooks.SummarySaverHook( - save_secs=None, save_steps=None, output_dir=self.logdir) + save_secs=10, save_steps=20, summary_writer=self.summary_writer) - def test_raise_when_secs_and_steps_both_present(self): + def test_raise_in_none_secs_and_steps(self): with self.assertRaises(ValueError): basic_session_run_hooks.SummarySaverHook( - save_secs=10, save_steps=20, output_dir=self.logdir) + save_secs=None, save_steps=None, summary_writer=self.summary_writer) - def _makeHook(self, **kwargs): - kwargs['output_dir'] = self.logdir - kwargs['scaffold'] = monitored_session.Scaffold() - return basic_session_run_hooks.SummarySaverHook(**kwargs) + def test_save_steps(self): + hook = basic_session_run_hooks.SummarySaverHook( + save_steps=8, + summary_writer=self.summary_writer, + summary_op=self.summary_op) - def _runForSteps(self, hook, steps, loop_body_fn=None): - train_op = training_util.get_global_step().assign_add(1) with self.test_session() as sess: hook.begin() sess.run(variables_lib.global_variables_initializer()) - scaffold = hook._scaffold # pylint: disable=protected-access - if scaffold is not None: - scaffold.finalize() - sess.run(scaffold.init_op) mon_sess = monitored_session._HookedSession(sess, [hook]) - for _ in range(steps): - mon_sess.run(train_op) - if loop_body_fn is not None: - loop_body_fn() + for _ in range(30): + mon_sess.run(self.train_op) hook.end(sess) - def _assertSessionEvent(self, event, step, session_status): - self.assertEqual(step, event.step) - self.assertEqual(session_status, event.session_log.status) - - def _assertSummaryEvent(self, event, step, tag_value_list): - self.assertEqual(step, event.step) - tag_value_actual_list = [ - (value.tag, value.simple_value) for value in event.summary.value - ] - self.assertItemsEqual(tag_value_list, tag_value_actual_list) - - def test_no_summaries(self): - hook = self._makeHook(save_steps=1) - self._runForSteps(hook, 3) - events = iter(load_eventfile_contents(self.logdir)) - next(events) # Skip version event that's always there. - self._assertSessionEvent(next(events), 0, SessionLog.START) - self.assertRaises(StopIteration, lambda: next(events)) - - def test_basic_summaries(self): - summary_lib.scalar('foo-v1', 1.0) - with summary_ops_v2.create_file_writer(self.logdir).as_default(): - with summary_ops_v2.always_record_summaries(): - summary_ops_v2.scalar('foo-v2', 2.0) - hook = self._makeHook(save_steps=1) - self._runForSteps(hook, 3) - events = iter(load_eventfile_contents(self.logdir)) - next(events) # Skip version event that's always there. - self._assertSessionEvent(next(events), 0, SessionLog.START) - - self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 1, [('foo-v1', 1.0)]) - - self._assertSummaryEvent(next(events), 1, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 2, [('foo-v1', 1.0)]) - - self._assertSummaryEvent(next(events), 2, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 3, [('foo-v1', 1.0)]) - self.assertRaises(StopIteration, lambda: next(events)) + self.summary_writer.assert_summaries( + test_case=self, + expected_logdir=self.log_dir, + expected_summaries={ + 1: { + 'my_summary': 1.0 + }, + 9: { + 'my_summary': 2.0 + }, + 17: { + 'my_summary': 3.0 + }, + 25: { + 'my_summary': 4.0 + }, + }) def test_multiple_summaries(self): - summary_lib.scalar('foo-v1', 1.0) - summary_lib.scalar('bar-v1', 10.0) - with summary_ops_v2.create_file_writer(self.logdir).as_default(): - with summary_ops_v2.always_record_summaries(): - foo = summary_ops_v2.scalar('foo-v2', 2.0) - # Ensure deterministic write order - with ops.control_dependencies([foo]): - summary_ops_v2.scalar('bar-v2', 20.0) - hook = self._makeHook(save_steps=1) - self._runForSteps(hook, 1) - events = iter(load_eventfile_contents(self.logdir)) - next(events) # Skip version event that's always there. - self._assertSessionEvent(next(events), 0, SessionLog.START) - self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 0, [('bar-v2', 20.0)]) - self._assertSummaryEvent( - next(events), 1, [('foo-v1', 1.0), ('bar-v1', 10.0)]) - self.assertRaises(StopIteration, lambda: next(events)) - - def test_v2_summaries_only(self): - with summary_ops_v2.create_file_writer(self.logdir).as_default(): - with summary_ops_v2.always_record_summaries(): - summary_ops_v2.scalar('foo-v2', 2.0) - hook = self._makeHook(save_steps=1) - self._runForSteps(hook, 1) - events = iter(load_eventfile_contents(self.logdir)) - next(events) # Skip version event that's always there. - self._assertSessionEvent(next(events), 0, SessionLog.START) - self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) - self.assertRaises(StopIteration, lambda: next(events)) - - def test_v2_summaries_custom_file_writer(self): - other_dir = os.path.join(self.logdir, 'other') - other_writer = summary_ops_v2.create_file_writer(other_dir) - # SummarySaverHook only flushes the writer for logdir; this one needs to be - # manually flushed. - flush_op = other_writer.flush() - with summary_ops_v2.always_record_summaries(): - with summary_ops_v2.create_file_writer(self.logdir).as_default(): - summary_ops_v2.scalar('foo-v2', 2.0) - with other_writer.as_default(): - summary_ops_v2.scalar('other-v2', 3.0) - hook = self._makeHook(save_steps=1) - self._runForSteps(hook, 1) - with self.test_session() as sess: - sess.run(flush_op) - - events = iter(load_eventfile_contents(self.logdir)) - next(events) # Skip version event that's always there. - self._assertSessionEvent(next(events), 0, SessionLog.START) - self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) - self.assertRaises(StopIteration, lambda: next(events)) - - events = iter(load_eventfile_contents(other_dir)) - next(events) # Skip version event that's always there. - self._assertSummaryEvent(next(events), 0, [('other-v2', 3.0)]) - self.assertRaises(StopIteration, lambda: next(events)) - - def test_save_steps(self): - summary_lib.scalar('foo-v1', 1.0) - placeholder = array_ops.placeholder_with_default(False, shape=[]) - with summary_ops_v2.create_file_writer(self.logdir).as_default(): - with summary_ops_v2.record_summaries_if(placeholder): - summary_ops_v2.scalar('foo-v2', 2.0) - - basic_session_run_hooks.SummarySaverHook._set_placeholder(placeholder) - hook = self._makeHook(save_steps=8) - self._runForSteps(hook, 30) - - events = load_eventfile_contents(self.logdir) - print('TEST SAVE STEPS EVENTS', str(events), file=sys.stderr) - events = iter(events) - next(events) # Skip version event that's always there. - self._assertSessionEvent(next(events), 0, SessionLog.START) - - self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 1, [('foo-v1', 1.0)]) - - self._assertSummaryEvent(next(events), 8, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 9, [('foo-v1', 1.0)]) - - self._assertSummaryEvent(next(events), 16, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 17, [('foo-v1', 1.0)]) - - self._assertSummaryEvent(next(events), 24, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 25, [('foo-v1', 1.0)]) - self.assertRaises(StopIteration, lambda: next(events)) - - @test.mock.patch.object(time, 'time') - def test_save_secs_saving_once_every_step(self, mock_time): - mock_time.return_value = 1000.0 - summary_lib.scalar('foo-v1', 1.0) - placeholder = array_ops.placeholder_with_default(False, shape=[]) - with summary_ops_v2.create_file_writer(self.logdir).as_default(): - with summary_ops_v2.record_summaries_if(placeholder): - summary_ops_v2.scalar('foo-v2', 2.0) - - basic_session_run_hooks.SummarySaverHook._set_placeholder(placeholder) - hook = self._makeHook(save_secs=0.5) - def fake_sleep(): - mock_time.return_value += 0.5 - self._runForSteps(hook, 4, fake_sleep) + hook = basic_session_run_hooks.SummarySaverHook( + save_steps=8, + summary_writer=self.summary_writer, + summary_op=[self.summary_op, self.summary_op2]) - events = iter(load_eventfile_contents(self.logdir)) - next(events) # Skip version event that's always there. - self._assertSessionEvent(next(events), 0, SessionLog.START) + with self.test_session() as sess: + hook.begin() + sess.run(variables_lib.global_variables_initializer()) + mon_sess = monitored_session._HookedSession(sess, [hook]) + for _ in range(10): + mon_sess.run(self.train_op) + hook.end(sess) - self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 1, [('foo-v1', 1.0)]) + self.summary_writer.assert_summaries( + test_case=self, + expected_logdir=self.log_dir, + expected_summaries={ + 1: { + 'my_summary': 1.0, + 'my_summary2': 2.0 + }, + 9: { + 'my_summary': 2.0, + 'my_summary2': 4.0 + }, + }) - self._assertSummaryEvent(next(events), 1, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 2, [('foo-v1', 1.0)]) + def test_save_secs_saving_once_every_step(self): + hook = basic_session_run_hooks.SummarySaverHook( + save_secs=0.5, + summary_writer=self.summary_writer, + summary_op=self.summary_op) - self._assertSummaryEvent(next(events), 2, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 3, [('foo-v1', 1.0)]) + with self.test_session() as sess: + hook.begin() + sess.run(variables_lib.global_variables_initializer()) + mon_sess = monitored_session._HookedSession(sess, [hook]) + for _ in range(4): + mon_sess.run(self.train_op) + time.sleep(0.5) + hook.end(sess) - self._assertSummaryEvent(next(events), 3, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 4, [('foo-v1', 1.0)]) - self.assertRaises(StopIteration, lambda: next(events)) + self.summary_writer.assert_summaries( + test_case=self, + expected_logdir=self.log_dir, + expected_summaries={ + 1: { + 'my_summary': 1.0 + }, + 2: { + 'my_summary': 2.0 + }, + 3: { + 'my_summary': 3.0 + }, + 4: { + 'my_summary': 4.0 + }, + }) @test.mock.patch.object(time, 'time') def test_save_secs_saving_once_every_three_steps(self, mock_time): - mock_time.return_value = 1000.0 - summary_lib.scalar('foo-v1', 1.0) - placeholder = array_ops.placeholder_with_default(False, shape=[]) - with summary_ops_v2.create_file_writer(self.logdir).as_default(): - with summary_ops_v2.record_summaries_if(placeholder): - summary_ops_v2.scalar('foo-v2', 2.0) - - basic_session_run_hooks.SummarySaverHook._set_placeholder(placeholder) - hook = self._makeHook(save_secs=9) - def fake_sleep(): - mock_time.return_value += 3.1 - self._runForSteps(hook, 8, fake_sleep) - - events = iter(load_eventfile_contents(self.logdir)) - next(events) # Skip version event that's always there. - self._assertSessionEvent(next(events), 0, SessionLog.START) - - # 24.8 seconds passed (3.1*8), it saves every 9 seconds starting from first: - self._assertSummaryEvent(next(events), 0, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 1, [('foo-v1', 1.0)]) - - self._assertSummaryEvent(next(events), 3, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 4, [('foo-v1', 1.0)]) + mock_time.return_value = 1484695987.209386 + hook = basic_session_run_hooks.SummarySaverHook( + save_secs=9., + summary_writer=self.summary_writer, + summary_op=self.summary_op) - self._assertSummaryEvent(next(events), 6, [('foo-v2', 2.0)]) - self._assertSummaryEvent(next(events), 7, [('foo-v1', 1.0)]) - self.assertRaises(StopIteration, lambda: next(events)) + with self.test_session() as sess: + hook.begin() + sess.run(variables_lib.global_variables_initializer()) + mon_sess = monitored_session._HookedSession(sess, [hook]) + for _ in range(8): + mon_sess.run(self.train_op) + mock_time.return_value += 3.1 + hook.end(sess) - def test_explicit_summary_writer_and_op(self): - summary_writer = fake_summary_writer.FakeSummaryWriter(self.logdir) - hook = basic_session_run_hooks.SummarySaverHook( - save_steps=1, - summary_writer=summary_writer, - summary_op=summary_lib.scalar('foo-v1', 1.0)) - self._runForSteps(hook, 3) - summary_writer.assert_summaries( + # 24.8 seconds passed (3.1*8), it saves every 9 seconds starting from first: + self.summary_writer.assert_summaries( test_case=self, - expected_logdir=self.logdir, + expected_logdir=self.log_dir, expected_summaries={ - 1: {'foo-v1': 1.0}, - 2: {'foo-v1': 1.0}, - 3: {'foo-v1': 1.0}, + 1: { + 'my_summary': 1.0 + }, + 4: { + 'my_summary': 2.0 + }, + 7: { + 'my_summary': 3.0 + }, }) @@ -1701,23 +1518,18 @@ class ProfilerHookTest(test.TestCase): sess.run(self.train_op) # Saved. self.assertEqual(3, self._count_timeline_files()) - def test_run_metadata_summary_saving(self): + def test_run_metadata_saves_in_first_step(self): + writer_cache.FileWriterCache.clear() + fake_summary_writer.FakeSummaryWriter.install() + fake_writer = writer_cache.FileWriterCache.get(self.output_dir) with self.graph.as_default(): hook = basic_session_run_hooks.ProfilerHook( - save_steps=2, output_dir=self.output_dir) + save_secs=2, output_dir=self.output_dir) with monitored_session.SingularMonitoredSession(hooks=[hook]) as sess: sess.run(self.train_op) # Saved. - sess.run(self.train_op) # Not saved. - sess.run(self.train_op) # Saved. - events = iter(load_eventfile_contents(self.output_dir)) - next(events) # Skip version event that's always there. - event = next(events) - self.assertEqual(1, event.step) - self.assertEqual('step_1', event.tagged_run_metadata.tag) - event = next(events) - self.assertEqual(3, event.step) - self.assertEqual('step_3', event.tagged_run_metadata.tag) - self.assertRaises(StopIteration, lambda: next(events)) # No more events. + self.assertEqual( + list(fake_writer._added_run_metadata.keys()), ['step_1']) + fake_summary_writer.FakeSummaryWriter.uninstall() if __name__ == '__main__': diff --git a/tensorflow/python/training/monitored_session.py b/tensorflow/python/training/monitored_session.py index 8a4ca04b1e..7b06bffa4b 100644 --- a/tensorflow/python/training/monitored_session.py +++ b/tensorflow/python/training/monitored_session.py @@ -31,7 +31,6 @@ from tensorflow.python.ops import array_ops from tensorflow.python.ops import control_flow_ops from tensorflow.python.ops import lookup_ops from tensorflow.python.ops import resources -from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary import summary @@ -205,17 +204,13 @@ class Scaffold(object): 'local_init_op', ops.GraphKeys.LOCAL_INIT_OP, Scaffold.default_local_init_op) if self._summary_op is None: - def default_summary_op(): - v1_op = summary.merge_all() - v2_ops = summary_ops_v2.all_summary_ops() or [] - if v1_op is not None: - return control_flow_ops.with_dependencies(v2_ops, v1_op) - return control_flow_ops.group(v2_ops) if v2_ops else None self._summary_op = Scaffold.get_or_default('summary_op', ops.GraphKeys.SUMMARY_OP, - default_summary_op) + summary.merge_all) + # pylint: disable=g-long-lambda if self._saver is None: self._saver = training_saver._get_saver_or_default() # pylint: disable=protected-access + # pylint: enable=g-long-lambda self._saver.build() ops.get_default_graph().finalize() diff --git a/tensorflow/python/training/optimizer.py b/tensorflow/python/training/optimizer.py index b9d42b034e..f75db08059 100644 --- a/tensorflow/python/training/optimizer.py +++ b/tensorflow/python/training/optimizer.py @@ -611,8 +611,10 @@ class Optimizer( if isinstance(global_step, resource_variable_ops.ResourceVariable): # TODO(apassos): the implicit read in assign_add is slow; consider # making it less so. - apply_updates = global_step.assign_add( - 1, name=name, read_value=False) + apply_updates = resource_variable_ops.assign_add_variable_op( + global_step.handle, + ops.convert_to_tensor(1, dtype=global_step.dtype), + name=name) else: apply_updates = state_ops.assign_add(global_step, 1, name=name) -- GitLab From 3ca47448added403f065b43395f38c538c1602f9 Mon Sep 17 00:00:00 2001 From: Reed Wanderman-Milne Date: Tue, 24 Jul 2018 13:00:41 -0700 Subject: [PATCH 340/519] Automated rollback of commit 09c4c387913c86247121589caa7fb2e85351fa58 PiperOrigin-RevId: 205877002 --- tensorflow/core/BUILD | 11 ---- .../core/common_runtime/gpu/gpu_device.cc | 51 +------------------ .../core/common_runtime/gpu/gpu_device.h | 11 ---- .../gpu/gpu_device_kernel_check.cu.cc | 37 -------------- .../gpu/gpu_device_kernel_check.h | 32 ------------ 5 files changed, 1 insertion(+), 141 deletions(-) delete mode 100644 tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.cu.cc delete mode 100644 tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index a960736295..84555b60da 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -90,7 +90,6 @@ load( "tf_genrule_cmd_append_to_srcs", "tf_opts_nortti_if_android", "tf_features_nomodules_if_android", - "tf_gpu_kernel_library", ) load("//tensorflow:tensorflow.bzl", "tf_cc_test_mkl") load("//tensorflow:tensorflow.bzl", "tf_cc_test_gpu") @@ -2950,15 +2949,6 @@ cc_library( ], ) -tf_gpu_kernel_library( - name = "gpu_device_kernel_check", - srcs = ["common_runtime/gpu/gpu_device_kernel_check.cu.cc"], - hdrs = ["common_runtime/gpu/gpu_device_kernel_check.h"], - deps = [ - "//tensorflow/core:stream_executor", - ], -) - GPU_RUNTIME_HEADERS = [ "common_runtime/gpu/cuda_host_allocator.h", "common_runtime/gpu/gpu_bfc_allocator.h", @@ -2997,7 +2987,6 @@ tf_cuda_library( ":core_cpu_lib", ":framework", ":framework_internal", - ":gpu_device_kernel_check", ":gpu_id_impl", ":gpu_init_impl", ":gpu_lib", diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.cc b/tensorflow/core/common_runtime/gpu/gpu_device.cc index fbe158c777..3292ef2f62 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_device.cc +++ b/tensorflow/core/common_runtime/gpu/gpu_device.cc @@ -31,7 +31,6 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/core/common_runtime/device_factory.h" -#include "tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h" #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" #include "tensorflow/core/common_runtime/gpu/gpu_id.h" #include "tensorflow/core/common_runtime/gpu/gpu_id_manager.h" @@ -378,7 +377,7 @@ Status BaseGPUDevice::Init(const SessionOptions& options) { } } - return CheckGPU(); + return Status::OK(); } bool BaseGPUDevice::RequiresRecordingAccessedTensors() const { @@ -895,54 +894,6 @@ Allocator* BaseGPUDevice::GetScopedAllocator(AllocatorAttributes attr, return gpu_allocator_; } -Status BaseGPUDevice::CheckGPU() { - se::Stream* stream = tensorflow_gpu_device_info()->stream; - TF_RETURN_IF_ERROR(stream->BlockHostUntilDone()); - Tensor device_tensor(gpu_allocator_, DT_FLOAT, {}); - if (!device_tensor.IsInitialized()) { - return errors::ResourceExhausted("Failed to allocate ", sizeof(float), - " bytes on the GPU for initialization " - "checks"); - } - float* val_dev = device_tensor.scalar().data(); - const cudaStream_t cu_stream = *reinterpret_cast( - stream->implementation()->GpuStreamMemberHack()); - { - se::cuda::ScopedActivateExecutorContext scoped_activation{stream->parent()}; - run_test_kernel(val_dev, cu_stream); - // We have to use the CUDA runtime function cudaPeekAtLastError here, - // because 'stream' does not provide a way to check if a kernel launch - // succeeds. Calling 'stream->BlockHostUntilDone()', which internally calls - // 'cuCtxSynchronize()', does not catch all kernel launch errors. - cudaError_t cuda_error = cudaPeekAtLastError(); - if (cuda_error == cudaSuccess) { - cuda_error = cudaDeviceSynchronize(); - } - TF_RETURN_IF_ERROR(CudaErrorToStatus(cuda_error, *stream)); - } - - float val_host = 0.; - stream->ThenMemcpy(&val_host, se::DeviceMemoryBase(val_dev, sizeof(float)), - sizeof(float)); - TF_RETURN_IF_ERROR(stream->BlockHostUntilDone()); - if (val_host != 12345.) { - return errors::Internal( - "GPU kernel for initialization returned wrong value: ", val_host); - } - return Status::OK(); -} - -Status BaseGPUDevice::CudaErrorToStatus(cudaError_t cuda_error, - const se::Stream& stream) { - if (cuda_error != cudaSuccess) { - return errors::Internal( - "Failed to run GPU kernel for the initialization check. Received " - "error ", - cudaGetErrorName(cuda_error), " after running GPU kernel."); - } - return Status::OK(); -} - const int BaseGPUDeviceFactory::InterconnectMap::kSameDeviceStrength = 1000; const int BaseGPUDeviceFactory::InterconnectMap::kStreamExecutorStrength = 1; diff --git a/tensorflow/core/common_runtime/gpu/gpu_device.h b/tensorflow/core/common_runtime/gpu/gpu_device.h index d02901a7ae..56d03d7a8c 100644 --- a/tensorflow/core/common_runtime/gpu/gpu_device.h +++ b/tensorflow/core/common_runtime/gpu/gpu_device.h @@ -26,7 +26,6 @@ limitations under the License. #include #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" -#include "cuda/include/cuda_runtime_api.h" #include "tensorflow/core/common_runtime/device_factory.h" #include "tensorflow/core/common_runtime/gpu/gpu_event_mgr.h" #include "tensorflow/core/common_runtime/gpu/gpu_id.h" @@ -116,12 +115,6 @@ class BaseGPUDevice : public LocalDevice { se::StreamExecutor* executor_; // not owned std::unique_ptr scoped_allocator_mgr_; - // Returns a Status corresponding to a cudaError_t. The CUDA error must have - // been obtained from a CUDA kernel launch used to check if the GPU is - // initialized properly. - virtual Status CudaErrorToStatus(cudaError_t cuda_error, - const se::Stream& stream); - private: struct StreamGroup { se::Stream* compute = nullptr; @@ -158,10 +151,6 @@ class BaseGPUDevice : public LocalDevice { Status MaybeCopyTensorToGPU(const AllocatorAttributes& alloc_attrs, const Tensor& from, Tensor* to, StatusCallback done); - - // Checks that the GPU is capable of doing work, by running a test kernel on - // it. - Status CheckGPU(); }; class BaseGPUDeviceFactory : public DeviceFactory { diff --git a/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.cu.cc b/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.cu.cc deleted file mode 100644 index 017565195b..0000000000 --- a/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.cu.cc +++ /dev/null @@ -1,37 +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. -==============================================================================*/ - -#if GOOGLE_CUDA - -#include "tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h" -#include "tensorflow/stream_executor/cuda/cuda_activation.h" - -namespace { -__global__ void test_kernel(float* val) { - if (blockIdx.x == 0 && threadIdx.x == 0) { - (*val) = 12345.; - } -} -} // namespace - -namespace tensorflow { - -void run_test_kernel(float* val, cudaStream_t cu_stream) { - test_kernel<<<1, 1, 0, cu_stream>>>(val); -} - -} // namespace tensorflow - -#endif // GOOGLE_CUDA diff --git a/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h b/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h deleted file mode 100644 index 064fb7a49f..0000000000 --- a/tensorflow/core/common_runtime/gpu/gpu_device_kernel_check.h +++ /dev/null @@ -1,32 +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. -==============================================================================*/ - -#ifndef TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_DEVICE_KERNEL_CHECK_H_ -#define TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_DEVICE_KERNEL_CHECK_H_ - -#if GOOGLE_CUDA - -#include "tensorflow/core/platform/stream_executor.h" - -namespace tensorflow { - -// Runs a GPU kernel to test that it functions correctly. Sets 'val' to 12345. -void run_test_kernel(float* val, cudaStream_t cu_stream); - -} // namespace tensorflow - -#endif // GOOGLE_CUDA - -#endif // TENSORFLOW_CORE_COMMON_RUNTIME_GPU_GPU_DEVICE_KERNEL_CHECK_H_ -- GitLab From bb384118db531a7951735dcdc809b5735bc02a76 Mon Sep 17 00:00:00 2001 From: Yifei Feng Date: Tue, 24 Jul 2018 13:12:54 -0700 Subject: [PATCH 341/519] Upgrade bazel to 0.15.0. PiperOrigin-RevId: 205878953 --- WORKSPACE | 2 +- configure.py | 2 +- tensorflow/tools/ci_build/ci_sanity.sh | 2 +- tensorflow/tools/ci_build/install/install_bazel.sh | 2 +- .../tools/ci_build/install/install_bazel_from_source.sh | 2 +- tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh | 8 ++++---- tensorflow/tools/docker/Dockerfile.devel | 2 +- tensorflow/tools/docker/Dockerfile.devel-gpu | 2 +- tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7 | 2 +- 9 files changed, 12 insertions(+), 12 deletions(-) diff --git a/WORKSPACE b/WORKSPACE index e7cf23a159..17961829a6 100644 --- a/WORKSPACE +++ b/WORKSPACE @@ -18,7 +18,7 @@ closure_repositories() # files, in case the parsing of those build files depends on the bazel # version we require here. load("//tensorflow:version_check.bzl", "check_bazel_version_at_least") -check_bazel_version_at_least("0.13.0") +check_bazel_version_at_least("0.15.0") load("//tensorflow:workspace.bzl", "tf_workspace") diff --git a/configure.py b/configure.py index 1df7bc736f..f97bf8a668 100644 --- a/configure.py +++ b/configure.py @@ -1451,7 +1451,7 @@ def main(): # environment variables. environ_cp = dict(os.environ) - check_bazel_version('0.13.0') + check_bazel_version('0.15.0') reset_tf_configure_bazelrc(args.workspace) cleanup_makefile() diff --git a/tensorflow/tools/ci_build/ci_sanity.sh b/tensorflow/tools/ci_build/ci_sanity.sh index db37edf809..866fe95d2b 100755 --- a/tensorflow/tools/ci_build/ci_sanity.sh +++ b/tensorflow/tools/ci_build/ci_sanity.sh @@ -354,7 +354,7 @@ 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" -e "@com_github_googlecloudplatform_google_cloud_cpp//" -v ${EXTRA_LICENSES_FILE} > temp.txt + grep -e "@bazel_tools//src" -e "@bazel_tools//tools/" -e "@com_google_absl//" -e "//external" -e "@local" -e "@com_github_googlecloudplatform_google_cloud_cpp//" -e "@embedded_jdk//" -v ${EXTRA_LICENSES_FILE} > temp.txt mv temp.txt ${EXTRA_LICENSES_FILE} diff --git a/tensorflow/tools/ci_build/install/install_bazel.sh b/tensorflow/tools/ci_build/install/install_bazel.sh index adbff8f6ef..e284401b8a 100755 --- a/tensorflow/tools/ci_build/install/install_bazel.sh +++ b/tensorflow/tools/ci_build/install/install_bazel.sh @@ -15,7 +15,7 @@ # ============================================================================== # Select bazel version. -BAZEL_VERSION="0.14.1" +BAZEL_VERSION="0.15.0" set +e local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}') diff --git a/tensorflow/tools/ci_build/install/install_bazel_from_source.sh b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh index 9d24b3e421..87be81577d 100755 --- a/tensorflow/tools/ci_build/install/install_bazel_from_source.sh +++ b/tensorflow/tools/ci_build/install/install_bazel_from_source.sh @@ -18,7 +18,7 @@ # It will compile bazel from source and install it in /usr/local/bin # Select bazel version. -BAZEL_VERSION="0.14.1" +BAZEL_VERSION="0.15.0" set +e local_bazel_ver=$(bazel version 2>&1 | grep -i label | awk '{print $3}') diff --git a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh index c03cbd9c66..0482cf619a 100644 --- a/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh +++ b/tensorflow/tools/ci_build/windows/bazel/bazel_test_lib.sh @@ -33,10 +33,10 @@ function set_remote_cache_options { echo "build --tls_enabled=true" >> "${TMP_BAZELRC}" echo "build --remote_timeout=3600" >> "${TMP_BAZELRC}" echo "build --auth_enabled=true" >> "${TMP_BAZELRC}" - echo "build --spawn_strategy=remote" >> "${TMP_BAZELRC}" - echo "build --strategy=Javac=remote" >> "${TMP_BAZELRC}" - echo "build --strategy=Closure=remote" >> "${TMP_BAZELRC}" - echo "build --genrule_strategy=remote" >> "${TMP_BAZELRC}" + echo "build --spawn_strategy=standalone" >> "${TMP_BAZELRC}" + echo "build --strategy=Javac=standalone" >> "${TMP_BAZELRC}" + echo "build --strategy=Closure=standalone" >> "${TMP_BAZELRC}" + echo "build --genrule_strategy=standalone" >> "${TMP_BAZELRC}" echo "build --google_credentials=$GOOGLE_CLOUD_CREDENTIAL" >> "${TMP_BAZELRC}" } diff --git a/tensorflow/tools/docker/Dockerfile.devel b/tensorflow/tools/docker/Dockerfile.devel index fd94d64268..f7fe4119da 100644 --- a/tensorflow/tools/docker/Dockerfile.devel +++ b/tensorflow/tools/docker/Dockerfile.devel @@ -63,7 +63,7 @@ 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 0.14.1 +ENV BAZEL_VERSION 0.15.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu b/tensorflow/tools/docker/Dockerfile.devel-gpu index a5560e459c..340f96df48 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu @@ -79,7 +79,7 @@ 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 0.14.1 +ENV BAZEL_VERSION 0.15.0 WORKDIR / RUN mkdir /bazel && \ cd /bazel && \ diff --git a/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7 b/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7 index 3bedc8cf34..30bc2d2806 100644 --- a/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7 +++ b/tensorflow/tools/docker/Dockerfile.devel-gpu-cuda9-cudnn7 @@ -4,7 +4,7 @@ LABEL maintainer="Gunhan Gulsoy " # It is possible to override these for releases. ARG TF_BRANCH=master -ARG BAZEL_VERSION=0.5.4 +ARG BAZEL_VERSION=0.15.0 ARG TF_AVAILABLE_CPUS=32 RUN apt-get update && apt-get install -y --no-install-recommends \ -- GitLab From e2f8d4a8bdfc4e3970cacc89a6b184297205a1cc Mon Sep 17 00:00:00 2001 From: Smit Hinsu Date: Tue, 24 Jul 2018 13:16:31 -0700 Subject: [PATCH 342/519] Add data format as a parameter in ConvParameters to support NHWC format PiperOrigin-RevId: 205879506 --- .../fused_conv2d_bias_activation_op.cc | 3 + .../fused_conv/kernels/fused_conv_ops_gpu.h | 12 ++-- .../core/kernels/conv_grad_filter_ops.cc | 1 + .../core/kernels/conv_grad_input_ops.cc | 1 + tensorflow/core/kernels/conv_grad_ops_3d.cc | 2 + tensorflow/core/kernels/conv_ops.cc | 1 + tensorflow/core/kernels/conv_ops_3d.cc | 1 + tensorflow/core/kernels/conv_ops_gpu.h | 20 +++--- tensorflow/core/kernels/conv_ops_test.cc | 62 ++++++++++--------- 9 files changed, 60 insertions(+), 43 deletions(-) diff --git a/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc index 4554a3d89a..0ccb4583ab 100644 --- a/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc +++ b/tensorflow/contrib/fused_conv/kernels/fused_conv2d_bias_activation_op.cc @@ -443,6 +443,8 @@ void LaunchFusedConv2DBiasActivationOp:: : dnn::DataLayout::kBatchDepthYX; constexpr auto filter_layout = is_int8x4 ? dnn::FilterLayout::kOutputInputYX4 : dnn::FilterLayout::kOutputInputYX; + constexpr auto compute_data_format = + is_int8x4 ? FORMAT_NCHW_VECT_C : FORMAT_NCHW; dnn::BatchDescriptor conv_input_desc; conv_input_desc.set_count(batch_size) @@ -529,6 +531,7 @@ void LaunchFusedConv2DBiasActivationOp:: batch_size, conv_input_depth, {{conv_input_rows, conv_input_cols}}, + compute_data_format, output_depth, {{filter_rows, filter_cols}}, // TODO(yangzihao): Add support for arbitrary dilations for fused conv. diff --git a/tensorflow/contrib/fused_conv/kernels/fused_conv_ops_gpu.h b/tensorflow/contrib/fused_conv/kernels/fused_conv_ops_gpu.h index ba52697679..b9c131a2e9 100644 --- a/tensorflow/contrib/fused_conv/kernels/fused_conv_ops_gpu.h +++ b/tensorflow/contrib/fused_conv/kernels/fused_conv_ops_gpu.h @@ -29,13 +29,13 @@ namespace tensorflow { class FusedConvParameters : public ConvParameters { public: FusedConvParameters(int64 batch, int64 in_depths, const SpatialArray& in, - int64 out_depths, const SpatialArray& filter, - const SpatialArray& dilation, const SpatialArray& stride, - const SpatialArray& padding, DataType dtype, - int device_id, bool has_side_input, + TensorFormat data_format, int64 out_depths, + const SpatialArray& filter, const SpatialArray& dilation, + const SpatialArray& stride, const SpatialArray& padding, + DataType dtype, int device_id, bool has_side_input, ActivationMode activation_mode) - : ConvParameters(batch, in_depths, in, out_depths, filter, dilation, - stride, padding, dtype, device_id), + : ConvParameters(batch, in_depths, in, data_format, out_depths, filter, + dilation, stride, padding, dtype, device_id), activation_mode_(activation_mode), has_side_input_(has_side_input) { hash_code_ = Hash64Combine(hash_code_, has_side_input); diff --git a/tensorflow/core/kernels/conv_grad_filter_ops.cc b/tensorflow/core/kernels/conv_grad_filter_ops.cc index aca75176a5..63b1bcda43 100644 --- a/tensorflow/core/kernels/conv_grad_filter_ops.cc +++ b/tensorflow/core/kernels/conv_grad_filter_ops.cc @@ -909,6 +909,7 @@ void LaunchConv2DBackpropFilterOp::operator()( dims.in_depth, // in_depths {{input_desc.height(), // in_rows input_desc.width()}}, // in_cols + FORMAT_NCHW, // compute_data_format dims.out_depth, // out_depths {{dims.spatial_dims[0].filter_size, // filter_rows dims.spatial_dims[1].filter_size, // filter_cols diff --git a/tensorflow/core/kernels/conv_grad_input_ops.cc b/tensorflow/core/kernels/conv_grad_input_ops.cc index 63a775afa8..d664a11e73 100644 --- a/tensorflow/core/kernels/conv_grad_input_ops.cc +++ b/tensorflow/core/kernels/conv_grad_input_ops.cc @@ -957,6 +957,7 @@ void LaunchConv2DBackpropInputOp::operator()( dims.in_depth, // in_depths {{input_desc.height(), // in_rows input_desc.width()}}, // in_cols + FORMAT_NCHW, // compute_data_format dims.out_depth, // out_depths {{dims.spatial_dims[0].filter_size, // filter_rows dims.spatial_dims[1].filter_size, // filter_cols diff --git a/tensorflow/core/kernels/conv_grad_ops_3d.cc b/tensorflow/core/kernels/conv_grad_ops_3d.cc index 980b1063de..15f1bf9aba 100644 --- a/tensorflow/core/kernels/conv_grad_ops_3d.cc +++ b/tensorflow/core/kernels/conv_grad_ops_3d.cc @@ -716,6 +716,7 @@ class Conv3DBackpropInputOp : public OpKernel { batch, in_depth, {{input_size[0], input_size[1], input_size[2]}}, + FORMAT_NCHW, out_depth, {{filter_size[0], filter_size[1], filter_size[2]}}, {{dilations[0], dilations[1], dilations[2]}}, @@ -1112,6 +1113,7 @@ class Conv3DBackpropFilterOp : public OpKernel { batch, in_depth, {{input_size[0], input_size[1], input_size[2]}}, + FORMAT_NCHW, out_depth, {{filter_size[0], filter_size[1], filter_size[2]}}, {{dilations[0], dilations[1], dilations[2]}}, diff --git a/tensorflow/core/kernels/conv_ops.cc b/tensorflow/core/kernels/conv_ops.cc index 3b9886eece..ef692418d6 100644 --- a/tensorflow/core/kernels/conv_ops.cc +++ b/tensorflow/core/kernels/conv_ops.cc @@ -713,6 +713,7 @@ void LaunchConv2DOp::operator()( in_depths, // in_depths {{in_rows, // in_rows in_cols}}, // in_cols + FORMAT_NCHW, // compute_data_format out_depths, // out_depths {{patch_rows, // filter_rows patch_cols, // filter_cols diff --git a/tensorflow/core/kernels/conv_ops_3d.cc b/tensorflow/core/kernels/conv_ops_3d.cc index 9ec16be67d..a1eed4e68c 100644 --- a/tensorflow/core/kernels/conv_ops_3d.cc +++ b/tensorflow/core/kernels/conv_ops_3d.cc @@ -415,6 +415,7 @@ struct LaunchConvOp { in_batch, in_depth, {{in_planes, in_rows, in_cols}}, + FORMAT_NCHW, out_depth, {{filter_planes, filter_rows, filter_cols}}, {{dilations[0], dilations[1], dilations[2]}}, diff --git a/tensorflow/core/kernels/conv_ops_gpu.h b/tensorflow/core/kernels/conv_ops_gpu.h index d2c8020bb6..afc611f277 100644 --- a/tensorflow/core/kernels/conv_ops_gpu.h +++ b/tensorflow/core/kernels/conv_ops_gpu.h @@ -85,13 +85,15 @@ class ConvParameters { public: using SpatialArray = gtl::InlinedVector; ConvParameters(int64 batch, int64 in_depths, const SpatialArray& in, - int64 out_depths, const SpatialArray& filter, - const SpatialArray& dilation, const SpatialArray& stride, - const SpatialArray& padding, DataType dtype, int device_id) + TensorFormat data_format, int64 out_depths, + const SpatialArray& filter, const SpatialArray& dilation, + const SpatialArray& stride, const SpatialArray& padding, + DataType dtype, int device_id) : batch_(batch), in_depths_(in_depths), out_depths_(out_depths), in_(in), + data_format_(data_format), filter_(filter), dilation_(dilation), stride_(stride), @@ -101,6 +103,7 @@ class ConvParameters { hash_code_ = batch; hash_code_ = Hash64Combine(hash_code_, in_depths); for (int64 val : in) hash_code_ = Hash64Combine(hash_code_, val); + hash_code_ = Hash64Combine(hash_code_, data_format); hash_code_ = Hash64Combine(hash_code_, out_depths); for (int64 val : filter) hash_code_ = Hash64Combine(hash_code_, val); for (int64 val : dilation) hash_code_ = Hash64Combine(hash_code_, val); @@ -123,6 +126,7 @@ class ConvParameters { return strings::StrCat( batch_, ", ", in_depths_, ", ", "(", str_util::Join(in_, ", "), "), ", + ::tensorflow::ToString(data_format_), ", ", out_depths_, ", ", "(", str_util::Join(filter_, ", "), "), ", "(", str_util::Join(dilation_, ", "), "), ", @@ -148,12 +152,13 @@ class ConvParameters { protected: using ParameterDataType = - std::tuple; + std::tuple; ParameterDataType get_data_as_tuple() const { - return std::make_tuple(batch_, in_depths_, in_, out_depths_, filter_, - dilation_, stride_, padding_, dtype_, device_id_); + return std::make_tuple(batch_, in_depths_, in_, data_format_, out_depths_, + filter_, dilation_, stride_, padding_, dtype_, + device_id_); } uint64 hash_code_; @@ -178,6 +183,7 @@ class ConvParameters { int64 in_depths_; int64 out_depths_; SpatialArray in_; + TensorFormat data_format_; SpatialArray filter_; SpatialArray dilation_; SpatialArray stride_; diff --git a/tensorflow/core/kernels/conv_ops_test.cc b/tensorflow/core/kernels/conv_ops_test.cc index 4f9a96ce17..c281153795 100644 --- a/tensorflow/core/kernels/conv_ops_test.cc +++ b/tensorflow/core/kernels/conv_ops_test.cc @@ -44,41 +44,43 @@ struct ConvParametersPeer { TEST(ConvParameters, WinogradNonfusedAlgoSize) { ConvParametersPeer conv_params_small = {{ - 1, // batch - 32, // in_depths - {{300, // in_rows - 300}}, // in_cols - 128, // out_depths - {{3, // filter_rows - 3}}, // filter_cols - {{1, // dilation_rows - 1}}, // dilation_cols - {{1, // stride_rows - 1}}, // stride_cols - {{0, // padding_rows - 0}}, // padding_cols - DT_FLOAT, // tensor datatype - 0, // device_id + 1, // batch + 32, // in_depths + {{300, // in_rows + 300}}, // in_cols + FORMAT_NCHW, // compute_data_format + 128, // out_depths + {{3, // filter_rows + 3}}, // filter_cols + {{1, // dilation_rows + 1}}, // dilation_cols + {{1, // stride_rows + 1}}, // stride_cols + {{0, // padding_rows + 0}}, // padding_cols + DT_FLOAT, // tensor datatype + 0, // device_id }}; EXPECT_TRUE( conv_params_small.ShouldIncludeWinogradNonfusedAlgoPreCudnn7()); ConvParametersPeer conv_params_large = {{ - 1, // batch - 128, // in_depths - {{300, // in_rows - 300}}, // in_cols - 768, // out_depths - {{3, // filter_rows - 3}}, // filter_cols - {{1, // dilation_rows - 1}}, // dilation_cols - {{1, // stride_rows - 1}}, // stride_cols - {{0, // padding_rows - 0}}, // padding_cols - DT_FLOAT, // tensor datatype - 0, // device_id + 1, // batch + 128, // in_depths + {{300, // in_rows + 300}}, // in_cols + FORMAT_NCHW, // compute_data_format + 768, // out_depths + {{3, // filter_rows + 3}}, // filter_cols + {{1, // dilation_rows + 1}}, // dilation_cols + {{1, // stride_rows + 1}}, // stride_cols + {{0, // padding_rows + 0}}, // padding_cols + DT_FLOAT, // tensor datatype + 0, // device_id }}; EXPECT_FALSE( conv_params_large.ShouldIncludeWinogradNonfusedAlgoPreCudnn7()); -- GitLab From d6d95beac43b9f85c57e8302711db53a48b96e65 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Tue, 24 Jul 2018 13:28:26 -0700 Subject: [PATCH 343/519] Remove BufferAllocation::is_reusable() and introduce is_readonly(); NFC - Instead of remembering is_reusable_, remember BufferAllocation::is_tuple_ and compute is_reusable() from is_tuple() and is_thread_local(). - Introduce is_readonly() which tells us whether an allocation holds readonly data. In the future this will return true for constant buffer allocations but today only entry parameters are readonly. is_reusable() is about lifetime whereas is_readonly() is about write access. In particular, we sometimes "re-use" readonly allocations e.g. when the init value of a while loop is an entry parameter and the while body is readonly. PiperOrigin-RevId: 205881338 --- .../compiler/xla/service/buffer_assignment.cc | 51 ++++++++----------- .../compiler/xla/service/buffer_assignment.h | 41 +++++++++------ tensorflow/compiler/xla/service/hlo.proto | 2 +- .../xla/service/llvm_ir/alias_analysis.cc | 3 +- 4 files changed, 48 insertions(+), 49 deletions(-) diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index 783e3f7e73..bcca9f46d3 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -270,7 +270,7 @@ BufferAllocationProto BufferAllocation::ToProto() const { proto.set_index(index_); proto.set_size(size_); proto.set_is_thread_local(is_thread_local_); - proto.set_is_reusable(is_reusable_); + proto.set_is_tuple(is_tuple_); proto.set_color(color_.value()); if (is_entry_computation_parameter_) { proto.set_is_entry_computation_parameter(true); @@ -491,20 +491,16 @@ BufferAssignment::GetUniqueTopLevelOutputSlice() const { } BufferAllocation* BufferAssignment::NewEmptyAllocation( - int64 size, bool is_thread_local, bool is_reusable, - LogicalBuffer::Color color) { + int64 size, LogicalBuffer::Color color) { BufferAllocation::Index index = allocations_.size(); - allocations_.emplace_back(index, size, is_thread_local, is_reusable, color); + allocations_.emplace_back(index, size, color); BufferAllocation* allocation = &allocations_.back(); return allocation; } BufferAllocation* BufferAssignment::NewAllocation(const LogicalBuffer& buffer, - int64 size, - bool is_thread_local, - bool is_reusable) { - BufferAllocation* allocation = - NewEmptyAllocation(size, is_thread_local, is_reusable, buffer.color()); + int64 size) { + BufferAllocation* allocation = NewEmptyAllocation(size, buffer.color()); AddAssignment(allocation, buffer, /*offset=*/0, size); allocation->peak_buffers_.push_back(&buffer); return allocation; @@ -517,7 +513,8 @@ void BufferAssignment::AddAssignment(BufferAllocation* allocation, CHECK_EQ(0, allocation_index_for_buffer_.count(&buffer)) << "LogicalBuffer " << buffer << " already has an allocation."; CHECK(allocation->is_reusable() || allocation->assigned_buffers().empty()) - << "Non-reusable allocation already assigned a buffer"; + << "Non-reusable allocation already assigned a buffer: " + << allocation->ToString(); TF_CHECK_OK(points_to_analysis().VerifyBuffer(buffer)); @@ -751,8 +748,8 @@ bool BufferAssigner::MaybeAssignBuffer(BufferAllocation* allocation, return false; } - if (allocation->is_entry_computation_parameter()) { - VLOG(4) << "Can't assign: allocation holds parameter"; + if (allocation->is_readonly()) { + VLOG(4) << "Can't assign: allocation is readonly"; return false; } @@ -923,9 +920,7 @@ Status BufferAssigner::AssignBuffersForComputation( // computations do not need special allocations because they live inside // callers. BufferAllocation* allocation = - assignment->NewAllocation(*buffer, buffer_size, - /*is_thread_local=*/false, - /*is_reusable=*/false); + assignment->NewAllocation(*buffer, buffer_size); allocation->set_entry_computation_parameter( instruction->parameter_number(), buffer->index()); VLOG(3) << "New allocation #" << allocation->index() @@ -934,20 +929,18 @@ Status BufferAssigner::AssignBuffersForComputation( } if (is_thread_local) { - // We do not reuse thread-local buffers for now, because they are - // dynamically allocated and their lifetimes are hard to compute. - BufferAllocation* allocation = assignment->NewAllocation( - *buffer, buffer_size, is_thread_local, /*is_reusable=*/false); + BufferAllocation* allocation = + assignment->NewAllocation(*buffer, buffer_size); + allocation->set_is_thread_local(true); VLOG(3) << "New allocation #" << allocation->index() << " for thread-local: " << *buffer; continue; } if (ShapeUtil::IsTuple(buffer->shape())) { - // TODO(b/34669761): Don't reuse tuple buffers because the GPU backend - // assumes longer buffer liveness than indicated by the analysis. - BufferAllocation* allocation = assignment->NewAllocation( - *buffer, buffer_size, is_thread_local, /*is_reusable=*/false); + BufferAllocation* allocation = + assignment->NewAllocation(*buffer, buffer_size); + allocation->set_is_tuple(true); VLOG(3) << "New allocation #" << allocation->index() << " for tuple-shaped buffer: " << *buffer; continue; @@ -1030,8 +1023,8 @@ Status BufferAssigner::AssignBuffersForComputation( } if (!assignment->HasAllocation(*buffer)) { - BufferAllocation* allocation = assignment->NewAllocation( - *buffer, buffer_size, is_thread_local, /*is_reusable=*/true); + BufferAllocation* allocation = + assignment->NewAllocation(*buffer, buffer_size); allocation_indices.push_back(allocation->index()); VLOG(3) << "New allocation #" << allocation->index() << " for: " << *buffer; @@ -1227,8 +1220,8 @@ void BufferAssigner::AssignBuffersFromHeapSimulator( result.fragmentation_size; } - BufferAllocation* allocation = assignment->NewEmptyAllocation( - result.heap_size, /*is_thread_local=*/false, /*is_reusable=*/true, color); + BufferAllocation* allocation = + assignment->NewEmptyAllocation(result.heap_size, color); for (const auto& buffer_chunk : result.chunk_map) { // TODO(lauj) Remove this down_cast after downstream users of // BufferAllocation::assigned_buffers() are updated to use BufferValue. @@ -1584,9 +1577,7 @@ void BufferAssigner::AssignColocatedBufferSets( // allocations for each colocated buffer set. When liveness has // module-level scope, we can allow buffers to be shared across // computations (in some cases). - allocation = assignment->NewAllocation(*buffer, buffer_size, - /*is_thread_local=*/false, - /*is_reusable=*/true); + allocation = assignment->NewAllocation(*buffer, buffer_size); if (entry_parameter_number >= 0) { // This colocated buffer set contains an entry parameter and other // logical buffers which use the parameter as read-only in a while diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h index ad0b0bf7c2..8844b6e3ba 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.h +++ b/tensorflow/compiler/xla/service/buffer_assignment.h @@ -58,13 +58,8 @@ class BufferAllocation { // contiguously and can be used as array indexes. using Index = int64; - BufferAllocation(Index index, int64 size, bool is_thread_local, - bool is_reusable, LogicalBuffer::Color color) - : index_(index), - size_(size), - is_thread_local_(is_thread_local), - is_reusable_(is_reusable), - color_(color) {} + BufferAllocation(Index index, int64 size, LogicalBuffer::Color color) + : index_(index), size_(size), color_(color) {} ~BufferAllocation() {} // Returns the index of this allocation. @@ -74,9 +69,26 @@ class BufferAllocation { // inside of a map or reduce computation. Such allocations need to be thread // local. bool is_thread_local() const { return is_thread_local_; } + void set_is_thread_local(bool is_thread_local) { + is_thread_local_ = is_thread_local; + } // Whether this allocation can be used by more than one logical buffer. - bool is_reusable() const { return is_reusable_; } + bool is_reusable() const { + // We do not reuse thread-local buffers for now, because they are + // dynamically allocated and their lifetimes are hard to compute. + // + // TODO(b/34669761): Don't reuse tuple buffers because the GPU backend + // assumes longer buffer liveness than indicated by the analysis. + return !is_thread_local() && !is_tuple(); + } + + // Whether this allocation is readonly i.e. backed by memory we cannot write + // to. + bool is_readonly() const { return is_entry_computation_parameter(); } + + bool is_tuple() const { return is_tuple_; } + void set_is_tuple(bool is_tuple) { is_tuple_ = is_tuple; } // Whether this allocation holds a LogicalBuffer from a parameter of the entry // computation. These buffers have lifetimes which may be longer than the @@ -256,10 +268,10 @@ class BufferAllocation { int64 size_; // Whether this buffer needs to be thread-local. - bool is_thread_local_; + bool is_thread_local_ = false; - // Whether this buffer is usable by more than one logical buffer. - bool is_reusable_; + // Whether this buffer holds a tuple. + bool is_tuple_ = false; // Color of the allocation. LogicalBuffer::Color color_; @@ -426,14 +438,11 @@ class BufferAssignment { // Creates and returns a new BufferAllocation, with no assigned // LogicalBuffers. Ownership is maintained internally. - BufferAllocation* NewEmptyAllocation(int64 size, bool is_thread_local, - bool is_reusable, - LogicalBuffer::Color color); + BufferAllocation* NewEmptyAllocation(int64 size, LogicalBuffer::Color color); // Helper that calls NewEmptyAllocation and AddAssignment in one call, // creating an allocation containing a single LogicalBuffer. - BufferAllocation* NewAllocation(const LogicalBuffer& buffer, int64 size, - bool is_thread_local, bool is_reusable); + BufferAllocation* NewAllocation(const LogicalBuffer& buffer, int64 size); // Adds a LogicalBuffer to the set assigned to the given allocation. void AddAssignment(BufferAllocation* allocation, const LogicalBuffer& buffer, diff --git a/tensorflow/compiler/xla/service/hlo.proto b/tensorflow/compiler/xla/service/hlo.proto index 87abc0e74f..50d7f1823c 100644 --- a/tensorflow/compiler/xla/service/hlo.proto +++ b/tensorflow/compiler/xla/service/hlo.proto @@ -244,7 +244,7 @@ message BufferAllocationProto { int64 index = 1; int64 size = 2; bool is_thread_local = 3; - bool is_reusable = 4; + bool is_tuple = 11; bool is_entry_computation_parameter = 5; int64 parameter_number = 6; repeated int64 parameter_shape_index = 10; diff --git a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc index 93a8c130e1..e5370eca56 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis.cc @@ -28,8 +28,7 @@ namespace llvm_ir { // Sentry allocation used to represent parameters of the entry computation in // alias_scope_metadata_ and noalias_metadata_. static const BufferAllocation* kParameterAllocation = new BufferAllocation( - /*index=*/-1, /*size=*/0, /*is_thread_local=*/false, /*is_reusable=*/false, - LogicalBuffer::Color(0)); + /*index=*/-1, /*size=*/0, LogicalBuffer::Color(0)); void AliasAnalysis::AddAliasingInformationToIrArray(const HloInstruction& hlo, llvm_ir::IrArray* array, -- GitLab From e25386e18a2bea60886daa3157dfb3a32781d863 Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Tue, 24 Jul 2018 13:33:37 -0700 Subject: [PATCH 344/519] Remove functions from TFLite public Python API. PiperOrigin-RevId: 205882419 --- tensorflow/contrib/lite/python/lite.py | 61 ++++++++++++-------------- 1 file changed, 29 insertions(+), 32 deletions(-) diff --git a/tensorflow/contrib/lite/python/lite.py b/tensorflow/contrib/lite/python/lite.py index 29a1487c1f..2f9b9d469a 100644 --- a/tensorflow/contrib/lite/python/lite.py +++ b/tensorflow/contrib/lite/python/lite.py @@ -40,24 +40,23 @@ from google.protobuf import text_format as _text_format from google.protobuf.message import DecodeError from tensorflow.contrib.lite.python import lite_constants as constants from tensorflow.contrib.lite.python.convert import build_toco_convert_protos # pylint: disable=unused-import -from tensorflow.contrib.lite.python.convert import tensor_name +from tensorflow.contrib.lite.python.convert import tensor_name as _tensor_name from tensorflow.contrib.lite.python.convert import toco_convert from tensorflow.contrib.lite.python.convert import toco_convert_protos # pylint: disable=unused-import -from tensorflow.contrib.lite.python.convert_saved_model import freeze_saved_model -from tensorflow.contrib.lite.python.convert_saved_model import get_tensors_from_tensor_names -from tensorflow.contrib.lite.python.convert_saved_model import set_tensor_shapes +from tensorflow.contrib.lite.python.convert_saved_model import freeze_saved_model as _freeze_saved_model +from tensorflow.contrib.lite.python.convert_saved_model import get_tensors_from_tensor_names as _get_tensors_from_tensor_names +from tensorflow.contrib.lite.python.convert_saved_model import set_tensor_shapes as _set_tensor_shapes from tensorflow.contrib.lite.python.interpreter import Interpreter # pylint: disable=unused-import from tensorflow.contrib.lite.python.op_hint import convert_op_hints_to_stubs # pylint: disable=unused-import from tensorflow.contrib.lite.python.op_hint import OpHint # pylint: disable=unused-import from tensorflow.core.framework import graph_pb2 as _graph_pb2 from tensorflow.python import keras as _keras from tensorflow.python.client import session as _session -from tensorflow.python.framework import graph_util as tf_graph_util -from tensorflow.python.framework.importer import import_graph_def -from tensorflow.python.ops.variables import global_variables_initializer -from tensorflow.python.saved_model import signature_constants -from tensorflow.python.saved_model import tag_constants -# from tensorflow.python.util.all_util import remove_undocumented +from tensorflow.python.framework import graph_util as _tf_graph_util +from tensorflow.python.framework.importer import import_graph_def as _import_graph_def +from tensorflow.python.ops.variables import global_variables_initializer as _global_variables_initializer +from tensorflow.python.saved_model import signature_constants as _signature_constants +from tensorflow.python.saved_model import tag_constants as _tag_constants class TocoConverter(object): @@ -196,7 +195,7 @@ class TocoConverter(object): input_arrays or output_arrays contains an invalid tensor name. """ with _session.Session() as sess: - sess.run(global_variables_initializer()) + sess.run(_global_variables_initializer()) # Read GraphDef from file. graph_def = _graph_pb2.GraphDef() @@ -218,12 +217,12 @@ class TocoConverter(object): raise ValueError( "Unable to parse input file '{}'.".format(graph_def_file)) sess.graph.as_default() - import_graph_def(graph_def, name="") + _import_graph_def(graph_def, name="") # Get input and output tensors. - input_tensors = get_tensors_from_tensor_names(sess.graph, input_arrays) - output_tensors = get_tensors_from_tensor_names(sess.graph, output_arrays) - set_tensor_shapes(input_tensors, input_shapes) + input_tensors = _get_tensors_from_tensor_names(sess.graph, input_arrays) + output_tensors = _get_tensors_from_tensor_names(sess.graph, output_arrays) + _set_tensor_shapes(input_tensors, input_shapes) # Check if graph is frozen. if not _is_frozen_graph(sess): @@ -261,12 +260,12 @@ class TocoConverter(object): TocoConverter class. """ if tag_set is None: - tag_set = set([tag_constants.SERVING]) + tag_set = set([_tag_constants.SERVING]) if signature_key is None: - signature_key = signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY + signature_key = _signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY - result = freeze_saved_model(saved_model_dir, input_arrays, input_shapes, - output_arrays, tag_set, signature_key) + result = _freeze_saved_model(saved_model_dir, input_arrays, input_shapes, + output_arrays, tag_set, signature_key) return cls( graph_def=result[0], input_tensors=result[1], output_tensors=result[2]) @@ -299,15 +298,15 @@ class TocoConverter(object): # Get input and output tensors. if input_arrays: - input_tensors = get_tensors_from_tensor_names(sess.graph, input_arrays) + input_tensors = _get_tensors_from_tensor_names(sess.graph, input_arrays) else: input_tensors = keras_model.inputs if output_arrays: - output_tensors = get_tensors_from_tensor_names(sess.graph, output_arrays) + output_tensors = _get_tensors_from_tensor_names(sess.graph, output_arrays) else: output_tensors = keras_model.outputs - set_tensor_shapes(input_tensors, input_shapes) + _set_tensor_shapes(input_tensors, input_shapes) graph_def = _freeze_graph(sess, output_tensors) return cls(graph_def, input_tensors, output_tensors) @@ -328,12 +327,12 @@ class TocoConverter(object): for tensor in self._input_tensors: if not tensor.get_shape(): raise ValueError("Provide an input shape for input array '{0}'.".format( - tensor_name(tensor))) + _tensor_name(tensor))) shape = tensor.get_shape().as_list() if None in shape[1:]: raise ValueError( "None is only supported in the 1st dimension. Tensor '{0}' has " - "invalid shape '{1}'.".format(tensor_name(tensor), shape)) + "invalid shape '{1}'.".format(_tensor_name(tensor), shape)) elif shape[0] is None: self._set_batch_size(batch_size=1) @@ -343,7 +342,7 @@ class TocoConverter(object): quantized_stats = [] invalid_stats = [] for tensor in self._input_tensors: - name = tensor_name(tensor) + name = _tensor_name(tensor) if name in self.quantized_input_stats: quantized_stats.append(self.quantized_input_stats[name]) else: @@ -381,7 +380,7 @@ class TocoConverter(object): Returns: List of strings. """ - return [tensor_name(tensor) for tensor in self._input_tensors] + return [_tensor_name(tensor) for tensor in self._input_tensors] def _set_batch_size(self, batch_size): """Sets the first dimension of the input tensor to `batch_size`. @@ -428,11 +427,9 @@ def _freeze_graph(sess, output_tensors): Frozen GraphDef. """ if not _is_frozen_graph(sess): - sess.run(global_variables_initializer()) - output_arrays = [tensor_name(tensor) for tensor in output_tensors] - return tf_graph_util.convert_variables_to_constants(sess, sess.graph_def, - output_arrays) + sess.run(_global_variables_initializer()) + output_arrays = [_tensor_name(tensor) for tensor in output_tensors] + return _tf_graph_util.convert_variables_to_constants( + sess, sess.graph_def, output_arrays) else: return sess.graph_def - -# remove_undocumented(__name__) -- GitLab From 6b2ae9b3da572d2f8e1eccdf2922efce43cbecd4 Mon Sep 17 00:00:00 2001 From: Youlong Cheng Date: Tue, 24 Jul 2018 13:44:46 -0700 Subject: [PATCH 345/519] PUBLIC: Enable eval and predict on multi-hosts in broadcast mode. PiperOrigin-RevId: 205884309 --- tensorflow/contrib/tpu/python/tpu/tpu_context.py | 4 ++-- tensorflow/contrib/tpu/python/tpu/tpu_estimator.py | 3 ++- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_context.py b/tensorflow/contrib/tpu/python/tpu/tpu_context.py index 2cb68f74a0..a9cf54f77d 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_context.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_context.py @@ -594,7 +594,7 @@ class _InternalTPUContext(object): raise ValueError( 'eval batch size {} must be divisible by number of replicas {}' .format(self._eval_batch_size, num_replicas)) - if num_hosts > 1: + if num_hosts > 1 and not self.is_input_broadcast_with_iterators(): raise ValueError( 'TPUEstimator.evaluate should be running on single TPU worker. ' 'got {}.'.format(num_hosts)) @@ -609,7 +609,7 @@ class _InternalTPUContext(object): raise ValueError( 'predict batch size {} must be divisible by number of replicas {}' .format(self._predict_batch_size, num_replicas)) - if num_hosts > 1: + if num_hosts > 1 and not self.is_input_broadcast_with_iterators(): raise ValueError( 'TPUEstimator.predict should be running on single TPU worker. ' 'got {}.'.format(num_hosts)) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 42406db88a..2c7e7d84c0 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -1764,7 +1764,8 @@ class TPUEstimator(estimator_lib.Estimator): Current limitations: -------------------- - 1. TPU evaluation only works on a single host (one TPU worker). + 1. TPU evaluation only works on a single host (one TPU worker) except + BROADCAST mode. 2. `input_fn` for evaluation should **NOT** raise an end-of-input exception (`OutOfRangeError` or `StopIteration`). And all evaluation steps and all -- GitLab From ee0bd6ef450b388fadea63b31b65b13bd12f17d6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 24 Jul 2018 13:50:22 -0700 Subject: [PATCH 346/519] Automated rollback of commit 0ea6847c892497afdd20c1150fee1e532612ca17 PiperOrigin-RevId: 205885304 --- .../compiler/jit/xla_compilation_cache.cc | 18 ++- tensorflow/compiler/jit/xla_device_context.cc | 117 ++++++++++++------ tensorflow/compiler/jit/xla_device_context.h | 5 +- tensorflow/compiler/jit/xla_tensor.cc | 4 +- tensorflow/compiler/xla/service/executable.cc | 13 +- tensorflow/compiler/xla/service/hlo_runner.cc | 9 +- .../xla/tests/local_client_execute_test.cc | 4 + .../xla/tests/local_client_test_base.cc | 14 ++- .../xla/tests/xla_hlo_profile_test.cc | 1 + .../stream_executor/host/host_gpu_executor.cc | 2 +- tensorflow/stream_executor/stream.cc | 6 + 11 files changed, 143 insertions(+), 50 deletions(-) diff --git a/tensorflow/compiler/jit/xla_compilation_cache.cc b/tensorflow/compiler/jit/xla_compilation_cache.cc index 7ed609c437..54a41a4daa 100644 --- a/tensorflow/compiler/jit/xla_compilation_cache.cc +++ b/tensorflow/compiler/jit/xla_compilation_cache.cc @@ -40,7 +40,23 @@ namespace tensorflow { XlaCompilationCache::XlaCompilationCache(xla::LocalClient* client, DeviceType device_type) : client_(client), device_type_(std::move(device_type)) {} -XlaCompilationCache::~XlaCompilationCache() = default; +XlaCompilationCache::~XlaCompilationCache() { + // Ensure any use of our programs have completed by waiting for all stream + // executors to complete. + for (auto* executor : client_->backend().stream_executors()) { + bool ok = executor->SynchronizeAllActivity(); + if (!ok) { + LOG(ERROR) << "Error synchronizing activity while waiting for all " + "programs to complete"; + } + } + // TODO(b/110813685): Think about the program ownership model. Programs are + // currently owned by the compilation cache which means we must wait for + // program completion in the destructor. There are multiple compilation caches + // around, which complicates things a little. Perhaps having programs be + // shared_ptrs (an invasive change) would make the model easier to reason + // about? +} string XlaCompilationCache::DebugString() { return "XLA JIT compilation cache"; diff --git a/tensorflow/compiler/jit/xla_device_context.cc b/tensorflow/compiler/jit/xla_device_context.cc index 04778c0090..8cf198239c 100644 --- a/tensorflow/compiler/jit/xla_device_context.cc +++ b/tensorflow/compiler/jit/xla_device_context.cc @@ -74,43 +74,64 @@ Status XlaTransferManager::TransferLiteralToDevice( xla::Shape xla_shape; TF_RETURN_IF_ERROR(TensorShapeToXLAShape(host_tensor.dtype(), host_tensor.shape(), &xla_shape)); - xla::BorrowingLiteral literal( + // Create a reference to hold onto host_tensor until after the literal has + // been transferred. Also make sure the literal exists until the function + // asynchronously completes, as it will be wrapped in an xla::LiteralSlice. + TensorReference ref(host_tensor); + auto literal = std::make_shared( static_cast(DMAHelper::base(&host_tensor)), xla_shape); XlaTensor* xla_tensor = XlaTensor::FromTensor(device_tensor); const xla::ShapedBuffer& shaped_buffer = xla_tensor->shaped_buffer(); - VLOG(1) << "Transfer to device as literal: " << literal.ToString() << " " + VLOG(1) << "Transfer to device as literal: " << literal->ToString() << " " << shaped_buffer.ToString(); - TF_RETURN_IF_ERROR(transfer_manager_->TransferLiteralToDevice( - host_to_device_stream_, literal, shaped_buffer)); + if (UseMultipleStreams()) { + // Initially wait for the compute stream so that memory allocations are + // synchronized. + host_to_device_stream_->ThenWaitFor(stream_); + } + TF_RETURN_IF_ERROR(transfer_manager_->TransferLiteralToDeviceAsync( + host_to_device_stream_, *literal, shaped_buffer)); if (UseMultipleStreams()) { se::Event event(stream_->parent()); TF_RET_CHECK(event.Init()) << "Event failed to initialize!"; host_to_device_stream_->ThenRecordEvent(&event); xla_tensor->SetDefinedOn(host_to_device_stream_, std::move(event)); } + // Unref the host tensor, and capture the literal shared_ptr too so it goes + // out of scope when the lambda completes. + host_to_device_stream_->ThenDoHostCallback([ref, literal]() { ref.Unref(); }); return Status::OK(); } -Status XlaTransferManager::TransferLiteralFromDevice( - Tensor* host_tensor, const Tensor& device_tensor) const { +void XlaTransferManager::TransferLiteralFromDevice( + Tensor* host_tensor, const Tensor& device_tensor, + const StatusCallback& done) const { const xla::ShapedBuffer& shaped_buffer = XlaTensor::FromTensor(&device_tensor)->shaped_buffer(); - TF_ASSIGN_OR_RETURN(std::unique_ptr literal, - transfer_manager_->TransferLiteralFromDevice( - device_to_host_stream_, shaped_buffer)); - VLOG(1) << "Transfer from device as literal: " << literal->ToString() << " " - << shaped_buffer.ToString(); - Tensor tensor; - TF_RETURN_IF_ERROR( - LiteralToHostTensor(*literal, host_tensor->dtype(), &tensor)); - // Reshape the tensor back to its declared shape. - if (!host_tensor->CopyFrom(tensor, device_tensor.shape())) { - return errors::Internal( - "Tensor::CopyFrom failed when copying from XLA device to CPU"); - } - return Status::OK(); + TensorReference ref(device_tensor); + transfer_manager_->TransferLiteralFromDevice( + device_to_host_stream_, shaped_buffer, + [=, &shaped_buffer]( + xla::StatusOr > literal_or) { + ref.Unref(); + done([&]() -> Status { + TF_ASSIGN_OR_RETURN(auto literal, std::move(literal_or)); + VLOG(1) << "Transfer from device as literal: " << literal->ToString() + << " " << shaped_buffer.ToString(); + Tensor tensor; + TF_RETURN_IF_ERROR( + LiteralToHostTensor(*literal, host_tensor->dtype(), &tensor)); + // Reshape the tensor back to its declared shape. + Status status; + if (!host_tensor->CopyFrom(tensor, device_tensor.shape())) { + status = errors::Internal( + "Tensor::CopyFrom failed when copying from XLA device to CPU"); + } + return status; + }()); + }); } void XlaTransferManager::CopyCPUTensorToDevice(const Tensor* cpu_tensor, @@ -163,6 +184,12 @@ void XlaTransferManager::CopyCPUTensorToDevice(const Tensor* cpu_tensor, return; } status = TransferLiteralToDevice(reshaped_cpu_tensor, device_tensor); + if (status.ok()) { + xla_tensor->set_host_tensor(*cpu_tensor); + host_to_device_stream_->ThenDoHostCallback( + [done]() { done(Status::OK()); }); + return; + } } else { se::DeviceMemoryBase dev_dst_ptr = XlaTensor::DeviceMemoryFromTensor(*device_tensor); @@ -212,7 +239,8 @@ void XlaTransferManager::CopyDeviceTensorToCPU(const Tensor* device_tensor, Status status; if (transfer_as_literal_) { - status = TransferLiteralFromDevice(cpu_tensor, *device_tensor); + TransferLiteralFromDevice(cpu_tensor, *device_tensor, done); + return; } else { device_to_host_stream_->ThenMemcpy(dst_ptr, dev_src_ptr, total_bytes); // TODO(hpucha): Make this asynchronous. @@ -234,15 +262,15 @@ void XlaTransferManager::CopyDeviceTensorToDevice(const Tensor& src_tensor, << reinterpret_cast(src_tensor.tensor_data().data()) << " " << reinterpret_cast(dst_tensor->tensor_data().data()); - // TODO(phawkins): replace this code with an asynchronous implementation. - auto body = [&]() { + // Perform memory allocation now, and enqueue the device-to-device transfer. + Status status = [&]() -> Status { if (src_tensor.NumElements() == 0) { return Status::OK(); } // TODO(jmolloy): We co-opt the device_to_host stream for device to device // transfers; perhaps we should have a dedicated device to device stream? or // one per device? - auto device_to_device_stream = device_to_host_stream_; + auto device_to_device_stream = stream_; XlaTensor* xla_src = XlaTensor::FromTensor(&src_tensor); XlaTensor* xla_dst = XlaTensor::FromTensor(dst_tensor); CHECK(xla_src && xla_dst) @@ -254,29 +282,40 @@ void XlaTransferManager::CopyDeviceTensorToDevice(const Tensor& src_tensor, TF_RETURN_IF_ERROR( xla_dst->AllocateShapedBuffer(src_tensor.dtype(), shape, client_, stream_->parent()->device_ordinal())); + if (stream_ != device_to_device_stream) { + // Initially wait for the compute stream so that memory allocations are + // synchronized. + device_to_device_stream->ThenWaitFor(stream_); + } } if (se::Event* event = xla_src->GetDefinitionEvent(device_to_device_stream)) { device_to_device_stream->ThenWaitFor(event); xla_src->SetDefinedOn(device_to_device_stream); - TF_RETURN_IF_ERROR(device_to_device_stream->BlockHostUntilDone()); } - TF_RETURN_IF_ERROR( - xla_dst->shaped_buffer().buffers().ForEachMutableElementWithStatus( - [&](const xla::ShapeIndex& index, se::DeviceMemoryBase* buffer) { - const se::DeviceMemoryBase& from_buffer = - xla_src->shaped_buffer().buffers().element(index); - CHECK_EQ(buffer->size(), from_buffer.size()); - if (!stream_->parent()->SynchronousMemcpy(buffer, from_buffer, - buffer->size())) { - return errors::Internal("Device to device memcpy failed"); - } - return Status::OK(); - })); + + auto from_iter = xla_src->shaped_buffer().buffers().begin(); + auto to_iter = xla_dst->shaped_buffer().buffers().begin(); + for (auto end_iter = xla_src->shaped_buffer().buffers().end(); + from_iter != end_iter; ++from_iter, ++to_iter) { + device_to_device_stream->ThenMemcpyD2D( + &to_iter->second, from_iter->second, to_iter->second.size()); + } + + if (UseMultipleStreams()) { + se::Event event(stream_->parent()); + CHECK(event.Init()); + device_to_device_stream->ThenRecordEvent(&event); + xla_dst->SetDefinedOn(device_to_device_stream, std::move(event)); + } return Status::OK(); - }; - done(body()); + }(); + if (!status.ok()) { + return done(status); + } else { + stream_->ThenDoHostCallback([=]() { done(Status::OK()); }); + } } XlaDeviceContext::XlaDeviceContext( diff --git a/tensorflow/compiler/jit/xla_device_context.h b/tensorflow/compiler/jit/xla_device_context.h index c726495f96..912f8d779e 100644 --- a/tensorflow/compiler/jit/xla_device_context.h +++ b/tensorflow/compiler/jit/xla_device_context.h @@ -66,8 +66,9 @@ class XlaTransferManager { private: Status TransferLiteralToDevice(const Tensor& host_tensor, Tensor* device_tensor) const; - Status TransferLiteralFromDevice(Tensor* host_tensor, - const Tensor& device_tensor) const; + void TransferLiteralFromDevice(Tensor* host_tensor, + const Tensor& device_tensor, + const StatusCallback& done) const; bool UseMultipleStreams() const { return stream_ != host_to_device_stream_; } // The main compute stream of the device, used to synchronize the transfer diff --git a/tensorflow/compiler/jit/xla_tensor.cc b/tensorflow/compiler/jit/xla_tensor.cc index 5dff187fff..d777dfa5a3 100644 --- a/tensorflow/compiler/jit/xla_tensor.cc +++ b/tensorflow/compiler/jit/xla_tensor.cc @@ -92,10 +92,8 @@ se::Event* XlaTensor::GetDefinitionEvent(se::Stream* stream) { void XlaTensor::SetDefinedOn(se::Stream* stream, se::Event event) { mutex_lock lock(mu_); - CHECK(!definition_event_.has_value()) - << "SetDefinedOn must only be called once!"; definition_event_ = std::move(event); - streams_defined_on_.push_back(stream); + streams_defined_on_ = {stream}; } void XlaTensor::SetDefinedOn(se::Stream* stream) { diff --git a/tensorflow/compiler/xla/service/executable.cc b/tensorflow/compiler/xla/service/executable.cc index 7cf2746947..fd75847d0c 100644 --- a/tensorflow/compiler/xla/service/executable.cc +++ b/tensorflow/compiler/xla/service/executable.cc @@ -82,7 +82,18 @@ StatusOr Executable::ExecuteOnStreamWrapper( StatusOr return_value = ExecuteOnStream(run_options, arguments, profile_ptr.get()); - TF_RETURN_IF_ERROR(return_value.status()); + if (!return_value.status().ok()) { + if (profile != nullptr) { + // Ensure the ThenStartTimer call has completed before we destroy timer. + // We already have a failure status to return, so just log this if it + // fails. + Status status = stream->BlockHostUntilDone(); + if (!status.ok()) { + LOG(ERROR) << "Failed to BlockHostUntilDone: " << status; + } + } + return return_value.status(); + } if (profile != nullptr) { VLOG(1) << "enqueueing 'stop timer' and blocking host until done..."; diff --git a/tensorflow/compiler/xla/service/hlo_runner.cc b/tensorflow/compiler/xla/service/hlo_runner.cc index 4f0569f405..b2725e2918 100644 --- a/tensorflow/compiler/xla/service/hlo_runner.cc +++ b/tensorflow/compiler/xla/service/hlo_runner.cc @@ -180,8 +180,12 @@ StatusOr HloRunner::ExecuteWithDeviceBuffers( TF_ASSIGN_OR_RETURN(std::unique_ptr executable, CreateExecutable(std::move(module), run_hlo_passes)); - return executable->ExecuteOnStreamWrapper(&service_run_options, - /*profile=*/profile, arguments); + TF_ASSIGN_OR_RETURN( + ScopedShapedBuffer retval, + executable->ExecuteOnStreamWrapper(&service_run_options, + /*profile=*/profile, arguments)); + TF_RETURN_IF_ERROR(stream.BlockHostUntilDone()); + return std::move(retval); } StatusOr HloRunner::ExecuteWithDeviceBuffers( @@ -309,6 +313,7 @@ StatusOr>> HloRunner::ExecuteReplicated( std::vector> exec_results; for (int64 i = 0; i < options.num_replicas; ++i) { + TF_RETURN_IF_ERROR(streams[i]->BlockHostUntilDone()); TF_ASSIGN_OR_RETURN(std::unique_ptr literal, backend().transfer_manager()->TransferLiteralFromDevice( streams[i].get(), results[i])); diff --git a/tensorflow/compiler/xla/tests/local_client_execute_test.cc b/tensorflow/compiler/xla/tests/local_client_execute_test.cc index 2f4d197ae6..5c3498c84c 100644 --- a/tensorflow/compiler/xla/tests/local_client_execute_test.cc +++ b/tensorflow/compiler/xla/tests/local_client_execute_test.cc @@ -772,6 +772,10 @@ XLA_TEST_F(LocalClientExecuteTest, CompileExecutable) { ScopedShapedBuffer result = executable->Run({&x_array}, DefaultExecutableRunOptions()) .ConsumeValueOrDie(); + ASSERT_IS_OK(local_client_->mutable_backend() + ->BorrowStream(0) + .ValueOrDie() + ->BlockHostUntilDone()); LiteralTestUtil::ExpectR1Near( {2.0f, 4.0f, 6.0f}, *ShapedBufferToLiteral(result), error_spec_); diff --git a/tensorflow/compiler/xla/tests/local_client_test_base.cc b/tensorflow/compiler/xla/tests/local_client_test_base.cc index 88797a7d0a..c31ba0e713 100644 --- a/tensorflow/compiler/xla/tests/local_client_test_base.cc +++ b/tensorflow/compiler/xla/tests/local_client_test_base.cc @@ -189,7 +189,19 @@ StatusOr LocalClientTestBase::ExecuteLocally( TF_ASSIGN_OR_RETURN( std::unique_ptr executable, local_client_->Compile(computation, argument_layouts, build_options)); - return executable->Run(arguments, run_options); + TF_ASSIGN_OR_RETURN(auto ret, executable->Run(arguments, run_options)); + + auto device_ordinal = + build_options.device_ordinal() == -1 ? 0 : build_options.device_ordinal(); + auto* stream = run_options.stream(); + if (!stream) { + stream = local_client_->mutable_backend() + ->BorrowStream(device_ordinal) + .ValueOrDie() + .get(); + } + TF_RETURN_IF_ERROR(stream->BlockHostUntilDone()); + return std::move(ret); } } // namespace xla diff --git a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc index 4d4dd62a3f..c000ff4dc8 100644 --- a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc +++ b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc @@ -172,6 +172,7 @@ void ExecuteAndFetchProfile(string* profile_output, LocalClient* client, auto execution_result, executable->ExecuteOnStream(&run_options, {&lhs_arg, &rhs_arg}, &hlo_execution_profile)); + TF_ASSERT_OK(stream_ptr->BlockHostUntilDone()); (void)execution_result; *profile_output = diff --git a/tensorflow/stream_executor/host/host_gpu_executor.cc b/tensorflow/stream_executor/host/host_gpu_executor.cc index 3cd97b3cf1..8adf739b17 100644 --- a/tensorflow/stream_executor/host/host_gpu_executor.cc +++ b/tensorflow/stream_executor/host/host_gpu_executor.cc @@ -93,7 +93,7 @@ bool HostExecutor::MemcpyDeviceToDevice(Stream *stream, // the nature of the HostExecutor) memcpy on the stream (HostStream) // associated with the HostExecutor. AsHostStream(stream)->EnqueueTask( - [src_mem, dst_mem, size]() { memcpy(src_mem, dst_mem, size); }); + [src_mem, dst_mem, size]() { memcpy(dst_mem, src_mem, size); }); return true; } diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc index ca1b8e28e6..2c495c99e1 100644 --- a/tensorflow/stream_executor/stream.cc +++ b/tensorflow/stream_executor/stream.cc @@ -268,6 +268,12 @@ Stream::~Stream() { VLOG_CALL(); temporary_memory_manager_.ForceDeallocateAll(); + // Ensure the stream is completed. + auto status = BlockHostUntilDone(); + if (!status.ok()) { + LOG(WARNING) << "Error blocking host until done in stream destructor: " + << status; + } if (allocated_) { parent_->DeallocateStream(this); -- GitLab From 57d051e7b156313c0beef6eb1fd9e6ca955a568a Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Tue, 24 Jul 2018 13:50:42 -0700 Subject: [PATCH 347/519] Don't cache zero tensors in graph at all PiperOrigin-RevId: 205885372 --- tensorflow/python/eager/backprop.py | 16 ++++++--- tensorflow/python/eager/backprop_test.py | 43 ++++++++++-------------- 2 files changed, 28 insertions(+), 31 deletions(-) diff --git a/tensorflow/python/eager/backprop.py b/tensorflow/python/eager/backprop.py index da8b93dba8..c59ad09bf1 100644 --- a/tensorflow/python/eager/backprop.py +++ b/tensorflow/python/eager/backprop.py @@ -599,15 +599,18 @@ def _fast_fill(value, shape, dtype): def _zeros(shape, dtype): - """Wraps array_ops.zeros to cache last zero for a given shape and dtype.""" - device = context.context().device_name + """Helper to return (possibly cached) zero tensors in eager mode.""" if dtype == dtypes.variant: # TODO(apassos): need to save enough information about variant tensors to do # a zeros return None - # pylint: disable=protected-access - cache_key = shape, dtype, device, context.context()._eager_context.mode - # pylint: enable=protected-access + + ctx = context.context() + if not ctx.executing_eagerly(): + return array_ops.zeros(shape, dtype) + + device = ctx.device_name + cache_key = shape, dtype, device cached = _zeros_cache.get(cache_key) if cached is None: cached = _fast_fill(0, shape, dtype) @@ -616,6 +619,9 @@ def _zeros(shape, dtype): def _ones(shape, dtype): + if not context.context().executing_eagerly(): + return array_ops.ones(shape, dtype) + if shape == (): # pylint: disable=g-explicit-bool-comparison return constant_op.constant(1, dtype=dtype) return _fast_fill(1, shape, dtype) diff --git a/tensorflow/python/eager/backprop_test.py b/tensorflow/python/eager/backprop_test.py index 95a3a8b629..3d3f54b9c4 100644 --- a/tensorflow/python/eager/backprop_test.py +++ b/tensorflow/python/eager/backprop_test.py @@ -925,32 +925,23 @@ class BackpropTest(test.TestCase): 'did you forget to return a value from fn?'): val_and_grads_fn(x, y) - def testZerosCacheDoesntLeakAcrossModes(self): - with ops.Graph().as_default(): - t = random_ops.random_normal(shape=[100, 2]) - x = random_ops.random_normal(shape=[100, 4]) - dy = random_ops.random_normal(shape=[100, 4]) - with backprop.GradientTape() as gradient_tape: - gradient_tape.watch(x) - x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1) - y1 = x1 ** 2. - y = array_ops.concat([y1, t], axis=1) - - dx = gradient_tape.gradient(y, x, output_gradients=dy) - with self.test_session() as sess: - sess.run(variables.global_variables_initializer()) - sess.run(dx) - - t = random_ops.random_normal(shape=[100, 2]) - x = random_ops.random_normal(shape=[100, 4]) - dy = random_ops.random_normal(shape=[100, 4]) - with backprop.GradientTape() as gradient_tape: - gradient_tape.watch(x) - x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1) - y1 = x1 ** 2. - y = array_ops.concat([y1, t], axis=1) - - dx = gradient_tape.gradient(y, x, output_gradients=dy) + def testZerosCacheDoesntLeakAcrossGraphs(self): + with context.graph_mode(): + def get_grad(): + with ops.Graph().as_default(), self.test_session(): + t = constant_op.constant(1, dtype=dtypes.float32, shape=(10, 4)) + x = constant_op.constant(2, dtype=dtypes.float32, shape=(10, 4)) + with backprop.GradientTape() as gt: + tape.watch(x) + x1, _ = array_ops.split(x, num_or_size_splits=2, axis=1) + y1 = x1**2 + y = array_ops.concat([y1, t], axis=1) + return self.evaluate(gt.gradient(y, x)) + + grad1 = get_grad() + grad2 = get_grad() + + self.assertAllEqual(grad1, grad2) if __name__ == '__main__': -- GitLab From 76e8f7b7fdf89b131e0406022129d5dde6b89e40 Mon Sep 17 00:00:00 2001 From: Anna R Date: Tue, 24 Jul 2018 14:02:04 -0700 Subject: [PATCH 348/519] Initial API compatibility script for TF2.0. I am pretty much reusing 1.0 conversion script but passing V2 data. Also, remove code from tf_update.py which is also in ast_edits.py. PiperOrigin-RevId: 205887317 --- tensorflow/tools/compatibility/BUILD | 57 ++ tensorflow/tools/compatibility/renames_v2.py | 134 +++++ .../compatibility/testdata/test_file_v1_10.py | 34 ++ tensorflow/tools/compatibility/tf_upgrade.py | 486 +----------------- .../tools/compatibility/tf_upgrade_test.py | 5 +- .../tools/compatibility/tf_upgrade_v2.py | 115 +++++ .../tools/compatibility/tf_upgrade_v2_test.py | 83 +++ tensorflow/tools/compatibility/update/BUILD | 15 + .../update/generate_v2_renames_map.py | 103 ++++ 9 files changed, 547 insertions(+), 485 deletions(-) create mode 100644 tensorflow/tools/compatibility/renames_v2.py create mode 100644 tensorflow/tools/compatibility/testdata/test_file_v1_10.py create mode 100644 tensorflow/tools/compatibility/tf_upgrade_v2.py create mode 100644 tensorflow/tools/compatibility/tf_upgrade_v2_test.py create mode 100644 tensorflow/tools/compatibility/update/BUILD create mode 100644 tensorflow/tools/compatibility/update/generate_v2_renames_map.py diff --git a/tensorflow/tools/compatibility/BUILD b/tensorflow/tools/compatibility/BUILD index b7bfb29aae..55792c51fe 100644 --- a/tensorflow/tools/compatibility/BUILD +++ b/tensorflow/tools/compatibility/BUILD @@ -8,10 +8,17 @@ load( "tf_cc_test", # @unused ) +py_library( + name = "ast_edits", + srcs = ["ast_edits.py"], + srcs_version = "PY2AND3", +) + py_binary( name = "tf_upgrade", srcs = ["tf_upgrade.py"], srcs_version = "PY2AND3", + deps = [":ast_edits"], ) py_test( @@ -26,6 +33,28 @@ py_test( ], ) +py_binary( + name = "tf_upgrade_v2", + srcs = [ + "renames_v2.py", + "tf_upgrade_v2.py", + ], + srcs_version = "PY2AND3", + deps = [":ast_edits"], +) + +py_test( + name = "tf_upgrade_v2_test", + srcs = ["tf_upgrade_v2_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":tf_upgrade_v2", + "//tensorflow/python:client_testlib", + "//tensorflow/python:framework_test_lib", + "@six_archive//:six", + ], +) + # Keep for reference, this test will succeed in 0.11 but fail in 1.0 # py_test( # name = "test_file_v0_11", @@ -62,9 +91,37 @@ py_test( ], ) +genrule( + name = "generate_upgraded_file_v2", + testonly = 1, + srcs = ["testdata/test_file_v1_10.py"], + outs = [ + "test_file_v2_0.py", + "report_v2.txt", + ], + cmd = ("$(location :tf_upgrade_v2)" + + " --infile $(location testdata/test_file_v1_10.py)" + + " --outfile $(location test_file_v2_0.py)" + + " --reportfile $(location report_v2.txt)"), + tools = [":tf_upgrade_v2"], +) + +py_test( + name = "test_file_v2_0", + size = "small", + srcs = ["test_file_v2_0.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + ], +) + exports_files( [ + "ast_edits.py", "tf_upgrade.py", + "renames_v2.py", "testdata/test_file_v0_11.py", + "testdata/test_file_v1_10.py", ], ) diff --git a/tensorflow/tools/compatibility/renames_v2.py b/tensorflow/tools/compatibility/renames_v2.py new file mode 100644 index 0000000000..216aa41b60 --- /dev/null +++ b/tensorflow/tools/compatibility/renames_v2.py @@ -0,0 +1,134 @@ +# 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=line-too-long +"""List of renames to apply when converting from TF 1.0 to TF 2.0. + +THIS FILE IS AUTOGENERATED: To update, please run: + bazel build tensorflow/tools/compatibility/update:generate_v2_renames_map + bazel-bin/tensorflow/tools/compatibility/update/generate_v2_renames_map +This file should be updated whenever endpoints are deprecated. +""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +renames = { + 'tf.acos': 'tf.math.acos', + 'tf.acosh': 'tf.math.acosh', + 'tf.add': 'tf.math.add', + 'tf.as_string': 'tf.dtypes.as_string', + 'tf.asin': 'tf.math.asin', + 'tf.asinh': 'tf.math.asinh', + 'tf.atan': 'tf.math.atan', + 'tf.atan2': 'tf.math.atan2', + 'tf.atanh': 'tf.math.atanh', + 'tf.batch_to_space_nd': 'tf.manip.batch_to_space_nd', + 'tf.betainc': 'tf.math.betainc', + 'tf.ceil': 'tf.math.ceil', + 'tf.check_numerics': 'tf.debugging.check_numerics', + 'tf.cholesky': 'tf.linalg.cholesky', + 'tf.cos': 'tf.math.cos', + 'tf.cosh': 'tf.math.cosh', + 'tf.cross': 'tf.linalg.cross', + 'tf.decode_base64': 'tf.io.decode_base64', + 'tf.decode_compressed': 'tf.io.decode_compressed', + 'tf.decode_json_example': 'tf.io.decode_json_example', + 'tf.decode_raw': 'tf.io.decode_raw', + 'tf.dequantize': 'tf.quantization.dequantize', + 'tf.diag': 'tf.linalg.tensor_diag', + 'tf.diag_part': 'tf.linalg.tensor_diag_part', + 'tf.digamma': 'tf.math.digamma', + 'tf.encode_base64': 'tf.io.encode_base64', + 'tf.equal': 'tf.math.equal', + 'tf.erfc': 'tf.math.erfc', + 'tf.exp': 'tf.math.exp', + 'tf.expm1': 'tf.math.expm1', + 'tf.extract_image_patches': 'tf.image.extract_image_patches', + 'tf.fake_quant_with_min_max_args': 'tf.quantization.fake_quant_with_min_max_args', + 'tf.fake_quant_with_min_max_args_gradient': 'tf.quantization.fake_quant_with_min_max_args_gradient', + 'tf.fake_quant_with_min_max_vars': 'tf.quantization.fake_quant_with_min_max_vars', + 'tf.fake_quant_with_min_max_vars_gradient': 'tf.quantization.fake_quant_with_min_max_vars_gradient', + 'tf.fake_quant_with_min_max_vars_per_channel': 'tf.quantization.fake_quant_with_min_max_vars_per_channel', + 'tf.fake_quant_with_min_max_vars_per_channel_gradient': 'tf.quantization.fake_quant_with_min_max_vars_per_channel_gradient', + 'tf.fft': 'tf.spectral.fft', + 'tf.floor': 'tf.math.floor', + 'tf.gather_nd': 'tf.manip.gather_nd', + 'tf.greater': 'tf.math.greater', + 'tf.greater_equal': 'tf.math.greater_equal', + 'tf.ifft': 'tf.spectral.ifft', + 'tf.igamma': 'tf.math.igamma', + 'tf.igammac': 'tf.math.igammac', + 'tf.invert_permutation': 'tf.math.invert_permutation', + 'tf.is_finite': 'tf.debugging.is_finite', + 'tf.is_inf': 'tf.debugging.is_inf', + 'tf.is_nan': 'tf.debugging.is_nan', + 'tf.less': 'tf.math.less', + 'tf.less_equal': 'tf.math.less_equal', + 'tf.lgamma': 'tf.math.lgamma', + 'tf.log': 'tf.math.log', + 'tf.log1p': 'tf.math.log1p', + 'tf.logical_and': 'tf.math.logical_and', + 'tf.logical_not': 'tf.math.logical_not', + 'tf.logical_or': 'tf.math.logical_or', + 'tf.matching_files': 'tf.io.matching_files', + 'tf.matrix_band_part': 'tf.linalg.band_part', + 'tf.matrix_determinant': 'tf.linalg.det', + 'tf.matrix_diag': 'tf.linalg.diag', + 'tf.matrix_diag_part': 'tf.linalg.diag_part', + 'tf.matrix_inverse': 'tf.linalg.inv', + 'tf.matrix_set_diag': 'tf.linalg.set_diag', + 'tf.matrix_solve': 'tf.linalg.solve', + 'tf.matrix_triangular_solve': 'tf.linalg.triangular_solve', + 'tf.maximum': 'tf.math.maximum', + 'tf.minimum': 'tf.math.minimum', + 'tf.not_equal': 'tf.math.not_equal', + 'tf.parse_tensor': 'tf.io.parse_tensor', + 'tf.polygamma': 'tf.math.polygamma', + 'tf.qr': 'tf.linalg.qr', + 'tf.quantized_concat': 'tf.quantization.quantized_concat', + 'tf.read_file': 'tf.io.read_file', + 'tf.reciprocal': 'tf.math.reciprocal', + 'tf.regex_replace': 'tf.strings.regex_replace', + 'tf.reshape': 'tf.manip.reshape', + 'tf.reverse': 'tf.manip.reverse', + 'tf.reverse_v2': 'tf.manip.reverse', + 'tf.rint': 'tf.math.rint', + 'tf.rsqrt': 'tf.math.rsqrt', + 'tf.scatter_nd': 'tf.manip.scatter_nd', + 'tf.segment_max': 'tf.math.segment_max', + 'tf.segment_mean': 'tf.math.segment_mean', + 'tf.segment_min': 'tf.math.segment_min', + 'tf.segment_prod': 'tf.math.segment_prod', + 'tf.segment_sum': 'tf.math.segment_sum', + 'tf.sin': 'tf.math.sin', + 'tf.sinh': 'tf.math.sinh', + 'tf.space_to_batch_nd': 'tf.manip.space_to_batch_nd', + 'tf.squared_difference': 'tf.math.squared_difference', + 'tf.string_join': 'tf.strings.join', + 'tf.string_strip': 'tf.strings.strip', + 'tf.string_to_hash_bucket': 'tf.strings.to_hash_bucket', + 'tf.string_to_hash_bucket_fast': 'tf.strings.to_hash_bucket_fast', + 'tf.string_to_hash_bucket_strong': 'tf.strings.to_hash_bucket_strong', + 'tf.string_to_number': 'tf.strings.to_number', + 'tf.substr': 'tf.strings.substr', + 'tf.tan': 'tf.math.tan', + 'tf.tile': 'tf.manip.tile', + 'tf.unsorted_segment_max': 'tf.math.unsorted_segment_max', + 'tf.unsorted_segment_min': 'tf.math.unsorted_segment_min', + 'tf.unsorted_segment_prod': 'tf.math.unsorted_segment_prod', + 'tf.unsorted_segment_sum': 'tf.math.unsorted_segment_sum', + 'tf.write_file': 'tf.io.write_file', + 'tf.zeta': 'tf.math.zeta' +} diff --git a/tensorflow/tools/compatibility/testdata/test_file_v1_10.py b/tensorflow/tools/compatibility/testdata/test_file_v1_10.py new file mode 100644 index 0000000000..a49035a1a0 --- /dev/null +++ b/tensorflow/tools/compatibility/testdata/test_file_v1_10.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. +# ============================================================================== +"""Tests for tf upgrader.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import tensorflow as tf +from tensorflow.python.framework import test_util +from tensorflow.python.platform import test as test_lib + + +class TestUpgrade(test_util.TensorFlowTestCase): + """Test various APIs that have been changed in 2.0.""" + + def testRenames(self): + with self.test_session(): + self.assertAllClose(1.04719755, tf.acos(0.5).eval()) + self.assertAllClose(0.5, tf.rsqrt(4.0).eval()) + +if __name__ == "__main__": + test_lib.main() diff --git a/tensorflow/tools/compatibility/tf_upgrade.py b/tensorflow/tools/compatibility/tf_upgrade.py index 1f8833582a..96705b1a4c 100644 --- a/tensorflow/tools/compatibility/tf_upgrade.py +++ b/tensorflow/tools/compatibility/tf_upgrade.py @@ -19,491 +19,11 @@ from __future__ import division from __future__ import print_function import argparse -import ast -import collections -import os -import shutil -import sys -import tempfile -import traceback +from tensorflow.tools.compatibility import ast_edits -class APIChangeSpec(object): - """This class defines the transformations that need to happen. - This class must provide the following fields: - - * `function_keyword_renames`: maps function names to a map of old -> new - argument names - * `function_renames`: maps function names to new function names - * `change_to_function`: a set of function names that have changed (for - notifications) - * `function_reorders`: maps functions whose argument order has changed to the - list of arguments in the new order - * `function_handle`: maps function names to custom handlers for the function - - For an example, see `TFAPIChangeSpec`. - """ - - -class _FileEditTuple( - collections.namedtuple("_FileEditTuple", - ["comment", "line", "start", "old", "new"])): - """Each edit that is recorded by a _FileEditRecorder. - - Fields: - comment: A description of the edit and why it was made. - line: The line number in the file where the edit occurs (1-indexed). - start: The line number in the file where the edit occurs (0-indexed). - old: text string to remove (this must match what was in file). - new: text string to add in place of `old`. - """ - - __slots__ = () - - -class _FileEditRecorder(object): - """Record changes that need to be done to the file.""" - - def __init__(self, filename): - # all edits are lists of chars - self._filename = filename - - self._line_to_edit = collections.defaultdict(list) - self._errors = [] - - def process(self, text): - """Process a list of strings, each corresponding to the recorded changes. - - Args: - text: A list of lines of text (assumed to contain newlines) - Returns: - A tuple of the modified text and a textual description of what is done. - Raises: - ValueError: if substitution source location does not have expected text. - """ - - change_report = "" - - # Iterate of each line - for line, edits in self._line_to_edit.items(): - offset = 0 - # sort by column so that edits are processed in order in order to make - # indexing adjustments cumulative for changes that change the string - # length - edits.sort(key=lambda x: x.start) - - # Extract each line to a list of characters, because mutable lists - # are editable, unlike immutable strings. - char_array = list(text[line - 1]) - - # Record a description of the change - change_report += "%r Line %d\n" % (self._filename, line) - change_report += "-" * 80 + "\n\n" - for e in edits: - change_report += "%s\n" % e.comment - change_report += "\n Old: %s" % (text[line - 1]) - - # Make underscore buffers for underlining where in the line the edit was - change_list = [" "] * len(text[line - 1]) - change_list_new = [" "] * len(text[line - 1]) - - # Iterate for each edit - for e in edits: - # Create effective start, end by accounting for change in length due - # to previous edits - start_eff = e.start + offset - end_eff = start_eff + len(e.old) - - # Make sure the edit is changing what it should be changing - old_actual = "".join(char_array[start_eff:end_eff]) - if old_actual != e.old: - raise ValueError("Expected text %r but got %r" % - ("".join(e.old), "".join(old_actual))) - # Make the edit - char_array[start_eff:end_eff] = list(e.new) - - # Create the underline highlighting of the before and after - change_list[e.start:e.start + len(e.old)] = "~" * len(e.old) - change_list_new[start_eff:end_eff] = "~" * len(e.new) - - # Keep track of how to generate effective ranges - offset += len(e.new) - len(e.old) - - # Finish the report comment - change_report += " %s\n" % "".join(change_list) - text[line - 1] = "".join(char_array) - change_report += " New: %s" % (text[line - 1]) - change_report += " %s\n\n" % "".join(change_list_new) - return "".join(text), change_report, self._errors - - def add(self, comment, line, start, old, new, error=None): - """Add a new change that is needed. - - Args: - comment: A description of what was changed - line: Line number (1 indexed) - start: Column offset (0 indexed) - old: old text - new: new text - error: this "edit" is something that cannot be fixed automatically - Returns: - None - """ - - self._line_to_edit[line].append( - _FileEditTuple(comment, line, start, old, new)) - if error: - self._errors.append("%s:%d: %s" % (self._filename, line, error)) - - -class _ASTCallVisitor(ast.NodeVisitor): - """AST Visitor that processes function calls. - - Updates function calls from old API version to new API version using a given - change spec. - """ - - def __init__(self, filename, lines, api_change_spec): - self._filename = filename - self._file_edit = _FileEditRecorder(filename) - self._lines = lines - self._api_change_spec = api_change_spec - - def process(self, lines): - return self._file_edit.process(lines) - - def generic_visit(self, node): - ast.NodeVisitor.generic_visit(self, node) - - def _rename_functions(self, node, full_name): - function_renames = self._api_change_spec.function_renames - try: - new_name = function_renames[full_name] - self._file_edit.add("Renamed function %r to %r" % (full_name, new_name), - node.lineno, node.col_offset, full_name, new_name) - except KeyError: - pass - - def _get_attribute_full_path(self, node): - """Traverse an attribute to generate a full name e.g. tf.foo.bar. - - Args: - node: A Node of type Attribute. - - Returns: - a '.'-delimited full-name or None if the tree was not a simple form. - i.e. `foo()+b).bar` returns None, while `a.b.c` would return "a.b.c". - """ - curr = node - items = [] - while not isinstance(curr, ast.Name): - if not isinstance(curr, ast.Attribute): - return None - items.append(curr.attr) - curr = curr.value - items.append(curr.id) - return ".".join(reversed(items)) - - def _find_true_position(self, node): - """Return correct line number and column offset for a given node. - - This is necessary mainly because ListComp's location reporting reports - the next token after the list comprehension list opening. - - Args: - node: Node for which we wish to know the lineno and col_offset - """ - import re - find_open = re.compile("^\s*(\\[).*$") - find_string_chars = re.compile("['\"]") - - if isinstance(node, ast.ListComp): - # Strangely, ast.ListComp returns the col_offset of the first token - # after the '[' token which appears to be a bug. Workaround by - # explicitly finding the real start of the list comprehension. - line = node.lineno - col = node.col_offset - # loop over lines - while 1: - # Reverse the text to and regular expression search for whitespace - text = self._lines[line - 1] - reversed_preceding_text = text[:col][::-1] - # First find if a [ can be found with only whitespace between it and - # col. - m = find_open.match(reversed_preceding_text) - if m: - new_col_offset = col - m.start(1) - 1 - return line, new_col_offset - else: - if (reversed_preceding_text == "" or - reversed_preceding_text.isspace()): - line = line - 1 - prev_line = self._lines[line - 1] - # TODO(aselle): - # this is poor comment detection, but it is good enough for - # cases where the comment does not contain string literal starting/ - # ending characters. If ast gave us start and end locations of the - # ast nodes rather than just start, we could use string literal - # node ranges to filter out spurious #'s that appear in string - # literals. - comment_start = prev_line.find("#") - if comment_start == -1: - col = len(prev_line) - 1 - elif find_string_chars.search(prev_line[comment_start:]) is None: - col = comment_start - else: - return None, None - else: - return None, None - # Most other nodes return proper locations (with notably does not), but - # it is not possible to use that in an argument. - return node.lineno, node.col_offset - - def visit_Call(self, node): # pylint: disable=invalid-name - """Handle visiting a call node in the AST. - - Args: - node: Current Node - """ - - # Find a simple attribute name path e.g. "tf.foo.bar" - full_name = self._get_attribute_full_path(node.func) - - # Make sure the func is marked as being part of a call - node.func.is_function_for_call = True - - if full_name: - # Call special handlers - function_handles = self._api_change_spec.function_handle - if full_name in function_handles: - function_handles[full_name](self._file_edit, node) - - # Examine any non-keyword argument and make it into a keyword argument - # if reordering required. - function_reorders = self._api_change_spec.function_reorders - function_keyword_renames = ( - self._api_change_spec.function_keyword_renames) - - if full_name in function_reorders: - reordered = function_reorders[full_name] - for idx, arg in enumerate(node.args): - lineno, col_offset = self._find_true_position(arg) - if lineno is None or col_offset is None: - self._file_edit.add( - "Failed to add keyword %r to reordered function %r" % - (reordered[idx], full_name), - arg.lineno, - arg.col_offset, - "", - "", - error="A necessary keyword argument failed to be inserted.") - else: - keyword_arg = reordered[idx] - if (full_name in function_keyword_renames and - keyword_arg in function_keyword_renames[full_name]): - keyword_arg = function_keyword_renames[full_name][keyword_arg] - self._file_edit.add("Added keyword %r to reordered function %r" % - (reordered[idx], full_name), lineno, col_offset, - "", keyword_arg + "=") - - # Examine each keyword argument and convert it to the final renamed form - renamed_keywords = ({} if full_name not in function_keyword_renames else - function_keyword_renames[full_name]) - for keyword in node.keywords: - argkey = keyword.arg - argval = keyword.value - - if argkey in renamed_keywords: - argval_lineno, argval_col_offset = self._find_true_position(argval) - if argval_lineno is not None and argval_col_offset is not None: - # TODO(aselle): We should scan backward to find the start of the - # keyword key. Unfortunately ast does not give you the location of - # keyword keys, so we are forced to infer it from the keyword arg - # value. - key_start = argval_col_offset - len(argkey) - 1 - key_end = key_start + len(argkey) + 1 - if (self._lines[argval_lineno - 1][key_start:key_end] == argkey + - "="): - self._file_edit.add("Renamed keyword argument from %r to %r" % - (argkey, - renamed_keywords[argkey]), argval_lineno, - argval_col_offset - len(argkey) - 1, - argkey + "=", renamed_keywords[argkey] + "=") - continue - self._file_edit.add( - "Failed to rename keyword argument from %r to %r" % - (argkey, renamed_keywords[argkey]), - argval.lineno, - argval.col_offset - len(argkey) - 1, - "", - "", - error="Failed to find keyword lexographically. Fix manually.") - - ast.NodeVisitor.generic_visit(self, node) - - def visit_Attribute(self, node): # pylint: disable=invalid-name - """Handle bare Attributes i.e. [tf.foo, tf.bar]. - - Args: - node: Node that is of type ast.Attribute - """ - full_name = self._get_attribute_full_path(node) - if full_name: - self._rename_functions(node, full_name) - if full_name in self._api_change_spec.change_to_function: - if not hasattr(node, "is_function_for_call"): - new_text = full_name + "()" - self._file_edit.add("Changed %r to %r" % (full_name, new_text), - node.lineno, node.col_offset, full_name, new_text) - - ast.NodeVisitor.generic_visit(self, node) - - -class ASTCodeUpgrader(object): - """Handles upgrading a set of Python files using a given API change spec.""" - - def __init__(self, api_change_spec): - if not isinstance(api_change_spec, APIChangeSpec): - raise TypeError("Must pass APIChangeSpec to ASTCodeUpgrader, got %s" % - type(api_change_spec)) - self._api_change_spec = api_change_spec - - def process_file(self, in_filename, out_filename): - """Process the given python file for incompatible changes. - - Args: - in_filename: filename to parse - out_filename: output file to write to - Returns: - A tuple representing number of files processed, log of actions, errors - """ - - # Write to a temporary file, just in case we are doing an implace modify. - with open(in_filename, "r") as in_file, \ - tempfile.NamedTemporaryFile("w", delete=False) as temp_file: - ret = self.process_opened_file(in_filename, in_file, out_filename, - temp_file) - - shutil.move(temp_file.name, out_filename) - return ret - - # Broad exceptions are required here because ast throws whatever it wants. - # pylint: disable=broad-except - def process_opened_file(self, in_filename, in_file, out_filename, out_file): - """Process the given python file for incompatible changes. - - This function is split out to facilitate StringIO testing from - tf_upgrade_test.py. - - Args: - in_filename: filename to parse - in_file: opened file (or StringIO) - out_filename: output file to write to - out_file: opened file (or StringIO) - Returns: - A tuple representing number of files processed, log of actions, errors - """ - process_errors = [] - text = "-" * 80 + "\n" - text += "Processing file %r\n outputting to %r\n" % (in_filename, - out_filename) - text += "-" * 80 + "\n\n" - - parsed_ast = None - lines = in_file.readlines() - try: - parsed_ast = ast.parse("".join(lines)) - except Exception: - text += "Failed to parse %r\n\n" % in_filename - text += traceback.format_exc() - if parsed_ast: - visitor = _ASTCallVisitor(in_filename, lines, self._api_change_spec) - visitor.visit(parsed_ast) - out_text, new_text, process_errors = visitor.process(lines) - text += new_text - if out_file: - out_file.write(out_text) - text += "\n" - return 1, text, process_errors - - # pylint: enable=broad-except - - def process_tree(self, root_directory, output_root_directory, - copy_other_files): - """Processes upgrades on an entire tree of python files in place. - - Note that only Python files. If you have custom code in other languages, - you will need to manually upgrade those. - - Args: - root_directory: Directory to walk and process. - output_root_directory: Directory to use as base. - copy_other_files: Copy files that are not touched by this converter. - - Returns: - A tuple of files processed, the report string ofr all files, and errors - """ - - # make sure output directory doesn't exist - if output_root_directory and os.path.exists(output_root_directory): - print("Output directory %r must not already exist." % - (output_root_directory)) - sys.exit(1) - - # make sure output directory does not overlap with root_directory - norm_root = os.path.split(os.path.normpath(root_directory)) - norm_output = os.path.split(os.path.normpath(output_root_directory)) - if norm_root == norm_output: - print("Output directory %r same as input directory %r" % - (root_directory, output_root_directory)) - sys.exit(1) - - # Collect list of files to process (we do this to correctly handle if the - # user puts the output directory in some sub directory of the input dir) - files_to_process = [] - files_to_copy = [] - for dir_name, _, file_list in os.walk(root_directory): - py_files = [f for f in file_list if f.endswith(".py")] - copy_files = [f for f in file_list if not f.endswith(".py")] - for filename in py_files: - fullpath = os.path.join(dir_name, filename) - fullpath_output = os.path.join(output_root_directory, - os.path.relpath(fullpath, - root_directory)) - files_to_process.append((fullpath, fullpath_output)) - if copy_other_files: - for filename in copy_files: - fullpath = os.path.join(dir_name, filename) - fullpath_output = os.path.join(output_root_directory, - os.path.relpath( - fullpath, root_directory)) - files_to_copy.append((fullpath, fullpath_output)) - - file_count = 0 - tree_errors = [] - report = "" - report += ("=" * 80) + "\n" - report += "Input tree: %r\n" % root_directory - report += ("=" * 80) + "\n" - - for input_path, output_path in files_to_process: - output_directory = os.path.dirname(output_path) - if not os.path.isdir(output_directory): - os.makedirs(output_directory) - file_count += 1 - _, l_report, l_errors = self.process_file(input_path, output_path) - tree_errors += l_errors - report += l_report - for input_path, output_path in files_to_copy: - output_directory = os.path.dirname(output_path) - if not os.path.isdir(output_directory): - os.makedirs(output_directory) - shutil.copy(input_path, output_path) - return file_count, report, tree_errors - - -class TFAPIChangeSpec(APIChangeSpec): +class TFAPIChangeSpec(ast_edits.APIChangeSpec): """List of maps that describe what changed in the API.""" def __init__(self): @@ -718,7 +238,7 @@ Simple usage: default="report.txt") args = parser.parse_args() - upgrade = ASTCodeUpgrader(TFAPIChangeSpec()) + upgrade = ast_edits.ASTCodeUpgrader(TFAPIChangeSpec()) report_text = None report_filename = args.report_filename files_processed = 0 diff --git a/tensorflow/tools/compatibility/tf_upgrade_test.py b/tensorflow/tools/compatibility/tf_upgrade_test.py index 3d02eacba6..66325ea2ad 100644 --- a/tensorflow/tools/compatibility/tf_upgrade_test.py +++ b/tensorflow/tools/compatibility/tf_upgrade_test.py @@ -22,6 +22,7 @@ import tempfile import six from tensorflow.python.framework import test_util from tensorflow.python.platform import test as test_lib +from tensorflow.tools.compatibility import ast_edits from tensorflow.tools.compatibility import tf_upgrade @@ -36,7 +37,7 @@ class TestUpgrade(test_util.TensorFlowTestCase): def _upgrade(self, old_file_text): in_file = six.StringIO(old_file_text) out_file = six.StringIO() - upgrader = tf_upgrade.ASTCodeUpgrader(tf_upgrade.TFAPIChangeSpec()) + upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade.TFAPIChangeSpec()) count, report, errors = ( upgrader.process_opened_file("test.py", in_file, "test_out.py", out_file)) @@ -139,7 +140,7 @@ class TestUpgradeFiles(test_util.TensorFlowTestCase): upgraded = "tf.multiply(a, b)\n" temp_file.write(original) temp_file.close() - upgrader = tf_upgrade.ASTCodeUpgrader(tf_upgrade.TFAPIChangeSpec()) + upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade.TFAPIChangeSpec()) upgrader.process_file(temp_file.name, temp_file.name) self.assertAllEqual(open(temp_file.name).read(), upgraded) os.unlink(temp_file.name) diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2.py b/tensorflow/tools/compatibility/tf_upgrade_v2.py new file mode 100644 index 0000000000..9702430a12 --- /dev/null +++ b/tensorflow/tools/compatibility/tf_upgrade_v2.py @@ -0,0 +1,115 @@ +# 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. +# ============================================================================== +"""Upgrader for Python scripts from 1.* TensorFlow to 2.0 TensorFlow.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import argparse + +from tensorflow.tools.compatibility import ast_edits +from tensorflow.tools.compatibility import renames_v2 + + +class TFAPIChangeSpec(ast_edits.APIChangeSpec): + """List of maps that describe what changed in the API.""" + + def __init__(self): + # Maps from a function name to a dictionary that describes how to + # map from an old argument keyword to the new argument keyword. + self.function_keyword_renames = {} + + # Mapping from function to the new name of the function + self.function_renames = renames_v2.renames + + # Variables that should be changed to functions. + self.change_to_function = {} + + # Functions that were reordered should be changed to the new keyword args + # for safety, if positional arguments are used. If you have reversed the + # positional arguments yourself, this could do the wrong thing. + self.function_reorders = {} + + # Specially handled functions. + self.function_handle = {} + + +if __name__ == "__main__": + parser = argparse.ArgumentParser( + formatter_class=argparse.RawDescriptionHelpFormatter, + description="""Convert a TensorFlow Python file to 2.0 + +Simple usage: + tf_convert_v2.py --infile foo.py --outfile bar.py + tf_convert_v2.py --intree ~/code/old --outtree ~/code/new +""") + parser.add_argument( + "--infile", + dest="input_file", + help="If converting a single file, the name of the file " + "to convert") + parser.add_argument( + "--outfile", + dest="output_file", + help="If converting a single file, the output filename.") + parser.add_argument( + "--intree", + dest="input_tree", + help="If converting a whole tree of files, the directory " + "to read from (relative or absolute).") + parser.add_argument( + "--outtree", + dest="output_tree", + help="If converting a whole tree of files, the output " + "directory (relative or absolute).") + parser.add_argument( + "--copyotherfiles", + dest="copy_other_files", + help=("If converting a whole tree of files, whether to " + "copy the other files."), + type=bool, + default=False) + parser.add_argument( + "--reportfile", + dest="report_filename", + help=("The name of the file where the report log is " + "stored." + "(default: %(default)s)"), + default="report.txt") + args = parser.parse_args() + + upgrade = ast_edits.ASTCodeUpgrader(TFAPIChangeSpec()) + report_text = None + report_filename = args.report_filename + files_processed = 0 + if args.input_file: + files_processed, report_text, errors = upgrade.process_file( + args.input_file, args.output_file) + files_processed = 1 + elif args.input_tree: + files_processed, report_text, errors = upgrade.process_tree( + args.input_tree, args.output_tree, args.copy_other_files) + else: + parser.print_help() + if report_text: + open(report_filename, "w").write(report_text) + print("TensorFlow 2.0 Upgrade Script") + print("-----------------------------") + print("Converted %d files\n" % files_processed) + print("Detected %d errors that require attention" % len(errors)) + print("-" * 80) + print("\n".join(errors)) + print("\nMake sure to read the detailed log %r\n" % report_filename) diff --git a/tensorflow/tools/compatibility/tf_upgrade_v2_test.py b/tensorflow/tools/compatibility/tf_upgrade_v2_test.py new file mode 100644 index 0000000000..57ac04de06 --- /dev/null +++ b/tensorflow/tools/compatibility/tf_upgrade_v2_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 tf 2.0 upgrader.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function +import os +import tempfile +import six +from tensorflow.python.framework import test_util +from tensorflow.python.platform import test as test_lib +from tensorflow.tools.compatibility import ast_edits +from tensorflow.tools.compatibility import tf_upgrade_v2 + + +class TestUpgrade(test_util.TensorFlowTestCase): + """Test various APIs that have been changed in 2.0. + + We also test whether a converted file is executable. test_file_v1_10.py + aims to exhaustively test that API changes are convertible and actually + work when run with current TensorFlow. + """ + + def _upgrade(self, old_file_text): + in_file = six.StringIO(old_file_text) + out_file = six.StringIO() + upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade_v2.TFAPIChangeSpec()) + count, report, errors = ( + upgrader.process_opened_file("test.py", in_file, + "test_out.py", out_file)) + return count, report, errors, out_file.getvalue() + + def testParseError(self): + _, report, unused_errors, unused_new_text = self._upgrade( + "import tensorflow as tf\na + \n") + self.assertTrue(report.find("Failed to parse") != -1) + + def testReport(self): + text = "tf.acos(a)\n" + _, report, unused_errors, unused_new_text = self._upgrade(text) + # This is not a complete test, but it is a sanity test that a report + # is generating information. + self.assertTrue(report.find("Renamed function `tf.acos` to `tf.math.acos`")) + + def testRename(self): + text = "tf.acos(a)\n" + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, "tf.math.acos(a)\n") + text = "tf.rsqrt(tf.log(3.8))\n" + _, unused_report, unused_errors, new_text = self._upgrade(text) + self.assertEqual(new_text, "tf.math.rsqrt(tf.math.log(3.8))\n") + + +class TestUpgradeFiles(test_util.TensorFlowTestCase): + + def testInplace(self): + """Check to make sure we don't have a file system race.""" + temp_file = tempfile.NamedTemporaryFile("w", delete=False) + original = "tf.acos(a, b)\n" + upgraded = "tf.math.acos(a, b)\n" + temp_file.write(original) + temp_file.close() + upgrader = ast_edits.ASTCodeUpgrader(tf_upgrade_v2.TFAPIChangeSpec()) + upgrader.process_file(temp_file.name, temp_file.name) + self.assertAllEqual(open(temp_file.name).read(), upgraded) + os.unlink(temp_file.name) + + +if __name__ == "__main__": + test_lib.main() diff --git a/tensorflow/tools/compatibility/update/BUILD b/tensorflow/tools/compatibility/update/BUILD new file mode 100644 index 0000000000..feb37c902e --- /dev/null +++ b/tensorflow/tools/compatibility/update/BUILD @@ -0,0 +1,15 @@ +licenses(["notice"]) # Apache 2.0 + +package(default_visibility = ["//visibility:private"]) + +py_binary( + name = "generate_v2_renames_map", + srcs = ["generate_v2_renames_map.py"], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow:tensorflow_py", + "//tensorflow/python:lib", + "//tensorflow/tools/common:public_api", + "//tensorflow/tools/common:traverse", + ], +) diff --git a/tensorflow/tools/compatibility/update/generate_v2_renames_map.py b/tensorflow/tools/compatibility/update/generate_v2_renames_map.py new file mode 100644 index 0000000000..567eceb0b6 --- /dev/null +++ b/tensorflow/tools/compatibility/update/generate_v2_renames_map.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. +# ============================================================================== +# pylint: disable=line-too-long +"""Script for updating tensorflow/tools/compatibility/renames_v2.py. + +To update renames_v2.py, run: + bazel build tensorflow/tools/compatibility/update:generate_v2_renames_map + bazel-bin/tensorflow/tools/compatibility/update/generate_v2_renames_map +""" +# pylint: enable=line-too-long + +import tensorflow as tf + +from tensorflow.python.lib.io import file_io +from tensorflow.python.util import tf_decorator +from tensorflow.python.util import tf_export +from tensorflow.tools.common import public_api +from tensorflow.tools.common import traverse + + +_OUTPUT_FILE_PATH = 'third_party/tensorflow/tools/compatibility/renames_v2.py' +_FILE_HEADER = """# 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=line-too-long +\"\"\"List of renames to apply when converting from TF 1.0 to TF 2.0. + +THIS FILE IS AUTOGENERATED: To update, please run: + bazel build tensorflow/tools/compatibility/update:generate_v2_renames_map + bazel-bin/tensorflow/tools/compatibility/update/generate_v2_renames_map +This file should be updated whenever endpoints are deprecated. +\"\"\" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +""" + + +def update_renames_v2(output_file_path): + """Writes a Python dictionary mapping deprecated to canonical API names. + + Args: + output_file_path: File path to write output to. Any existing contents + would be replaced. + """ + # Set of rename lines to write to output file in the form: + # 'tf.deprecated_name': 'tf.canonical_name' + rename_line_set = set() + # _tf_api_names attribute name + tensorflow_api_attr = tf_export.API_ATTRS[tf_export.TENSORFLOW_API_NAME].names + + def visit(unused_path, unused_parent, children): + """Visitor that collects rename strings to add to rename_line_set.""" + for child in children: + _, attr = tf_decorator.unwrap(child[1]) + if not hasattr(attr, '__dict__'): + continue + api_names = attr.__dict__.get(tensorflow_api_attr, []) + deprecated_api_names = attr.__dict__.get('_tf_deprecated_api_names', []) + canonical_name = tf_export.get_canonical_name( + api_names, deprecated_api_names) + for name in deprecated_api_names: + rename_line_set.add(' \'tf.%s\': \'tf.%s\'' % (name, canonical_name)) + + visitor = public_api.PublicAPIVisitor(visit) + visitor.do_not_descend_map['tf'].append('contrib') + traverse.traverse(tf, visitor) + + renames_file_text = '%srenames = {\n%s\n}\n' % ( + _FILE_HEADER, ',\n'.join(sorted(rename_line_set))) + file_io.write_string_to_file(output_file_path, renames_file_text) + + +def main(unused_argv): + update_renames_v2(_OUTPUT_FILE_PATH) + + +if __name__ == '__main__': + tf.app.run(main=main) -- GitLab From e9398c43cf470a7388df7d20baf6dd10a3b42edb Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Tue, 24 Jul 2018 14:10:01 -0700 Subject: [PATCH 349/519] Push tensors from client to workers. At times, a server cannot open a reverse connection to the client. This is required when using the _Send/_Recv ops and the client needs to send a tensor to the server (tensors are pulled). Instead, this adds a way to push the tensors directly from the client. Currently, pushing tensors always happens in sync mode. PiperOrigin-RevId: 205888825 --- .../core/common_runtime/eager/context.cc | 19 ++- .../core/common_runtime/eager/context.h | 9 +- .../core/common_runtime/eager/execute.cc | 122 +++++++++++++----- .../distributed_runtime/eager/eager_client.h | 1 + .../eager/eager_service_impl.cc | 36 +++++- .../eager/eager_service_impl.h | 3 + .../eager/eager_service_impl_test.cc | 79 +++++++++++- .../eager/remote_execute_node.h | 19 ++- .../rpc/eager/grpc_eager_client.cc | 1 + .../rpc/eager/grpc_eager_service.cc | 14 +- .../rpc/eager/grpc_eager_service.h | 15 +++ .../rpc/eager/grpc_eager_service_impl.cc | 1 + .../rpc/eager/grpc_eager_service_impl.h | 1 + tensorflow/core/protobuf/eager_service.proto | 23 ++++ tensorflow/python/eager/context.py | 5 + tensorflow/python/framework/ops.py | 5 +- 16 files changed, 300 insertions(+), 53 deletions(-) diff --git a/tensorflow/core/common_runtime/eager/context.cc b/tensorflow/core/common_runtime/eager/context.cc index 1c5e9a2a31..5e0f0a45f8 100644 --- a/tensorflow/core/common_runtime/eager/context.cc +++ b/tensorflow/core/common_runtime/eager/context.cc @@ -17,8 +17,20 @@ limitations under the License. #include "tensorflow/core/common_runtime/process_util.h" #include "tensorflow/core/lib/core/blocking_counter.h" +#include "tensorflow/core/util/env_var.h" namespace tensorflow { +namespace { + +bool ReadBoolFromEnvVar(StringPiece env_var_name, bool default_val) { + bool val; + if (ReadBoolFromEnvVar(env_var_name, default_val, &val).ok()) { + return val; + } + return default_val; +} + +} // namespace EagerContext::EagerContext(const SessionOptions& opts, ContextDevicePlacementPolicy default_policy, @@ -34,7 +46,8 @@ EagerContext::EagerContext(const SessionOptions& opts, local_device_manager_.get(), opts.env, TF_GRAPH_DEF_VERSION, &func_lib_def_, {}, thread_pool_.get())), log_device_placement_(opts.config.log_device_placement()), - async_default_(async) { + async_default_(async), + use_send_tensor_rpc_(false) { InitDeviceMapAndAsync(); if (opts.config.inter_op_parallelism_threads() > 0) { runner_ = [this](std::function closure) { @@ -66,7 +79,9 @@ EagerContext::EagerContext( remote_device_manager_(std::move(remote_device_manager)), server_(std::move(server)), remote_eager_workers_(std::move(remote_eager_workers)), - remote_contexts_(remote_contexts) { + remote_contexts_(remote_contexts), + use_send_tensor_rpc_( + ReadBoolFromEnvVar("TF_EAGER_REMOTE_USE_SEND_TENSOR_RPC", false)) { InitDeviceMapAndAsync(); } #endif diff --git a/tensorflow/core/common_runtime/eager/context.h b/tensorflow/core/common_runtime/eager/context.h index d0563280bf..4a180e074d 100644 --- a/tensorflow/core/common_runtime/eager/context.h +++ b/tensorflow/core/common_runtime/eager/context.h @@ -182,6 +182,11 @@ class EagerContext { #ifndef __ANDROID__ Status GetClientAndContextID(Device* device, eager::EagerClient** client, uint64* context_id); + + // If true, then tensors should be shipped across processes via the + // EagerService.SendTensor RPC. If false, _Send/_Recv ops should be used + // instead (which in-turn use WorkerService.RecvTensor RPCs. + bool UseSendTensorRPC() { return use_send_tensor_rpc_; } #endif private: void InitDeviceMapAndAsync(); @@ -239,16 +244,18 @@ class EagerContext { const std::unique_ptr remote_device_manager_; +#ifndef __ANDROID__ // The server_ is not const since we release it when the context is destroyed. // Therefore the server_ object is not marked as const (even though it should // be). -#ifndef __ANDROID__ std::unique_ptr server_; const std::unique_ptr remote_eager_workers_; const gtl::FlatMap remote_contexts_; gtl::FlatMap> device_to_client_cache_; + + const bool use_send_tensor_rpc_; #endif }; diff --git a/tensorflow/core/common_runtime/eager/execute.cc b/tensorflow/core/common_runtime/eager/execute.cc index 27d0cd611f..7ea78b63d9 100644 --- a/tensorflow/core/common_runtime/eager/execute.cc +++ b/tensorflow/core/common_runtime/eager/execute.cc @@ -585,6 +585,87 @@ Status EagerLocalExecute(EagerOperation* op, return status; } +std::function GetRemoteTensorDestructor( + EagerContext* ctx, eager::EagerClient* eager_client, uint64 context_id, + uint64 op_id, int output_num) { + return [ctx, eager_client, context_id, op_id, output_num]() { + std::unique_ptr request(new eager::EnqueueRequest); + request->set_context_id(context_id); + + auto* handle_to_decref = request->add_queue()->mutable_handle_to_decref(); + handle_to_decref->set_op_id(op_id); + handle_to_decref->set_output_num(output_num); + + if (ctx->Async()) { + tensorflow::uint64 id = ctx->NextId(); + auto* node = + new eager::RemoteExecuteNode(id, std::move(request), eager_client); + ctx->ExecutorAdd(node); + } else { + eager::EnqueueRequest* actual_request = request.release(); + eager::EnqueueResponse* response = new eager::EnqueueResponse; + eager_client->EnqueueAsync( + actual_request, response, + [actual_request, response](const tensorflow::Status& s) { + delete actual_request; + delete response; + }); + } + + return tensorflow::Status::OK(); + }; +} + +// When !ctx->UseSendTensorRPC(), then tensors are shipped between remote +// devices by the receiver invoking the WorkerService.RecvTensor RPC *on the +// sender* (Rendezvous::RecvAsync() invoked by the _Recv kernel). +// +// However, in some configurations the node that has the tensor to be copied +// isn't running a server (WorkerService RPC interface). For such cases, +// this function enables sending tensors using the EagerService.SendTensor RPC +// *on the receiver*. +Status EagerRemoteSendTensor(EagerContext* ctx, TensorHandle* h, + Device* recv_device, TensorHandle** result) { + eager::EagerClient* eager_client; + uint64 context_id; + TF_RETURN_IF_ERROR( + ctx->GetClientAndContextID(recv_device, &eager_client, &context_id)); + + eager::SendTensorRequest request; + eager::SendTensorResponse response; + + request.set_context_id(context_id); + request.set_op_id(ctx->NextId()); + request.set_device_name(recv_device->name()); + + const Tensor* tensor; + TF_RETURN_IF_ERROR(h->Tensor(&tensor)); + tensor->AsProtoTensorContent(request.add_tensors()); + + const tensorflow::uint64 id = request.op_id(); + + // TODO(nareshmodi): support making this call async. + Notification n; + Status status; + eager_client->SendTensorAsync(&request, &response, + [&n, &status](const Status& s) { + status = s; + n.Notify(); + }); + n.WaitForNotification(); + if (!status.ok()) return status; + + std::function destructor = + GetRemoteTensorDestructor(ctx, eager_client, context_id, id, 0); + + *result = new TensorHandle(id, /*output_num=*/0, /*remote_shape_node_id=*/0, + tensor->dtype(), std::move(destructor), + recv_device, recv_device, ctx); + (*result)->SetRemoteShape(MakeUnique(tensor->shape())); + + return Status::OK(); +} + Status EagerRemoteExecute(EagerOperation* op, TensorHandle** retvals, int* num_retvals) { #ifdef __ANDROID__ @@ -598,10 +679,12 @@ Status EagerRemoteExecute(EagerOperation* op, TensorHandle** retvals, TF_RETURN_IF_ERROR( ctx->GetClientAndContextID(op->Device(), &eager_client, &context_id)); - eager::EnqueueRequest request; + std::unique_ptr request(new eager::EnqueueRequest); eager::EnqueueResponse response; - auto* remote_op = request.add_queue()->mutable_operation(); + request->set_context_id(context_id); + + auto* remote_op = request->add_queue()->mutable_operation(); for (int i = 0; i < op->Inputs().size(); i++) { tensorflow::Device* input_device; @@ -631,8 +714,6 @@ Status EagerRemoteExecute(EagerOperation* op, TensorHandle** retvals, op->Attrs().FillAttrValueMap(remote_op->mutable_attrs()); remote_op->set_device(op->Device()->name()); - request.set_context_id(context_id); - DataTypeVector output_dtypes; TF_RETURN_IF_ERROR(GetOutputDTypes(op, &output_dtypes)); @@ -654,32 +735,11 @@ Status EagerRemoteExecute(EagerOperation* op, TensorHandle** retvals, for (int i = 0; i < *num_retvals; i++) { // TODO(nareshmodi): Change the callback to instead add the decref to a list // of pending decrefs that we can send as a batch with the next execute. - std::function callback = [ctx, eager_client, context_id, id, i]() { - eager::EnqueueRequest request; - request.set_context_id(context_id); - - auto* handle_to_decref = request.add_queue()->mutable_handle_to_decref(); - handle_to_decref->set_op_id(id); - handle_to_decref->set_output_num(i); - - if (ctx->Async()) { - tensorflow::uint64 id = ctx->NextId(); - auto* node = new eager::RemoteExecuteNode(id, request, eager_client); - ctx->ExecutorAdd(node); - } else { - Notification n; - eager::EnqueueResponse response; - eager_client->EnqueueAsync( - &request, &response, - [&n](const tensorflow::Status& s) { n.Notify(); }); - n.WaitForNotification(); - } - - return tensorflow::Status::OK(); - }; + std::function destructor = + GetRemoteTensorDestructor(ctx, eager_client, context_id, id, i); retvals[i] = new TensorHandle(remote_op->id(), i, remote_node_id, - output_dtypes[i], std::move(callback), + output_dtypes[i], std::move(destructor), op_device, op_device, op->EagerContext()); } @@ -693,7 +753,7 @@ Status EagerRemoteExecute(EagerOperation* op, TensorHandle** retvals, } // Unable to capture via std::move, so bind instead. auto* node = new eager::RemoteExecuteNode( - remote_node_id, request, eager_client, op->Inputs(), + remote_node_id, std::move(request), eager_client, op->Inputs(), std::bind( [](const gtl::InlinedVector& retvals, const Status& status, const eager::EnqueueResponse& response) { @@ -710,7 +770,7 @@ Status EagerRemoteExecute(EagerOperation* op, TensorHandle** retvals, } else { Notification n; Status status; - eager_client->EnqueueAsync(&request, &response, + eager_client->EnqueueAsync(request.get(), &response, [&n, &status](const Status& s) { status = s; n.Notify(); @@ -939,6 +999,8 @@ Status EagerCopyToDevice(TensorHandle* h, EagerContext* ctx, if (sender_is_local && recver_is_local) { return LocalEagerCopyToDevice(h, ctx, recv_device, result); + } else if (ctx->UseSendTensorRPC() && sender_is_local && !recver_is_local) { + return EagerRemoteSendTensor(ctx, h, recv_device, result); } else { string wire_id = GetUniqueWireID(); diff --git a/tensorflow/core/distributed_runtime/eager/eager_client.h b/tensorflow/core/distributed_runtime/eager/eager_client.h index 9ba8c8d80c..707f3234b9 100644 --- a/tensorflow/core/distributed_runtime/eager/eager_client.h +++ b/tensorflow/core/distributed_runtime/eager/eager_client.h @@ -39,6 +39,7 @@ class EagerClient { CLIENT_METHOD(KeepAlive); CLIENT_METHOD(CloseContext); CLIENT_METHOD(RegisterFunction); + CLIENT_METHOD(SendTensor); #undef CLIENT_METHOD }; diff --git a/tensorflow/core/distributed_runtime/eager/eager_service_impl.cc b/tensorflow/core/distributed_runtime/eager/eager_service_impl.cc index 466e779fab..916c8720f0 100644 --- a/tensorflow/core/distributed_runtime/eager/eager_service_impl.cc +++ b/tensorflow/core/distributed_runtime/eager/eager_service_impl.cc @@ -81,10 +81,11 @@ Status GetNumRetvals(tensorflow::EagerContext* context, const string& op_name, Status EagerServiceImpl::CreateContext(const CreateContextRequest* request, CreateContextResponse* response) { - //make sure env_ , env_->rendezvous_mgr available + // make sure env_ , env_->rendezvous_mgr available if (env_ == nullptr || env_->rendezvous_mgr == nullptr) { - return tensorflow::errors::Internal("invalid eager env_ or env_->rendezvous_mgr."); - } + return tensorflow::errors::Internal( + "invalid eager env_ or env_->rendezvous_mgr."); + } std::vector devices; TF_RETURN_IF_ERROR(tensorflow::DeviceFactory::AddDevices( @@ -266,6 +267,35 @@ Status EagerServiceImpl::RegisterFunction( return context->Context()->AddFunctionDef(request->function_def()); } +Status EagerServiceImpl::SendTensor(const SendTensorRequest* request, + SendTensorResponse* response) { + ServerContext* context = nullptr; + TF_RETURN_IF_ERROR(GetServerContext(request->context_id(), &context)); + core::ScopedUnref context_unref(context); + + tensorflow::gtl::InlinedVector tensors; + for (const auto& tensor_proto : request->tensors()) { + Tensor tensor; + if (!tensor.FromProto(tensor_proto)) { + return errors::InvalidArgument("Unable to parse tensor proto"); + } + + TensorHandle* tensor_handle = + new TensorHandle(tensor, nullptr, nullptr, nullptr); + + TensorHandle* copied_handle = nullptr; + TF_RETURN_IF_ERROR(EagerCopyToDevice(tensor_handle, context->Context(), + request->device_name().c_str(), + &copied_handle)); + tensors.push_back(copied_handle); + tensor_handle->Unref(); + } + + context->AddOperationOutputs(tensors, request->op_id()); + + return Status::OK(); +} + tensorflow::Status EagerServiceImpl::GetServerContext( uint64 context_id, ServerContext** server_context) { mutex_lock l(contexts_mu_); diff --git a/tensorflow/core/distributed_runtime/eager/eager_service_impl.h b/tensorflow/core/distributed_runtime/eager/eager_service_impl.h index b0e4aa84b9..718b4e2457 100644 --- a/tensorflow/core/distributed_runtime/eager/eager_service_impl.h +++ b/tensorflow/core/distributed_runtime/eager/eager_service_impl.h @@ -62,6 +62,9 @@ class EagerServiceImpl { Status RegisterFunction(const RegisterFunctionRequest* request, RegisterFunctionResponse* response); + Status SendTensor(const SendTensorRequest* request, + SendTensorResponse* response); + protected: // This is the server-side execution context. All state regarding execution of // a client's ops is held in this server-side context (all generated tensors, diff --git a/tensorflow/core/distributed_runtime/eager/eager_service_impl_test.cc b/tensorflow/core/distributed_runtime/eager/eager_service_impl_test.cc index b98386ba86..d1f2a6da8f 100644 --- a/tensorflow/core/distributed_runtime/eager/eager_service_impl_test.cc +++ b/tensorflow/core/distributed_runtime/eager/eager_service_impl_test.cc @@ -84,7 +84,7 @@ class EagerServiceImplTest : public ::testing::Test { std::unique_ptr device_mgr_; }; -void SetTensorProto(AttrValue* val) { +void SetTensorProto(TensorProto* tensor_proto) { int64_t dims[] = {2, 2}; float data[] = {1.0f, 2.0f, 3.0f, 4.0f}; TF_Tensor* t = TF_AllocateTensor( @@ -92,7 +92,7 @@ void SetTensorProto(AttrValue* val) { memcpy(TF_TensorData(t), &data[0], TF_TensorByteSize(t)); tensorflow::Tensor tensor; TF_ASSERT_OK(tensorflow::TF_TensorToTensor(t, &tensor)); - tensor.AsProtoTensorContent(val->mutable_tensor()); + tensor.AsProtoTensorContent(tensor_proto); TF_DeleteTensor(t); } @@ -175,7 +175,7 @@ TEST_F(EagerServiceImplTest, BasicTest) { val.set_type(tensorflow::DataType::DT_FLOAT); const_attrs.insert({"dtype", val}); val.Clear(); - SetTensorProto(&val); + SetTensorProto(val.mutable_tensor()); const_attrs.insert({"value", val}); AddOperationToEnqueueRequest(1, "Const", {}, const_attrs, @@ -260,7 +260,7 @@ TEST_F(EagerServiceImplTest, BasicFunctionTest) { const_attrs.insert({"dtype", val}); val.Clear(); - SetTensorProto(&val); + SetTensorProto(val.mutable_tensor()); const_attrs.insert({"value", val}); AddOperationToEnqueueRequest(1, "Const", {}, const_attrs, @@ -294,6 +294,77 @@ TEST_F(EagerServiceImplTest, BasicFunctionTest) { &close_context_response)); } +// Test creates a context and attempts to send a tensor (using the RPC), and +// then use the tensor. +TEST_F(EagerServiceImplTest, SendTensorTest) { + TestEagerServiceImpl eager_service_impl(&worker_env_); + + CreateContextRequest request; + request.mutable_server_def()->set_job_name("localhost"); + request.mutable_server_def()->set_task_index(0); + request.set_rendezvous_id(random::New64()); + CreateContextResponse response; + + TF_ASSERT_OK(eager_service_impl.CreateContext(&request, &response)); + + uint64 context_id = response.context_id(); + + SendTensorRequest send_tensor_request; + send_tensor_request.set_context_id(context_id); + send_tensor_request.set_op_id(1); + SetTensorProto(send_tensor_request.add_tensors()); + SendTensorResponse send_tensor_response; + + TF_ASSERT_OK(eager_service_impl.SendTensor(&send_tensor_request, + &send_tensor_response)); + + EnqueueRequest remote_enqueue_request; + remote_enqueue_request.set_context_id(context_id); + EnqueueResponse remote_enqueue_response; + + std::unordered_map attrs; + AttrValue val; + val.Clear(); + val.set_type(tensorflow::DataType::DT_FLOAT); + attrs.insert({"T", val}); + val.Clear(); + val.set_b(false); + attrs.insert({"transpose_a", val}); + attrs.insert({"transpose_b", val}); + + AddOperationToEnqueueRequest(2, "MatMul", {{1, 0}, {1, 0}}, attrs, + "/job:localhost/replica:0/task:0/device:CPU:0", + &remote_enqueue_request); + + TF_ASSERT_OK(eager_service_impl.Enqueue(&remote_enqueue_request, + &remote_enqueue_response)); + + const tensorflow::Tensor* t = nullptr; + tensorflow::TensorHandle* tensor_handle; + TF_ASSERT_OK(eager_service_impl.GetTensorHandle( + response.context_id(), RemoteTensorHandleInternal(2, 0), &tensor_handle)); + TF_ASSERT_OK(tensor_handle->Tensor(&t)); + + Device* device = nullptr; + TF_ASSERT_OK(tensor_handle->Device(&device)); + EXPECT_NE(device, nullptr); + EXPECT_EQ(device->name(), "/job:localhost/replica:0/task:0/device:CPU:0"); + + auto actual = t->flat(); + EXPECT_EQ(4, actual.size()); + + EXPECT_EQ(7, actual(0)); + EXPECT_EQ(10, actual(1)); + EXPECT_EQ(15, actual(2)); + EXPECT_EQ(22, actual(3)); + + CloseContextRequest close_context_request; + close_context_request.set_context_id(context_id); + CloseContextResponse close_context_response; + TF_ASSERT_OK(eager_service_impl.CloseContext(&close_context_request, + &close_context_response)); +} + } // namespace } // namespace eager } // namespace tensorflow diff --git a/tensorflow/core/distributed_runtime/eager/remote_execute_node.h b/tensorflow/core/distributed_runtime/eager/remote_execute_node.h index 28b68c3b88..0e3a68c4d8 100644 --- a/tensorflow/core/distributed_runtime/eager/remote_execute_node.h +++ b/tensorflow/core/distributed_runtime/eager/remote_execute_node.h @@ -29,8 +29,8 @@ namespace eager { class RemoteExecuteNode : public tensorflow::EagerNode { public: RemoteExecuteNode( - tensorflow::uint64 id, const tensorflow::eager::EnqueueRequest& request, - tensorflow::eager::EagerClient* eager_client, + tensorflow::uint64 id, std::unique_ptr request, + EagerClient* eager_client, const gtl::InlinedVector& inputs, std::function done_callback) @@ -45,8 +45,8 @@ class RemoteExecuteNode : public tensorflow::EagerNode { } RemoteExecuteNode(tensorflow::uint64 id, - const tensorflow::eager::EnqueueRequest& request, - tensorflow::eager::EagerClient* eager_client) + std::unique_ptr request, + EagerClient* eager_client) : tensorflow::EagerNode(id), request_(std::move(request)), eager_client_(eager_client) {} @@ -58,10 +58,10 @@ class RemoteExecuteNode : public tensorflow::EagerNode { } tensorflow::Status Run() override { - tensorflow::eager::EnqueueResponse response; - tensorflow::Status status; + EnqueueResponse response; + Status status; Notification n; - eager_client_->EnqueueAsync(&request_, &response, + eager_client_->EnqueueAsync(request_.get(), &response, [&n, &status](const tensorflow::Status& s) { status.Update(s); n.Notify(); @@ -76,9 +76,8 @@ class RemoteExecuteNode : public tensorflow::EagerNode { } private: - EnqueueRequest request_; - tensorflow::eager::EagerClient* - eager_client_; // Not owned, and must outlive the RemoteExecuteNode. + std::unique_ptr request_; + EagerClient* eager_client_; // Not owned, and must outlive this node. // This is required to ensure that the tensor handles stay alive across the // execution. diff --git a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_client.cc b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_client.cc index b23466037f..181422118c 100644 --- a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_client.cc +++ b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_client.cc @@ -49,6 +49,7 @@ class GrpcEagerClient : public EagerClient { CLIENT_METHOD(KeepAlive); CLIENT_METHOD(CloseContext); CLIENT_METHOD(RegisterFunction); + CLIENT_METHOD(SendTensor); #undef CLIENT_METHOD diff --git a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service.cc b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service.cc index 39ab6856c5..ab3aa3fd1d 100644 --- a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service.cc +++ b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service.cc @@ -36,6 +36,7 @@ static const char* grpcEagerService_method_names[] = { "/tensorflow.eager.EagerService/KeepAlive", "/tensorflow.eager.EagerService/CloseContext", "/tensorflow.eager.EagerService/RegisterFunction", + "/tensorflow.eager.EagerService/SendTensor", }; std::unique_ptr EagerService::NewStub( @@ -62,7 +63,9 @@ EagerService::Stub::Stub( ::grpc::internal::RpcMethod::NORMAL_RPC, channel), rpcmethod_RegisterFunction_(grpcEagerService_method_names[5], ::grpc::internal::RpcMethod::NORMAL_RPC, - channel) {} + channel), + rpcmethod_SendTensor_(grpcEagerService_method_names[6], + ::grpc::internal::RpcMethod::NORMAL_RPC, channel) {} ::grpc::Status EagerService::Stub::CreateContext( ::grpc::ClientContext* context, const CreateContextRequest& request, @@ -106,8 +109,15 @@ EagerService::Stub::Stub( channel_.get(), rpcmethod_RegisterFunction_, context, request, response); } +::grpc::Status EagerService::Stub::SendTensor(::grpc::ClientContext* context, + const SendTensorRequest& request, + SendTensorResponse* response) { + return ::grpc::internal::BlockingUnaryCall( + channel_.get(), rpcmethod_SendTensor_, context, request, response); +} + EagerService::AsyncService::AsyncService() { - for (int i = 0; i < 6; ++i) { + for (int i = 0; i < 7; ++i) { AddMethod(new ::grpc::internal::RpcServiceMethod( grpcEagerService_method_names[i], ::grpc::internal::RpcMethod::NORMAL_RPC, nullptr)); diff --git a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service.h b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service.h index 66458186ad..521e0ac4fa 100644 --- a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service.h +++ b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service.h @@ -69,6 +69,9 @@ class EagerService final { virtual ::grpc::Status RegisterFunction( ::grpc::ClientContext* context, const RegisterFunctionRequest& request, RegisterFunctionResponse* response) = 0; + virtual ::grpc::Status SendTensor(::grpc::ClientContext* context, + const SendTensorRequest& request, + SendTensorResponse* response) = 0; }; class Stub final : public StubInterface { public: @@ -91,6 +94,9 @@ class EagerService final { ::grpc::Status RegisterFunction( ::grpc::ClientContext* context, const RegisterFunctionRequest& request, RegisterFunctionResponse* response) override; + ::grpc::Status SendTensor(::grpc::ClientContext* context, + const SendTensorRequest& request, + SendTensorResponse* response) override; private: std::shared_ptr< ::grpc::ChannelInterface> channel_; @@ -100,6 +106,7 @@ class EagerService final { const ::grpc::internal::RpcMethod rpcmethod_KeepAlive_; const ::grpc::internal::RpcMethod rpcmethod_CloseContext_; const ::grpc::internal::RpcMethod rpcmethod_RegisterFunction_; + const ::grpc::internal::RpcMethod rpcmethod_SendTensor_; }; static std::unique_ptr NewStub( const std::shared_ptr< ::grpc::ChannelInterface>& channel, @@ -157,6 +164,14 @@ class EagerService final { ::grpc::Service::RequestAsyncUnary(5, context, request, response, new_call_cq, notification_cq, tag); } + void RequestSendTensor( + ::grpc::ServerContext* context, SendTensorRequest* request, + ::grpc::ServerAsyncResponseWriter* response, + ::grpc::CompletionQueue* new_call_cq, + ::grpc::ServerCompletionQueue* notification_cq, void* tag) { + ::grpc::Service::RequestAsyncUnary(6, context, request, response, + new_call_cq, notification_cq, tag); + } }; }; diff --git a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc index 44e880de04..f511674e1f 100644 --- a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc +++ b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.cc @@ -48,6 +48,7 @@ void GrpcEagerServiceImpl::HandleRPCsLoop() { ENQUEUE_REQUEST(KeepAlive); ENQUEUE_REQUEST(CloseContext); ENQUEUE_REQUEST(RegisterFunction); + ENQUEUE_REQUEST(SendTensor); #undef ENQUEUE_REQUEST void* tag; // Matches the operation started against this cq_. diff --git a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h index 502f3ef529..537e9043bd 100644 --- a/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h +++ b/tensorflow/core/distributed_runtime/rpc/eager/grpc_eager_service_impl.h @@ -62,6 +62,7 @@ class GrpcEagerServiceImpl : public AsyncServiceInterface { HANDLER(KeepAlive); HANDLER(CloseContext); HANDLER(RegisterFunction); + HANDLER(SendTensor); #undef HANDLER const WorkerEnv* const env_; // Not owned. diff --git a/tensorflow/core/protobuf/eager_service.proto b/tensorflow/core/protobuf/eager_service.proto index 5b05a1b3ee..63ba4eb173 100644 --- a/tensorflow/core/protobuf/eager_service.proto +++ b/tensorflow/core/protobuf/eager_service.proto @@ -8,6 +8,7 @@ import "tensorflow/core/framework/function.proto"; import "tensorflow/core/framework/versions.proto"; import "tensorflow/core/protobuf/tensorflow_server.proto"; import "tensorflow/core/framework/tensor_shape.proto"; +import "tensorflow/core/framework/tensor.proto"; message RemoteTensorHandle { // The ID of the operation that produced this tensor. @@ -128,6 +129,24 @@ message RegisterFunctionRequest { message RegisterFunctionResponse { } +message SendTensorRequest { + fixed64 context_id = 1; + + // All remote tensors are identified by . To mimic this + // situation when directly sending tensors, we include an "artificial" op ID + // (which would have corresponded to the _Recv op when not using SendTensor). + int64 op_id = 2; + // The index within the repeated field is the output number that will help + // uniquely identify (along with the above op_id) the particular tensor. + repeated TensorProto tensors = 3; + + // The device on which the tensors should be resident. + string device_name = 4; +} + +message SendTensorResponse { +} + //////////////////////////////////////////////////////////////////////////////// // // Eager Service defines a TensorFlow service that executes operations eagerly @@ -174,4 +193,8 @@ service EagerService { // Takes a FunctionDef and makes it enqueable on the remote worker. rpc RegisterFunction(RegisterFunctionRequest) returns (RegisterFunctionResponse); + + // An RPC to push tensors to the server. At times, certain environments don't + // allow the server to connect back to the client. + rpc SendTensor(SendTensorRequest) returns (SendTensorResponse); } diff --git a/tensorflow/python/eager/context.py b/tensorflow/python/eager/context.py index 85b9491903..495a674526 100644 --- a/tensorflow/python/eager/context.py +++ b/tensorflow/python/eager/context.py @@ -177,6 +177,11 @@ class Context(object): - tf.contrib.eager.SYNC: executes each operation synchronously. - tf.contrib.eager.ASYNC: executes each operation asynchronously. These operations may return "non-ready" handles. + server_def: (Optional.) A tensorflow::ServerDef proto. + Enables execution on remote devices. GrpcServers need to be started by + creating an identical server_def to this, and setting the appropriate + task_indexes, so that the servers can communicate. It will then be + possible to execute operations on remote devices. Raises: ValueError: If execution_mode is not valid. diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index b813cd6c06..6a5c44e4d9 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -5237,7 +5237,10 @@ def enable_eager_execution(config=None, to this function. """ return enable_eager_execution_internal( - config, device_policy, execution_mode, None) + config=config, + device_policy=device_policy, + execution_mode=execution_mode, + server_def=None) def enable_eager_execution_internal(config=None, -- GitLab From 2ddf7fab12e38099a4a527237bc8b893ed4c21d0 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Tue, 24 Jul 2018 14:22:08 -0700 Subject: [PATCH 350/519] Assign buffers for constants (Still disabled on all backends by default) PiperOrigin-RevId: 205890903 --- .../compiler/xla/service/buffer_assignment.cc | 46 +++++-- .../compiler/xla/service/buffer_assignment.h | 28 +++- .../xla/service/buffer_assignment_test.cc | 128 +++++++++++++++--- tensorflow/compiler/xla/service/hlo.proto | 1 + 4 files changed, 171 insertions(+), 32 deletions(-) diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index bcca9f46d3..b4c7cf0dd8 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -279,6 +279,7 @@ BufferAllocationProto BufferAllocation::ToProto() const { } proto.set_parameter_number(parameter_number_); } + proto.set_is_constant(is_constant_); proto.set_maybe_live_out(maybe_live_out_); for (const auto& buffer_offset_size : assigned_buffers_) { BufferAllocationProto::Assigned* proto_assigned = proto.add_assigned(); @@ -304,6 +305,9 @@ string BufferAllocation::ToString() const { StrAppend(&output, ", parameter ", parameter_number(), " at ShapeIndex ", param_shape_index().ToString()); } + if (is_constant()) { + StrAppend(&output, ", constant"); + } if (is_thread_local()) { StrAppend(&output, ", thread-local"); } @@ -606,6 +610,10 @@ Status BufferAssignment::ComputeSummaryStats() { stats_.parameter_allocation_count++; stats_.parameter_allocation_bytes += allocation.size(); } + if (allocation.is_constant()) { + stats_.constant_allocation_count++; + stats_.constant_allocation_bytes += allocation.size(); + } if (allocation.maybe_live_out()) { stats_.maybe_live_out_allocation_count++; stats_.maybe_live_out_allocation_bytes += allocation.size(); @@ -642,6 +650,8 @@ string BufferAssignment::Stats::ToString() const { Appendf(&s, "BufferAssignment stats:\n"); Appendf(&s, " parameter allocation: %10s\n", HumanReadableNumBytes(parameter_allocation_bytes).c_str()); + Appendf(&s, " constant allocation: %10s\n", + HumanReadableNumBytes(constant_allocation_bytes).c_str()); Appendf(&s, " maybe_live_out allocation: %10s\n", HumanReadableNumBytes(maybe_live_out_allocation_bytes).c_str()); Appendf(&s, " preallocated temp allocation: %10s\n", @@ -719,8 +729,10 @@ StatusOr> BufferAssigner::Run( const HloModule* module, std::unique_ptr hlo_ordering, LogicalBuffer::SizeFunction buffer_size, LogicalBuffer::AlignmentFunction color_alignment, - bool allow_input_output_aliasing, BufferLiveness::Colorer colorer) { - BufferAssigner assigner(allow_input_output_aliasing, std::move(colorer)); + bool allow_input_output_aliasing, bool allocate_buffers_for_constants, + BufferLiveness::Colorer colorer) { + BufferAssigner assigner(allow_input_output_aliasing, + allocate_buffers_for_constants, std::move(colorer)); return assigner.CreateAssignment(module, std::move(hlo_ordering), std::move(buffer_size), std::move(color_alignment)); @@ -902,15 +914,19 @@ Status BufferAssigner::AssignBuffersForComputation( TF_RET_CHECK(!assignment->HasAllocation(*buffer)); const HloInstruction* instruction = buffer->instruction(); + const int64 buffer_size = assignment->buffer_size_(*buffer); + if (instruction->opcode() == HloOpcode::kConstant) { - // No BufferAllocations for constants. - // TODO(b/32248867): For consistency, constants should get allocations. - VLOG(3) << "Skipping constant: " << *buffer; + if (allocate_buffers_for_constants_) { + BufferAllocation* allocation = + assignment->NewAllocation(*buffer, buffer_size); + allocation->set_constant(true); + VLOG(3) << "New allocation #" << allocation->index() << " for constant " + << *buffer; + } continue; } - const int64 buffer_size = assignment->buffer_size_(*buffer); - const bool is_entry_parameter = instruction->opcode() == HloOpcode::kParameter && computation == computation->parent()->entry_computation(); @@ -1078,6 +1094,7 @@ Status BufferAssigner::AssignBuffersWithSequentialOrdering( VLOG(2) << "Simulating heap for color " << color; int64 alignment = assignment->color_alignment_(color); HeapSimulator::Options options; + options.alloc_constants = allocate_buffers_for_constants_; BufferValueFlatSet buffer_value_set = ToBufferValueFlatSet(single_colored_set.second); options.buffers_to_assign = &buffer_value_set; @@ -1559,6 +1576,7 @@ void BufferAssigner::AssignColocatedBufferSets( // param in 'colocated_buffer_set'. int64 entry_parameter_number = -1; const ShapeIndex* entry_parameter_shape_idx = nullptr; + bool is_constant = false; for (const LogicalBuffer* buffer : colocated_buffer_set) { const HloInstruction* instruction = buffer->instruction(); const HloComputation* computation = instruction->parent(); @@ -1566,10 +1584,14 @@ void BufferAssigner::AssignColocatedBufferSets( computation == computation->parent()->entry_computation()) { entry_parameter_number = instruction->parameter_number(); entry_parameter_shape_idx = &buffer->index(); - break; + } else if (instruction->opcode() == HloOpcode::kConstant) { + is_constant = true; } } + CHECK(!is_constant || entry_parameter_number == -1) + << "Copy insertion should have inserted copies to prevent this."; + for (const LogicalBuffer* buffer : colocated_buffer_set) { const int64 buffer_size = assignment->buffer_size_(*buffer); if (allocation == nullptr) { @@ -1579,14 +1601,12 @@ void BufferAssigner::AssignColocatedBufferSets( // computations (in some cases). allocation = assignment->NewAllocation(*buffer, buffer_size); if (entry_parameter_number >= 0) { - // This colocated buffer set contains an entry parameter and other - // logical buffers which use the parameter as read-only in a while - // body computation (which updates in place). - // Set 'entry_computation_parameter' to indicate that it contains - // an entry parameter, and to prevent reuse in MaybeAssignBuffer. allocation->set_entry_computation_parameter( entry_parameter_number, *entry_parameter_shape_idx); } + if (is_constant) { + allocation->set_constant(true); + } colocated_allocations->insert(allocation->index()); } else { CHECK_EQ(buffer_size, allocation->size()) diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h index 8844b6e3ba..4fcf1fc73d 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.h +++ b/tensorflow/compiler/xla/service/buffer_assignment.h @@ -85,7 +85,9 @@ class BufferAllocation { // Whether this allocation is readonly i.e. backed by memory we cannot write // to. - bool is_readonly() const { return is_entry_computation_parameter(); } + bool is_readonly() const { + return is_entry_computation_parameter() || is_constant(); + } bool is_tuple() const { return is_tuple_; } void set_is_tuple(bool is_tuple) { is_tuple_ = is_tuple; } @@ -96,6 +98,13 @@ class BufferAllocation { bool is_entry_computation_parameter() const { return is_entry_computation_parameter_; } + + // Whether this allocation holds a constant. On the CPU and GPU backends + // constant allocations are not allocated dynamically, instead we resolve + // references to these buffer allocations to a global in the readonly section + // of the binary. + bool is_constant() const { return is_constant_; } + // If this allocation holds a Buffer from a parameter of the entry // computation, this methods returns the parameter number. CHECKs otherwise. int64 parameter_number() const { @@ -201,7 +210,9 @@ class BufferAllocation { // of the computation. !maybe_live_out() && // Thread-local buffers are allocated using `alloca`s. - !is_thread_local(); + !is_thread_local() && + // Constant buffers are allocated as global values. + !is_constant(); } // Add a heap trace which was used to assign slices to logical buffers in this @@ -257,6 +268,8 @@ class BufferAllocation { parameter_number_ = parameter_number; param_shape_index_ = std::move(param_shape_index); } + + void set_constant(bool is_constant) { is_constant_ = is_constant; } void set_maybe_live_out(bool value) { maybe_live_out_ = value; } void set_index(Index index) { index_ = index; } void set_size(int64 size) { size_ = size; } @@ -295,6 +308,9 @@ class BufferAllocation { // might not actually escape. bool maybe_live_out_ = false; + // See comment on the is_constant() accessor. + bool is_constant_ = false; + // Mapping from the set of buffers assigned to this allocation to their // logical offsets and sizes. tensorflow::gtl::FlatMap assigned_buffers_; @@ -410,6 +426,8 @@ class BufferAssignment { struct Stats { int64 parameter_allocation_count = 0; int64 parameter_allocation_bytes = 0; + int64 constant_allocation_count = 0; + int64 constant_allocation_bytes = 0; int64 maybe_live_out_allocation_count = 0; int64 maybe_live_out_allocation_bytes = 0; int64 preallocated_temp_allocation_count = 0; @@ -502,12 +520,15 @@ class BufferAssigner { LogicalBuffer::SizeFunction buffer_size, LogicalBuffer::AlignmentFunction color_alignment, bool allow_input_output_aliasing = false, + bool allocate_buffers_for_constants = false, BufferLiveness::Colorer colorer = BufferLiveness::DefaultColorer()); private: BufferAssigner(bool allow_input_output_aliasing, + bool allocate_buffers_for_constants, BufferLiveness::Colorer colorer) : allow_input_output_aliasing_(allow_input_output_aliasing), + allocate_buffers_for_constants_(allocate_buffers_for_constants), colorer_(colorer) {} virtual ~BufferAssigner() = default; @@ -604,6 +625,9 @@ class BufferAssigner { // buffers can be shared if their sizes match. bool allow_input_output_aliasing_; + // If true, allocate buffers for constant instructions. + bool allocate_buffers_for_constants_; + // Functor used to assign colors to newly allocated logical buffers. BufferLiveness::Colorer colorer_; diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc index bfd20921e2..dea855d39a 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc @@ -89,7 +89,20 @@ class BufferAssignmentTest : public HloTestBase { return BufferAssigner::Run( module, xla::MakeUnique(module), backend().compiler()->BufferSizeBytesFunction(), - [alignment](LogicalBuffer::Color) { return alignment; }) + [alignment](LogicalBuffer::Color) { return alignment; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true) + .ConsumeValueOrDie(); + } + + std::unique_ptr RunBufferAssignmentNoBuffersForConstants( + HloModule* module, int64 alignment = 1) { + return BufferAssigner::Run( + module, xla::MakeUnique(module), + backend().compiler()->BufferSizeBytesFunction(), + [alignment](LogicalBuffer::Color) { return alignment; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/false) .ConsumeValueOrDie(); } @@ -98,8 +111,9 @@ class BufferAssignmentTest : public HloTestBase { return BufferAssigner::Run( module, xla::MakeUnique(module), backend().compiler()->BufferSizeBytesFunction(), - [alignment](LogicalBuffer::Color) { return alignment; }, false, - std::move(colorer)) + [alignment](LogicalBuffer::Color) { return alignment; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true, std::move(colorer)) .ConsumeValueOrDie(); } @@ -115,7 +129,9 @@ class BufferAssignmentTest : public HloTestBase { module, xla::MakeUnique(module, module_sequence), backend().compiler()->BufferSizeBytesFunction(), - [alignment](LogicalBuffer::Color) { return alignment; }) + [alignment](LogicalBuffer::Color) { return alignment; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true) .ConsumeValueOrDie(); } @@ -294,9 +310,15 @@ TEST_F(BufferAssignmentTest, ScalarConstant) { auto module = CreateNewModule(); module->AddEntryComputation(builder.Build()); - auto buffers = RunBufferAssignment(module.get()); - // Check that the constant does not have a buffer assigned. - EXPECT_FALSE(buffers->HasTopLevelAllocation(const0)); + { + auto buffers = RunBufferAssignment(module.get()); + EXPECT_TRUE(buffers->HasTopLevelAllocation(const0)); + } + + { + auto buffers = RunBufferAssignmentNoBuffersForConstants(module.get()); + EXPECT_FALSE(buffers->HasTopLevelAllocation(const0)); + } } TEST_F(BufferAssignmentTest, BufferForConst) { @@ -312,12 +334,18 @@ TEST_F(BufferAssignmentTest, BufferForConst) { auto module = CreateNewModule(); module->AddEntryComputation(builder.Build()); - auto buffers = RunBufferAssignment(module.get()); - // The two constant nodes have no buffers assigned. - EXPECT_FALSE(buffers->HasTopLevelAllocation(const0)); - EXPECT_FALSE(buffers->HasTopLevelAllocation(const1)); - // The add node has an output buffer. - GetAssignedOutputAllocation(*buffers, add); + { + auto buffers = RunBufferAssignment(module.get()); + EXPECT_TRUE(buffers->HasTopLevelAllocation(const0)); + EXPECT_TRUE(buffers->HasTopLevelAllocation(const1)); + GetAssignedOutputAllocation(*buffers, add); + } + { + auto buffers = RunBufferAssignmentNoBuffersForConstants(module.get()); + EXPECT_FALSE(buffers->HasTopLevelAllocation(const0)); + EXPECT_FALSE(buffers->HasTopLevelAllocation(const1)); + GetAssignedOutputAllocation(*buffers, add); + } } TEST_F(BufferAssignmentTest, HasAllocationAt) { @@ -1196,7 +1224,7 @@ TEST_F(BufferAssignmentTest, ElementOfNestedTupleParameterAsOutput) { // TODO(b/32248867): Enable when buffer assignment gives allocations to // constants. -TEST_F(BufferAssignmentTest, DISABLED_TupleConstantAsOutput) { +TEST_F(BufferAssignmentTest, TupleConstantAsOutput) { // Test that a tuple constant which is forwarded to the computation output // is properly handled. auto builder = HloComputation::Builder(TestName()); @@ -1644,6 +1672,66 @@ TEST_F(BufferAssignmentTest, PeakBuffersWhile) { nonbcast_buffer->instruction() == condition->parameter_instruction(0)); } +TEST_F(BufferAssignmentTest, ConstantBuffersAreNotReused) { + const char* hlo_text = R"( +HloModule Module + +True { + ROOT x.0.1 = f32[] parameter(0) +} + +False { + x.0.0 = f32[] parameter(0) + ROOT copy.1 = f32[] copy(x.0.0) +} + +ENTRY main { + pred.1.0 = pred[] parameter(0) + constant.1.1 = f32[] constant(56) + copy.2 = f32[] copy(constant.1.1) + constant.1.2 = f32[] constant(12) + ROOT conditional.1.3 = f32[] conditional(pred.1.0, copy.2, constant.1.2), + true_computation=True, false_computation=False +} +)"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseHloString(hlo_text)); + + HloInstruction* constant_1 = + module->entry_computation()->GetInstructionWithName("constant.1.1"); + HloInstruction* constant_2 = + module->entry_computation()->GetInstructionWithName("constant.1.2"); + + auto buffers = RunBufferAssignment(module.get()); + + { + const BufferAllocation& allocation_for_const_1 = + GetTopLevelAllocation(*buffers, constant_1); + EXPECT_TRUE(allocation_for_const_1.is_constant()); + for (const auto& buffer_offset_pair : + allocation_for_const_1.assigned_buffers()) { + EXPECT_NE(buffer_offset_pair.first->instruction()->opcode(), + HloOpcode::kCopy); + EXPECT_NE(buffer_offset_pair.first->instruction()->opcode(), + HloOpcode::kConditional); + } + } + + { + const BufferAllocation& allocation_for_const_2 = + GetTopLevelAllocation(*buffers, constant_2); + EXPECT_TRUE(allocation_for_const_2.is_constant()); + for (const auto& buffer_offset_pair : + allocation_for_const_2.assigned_buffers()) { + EXPECT_NE(buffer_offset_pair.first->instruction()->opcode(), + HloOpcode::kCopy); + EXPECT_NE(buffer_offset_pair.first->instruction()->opcode(), + HloOpcode::kConditional); + } + } +} + class WhileBufferAssignmentTest : public HloTestBase { protected: std::unique_ptr BuildWhileConditionComputation( @@ -1683,7 +1771,9 @@ class WhileBufferAssignmentTest : public HloTestBase { return BufferAssigner::Run( module, xla::MakeUnique(module, sequence), ByteSizeOf, - [alignment](LogicalBuffer::Color) { return alignment; }) + [alignment](LogicalBuffer::Color) { return alignment; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true) .ConsumeValueOrDie(); } @@ -1927,7 +2017,9 @@ TEST_F(WhileBufferAssignmentTest, ColocatedBuffers) { module.get(), xla::MakeUnique(module.get(), sequence), backend().compiler()->BufferSizeBytesFunction(), - [](LogicalBuffer::Color) { return 1; })); + [](LogicalBuffer::Color) { return 1; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true)); // The result tuple elements must be assigned with different buffers. TF_ASSERT_OK_AND_ASSIGN(auto slice0, assignment->GetUniqueSlice(tuple, {0})); @@ -2181,7 +2273,9 @@ TEST_F(WhileBufferAssignmentTest, WhileLoopsInterferingResultRange) { BufferAssigner::Run( module.get(), xla::MakeUnique(module.get(), sequence), - ByteSizeOf, [](LogicalBuffer::Color) { return 1; }) + ByteSizeOf, [](LogicalBuffer::Color) { return 1; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true) .ConsumeValueOrDie(); EXPECT_TRUE(BuffersDistinct({while0}, {while1}, *assignment)); diff --git a/tensorflow/compiler/xla/service/hlo.proto b/tensorflow/compiler/xla/service/hlo.proto index 50d7f1823c..63a8a813cd 100644 --- a/tensorflow/compiler/xla/service/hlo.proto +++ b/tensorflow/compiler/xla/service/hlo.proto @@ -246,6 +246,7 @@ message BufferAllocationProto { bool is_thread_local = 3; bool is_tuple = 11; bool is_entry_computation_parameter = 5; + bool is_constant = 12; int64 parameter_number = 6; repeated int64 parameter_shape_index = 10; bool maybe_live_out = 7; -- GitLab From 62696ff0ede36f05b945d6465c709c5f586d63ed Mon Sep 17 00:00:00 2001 From: Igor Ganichev Date: Tue, 24 Jul 2018 14:24:59 -0700 Subject: [PATCH 351/519] Use metric's dtype to represent matches Before this change, even when the actual metric is using float32, we used float64 to represent boolean matches. PiperOrigin-RevId: 205891351 --- tensorflow/contrib/eager/python/metrics_impl.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/eager/python/metrics_impl.py b/tensorflow/contrib/eager/python/metrics_impl.py index efa6ba0626..6efafccd6b 100644 --- a/tensorflow/contrib/eager/python/metrics_impl.py +++ b/tensorflow/contrib/eager/python/metrics_impl.py @@ -291,8 +291,6 @@ class Metric(checkpointable.CheckpointableBase): class Mean(Metric): """Computes the (weighted) mean of the given values.""" - # 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, use_global_variables=False): @@ -377,7 +375,7 @@ class Accuracy(Mean): array_ops.shape(labels), array_ops.shape(predictions), message="Shapes of labels and predictions are unequal") matches = math_ops.equal(labels, predictions) - matches = math_ops.cast(matches, dtypes.float64) + matches = math_ops.cast(matches, self.dtype) super(Accuracy, self).call(matches, weights=weights) if weights is None: return labels, predictions @@ -421,7 +419,7 @@ class CategoricalAccuracy(Mean): labels = math_ops.argmax(labels, axis=-1) predictions = math_ops.argmax(predictions, axis=-1) matches = math_ops.equal(labels, predictions) - matches = math_ops.cast(matches, dtypes.float64) + matches = math_ops.cast(matches, self.dtype) super(CategoricalAccuracy, self).call(matches, weights=weights) if weights is None: return labels, predictions @@ -472,7 +470,7 @@ class BinaryAccuracy(Mean): predictions = ops.convert_to_tensor(predictions) predictions = predictions > self.threshold matches = math_ops.equal(labels, predictions) - matches = math_ops.cast(matches, dtypes.float64) + matches = math_ops.cast(matches, self.dtype) super(BinaryAccuracy, self).call(matches, weights=weights) if weights is None: return labels, predictions @@ -520,7 +518,7 @@ class SparseAccuracy(Mean): predictions = math_ops.argmax(predictions, axis=-1) labels = math_ops.cast(labels, dtypes.int64) matches = math_ops.equal(labels, predictions) - matches = math_ops.cast(matches, dtypes.float64) + matches = math_ops.cast(matches, self.dtype) super(SparseAccuracy, self).call(matches, weights=weights) if weights is None: return labels, predictions -- GitLab From b6b6d39ade422e3fe545fe24f1b06ab0bfd72efc Mon Sep 17 00:00:00 2001 From: Russell Power Date: Tue, 24 Jul 2018 14:33:23 -0700 Subject: [PATCH 352/519] Disable wait timeout for TPUEstimator. If the main loop triggers an error, this will raise immediately. Fixes testing timeouts. PiperOrigin-RevId: 205892842 --- tensorflow/contrib/tpu/python/tpu/error_handling.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/tpu/python/tpu/error_handling.py b/tensorflow/contrib/tpu/python/tpu/error_handling.py index 182cac6f0f..14659fe68f 100644 --- a/tensorflow/contrib/tpu/python/tpu/error_handling.py +++ b/tensorflow/contrib/tpu/python/tpu/error_handling.py @@ -101,7 +101,7 @@ class ErrorRendezvous(object): except Exception as e: # pylint: disable=broad-except self.record_error(source, e, session) - def raise_errors(self, timeout_sec=5): + def raise_errors(self, timeout_sec=0): """Wait for up to `timeout` seconds for all error sources to finish. Preferentially raise "interesting" errors (errors not in the -- GitLab From e4cfe8a009a8e136ace5d74eb1bebf1ef00ea344 Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Tue, 24 Jul 2018 14:47:22 -0700 Subject: [PATCH 353/519] eager: Add a benchmark to contrast subclassed and functional keras models. This is a trivial, not very representative model which likely accentuates overheads. May revisit the model in the future, for now just setting up the plumbing for the benchmark. PiperOrigin-RevId: 205895247 --- tensorflow/python/eager/BUILD | 1 + tensorflow/python/eager/benchmarks_test.py | 60 +++++++++++++++++++--- 2 files changed, 54 insertions(+), 7 deletions(-) diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD index 6ede8e4f4d..58b287fe4b 100644 --- a/tensorflow/python/eager/BUILD +++ b/tensorflow/python/eager/BUILD @@ -322,6 +322,7 @@ cuda_py_test( "//tensorflow/python:math_ops", "//tensorflow/python:pywrap_tensorflow", "//tensorflow/python:random_ops", + "//tensorflow/python/keras", ], ) diff --git a/tensorflow/python/eager/benchmarks_test.py b/tensorflow/python/eager/benchmarks_test.py index 3aad4a114a..afc4bf0066 100644 --- a/tensorflow/python/eager/benchmarks_test.py +++ b/tensorflow/python/eager/benchmarks_test.py @@ -31,6 +31,7 @@ import numpy as np import six from six.moves import xrange # pylint: disable=redefined-builtin +from tensorflow.python import keras from tensorflow.python import pywrap_tensorflow from tensorflow.python.eager import backprop # pylint: disable=unused-import from tensorflow.python.eager import context @@ -70,6 +71,25 @@ def c_tfe_py_fastpath_execute(a, six.raise_from(core._status_to_exception(e.code, message), None) +class SubclassedKerasModel(keras.Model): + + def __init__(self): + super(SubclassedKerasModel, self).__init__() + self.layer = keras.layers.Dense( + 10, kernel_initializer="ones", bias_initializer="zeros") + + def call(self, x): + return self.layer(x) + + +def make_keras_model(): + x = keras.Input(shape=(10,)) + y = keras.layers.Dense( + 10, kernel_initializer="ones", bias_initializer="zeros")( + x) + return keras.Model(inputs=x, outputs=y) + + class MicroBenchmarks(test.Benchmark): def __init__(self): @@ -115,6 +135,7 @@ class MicroBenchmarks(test.Benchmark): def func(): ops.EagerTensor(value, context=handle, device=device, dtype=dtype) + self._run(func, 30000) def benchmark_create_float_tensor_from_list_CPU(self): @@ -211,8 +232,8 @@ class MicroBenchmarks(test.Benchmark): inputs = [m] def f(): - pywrap_tensorflow.TFE_Py_Execute( - ctx_handle, None, "Identity", inputs, attrs, 1) + pywrap_tensorflow.TFE_Py_Execute(ctx_handle, None, "Identity", inputs, + attrs, 1) self._run(f, 30000) @@ -234,14 +255,13 @@ class MicroBenchmarks(test.Benchmark): def f(): with backprop.GradientTape(): pass + self._run(f, 30000) def benchmark_tf_gradient_function_no_op(self): with context.device(CPU): m = gen_array_ops.identity(self._m_2) - self._run( - lambda: backprop.gradients_function(lambda x: x, [0])(m), - 30000) + self._run(lambda: backprop.gradients_function(lambda x: x, [0])(m), 30000) def _benchmark_np_matmul(self, m, transpose_b, num_iters): a = m.cpu().numpy() @@ -255,6 +275,7 @@ class MicroBenchmarks(test.Benchmark): self._run(func, num_iters, execution_mode=execution_mode) 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) @@ -276,9 +297,10 @@ class MicroBenchmarks(test.Benchmark): device = context.context().device_name attrs = ("transpose_a", False, "transpose_b", transpose_b, "T", m.dtype.as_datatype_enum) + def func(): - pywrap_tensorflow.TFE_Py_Execute(ctx_handle, device, "MatMul", - inputs, attrs, 1) + pywrap_tensorflow.TFE_Py_Execute(ctx_handle, device, "MatMul", inputs, + attrs, 1) self._run(func, num_iters) @@ -542,6 +564,30 @@ class MicroBenchmarks(test.Benchmark): self._benchmark_read_variable_with_tape( m, num_iters=self._num_iters_2_by_2) + def benchmark_keras_model_subclassed(self): + model = SubclassedKerasModel() + data = random_ops.random_uniform((10, 10)) + + func = lambda: model(data) + # First call is more expensive (creates variables etc.), discount that. + func() + + # The whole point of this test is to contrast subclassing with + # the functional style of keras model building, so validate that + # the models are equivalent. + assert np.equal(func(), make_keras_model()(data)).all() + + self._run(func, 30000) + + def benchmark_keras_model_functional(self): + model = make_keras_model() + data = random_ops.random_uniform((10, 10)) + func = lambda: model(data) + # Symmetry with benchmark_keras_model_subclassed + func() + assert np.equal(func(), SubclassedKerasModel()(data)).all() + self._run(func, 30000) + if __name__ == "__main__": test.main() -- GitLab From 779b789cc02ba1466da46158359c3132ef04c3ab Mon Sep 17 00:00:00 2001 From: Nick Felt Date: Tue, 24 Jul 2018 14:48:52 -0700 Subject: [PATCH 354/519] Update tensorboard dependency to 1.10.x PiperOrigin-RevId: 205895470 --- tensorflow/tools/pip_package/setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tools/pip_package/setup.py b/tensorflow/tools/pip_package/setup.py index 2e278aa60b..1f4c3d47bf 100644 --- a/tensorflow/tools/pip_package/setup.py +++ b/tensorflow/tools/pip_package/setup.py @@ -55,7 +55,7 @@ REQUIRED_PACKAGES = [ 'six >= 1.10.0', 'protobuf >= 3.6.0', 'setuptools <= 39.1.0', - 'tensorboard >= 1.8.0, < 1.9.0', + 'tensorboard >= 1.10.0, < 1.11.0', 'termcolor >= 1.1.0', ] -- GitLab From d09afb711610b88f394d318622e862fcd327f440 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Tue, 24 Jul 2018 15:03:21 -0700 Subject: [PATCH 355/519] Add dataset specific parameters in config file. PiperOrigin-RevId: 205898175 --- .../eager/python/examples/revnet/config.py | 23 ++++++++++++------- 1 file changed, 15 insertions(+), 8 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/revnet/config.py b/tensorflow/contrib/eager/python/examples/revnet/config.py index e108686b66..821a4878c1 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/config.py +++ b/tensorflow/contrib/eager/python/examples/revnet/config.py @@ -33,7 +33,8 @@ def get_hparams_cifar_38(): """RevNet-38 configurations for CIFAR-10/CIFAR-100.""" config = tf.contrib.training.HParams() - # Hyperparameters from the RevNet paper + config.add_hparam("num_train_images", 50000) + config.add_hparam("num_eval_images", 10000) config.add_hparam("init_filters", 32) config.add_hparam("init_kernel", 3) config.add_hparam("init_stride", 1) @@ -67,7 +68,8 @@ def get_hparams_cifar_38(): config.add_hparam("div255", True) # This is imprecise, when training with validation set, # we only have 40k images in training data - config.add_hparam("iters_per_epoch", 50000 // config.batch_size) + config.add_hparam("iters_per_epoch", + config.num_train_images // config.batch_size) config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) # Customized TPU hyperparameters due to differing batch size caused by @@ -76,7 +78,8 @@ def get_hparams_cifar_38(): # https://cloud.google.com/tpu/docs/troubleshooting config.add_hparam("tpu_batch_size", 1024) config.add_hparam("tpu_eval_batch_size", 1024) - config.add_hparam("tpu_iters_per_epoch", 50000 // config.tpu_batch_size) + config.add_hparam("tpu_iters_per_epoch", + config.num_train_images // config.tpu_batch_size) config.add_hparam("tpu_epochs", config.max_train_iter // config.tpu_iters_per_epoch) @@ -109,6 +112,8 @@ def get_hparams_imagenet_56(): config = tf.contrib.training.HParams() config.add_hparam("n_classes", 1000) config.add_hparam("dataset", "ImageNet") + config.add_hparam("num_train_images", 1281167) + config.add_hparam("num_eval_images", 50000) config.add_hparam("init_filters", 128) config.add_hparam("init_kernel", 7) config.add_hparam("init_stride", 2) @@ -126,6 +131,9 @@ def get_hparams_imagenet_56(): else: config.add_hparam("input_shape", (224, 224, 3)) config.add_hparam("data_format", "channels_last") + # Due to bottleneck residual blocks + filters = [f * 4 for f in config.filters] + config.filters = filters # Training details config.add_hparam("weight_decay", 1e-4) @@ -140,11 +148,9 @@ def get_hparams_imagenet_56(): config.add_hparam("dtype", tf.float32) config.add_hparam("eval_batch_size", 256) config.add_hparam("div255", True) - config.add_hparam("iters_per_epoch", 1281167 // config.batch_size) + config.add_hparam("iters_per_epoch", + config.num_train_images // config.batch_size) config.add_hparam("epochs", config.max_train_iter // config.iters_per_epoch) - # Due to bottleneck residual blocks - filters = [f * 4 for f in config.filters] - config.filters = filters # Customized TPU hyperparameters due to differing batch size caused by # TPU architecture specifics @@ -152,7 +158,8 @@ def get_hparams_imagenet_56(): # https://cloud.google.com/tpu/docs/troubleshooting config.add_hparam("tpu_batch_size", 1024) config.add_hparam("tpu_eval_batch_size", 1024) - config.add_hparam("tpu_iters_per_epoch", 1281167 // config.tpu_batch_size) + config.add_hparam("tpu_iters_per_epoch", + config.num_train_images // config.tpu_batch_size) config.add_hparam("tpu_epochs", config.max_train_iter // config.tpu_iters_per_epoch) -- GitLab From eabda97225faf53ec528621299f5b6c57a7847b0 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Tue, 24 Jul 2018 15:03:46 -0700 Subject: [PATCH 356/519] Dictionary tracking for tf.keras.Model attribute assignment Does not inherit from dict (and so won't pass isinstance checks). I've written a small tome about why in a comment on the class definition. This seems not to break anyone, but if it does we can add Mapping to the problematic isinstance checks (as I've done for TF's nest util and Session fetching); ideally custom mappings would be supported everywhere dicts are anyway. PiperOrigin-RevId: 205898305 --- tensorflow/python/client/session.py | 3 +- tensorflow/python/estimator/keras.py | 5 +- tensorflow/python/framework/test_util.py | 5 +- .../checkpointable/data_structures.py | 189 +++++++++++++++++- .../checkpointable/data_structures_test.py | 119 +++++++++++ .../training/checkpointable/tracking_test.py | 37 +++- .../python/training/checkpointable/util.py | 38 +++- tensorflow/python/util/util.cc | 9 +- 8 files changed, 380 insertions(+), 25 deletions(-) diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index 8ede6ab54c..180bb74d00 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -18,6 +18,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections import functools import re import threading @@ -243,7 +244,7 @@ class _FetchMapper(object): elif isinstance(fetch, (list, tuple)): # NOTE(touts): This is also the code path for namedtuples. return _ListFetchMapper(fetch) - elif isinstance(fetch, dict): + elif isinstance(fetch, collections.Mapping): return _DictFetchMapper(fetch) else: # Look for a handler in the registered expansions. diff --git a/tensorflow/python/estimator/keras.py b/tensorflow/python/estimator/keras.py index 682be8e7cc..70517ae278 100644 --- a/tensorflow/python/estimator/keras.py +++ b/tensorflow/python/estimator/keras.py @@ -184,7 +184,7 @@ def _in_place_subclassed_model_reset(model): # Replace layers on the model with fresh layers layers_to_names = {value: key for key, value in attributes_cache.items()} original_layers = model._layers[:] - model._layers = [] + model._layers = data_structures.NoDependency([]) for layer in original_layers: # We preserve layer order. config = layer.get_config() # This will not work for nested subclassed models used as layers. @@ -232,7 +232,8 @@ def _in_place_subclassed_model_reset(model): ] for name in attributes_to_cache: attributes_cache[name] = getattr(model, name) - model._original_attributes_cache = attributes_cache + model._original_attributes_cache = data_structures.NoDependency( + attributes_cache) # Reset built state model.built = False model.inputs = None diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index 2bc2a189fa..d7e7a2c111 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -19,6 +19,7 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import collections import contextlib import gc import itertools @@ -1227,8 +1228,8 @@ class TensorFlowTestCase(googletest.TestCase): a = a._asdict() if hasattr(b, "_asdict"): b = b._asdict() - a_is_dict = isinstance(a, dict) - if a_is_dict != isinstance(b, dict): + a_is_dict = isinstance(a, collections.Mapping) + if a_is_dict != isinstance(b, collections.Mapping): raise ValueError("Can't compare dict to non-dict, a%s vs b%s. %s" % (path_str, path_str, msg)) if a_is_dict: diff --git a/tensorflow/python/training/checkpointable/data_structures.py b/tensorflow/python/training/checkpointable/data_structures.py index 019d43f09c..507cda8734 100644 --- a/tensorflow/python/training/checkpointable/data_structures.py +++ b/tensorflow/python/training/checkpointable/data_structures.py @@ -57,6 +57,8 @@ def _wrap_or_unwrap(value): return value.value if isinstance(value, base.CheckpointableBase): return value # Skip conversion for already checkpointable objects. + elif isinstance(value, dict): + return _DictWrapper(value) elif isinstance(value, list): return _ListWrapper(value) else: @@ -438,12 +440,15 @@ class Mapping(CheckpointableDataStructure, collections.Mapping): def __init__(self, *args, **kwargs): """Construct a new sequence. Arguments are passed to `dict()`.""" super(Mapping, self).__init__() - self._storage = dict(*args, **kwargs) + self._storage = self._make_storage(*args, **kwargs) self._storage.update( {key: self._track_value( value, name=self._name_element(key)) for key, value in self._storage.items()}) + def _make_storage(self, *args, **kwargs): + return dict(*args, **kwargs) + def _name_element(self, key): if not isinstance(key, six.string_types): raise TypeError( @@ -476,3 +481,185 @@ class Mapping(CheckpointableDataStructure, collections.Mapping): def __iter__(self): return iter(self._storage) + + +# Unlike _ListWrapper, having _DictWrapper inherit from dict and pass isinstance +# checks seems infeasible. CPython will not call Python methods/properties on +# dictionary subclasses when running e.g. {}.update(dict_subclass), and instead +# collects elements directly from dict_subclass's C structs. So subclassing dict +# implies that the storage has to be "self" (i.e. the C structs for the object +# must be updated correctly), but we also need that storage to be the wrapped +# dictionary to avoid synchronization bugs (un-tracked external modifications +# should still show up when the dict is accessed through the wrapper). Monkey +# patching all of the "wrapped" dict's methods instead of creating a wrapper +# object is an option, but not a very attractive one (replacing methods without +# creating reference cycles is difficult, and then dicts would need to be +# special cased everywhere as being checkpointable). +class _DictWrapper(Mapping, collections.MutableMapping): + """Wraps built-in dicts to support restore-on-create for variables. + + _DictWrapper is to Mapping as _ListWrapper is to List. Unlike Mapping, + _DictWrapper allows non-string keys and values and arbitrary mutations (delete + keys, reassign values). Like _ListWrapper, these mutations mean that + _DictWrapper will raise an exception on save. + """ + + def __new__(cls, *args): + if len(args) == 1 and isinstance(args[0], dict): + return super(_DictWrapper, cls).__new__(cls) + else: + # Allow construction from a sequence, e.g. for nest.pack_sequence_as. In + # this case there's nothing to wrap, so we make a normal dictionary. Also + # allows constructing empty instances of the _DictWrapper type, as Session + # is wont to do (and again there's nothing to wrap, so a normal dictionary + # makes more sense). + return dict(*args) + + def __init__(self, wrapped_dict): + self._non_string_key = False + self._non_append_mutation = False + self._external_modification = False + super(_DictWrapper, self).__init__(wrapped_dict) + self._update_snapshot() + + def _make_storage(self, wrapped_dict): + """Re-use the wrapped dict for storage (to force them to be in sync).""" + return wrapped_dict + + @property + def _checkpoint_dependencies(self): + """Check that the object is saveable before listing its dependencies.""" + self._check_external_modification() + if self._non_string_key: + raise ValueError( + "Unable to save the object %s (a dictionary wrapper constructed " + "automatically on attribute assignment). The wrapped dictionary " + "contains a non-string key which maps to a checkpointable object or " + "mutable data structure.\n\nIf you don't need this dictionary " + "checkpointed, wrap it in a tf.contrib.checkpoint.NoDependency " + "object; it will be automatically un-wrapped and subsequently " + "ignored." % (self,)) + if self._non_append_mutation: + raise ValueError( + "Unable to save the object %s (a dictionary wrapper constructed " + "automatically on attribute assignment). A key mapping to a " + "checkpointable object was overwritten or deleted, which would " + "cause problems for restoration.\n\nIf you don't need this " + "dictionary checkpointed, wrap it in a " + "tf.contrib.checkpoint.NoDependency object; it will be automatically " + "un-wrapped and subsequently ignored." % (self,)) + if self._external_modification: + raise ValueError( + "Unable to save the object %s (a dictionary wrapper constructed " + "automatically on attribute assignment). The wrapped dictionary was " + "modified outside the wrapper (its final value was %s, its value " + "when a checkpoint dependency was added was %s), which breaks " + "restoration on object creation.\n\nIf you don't need this " + "dictionary checkpointed, wrap it in a " + "tf.contrib.checkpoint.NoDependency object; it will be automatically " + "un-wrapped and subsequently ignored." % ( + self, self, self._last_wrapped_dict_snapshot)) + assert not self._dirty # Any reason for dirtiness should have an exception. + return super(_DictWrapper, self)._checkpoint_dependencies + + @property + def _dirty(self): + """Check if there has already been a mutation which prevents saving.""" + return (self._external_modification + or self._non_append_mutation + or self._non_string_key) + + def _check_external_modification(self): + """Checks for any changes to the wrapped dict not through the wrapper.""" + if self._dirty: + return + if self != self._last_wrapped_dict_snapshot: + self._external_modification = True + self._last_wrapped_dict_snapshot = None + + def _update_snapshot(self): + """Acknowledges tracked changes to the wrapped dict.""" + if self._dirty: + return + self._last_wrapped_dict_snapshot = dict(self) + + def _track_value(self, value, name): + """Allows storage of non-checkpointable objects.""" + if isinstance(name, six.string_types): + string_key = True + else: + name = "-non_string_key" + string_key = False + try: + no_dependency = isinstance(value, NoDependency) + value = super(_DictWrapper, self)._track_value(value=value, name=name) + if not (string_key or no_dependency): + # A non-string key maps to a checkpointable value. This data structure + # is not saveable. + self._non_string_key = True + return value + except ValueError: + # Even if this value isn't checkpointable, we need to make sure + # NoDependency objects get unwrapped. + return sticky_attribute_assignment( + checkpointable=self, value=value, name=name) + + def _name_element(self, key): + """Don't throw errors for non-string keys.""" + if isinstance(key, six.string_types): + return super(_DictWrapper, self)._name_element(key) + else: + return key + + def __setitem__(self, key, value): + """Allow any modifications, but possibly mark the wrapper as unsaveable.""" + self._check_external_modification() + no_dep = isinstance(value, NoDependency) + if isinstance(key, six.string_types): + existing_dependency = self._lookup_dependency(key) + value = self._track_value(value, name=key) + else: + value = _wrap_or_unwrap(value) + existing_dependency = None + if not no_dep and isinstance(value, base.CheckpointableBase): + # Non-string keys are OK as long as we have no reason to add a + # dependency on the value (either because the value is not + # checkpointable, or because it was wrapped in a NoDependency object). + self._non_string_key = True + current_value = self._storage.setdefault(key, value) + if current_value is not value: + if ((not no_dep and isinstance(value, base.CheckpointableBase)) + # We don't want to just check that the existing object is + # checkpointable, since it may have been wrapped in a NoDependency + # object. + or existing_dependency is not None): + # A checkpointable object was replaced under the same key; this means + # that restoring would be error-prone, so we'll throw an exception on + # save. + self._non_append_mutation = True + self._storage[key] = value + + self._update_snapshot() + + def __delitem__(self, key): + self._check_external_modification() + existing_value = self[key] + if isinstance(existing_value, base.CheckpointableBase): + # Deleting tracked checkpointable values means restoring is problematic, + # so we'll throw an exception on save. + self._non_append_mutation = True + del self._storage[key] + self._update_snapshot() + + def __repr__(self): + return "DictWrapper(%s)" % (repr(self._storage),) + + def __hash__(self): + raise TypeError("unhashable type: 'DictWrapper'") + + def __eq__(self, other): + return self._storage == getattr(other, "_storage", other) + + def update(self, *args, **kwargs): + for key, value in dict(*args, **kwargs).items(): + self[key] = value diff --git a/tensorflow/python/training/checkpointable/data_structures_test.py b/tensorflow/python/training/checkpointable/data_structures_test.py index 7bee00a927..472b7c32b4 100644 --- a/tensorflow/python/training/checkpointable/data_structures_test.py +++ b/tensorflow/python/training/checkpointable/data_structures_test.py @@ -33,6 +33,7 @@ from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.training.checkpointable import data_structures from tensorflow.python.training.checkpointable import tracking +from tensorflow.python.training.checkpointable import util class HasList(training.Model): @@ -303,6 +304,124 @@ class MappingTests(test.TestCase): data_structures.Mapping()]) self.assertEqual(2, len(has_mappings)) self.assertNotIn(data_structures.Mapping(), has_mappings) + # In contrast to Mapping, dict wrappers are not hashable + a = tracking.Checkpointable() + a.d = {} + self.assertEqual({}, a.d) + self.assertFalse({} != a.d) # pylint: disable=g-explicit-bool-comparison + self.assertNotEqual({1: 2}, a.d) + with self.assertRaisesRegexp(TypeError, "unhashable"): + set([a.d]) + + def testDictWrapperBadKeys(self): + a = tracking.Checkpointable() + a.d = {} + a.d[1] = data_structures.List() + model = training.Model() + model.sub = a + save_path = os.path.join(self.get_temp_dir(), "ckpt") + with self.assertRaisesRegexp(ValueError, "non-string key"): + model.save_weights(save_path) + + def testDictWrapperNoDependency(self): + a = tracking.Checkpointable() + a.d = data_structures.NoDependency({}) + a.d[1] = [3] + self.assertEqual([a], util.list_objects(a)) + model = training.Model() + model.sub = a + save_path = os.path.join(self.get_temp_dir(), "ckpt") + model.save_weights(save_path) + model.load_weights(save_path) + + def testNonStringKeyNotCheckpointableValue(self): + a = tracking.Checkpointable() + a.d = {} + a.d["a"] = [3] + a.d[1] = data_structures.NoDependency([3]) + self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a)) + model = training.Model() + model.sub = a + save_path = os.path.join(self.get_temp_dir(), "ckpt") + model.save_weights(save_path) + model.load_weights(save_path) + + def testNonAppendNotCheckpointable(self): + # Non-append mutations (deleting or overwriting values) are OK when the + # values aren't tracked. + a = tracking.Checkpointable() + a.d = {} + a.d["a"] = [3] + a.d[1] = 3 + a.d[1] = 2 + self.assertEqual(2, a.d[1]) + del a.d[1] + a.d[2] = data_structures.NoDependency(tracking.Checkpointable()) + second = tracking.Checkpointable() + a.d[2] = data_structures.NoDependency(second) + self.assertIs(second, a.d[2]) + self.assertEqual([a, a.d, a.d["a"]], util.list_objects(a)) + model = training.Model() + model.sub = a + save_path = os.path.join(self.get_temp_dir(), "ckpt") + model.save_weights(save_path) + model.load_weights(save_path) + + def testDelNoSave(self): + model = training.Model() + model.d = {} + model.d["a"] = [] + del model.d["a"] + save_path = os.path.join(self.get_temp_dir(), "ckpt") + with self.assertRaisesRegexp(ValueError, "overwritten or deleted"): + model.save_weights(save_path) + + def testPopNoSave(self): + model = training.Model() + model.d = {} + model.d["a"] = [] + model.d.pop("a") + save_path = os.path.join(self.get_temp_dir(), "ckpt") + with self.assertRaisesRegexp(ValueError, "overwritten or deleted"): + model.save_weights(save_path) + + def testExternalModificationNoSave(self): + model = training.Model() + external_reference = {} + model.d = external_reference + external_reference["a"] = [] + save_path = os.path.join(self.get_temp_dir(), "ckpt") + with self.assertRaisesRegexp(ValueError, "modified outside the wrapper"): + model.save_weights(save_path) + + def testOverwriteNoSave(self): + model = training.Model() + model.d = {} + model.d["a"] = {} + model.d["a"] = {} + save_path = os.path.join(self.get_temp_dir(), "ckpt") + with self.assertRaisesRegexp(ValueError, "overwritten or deleted"): + model.save_weights(save_path) + + def testIter(self): + model = training.Model() + model.d = {1: 3} + model.d[1] = 3 + self.assertEqual([1], list(model.d)) + new_dict = {} + # This update() is super tricky. If the dict wrapper subclasses dict, + # CPython will access its storage directly instead of calling any + # methods/properties on the object. So the options are either not to + # subclass dict (in which case update will call normal iter methods, but the + # object won't pass isinstance checks) or to subclass dict and keep that + # storage updated (no shadowing all its methods like _ListWrapper). + new_dict.update(model.d) + self.assertEqual({1: 3}, new_dict) + + def testConstructableFromSequence(self): + result = data_structures._DictWrapper([(1, 2), (3, 4)]) + self.assertIsInstance(result, dict) + self.assertEqual({1: 2, 3: 4}, result) if __name__ == "__main__": test.main() diff --git a/tensorflow/python/training/checkpointable/tracking_test.py b/tensorflow/python/training/checkpointable/tracking_test.py index 96da0d6e47..f8d17cd417 100644 --- a/tensorflow/python/training/checkpointable/tracking_test.py +++ b/tensorflow/python/training/checkpointable/tracking_test.py @@ -19,6 +19,7 @@ from __future__ import print_function import os import numpy +import six from tensorflow.python.framework import test_util from tensorflow.python.keras.engine import training @@ -143,6 +144,29 @@ class InterfaceTests(test.TestCase): with self.assertRaisesRegexp(ValueError, "A list element was replaced"): checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt")) + @test_util.run_in_graph_and_eager_modes + def testDictionariesBasic(self): + a = training.Model() + b = training.Model() + a.attribute = {"b": b} + c = training.Model() + a.attribute["c"] = [] + a.attribute["c"].append(c) + a_deps = util.list_objects(a) + self.assertIn(b, a_deps) + self.assertIn(c, a_deps) + self.assertIs(b, a.attribute["b"]) + six.assertCountEqual( + self, + ["b", "c"], + [dep.name for dep in a.attribute._checkpoint_dependencies]) + self.assertEqual([b, c], a.layers) + self.assertEqual([b, c], a.attribute.layers) + self.assertEqual([c], a.attribute["c"].layers) + checkpoint = util.Checkpoint(a=a) + save_path = checkpoint.save(os.path.join(self.get_temp_dir(), "ckpt")) + checkpoint.restore(save_path).assert_consumed() + @test_util.run_in_graph_and_eager_modes def testNoDepList(self): a = training.Model() @@ -159,12 +183,13 @@ class InterfaceTests(test.TestCase): @test_util.run_in_graph_and_eager_modes def testAssertions(self): a = tracking.Checkpointable() - a.l = [numpy.zeros([2, 2])] - self.assertAllEqual([numpy.zeros([2, 2])], a.l) - self.assertAllClose([numpy.zeros([2, 2])], a.l) - nest.map_structure(self.assertAllClose, a.l, [numpy.zeros([2, 2])]) - a.tensors = [array_ops.ones([2, 2]), array_ops.zeros([3, 3])] - self.assertAllClose([numpy.ones([2, 2]), numpy.zeros([3, 3])], + a.l = {"k": [numpy.zeros([2, 2])]} + self.assertAllEqual(nest.flatten({"k": [numpy.zeros([2, 2])]}), + nest.flatten(a.l)) + self.assertAllClose({"k": [numpy.zeros([2, 2])]}, a.l) + nest.map_structure(self.assertAllClose, a.l, {"k": [numpy.zeros([2, 2])]}) + a.tensors = {"k": [array_ops.ones([2, 2]), array_ops.zeros([3, 3])]} + self.assertAllClose({"k": [numpy.ones([2, 2]), numpy.zeros([3, 3])]}, self.evaluate(a.tensors)) if __name__ == "__main__": diff --git a/tensorflow/python/training/checkpointable/util.py b/tensorflow/python/training/checkpointable/util.py index 686232fe27..5d26a817d4 100644 --- a/tensorflow/python/training/checkpointable/util.py +++ b/tensorflow/python/training/checkpointable/util.py @@ -361,24 +361,42 @@ class _ObjectIdentityWeakKeyDictionary(_ObjectIdentityDictionary): yield unwrapped -class _ObjectIdentityWeakSet(collections.MutableSet): - """Like weakref.WeakSet, but compares objects with "is".""" +class _ObjectIdentitySet(collections.MutableSet): + """Like the built-in set, but compares objects with "is".""" - def __init__(self): - self._storage = set() + def __init__(self, *args): + self._storage = set([self._wrap_key(obj) for obj in list(*args)]) + + def _wrap_key(self, key): + return _ObjectIdentityWrapper(key) def __contains__(self, key): - return _WeakObjectIdentityWrapper(key) in self._storage + return self._wrap_key(key) in self._storage def discard(self, key): - self._storage.discard(_WeakObjectIdentityWrapper(key)) + self._storage.discard(self._wrap_key(key)) def add(self, key): - self._storage.add(_WeakObjectIdentityWrapper(key)) + self._storage.add(self._wrap_key(key)) + + def __len__(self): + return len(self._storage) + + def __iter__(self): + keys = list(self._storage) + for key in keys: + yield key.unwrapped + + +class _ObjectIdentityWeakSet(_ObjectIdentitySet): + """Like weakref.WeakSet, but compares objects with "is".""" + + def _wrap_key(self, key): + return _WeakObjectIdentityWrapper(key) def __len__(self): # Iterate, discarding old weak refs - return len(list(self)) + return len([_ for _ in self]) def __iter__(self): keys = list(self._storage) @@ -857,8 +875,8 @@ class CheckpointLoadStatus(_LoadStatus): for checkpointable_object in list_objects(self._root_checkpointable): self._checkpoint.all_python_objects.add(checkpointable_object) unused_python_objects = ( - set(self._checkpoint.all_python_objects) - - set(self._checkpoint.object_by_proto_id.values())) + _ObjectIdentitySet(self._checkpoint.all_python_objects) + - _ObjectIdentitySet(self._checkpoint.object_by_proto_id.values())) if unused_python_objects: raise AssertionError( ("Some Python objects were not bound to checkpointed values, likely " diff --git a/tensorflow/python/util/util.cc b/tensorflow/python/util/util.cc index f9e0b7e4d2..ad85a44f8d 100644 --- a/tensorflow/python/util/util.cc +++ b/tensorflow/python/util/util.cc @@ -506,9 +506,12 @@ bool AssertSameStructureHelper(PyObject* o1, PyObject* o2, bool check_types, } } else if (type1 != type2 /* If both sequences are list types, don't complain. This allows - one to be a list subclass (e.g. _ListWrapper used for automatic - dependency tracking.) */ - && !(PyList_Check(o1) && PyList_Check(o2))) { + one to be a list subclass (e.g. _ListWrapper used for + automatic dependency tracking.) */ + && !(PyList_Check(o1) && PyList_Check(o2)) + /* Two mapping types will also compare equal, making _DictWrapper + and dict compare equal. */ + && !(IsMappingHelper(o1) && IsMappingHelper(o2))) { *is_type_error = true; *error_msg = tensorflow::strings::StrCat( "The two namedtuples don't have the same sequence type. " -- GitLab From 6de72a32056c68da3736e232ff3ba3fec390d81b Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Tue, 24 Jul 2018 15:22:50 -0700 Subject: [PATCH 357/519] Fix review comments --- .../contrib/tensorrt/kernels/trt_engine_op.cc | 31 ++++++++++++------- 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc index f45de29a14..6699b71d28 100644 --- a/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc +++ b/tensorflow/contrib/tensorrt/kernels/trt_engine_op.cc @@ -293,6 +293,7 @@ void TRTEngineOp::ComputeAsync(OpKernelContext* ctx, const bool retry = ExecuteTrtEngine(ctx, num_batch, trt_engine_ptr.get(), engine_ctx_pair.second.get()); if (retry) { + LOG(WARNING) << "Failed to execute engine, retrying with native segment"; ExecuteNativeSegment(ctx, helper); return; } @@ -310,15 +311,15 @@ bool TRTEngineOp::ExecuteTrtEngine( const size_t binding_index = trt_engine_ptr->getBindingIndex(input_name.c_str()); if (binding_index == -1) { - LOG(WARNING) << "Iutput node not found, at " << input_name; + LOG(ERROR) << "Input node not found, at " << input_name; return kRetry; } const Tensor& input_tensor = ctx->input(i); const TensorShape& input_shape = input_tensor.shape(); if (num_batch != input_shape.dim_size(0)) { - LOG(WARNING) << "Input data has inconsistent batch size: " << num_batch - << " vs " << input_shape.dim_size(0); + LOG(ERROR) << "Input data has inconsistent batch size: " << num_batch + << " vs " << input_shape.dim_size(0); return kRetry; } auto dtype = trt_engine_ptr->getBindingDataType(binding_index); @@ -327,10 +328,10 @@ bool TRTEngineOp::ExecuteTrtEngine( buffers[binding_index] = (void*)(input_tensor.flat().data()); break; case nvinfer1::DataType::kHALF: - LOG(WARNING) << "FP16 inputs are not supported yet!"; + LOG(ERROR) << "FP16 inputs are not supported yet!"; return kRetry; case nvinfer1::DataType::kINT8: - LOG(WARNING) << "INT8 inputs are not supported yet!"; + LOG(ERROR) << "INT8 inputs are not supported yet!"; return kRetry; #if NV_TENSORRT_MAJOR > 3 case nvinfer1::DataType::kINT32: @@ -338,7 +339,7 @@ bool TRTEngineOp::ExecuteTrtEngine( break; #endif default: - LOG(WARNING) << "Unknown TRT data type: " << int(dtype); + LOG(ERROR) << "Unknown TRT data type: " << int(dtype); return kRetry; } } @@ -359,16 +360,16 @@ bool TRTEngineOp::ExecuteTrtEngine( auto status = TensorShapeUtils::MakeShape( trt_shape.data(), trt_shape.size(), &output_shape); if (!status.ok()) { - LOG(WARNING) << "Failed to get output shape: " << status; + LOG(ERROR) << "Failed to get output shape: " << status; return kRetry; } } else { - LOG(WARNING) << "Output node not found, at " << output_name; + LOG(ERROR) << "Output node not found, at " << output_name; return kRetry; } auto status = ctx->allocate_output(i, output_shape, &output_tensor); if (!status.ok()) { - LOG(WARNING) << "Allocating output failed with " << status; + LOG(ERROR) << "Allocating output failed with " << status; ctx->SetStatus(status); // Do not retry since we cannot allocate the same output twice. // TODO(aaroey): ideally we should retry, fix this. @@ -457,7 +458,9 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, TrtUniquePtrType infer(nvinfer1::createInferRuntime(logger)); #if NV_TENSORRT_MAJOR > 3 auto allocator = GetAllocator(ctx); - if (allocator == nullptr) return null_pair; + if (allocator == nullptr) { + return null_pair; + } infer->setGpuAllocator(allocator); #endif TrtUniquePtrType static_engine( @@ -472,7 +475,9 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, raw_static_engine->createExecutionContext())}; // Runtime is safe to delete after engine creation serialized_segment_.clear(); - if (max_batch_size < batch_size) return null_pair; + if (max_batch_size < batch_size) { + return null_pair; + } return engine_map_.at(max_batch_size); } // static_engine_ @@ -483,7 +488,9 @@ TRTEngineOp::EngineCtxPair& TRTEngineOp::GetEngine(int batch_size, nvinfer1::IGpuAllocator* allocator = nullptr; #if NV_TENSORRT_MAJOR > 3 allocator = GetAllocator(ctx); - if (allocator == nullptr) return null_pair; + if (allocator == nullptr) { + return null_pair; + } #endif std::vector shapes; for (int i = 0; i < ctx->num_inputs(); ++i) { -- GitLab From 74a75900faf88d7ce4e05f4bebd2b872abdf16a9 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 24 Jul 2018 15:23:12 -0700 Subject: [PATCH 358/519] Adding defun PiperOrigin-RevId: 205901720 --- .../examples/generative_examples/dcgan.ipynb | 24 +++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb index 54cc4dc5da..44ff43a111 100644 --- a/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb +++ b/tensorflow/contrib/eager/python/examples/generative_examples/dcgan.ipynb @@ -29,7 +29,7 @@ "source": [ "This notebook demonstrates how to generate images of handwritten digits using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). To do so, we use Deep Convolutional Generative Adverserial Networks ([DCGAN](https://arxiv.org/pdf/1511.06434.pdf)).\n", "\n", - "This model takes about 40 seconds per epoch to train on a single Tesla K80 on Colab, as of July 2018.\n", + "This model takes about ~30 seconds per epoch (using tf.contrib.eager.defun to create graph functions) to train on a single Tesla K80 on Colab, as of July 2018.\n", "\n", "Below is the output generated after training the generator and discriminator models for 150 epochs.\n", "\n", @@ -203,7 +203,7 @@ "## Write the generator and discriminator models\n", "\n", "* **Generator** \n", - " * It is responsible for **creating the convincing images good enough to fool the discriminator**.\n", + " * It is responsible for **creating convincing images that are good enough to fool the discriminator**.\n", " * It consists of Conv2DTranspose (Upsampling) layers. We start with a fully connected layer and upsample the image 2 times so as to reach the desired image size (mnist image size) which is (28, 28, 1). \n", " * We use **leaky relu** activation except for the **last layer** which uses **tanh** activation.\n", " \n", @@ -314,6 +314,26 @@ "discriminator = Discriminator()" ] }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "k1HpMSLImuRi" + }, + "outputs": [], + "source": [ + "# Defun gives 10 secs/epoch performance boost\n", + "generator.call = tf.contrib.eager.defun(generator.call)\n", + "discriminator.call = tf.contrib.eager.defun(discriminator.call)" + ] + }, { "cell_type": "markdown", "metadata": { -- GitLab From ba4ccc83adc397936ac01f80dd04ee8b2686c929 Mon Sep 17 00:00:00 2001 From: Yu-Cheng Ling Date: Tue, 24 Jul 2018 15:26:39 -0700 Subject: [PATCH 359/519] Improve TFLite Python error handling. When `InterpreterBuilder` fails, now it fails silently and later user sees "Interpreter was not initialized.". There's no way to get the error message and troubleshoot. This fixes the issue and displays the error message. PiperOrigin-RevId: 205902280 --- .../interpreter_wrapper.cc | 48 +++++++++++++------ .../interpreter_wrapper/interpreter_wrapper.h | 18 ++++++- 2 files changed, 50 insertions(+), 16 deletions(-) diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc index f97919363b..9ab05f3068 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.cc @@ -108,7 +108,9 @@ std::unique_ptr CreateInterpreter( ImportNumpy(); std::unique_ptr interpreter; - tflite::InterpreterBuilder(*model, resolver)(&interpreter); + if (tflite::InterpreterBuilder(*model, resolver)(&interpreter) != kTfLiteOk) { + return nullptr; + } return interpreter; } @@ -182,13 +184,37 @@ PyObject* PyTupleFromQuantizationParam(const TfLiteQuantizationParams& param) { } // namespace +InterpreterWrapper* InterpreterWrapper::CreateInterpreterWrapper( + std::unique_ptr model, + std::unique_ptr error_reporter, + std::string* error_msg) { + if (!model) { + *error_msg = error_reporter->message(); + return nullptr; + } + + auto resolver = absl::make_unique(); + auto interpreter = CreateInterpreter(model.get(), *resolver); + if (!interpreter) { + *error_msg = error_reporter->message(); + return nullptr; + } + + InterpreterWrapper* wrapper = + new InterpreterWrapper(std::move(model), std::move(error_reporter), + std::move(resolver), std::move(interpreter)); + return wrapper; +} + InterpreterWrapper::InterpreterWrapper( std::unique_ptr model, - std::unique_ptr error_reporter) + std::unique_ptr error_reporter, + std::unique_ptr resolver, + std::unique_ptr interpreter) : model_(std::move(model)), error_reporter_(std::move(error_reporter)), - resolver_(absl::make_unique()), - interpreter_(CreateInterpreter(model_.get(), *resolver_)) {} + resolver_(std::move(resolver)), + interpreter_(std::move(interpreter)) {} InterpreterWrapper::~InterpreterWrapper() {} @@ -421,11 +447,8 @@ InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromFile( std::unique_ptr error_reporter(new PythonErrorReporter); std::unique_ptr model = tflite::FlatBufferModel::BuildFromFile(model_path, error_reporter.get()); - if (!model) { - *error_msg = error_reporter->message(); - return nullptr; - } - return new InterpreterWrapper(std::move(model), std::move(error_reporter)); + return CreateInterpreterWrapper(std::move(model), std::move(error_reporter), + error_msg); } InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromBuffer( @@ -439,11 +462,8 @@ InterpreterWrapper* InterpreterWrapper::CreateWrapperCPPFromBuffer( std::unique_ptr model = tflite::FlatBufferModel::BuildFromBuffer(buf, length, error_reporter.get()); - if (!model) { - *error_msg = error_reporter->message(); - return nullptr; - } - return new InterpreterWrapper(std::move(model), std::move(error_reporter)); + return CreateInterpreterWrapper(std::move(model), std::move(error_reporter), + error_msg); } PyObject* InterpreterWrapper::ResetVariableTensorsToZero() { diff --git a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h index 556ec7117a..3e03751da4 100644 --- a/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h +++ b/tensorflow/contrib/lite/python/interpreter_wrapper/interpreter_wrapper.h @@ -69,14 +69,28 @@ class InterpreterWrapper { PyObject* tensor(PyObject* base_object, int i); private: - InterpreterWrapper(std::unique_ptr model, - std::unique_ptr error_reporter); + // Helper function to construct an `InterpreterWrapper` object. + // It only returns InterpreterWrapper if it can construct an `Interpreter`. + // Otherwise it returns `nullptr`. + static InterpreterWrapper* CreateInterpreterWrapper( + std::unique_ptr model, + std::unique_ptr error_reporter, + std::string* error_msg); + + InterpreterWrapper( + std::unique_ptr model, + std::unique_ptr error_reporter, + std::unique_ptr resolver, + std::unique_ptr interpreter); // InterpreterWrapper is not copyable or assignable. We avoid the use of // InterpreterWrapper() = delete here for SWIG compatibility. InterpreterWrapper(); InterpreterWrapper(const InterpreterWrapper& rhs); + // The public functions which creates `InterpreterWrapper` should ensure all + // these member variables are initialized successfully. Otherwise it should + // report the error and return `nullptr`. const std::unique_ptr model_; const std::unique_ptr error_reporter_; const std::unique_ptr resolver_; -- GitLab From 74d4bd78363b75538ce3aa7ec5ec20678da69c28 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 24 Jul 2018 15:32:45 -0700 Subject: [PATCH 360/519] Internal Change PiperOrigin-RevId: 205903203 --- tensorflow/contrib/lite/testing/BUILD | 11 ++--------- tensorflow/contrib/lite/testing/generate_testspec.cc | 12 +++++++----- tensorflow/contrib/lite/testing/generate_testspec.h | 2 ++ tensorflow/contrib/lite/testing/join.h | 3 ++- tensorflow/contrib/lite/testing/test_runner.h | 2 +- tensorflow/contrib/lite/testing/tf_driver.cc | 4 ++-- tensorflow/contrib/lite/testing/tflite_diff_flags.h | 2 ++ 7 files changed, 18 insertions(+), 18 deletions(-) diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index 4c37bcb3c9..6c7f494e9b 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -112,7 +112,6 @@ cc_library( cc_test( name = "message_test", srcs = ["message_test.cc"], - tags = ["no_oss"], deps = [ ":message", "@com_google_googletest//:gtest_main", @@ -132,7 +131,6 @@ cc_test( name = "split_test", size = "small", srcs = ["split_test.cc"], - tags = ["no_oss"], deps = [ ":split", "@com_google_googletest//:gtest_main", @@ -142,13 +140,13 @@ cc_test( cc_library( name = "join", hdrs = ["join.h"], + deps = ["//tensorflow/contrib/lite:string"], ) cc_test( name = "join_test", size = "small", srcs = ["join_test.cc"], - tags = ["no_oss"], deps = [ ":join", "@com_google_googletest//:gtest_main", @@ -174,7 +172,6 @@ cc_test( srcs = ["tflite_driver_test.cc"], data = ["//tensorflow/contrib/lite:testdata/multi_add.bin"], tags = [ - "no_oss", "tflite_not_portable_android", "tflite_not_portable_ios", ], @@ -196,7 +193,6 @@ cc_library( cc_test( name = "tokenize_test", srcs = ["tokenize_test.cc"], - tags = ["no_oss"], deps = [ ":tokenize", "@com_google_googletest//:gtest_main", @@ -219,7 +215,6 @@ cc_library( cc_test( name = "test_runner_test", srcs = ["test_runner_test.cc"], - tags = ["no_oss"], deps = [ ":test_runner", "@com_google_googletest//:gtest_main", @@ -258,7 +253,6 @@ cc_test( srcs = ["tf_driver_test.cc"], data = ["//tensorflow/contrib/lite:testdata/multi_add.pb"], tags = [ - "no_oss", "tflite_not_portable", ], deps = [ @@ -275,6 +269,7 @@ cc_library( ":join", ":split", ":tf_driver", + "//tensorflow/contrib/lite:string", "//tensorflow/core:framework", ], ) @@ -284,7 +279,6 @@ cc_test( size = "small", srcs = ["generate_testspec_test.cc"], tags = [ - "no_oss", "tflite_not_portable", ], deps = [ @@ -341,7 +335,6 @@ tf_cc_test( ], tags = [ "no_cuda_on_cpu_tap", - "no_oss", "tflite_not_portable", ], deps = [ diff --git a/tensorflow/contrib/lite/testing/generate_testspec.cc b/tensorflow/contrib/lite/testing/generate_testspec.cc index c1092e4d25..f29c188e6c 100644 --- a/tensorflow/contrib/lite/testing/generate_testspec.cc +++ b/tensorflow/contrib/lite/testing/generate_testspec.cc @@ -13,6 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include + #include "tensorflow/contrib/lite/testing/generate_testspec.h" #include "tensorflow/contrib/lite/testing/join.h" #include "tensorflow/contrib/lite/testing/split.h" @@ -88,13 +90,13 @@ bool GenerateTestSpecFromTensorflowModel( TfDriver runner(input_layer, input_layer_type, input_layer_shape, output_layer); if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; + std::cerr << runner.GetErrorMessage() << std::endl; return false; } runner.LoadModel(tensorflow_model_path); if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; + std::cerr << runner.GetErrorMessage() << std::endl; return false; } @@ -118,14 +120,14 @@ bool GenerateTestSpecFromTensorflowModel( for (int j = 0; j < input_values.size(); j++) { runner.SetInput(j, input_values[j]); if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; + std::cerr << runner.GetErrorMessage() << std::endl; return false; } } runner.Invoke(); if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; + std::cerr << runner.GetErrorMessage() << std::endl; return false; } @@ -137,7 +139,7 @@ bool GenerateTestSpecFromTensorflowModel( for (int j = 0; j < output_layer.size(); j++) { stream << " output: \"" << runner.ReadOutput(j) << "\"\n"; if (!runner.IsValid()) { - cerr << runner.GetErrorMessage() << endl; + std::cerr << runner.GetErrorMessage() << std::endl; return false; } } diff --git a/tensorflow/contrib/lite/testing/generate_testspec.h b/tensorflow/contrib/lite/testing/generate_testspec.h index bfaf5e7ec8..b3d0db31c0 100644 --- a/tensorflow/contrib/lite/testing/generate_testspec.h +++ b/tensorflow/contrib/lite/testing/generate_testspec.h @@ -19,6 +19,8 @@ limitations under the License. #include #include +#include "tensorflow/contrib/lite/string.h" + namespace tflite { namespace testing { diff --git a/tensorflow/contrib/lite/testing/join.h b/tensorflow/contrib/lite/testing/join.h index 1edee01cf9..4be19ad756 100644 --- a/tensorflow/contrib/lite/testing/join.h +++ b/tensorflow/contrib/lite/testing/join.h @@ -17,7 +17,8 @@ limitations under the License. #include #include -#include + +#include "tensorflow/contrib/lite/string.h" namespace tflite { namespace testing { diff --git a/tensorflow/contrib/lite/testing/test_runner.h b/tensorflow/contrib/lite/testing/test_runner.h index 96ab6be54e..fac7d01aab 100644 --- a/tensorflow/contrib/lite/testing/test_runner.h +++ b/tensorflow/contrib/lite/testing/test_runner.h @@ -90,7 +90,7 @@ class TestRunner { // Invalidate the test runner, preventing it from executing any further. void Invalidate(const string& error_message) { - cerr << error_message << std::endl; + std::cerr << error_message << std::endl; error_message_ = error_message; } bool IsValid() const { return error_message_.empty(); } diff --git a/tensorflow/contrib/lite/testing/tf_driver.cc b/tensorflow/contrib/lite/testing/tf_driver.cc index 3b27f6f3da..d6a6ff8f56 100644 --- a/tensorflow/contrib/lite/testing/tf_driver.cc +++ b/tensorflow/contrib/lite/testing/tf_driver.cc @@ -28,8 +28,8 @@ namespace { tensorflow::Tensor CreateTensor(const tensorflow::DataType type, const std::vector& dim) { - tensorflow::TensorShape shape{gtl::ArraySlice{ - reinterpret_cast(dim.data()), dim.size()}}; + tensorflow::TensorShape shape{tensorflow::gtl::ArraySlice{ + reinterpret_cast(dim.data()), dim.size()}}; return {type, shape}; } diff --git a/tensorflow/contrib/lite/testing/tflite_diff_flags.h b/tensorflow/contrib/lite/testing/tflite_diff_flags.h index 7a57e8d3fb..695c2a3de6 100644 --- a/tensorflow/contrib/lite/testing/tflite_diff_flags.h +++ b/tensorflow/contrib/lite/testing/tflite_diff_flags.h @@ -15,6 +15,8 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_FLAGS_H_ #define TENSORFLOW_CONTRIB_LITE_TESTING_TFLITE_DIFF_FLAGS_H_ +#include + #include "tensorflow/contrib/lite/testing/split.h" #include "tensorflow/contrib/lite/testing/tflite_diff_util.h" #include "tensorflow/core/util/command_line_flags.h" -- GitLab From 3aeaae05f94ea19b60bf9220697e058673a11e85 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Tue, 24 Jul 2018 15:35:29 -0700 Subject: [PATCH 361/519] Fix //tensorflow/python/eager:memory_test in python 3 Just a tiny xrange issue. Was running a bunch of tests and noticed this failure. PiperOrigin-RevId: 205903619 --- tensorflow/python/eager/BUILD | 1 + tensorflow/python/eager/memory_test.py | 4 +++- 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/eager/BUILD b/tensorflow/python/eager/BUILD index 58b287fe4b..32a8452f62 100644 --- a/tensorflow/python/eager/BUILD +++ b/tensorflow/python/eager/BUILD @@ -405,6 +405,7 @@ cuda_py_test( "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:framework_test_lib", + "@six_archive//:six", ], tags = [ "optonly", # The test is too slow in non-opt mode diff --git a/tensorflow/python/eager/memory_test.py b/tensorflow/python/eager/memory_test.py index 74c6cbdd31..a1a59d511f 100644 --- a/tensorflow/python/eager/memory_test.py +++ b/tensorflow/python/eager/memory_test.py @@ -24,6 +24,8 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +import six + from tensorflow.python import keras from tensorflow.python.eager import backprop from tensorflow.python.eager import context @@ -63,7 +65,7 @@ class MemoryTest(test.TestCase): initial = memory_profiler.memory_usage(-1)[0] - for _ in xrange(num_iters): + for _ in six.moves.range(num_iters): f() increase = memory_profiler.memory_usage(-1)[0] - initial -- GitLab From da5126b19faabade710d50a1d600dfa4c931e89b Mon Sep 17 00:00:00 2001 From: Shivani Agrawal Date: Tue, 24 Jul 2018 15:55:08 -0700 Subject: [PATCH 362/519] quick fix on typo. PiperOrigin-RevId: 205906547 --- tensorflow/core/kernels/data/stats_dataset_ops.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/data/stats_dataset_ops.cc b/tensorflow/core/kernels/data/stats_dataset_ops.cc index 754c32b6ca..58ec3d4495 100644 --- a/tensorflow/core/kernels/data/stats_dataset_ops.cc +++ b/tensorflow/core/kernels/data/stats_dataset_ops.cc @@ -390,7 +390,7 @@ class FeatureStatsDatasetOp : public UnaryDatasetOpKernel { for (const auto& feature_list : example.feature_lists().feature_list()) { - stats_aggregator->IncrementCounter("feature_lists_count", "reainer", + stats_aggregator->IncrementCounter("feature_lists_count", "trainer", 1); for (const auto& feature : feature_list.second.feature()) { feature_values_list_size_sum += AddStatsFeatureValues(feature); -- GitLab From 1d35ecc2e25edeadcf416e11f2a11f00db58af61 Mon Sep 17 00:00:00 2001 From: Zhenyu Tan Date: Tue, 24 Jul 2018 16:09:27 -0700 Subject: [PATCH 363/519] Correct natural exp decay documentation. PiperOrigin-RevId: 205908932 --- tensorflow/python/training/learning_rate_decay.py | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/tensorflow/python/training/learning_rate_decay.py b/tensorflow/python/training/learning_rate_decay.py index 51190264e8..fd195a7965 100644 --- a/tensorflow/python/training/learning_rate_decay.py +++ b/tensorflow/python/training/learning_rate_decay.py @@ -356,7 +356,15 @@ def natural_exp_decay(learning_rate, The function returns the decayed learning rate. It is computed as: ```python - decayed_learning_rate = learning_rate * exp(-decay_rate * global_step) + decayed_learning_rate = learning_rate * exp(-decay_rate * global_step / + decay_step) + ``` + + or, if `staircase` is `True`, as: + + ```python + decayed_learning_rate = learning_rate * exp(-decay_rate * floor(global_step / + decay_step)) ``` Example: decay exponentially with a base of 0.96: @@ -365,8 +373,10 @@ def natural_exp_decay(learning_rate, ... global_step = tf.Variable(0, trainable=False) learning_rate = 0.1 + decay_steps = 5 k = 0.5 - learning_rate = tf.train.exponential_time_decay(learning_rate, global_step, k) + learning_rate = tf.train.natural_exp_decay(learning_rate, global_step, + decay_steps, k) # Passing global_step to minimize() will increment it at each step. learning_step = ( -- GitLab From 20a3f82b92cec4e31eea8f59f609961cbc3a1454 Mon Sep 17 00:00:00 2001 From: Michael Kuperstein Date: Tue, 24 Jul 2018 16:33:33 -0700 Subject: [PATCH 364/519] [XLA] Fix up table formatting in Map semantics. PiperOrigin-RevId: 205912552 --- tensorflow/docs_src/performance/xla/operation_semantics.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md index 26a7b9e42c..fe9afc4ecb 100644 --- a/tensorflow/docs_src/performance/xla/operation_semantics.md +++ b/tensorflow/docs_src/performance/xla/operation_semantics.md @@ -1334,7 +1334,7 @@ See also | Arguments | Type | Semantics | | ----------------- | ---------------------- | ------------------------------ | | `operands` | sequence of N `XlaOp`s | N arrays of types T_0..T_{N-1} | -| `computation` | `XlaComputation` | computation of type `T_0, T_1, | +| `computation` | `XlaComputation` | computation of type `T_0, T_1, | : : : ..., T_{N + M -1} -> S` with N : : : : parameters of type T and M of : : : : arbitrary type : -- GitLab From 55c5f1289845649ee6e13bd90bc51e113f74c143 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 24 Jul 2018 16:38:02 -0700 Subject: [PATCH 365/519] Tabularized the VLOGs printing per-op execution times to make it easier to see what ops take much time. PiperOrigin-RevId: 205913222 --- .../core/grappler/costs/virtual_scheduler.cc | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/tensorflow/core/grappler/costs/virtual_scheduler.cc b/tensorflow/core/grappler/costs/virtual_scheduler.cc index 7f68272950..6a1b0aebfa 100644 --- a/tensorflow/core/grappler/costs/virtual_scheduler.cc +++ b/tensorflow/core/grappler/costs/virtual_scheduler.cc @@ -30,6 +30,7 @@ limitations under the License. #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/stringprintf.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/util/device_name_utils.h" @@ -858,8 +859,9 @@ Costs VirtualScheduler::Summary() const { const auto& memory_cost = op_cost_pair.second.memory_time.count(); const bool is_op_cost_accurate = !op_cost_pair.second.inaccurate; if (cost) { // Skip printing out zero-cost ops. - VLOG(1) << " + " << op << " : " << (is_op_cost_accurate ? "" : "~") - << cost << " / " << compute_cost << " / " << memory_cost; + VLOG(1) << strings::Printf(" + %30s : %c %10ld / %10ld / %10ld", + op.c_str(), (is_op_cost_accurate ? ' ' : '~'), + cost, compute_cost, memory_cost); } } @@ -934,9 +936,11 @@ Costs VirtualScheduler::Summary() const { : 0.0; if (cost || mem_usage_percent > 1.0) { // Print out only non-zero cost ops or ops with > 1% memory usage. - VLOG(1) << " + " << op << " : " << (is_op_cost_accurate ? "" : "~") - << cost << " / " << compute_cost << " / " << memory_cost << " (" - << strings::HumanReadableNumBytes(op_mem_usage) << " [" + VLOG(1) << strings::Printf(" + %30s : %c %10ld / %10ld / %10ld", + op.c_str(), + (is_op_cost_accurate ? ' ' : '~'), cost, + compute_cost, memory_cost) + << " (" << strings::HumanReadableNumBytes(op_mem_usage) << " [" << mem_usage_percent << "%] " << (persisent_ops.count(op) > 0 ? ": persistent op)" : ")"); } -- GitLab From 4c161d7306eb934232e3fe65de2c31c3bb7cf875 Mon Sep 17 00:00:00 2001 From: Zhenyu Tan Date: Tue, 24 Jul 2018 16:50:28 -0700 Subject: [PATCH 366/519] use parameterized test in rmsprop PiperOrigin-RevId: 205914985 --- tensorflow/contrib/optimizer_v2/BUILD | 1 + .../contrib/optimizer_v2/rmsprop_test.py | 718 +++++++++--------- tensorflow/python/framework/test_util.py | 73 ++ .../keras/layers/cudnn_recurrent_test.py | 4 +- tensorflow/python/keras/testing_utils.py | 73 -- 5 files changed, 435 insertions(+), 434 deletions(-) diff --git a/tensorflow/contrib/optimizer_v2/BUILD b/tensorflow/contrib/optimizer_v2/BUILD index 5225ecc14f..3ba3ee29ec 100644 --- a/tensorflow/contrib/optimizer_v2/BUILD +++ b/tensorflow/contrib/optimizer_v2/BUILD @@ -193,6 +193,7 @@ cuda_py_test( srcs = ["rmsprop_test.py"], additional_deps = [ ":training", + "@absl_py//absl/testing:parameterized", "//tensorflow/python:array_ops", "//tensorflow/python:embedding_ops", "//tensorflow/python:framework", diff --git a/tensorflow/contrib/optimizer_v2/rmsprop_test.py b/tensorflow/contrib/optimizer_v2/rmsprop_test.py index ed68f6afbf..dc23ef241a 100644 --- a/tensorflow/contrib/optimizer_v2/rmsprop_test.py +++ b/tensorflow/contrib/optimizer_v2/rmsprop_test.py @@ -19,15 +19,16 @@ from __future__ import division from __future__ import print_function import copy -import itertools import math +from absl.testing import parameterized import numpy as np from tensorflow.contrib.optimizer_v2 import rmsprop 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.ops import embedding_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops import resource_variable_ops @@ -48,13 +49,8 @@ _TEST_PARAM_VALUES = [ [0.5, 0.95, 0.9, 1e-5, True, False], ] -_TESTPARAMS = [ - [data_type] + values - for data_type, values in itertools.product(_DATA_TYPES, _TEST_PARAM_VALUES) -] - -class RMSPropOptimizerTest(test.TestCase): +class RMSPropOptimizerTest(test.TestCase, parameterized.TestCase): def _rmsprop_update_numpy(self, var, g, mg, rms, mom, lr, decay, momentum, epsilon, centered): @@ -87,362 +83,366 @@ class RMSPropOptimizerTest(test.TestCase): var_t[gindex] = var[gindex] - mom_t[gindex] return var_t, mg_t, rms_t, mom_t - def testDense(self): - # TODO(yori): Use ParameterizedTest when available - for (dtype, learning_rate, decay, momentum, - epsilon, centered, use_resource) in _TESTPARAMS: - with self.test_session(use_gpu=True): - # Initialize variables for numpy implementation. - var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) - grads0_np = np.array([0.1, 0.2], dtype=dtype.as_numpy_dtype) - var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) - grads1_np = np.array([0.01, 0.2], dtype=dtype.as_numpy_dtype) - - if use_resource: - var0 = resource_variable_ops.ResourceVariable(var0_np) - var1 = resource_variable_ops.ResourceVariable(var1_np) - else: - var0 = variables.Variable(var0_np) - var1 = variables.Variable(var1_np) - grads0 = constant_op.constant(grads0_np) - grads1 = constant_op.constant(grads1_np) - opt = rmsprop.RMSPropOptimizer( - learning_rate=learning_rate, - decay=decay, - momentum=momentum, - epsilon=epsilon, - centered=centered) - - update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - - mg0 = opt.get_slot(var0, "mg") - self.assertEqual(mg0 is not None, centered) - mg1 = opt.get_slot(var1, "mg") - self.assertEqual(mg1 is not None, centered) - rms0 = opt.get_slot(var0, "rms") - self.assertTrue(rms0 is not None) - rms1 = opt.get_slot(var1, "rms") - self.assertTrue(rms1 is not None) - mom0 = opt.get_slot(var0, "momentum") - self.assertTrue(mom0 is not None) - mom1 = opt.get_slot(var1, "momentum") - self.assertTrue(mom1 is not None) - - mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) - rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) - mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - - # Fetch params to validate initial values - self.assertAllClose([1.0, 2.0], var0.eval()) - self.assertAllClose([3.0, 4.0], var1.eval()) - - # Run 4 steps of RMSProp - for _ in range(1, 5): - update.run() - - var0_np, mg0_np, rms0_np, mom0_np = self._rmsprop_update_numpy( - var0_np, grads0_np, mg0_np, rms0_np, mom0_np, learning_rate, - decay, momentum, epsilon, centered) - var1_np, mg1_np, rms1_np, mom1_np = self._rmsprop_update_numpy( - var1_np, grads1_np, mg1_np, rms1_np, mom1_np, learning_rate, - decay, momentum, epsilon, centered) - - # Validate updated params - if centered: - self.assertAllCloseAccordingToType(mg0_np, mg0.eval()) - self.assertAllCloseAccordingToType(mg1_np, mg1.eval()) - self.assertAllCloseAccordingToType(rms0_np, rms0.eval()) - self.assertAllCloseAccordingToType(rms1_np, rms1.eval()) - self.assertAllCloseAccordingToType(mom0_np, mom0.eval()) - self.assertAllCloseAccordingToType(mom1_np, mom1.eval()) - self.assertAllCloseAccordingToType(var0_np, var0.eval()) - self.assertAllCloseAccordingToType(var1_np, var1.eval()) - - def testMinimizeSparseResourceVariable(self): - for dtype in [dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) - x = constant_op.constant([[4.0], [5.0]], dtype=dtype) - pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) - loss = pred * pred - sgd_op = rmsprop.RMSPropOptimizer( - learning_rate=1.0, - decay=0.0, - momentum=0.0, - epsilon=0.0, - centered=False).minimize(loss) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval()) - # Run 1 step of sgd - sgd_op.run() - # Validate updated params - self.assertAllCloseAccordingToType( - [[0., 1.]], var0.eval(), atol=0.01) - - def testMinimizeSparseResourceVariableCentered(self): - for dtype in [dtypes.float32, dtypes.float64]: - with self.test_session(): - var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) - x = constant_op.constant([[4.0], [5.0]], dtype=dtype) - pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) - loss = pred * pred - sgd_op = rmsprop.RMSPropOptimizer( - learning_rate=1.0, - decay=0.0, - momentum=0.0, - epsilon=1.0, - centered=True).minimize(loss) - variables.global_variables_initializer().run() - # Fetch params to validate initial values - self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval()) - # Run 1 step of sgd - sgd_op.run() - # Validate updated params - self.assertAllCloseAccordingToType( - [[-111, -138]], var0.eval(), atol=0.01) - - def testSparse(self): - # TODO(yori): Use ParameterizedTest when available - for (dtype, learning_rate, decay, - momentum, epsilon, centered, _) in _TESTPARAMS: - with self.test_session(use_gpu=True): - # Initialize variables for numpy implementation. - var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) - grads0_np = np.array([0.1], dtype=dtype.as_numpy_dtype) - var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) - grads1_np = np.array([0.01], dtype=dtype.as_numpy_dtype) - + @parameterized.named_parameters( + *test_util.generate_combinations_with_testcase_name( + dtype=_DATA_TYPES, param_value=_TEST_PARAM_VALUES)) + def testDense(self, dtype, param_value): + (learning_rate, decay, momentum, epsilon, centered, use_resource) = tuple( + param_value) + with self.test_session(use_gpu=True): + # Initialize variables for numpy implementation. + var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) + grads0_np = np.array([0.1, 0.2], dtype=dtype.as_numpy_dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) + grads1_np = np.array([0.01, 0.2], dtype=dtype.as_numpy_dtype) + + if use_resource: + var0 = resource_variable_ops.ResourceVariable(var0_np) + var1 = resource_variable_ops.ResourceVariable(var1_np) + else: var0 = variables.Variable(var0_np) var1 = variables.Variable(var1_np) - grads0_np_indices = np.array([0], dtype=np.int32) - grads0 = ops.IndexedSlices( - constant_op.constant(grads0_np), - constant_op.constant(grads0_np_indices), constant_op.constant([1])) - grads1_np_indices = np.array([1], dtype=np.int32) - grads1 = ops.IndexedSlices( - constant_op.constant(grads1_np), - constant_op.constant(grads1_np_indices), constant_op.constant([1])) - opt = rmsprop.RMSPropOptimizer( - learning_rate=learning_rate, - decay=decay, - momentum=momentum, - epsilon=epsilon, - centered=centered) - update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - - mg0 = opt.get_slot(var0, "mg") - self.assertEqual(mg0 is not None, centered) - mg1 = opt.get_slot(var1, "mg") - self.assertEqual(mg1 is not None, centered) - rms0 = opt.get_slot(var0, "rms") - self.assertTrue(rms0 is not None) - rms1 = opt.get_slot(var1, "rms") - self.assertTrue(rms1 is not None) - mom0 = opt.get_slot(var0, "momentum") - self.assertTrue(mom0 is not None) - mom1 = opt.get_slot(var1, "momentum") - self.assertTrue(mom1 is not None) - - mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) - rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) - mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) - - # Fetch params to validate initial values - self.assertAllClose([1.0, 2.0], var0.eval()) - self.assertAllClose([3.0, 4.0], var1.eval()) - - # Run 4 steps of RMSProp - for _ in range(1, 5): - update.run() - - var0_np, mg0_np, rms0_np, mom0_np = self._sparse_rmsprop_update_numpy( - var0_np, grads0_np_indices, grads0_np, mg0_np, rms0_np, mom0_np, - learning_rate, decay, momentum, epsilon, centered) - var1_np, mg1_np, rms1_np, mom1_np = self._sparse_rmsprop_update_numpy( - var1_np, grads1_np_indices, grads1_np, mg1_np, rms1_np, mom1_np, - learning_rate, decay, momentum, epsilon, centered) - - # Validate updated params - if centered: - self.assertAllCloseAccordingToType(mg0_np, mg0.eval()) - self.assertAllCloseAccordingToType(mg1_np, mg1.eval()) - self.assertAllCloseAccordingToType(rms0_np, rms0.eval()) - self.assertAllCloseAccordingToType(rms1_np, rms1.eval()) - self.assertAllCloseAccordingToType(mom0_np, mom0.eval()) - self.assertAllCloseAccordingToType(mom1_np, mom1.eval()) - self.assertAllCloseAccordingToType(var0_np, var0.eval()) - self.assertAllCloseAccordingToType(var1_np, var1.eval()) - - def testWithoutMomentum(self): - for dtype in [dtypes.half, dtypes.float32]: - with self.test_session(use_gpu=True): - var0 = variables.Variable([1.0, 2.0], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - opt = rmsprop.RMSPropOptimizer( - learning_rate=2.0, decay=0.9, momentum=0.0, epsilon=1.0) - update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - - rms0 = opt.get_slot(var0, "rms") - self.assertTrue(rms0 is not None) - rms1 = opt.get_slot(var1, "rms") - self.assertTrue(rms1 is not None) - mom0 = opt.get_slot(var0, "momentum") - self.assertTrue(mom0 is not None) - mom1 = opt.get_slot(var1, "momentum") - self.assertTrue(mom1 is not None) - - # Fetch params to validate initial values - self.assertAllClose([1.0, 2.0], var0.eval()) - self.assertAllClose([3.0, 4.0], var1.eval()) - # Step 1: the rms accumulators where 1. So we should see a normal - # update: v -= grad * learning_rate - update.run() - # Check the root mean square accumulators. - self.assertAllCloseAccordingToType( - np.array([0.901, 0.901]), rms0.eval()) - self.assertAllCloseAccordingToType( - np.array([0.90001, 0.90001]), rms1.eval()) - # Check the parameters. - self.assertAllCloseAccordingToType( - np.array([ - 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)), - 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - ]), var0.eval()) - self.assertAllCloseAccordingToType( - np.array([ - 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)), - 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - ]), var1.eval()) - # Step 2: the root mean square accumulators contain the previous update. - update.run() - # Check the rms accumulators. - self.assertAllCloseAccordingToType( - np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), rms0.eval()) - self.assertAllCloseAccordingToType( - np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), rms1.eval()) - # Check the parameters. - self.assertAllCloseAccordingToType( - np.array([ - 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)), - 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)) - ]), var0.eval()) - self.assertAllCloseAccordingToType( - np.array([ - 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)), - 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)) - ]), var1.eval()) - - def testWithMomentum(self): - for dtype in [dtypes.half, dtypes.float32]: - with self.test_session(use_gpu=True): - var0 = variables.Variable([1.0, 2.0], dtype=dtype) - var1 = variables.Variable([3.0, 4.0], dtype=dtype) - grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) - grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) - - opt = rmsprop.RMSPropOptimizer( - learning_rate=2.0, decay=0.9, momentum=0.5, epsilon=1e-5) - update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) - variables.global_variables_initializer().run() - - rms0 = opt.get_slot(var0, "rms") - self.assertTrue(rms0 is not None) - rms1 = opt.get_slot(var1, "rms") - self.assertTrue(rms1 is not None) - mom0 = opt.get_slot(var0, "momentum") - self.assertTrue(mom0 is not None) - mom1 = opt.get_slot(var1, "momentum") - self.assertTrue(mom1 is not None) - - # Fetch params to validate initial values - self.assertAllClose([1.0, 2.0], var0.eval()) - self.assertAllClose([3.0, 4.0], var1.eval()) - # Step 1: rms = 1, mom = 0. So we should see a normal - # update: v -= grad * learning_rate + grads0 = constant_op.constant(grads0_np) + grads1 = constant_op.constant(grads1_np) + opt = rmsprop.RMSPropOptimizer( + learning_rate=learning_rate, + decay=decay, + momentum=momentum, + epsilon=epsilon, + centered=centered) + + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + variables.global_variables_initializer().run() + + mg0 = opt.get_slot(var0, "mg") + self.assertEqual(mg0 is not None, centered) + mg1 = opt.get_slot(var1, "mg") + self.assertEqual(mg1 is not None, centered) + rms0 = opt.get_slot(var0, "rms") + self.assertIsNotNone(rms0) + rms1 = opt.get_slot(var1, "rms") + self.assertIsNotNone(rms1) + mom0 = opt.get_slot(var0, "momentum") + self.assertIsNotNone(mom0) + mom1 = opt.get_slot(var1, "momentum") + self.assertIsNotNone(mom1) + + mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + # Run 4 steps of RMSProp + for _ in range(4): update.run() - # Check the root mean square accumulators. - self.assertAllCloseAccordingToType( - np.array([0.901, 0.901]), rms0.eval()) - self.assertAllCloseAccordingToType( - np.array([0.90001, 0.90001]), rms1.eval()) - # Check the momentum accumulators - self.assertAllCloseAccordingToType( - np.array([(0.1 * 2.0 / math.sqrt(0.901 + 1e-5)), - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5))]), mom0.eval()) - self.assertAllCloseAccordingToType( - np.array([(0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)), - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5))]), mom1.eval()) - - # Check that the parameters. - self.assertAllCloseAccordingToType( - np.array([ - 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)), - 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - ]), var0.eval()) - self.assertAllCloseAccordingToType( - np.array([ - 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)), - 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - ]), var1.eval()) - - # Step 2: the root mean square accumulators contain the previous update. + + var0_np, mg0_np, rms0_np, mom0_np = self._rmsprop_update_numpy( + var0_np, grads0_np, mg0_np, rms0_np, mom0_np, learning_rate, + decay, momentum, epsilon, centered) + var1_np, mg1_np, rms1_np, mom1_np = self._rmsprop_update_numpy( + var1_np, grads1_np, mg1_np, rms1_np, mom1_np, learning_rate, + decay, momentum, epsilon, centered) + + # Validate updated params + if centered: + self.assertAllCloseAccordingToType(mg0_np, mg0.eval()) + self.assertAllCloseAccordingToType(mg1_np, mg1.eval()) + self.assertAllCloseAccordingToType(rms0_np, rms0.eval()) + self.assertAllCloseAccordingToType(rms1_np, rms1.eval()) + self.assertAllCloseAccordingToType(mom0_np, mom0.eval()) + self.assertAllCloseAccordingToType(mom1_np, mom1.eval()) + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + + @parameterized.parameters([dtypes.float32, dtypes.float64]) + def testMinimizeSparseResourceVariable(self, dtype): + with self.test_session(): + var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) + x = constant_op.constant([[4.0], [5.0]], dtype=dtype) + pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) + loss = pred * pred + sgd_op = rmsprop.RMSPropOptimizer( + learning_rate=1.0, + decay=0.0, + momentum=0.0, + epsilon=0.0, + centered=False).minimize(loss) + variables.global_variables_initializer().run() + # Fetch params to validate initial values + self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval()) + # Run 1 step of sgd + sgd_op.run() + # Validate updated params + self.assertAllCloseAccordingToType( + [[0., 1.]], var0.eval(), atol=0.01) + + @parameterized.parameters([dtypes.float32, dtypes.float64]) + def testMinimizeSparseResourceVariableCentered(self, dtype): + with self.test_session(): + var0 = resource_variable_ops.ResourceVariable([[1.0, 2.0]], dtype=dtype) + x = constant_op.constant([[4.0], [5.0]], dtype=dtype) + pred = math_ops.matmul(embedding_ops.embedding_lookup([var0], [0]), x) + loss = pred * pred + sgd_op = rmsprop.RMSPropOptimizer( + learning_rate=1.0, + decay=0.0, + momentum=0.0, + epsilon=1.0, + centered=True).minimize(loss) + variables.global_variables_initializer().run() + # Fetch params to validate initial values + self.assertAllCloseAccordingToType([[1.0, 2.0]], var0.eval()) + # Run 1 step of sgd + sgd_op.run() + # Validate updated params + self.assertAllCloseAccordingToType( + [[-111, -138]], var0.eval(), atol=0.01) + + @parameterized.named_parameters( + *test_util.generate_combinations_with_testcase_name( + dtype=_DATA_TYPES, param_value=_TEST_PARAM_VALUES)) + def testSparse(self, dtype, param_value): + (learning_rate, decay, momentum, epsilon, centered, _) = tuple( + param_value) + with self.test_session(use_gpu=True): + # Initialize variables for numpy implementation. + var0_np = np.array([1.0, 2.0], dtype=dtype.as_numpy_dtype) + grads0_np = np.array([0.1], dtype=dtype.as_numpy_dtype) + var1_np = np.array([3.0, 4.0], dtype=dtype.as_numpy_dtype) + grads1_np = np.array([0.01], dtype=dtype.as_numpy_dtype) + + var0 = variables.Variable(var0_np) + var1 = variables.Variable(var1_np) + grads0_np_indices = np.array([0], dtype=np.int32) + grads0 = ops.IndexedSlices( + constant_op.constant(grads0_np), + constant_op.constant(grads0_np_indices), constant_op.constant([1])) + grads1_np_indices = np.array([1], dtype=np.int32) + grads1 = ops.IndexedSlices( + constant_op.constant(grads1_np), + constant_op.constant(grads1_np_indices), constant_op.constant([1])) + opt = rmsprop.RMSPropOptimizer( + learning_rate=learning_rate, + decay=decay, + momentum=momentum, + epsilon=epsilon, + centered=centered) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + variables.global_variables_initializer().run() + + mg0 = opt.get_slot(var0, "mg") + self.assertEqual(mg0 is not None, centered) + mg1 = opt.get_slot(var1, "mg") + self.assertEqual(mg1 is not None, centered) + rms0 = opt.get_slot(var0, "rms") + self.assertIsNotNone(rms0) + rms1 = opt.get_slot(var1, "rms") + self.assertIsNotNone(rms1) + mom0 = opt.get_slot(var0, "momentum") + self.assertIsNotNone(mom0) + mom1 = opt.get_slot(var1, "momentum") + self.assertIsNotNone(mom1) + + mg0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + mg1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + rms0_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + rms1_np = np.array([1.0, 1.0], dtype=dtype.as_numpy_dtype) + mom0_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + mom1_np = np.array([0.0, 0.0], dtype=dtype.as_numpy_dtype) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + + # Run 4 steps of RMSProp + for _ in range(4): update.run() - # Check the rms accumulators. - self.assertAllCloseAccordingToType( - np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), rms0.eval()) - self.assertAllCloseAccordingToType( - np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), rms1.eval()) - self.assertAllCloseAccordingToType( - np.array([ - 0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)), - 0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)) - ]), mom0.eval()) - self.assertAllCloseAccordingToType( - np.array([ - 0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)), - 0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)) - ]), mom1.eval()) - - # Check the parameters. - self.assertAllCloseAccordingToType( - np.array([ - 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - - (0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))), - 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - - (0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + - (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))) - ]), var0.eval()) - - self.assertAllCloseAccordingToType( - np.array([ - 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - - (0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))), - 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - - (0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + - (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))) - ]), var1.eval()) + + var0_np, mg0_np, rms0_np, mom0_np = self._sparse_rmsprop_update_numpy( + var0_np, grads0_np_indices, grads0_np, mg0_np, rms0_np, mom0_np, + learning_rate, decay, momentum, epsilon, centered) + var1_np, mg1_np, rms1_np, mom1_np = self._sparse_rmsprop_update_numpy( + var1_np, grads1_np_indices, grads1_np, mg1_np, rms1_np, mom1_np, + learning_rate, decay, momentum, epsilon, centered) + + # Validate updated params + if centered: + self.assertAllCloseAccordingToType(mg0_np, mg0.eval()) + self.assertAllCloseAccordingToType(mg1_np, mg1.eval()) + self.assertAllCloseAccordingToType(rms0_np, rms0.eval()) + self.assertAllCloseAccordingToType(rms1_np, rms1.eval()) + self.assertAllCloseAccordingToType(mom0_np, mom0.eval()) + self.assertAllCloseAccordingToType(mom1_np, mom1.eval()) + self.assertAllCloseAccordingToType(var0_np, var0.eval()) + self.assertAllCloseAccordingToType(var1_np, var1.eval()) + + @parameterized.parameters(_DATA_TYPES) + def testWithoutMomentum(self, dtype): + with self.test_session(use_gpu=True): + var0 = variables.Variable([1.0, 2.0], dtype=dtype) + var1 = variables.Variable([3.0, 4.0], dtype=dtype) + grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) + grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) + opt = rmsprop.RMSPropOptimizer( + learning_rate=2.0, decay=0.9, momentum=0.0, epsilon=1.0) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + variables.global_variables_initializer().run() + + rms0 = opt.get_slot(var0, "rms") + self.assertIsNotNone(rms0) + rms1 = opt.get_slot(var1, "rms") + self.assertIsNotNone(rms1) + mom0 = opt.get_slot(var0, "momentum") + self.assertIsNotNone(mom0) + mom1 = opt.get_slot(var1, "momentum") + self.assertIsNotNone(mom1) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + # Step 1: the rms accumulators where 1. So we should see a normal + # update: v -= grad * learning_rate + update.run() + # Check the root mean square accumulators. + self.assertAllCloseAccordingToType( + np.array([0.901, 0.901]), rms0.eval()) + self.assertAllCloseAccordingToType( + np.array([0.90001, 0.90001]), rms1.eval()) + # Check the parameters. + self.assertAllCloseAccordingToType( + np.array([ + 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)), + 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) + ]), var0.eval()) + self.assertAllCloseAccordingToType( + np.array([ + 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)), + 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) + ]), var1.eval()) + # Step 2: the root mean square accumulators contain the previous update. + update.run() + # Check the rms accumulators. + self.assertAllCloseAccordingToType( + np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), rms0.eval()) + self.assertAllCloseAccordingToType( + np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), rms1.eval()) + # Check the parameters. + self.assertAllCloseAccordingToType( + np.array([ + 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)), + 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1.0)) - + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1.0)) + ]), var0.eval()) + self.assertAllCloseAccordingToType( + np.array([ + 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)), + 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1.0)) - + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 1e-5 + 1.0)) + ]), var1.eval()) + + @parameterized.parameters(_DATA_TYPES) + def testWithMomentum(self, dtype): + with self.test_session(use_gpu=True): + var0 = variables.Variable([1.0, 2.0], dtype=dtype) + var1 = variables.Variable([3.0, 4.0], dtype=dtype) + grads0 = constant_op.constant([0.1, 0.1], dtype=dtype) + grads1 = constant_op.constant([0.01, 0.01], dtype=dtype) + + opt = rmsprop.RMSPropOptimizer( + learning_rate=2.0, decay=0.9, momentum=0.5, epsilon=1e-5) + update = opt.apply_gradients(zip([grads0, grads1], [var0, var1])) + variables.global_variables_initializer().run() + + rms0 = opt.get_slot(var0, "rms") + self.assertIsNotNone(rms0) + rms1 = opt.get_slot(var1, "rms") + self.assertIsNotNone(rms1) + mom0 = opt.get_slot(var0, "momentum") + self.assertIsNotNone(mom0) + mom1 = opt.get_slot(var1, "momentum") + self.assertIsNotNone(mom1) + + # Fetch params to validate initial values + self.assertAllClose([1.0, 2.0], var0.eval()) + self.assertAllClose([3.0, 4.0], var1.eval()) + # Step 1: rms = 1, mom = 0. So we should see a normal + # update: v -= grad * learning_rate + update.run() + # Check the root mean square accumulators. + self.assertAllCloseAccordingToType( + np.array([0.901, 0.901]), rms0.eval()) + self.assertAllCloseAccordingToType( + np.array([0.90001, 0.90001]), rms1.eval()) + # Check the momentum accumulators + self.assertAllCloseAccordingToType( + np.array([(0.1 * 2.0 / math.sqrt(0.901 + 1e-5)), + (0.1 * 2.0 / math.sqrt(0.901 + 1e-5))]), mom0.eval()) + self.assertAllCloseAccordingToType( + np.array([(0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)), + (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5))]), mom1.eval()) + + # Check that the parameters. + self.assertAllCloseAccordingToType( + np.array([ + 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)), + 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + ]), var0.eval()) + self.assertAllCloseAccordingToType( + np.array([ + 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)), + 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + ]), var1.eval()) + + # Step 2: the root mean square accumulators contain the previous update. + update.run() + # Check the rms accumulators. + self.assertAllCloseAccordingToType( + np.array([0.901 * 0.9 + 0.001, 0.901 * 0.9 + 0.001]), rms0.eval()) + self.assertAllCloseAccordingToType( + np.array([0.90001 * 0.9 + 1e-5, 0.90001 * 0.9 + 1e-5]), rms1.eval()) + self.assertAllCloseAccordingToType( + np.array([ + 0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)), + 0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5)) + ]), mom0.eval()) + self.assertAllCloseAccordingToType( + np.array([ + 0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)), + 0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5)) + ]), mom1.eval()) + + # Check the parameters. + self.assertAllCloseAccordingToType( + np.array([ + 1.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - + (0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))), + 2.0 - (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) - + (0.5 * (0.1 * 2.0 / math.sqrt(0.901 + 1e-5)) + + (0.1 * 2.0 / math.sqrt(0.901 * 0.9 + 0.001 + 1e-5))) + ]), var0.eval()) + + self.assertAllCloseAccordingToType( + np.array([ + 3.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - + (0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))), + 4.0 - (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) - + (0.5 * (0.01 * 2.0 / math.sqrt(0.90001 + 1e-5)) + + (0.01 * 2.0 / math.sqrt(0.90001 * 0.9 + 2e-5))) + ]), var1.eval()) if __name__ == "__main__": diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index d7e7a2c111..fc47b1cca5 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -20,6 +20,7 @@ from __future__ import division from __future__ import print_function import collections +from collections import OrderedDict import contextlib import gc import itertools @@ -572,6 +573,78 @@ def assert_no_garbage_created(f): return decorator +def _combine_named_parameters(**kwargs): + """Generate combinations based on its keyword arguments. + + Two sets of returned combinations can be concatenated using +. Their product + can be computed using `times()`. + + Args: + **kwargs: keyword arguments of form `option=[possibilities, ...]` + or `option=the_only_possibility`. + + Returns: + a list of dictionaries for each combination. Keys in the dictionaries are + the keyword argument names. Each key has one value - one of the + corresponding keyword argument values. + """ + if not kwargs: + return [OrderedDict()] + + sort_by_key = lambda k: k[0][0] + kwargs = OrderedDict(sorted(kwargs.items(), key=sort_by_key)) + first = list(kwargs.items())[0] + + rest = dict(list(kwargs.items())[1:]) + rest_combined = _combine_named_parameters(**rest) + + key = first[0] + values = first[1] + if not isinstance(values, list): + values = [values] + + combinations = [ + OrderedDict(sorted(list(combined.items()) + [(key, v)], key=sort_by_key)) + for v in values + for combined in rest_combined + ] + return combinations + + +def generate_combinations_with_testcase_name(**kwargs): + """Generate combinations based on its keyword arguments using combine(). + + This function calls combine() and appends a testcase name to the list of + dictionaries returned. The 'testcase_name' key is a required for named + parameterized tests. + + Args: + **kwargs: keyword arguments of form `option=[possibilities, ...]` + or `option=the_only_possibility`. + + Returns: + a list of dictionaries for each combination. Keys in the dictionaries are + the keyword argument names. Each key has one value - one of the + corresponding keyword argument values. + """ + combinations = _combine_named_parameters(**kwargs) + named_combinations = [] + for combination in combinations: + assert isinstance(combination, OrderedDict) + name = "".join([ + "_{}_{}".format( + "".join(filter(str.isalnum, key)), + "".join(filter(str.isalnum, str(value)))) + for key, value in combination.items() + ]) + named_combinations.append( + OrderedDict( + list(combination.items()) + [("testcase_name", + "_test{}".format(name))])) + + return named_combinations + + def run_all_in_graph_and_eager_modes(cls): """Execute all test methods in the given class with and without eager.""" base_decorator = run_in_graph_and_eager_modes diff --git a/tensorflow/python/keras/layers/cudnn_recurrent_test.py b/tensorflow/python/keras/layers/cudnn_recurrent_test.py index 8fd970239f..2ed0aa8f26 100644 --- a/tensorflow/python/keras/layers/cudnn_recurrent_test.py +++ b/tensorflow/python/keras/layers/cudnn_recurrent_test.py @@ -220,7 +220,7 @@ class CuDNNTest(test.TestCase, parameterized.TestCase): self.assertNotEqual(out4.max(), out5.max()) @parameterized.named_parameters( - *testing_utils.generate_combinations_with_testcase_name( + *test_util.generate_combinations_with_testcase_name( rnn_type=['LSTM', 'GRU'], to_cudnn=[True, False], bidirectional=[True, False], implementation=[1, 2], model_nest_level=[1, 2], model_type=['seq', 'func'])) @@ -301,7 +301,7 @@ class CuDNNTest(test.TestCase, parameterized.TestCase): os.remove(fname) @parameterized.named_parameters( - *testing_utils.generate_combinations_with_testcase_name( + *test_util.generate_combinations_with_testcase_name( rnn_type=['LSTM', 'GRU'], to_cudnn=[True, False])) def test_load_weights_between_noncudnn_rnn_time_distributed(self, rnn_type, to_cudnn): diff --git a/tensorflow/python/keras/testing_utils.py b/tensorflow/python/keras/testing_utils.py index 17aba7d86c..6e8ee06ff5 100644 --- a/tensorflow/python/keras/testing_utils.py +++ b/tensorflow/python/keras/testing_utils.py @@ -18,7 +18,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -from collections import OrderedDict import numpy as np from tensorflow.python import keras @@ -185,75 +184,3 @@ def layer_test(layer_cls, kwargs=None, input_shape=None, input_dtype=None, # for further checks in the caller function return actual_output - -def _combine_named_parameters(**kwargs): - """Generate combinations based on its keyword arguments. - - Two sets of returned combinations can be concatenated using +. Their product - can be computed using `times()`. - - Args: - **kwargs: keyword arguments of form `option=[possibilities, ...]` - or `option=the_only_possibility`. - - Returns: - a list of dictionaries for each combination. Keys in the dictionaries are - the keyword argument names. Each key has one value - one of the - corresponding keyword argument values. - """ - if not kwargs: - return [OrderedDict()] - - sort_by_key = lambda k: k[0][0] - kwargs = OrderedDict(sorted(kwargs.items(), key=sort_by_key)) - first = list(kwargs.items())[0] - - rest = dict(list(kwargs.items())[1:]) - rest_combined = _combine_named_parameters(**rest) - - key = first[0] - values = first[1] - if not isinstance(values, list): - values = [values] - - combinations = [ - OrderedDict(sorted(list(combined.items()) + [(key, v)], key=sort_by_key)) - for v in values - for combined in rest_combined - ] - return combinations - - -def generate_combinations_with_testcase_name(**kwargs): - """Generate combinations based on its keyword arguments using combine(). - - This function calls combine() and appends a testcase name to the list of - dictionaries returned. The 'testcase_name' key is a required for named - parameterized tests. - - Args: - **kwargs: keyword arguments of form `option=[possibilities, ...]` - or `option=the_only_possibility`. - - Returns: - a list of dictionaries for each combination. Keys in the dictionaries are - the keyword argument names. Each key has one value - one of the - corresponding keyword argument values. - """ - combinations = _combine_named_parameters(**kwargs) - named_combinations = [] - for combination in combinations: - assert isinstance(combination, OrderedDict) - name = ''.join([ - '_{}_{}'.format( - ''.join(filter(str.isalnum, key)), - ''.join(filter(str.isalnum, str(value)))) - for key, value in combination.items() - ]) - named_combinations.append( - OrderedDict( - list(combination.items()) + [('testcase_name', - '_test{}'.format(name))])) - - return named_combinations - -- GitLab From 0cf2c612e5e6ff8c5026011e8186056801def747 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 24 Jul 2018 17:07:15 -0700 Subject: [PATCH 367/519] Keras ReLU Consolidation Consolidate functionality of ThresholdedReLU and LeakyReLU layers into ReLU layer PiperOrigin-RevId: 205917439 --- tensorflow/python/keras/activations.py | 22 ++++--- tensorflow/python/keras/backend.py | 32 ++++++++-- tensorflow/python/keras/backend_test.py | 61 +++++++++++++++++++ .../keras/layers/advanced_activations.py | 37 +++++++++-- .../keras/layers/advanced_activations_test.py | 8 +++ .../golden/tensorflow.keras.activations.pbtxt | 2 +- .../api/golden/tensorflow.keras.backend.pbtxt | 2 +- .../tensorflow.keras.layers.-re-l-u.pbtxt | 2 +- 8 files changed, 144 insertions(+), 22 deletions(-) diff --git a/tensorflow/python/keras/activations.py b/tensorflow/python/keras/activations.py index f608dea430..99645de736 100644 --- a/tensorflow/python/keras/activations.py +++ b/tensorflow/python/keras/activations.py @@ -128,20 +128,26 @@ def softsign(x): @tf_export('keras.activations.relu') -def relu(x, alpha=0., max_value=None): +def relu(x, alpha=0., max_value=None, threshold=0): """Rectified Linear Unit. + With default values, it returns element-wise `max(x, 0)`. + + Otherwise, it follows: + `f(x) = max_value` for `x >= max_value`, + `f(x) = x` for `threshold <= x < max_value`, + `f(x) = alpha * (x - threshold)` otherwise. + Arguments: - x: Input tensor. - alpha: Slope of the negative part. Defaults to zero. - max_value: Maximum value for the output. + x: A tensor or variable. + alpha: A scalar, slope of negative section (default=`0.`). + max_value: float. Saturation threshold. + threshold: float. Threshold value for thresholded activation. Returns: - The (leaky) rectified linear unit activation: `x` if `x > 0`, - `alpha * x` if `x < 0`. If `max_value` is defined, the result - is truncated to this value. + A tensor. """ - return K.relu(x, alpha=alpha, max_value=max_value) + return K.relu(x, alpha=alpha, max_value=max_value, threshold=threshold) @tf_export('keras.activations.tanh') diff --git a/tensorflow/python/keras/backend.py b/tensorflow/python/keras/backend.py index 333f927d2f..38794f1612 100644 --- a/tensorflow/python/keras/backend.py +++ b/tensorflow/python/keras/backend.py @@ -3372,26 +3372,48 @@ def in_test_phase(x, alt, training=None): @tf_export('keras.backend.relu') -def relu(x, alpha=0., max_value=None): +def relu(x, alpha=0., max_value=None, threshold=0): """Rectified linear unit. With default values, it returns element-wise `max(x, 0)`. + Otherwise, it follows: + `f(x) = max_value` for `x >= max_value`, + `f(x) = x` for `threshold <= x < max_value`, + `f(x) = alpha * (x - threshold)` otherwise. + Arguments: x: A tensor or variable. alpha: A scalar, slope of negative section (default=`0.`). - max_value: Saturation threshold. + max_value: float. Saturation threshold. + threshold: float. Threshold value for thresholded activation. Returns: A tensor. """ + clip_max = max_value is not None + if alpha != 0.: - negative_part = nn.relu(-x) - x = nn.relu(x) - if max_value is not None: + if threshold != 0: + negative_part = nn.relu(-x + threshold) + else: + negative_part = nn.relu(-x) + + if threshold != 0: + # computes x for x > threshold else 0 + x = x * math_ops.cast(math_ops.greater(x, threshold), floatx()) + elif max_value == 6: + # if no threshold, then can use nn.relu6 native TF op for performance + x = nn.relu6(x) + clip_max = False + else: + x = nn.relu(x) + + if clip_max: max_value = _to_tensor(max_value, x.dtype.base_dtype) zero = _to_tensor(0., x.dtype.base_dtype) x = clip_ops.clip_by_value(x, zero, max_value) + if alpha != 0.: alpha = _to_tensor(alpha, x.dtype.base_dtype) x -= alpha * negative_part diff --git a/tensorflow/python/keras/backend_test.py b/tensorflow/python/keras/backend_test.py index 36478ea089..40e7910061 100644 --- a/tensorflow/python/keras/backend_test.py +++ b/tensorflow/python/keras/backend_test.py @@ -23,6 +23,7 @@ import scipy.sparse from tensorflow.python import keras from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops from tensorflow.python.framework import sparse_tensor from tensorflow.python.ops import variables from tensorflow.python.platform import test @@ -490,6 +491,66 @@ class BackendLinearAlgebraTest(test.TestCase): input_shape_a=(4, 7), input_shape_b=(4, 7)) + def test_relu(self): + x = ops.convert_to_tensor([[-4, 0], [2, 7]], 'float32') + with self.test_session(): + # standard relu + relu_op = keras.backend.relu(x) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]]) + + # alpha + relu_op = keras.backend.relu(x, alpha=0.5) + self.assertAllClose(keras.backend.eval(relu_op), [[-2, 0], [2, 7]]) + + # max_value < some elements + relu_op = keras.backend.relu(x, max_value=5) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 5]]) + + # nn.relu6 used + relu_op = keras.backend.relu(x, max_value=6) + self.assertTrue('Relu6' in relu_op.name) # uses tf.nn.relu6 + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 6]]) + + # max value > 6 + relu_op = keras.backend.relu(x, max_value=10) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]]) + + # max value is float + relu_op = keras.backend.relu(x, max_value=4.3) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 4.3]]) + + # max value == 0 + relu_op = keras.backend.relu(x, max_value=0) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 0]]) + + # alpha and max_value + relu_op = keras.backend.relu(x, alpha=0.25, max_value=3) + self.assertAllClose(keras.backend.eval(relu_op), [[-1, 0], [2, 3]]) + + # threshold + relu_op = keras.backend.relu(x, threshold=3) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 7]]) + + # threshold is float + relu_op = keras.backend.relu(x, threshold=1.5) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [2, 7]]) + + # threshold is negative + relu_op = keras.backend.relu(x, threshold=-5) + self.assertAllClose(keras.backend.eval(relu_op), [[-4, 0], [2, 7]]) + + # threshold and max_value + relu_op = keras.backend.relu(x, threshold=3, max_value=5) + self.assertAllClose(keras.backend.eval(relu_op), [[0, 0], [0, 5]]) + + # threshold and alpha + relu_op = keras.backend.relu(x, alpha=0.25, threshold=4) + self.assertAllClose(keras.backend.eval(relu_op), [[-2, -1], [-0.5, 7]]) + + # threshold, alpha, and max_value + relu_op = keras.backend.relu(x, alpha=0.25, threshold=4, max_value=5) + self.assertAllClose(keras.backend.eval(relu_op), [[-2, -1], [-0.5, 5]]) + class BackendShapeOpsTest(test.TestCase): diff --git a/tensorflow/python/keras/layers/advanced_activations.py b/tensorflow/python/keras/layers/advanced_activations.py index eba10da6f3..61ab69c16f 100644 --- a/tensorflow/python/keras/layers/advanced_activations.py +++ b/tensorflow/python/keras/layers/advanced_activations.py @@ -284,6 +284,13 @@ class Softmax(Layer): class ReLU(Layer): """Rectified Linear Unit activation function. + With default values, it returns element-wise `max(x, 0)`. + + Otherwise, it follows: + `f(x) = max_value` for `x >= max_value`, + `f(x) = x` for `threshold <= x < max_value`, + `f(x) = negative_slope * (x - threshold)` otherwise. + Input shape: Arbitrary. Use the keyword argument `input_shape` (tuple of integers, does not include the samples axis) @@ -294,21 +301,39 @@ class ReLU(Layer): Arguments: max_value: float >= 0. Maximum activation value. + negative_slope: float >= 0. Negative slope coefficient. + threshold: float. Threshold value for thresholded activation. """ - def __init__(self, max_value=None, **kwargs): + def __init__(self, max_value=None, negative_slope=0, threshold=0, **kwargs): super(ReLU, self).__init__(**kwargs) - self.support_masking = True - self.max_value = K.cast_to_floatx(max_value) - if self.max_value < 0.: + if max_value is not None and max_value < 0.: raise ValueError('max_value of Relu layer ' 'cannot be negative value: ' + str(max_value)) + if negative_slope < 0.: + raise ValueError('negative_slope of Relu layer ' + 'cannot be negative value: ' + str(negative_slope)) + + self.support_masking = True + self.max_value = K.cast_to_floatx(max_value) + self.negative_slope = K.cast_to_floatx(negative_slope) + self.threshold = K.cast_to_floatx(threshold) def call(self, inputs): - return activations.relu(inputs, max_value=self.max_value) + # alpha is used for leaky relu slope in activations instead of + # negative_slope. + return activations.relu( + inputs, + alpha=self.negative_slope, + max_value=self.max_value, + threshold=self.threshold) def get_config(self): - config = {'max_value': self.max_value} + config = { + 'max_value': self.max_value, + 'negative_slope': self.negative_slope, + 'threshold': self.threshold + } base_config = super(ReLU, self).get_config() return dict(list(base_config.items()) + list(config.items())) diff --git a/tensorflow/python/keras/layers/advanced_activations_test.py b/tensorflow/python/keras/layers/advanced_activations_test.py index 9e1f15b1bc..53c1baa2bb 100644 --- a/tensorflow/python/keras/layers/advanced_activations_test.py +++ b/tensorflow/python/keras/layers/advanced_activations_test.py @@ -75,6 +75,14 @@ class AdvancedActivationsTest(test.TestCase): testing_utils.layer_test(keras.layers.ReLU, kwargs={'max_value': -10}, input_shape=(2, 3, 4)) + with self.assertRaisesRegexp( + ValueError, + 'negative_slope of Relu layer cannot be negative value: -2'): + with self.test_session(): + testing_utils.layer_test( + keras.layers.ReLU, + kwargs={'negative_slope': -2}, + input_shape=(2, 3, 4)) if __name__ == '__main__': diff --git a/tensorflow/tools/api/golden/tensorflow.keras.activations.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.activations.pbtxt index 2cd83baf65..2e9de9ebb2 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.activations.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.activations.pbtxt @@ -22,7 +22,7 @@ tf_module { } member_method { name: "relu" - argspec: "args=[\'x\', \'alpha\', \'max_value\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\'], " + argspec: "args=[\'x\', \'alpha\', \'max_value\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\', \'0\'], " } member_method { name: "selu" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt index fddac63b78..126ce8db6a 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.backend.pbtxt @@ -366,7 +366,7 @@ tf_module { } member_method { name: "relu" - argspec: "args=[\'x\', \'alpha\', \'max_value\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\'], " + argspec: "args=[\'x\', \'alpha\', \'max_value\', \'threshold\'], varargs=None, keywords=None, defaults=[\'0.0\', \'None\', \'0\'], " } member_method { name: "repeat" diff --git a/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt b/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt index c00fa79adf..4d3de58bd1 100644 --- a/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.keras.layers.-re-l-u.pbtxt @@ -82,7 +82,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'max_value\'], varargs=None, keywords=kwargs, defaults=[\'None\'], " + argspec: "args=[\'self\', \'max_value\', \'negative_slope\', \'threshold\'], varargs=None, keywords=kwargs, defaults=[\'None\', \'0\', \'0\'], " } member_method { name: "add_loss" -- GitLab From b3d6fd048c09acca275576873d69a49f54efcafd Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 24 Jul 2018 17:17:56 -0700 Subject: [PATCH 368/519] Added QuantizedMatMul to OpLevelCostEstimator. PiperOrigin-RevId: 205918819 --- .../grappler/costs/op_level_cost_estimator.cc | 135 +++++++++--------- 1 file changed, 71 insertions(+), 64 deletions(-) diff --git a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc index d34eecd009..5b303f6ccb 100644 --- a/tensorflow/core/grappler/costs/op_level_cost_estimator.cc +++ b/tensorflow/core/grappler/costs/op_level_cost_estimator.cc @@ -65,6 +65,7 @@ constexpr char kAvgPool[] = "AvgPool"; constexpr char kAvgPoolGrad[] = "AvgPoolGrad"; constexpr char kFusedBatchNorm[] = "FusedBatchNorm"; constexpr char kFusedBatchNormGrad[] = "FusedBatchNormGrad"; +constexpr char kQuantizedMatMulV2[] = "QuantizedMatMulV2"; static const Costs::Duration kMinComputeTime(1); @@ -226,6 +227,7 @@ OpLevelCostEstimator::OpLevelCostEstimator() { {kMatMul, wrap(&OpLevelCostEstimator::PredictMatMul)}, {kSparseMatMul, wrap(&OpLevelCostEstimator::PredictMatMul)}, {kBatchMatMul, wrap(&OpLevelCostEstimator::PredictBatchMatMul)}, + {kQuantizedMatMulV2, wrap(&OpLevelCostEstimator::PredictMatMul)}, {kNoOp, wrap(&OpLevelCostEstimator::PredictNoOp)}, {kGuaranteeConst, wrap(&OpLevelCostEstimator::PredictNoOp)}, @@ -268,67 +270,70 @@ OpLevelCostEstimator::OpLevelCostEstimator() { EIGEN_COST(scalar_product_op) + EIGEN_COST(scalar_max_op) + EIGEN_COST(scalar_min_op) + EIGEN_COST(scalar_round_op); - elementwise_ops_ = {// Unary ops alphabetically sorted - {"Acos", EIGEN_COST(scalar_acos_op)}, - {"Asin", EIGEN_COST(scalar_asin_op)}, - {"Atan", EIGEN_COST(scalar_atan_op)}, - {"Atan2", EIGEN_COST(scalar_quotient_op) + - EIGEN_COST(scalar_atan_op)}, - {"Ceil", EIGEN_COST(scalar_ceil_op)}, - {"Cos", EIGEN_COST(scalar_cos_op)}, - {"Dequantize", EIGEN_COST(scalar_product_op)}, - {"Erf", 1}, - {"Erfc", 1}, - {"Exp", EIGEN_COST(scalar_exp_op)}, - {"Expm1", EIGEN_COST(scalar_expm1_op)}, - {"Floor", EIGEN_COST(scalar_floor_op)}, - {"Inv", EIGEN_COST(scalar_inverse_op)}, - {"InvGrad", 1}, - {"Lgamma", 1}, - {"Log", EIGEN_COST(scalar_log_op)}, - {"Log1p", EIGEN_COST(scalar_log1p_op)}, - {"Neg", EIGEN_COST(scalar_opposite_op)}, - {"QuantizeV2", quantize_v2_cost}, - {"Reciprocal", EIGEN_COST(scalar_inverse_op)}, - {"Rint", 1}, - {"Round", EIGEN_COST(scalar_round_op)}, - {"Rsqrt", EIGEN_COST(scalar_rsqrt_op)}, - {"Sqrt", EIGEN_COST(scalar_sqrt_op)}, - {"Square", EIGEN_COST(scalar_square_op)}, - {"Tanh", EIGEN_COST(scalar_tanh_op)}, - {"Relu", EIGEN_COST(scalar_max_op)}, - {"Sigmoid", EIGEN_COST(scalar_sigmoid_op)}, - {"Sign", EIGEN_COST(scalar_sign_op)}, - {"Sin", EIGEN_COST(scalar_sin_op)}, - {"Tan", EIGEN_COST(scalar_tan_op)}, - // Binary ops alphabetically sorted - {"Add", EIGEN_COST(scalar_sum_op)}, - {"ApproximateEqual", 1}, - {"BiasAdd", EIGEN_COST(scalar_sum_op)}, - {"Div", EIGEN_COST(scalar_quotient_op)}, - {"Equal", 1}, - {"FloorDiv", EIGEN_COST(scalar_quotient_op)}, - {"FloorMod", EIGEN_COST(scalar_mod_op)}, - {"Greater", 1}, - {"GreaterEqual", 1}, - {"Less", 1}, - {"LessEqual", 1}, - {"LogicalAnd", EIGEN_COST(scalar_boolean_and_op)}, - {"LogicalNot", 1}, - {"LogicalOr", EIGEN_COST(scalar_boolean_or_op)}, - {"Maximum", EIGEN_COST(scalar_max_op)}, - {"Minimum", EIGEN_COST(scalar_min_op)}, - {"Mod", EIGEN_COST(scalar_mod_op)}, - {"Mul", EIGEN_COST(scalar_product_op)}, - {"NotEqual", 1}, - {"QuantizedAdd", EIGEN_COST(scalar_sum_op)}, - {"QuantizedMul", EIGEN_COST(scalar_product_op)}, - {"RealDiv", EIGEN_COST(scalar_quotient_op)}, - {"ReluGrad", EIGEN_COST(scalar_max_op)}, - {"SquareDifference", 1}, - {"Sub", EIGEN_COST(scalar_difference_op)}, - {"TruncateDiv", EIGEN_COST(scalar_quotient_op)}, - {"TruncateMod", EIGEN_COST(scalar_mod_op)}}; + elementwise_ops_ = { + // Unary ops alphabetically sorted + {"Acos", EIGEN_COST(scalar_acos_op)}, + {"Asin", EIGEN_COST(scalar_asin_op)}, + {"Atan", EIGEN_COST(scalar_atan_op)}, + {"Atan2", EIGEN_COST(scalar_quotient_op) + + EIGEN_COST(scalar_atan_op)}, + {"Ceil", EIGEN_COST(scalar_ceil_op)}, + {"Cos", EIGEN_COST(scalar_cos_op)}, + {"Dequantize", EIGEN_COST(scalar_product_op)}, + {"Erf", 1}, + {"Erfc", 1}, + {"Exp", EIGEN_COST(scalar_exp_op)}, + {"Expm1", EIGEN_COST(scalar_expm1_op)}, + {"Floor", EIGEN_COST(scalar_floor_op)}, + {"Inv", EIGEN_COST(scalar_inverse_op)}, + {"InvGrad", 1}, + {"Lgamma", 1}, + {"Log", EIGEN_COST(scalar_log_op)}, + {"Log1p", EIGEN_COST(scalar_log1p_op)}, + {"Neg", EIGEN_COST(scalar_opposite_op)}, + {"QuantizeV2", quantize_v2_cost}, + {"Reciprocal", EIGEN_COST(scalar_inverse_op)}, + {"Rint", 1}, + {"Round", EIGEN_COST(scalar_round_op)}, + {"Rsqrt", EIGEN_COST(scalar_rsqrt_op)}, + {"Sqrt", EIGEN_COST(scalar_sqrt_op)}, + {"Square", EIGEN_COST(scalar_square_op)}, + {"Tanh", EIGEN_COST(scalar_tanh_op)}, + {"Relu", EIGEN_COST(scalar_max_op)}, + {"Sigmoid", EIGEN_COST(scalar_sigmoid_op)}, + {"QuantizedSigmoid", EIGEN_COST(scalar_sigmoid_op)}, + {"Sign", EIGEN_COST(scalar_sign_op)}, + {"Sin", EIGEN_COST(scalar_sin_op)}, + {"Tan", EIGEN_COST(scalar_tan_op)}, + // Binary ops alphabetically sorted + {"Add", EIGEN_COST(scalar_sum_op)}, + {"ApproximateEqual", 1}, + {"BiasAdd", EIGEN_COST(scalar_sum_op)}, + {"QuantizedBiasAdd", EIGEN_COST(scalar_sum_op)}, + {"Div", EIGEN_COST(scalar_quotient_op)}, + {"Equal", 1}, + {"FloorDiv", EIGEN_COST(scalar_quotient_op)}, + {"FloorMod", EIGEN_COST(scalar_mod_op)}, + {"Greater", 1}, + {"GreaterEqual", 1}, + {"Less", 1}, + {"LessEqual", 1}, + {"LogicalAnd", EIGEN_COST(scalar_boolean_and_op)}, + {"LogicalNot", 1}, + {"LogicalOr", EIGEN_COST(scalar_boolean_or_op)}, + {"Maximum", EIGEN_COST(scalar_max_op)}, + {"Minimum", EIGEN_COST(scalar_min_op)}, + {"Mod", EIGEN_COST(scalar_mod_op)}, + {"Mul", EIGEN_COST(scalar_product_op)}, + {"NotEqual", 1}, + {"QuantizedAdd", EIGEN_COST(scalar_sum_op)}, + {"QuantizedMul", EIGEN_COST(scalar_product_op)}, + {"RealDiv", EIGEN_COST(scalar_quotient_op)}, + {"ReluGrad", EIGEN_COST(scalar_max_op)}, + {"SquareDifference", 1}, + {"Sub", EIGEN_COST(scalar_difference_op)}, + {"TruncateDiv", EIGEN_COST(scalar_quotient_op)}, + {"TruncateMod", EIGEN_COST(scalar_mod_op)}}; #undef EIGEN_COST @@ -675,7 +680,7 @@ int64 OpLevelCostEstimator::CountMatMulOperations( } ops = m_dim * n_dim * k_dim * 2; - VLOG(1) << "Operations for Matmul" << ops; + VLOG(1) << "Operations for Matmul: " << ops; if (mat_mul != nullptr) { mat_mul->m = m_dim; @@ -972,8 +977,10 @@ int64 OpLevelCostEstimator::CalculateTensorElementCount( int64 OpLevelCostEstimator::CalculateTensorSize( const OpInfo::TensorProperties& tensor, bool* found_unknown_shapes) const { - return CalculateTensorElementCount(tensor, found_unknown_shapes) * - DataTypeSize(BaseType(tensor.dtype())); + int64 count = CalculateTensorElementCount(tensor, found_unknown_shapes); + int size = DataTypeSize(BaseType(tensor.dtype())); + VLOG(2) << "Count: " << count << " DataTypeSize: " << size; + return count * size; } int64 OpLevelCostEstimator::CalculateInputSize( -- GitLab From d2f3441c0c3d552293f5c64784261e267653484b Mon Sep 17 00:00:00 2001 From: Raymond Yuan Date: Tue, 24 Jul 2018 18:00:08 -0700 Subject: [PATCH 369/519] Added support for build on subclass models PiperOrigin-RevId: 205923522 --- tensorflow/python/keras/engine/base_layer.py | 10 +- tensorflow/python/keras/engine/training.py | 91 ++++++ .../python/keras/model_subclassing_test.py | 270 +++++++++++++++++- 3 files changed, 363 insertions(+), 8 deletions(-) diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py index e02792208b..b41f6ee03b 100644 --- a/tensorflow/python/keras/engine/base_layer.py +++ b/tensorflow/python/keras/engine/base_layer.py @@ -723,9 +723,17 @@ class Layer(checkpointable.CheckpointableBase): self._dtype = input_list[0].dtype.base_dtype.name except AttributeError: pass + if all(hasattr(x, 'shape') for x in input_list): input_shapes = nest.map_structure(lambda x: x.shape, inputs) - self.build(input_shapes) + + if (not hasattr(self, '_is_graph_network') or + self.__class__.__name__ == 'Sequential'): + # Only if self is a layer or an instance of a sequential model do we + # need to build it. + self.build(input_shapes) + # We must set self.built since user defined build functions are not + # constrained to set self.built. self.built = True # Check input assumptions set after layer building, e.g. input shape. diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 1d4ab1fe37..4df739254b 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -27,6 +27,7 @@ from tensorflow.python.eager import context from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend as K from tensorflow.python.keras import losses @@ -43,6 +44,7 @@ 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.training.checkpointable import base as checkpointable +from tensorflow.python.util import tf_inspect from tensorflow.python.util.tf_export import tf_export @@ -560,6 +562,95 @@ class Model(Network): trainable_weights = self.trainable_weights self._collected_trainable_weights = trainable_weights + def build(self, input_shape): + """Build the model based on input shapes received. + + This is to be used for subclassed models, which do not know at instantiation + time what their inputs look like. + + Args: + input_shape: Single tuple, TensorShape, or list of shapes, where shapes + are tuples, integers, or TensorShapes. + + Raises: + ValueError: + 1. In case of invalid user-provided data (not of type tuple, + list, or TensorShape). + 2. If the model requires call arguments that are agnostic + to the input shapes (positional or kwarg in call signature). + 3. If not all layers were properly built. + 4. If float type inputs are not supported within the layers. + + In each of these cases, the user should build their model by calling it + on real tensor data. + """ + if self._is_graph_network: + self.built = True + return + + # If subclass network + if input_shape is None: + raise ValueError('Input shape must be defined when calling build on a ' + 'model subclass network.') + valid_types = (tuple, list, tensor_shape.TensorShape) + if not isinstance(input_shape, valid_types): + raise ValueError('Specified input shape is not one of the valid types. ' + 'Please specify a batch input shape of type tuple or ' + 'list of input shapes. User provided ' + 'input type: {}'.format(type(input_shape))) + + def _generate_dummy_data_from_shape(shape): + if isinstance(shape, tensor_shape.TensorShape): + shape = shape.as_list() + + # Replace Nones in input shape with dummy `1` value + shape = [x.value if isinstance(x, tensor_shape.Dimension) else x + for x in shape] + shape = [1 if x is None else x for x in shape] + return array_ops.ones(shape, dtype=K.floatx()) + + if input_shape and not self.inputs: + if isinstance(input_shape, list): + # List of input shapes + x = [_generate_dummy_data_from_shape(shape) for shape in input_shape] + else: + x = _generate_dummy_data_from_shape(input_shape) + + kwargs = {} + num_call_args = len(tf_inspect.getargspec(self.call).args) + if self._expects_training_arg and num_call_args == 3: + # Has call signature of call(self, input, training) + kwargs['training'] = False + elif num_call_args > 2: + # Has invalid call signature of call(self, input, *args, **kwargs) + raise ValueError('Currently, you cannot build your model if it has ' + 'positional or keyword arguments that are not ' + 'inputs to the model, but are required for its ' + '`call` method. Instead, in order to instantiate ' + 'and build your model, `call` your model on real ' + 'tensor data with all expected call arguments.') + + try: + self.call(x, **kwargs) + except (errors.InvalidArgumentError, TypeError): + raise ValueError('You cannot build your model by calling `build` ' + 'if your layers do not support float type inputs. ' + 'Instead, in order to instantiate and build your ' + 'model, `call` your model on real tensor data (of ' + 'the correct dtype).') + + if self._layers: + self._track_layers(self._layers) + if self.layers: + for layer in self.layers: + if not layer.built: + raise ValueError('Layer: {} was not built in your model. Calling ' + '`build` manually on a subclassed model is only ' + 'allowed for models with a static topology. ' + 'In this case, you can build your model by ' + 'calling it on real tensor data.'.format(layer)) + self.built = True + def _check_trainable_weights_consistency(self): """Check trainable weights count consistency. diff --git a/tensorflow/python/keras/model_subclassing_test.py b/tensorflow/python/keras/model_subclassing_test.py index 3ac4852eff..5fbc191e78 100644 --- a/tensorflow/python/keras/model_subclassing_test.py +++ b/tensorflow/python/keras/model_subclassing_test.py @@ -29,6 +29,8 @@ from tensorflow.python.eager import context 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 embedding_ops +from tensorflow.python.ops import init_ops from tensorflow.python.ops import resource_variable_ops from tensorflow.python.platform import test from tensorflow.python.training.checkpointable import data_structures @@ -65,6 +67,22 @@ class SimpleTestModel(keras.Model): return self.dense2(x) +class SimpleConvTestModel(keras.Model): + + def __init__(self, num_classes=10): + super(SimpleConvTestModel, self).__init__(name='test_model') + self.num_classes = num_classes + + self.conv1 = keras.layers.Conv2D(32, (3, 3), activation='relu') + self.flatten = keras.layers.Flatten() + self.dense1 = keras.layers.Dense(num_classes, activation='softmax') + + def call(self, x): + x = self.conv1(x) + x = self.flatten(x) + return self.dense1(x) + + class MultiIOTestModel(keras.Model): def __init__(self, use_bn=False, use_dp=False, num_classes=(2, 3)): @@ -173,6 +191,213 @@ def get_nested_model_3(input_dim, num_classes): class ModelSubclassingTest(test.TestCase): + @test_util.run_in_graph_and_eager_modes + def test_invalid_input_shape_build(self): + num_classes = 2 + input_dim = 50 + + model = SimpleTestModel(num_classes=num_classes, + use_dp=True, + use_bn=True) + + self.assertFalse(model.built, 'Model should not have been built') + self.assertFalse(model.weights, ('Model should have no weights since it ' + 'has not been built.')) + with self.assertRaisesRegexp( + ValueError, 'input shape is not one of the valid types'): + model.build(input_shape=tensor_shape.Dimension(input_dim)) + + @test_util.run_in_graph_and_eager_modes + def test_embed_dtype_with_subclass_build(self): + class Embedding(keras.layers.Layer): + """An Embedding layer.""" + + def __init__(self, vocab_size, embedding_dim, **kwargs): + super(Embedding, self).__init__(**kwargs) + self.vocab_size = vocab_size + self.embedding_dim = embedding_dim + + def build(self, _): + self.embedding = self.add_variable( + 'embedding_kernel', + shape=[self.vocab_size, self.embedding_dim], + dtype=np.float32, + initializer=init_ops.random_uniform_initializer(-0.1, 0.1), + trainable=True) + + def call(self, x): + return embedding_ops.embedding_lookup(self.embedding, x) + + class EmbedModel(keras.Model): + + def __init__(self, vocab_size, embed_size): + super(EmbedModel, self).__init__() + self.embed1 = Embedding(vocab_size, embed_size) + + def call(self, inputs): + return self.embed1(inputs) + + model = EmbedModel(100, 20) + self.assertFalse(model.built, 'Model should not have been built') + self.assertFalse(model.weights, ('Model should have no weights since it ' + 'has not been built.')) + with self.assertRaisesRegexp( + ValueError, 'if your layers do not support float type inputs'): + model.build(input_shape=(35, 20)) + + @test_util.run_in_graph_and_eager_modes + def test_single_time_step_rnn_build(self): + dim = 4 + timesteps = 1 + batch_input_shape = (None, timesteps, dim) + units = 3 + + class SimpleRNNModel(keras.Model): + + def __init__(self): + super(SimpleRNNModel, self).__init__() + self.lstm = keras.layers.LSTM(units) + + def call(self, inputs): + return self.lstm(inputs) + + model = SimpleRNNModel() + self.assertFalse(model.built, 'Model should not have been built') + self.assertFalse(model.weights, ('Model should have no weights since it ' + 'has not been built.')) + model.build(batch_input_shape) + self.assertTrue(model.weights, ('Model should have weights now that it ' + 'has been properly built.')) + self.assertTrue(model.built, 'Model should be built after calling `build`.') + model(array_ops.ones((32, timesteps, dim))) + + @test_util.run_in_graph_and_eager_modes + def test_single_io_subclass_build(self): + num_classes = 2 + input_dim = 50 + batch_size = None + + model = SimpleTestModel(num_classes=num_classes, + use_dp=True, + use_bn=True) + + self.assertFalse(model.built, 'Model should not have been built') + self.assertFalse(model.weights, ('Model should have no weights since it ' + 'has not been built.')) + model.build(input_shape=(batch_size, input_dim)) + self.assertTrue(model.weights, ('Model should have weights now that it ' + 'has been properly built.')) + self.assertTrue(model.built, 'Model should be built after calling `build`.') + model(array_ops.ones((32, input_dim))) + + @test_util.run_in_graph_and_eager_modes + def test_single_io_dimension_subclass_build(self): + num_classes = 2 + input_dim = tensor_shape.Dimension(50) + batch_size = tensor_shape.Dimension(None) + + model = SimpleTestModel(num_classes=num_classes, + use_dp=True, + use_bn=True) + + self.assertFalse(model.built, 'Model should not have been built') + self.assertFalse(model.weights, ('Model should have no weights since it ' + 'has not been built.')) + model.build(input_shape=(batch_size, input_dim)) + self.assertTrue(model.weights, ('Model should have weights now that it ' + 'has been properly built.')) + self.assertTrue(model.built, 'Model should be built after calling `build`.') + model(array_ops.ones((32, input_dim))) + + @test_util.run_in_graph_and_eager_modes + def test_multidim_io_subclass_build(self): + num_classes = 10 + # Input size, e.g. image + batch_size = 32 + input_shape = (32, 32, 3) + + model = SimpleConvTestModel(num_classes) + self.assertFalse(model.built, 'Model should not have been built') + self.assertFalse(model.weights, ('Model should have no weights since it ' + 'has not been built.')) + batch_input_shape = (batch_size,) + input_shape + model.build(input_shape=batch_input_shape) + self.assertTrue(model.weights, ('Model should have weights now that it ' + 'has been properly built.')) + self.assertTrue(model.built, 'Model should be built after calling `build`.') + + model(array_ops.ones(batch_input_shape)) + + @test_util.run_in_graph_and_eager_modes + def test_tensorshape_io_subclass_build(self): + num_classes = 10 + # Input size, e.g. image + batch_size = None + input_shape = (32, 32, 3) + + model = SimpleConvTestModel(num_classes) + self.assertFalse(model.built, 'Model should not have been built') + self.assertFalse(model.weights, ('Model should have no weights since it ' + 'has not been built.')) + model.build( + input_shape=tensor_shape.TensorShape((batch_size,) + input_shape)) + self.assertTrue(model.weights, ('Model should have weights now that it ' + 'has been properly built.')) + self.assertTrue(model.built, 'Model should be built after calling `build`.') + + model(array_ops.ones((32,) + input_shape)) + + def test_subclass_save_model(self): + num_classes = 10 + # Input size, e.g. image + batch_size = None + input_shape = (32, 32, 3) + + model = SimpleConvTestModel(num_classes) + self.assertFalse(model.built, 'Model should not have been built') + self.assertFalse(model.weights, ('Model should have no weights since it ' + 'has not been built.')) + model.build( + input_shape=tensor_shape.TensorShape((batch_size,) + input_shape)) + self.assertTrue(model.weights, ('Model should have weights now that it ' + 'has been properly built.')) + self.assertTrue(model.built, 'Model should be built after calling `build`.') + weights = model.get_weights() + + tf_format_name = os.path.join(self.get_temp_dir(), 'ckpt') + model.save_weights(tf_format_name) + if h5py is not None: + hdf5_format_name = os.path.join(self.get_temp_dir(), 'weights.h5') + model.save_weights(hdf5_format_name) + + model = SimpleConvTestModel(num_classes) + model.build( + input_shape=tensor_shape.TensorShape((batch_size,) + input_shape)) + if h5py is not None: + model.load_weights(hdf5_format_name) + self.assertAllClose(weights, model.get_weights()) + model.load_weights(tf_format_name) + self.assertAllClose(weights, model.get_weights()) + + @test_util.run_in_graph_and_eager_modes + def test_multi_io_subclass_build(self): + batch_size = None + num_samples = 1000 + input_dim = 50 + model = MultiIOTestModel() + self.assertFalse(model.built, 'Model should not have been built') + self.assertFalse(model.weights, ('Model should have no weights since it ' + 'has not been built.')) + batch_input_shape = tensor_shape.TensorShape((batch_size, input_dim)) + model.build( + input_shape=[batch_input_shape, batch_input_shape]) + self.assertTrue(model.weights, ('Model should have weights now that it ' + 'has been properly built.')) + self.assertTrue(model.built, 'Model should be built after calling `build`.') + x1 = array_ops.ones((num_samples, input_dim)) + x2 = array_ops.ones((num_samples, input_dim)) + model([x1, x2]) + @test_util.run_in_graph_and_eager_modes def test_single_io_workflow_with_np_arrays(self): num_classes = 2 @@ -750,6 +975,16 @@ class CustomCallModel(keras.Model): return combined +class TrainingNoDefaultModel(keras.Model): + + def __init__(self): + super(TrainingNoDefaultModel, self).__init__() + self.dense1 = keras.layers.Dense(1) + + def call(self, x, training): + return self.dense1(x) + + class CustomCallSignatureTests(test.TestCase): @test_util.run_in_graph_and_eager_modes @@ -766,6 +1001,32 @@ class CustomCallSignatureTests(test.TestCase): output = model(first, second=second, training=False) self.assertAllClose(expected_output, self.evaluate(output)) + @test_util.run_in_graph_and_eager_modes + def test_training_args_call_build(self): + input_dim = 2 + + model = TrainingNoDefaultModel() + self.assertFalse(model.built, 'Model should not have been built') + self.assertFalse(model.weights, ('Model should have no weights since it ' + 'has not been built.')) + model.build((None, input_dim)) + self.assertTrue(model.weights, ('Model should have weights now that it ' + 'has been properly built.')) + self.assertTrue(model.built, 'Model should be built after calling `build`.') + + @test_util.run_in_graph_and_eager_modes + def test_custom_call_kwargs_and_build(self): + first_input_shape = (2, 3) + second_input_shape = (2, 5) + + model = CustomCallModel() + self.assertFalse(model.built, 'Model should not have been built') + self.assertFalse(model.weights, ('Model should have no weights since it ' + 'has not been built.')) + with self.assertRaisesRegexp( + ValueError, 'cannot build your model if it has positional'): + model.build(input_shape=[first_input_shape, second_input_shape]) + @test_util.run_in_graph_and_eager_modes def test_inputs_in_signature(self): @@ -829,14 +1090,9 @@ class CustomCallSignatureTests(test.TestCase): def test_training_no_default(self): - class TrainingNoDefault(keras.Model): - - def call(self, x, training): - return x - with context.graph_mode(): - model = TrainingNoDefault() - arg = array_ops.ones([]) + model = TrainingNoDefaultModel() + arg = array_ops.ones([1, 1]) model(arg, True) six.assertCountEqual(self, [arg], model.inputs) -- GitLab From a5a4bb31c43bfef94bf8998ce07766f1597a7a02 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Tue, 24 Jul 2018 18:03:21 -0700 Subject: [PATCH 370/519] Internal change. PiperOrigin-RevId: 205923892 --- tensorflow/python/keras/BUILD | 1 - tensorflow/python/kernel_tests/BUILD | 1 - 2 files changed, 2 deletions(-) diff --git a/tensorflow/python/keras/BUILD b/tensorflow/python/keras/BUILD index a495d48545..df409d2aa5 100755 --- a/tensorflow/python/keras/BUILD +++ b/tensorflow/python/keras/BUILD @@ -732,7 +732,6 @@ py_test( size = "medium", srcs = ["preprocessing/image_test.py"], srcs_version = "PY2AND3", - tags = ["nomsan"], # TODO(b/110990716) reenable deps = [ ":keras", "//tensorflow/python:client_testlib", diff --git a/tensorflow/python/kernel_tests/BUILD b/tensorflow/python/kernel_tests/BUILD index db2e7e2c2a..adf97569ab 100644 --- a/tensorflow/python/kernel_tests/BUILD +++ b/tensorflow/python/kernel_tests/BUILD @@ -2845,7 +2845,6 @@ cuda_py_test( "//tensorflow/python:math_ops", ], shard_count = 20, - tags = ["nomsan"], # TODO(b/110990716) reenable ) cuda_py_test( -- GitLab From 2952ff32b21332e9a01cd522bc6e38fd7af55ce6 Mon Sep 17 00:00:00 2001 From: Adrian Kuegel Date: Wed, 25 Jul 2018 05:56:06 -0700 Subject: [PATCH 371/519] Remove duplicate code. Now that we made EmitOperandArrayLoopNest() a method of ForLoopNest in cr/205200030, dot_op_emitter can also use it instead of having a duplicate implementation itself. PiperOrigin-RevId: 205981516 --- .../xla/service/cpu/dot_op_emitter.cc | 38 ++----------------- .../compiler/xla/service/cpu/dot_op_emitter.h | 11 ------ 2 files changed, 4 insertions(+), 45 deletions(-) diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc index 1fdeceb860..645888de78 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.cc @@ -1278,10 +1278,10 @@ Status DotOpEmitter::Emit() { // operand dimensions. The reduction dimension of the LHS and RHS are handled // in a separate innermost loop which performs the sum of products. llvm_ir::ForLoopNest loop_nest(llvm_ir::IrName(&dot_), b_); - llvm_ir::IrArray::Index lhs_index = EmitOperandArrayLoopNest( - &loop_nest, lhs_array_, lhs_reduction_dimension, "lhs"); - llvm_ir::IrArray::Index rhs_index = EmitOperandArrayLoopNest( - &loop_nest, rhs_array_, rhs_reduction_dimension, "rhs"); + llvm_ir::IrArray::Index lhs_index = loop_nest.EmitOperandArrayLoopNest( + lhs_array_, /*dimension_to_skip=*/lhs_reduction_dimension, "lhs"); + llvm_ir::IrArray::Index rhs_index = loop_nest.EmitOperandArrayLoopNest( + rhs_array_, /*dimension_to_skip=*/rhs_reduction_dimension, "rhs"); // Create the loop which does the sum of products reduction. // @@ -1537,36 +1537,6 @@ DotOpEmitter::MatMultDims DotOpEmitter::GetMatMultDims() const { LayoutUtil::Minor(target_array_.GetShape().layout(), 0) == 0}; } -llvm_ir::IrArray::Index DotOpEmitter::EmitOperandArrayLoopNest( - llvm_ir::ForLoopNest* loop_nest, const llvm_ir::IrArray& operand_array, - int64 reduction_dimension, tensorflow::StringPiece name_suffix) { - // Prepares the dimension list we will use to emit the loop nest. Outermost - // loops are added first. Add loops in major-to-minor order, and skip the - // reduction dimension. - std::vector dimensions; - const Shape& shape = operand_array.GetShape(); - for (int i = LayoutUtil::MinorToMajor(shape).size() - 1; i >= 0; --i) { - int64 dimension = LayoutUtil::Minor(shape.layout(), i); - if (dimension != reduction_dimension) { - dimensions.push_back(dimension); - } - } - - // Create loop nest with one for-loop for each dimension of the - // output. - llvm_ir::IrArray::Index index = - loop_nest->AddLoopsForShapeOnDimensions(shape, dimensions, name_suffix); - // Verify every dimension except the reduction dimension was set in the index. - for (int dimension = 0; dimension < index.size(); ++dimension) { - if (dimension == reduction_dimension) { - DCHECK_EQ(nullptr, index[dimension]); - } else { - DCHECK_NE(nullptr, index[dimension]); - } - } - return index; -} - // Return whether the given shape is a matrix with no padding. static bool IsRank2WithNoPadding(const Shape& shape) { return ShapeUtil::Rank(shape) == 2 && !LayoutUtil::IsPadded(shape); diff --git a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h index c2eeb0a1f9..590032fbe9 100644 --- a/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/dot_op_emitter.h @@ -88,17 +88,6 @@ class DotOpEmitter { // Emits a call to the CPU runtime to perform the matrix multiply. Status EmitCallToRuntime(); - // Emits a series of nested loops for iterating over an operand array in the - // dot operation. Loops are constructed in major to minor dimension layout - // order. No loop is emitted for the given reduction_dimension. The function - // returns an IrArray index for the given operand_array containing the indvars - // of the loops. All dimensions of the index are filled except for the - // reduction dimension. name_suffix is the string to append to the names of - // LLVM constructs (eg, basic blocks) constructed by this method. - llvm_ir::IrArray::Index EmitOperandArrayLoopNest( - llvm_ir::ForLoopNest* loop_nest, const llvm_ir::IrArray& operand_array, - int64 reduction_dimension, tensorflow::StringPiece name_suffix); - // Represents the dimensions of a matrix-matrix multiply operation. struct MatMultDims { // The number of rows in the LHS. -- GitLab From 35591516feb0d4500aae29b7db31f542dd2fc0c2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 06:56:58 -0700 Subject: [PATCH 372/519] In grpc_server_lib.cc initialize master_env_.collective_executor_mgr from the worker_env_ value. PiperOrigin-RevId: 205987011 --- tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc index db14f6473e..8a6903be9e 100644 --- a/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc +++ b/tensorflow/core/distributed_runtime/rpc/grpc_server_lib.cc @@ -244,6 +244,7 @@ Status GrpcServer::Init( // Finish setting up master environment. master_env_.ops = OpRegistry::Global(); master_env_.worker_cache = worker_cache; + master_env_.collective_executor_mgr = worker_env_.collective_executor_mgr; master_env_.master_session_factory = [config, stats_factory]( SessionOptions options, const MasterEnv* env, -- GitLab From fa5e84e7a498eb1386f4bc7d7076b957484e0972 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 07:04:01 -0700 Subject: [PATCH 373/519] Colab demonstrating the overhead of AutoGraph and Eager vs. a vectorized graph operation. PiperOrigin-RevId: 205987803 --- .../graph_vs_ag_vs_eager_sum_speed_test.ipynb | 519 ++++++++++++++++++ 1 file changed, 519 insertions(+) create mode 100644 tensorflow/contrib/autograph/examples/notebooks/graph_vs_ag_vs_eager_sum_speed_test.ipynb diff --git a/tensorflow/contrib/autograph/examples/notebooks/graph_vs_ag_vs_eager_sum_speed_test.ipynb b/tensorflow/contrib/autograph/examples/notebooks/graph_vs_ag_vs_eager_sum_speed_test.ipynb new file mode 100644 index 0000000000..32742bec7e --- /dev/null +++ b/tensorflow/contrib/autograph/examples/notebooks/graph_vs_ag_vs_eager_sum_speed_test.ipynb @@ -0,0 +1,519 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "moMkWaT_TTHi" + }, + "source": [ + "This Colab illustrates the differing overhead* between a custom, vectorized graph operation and a loop over a tensor\n", + "that computes the same function. The loop is implemented in TensorFlow Eager mode using Python syntax and control-flow, and using AutoGraph which takes a python function and converts it into graph mode. In AutoGraph the Python loop is converted into a tf.while_loop.\n", + "\n", + "The actual computation, summing a small number of scalar values, takes very little time to compute, so the graphs below are showing the overhead of the differing approaches. As such, this is more of a \"micro-benchmark\" than a representation of real-world performance of the three approaches.\n", + "\n", + "*Note the differing scales of the included plots" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "a0X_rfvuav98" + }, + "source": [ + "### Imports" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "EdxWv4Vn0ync" + }, + "outputs": [], + "source": [ + "!pip install -U -q tf-nightly" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "erq3_S7QsjkU" + }, + "outputs": [], + "source": [ + "from __future__ import absolute_import\n", + "from __future__ import division\n", + "from __future__ import print_function\n", + "\n", + "import numpy as np\n", + "import tensorflow as tf\n", + "import matplotlib.pyplot as plt\n", + "import math\n", + "import time\n", + "import random\n", + "from colabtools import adhoc_import\n", + "from tensorflow.contrib import autograph as ag\n", + "from tensorflow.python.framework import function" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "1JgnsXooa2RP" + }, + "source": [ + "### Testing boilerplate" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "UyD5LLjVZzny" + }, + "outputs": [], + "source": [ + "# Test-only parameters. Test checks successful completion not correctness. \n", + "burn_ins = 1\n", + "trials = 1\n", + "batches = 2\n", + "max_elements = 2" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4_NBL0RQa8gY" + }, + "source": [ + "### Speed comparison parameters" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "Yq6daecyiJV5" + }, + "outputs": [], + "source": [ + "#@test {\"skip\": true} \n", + "burn_ins = 3 # Batches not counted in the average\n", + "trials = 10 # Batches run per vector-size (and averaged)\n", + "batches = 1000 # Number of random vectors summed over per trial\n", + "max_elements = 100 # Vectors of size 0 to this-1 will be executed and plotted" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "fiR8m13CbKH2" + }, + "source": [ + "### Random input" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "d8vrTlyNXuxc" + }, + "outputs": [], + "source": [ + "# Construct a random num x 1 tensor\n", + "def get_elements(num):\n", + " return tf.random_uniform(shape=(num, 1), maxval=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "ILJ6SbF3bXFQ" + }, + "source": [ + "## Graph mode" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "vovRf597X55n" + }, + "outputs": [], + "source": [ + "def tf_sum(elements):\n", + " # Using custom vectorized op\n", + " return tf.reduce_sum(elements)\n", + "\n", + "def run_trial(num):\n", + " elements = get_elements(num)\n", + " return tf_sum(elements)\n", + "\n", + "\n", + "\n", + "graph_means = []\n", + "for num in range(max_elements):\n", + " with tf.Graph().as_default():\n", + " durations = []\n", + " foo = run_trial(num)\n", + " \n", + " with tf.Session() as sess:\n", + " \n", + " for _ in range(burn_ins):\n", + " for _ in range(batches):\n", + " sess.run(foo)\n", + " \n", + " for _ in range(trials):\n", + " \n", + " start = time.time()\n", + " for _ in range(batches):\n", + " sess.run(foo)\n", + " \n", + " duration = time.time() - start\n", + " durations.append(duration) \n", + " \n", + " graph_means.append(np.mean(durations)) " + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 301 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 278, + "status": "ok", + "timestamp": 1532447361278, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "Jm9Blkyx90Eq", + "outputId": "d83cd51f-7e56-4d73-f7df-bb157dee46df" + }, + "outputs": [ + { + "data": { + "image/png": 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yBLDSqPFwsyczr6xFj5pSgleAu8n3Pm0cUKmQgzYaUFpRxf7TmeiqjCSmF13X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\nH4gzFtcS9o9MAAAAAElFTkSuQmCC\n", + "text/plain": [ + "\u003cmatplotlib.figure.Figure at 0x7f47b20dd690\u003e" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(graph_means)\n", + "plt.ylabel('Time (seconds)')\n", + "plt.xlabel('Length of vector')\n", + "_ = plt.title('Time to sum the elements of 1000 vectors (vectorized TF operation)')\n", + "_ = plt.ylim(ymin=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "4KZg2WXjbhg5" + }, + "source": [ + "## AutoGraph" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "UQJBQWbCbinm" + }, + "outputs": [], + "source": [ + "# Sum written using for loop and converted with AutoGraph\n", + "def sum_all(elements):\n", + " sum_ = 0.0\n", + " length = len(elements)\n", + " for i in tf.range(length): \n", + " sum_ += elements[i][0]\n", + " return sum_\n", + "\n", + "def run_trial(num):\n", + " elements = get_elements(num)\n", + " return sum_all(elements)\n", + " \n", + "ag_means = []\n", + "ag_run_trial = ag.to_graph(run_trial)\n", + "\n", + "for num in range(max_elements):\n", + " with tf.Graph().as_default():\n", + " durations = []\n", + " foo = ag_run_trial(num)\n", + " with tf.Session() as sess:\n", + " for _ in range(burn_ins):\n", + " for _ in range(batches):\n", + " sess.run(foo)\n", + " \n", + " for _ in range(trials):\n", + " start = time.time()\n", + " for _ in range(batches):\n", + " sess.run(foo)\n", + " \n", + " duration = time.time() - start\n", + " durations.append(duration)\n", + " ag_means.append(np.mean(durations))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 301 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 310, + "status": "ok", + "timestamp": 1532448438694, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "DLDOmrRW99v5", + "outputId": "ae0e0573-39db-4004-a064-efc618dbf867" + }, + "outputs": [ + { + "data": { + "image/png": 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R4OLiwl/+8hfatWtnzZCEEHVIid7I+0sOk55dxIrt5xkW3YK4bkHorhazds9F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\n", + "text/plain": [ + "\u003cmatplotlib.figure.Figure at 0x7f47b218dbd0\u003e" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(ag_means)\n", + "plt.ylabel('Time(s)')\n", + "plt.xlabel('Length of vector')\n", + "_ = plt.title('Time to sum the elements of 1000 vectors (AutoGraph)')\n", + "_ = plt.ylim(ymin=0)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "colab_type": "text", + "id": "d7IAJ6Bwbk9t" + }, + "source": [ + "## Eager" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "XMu5-12yoOzY" + }, + "outputs": [], + "source": [ + "from tensorflow.python.eager import context" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + } + }, + "colab_type": "code", + "id": "_vt9MzpyjQ4T" + }, + "outputs": [], + "source": [ + "# Sum written using for loop and run with tf.eager\n", + "def sum_all(elements):\n", + " sum_ = 0.0\n", + " length = elements.shape[0]\n", + " for i in tf.range(length): \n", + " sum_ += elements[i][0]\n", + " return sum_\n", + "\n", + "eager_means = []\n", + "for num in range(max_elements):\n", + " with context.eager_mode():\n", + " durations = []\n", + " for i in range(trials + burn_ins):\n", + " \n", + " start = time.time()\n", + " for _ in range(batches):\n", + " run_trial(num)\n", + " \n", + " if i \u003c burn_ins:\n", + " continue\n", + " \n", + " duration = time.time() - start\n", + " durations.append(duration)\n", + " eager_means.append(np.mean(durations))" + ] + }, + { + "cell_type": "code", + "execution_count": 0, + "metadata": { + "colab": { + "autoexec": { + "startup": false, + "wait_interval": 0 + }, + "height": 301 + }, + "colab_type": "code", + "executionInfo": { + "elapsed": 422, + "status": "ok", + "timestamp": 1532460024499, + "user": { + "displayName": "", + "photoUrl": "", + "userId": "" + }, + "user_tz": 240 + }, + "id": "5gHVdMlD-A-T", + "outputId": "3b581cb7-7ef9-489c-92f1-3e52c0c2dc8a" + }, + "outputs": [ + { + "data": { + "image/png": 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hs+n8FZdBYbGOXj6uTBzc/ran9hZ1kyQJIcQdOZt8hU9+PkZ2fgkALg5WDOrp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\nIWqJUZKEUuq2yyx9JZTCvKIaiEYIIcS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+ "text/plain": [ + "\u003cmatplotlib.figure.Figure at 0x7f47b8e3bd90\u003e" + ] + }, + "metadata": { + "tags": [] + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(eager_means)\n", + "plt.ylabel('Time(s)')\n", + "plt.xlabel('Length of vector')\n", + "_ = plt.title('Time to sum the elements of 1000 vectors (Eager)')\n", + "_ = plt.ylim(ymin=0)" + ] + } + ], + "metadata": { + "colab": { + "collapsed_sections": [], + "default_view": {}, + "name": "Autograph vs. Eager vs Graph sum", + "provenance": [ + { + "file_id": "1olZkm32B7n7pQwlIAXR0_w8fZhRHCtkX", + "timestamp": 1531755808890 + } + ], + "version": "0.3.2", + "views": {} + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} -- GitLab From 10e0233d7f2f215577271d33a3b04506f93c13b1 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 07:50:20 -0700 Subject: [PATCH 374/519] Small changes to interpreter.{h,cc}: refactoring plus improved error message. PiperOrigin-RevId: 205992521 --- tensorflow/contrib/lite/interpreter.cc | 37 ++++++++++++++++---------- tensorflow/contrib/lite/interpreter.h | 10 ++++++- 2 files changed, 32 insertions(+), 15 deletions(-) diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc index 26fecceab0..5a5c907b6e 100644 --- a/tensorflow/contrib/lite/interpreter.cc +++ b/tensorflow/contrib/lite/interpreter.cc @@ -43,10 +43,13 @@ namespace { TfLiteStatus ReportOpError(TfLiteContext* context, const TfLiteNode& node, const TfLiteRegistration& registration, int node_index, const char* message) { - context->ReportError(context, "Node number %d (%s) %s.\n", node_index, - EnumNameBuiltinOperator(static_cast( - registration.builtin_code)), - message); + context->ReportError( + context, "Node number %d (%s) %s.\n", node_index, + registration.custom_name + ? registration.custom_name + : EnumNameBuiltinOperator( + static_cast(registration.builtin_code)), + message); return kTfLiteError; } @@ -131,9 +134,7 @@ Interpreter::Interpreter(ErrorReporter* error_reporter) context_.SetExternalContext = SetExternalContext; // Invalid to call these these except from TfLiteDelegate - SetForbiddenContextFunction(&context_.GetNodeAndRegistration); - SetForbiddenContextFunction(&context_.ReplaceSubgraphsWithDelegateKernels); - SetForbiddenContextFunction(&context_.GetExecutionPlan); + SwitchToKernelContext(); // Reserve some space for the tensors to avoid excessive resizing. tensors_.reserve(kTensorsReservedCapacity); @@ -924,6 +925,19 @@ void Interpreter::SetNumThreads(int num_threads) { } } +void Interpreter::SwitchToDelegateContext() { + context_.GetNodeAndRegistration = GetNodeAndRegistration; + context_.ReplaceSubgraphsWithDelegateKernels = + ReplaceSubgraphsWithDelegateKernels; + context_.GetExecutionPlan = GetExecutionPlan; +} + +void Interpreter::SwitchToKernelContext() { + SetForbiddenContextFunction(&context_.GetNodeAndRegistration); + SetForbiddenContextFunction(&context_.ReplaceSubgraphsWithDelegateKernels); + SetForbiddenContextFunction(&context_.GetExecutionPlan); +} + TfLiteStatus Interpreter::ModifyGraphWithDelegate(TfLiteDelegate* delegate, bool allow_dynamic_tensors) { if (!allow_dynamic_tensors) { @@ -950,17 +964,12 @@ TfLiteStatus Interpreter::ModifyGraphWithDelegate(TfLiteDelegate* delegate, // TODO(aselle): Consider if it is worth storing pointers to delegates. // Setup additional context interface. - context_.GetNodeAndRegistration = GetNodeAndRegistration; - context_.ReplaceSubgraphsWithDelegateKernels = - ReplaceSubgraphsWithDelegateKernels; - context_.GetExecutionPlan = GetExecutionPlan; + SwitchToDelegateContext(); TfLiteStatus status = delegate->Prepare(&context_, delegate); // Remove additional context info. - SetForbiddenContextFunction(&context_.GetNodeAndRegistration); - SetForbiddenContextFunction(&context_.ReplaceSubgraphsWithDelegateKernels); - SetForbiddenContextFunction(&context_.GetExecutionPlan); + SwitchToKernelContext(); TF_LITE_ENSURE_OK(&context_, status); diff --git a/tensorflow/contrib/lite/interpreter.h b/tensorflow/contrib/lite/interpreter.h index bc608e2fce..be149a8cc0 100644 --- a/tensorflow/contrib/lite/interpreter.h +++ b/tensorflow/contrib/lite/interpreter.h @@ -111,7 +111,7 @@ class Interpreter { // processing this model will be forwarded to the error_reporter object. // // Note, if error_reporter is nullptr, then a default StderrReporter is - // used. + // used. Ownership of 'error_reporter' remains with the caller. explicit Interpreter(ErrorReporter* error_reporter = DefaultErrorReporter()); ~Interpreter(); @@ -416,6 +416,13 @@ class Interpreter { private: friend class InterpreterTest; + // Prevent 'context_' from accessing functions that are only available to + // delegated kernels. + void SwitchToKernelContext(); + + // Add delegate-only functions to 'context_'. + void SwitchToDelegateContext(); + // Give 'op_reg' a chance to initialize itself using the contents of // 'buffer'. void* OpInit(const TfLiteRegistration& op_reg, const char* buffer, @@ -502,6 +509,7 @@ class Interpreter { // Update the execution graph to replace some of the nodes with stub // nodes. Specifically any node index that has `nodes[index]==1` will be // slated for replacement with a delegate kernel specified by registration. + // Ownership of 'nodes_to_replace' and 'delegate' remains with the caller. // WARNING: This is an experimental interface that is subject to change. TfLiteStatus ReplaceSubgraphsWithDelegateKernels( TfLiteRegistration registration, const TfLiteIntArray* nodes_to_replace, -- GitLab From 1170da749f442554219f207a7cc745811c528320 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 08:16:06 -0700 Subject: [PATCH 375/519] Internal Change PiperOrigin-RevId: 205995452 --- tensorflow/contrib/lite/build_def.bzl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 7c13f9011e..a8a49784c6 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -254,7 +254,7 @@ def generated_test_models(): "prelu", "pow", "reduce_max", - "reduce_prod", + #"reduce_prod", # disabled due to b/111823366 "relu", "relu1", "relu6", -- GitLab From be3d22844025e42e177a21479f3ae73bc5351c1f Mon Sep 17 00:00:00 2001 From: James Keeling Date: Wed, 25 Jul 2018 08:21:22 -0700 Subject: [PATCH 376/519] Warn if creating a MultiRNNCell with multiple identical cell objects. This is likely a bug. PiperOrigin-RevId: 205996090 --- tensorflow/python/ops/rnn_cell_impl.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/tensorflow/python/ops/rnn_cell_impl.py b/tensorflow/python/ops/rnn_cell_impl.py index 70805fd572..42806ba6ec 100644 --- a/tensorflow/python/ops/rnn_cell_impl.py +++ b/tensorflow/python/ops/rnn_cell_impl.py @@ -1261,6 +1261,11 @@ class MultiRNNCell(RNNCell): raise TypeError( "cells must be a list or tuple, but saw: %s." % cells) + if len(set([id(cell) for cell in cells])) < len(cells): + logging.log_first_n(logging.WARN, + "At least two cells provided to MultiRNNCell " + "are the same object and will share weights.", 1) + self._cells = cells for cell_number, cell in enumerate(self._cells): # Add Checkpointable dependencies on these cells so their variables get -- GitLab From b3771feab49e2122164737a860341727d08c2d8c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 08:23:57 -0700 Subject: [PATCH 377/519] This change started with an intention of adding an attribute to cast ops to decide whether bfloat16 casts should use truncation or rounding. This is a preparatory change before we switch the default float ==> bfloat16 cast to use rounding instead of truncation. The attribute added can then be specified on casts that rely on the truncation, e.g., the TensorFlow send/receive operations. It later emerged that the choice of doing truncation is useful more generally. Therefore, this change allows the new attribute to be used by all relevant casts to use truncation instead of rounding. PiperOrigin-RevId: 205996367 --- tensorflow/core/framework/bfloat16_test.cc | 3 +- tensorflow/core/graph/graph_partition.cc | 8 + tensorflow/core/kernels/cast_op.cc | 4 +- tensorflow/core/kernels/cast_op.h | 164 +++++++++++++++++- tensorflow/core/kernels/cast_op_gpu.cu.cc | 48 +++-- tensorflow/core/kernels/cast_op_impl.h | 152 ++++++---------- .../core/kernels/cast_op_impl_bfloat.cc | 11 +- tensorflow/core/kernels/cast_op_impl_bool.cc | 9 +- .../core/kernels/cast_op_impl_complex128.cc | 6 +- .../core/kernels/cast_op_impl_complex64.cc | 6 +- .../core/kernels/cast_op_impl_double.cc | 9 +- tensorflow/core/kernels/cast_op_impl_float.cc | 9 +- tensorflow/core/kernels/cast_op_impl_half.cc | 6 +- tensorflow/core/kernels/cast_op_impl_int16.cc | 9 +- tensorflow/core/kernels/cast_op_impl_int32.cc | 9 +- tensorflow/core/kernels/cast_op_impl_int64.cc | 9 +- tensorflow/core/kernels/cast_op_impl_int8.cc | 9 +- .../core/kernels/cast_op_impl_uint16.cc | 9 +- .../core/kernels/cast_op_impl_uint32.cc | 9 +- .../core/kernels/cast_op_impl_uint64.cc | 9 +- tensorflow/core/kernels/cast_op_impl_uint8.cc | 9 +- tensorflow/core/kernels/cast_op_test.cc | 29 +++- tensorflow/core/lib/bfloat16/bfloat16.h | 20 ++- tensorflow/core/ops/math_ops.cc | 1 + tensorflow/python/eager/pywrap_tensor.cc | 1 + 25 files changed, 342 insertions(+), 216 deletions(-) diff --git a/tensorflow/core/framework/bfloat16_test.cc b/tensorflow/core/framework/bfloat16_test.cc index 206396a25a..0a1b5e1975 100644 --- a/tensorflow/core/framework/bfloat16_test.cc +++ b/tensorflow/core/framework/bfloat16_test.cc @@ -45,7 +45,8 @@ class Bfloat16Test : public ::testing::Test, public ::testing::WithParamInterface {}; TEST_P(Bfloat16Test, TruncateTest) { - bfloat16 truncated(GetParam().input); + bfloat16 truncated = bfloat16::truncate_to_bfloat16((GetParam().input)); + if (std::isnan(GetParam().input)) { EXPECT_TRUE(std::isnan(float(truncated)) || std::isinf(float(truncated))); return; diff --git a/tensorflow/core/graph/graph_partition.cc b/tensorflow/core/graph/graph_partition.cc index 1b1941f9c1..ea0a814ab8 100644 --- a/tensorflow/core/graph/graph_partition.cc +++ b/tensorflow/core/graph/graph_partition.cc @@ -214,6 +214,14 @@ NodeDef* AddSend(const PartitionOptions& opts, const GraphInfo& g_info, cast_builder.Attr("_start_time", start_time); } cast_builder.Attr("DstT", cast_dtype); + + if (cast_dtype == DT_BFLOAT16) { + // the below attribute specifies that the cast to bfloat16 should use + // truncation. This is needed to retain legacy behavior when we change + // the default bfloat16 casts to use rounding instead of truncation + cast_builder.Attr("Truncate", true); + } + NodeDef* cast = gdef->add_node(); *status = cast_builder.Finalize(cast); if (!status->ok()) return nullptr; diff --git a/tensorflow/core/kernels/cast_op.cc b/tensorflow/core/kernels/cast_op.cc index b4c97df38b..0478c93280 100644 --- a/tensorflow/core/kernels/cast_op.cc +++ b/tensorflow/core/kernels/cast_op.cc @@ -59,6 +59,8 @@ CastOpBase::CastOpBase(OpKernelConstruction* ctx) : OpKernel(ctx) { OP_REQUIRES_OK(ctx, ctx->GetAttr("DstT", &external_dst_dtype_)); + OP_REQUIRES_OK(ctx, ctx->GetAttr("Truncate", &use_truncation_)); + // Quantized data types use the same underlying format as their non quantized // version so we use the non quantized implementation for casting. if (external_dst_dtype_ == DT_QUINT8) { @@ -100,7 +102,7 @@ void CastOpBase::Compute(OpKernelContext* ctx) { Tensor* out = nullptr; OP_REQUIRES_OK(ctx, ctx->allocate_output(0, in.shape(), &out)); out->set_dtype(dst_dtype_); - work_(ctx, in, out); + work_(ctx, in, out, use_truncation_); out->set_dtype(external_dst_dtype_); } } diff --git a/tensorflow/core/kernels/cast_op.h b/tensorflow/core/kernels/cast_op.h index aae1e7ff19..527ab528c9 100644 --- a/tensorflow/core/kernels/cast_op.h +++ b/tensorflow/core/kernels/cast_op.h @@ -24,8 +24,71 @@ limitations under the License. #include "tensorflow/core/platform/byte_order.h" #include "tensorflow/core/platform/types.h" +// Note that the GPU cast functor templates need to be instantiated unlike the +// CPU ones, and hence their specializations are different than that for CPUs. +#ifdef SPECIALIZE_FOR_GPUS +#define SPECIALIZE_CAST(DEVICE, OUT_TYPE, IN_OUT) \ + template \ + struct CastFunctor { \ + void operator()(const Device& d, \ + typename TTypes::Flat out_tensor, \ + typename TTypes::ConstFlat in_tensor, \ + bool truncate = false) { \ + if (truncate) { \ + out_tensor.device(d) = \ + in_tensor.unaryExpr(LSBZeroSetter()) \ + .template cast(); \ + } else { \ + out_tensor.device(d) = in_tensor.template cast(); \ + } \ + } \ + }; \ + template struct CastFunctor; +#else +#define SPECIALIZE_CAST(DEVICE, OUT_TYPE, IN_OUT) \ + template <> \ + struct CastFunctor { \ + void operator()(const DEVICE& d, \ + typename TTypes::Flat out_tensor, \ + typename TTypes::ConstFlat in_tensor, \ + bool truncate = false) { \ + if (truncate) { \ + out_tensor.device(d) = \ + in_tensor.unaryExpr(LSBZeroSetter()) \ + .template cast(); \ + } else { \ + out_tensor.device(d) = in_tensor.template cast(); \ + } \ + } \ + }; +#endif + +#define CAST_FUNCTORS(devname) \ + SPECIALIZE_CAST(devname, float, double) \ + SPECIALIZE_CAST(devname, float, std::complex) \ + SPECIALIZE_CAST(devname, std::complex, std::complex) \ + SPECIALIZE_CAST(devname, std::complex, double) \ + SPECIALIZE_CAST(devname, Eigen::half, double) \ + SPECIALIZE_CAST(devname, Eigen::half, float) \ + SPECIALIZE_CAST(devname, Eigen::half, std::complex) \ + SPECIALIZE_CAST(devname, Eigen::half, std::complex) \ + SPECIALIZE_CAST(devname, bfloat16, float) \ + template \ + struct CastFunctor { \ + void operator()(const devname& d, \ + typename TTypes::Flat out_tensor, \ + typename TTypes::ConstFlat in_tensor, \ + bool truncate = false) { \ + out_tensor.device(d) = in_tensor.template cast(); \ + } \ + }; + namespace tensorflow { +typedef std::function + CastFunctorType; + // Common base class of Cast kernels class CastOpBase : public OpKernel { public: @@ -38,8 +101,8 @@ class CastOpBase : public OpKernel { DataType dst_dtype_; DataType external_src_dtype_; DataType external_dst_dtype_; - std::function work_ = nullptr; - + bool use_truncation_; + CastFunctorType work_ = nullptr; Status Unimplemented(); TF_DISALLOW_COPY_AND_ASSIGN(CastOpBase); @@ -56,6 +119,23 @@ class CpuCastOp : public CastOpBase { namespace functor { +template +constexpr int MantissaWidth() { + return std::numeric_limits::digits; +} + +template <> +constexpr int MantissaWidth() { + // Remember, there's 1 hidden bit + return 10 + 1; +} + +template <> +constexpr int MantissaWidth() { + // Remember, there's 1 hidden bit + return 7 + 1; +} + template void Cast(const Device& d, typename TTypes::Flat o, typename TTypes::ConstFlat i) { @@ -65,7 +145,85 @@ void Cast(const Device& d, typename TTypes::Flat o, template struct CastFunctor { void operator()(const Device& d, typename TTypes::Flat o, - typename TTypes::ConstFlat i); + typename TTypes::ConstFlat i, bool truncate = false); +}; + +// Only enable LSBZeroSetterHelper for 64 and 32 bit input data types. +// Specialize for others if needed in future. +template +typename std::enable_if::type EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE static LSBZeroSetterHelper(I& t, int n) { + // Only zero the bits for non-NaNs. + // For NaNs, let the non-truncation version handle it. + if (!std::isnan(t)) { + uint64_t* p = reinterpret_cast(&t); + *p &= (0xFFFFFFFFFFFFFFFF << n); + } +} + +template +typename std::enable_if::type EIGEN_DEVICE_FUNC + EIGEN_STRONG_INLINE static LSBZeroSetterHelper(I& t, int n) { + // Only zero the bits for non-NaNs. + // For NaNs, let the non-truncation version handle it. + if (!std::isnan(t)) { + uint32_t* p = reinterpret_cast(&t); + *p &= (0xFFFFFFFF << n); + } +} + +// Set n least significant bits to 0 +template +struct LSBZeroSetter { + EIGEN_EMPTY_STRUCT_CTOR(LSBZeroSetter) + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const I operator()(const I& a) const { + constexpr int bits = MantissaWidth() - MantissaWidth(); + static_assert( + bits > 0, + "The output type must have fewer mantissa bits than the input type\n"); + I t = a; + LSBZeroSetterHelper(t, bits); + return t; + } +}; + +template +struct LSBZeroSetter, std::complex> { + EIGEN_EMPTY_STRUCT_CTOR(LSBZeroSetter) + + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const std::complex operator()( + const std::complex& a) const { + constexpr int bits = MantissaWidth() - MantissaWidth(); + static_assert( + bits > 0, + "The output type must have fewer mantissa bits than the input type\n"); + I re = std::real(a); + I img = std::imag(a); + LSBZeroSetterHelper(re, bits); + LSBZeroSetterHelper(img, bits); + std::complex toReturn(re, img); + return toReturn; + } +}; + +template +struct LSBZeroSetter, O> { + EIGEN_EMPTY_STRUCT_CTOR(LSBZeroSetter) + // Sets the 16 LSBits of the float to 0 + EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const std::complex operator()( + const std::complex& a) const { + constexpr int bits = MantissaWidth() - MantissaWidth(); + static_assert( + bits > 0, + "The output type must have fewer mantissa bits than the input type\n"); + I re = std::real(a); + I img = std::imag(a); + LSBZeroSetterHelper(re, bits); + LSBZeroSetterHelper(img, bits); + std::complex toReturn(re, img); + return toReturn; + } }; } // end namespace functor diff --git a/tensorflow/core/kernels/cast_op_gpu.cu.cc b/tensorflow/core/kernels/cast_op_gpu.cu.cc index 607e7f5efd..036996fca2 100644 --- a/tensorflow/core/kernels/cast_op_gpu.cu.cc +++ b/tensorflow/core/kernels/cast_op_gpu.cu.cc @@ -18,22 +18,19 @@ limitations under the License. #define EIGEN_USE_GPU #include "tensorflow/core/framework/bfloat16.h" +#define SPECIALIZE_FOR_GPUS #include "tensorflow/core/kernels/cast_op.h" +#undef SPECIALIZE_FOR_GPUS namespace tensorflow { namespace functor { typedef Eigen::GpuDevice GPUDevice; -template -struct CastFunctor { - void operator()(const GPUDevice& d, typename TTypes::Flat o, - typename TTypes::ConstFlat i) { - Cast(d, o, i); - } -}; +CAST_FUNCTORS(GPUDevice); #define DEFINE(O, I) template struct CastFunctor + #define DEFINE_ALL_FROM(in_type) \ DEFINE(in_type, bool); \ DEFINE(in_type, uint8); \ @@ -59,14 +56,43 @@ DEFINE_ALL_FROM(int8); DEFINE_ALL_FROM(int16); DEFINE_ALL_FROM(int32); DEFINE_ALL_FROM(int64); -DEFINE_ALL_FROM(Eigen::half); -DEFINE_ALL_FROM(float); DEFINE_ALL_FROM(double); -DEFINE_ALL_FROM(std::complex); DEFINE_ALL_FROM(std::complex); -DEFINE(bfloat16, float); DEFINE(float, bfloat16); +#define DEFINE_ALL_TO_FLOAT(out_type) \ + DEFINE(out_type, bool); \ + DEFINE(out_type, uint8); \ + DEFINE(out_type, uint16); \ + DEFINE(out_type, uint32); \ + DEFINE(out_type, uint64); \ + DEFINE(out_type, int8); \ + DEFINE(out_type, int16); \ + DEFINE(out_type, int32); \ + DEFINE(out_type, int64); \ + DEFINE(out_type, Eigen::half); \ + DEFINE(out_type, float); \ + DEFINE(out_type, std::complex) + +#define DEFINE_ALL_TO_HALF(out_type) \ + DEFINE(out_type, bool); \ + DEFINE(out_type, uint8); \ + DEFINE(out_type, uint16); \ + DEFINE(out_type, uint32); \ + DEFINE(out_type, uint64); \ + DEFINE(out_type, int8); \ + DEFINE(out_type, int16); \ + DEFINE(out_type, int32); \ + DEFINE(out_type, int64); \ + DEFINE(out_type, Eigen::half) + +DEFINE_ALL_TO_HALF(Eigen::half); +DEFINE_ALL_TO_HALF(bfloat16); +DEFINE_ALL_TO_FLOAT(float); +DEFINE_ALL_TO_FLOAT(std::complex); + +#undef DEFINE_ALL_TO_FLOAT +#undef DEFINE_ALL_TO_HALF #undef DEFINE_ALL_FROM #undef DEFINE diff --git a/tensorflow/core/kernels/cast_op_impl.h b/tensorflow/core/kernels/cast_op_impl.h index fe821b25df..b899bac681 100644 --- a/tensorflow/core/kernels/cast_op_impl.h +++ b/tensorflow/core/kernels/cast_op_impl.h @@ -25,22 +25,10 @@ namespace tensorflow { namespace functor { -template -struct CastFunctor { - void operator()(const Eigen::ThreadPoolDevice& d, typename TTypes::Flat o, - typename TTypes::ConstFlat i) { - o.device(d) = i.template cast(); - } -}; +CAST_FUNCTORS(Eigen::ThreadPoolDevice); #ifdef TENSORFLOW_USE_SYCL -template -struct CastFunctor { - void operator()(const Eigen::SyclDevice& d, typename TTypes::Flat o, - typename TTypes::ConstFlat i) { - o.device(d) = i.template cast(); - } -}; +CAST_FUNCTORS(Eigen::SyclDevice); #endif // TENSORFLOW_USE_SYCL } // namespace functor @@ -68,139 +56,103 @@ struct CastFunctor { CURRY_TYPES3_NO_BF16(FN, arg0, arg1) \ FN(arg0, arg1, bfloat16); -#define CAST_CASE(DEVICE, IN, OUT) \ - if (DataTypeToEnum::value == dst_dtype) { \ - return [](OpKernelContext* ctx, const Tensor& inp, Tensor* out) { \ - functor::CastFunctor func; \ - func(ctx->eigen_device(), out->flat(), inp.flat()); \ - }; \ +#define CAST_CASE(DEVICE, IN, OUT) \ + if (DataTypeToEnum::value == dst_dtype) { \ + return [](OpKernelContext* ctx, const Tensor& inp, Tensor* out, \ + bool truncate) { \ + functor::CastFunctor func; \ + func(ctx->eigen_device(), out->flat(), inp.flat(), \ + truncate); \ + }; \ } // The functions below are implemented in the cast_op_impl_*.cc files. -std::function -GetCpuCastFromBool(DataType dst_dtype); +CastFunctorType GetCpuCastFromBool(DataType dst_dtype); + +CastFunctorType GetCpuCastFromUint8(DataType dst_dtype); -std::function -GetCpuCastFromUint8(DataType dst_dtype); +CastFunctorType GetCpuCastFromUint16(DataType dst_dtype); -std::function -GetCpuCastFromUint16(DataType dst_dtype); +CastFunctorType GetCpuCastFromInt8(DataType dst_dtype); -std::function -GetCpuCastFromUint32(DataType dst_dtype); +CastFunctorType GetCpuCastFromUint32(DataType dst_dtype); -std::function -GetCpuCastFromUint64(DataType dst_dtype); +CastFunctorType GetCpuCastFromUint64(DataType dst_dtype); -std::function -GetCpuCastFromInt8(DataType dst_dtype); +CastFunctorType GetCpuCastFromInt8(DataType dst_dtype); -std::function -GetCpuCastFromInt16(DataType dst_dtype); +CastFunctorType GetCpuCastFromInt16(DataType dst_dtype); -std::function -GetCpuCastFromInt32(DataType dst_dtype); +CastFunctorType GetCpuCastFromInt32(DataType dst_dtype); -std::function -GetCpuCastFromInt64(DataType dst_dtype); +CastFunctorType GetCpuCastFromInt64(DataType dst_dtype); -std::function -GetCpuCastFromHalf(DataType dst_dtype); +CastFunctorType GetCpuCastFromHalf(DataType dst_dtype); -std::function -GetCpuCastFromFloat(DataType dst_dtype); +CastFunctorType GetCpuCastFromFloat(DataType dst_dtype); -std::function -GetCpuCastFromDouble(DataType dst_dtype); +CastFunctorType GetCpuCastFromDouble(DataType dst_dtype); -std::function -GetCpuCastFromComplex64(DataType dst_dtype); +CastFunctorType GetCpuCastFromComplex64(DataType dst_dtype); -std::function -GetCpuCastFromComplex128(DataType dst_dtype); +CastFunctorType GetCpuCastFromComplex128(DataType dst_dtype); -std::function -GetCpuCastFromBfloat(DataType dst_dtype); +CastFunctorType GetCpuCastFromBfloat(DataType dst_dtype); #if GOOGLE_CUDA // Same, for GPU. -std::function -GetGpuCastFromBool(DataType dst_dtype); +CastFunctorType GetGpuCastFromBool(DataType dst_dtype); -std::function -GetGpuCastFromUint8(DataType dst_dtype); +CastFunctorType GetGpuCastFromUint8(DataType dst_dtype); -std::function -GetGpuCastFromUint16(DataType dst_dtype); +CastFunctorType GetGpuCastFromUint16(DataType dst_dtype); -std::function -GetGpuCastFromUint32(DataType dst_dtype); +CastFunctorType GetGpuCastFromInt8(DataType dst_dtype); -std::function -GetGpuCastFromUint64(DataType dst_dtype); +CastFunctorType GetGpuCastFromUint32(DataType dst_dtype); -std::function -GetGpuCastFromInt8(DataType dst_dtype); +CastFunctorType GetGpuCastFromUint64(DataType dst_dtype); -std::function -GetGpuCastFromInt16(DataType dst_dtype); +CastFunctorType GetGpuCastFromInt16(DataType dst_dtype); -std::function -GetGpuCastFromInt32(DataType dst_dtype); +CastFunctorType GetGpuCastFromInt32(DataType dst_dtype); -std::function -GetGpuCastFromInt64(DataType dst_dtype); +CastFunctorType GetGpuCastFromInt64(DataType dst_dtype); -std::function -GetGpuCastFromHalf(DataType dst_dtype); +CastFunctorType GetGpuCastFromHalf(DataType dst_dtype); -std::function -GetGpuCastFromFloat(DataType dst_dtype); +CastFunctorType GetGpuCastFromFloat(DataType dst_dtype); -std::function -GetGpuCastFromDouble(DataType dst_dtype); +CastFunctorType GetGpuCastFromDouble(DataType dst_dtype); -std::function -GetGpuCastFromComplex64(DataType dst_dtype); +CastFunctorType GetGpuCastFromComplex64(DataType dst_dtype); -std::function -GetGpuCastFromComplex128(DataType dst_dtype); +CastFunctorType GetGpuCastFromComplex128(DataType dst_dtype); -std::function -GetGpuCastFromBfloat(DataType dst_dtype); +CastFunctorType GetGpuCastFromBfloat(DataType dst_dtype); #endif // GOOGLE_CUDA #ifdef TENSORFLOW_USE_SYCL -std::function -GetSyclCastFromBool(DataType dst_dtype); +CastFunctorType GetSyclCastFromBool(DataType dst_dtype); -std::function -GetSyclCastFromUint8(DataType dst_dtype); +CastFunctorType GetSyclCastFromUint8(DataType dst_dtype); -std::function -GetSyclCastFromUint16(DataType dst_dtype); +CastFunctorType GetSyclCastFromUint16(DataType dst_dtype); -std::function -GetSyclCastFromUint32(DataType dst_dtype); +CastFunctorType GetSyclCastFromUint32(DataType dst_dtype); -std::function -GetSyclCastFromUint64(DataType dst_dtype); +CastFunctorType GetSyclCastFromUint64(DataType dst_dtype); -std::function -GetSyclCastFromInt16(DataType dst_dtype); +CastFunctorType GetSyclCastFromInt16(DataType dst_dtype); -std::function -GetSyclCastFromInt32(DataType dst_dtype); +CastFunctorType GetSyclCastFromInt32(DataType dst_dtype); -std::function -GetSyclCastFromInt64(DataType dst_dtype); +CastFunctorType GetSyclCastFromInt64(DataType dst_dtype); -std::function -GetSyclCastFromFloat(DataType dst_dtype); +CastFunctorType GetSyclCastFromFloat(DataType dst_dtype); -std::function -GetSyclCastFromDouble(DataType dst_dtype); +CastFunctorType GetSyclCastFromDouble(DataType dst_dtype); #endif // TENSORFLOW_USE_SYCL } // namespace tensorflow diff --git a/tensorflow/core/kernels/cast_op_impl_bfloat.cc b/tensorflow/core/kernels/cast_op_impl_bfloat.cc index bfa7ba0d47..96aae15608 100644 --- a/tensorflow/core/kernels/cast_op_impl_bfloat.cc +++ b/tensorflow/core/kernels/cast_op_impl_bfloat.cc @@ -22,20 +22,19 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromBfloat(DataType dst_dtype) { +CastFunctorType GetCpuCastFromBfloat(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, bfloat16); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromBfloat(DataType dst_dtype) { +CastFunctorType GetGpuCastFromBfloat(DataType dst_dtype) { if (dst_dtype == DT_FLOAT) { - return [](OpKernelContext* ctx, const Tensor& inp, Tensor* out) { + return [](OpKernelContext* ctx, const Tensor& inp, Tensor* out, + bool truncate) { functor::CastFunctor func; func(ctx->eigen_device(), out->flat(), - inp.flat()); + inp.flat(), truncate); }; } return nullptr; diff --git a/tensorflow/core/kernels/cast_op_impl_bool.cc b/tensorflow/core/kernels/cast_op_impl_bool.cc index c5c7394b43..792d4781f2 100644 --- a/tensorflow/core/kernels/cast_op_impl_bool.cc +++ b/tensorflow/core/kernels/cast_op_impl_bool.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromBool(DataType dst_dtype) { +CastFunctorType GetCpuCastFromBool(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, bool); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromBool(DataType dst_dtype) { +CastFunctorType GetGpuCastFromBool(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, bool); return nullptr; } @@ -36,8 +34,7 @@ GetGpuCastFromBool(DataType dst_dtype) { #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -std::function -GetSyclCastFromBool(DataType dst_dtype) { +CastFunctorType GetSyclCastFromBool(DataType dst_dtype) { CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, bool); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_complex128.cc b/tensorflow/core/kernels/cast_op_impl_complex128.cc index 52899d58cd..9a184e5954 100644 --- a/tensorflow/core/kernels/cast_op_impl_complex128.cc +++ b/tensorflow/core/kernels/cast_op_impl_complex128.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromComplex128(DataType dst_dtype) { +CastFunctorType GetCpuCastFromComplex128(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, std::complex); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromComplex128(DataType dst_dtype) { +CastFunctorType GetGpuCastFromComplex128(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, std::complex); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_complex64.cc b/tensorflow/core/kernels/cast_op_impl_complex64.cc index 617bda53d5..77bc620b46 100644 --- a/tensorflow/core/kernels/cast_op_impl_complex64.cc +++ b/tensorflow/core/kernels/cast_op_impl_complex64.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromComplex64(DataType dst_dtype) { +CastFunctorType GetCpuCastFromComplex64(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, std::complex); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromComplex64(DataType dst_dtype) { +CastFunctorType GetGpuCastFromComplex64(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, std::complex); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_double.cc b/tensorflow/core/kernels/cast_op_impl_double.cc index 7dc485ddad..ff9056897f 100644 --- a/tensorflow/core/kernels/cast_op_impl_double.cc +++ b/tensorflow/core/kernels/cast_op_impl_double.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromDouble(DataType dst_dtype) { +CastFunctorType GetCpuCastFromDouble(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, double); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromDouble(DataType dst_dtype) { +CastFunctorType GetGpuCastFromDouble(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, double); return nullptr; } @@ -36,8 +34,7 @@ GetGpuCastFromDouble(DataType dst_dtype) { #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -std::function -GetSyclCastFromDouble(DataType dst_dtype) { +CastFunctorType GetSyclCastFromDouble(DataType dst_dtype) { CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, double); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_float.cc b/tensorflow/core/kernels/cast_op_impl_float.cc index 1c933914fd..f1e8f0e37b 100644 --- a/tensorflow/core/kernels/cast_op_impl_float.cc +++ b/tensorflow/core/kernels/cast_op_impl_float.cc @@ -22,15 +22,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromFloat(DataType dst_dtype) { +CastFunctorType GetCpuCastFromFloat(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, float); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromFloat(DataType dst_dtype) { +CastFunctorType GetGpuCastFromFloat(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, GPUDevice, float); return nullptr; } @@ -38,8 +36,7 @@ GetGpuCastFromFloat(DataType dst_dtype) { #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -std::function -GetSyclCastFromFloat(DataType dst_dtype) { +CastFunctorType GetSyclCastFromFloat(DataType dst_dtype) { CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, float); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_half.cc b/tensorflow/core/kernels/cast_op_impl_half.cc index ef4b94e326..5da3a01352 100644 --- a/tensorflow/core/kernels/cast_op_impl_half.cc +++ b/tensorflow/core/kernels/cast_op_impl_half.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromHalf(DataType dst_dtype) { +CastFunctorType GetCpuCastFromHalf(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, Eigen::half); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromHalf(DataType dst_dtype) { +CastFunctorType GetGpuCastFromHalf(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, Eigen::half); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_int16.cc b/tensorflow/core/kernels/cast_op_impl_int16.cc index 59360f7445..440ee88fb5 100644 --- a/tensorflow/core/kernels/cast_op_impl_int16.cc +++ b/tensorflow/core/kernels/cast_op_impl_int16.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromInt16(DataType dst_dtype) { +CastFunctorType GetCpuCastFromInt16(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, int16); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromInt16(DataType dst_dtype) { +CastFunctorType GetGpuCastFromInt16(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, int16); return nullptr; } @@ -36,8 +34,7 @@ GetGpuCastFromInt16(DataType dst_dtype) { #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -std::function -GetSyclCastFromInt16(DataType dst_dtype) { +CastFunctorType GetSyclCastFromInt16(DataType dst_dtype) { CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int16); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_int32.cc b/tensorflow/core/kernels/cast_op_impl_int32.cc index a867392fde..4b3e7efddc 100644 --- a/tensorflow/core/kernels/cast_op_impl_int32.cc +++ b/tensorflow/core/kernels/cast_op_impl_int32.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromInt32(DataType dst_dtype) { +CastFunctorType GetCpuCastFromInt32(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, int32); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromInt32(DataType dst_dtype) { +CastFunctorType GetGpuCastFromInt32(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, int32); return nullptr; } @@ -36,8 +34,7 @@ GetGpuCastFromInt32(DataType dst_dtype) { #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -std::function -GetSyclCastFromInt32(DataType dst_dtype) { +CastFunctorType GetSyclCastFromInt32(DataType dst_dtype) { CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int32); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_int64.cc b/tensorflow/core/kernels/cast_op_impl_int64.cc index 467a8f6c89..0f711aa560 100644 --- a/tensorflow/core/kernels/cast_op_impl_int64.cc +++ b/tensorflow/core/kernels/cast_op_impl_int64.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromInt64(DataType dst_dtype) { +CastFunctorType GetCpuCastFromInt64(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, int64); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromInt64(DataType dst_dtype) { +CastFunctorType GetGpuCastFromInt64(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, int64); return nullptr; } @@ -36,8 +34,7 @@ GetGpuCastFromInt64(DataType dst_dtype) { #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -std::function -GetSyclCastFromInt64(DataType dst_dtype) { +CastFunctorType GetSyclCastFromInt64(DataType dst_dtype) { CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int64); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_int8.cc b/tensorflow/core/kernels/cast_op_impl_int8.cc index 21002a4321..eac185d5a0 100644 --- a/tensorflow/core/kernels/cast_op_impl_int8.cc +++ b/tensorflow/core/kernels/cast_op_impl_int8.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromInt8(DataType dst_dtype) { +CastFunctorType GetCpuCastFromInt8(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, int8); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromInt8(DataType dst_dtype) { +CastFunctorType GetGpuCastFromInt8(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, int8); return nullptr; } @@ -36,8 +34,7 @@ GetGpuCastFromInt8(DataType dst_dtype) { #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -std::function -GetSyclCastFromInt8(DataType dst_dtype) { +CastFunctorType GetSyclCastFromInt8(DataType dst_dtype) { CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, int8); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_uint16.cc b/tensorflow/core/kernels/cast_op_impl_uint16.cc index cd829bae2a..3aebbdc1f3 100644 --- a/tensorflow/core/kernels/cast_op_impl_uint16.cc +++ b/tensorflow/core/kernels/cast_op_impl_uint16.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromUint16(DataType dst_dtype) { +CastFunctorType GetCpuCastFromUint16(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, uint16); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromUint16(DataType dst_dtype) { +CastFunctorType GetGpuCastFromUint16(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, uint16); return nullptr; } @@ -36,8 +34,7 @@ GetGpuCastFromUint16(DataType dst_dtype) { #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -std::function -GetSyclCastFromUint16(DataType dst_dtype) { +CastFunctorType GetSyclCastFromUint16(DataType dst_dtype) { CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, uint16); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_uint32.cc b/tensorflow/core/kernels/cast_op_impl_uint32.cc index d1a854d98b..86f5961bcc 100644 --- a/tensorflow/core/kernels/cast_op_impl_uint32.cc +++ b/tensorflow/core/kernels/cast_op_impl_uint32.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromUint32(DataType dst_dtype) { +CastFunctorType GetCpuCastFromUint32(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, uint32); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromUint32(DataType dst_dtype) { +CastFunctorType GetGpuCastFromUint32(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, uint32); return nullptr; } @@ -36,8 +34,7 @@ GetGpuCastFromUint32(DataType dst_dtype) { #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -std::function -GetSyclCastFromUint32(DataType dst_dtype) { +CastFunctorType GetSyclCastFromUint32(DataType dst_dtype) { CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, uint32); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_uint64.cc b/tensorflow/core/kernels/cast_op_impl_uint64.cc index 604e0424fc..6478c266ee 100644 --- a/tensorflow/core/kernels/cast_op_impl_uint64.cc +++ b/tensorflow/core/kernels/cast_op_impl_uint64.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromUint64(DataType dst_dtype) { +CastFunctorType GetCpuCastFromUint64(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, uint64); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromUint64(DataType dst_dtype) { +CastFunctorType GetGpuCastFromUint64(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, uint64); return nullptr; } @@ -36,8 +34,7 @@ GetGpuCastFromUint64(DataType dst_dtype) { #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -std::function -GetSyclCastFromUint64(DataType dst_dtype) { +CastFunctorType GetSyclCastFromUint64(DataType dst_dtype) { CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, uint64); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_impl_uint8.cc b/tensorflow/core/kernels/cast_op_impl_uint8.cc index 2d1a6f3a4e..b22547a23e 100644 --- a/tensorflow/core/kernels/cast_op_impl_uint8.cc +++ b/tensorflow/core/kernels/cast_op_impl_uint8.cc @@ -20,15 +20,13 @@ namespace tensorflow { typedef Eigen::ThreadPoolDevice CPUDevice; typedef Eigen::GpuDevice GPUDevice; -std::function -GetCpuCastFromUint8(DataType dst_dtype) { +CastFunctorType GetCpuCastFromUint8(DataType dst_dtype) { CURRY_TYPES3(CAST_CASE, CPUDevice, uint8); return nullptr; } #if GOOGLE_CUDA -std::function -GetGpuCastFromUint8(DataType dst_dtype) { +CastFunctorType GetGpuCastFromUint8(DataType dst_dtype) { CURRY_TYPES3_NO_BF16(CAST_CASE, GPUDevice, uint8); return nullptr; } @@ -36,8 +34,7 @@ GetGpuCastFromUint8(DataType dst_dtype) { #ifdef TENSORFLOW_USE_SYCL typedef Eigen::SyclDevice SYCLDevice; -std::function -GetSyclCastFromUint8(DataType dst_dtype) { +CastFunctorType GetSyclCastFromUint8(DataType dst_dtype) { CURRY_TYPES3_NO_HALF(CAST_CASE, SYCLDevice, uint8); return nullptr; } diff --git a/tensorflow/core/kernels/cast_op_test.cc b/tensorflow/core/kernels/cast_op_test.cc index 9bbf7afb16..cb305de5e3 100644 --- a/tensorflow/core/kernels/cast_op_test.cc +++ b/tensorflow/core/kernels/cast_op_test.cc @@ -40,17 +40,27 @@ static Graph* Cast(int num) { class CastOpTest : public OpsTestBase { protected: - void MakeOp(DataType src, DataType dst) { - TF_EXPECT_OK(NodeDefBuilder("cast_op", "Cast") - .Input(FakeInput(src)) - .Attr("SrcT", src) - .Attr("DstT", dst) - .Finalize(node_def())); + void MakeOp(DataType src, DataType dst, bool trunc = false) { + if (trunc) { + TF_EXPECT_OK(NodeDefBuilder("cast_op", "Cast") + .Input(FakeInput(src)) + .Attr("SrcT", src) + .Attr("DstT", dst) + .Attr("Truncate", true) + .Finalize(node_def())); + } else { + TF_EXPECT_OK(NodeDefBuilder("cast_op", "Cast") + .Input(FakeInput(src)) + .Attr("SrcT", src) + .Attr("DstT", dst) + .Finalize(node_def())); + } + TF_EXPECT_OK(InitOp()); } template - void CheckCast() { + void CheckCast(bool trunc = false) { DataType in_type = DataTypeToEnum::v(); DataType out_type = DataTypeToEnum::v(); MakeOp(in_type, out_type); @@ -64,8 +74,9 @@ class CastOpTest : public OpsTestBase { } }; -#define TEST_CAST(in, out) \ - TEST_F(CastOpTest, TestCast##_##in##_##out) { CheckCast(); } +#define TEST_CAST(in, out) \ + TEST_F(CastOpTest, TestCast##_##in##_##out) { CheckCast(); } \ + TEST_F(CastOpTest, TestCast2##_##in##_##out) { CheckCast(true); } #define TEST_ALL_CASTS_FROM(in) \ TEST_CAST(in, uint8); \ diff --git a/tensorflow/core/lib/bfloat16/bfloat16.h b/tensorflow/core/lib/bfloat16/bfloat16.h index 1c130ba300..d6f3f26cd5 100644 --- a/tensorflow/core/lib/bfloat16/bfloat16.h +++ b/tensorflow/core/lib/bfloat16/bfloat16.h @@ -45,17 +45,25 @@ typedef std::complex complex128; struct bfloat16 { B16_DEVICE_FUNC bfloat16() {} - B16_DEVICE_FUNC explicit bfloat16(const float v) { + B16_DEVICE_FUNC static bfloat16 truncate_to_bfloat16(const float v) { + bfloat16 output; if (float_isnan(v)) { - value = NAN_VALUE; - return; + output.value = NAN_VALUE; + return output; } const uint16_t* p = reinterpret_cast(&v); #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ - value = p[0]; + output.value = p[0]; #else - value = p[1]; + output.value = p[1]; #endif + return output; + } + + B16_DEVICE_FUNC explicit bfloat16(const float v) { + // TODO(asabne) : change the below line to + // value = round_to_bfloat16(v).value; + value = truncate_to_bfloat16(v).value; } B16_DEVICE_FUNC explicit bfloat16(const double val) @@ -169,8 +177,6 @@ struct bfloat16 { // Converts a float point to bfloat16, with round-nearest-to-even as rounding // method. - // TODO(b/69266521): Add a truncate_to_bfloat16 function and make this - // function as default behavior. // TODO: There is a slightly faster implementation (8% faster on CPU) // than this (documented in cl/175987786), that is exponentially harder to // understand and document. Switch to the faster version when converting to diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index 386ae9635a..77697756c4 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -114,6 +114,7 @@ REGISTER_OP("Cast") .Output("y: DstT") .Attr("SrcT: type") .Attr("DstT: type") + .Attr("Truncate: bool = false") .SetShapeFn(shape_inference::UnchangedShape); REGISTER_OP("_HostCast") diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc index cefd5b1206..15d2ccf9d2 100644 --- a/tensorflow/python/eager/pywrap_tensor.cc +++ b/tensorflow/python/eager/pywrap_tensor.cc @@ -154,6 +154,7 @@ TFE_TensorHandle* EagerCast(TFE_Context* ctx, TFE_TensorHandle* handle, if (TF_GetCode(out_status) != TF_OK) RETURN_ERROR TFE_OpSetAttrType(op, "SrcT", src_type_enum); TFE_OpSetAttrType(op, "DstT", dst_type_enum); + TFE_OpSetAttrBool(op, "Truncate", false); TFE_TensorHandle* output = nullptr; int num_outputs = 1; TFE_Execute(op, &output, &num_outputs, out_status); -- GitLab From 3604c3a3ca7d80b796b4ee4c3c6beb5cc68878a9 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Wed, 25 Jul 2018 09:02:50 -0700 Subject: [PATCH 378/519] [XLA:GPU] Limit number of operands on multi-output fusion nodes. Similar to the limits imposed on non-MOF nodes in a separate patch. PiperOrigin-RevId: 206001152 --- tensorflow/compiler/xla/service/gpu/BUILD | 2 + .../xla/service/gpu/instruction_fusion.cc | 105 +++++++++++------- .../xla/service/gpu/instruction_fusion.h | 17 ++- .../service/gpu/instruction_fusion_test.cc | 4 +- .../xla/service/gpu/multi_output_fusion.cc | 13 ++- .../service/gpu/multi_output_fusion_test.cc | 54 +++++++++ 6 files changed, 144 insertions(+), 51 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 72aff197fc..08429c5b4d 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -451,6 +451,7 @@ cc_library( srcs = ["multi_output_fusion.cc"], hdrs = ["multi_output_fusion.h"], deps = [ + ":instruction_fusion", ":ir_emission_utils", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/service:hlo", @@ -463,6 +464,7 @@ tf_cc_test( name = "multi_output_fusion_test", srcs = ["multi_output_fusion_test.cc"], deps = [ + ":instruction_fusion", ":multi_output_fusion", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:util", diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc index 8abae43a5a..af6259ae83 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc @@ -73,6 +73,67 @@ bool IsIEEEFloatingPointScalarConstant(const HloInstruction* constant) { } } +// This function limits the maximum number of operands to a fusion. +// +// There's a cap on how many parameters we can pass to a CUDA kernel, but +// exactly what that limit is is hazy, as it depends on (among other things) how +// much GPU constant memory is in use for other purposes. +// +// Moreover, we don't even know at the point that we're running fusion how many +// arguments the CUDA kernel for a fusion node will have: It depends on buffer +// assignment, where we will decide which of the fusion's operands live in XLA's +// big temp buffer versus in other allocations. +// +// As a heuristic, we simply cap the number of fusion operands plus outputs at +// kMaxOperandsAndOutputsPerFusion. This puts an upper bound on the number of +// parameters to the kernel, working around the correctness problem. +// +// This limit is also often good for performance. In a fusion with many +// operands, each GPU thread likely has to do a lot of work, and so possibly +// uses a lot of registers, thus limiting occupancy. +/*static*/ bool GpuInstructionFusion::FusionWouldBeTooLarge( + const HloInstruction* a, const HloInstruction* b) { + // Compute the number of outputs of the (possibly multi-output) fusion node + // we're considering creating. + // + // This isn't precise; we may be off by one if + // - We're creating a multi-output fusion out of two non-MOFs. Creating a + // MOF adds a new buffer, namely, the tuple buffer. + // - We're merging two MOFs. In this case, we should count the tuple buffer + // only once. + // - WLOG there's an edge from `a` to `b` and `b` is the only consumer of + // `a`. In this case the result of `a` is not part of the output of the + // fusion. + // + // But because this is a heuristic and our limit + // kMaxOperandsAndOutputsPerFusion is a large value (so +/- 1 doesn't make a + // big difference), we ignore this small inaccuracy in favor of simplicity. + int64 num_output_buffers = ShapeUtil::SubshapeCount(a->shape()) + + ShapeUtil::SubshapeCount(b->shape()); + + // The new fusion will have no more operands and outputs than + // producer_operands + consumer_operands - 1 + num_output_buffers + // (minus one because we may be fusing a producer->consumer edge between `a` + // and `b`). + // + // This fact may be enough to let us avoid having to compute the true total + // number of operands, which can be expensive. + if (a->operand_count() + b->operand_count() - 1 + num_output_buffers <= + kMaxOperandsAndOutputsPerFusion) { + return false; + } + + // Compute the precise number of operands to the new fusion. + tensorflow::gtl::FlatSet operands( + a->operands().begin(), a->operands().end()); + operands.insert(b->operands().begin(), b->operands().end()); + // If there's an edge between `a` and `b`, don't count it: We're fusing that + // producer -> consumer relationship. + operands.erase(a); + operands.erase(b); + return operands.size() + num_output_buffers > kMaxOperandsAndOutputsPerFusion; +} + bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, int64 operand_index) { HloInstruction* producer = consumer->mutable_operand(operand_index); @@ -188,48 +249,8 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, return false; } - // Limit the maximum number of operands to a fusion. - // - // There's a limit to how many parameters we can pass to a CUDA kernel, but - // exactly what that limit is is hazy, as it depends on (among other things) - // how much GPU constant memory is in use for other purposes. - // - // Moreover, we don't even know at this point how many arguments the CUDA - // kernel for this fusion node will have: It depends on buffer assignment, - // where we will decide which of the fusion's operands live in XLA's big temp - // buffer versus in other allocations. - // - // As a heuristic, we simply cap the number of fusion operands at - // kMaxOperandsPerFusion. This puts an upper bound on the number of - // parameters to the kernel, working around the correctness problem. - // - // This limit is also often good for performance. In a fusion with many - // operands, each GPU thread likely has to do a lot of work, and so possibly - // uses a lot of registers, thus limiting occupancy. - // - // We put this check last because it's expensive to compute. - - // The new fusion will have no more operands than - // producer_operands + consumer_operands - 1 - // (minus one because we're fusing the producer->consumer edge). This fact - // may be enough to let us avoid having to compute the true total number of - // operands, taking into account the fact that producer and consumer may share - // operands. - if (producer->operand_count() + consumer->operand_count() - 1 > - kMaxOperandsPerFusion) { - tensorflow::gtl::FlatSet producer_operands( - producer->operands().begin(), producer->operands().end()); - int64 new_num_operands = - producer->operand_count() + - c_count_if(consumer->operands(), [&](const HloInstruction* operand) { - return operand != producer && !producer_operands.count(operand); - }); - if (new_num_operands > kMaxOperandsPerFusion) { - return false; - } - } - - return true; + // We put this check last because it's potentially expensive. + return !FusionWouldBeTooLarge(consumer, producer); } bool GpuInstructionFusion::ShouldFuseIntoMultiOutput(HloInstruction* consumer, diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.h b/tensorflow/compiler/xla/service/gpu/instruction_fusion.h index 5ee1c004b6..c91f6343a6 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.h +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.h @@ -27,6 +27,19 @@ class GpuInstructionFusion : public InstructionFusion { explicit GpuInstructionFusion(bool may_duplicate) : InstructionFusion(GpuInstructionFusion::IsExpensive, may_duplicate) {} + // Maximum number of operands plus outputs allowed on a single fusion node. + // Exposed publicly mainly for tests. + static constexpr int64 kMaxOperandsAndOutputsPerFusion = 64; + + // Determines whether the combination of `a` and `b` into a (possibly + // multi-output) fusion would be "too large" -- i.e., have more operands and + // outputs than is allowed. + // + // `ShouldFuse` and `ShouldFuseIntoMultiOutput` call this; it's public so that + // other fusion passes (e.g. GPU multi-output fusion) can also call this. + static bool FusionWouldBeTooLarge(const HloInstruction* a, + const HloInstruction* b); + static bool IsExpensive(const HloInstruction& instruction); bool ShouldFuse(HloInstruction* consumer, int64 operand_index) override; @@ -36,10 +49,6 @@ class GpuInstructionFusion : public InstructionFusion { HloInstruction::FusionKind ChooseKind( const HloInstruction* producer, const HloInstruction* consumer) override; - - // Maximum number of operands allowed on a single fusion node. Exposed - // publicly mainly for tests. - static constexpr int64 kMaxOperandsPerFusion = 64; }; } // namespace gpu diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc index 229eb23f12..8d0522bd8f 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion_test.cc @@ -609,7 +609,7 @@ TEST_F(InstructionFusionTest, FuseScalarConstant) { // Check that we limit the number of operands to fusions we create. TEST_F(InstructionFusionTest, AvoidsLargeFusion) { constexpr int64 kNumParams = 200; - ASSERT_GT(kNumParams, GpuInstructionFusion::kMaxOperandsPerFusion); + ASSERT_GT(kNumParams, GpuInstructionFusion::kMaxOperandsAndOutputsPerFusion); // Compute p0 + p1 + ... + pN. HloComputation::Builder b(TestName()); @@ -631,7 +631,7 @@ TEST_F(InstructionFusionTest, AvoidsLargeFusion) { SCOPED_TRACE(module->ToString()); for (const HloInstruction* instr : computation->instructions()) { EXPECT_LE(instr->operand_count(), - GpuInstructionFusion::kMaxOperandsPerFusion) + GpuInstructionFusion::kMaxOperandsAndOutputsPerFusion) << instr->ToString(); } } diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc index f95fbb01f9..6fef720853 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion.cc @@ -24,6 +24,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/layout_util.h" +#include "tensorflow/compiler/xla/service/gpu/instruction_fusion.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" @@ -163,16 +164,22 @@ bool GpuMultiOutputFusion::LegalToFuse(HloInstruction* instr1, if (!MultiOutputFusion::LegalToFuse(instr1, instr2)) { return false; } + // If we're fusing fusions only do it if the fusion kind matches. Loop fusions // merge into bigger loop fusions and input (reduce) fusions become fusions // with multiple reduce outputs. We could fuse reduce and loop fusions // together too (the result being an input fusion) if we find cases where this // improves things. CHECK(instr1->opcode() == HloOpcode::kFusion); - if (instr2->opcode() == HloOpcode::kFusion) { - return instr1->fusion_kind() == instr2->fusion_kind(); + if ((instr2->opcode() == HloOpcode::kFusion && + instr1->fusion_kind() != instr2->fusion_kind()) || + (instr2->opcode() != HloOpcode::kFusion && + instr1->fusion_kind() == HloInstruction::FusionKind::kLoop)) { + return false; } - return instr1->fusion_kind() != HloInstruction::FusionKind::kLoop; + + // Do this check last, as it may be expensive. + return !GpuInstructionFusion::FusionWouldBeTooLarge(instr1, instr2); } bool GpuMultiOutputFusion::DoProducerConsumerMultiOutputFusion() { diff --git a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc index 451e49f23a..ec4234b8d9 100644 --- a/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc +++ b/tensorflow/compiler/xla/service/gpu/multi_output_fusion_test.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/multi_output_fusion.h" +#include "tensorflow/compiler/xla/service/gpu/instruction_fusion.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/status_macros.h" @@ -419,5 +420,58 @@ TEST_F(MultiOutputFusionTest, ASSERT_FALSE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); } +// Check that we limit the number of operands to fusions we create. +TEST_F(MultiOutputFusionTest, AvoidsLargeFusion) { + constexpr int64 kNumParams = 200; + ASSERT_GT(kNumParams, GpuInstructionFusion::kMaxOperandsAndOutputsPerFusion); + + // Compute + // p0 * p1, + // p0 * p1 + p1 * p2 + // p0 * p1 + p1 * p2 + p2 * p3 + // ... + // where each of the (pi * pj)'s is represented as a fusion node so that + // multi-output fusion will pay attention to it. + auto module = CreateNewModule(); + HloComputation::Builder b(TestName()); + Shape shape = ShapeUtil::MakeShape(F32, {10, 100}); + + std::vector params; + for (int64 i = 0; i < kNumParams; ++i) { + params.push_back( + b.AddInstruction(HloInstruction::CreateParameter(i, shape, "p"))); + } + + // Creates a fusion node that calculates x*y. + auto make_fusion = [&](HloInstruction* x, HloInstruction* y) { + HloComputation::Builder sub_builder("subcomp"); + auto* p0 = sub_builder.AddInstruction( + HloInstruction::CreateParameter(0, shape, "p")); + auto* p1 = sub_builder.AddInstruction( + HloInstruction::CreateParameter(1, shape, "p")); + sub_builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kMultiply, p0, p1)); + HloComputation* subcomp = + module->AddEmbeddedComputation(sub_builder.Build()); + return HloInstruction::CreateFusion( + shape, HloInstruction::FusionKind::kLoop, {x, y}, subcomp); + }; + + auto* sum = b.AddInstruction(make_fusion(params[0], params[1])); + for (int64 i = 2; i < kNumParams; ++i) { + sum = b.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kAdd, sum, + b.AddInstruction(make_fusion(params[i - 1], params[i])))); + } + auto computation = module->AddEntryComputation(b.Build()); + EXPECT_TRUE(GpuMultiOutputFusion().Run(module.get()).ValueOrDie()); + SCOPED_TRACE(module->ToString()); + for (const HloInstruction* instr : computation->instructions()) { + EXPECT_LE(instr->operand_count() + ShapeUtil::SubshapeCount(instr->shape()), + GpuInstructionFusion::kMaxOperandsAndOutputsPerFusion) + << instr->ToString(); + } +} + } // namespace gpu } // namespace xla -- GitLab From 0ff2abfaf7f418d0547eaa0cf9ee0a32b32fb490 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 09:18:41 -0700 Subject: [PATCH 379/519] Update ops-related pbtxt files. PiperOrigin-RevId: 206003699 --- .../core/ops/compat/ops_history.v1.pbtxt | 26 +++++++++++++++++++ tensorflow/core/ops/ops.pbtxt | 7 +++++ 2 files changed, 33 insertions(+) diff --git a/tensorflow/core/ops/compat/ops_history.v1.pbtxt b/tensorflow/core/ops/compat/ops_history.v1.pbtxt index 69351cd392..4ac8e15160 100644 --- a/tensorflow/core/ops/compat/ops_history.v1.pbtxt +++ b/tensorflow/core/ops/compat/ops_history.v1.pbtxt @@ -12073,6 +12073,32 @@ op { type: "type" } } +op { + name: "Cast" + input_arg { + name: "x" + type_attr: "SrcT" + } + output_arg { + name: "y" + type_attr: "DstT" + } + attr { + name: "SrcT" + type: "type" + } + attr { + name: "DstT" + type: "type" + } + attr { + name: "Truncate" + type: "bool" + default_value { + b: false + } + } +} op { name: "Ceil" input_arg { diff --git a/tensorflow/core/ops/ops.pbtxt b/tensorflow/core/ops/ops.pbtxt index 978bb0bbf4..22a2f423c2 100644 --- a/tensorflow/core/ops/ops.pbtxt +++ b/tensorflow/core/ops/ops.pbtxt @@ -4977,6 +4977,13 @@ op { name: "DstT" type: "type" } + attr { + name: "Truncate" + type: "bool" + default_value { + b: false + } + } } op { name: "Ceil" -- GitLab From fa69d6531cc6cfe865a3ffc63c58f3c2fe0ec4df Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 09:45:23 -0700 Subject: [PATCH 380/519] Go: Update generated wrapper functions for TensorFlow ops. PiperOrigin-RevId: 206007600 --- tensorflow/go/op/wrappers.go | 16 +++++++++++++++- 1 file changed, 15 insertions(+), 1 deletion(-) diff --git a/tensorflow/go/op/wrappers.go b/tensorflow/go/op/wrappers.go index 18d7425323..6c9bf1e714 100644 --- a/tensorflow/go/op/wrappers.go +++ b/tensorflow/go/op/wrappers.go @@ -5225,12 +5225,26 @@ func IsBoostedTreesEnsembleInitialized(scope *Scope, tree_ensemble_handle tf.Out return op.Output(0) } +// CastAttr is an optional argument to Cast. +type CastAttr func(optionalAttr) + +// CastTruncate sets the optional Truncate attribute to value. +// If not specified, defaults to false +func CastTruncate(value bool) CastAttr { + return func(m optionalAttr) { + m["Truncate"] = value + } +} + // Cast x of type SrcT to y of DstT. -func Cast(scope *Scope, x tf.Output, DstT tf.DataType) (y tf.Output) { +func Cast(scope *Scope, x tf.Output, DstT tf.DataType, optional ...CastAttr) (y tf.Output) { if scope.Err() != nil { return } attrs := map[string]interface{}{"DstT": DstT} + for _, a := range optional { + a(attrs) + } opspec := tf.OpSpec{ Type: "Cast", Input: []tf.Input{ -- GitLab From 21f139075de212ccaab69bb89bb96d8b98282523 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Wed, 25 Jul 2018 09:56:29 -0700 Subject: [PATCH 381/519] Fix dependency overwriting in _add_variable_with_custom_getter Removes an exception which should have been removed in cl/203156155 (there is an equivalent exception slightly deeper which is more nuanced). The conditional as-is makes no sense. PiperOrigin-RevId: 206009242 --- .../python/training/checkpointable/base.py | 6 ------ .../training/checkpointable/base_test.py | 20 +++++++++++++++++++ 2 files changed, 20 insertions(+), 6 deletions(-) diff --git a/tensorflow/python/training/checkpointable/base.py b/tensorflow/python/training/checkpointable/base.py index ee35b01328..f0703c8af4 100644 --- a/tensorflow/python/training/checkpointable/base.py +++ b/tensorflow/python/training/checkpointable/base.py @@ -501,12 +501,6 @@ class CheckpointableBase(object): ValueError: If the variable name is not unique. """ self._maybe_initialize_checkpointable() - if 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 " - "that name. Variable names must be unique within a Checkpointable " - "object.") % (name,)) with ops.init_scope(): if context.executing_eagerly(): # If this is a variable with a single Tensor stored in the checkpoint, diff --git a/tensorflow/python/training/checkpointable/base_test.py b/tensorflow/python/training/checkpointable/base_test.py index 950e9c5b53..fd935ac559 100644 --- a/tensorflow/python/training/checkpointable/base_test.py +++ b/tensorflow/python/training/checkpointable/base_test.py @@ -16,8 +16,11 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from tensorflow.python.framework import ops +from tensorflow.python.ops import variable_scope from tensorflow.python.platform import test from tensorflow.python.training.checkpointable import base +from tensorflow.python.training.checkpointable import util class InterfaceTests(test.TestCase): @@ -37,5 +40,22 @@ class InterfaceTests(test.TestCase): self.assertIs(duplicate_name_dep, current_dependency) self.assertEqual("leaf", current_name) + def testAddVariableOverwrite(self): + root = base.CheckpointableBase() + a = root._add_variable_with_custom_getter( + name="v", shape=[], getter=variable_scope.get_variable) + self.assertEqual([root, a], util.list_objects(root)) + with ops.Graph().as_default(): + b = root._add_variable_with_custom_getter( + name="v", shape=[], overwrite=True, + getter=variable_scope.get_variable) + self.assertEqual([root, b], util.list_objects(root)) + with ops.Graph().as_default(): + with self.assertRaisesRegexp( + ValueError, "already declared as a dependency"): + root._add_variable_with_custom_getter( + name="v", shape=[], overwrite=False, + getter=variable_scope.get_variable) + if __name__ == "__main__": test.main() -- GitLab From ec33cb09255dc88fb5fc3403cbfb9e0c48805eb3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 10:12:24 -0700 Subject: [PATCH 382/519] Support for shape attributes in custom ops for Toco PiperOrigin-RevId: 206012140 --- .../contrib/lite/g3doc/custom_operators.md | 36 ++++++++ .../propagate_array_data_types.cc | 4 +- .../propagate_fixed_sizes.cc | 13 ++- .../contrib/lite/toco/import_tensorflow.cc | 26 +++++- tensorflow/contrib/lite/toco/model.h | 82 ++++++++++--------- 5 files changed, 114 insertions(+), 47 deletions(-) diff --git a/tensorflow/contrib/lite/g3doc/custom_operators.md b/tensorflow/contrib/lite/g3doc/custom_operators.md index 2296f5a064..d979353bb3 100644 --- a/tensorflow/contrib/lite/g3doc/custom_operators.md +++ b/tensorflow/contrib/lite/g3doc/custom_operators.md @@ -136,3 +136,39 @@ operations instead of a single operator. 6. Use TF_LITE_ENSURE(context, condition) to check for a specific condition. Your code must not leave memory hanging when TF_LITE_ENSURE is done, i.e., these should be done before any resources are allocated that will leak. + +## Special TF Graph Attributes + +When Toco convertes a TF graph into TFLite format, it makes some assumption +about custom operations that might be not correct. In this case, the generated +graph can be not executable. + +It is possible to add aditional information about your custom op output to TF +graph before it is converted. The following attributes are supported: + +- **_output_quantized** a boolean attribute, true if the operation outputs are + quantized +- **_output_types** a list of types for output tensors +- **_output_shapes** a list of shapes for output tensors + +### Setting the Attributes + +This is an example how the attributes can be set: + +```python +frozen_graph_def = tf.graph_util.convert_variables_to_constants(...) +for node in frozen_graph_def.node: + if node.op == 'sin': + node.attr['_output_types'].list.type.extend([ + types_pb2.DT_FLOAT, + ]) + node.attr['_output_shapes'].list.shape.extend([ + tf.TensorShape([10]), + ]) + node.attr['_output_quantized'].b = False +tflite_model = tf.contrib.lite.toco_convert( + frozen_graph_def,...) +``` + +**Note:** After the attributes are set, the graph can not be executed by +Tensorflow, therefore it should be done just before the conversion. 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 9848d55c83..9c22497d5e 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 @@ -154,8 +154,8 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { return false; } for (int i = 0; i < op->outputs.size(); ++i) { - auto output = op->outputs[i]; - auto data_type = unsupported_op->output_data_types[i]; + const string& output = op->outputs[i]; + const ArrayDataType data_type = unsupported_op->output_data_types[i]; model->GetArray(output).data_type = data_type; } break; 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 62ed5c46e9..a03b589bae 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -1786,8 +1786,19 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { ProcessArgMinMaxOperator( model, static_cast(op)); break; - case OperatorType::kUnsupported: + case OperatorType::kUnsupported: { + const auto* unsupported_op = + static_cast(op); + // Attribute can be not specified, ignore it. + if (unsupported_op->output_shapes.size() < op->outputs.size()) { + return false; + } + for (int i = 0; i < op->outputs.size(); ++i) { + const string& output = op->outputs[i]; + model->GetArray(output).copy_shape(unsupported_op->output_shapes.at(i)); + } break; + } case OperatorType::kSvdf: ProcessSvdfOperator(model, static_cast(op)); break; diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index 032c863945..f36f720857 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -1045,6 +1045,11 @@ tensorflow::Status ConvertSimpleOperator( tensorflow::Status ConvertUnsupportedOperator( const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, Model* model) { + // Names of special attributes in TF graph that are used by Toco. + static constexpr char kAttrOutputQuantized[] = "_output_quantized"; + static constexpr char kAttrOutputTypes[] = "_output_types"; + static constexpr char kAttrOutputShapes[] = "_output_shapes"; + LOG(INFO) << "Converting unsupported operation: " << node.op(); auto* op = new TensorFlowUnsupportedOperator; const int num_inputs = GetInputsCount(node, tf_import_flags); @@ -1055,11 +1060,11 @@ tensorflow::Status ConvertUnsupportedOperator( op->tensorflow_op = node.op(); node.SerializeToString(&op->tensorflow_node_def); model->operators.emplace_back(op); - if (HasAttr(node, "_output_quantized")) { - op->quantized = GetBoolAttr(node, "_output_quantized"); + if (HasAttr(node, kAttrOutputQuantized)) { + op->quantized = GetBoolAttr(node, kAttrOutputQuantized); } - if (HasAttr(node, "_output_types")) { - const auto& output_types = GetListAttr(node, "_output_types"); + if (HasAttr(node, kAttrOutputTypes)) { + const auto& output_types = GetListAttr(node, kAttrOutputTypes); for (int i = 0; i < output_types.type_size(); ++i) { op->output_data_types.push_back(ConvertDataType(output_types.type(i))); } @@ -1067,6 +1072,19 @@ tensorflow::Status ConvertUnsupportedOperator( const auto& output_type = GetDataTypeAttr(node, "Tout"); op->output_data_types.push_back(ConvertDataType(output_type)); } + if (HasAttr(node, kAttrOutputShapes)) { + const auto& output_shapes = GetListAttr(node, kAttrOutputShapes); + Shape output_shape; + for (int i = 0; i < output_shapes.shape_size(); ++i) { + const auto status = + ImportShape(output_shapes.shape(i).dim(), /*input_flat_size=*/nullptr, + &output_shape); + if (!status.ok()) { + return status; + } + op->output_shapes.push_back(output_shape); + } + } return tensorflow::Status::OK(); } diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index d629787939..6459dccf64 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -292,6 +292,46 @@ struct Buffer : GenericBuffer { std::vector> data; }; +class Shape { + public: + // For Shape, we stick to half-way encapsulation for now: + // we hide the raw dims_ member, but expose it raw by accessors + // because from some brainstorming, it's not at all easy to + // anticipate which flavor of more hermetic encapsulation would + // actually buy us future-proof-ness without being needlessly + // cumbersome. + Shape() {} + Shape(std::initializer_list dim_list) : dims_(dim_list) {} + + void ReplaceDims(std::initializer_list dim_list) { + dims_ = std::vector(dim_list); + } + + const std::vector& dims() const { return dims_; } + std::vector* mutable_dims() { return &dims_; } + const int dimensions_count() const { return dims_.size(); } + + // We still have that one convenience accessor to avoid + // the awkward double bracket issue: shape.dims()[i]. + int dims(int i) const { + // Always check for out-of-bounds accesses, even in optimized builds where + // standard assertions are disabled. Out-of-bounds access here is a common + // occurrence. + CHECK_GE(i, 0); + CHECK_GT(dims_.size(), i); + return dims_[i]; + } + + bool operator==(const Shape& comp) const { + return (this->dims_ == comp.dims()); + } + + bool operator!=(const Shape& comp) const { return !((*this) == comp); } + + private: + std::vector dims_; +}; + // Base class for all operator classes. struct Operator { // Non-default-constructible: only OperatorType-specific subclass @@ -1469,6 +1509,8 @@ struct TensorFlowUnsupportedOperator : Operator { bool quantized = false; // Output data types std::vector output_data_types; + // Output shapes. + std::vector output_shapes; }; // Softmax activation function. @@ -1739,46 +1781,6 @@ inline bool operator<(const Alloc& a, const Alloc& b) { return a.start < b.start; } -class Shape { - public: - // For Shape, we stick to half-way encapsulation for now: - // we hide the raw dims_ member, but expose it raw by accessors - // because from some brainstorming, it's not at all easy to - // anticipate which flavor of more hermetic encapsulation would - // actually buy us future-proof-ness without being needlessly - // cumbersome. - Shape() {} - Shape(std::initializer_list dim_list) : dims_(dim_list) {} - - void ReplaceDims(std::initializer_list dim_list) { - dims_ = std::vector(dim_list); - } - - const std::vector& dims() const { return dims_; } - std::vector* mutable_dims() { return &dims_; } - const int dimensions_count() const { return dims_.size(); } - - // We still have that one convenience accessor to avoid - // the awkward double bracket issue: shape.dims()[i]. - int dims(int i) const { - // Always check for out-of-bounds accesses, even in optimized builds where - // standard assertions are disabled. Out-of-bounds access here is a common - // occurrence. - CHECK_GE(i, 0); - CHECK_GT(dims_.size(), i); - return dims_[i]; - } - - bool operator==(const Shape& comp) const { - return (this->dims_ == comp.dims()); - } - - bool operator!=(const Shape& comp) const { return !((*this) == comp); } - - private: - std::vector dims_; -}; - // Array represents an array (either a constant parameter array or an // activations array) in a Model. struct Array { -- GitLab From 67b9e8eafd826d3f430eb7f6780e815fcac5859e Mon Sep 17 00:00:00 2001 From: Russell Power Date: Wed, 25 Jul 2018 10:14:04 -0700 Subject: [PATCH 383/519] Reraise full exception context when handling errors. PiperOrigin-RevId: 206012390 --- .../contrib/tpu/python/tpu/error_handling.py | 32 +++++++++---------- .../contrib/tpu/python/tpu/tpu_estimator.py | 13 ++++---- 2 files changed, 23 insertions(+), 22 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/error_handling.py b/tensorflow/contrib/tpu/python/tpu/error_handling.py index 14659fe68f..52e1ea4237 100644 --- a/tensorflow/contrib/tpu/python/tpu/error_handling.py +++ b/tensorflow/contrib/tpu/python/tpu/error_handling.py @@ -19,10 +19,11 @@ from __future__ import division from __future__ import print_function import contextlib +import sys import threading import time -import traceback +import six from tensorflow.python.framework import errors from tensorflow.python.platform import tf_logging as logging @@ -51,7 +52,7 @@ class ErrorRendezvous(object): self._num_sources = num_sources self._session_cancel_timer = None - def record_error(self, source, exception, session=None): + def record_error(self, source, exc_info, session=None): """Report an exception from the given source. If a session is passed, a timer will be registered to close it after a few @@ -61,12 +62,12 @@ class ErrorRendezvous(object): Args: source: string, source of the error - exception: Exception being thrown + exc_info: Output from `sys.exc_info` (type, value, traceback) session: Session to close after delay. """ - logging.info('Error recorded from %s: %s', source, exception) - stack_trace = traceback.format_exc() - self._errors[source] = (exception, stack_trace) + _, value, _ = exc_info + self._errors[source] = exc_info + logging.info('Error recorded from %s: %s', source, value) if session is not None and self._session_cancel_timer is None: @@ -98,8 +99,8 @@ class ErrorRendezvous(object): """Context manager to report any errors within a block.""" try: yield - except Exception as e: # pylint: disable=broad-except - self.record_error(source, e, session) + except Exception: # pylint: disable=broad-except + self.record_error(source, sys.exc_info(), session) def raise_errors(self, timeout_sec=0): """Wait for up to `timeout` seconds for all error sources to finish. @@ -117,16 +118,15 @@ class ErrorRendezvous(object): kept_errors = [(k, v) for (k, v) in self._errors.items() if v is not None] - if not kept_errors: - return - # First check for any interesting errors, then fall back on the session # cancelled errors etc. - for k, (exc, _) in kept_errors: - if isinstance(exc, _UNINTERESTING_ERRORS): + for k, (typ, value, traceback) in kept_errors: + if isinstance(value, _UNINTERESTING_ERRORS): continue else: - raise exc + logging.warn('Reraising captured error') + six.reraise(typ, value, traceback) - for k, (exc, _) in kept_errors: - raise exc + for k, (typ, value, traceback) in kept_errors: + logging.warn('Reraising captured error') + six.reraise(typ, value, traceback) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 2c7e7d84c0..7c7c97638e 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -22,6 +22,7 @@ import collections import copy import os import signal +import sys import threading import time @@ -2308,8 +2309,8 @@ class TPUEstimator(estimator_lib.Estimator): input_fn=input_fn, hooks=hooks, steps=steps, max_steps=max_steps, saving_listeners=saving_listeners ) - except Exception as e: # pylint: disable=broad-except - rendezvous.record_error('training_loop', e) + except Exception: # pylint: disable=broad-except + rendezvous.record_error('training_loop', sys.exc_info()) finally: rendezvous.record_done('training_loop') rendezvous.raise_errors() @@ -2323,8 +2324,8 @@ class TPUEstimator(estimator_lib.Estimator): input_fn, steps=steps, hooks=hooks, checkpoint_path=checkpoint_path, name=name ) - except Exception as e: # pylint: disable=broad-except - rendezvous.record_error('evaluation_loop', e) + except Exception: # pylint: disable=broad-except + rendezvous.record_error('evaluation_loop', sys.exc_info()) finally: rendezvous.record_done('evaluation_loop') rendezvous.raise_errors() @@ -2345,8 +2346,8 @@ class TPUEstimator(estimator_lib.Estimator): checkpoint_path=checkpoint_path, yield_single_examples=yield_single_examples): yield result - except Exception as e: # pylint: disable=broad-except - rendezvous.record_error('prediction_loop', e) + except Exception: # pylint: disable=broad-except + rendezvous.record_error('prediction_loop', sys.exc_info()) finally: rendezvous.record_done('prediction_loop') rendezvous.raise_errors() -- GitLab From 0584b943f4eca8a5761480ebb524c930aa808f0d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 10:14:24 -0700 Subject: [PATCH 384/519] An ErrorReporter to be used in tests. PiperOrigin-RevId: 206012444 --- tensorflow/contrib/lite/interpreter.cc | 5 ++-- tensorflow/contrib/lite/interpreter_test.cc | 17 ++--------- tensorflow/contrib/lite/model_test.cc | 10 +------ tensorflow/contrib/lite/testing/BUILD | 4 +++ tensorflow/contrib/lite/testing/util.h | 31 +++++++++++++++++++++ 5 files changed, 42 insertions(+), 25 deletions(-) diff --git a/tensorflow/contrib/lite/interpreter.cc b/tensorflow/contrib/lite/interpreter.cc index 5a5c907b6e..e38597495d 100644 --- a/tensorflow/contrib/lite/interpreter.cc +++ b/tensorflow/contrib/lite/interpreter.cc @@ -279,8 +279,9 @@ TfLiteStatus Interpreter::ReplaceSubgraphsWithDelegateKernels( int node_index; TfLiteDelegateParams* params = CreateDelegateParams(delegate, subgraph); - AddNodeWithParameters(subgraph.input_tensors, subgraph.output_tensors, - nullptr, 0, params, ®istration, &node_index); + TF_LITE_ENSURE_STATUS(AddNodeWithParameters( + subgraph.input_tensors, subgraph.output_tensors, nullptr, 0, params, + ®istration, &node_index)); // Initialize the output tensors's delegate-related fields. for (int tensor_index : subgraph.output_tensors) { diff --git a/tensorflow/contrib/lite/interpreter_test.cc b/tensorflow/contrib/lite/interpreter_test.cc index 10119903fe..2bf598bad7 100644 --- a/tensorflow/contrib/lite/interpreter_test.cc +++ b/tensorflow/contrib/lite/interpreter_test.cc @@ -647,18 +647,6 @@ TEST(BasicInterpreter, AllocateTwice) { ASSERT_EQ(old_tensor1_ptr, interpreter.tensor(1)->data.raw); } -struct TestErrorReporter : public ErrorReporter { - int Report(const char* format, va_list args) override { - char buffer[1024]; - int size = vsnprintf(buffer, sizeof(buffer), format, args); - all_reports += buffer; - calls++; - return size; - } - int calls = 0; - std::string all_reports; -}; - TEST(BasicInterpreter, TestNullErrorReporter) { TestErrorReporter reporter; Interpreter interpreter; @@ -668,8 +656,9 @@ TEST(BasicInterpreter, TestCustomErrorReporter) { TestErrorReporter reporter; Interpreter interpreter(&reporter); ASSERT_NE(interpreter.Invoke(), kTfLiteOk); - ASSERT_EQ(reporter.all_reports, "Invoke called on model that is not ready."); - ASSERT_EQ(reporter.calls, 1); + ASSERT_EQ(reporter.error_messages(), + "Invoke called on model that is not ready."); + ASSERT_EQ(reporter.num_calls(), 1); } TEST(BasicInterpreter, TestUnsupportedDelegateFunctions) { diff --git a/tensorflow/contrib/lite/model_test.cc b/tensorflow/contrib/lite/model_test.cc index edfdec9315..df4f60d4ad 100644 --- a/tensorflow/contrib/lite/model_test.cc +++ b/tensorflow/contrib/lite/model_test.cc @@ -241,14 +241,6 @@ TEST(BasicFlatBufferModel, TestWithNullVerifier) { "tensorflow/contrib/lite/testdata/test_model.bin", nullptr)); } -struct TestErrorReporter : public ErrorReporter { - int Report(const char* format, va_list args) override { - calls++; - return 0; - } - int calls = 0; -}; - // This makes sure the ErrorReporter is marshalled from FlatBufferModel to // the Interpreter. TEST(BasicFlatBufferModel, TestCustomErrorReporter) { @@ -262,7 +254,7 @@ TEST(BasicFlatBufferModel, TestCustomErrorReporter) { TrivialResolver resolver; InterpreterBuilder(*model, resolver)(&interpreter); ASSERT_NE(interpreter->Invoke(), kTfLiteOk); - ASSERT_EQ(reporter.calls, 1); + ASSERT_EQ(reporter.num_calls(), 1); } // This makes sure the ErrorReporter is marshalled from FlatBufferModel to diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index 6c7f494e9b..8a2705950d 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -210,6 +210,10 @@ cc_library( cc_library( name = "util", hdrs = ["util.h"], + deps = [ + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:string", + ], ) cc_test( diff --git a/tensorflow/contrib/lite/testing/util.h b/tensorflow/contrib/lite/testing/util.h index 6d20aec141..8aa639157b 100644 --- a/tensorflow/contrib/lite/testing/util.h +++ b/tensorflow/contrib/lite/testing/util.h @@ -15,8 +15,39 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_TESTING_UTIL_H_ #define TENSORFLOW_CONTRIB_LITE_TESTING_UTIL_H_ +#include + +#include "tensorflow/contrib/lite/error_reporter.h" +#include "tensorflow/contrib/lite/string.h" + namespace tflite { +// An ErrorReporter that collects error message in a string, in addition +// to printing to stderr. +class TestErrorReporter : public ErrorReporter { + public: + int Report(const char* format, va_list args) override { + char buffer[1024]; + int size = vsnprintf(buffer, sizeof(buffer), format, args); + fprintf(stderr, "%s", buffer); + error_messages_ += buffer; + num_calls_++; + return size; + } + + void Reset() { + num_calls_ = 0; + error_messages_.clear(); + } + + int num_calls() const { return num_calls_; } + const string& error_messages() const { return error_messages_; } + + private: + int num_calls_ = 0; + string error_messages_; +}; + inline void LogToStderr() { #ifdef PLATFORM_GOOGLE FLAGS_logtostderr = true; -- GitLab From 2cd10fad524f99048033c99fdac310487fe8173b Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Wed, 25 Jul 2018 10:22:33 -0700 Subject: [PATCH 385/519] Refine JNI exception when tensor allocation fails PiperOrigin-RevId: 206013732 --- .../native/nativeinterpreterwrapper_jni.cc | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) 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 e2c1edd9af..fdcf00a0a0 100644 --- a/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc +++ b/tensorflow/contrib/lite/java/src/main/native/nativeinterpreterwrapper_jni.cc @@ -152,10 +152,11 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_allocateTensors( if (error_reporter == nullptr) return; if (interpreter->AllocateTensors() != kTfLiteOk) { - throwException(env, kNullPointerException, - "Internal error: Cannot allocate memory for the interpreter:" - " %s", - error_reporter->CachedErrorMessage()); + throwException( + env, kIllegalStateException, + "Internal error: Unexpected failure when preparing tensor allocations:" + " %s", + error_reporter->CachedErrorMessage()); } } @@ -336,10 +337,11 @@ Java_org_tensorflow_lite_NativeInterpreterWrapper_createInterpreter( // allocates memory status = interpreter->AllocateTensors(); if (status != kTfLiteOk) { - throwException(env, kNullPointerException, - "Internal error: Cannot allocate memory for the interpreter:" - " %s", - error_reporter->CachedErrorMessage()); + throwException( + env, kIllegalStateException, + "Internal error: Unexpected failure when preparing tensor allocations:" + " %s", + error_reporter->CachedErrorMessage()); return 0; } return reinterpret_cast(interpreter.release()); -- GitLab From f9619bb3f309166a2c1fe453713eb1c44da41f87 Mon Sep 17 00:00:00 2001 From: Michael Kuperstein Date: Wed, 25 Jul 2018 10:22:40 -0700 Subject: [PATCH 386/519] [XLA] DynamicSlice and DynamicUpdateSlice OOB behavior is now well-defined. This removes comments that refer to it as implementation-defined. PiperOrigin-RevId: 206013760 --- tensorflow/compiler/xla/service/elemental_ir_emitter.cc | 8 -------- .../compiler/xla/service/hlo_evaluator_typed_visitor.h | 8 -------- .../xla/service/llvm_ir/dynamic_update_slice_util.cc | 4 ---- 3 files changed, 20 deletions(-) diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index b58b87a978..47ed6162ed 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -1517,10 +1517,6 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicSlice( // Clamp the start index so that the sliced portion fits in the operand: // start_index = clamp(start_index, 0, operand_dim_size - output_dim_size) - - // TODO(b/74360564): This is implementation defined behavior, but is - // currently respected by all implementations. Change this if we ever decide - // to officially document different behavior. start_index_value = b_->CreateSExtOrTrunc(start_index_value, index_type); int64 largest_valid_start_index = input_hlo->shape().dimensions(i) - hlo->shape().dimensions(i); @@ -1671,10 +1667,6 @@ StatusOr ElementalIrEmitter::EmitElementalDynamicUpdateSlice( // Clamp the start index so that the update region fits in the operand. // start_index = clamp(start_index, 0, input_dim_size - update_dim_size) - - // TODO(b/74360564): This is implementation defined behavior, but is - // currently respected by all implementations. Change this if we ever decide - // to officially document different behavior. start_index_value = b_->CreateSExtOrTrunc(start_index_value, index_type); llvm::Value* update_dim_size = index_typed_const(update_hlo->shape().dimensions(i)); diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h index f5e477e115..d5b4be7e12 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h +++ b/tensorflow/compiler/xla/service/hlo_evaluator_typed_visitor.h @@ -2078,10 +2078,6 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { start_indices_typed.end()); // Clamp the start indices so the slice is in-bounds w.r.t the operand. - - // TODO(b/74360564): This is implementation defined behavior, but is - // currently respected by all implementations. Change this if we ever decide - // to officially document different behavior. for (int64 i = 0; i < start.size(); ++i) { start[i] = std::min( std::max(int64{0}, start[i]), @@ -2115,10 +2111,6 @@ class HloEvaluatorTypedVisitor : public DfsHloVisitorWithDefault { start_indices_typed.end()); // Clamp the update start indices so the slice is in-bounds w.r.t the // operand. - - // TODO(b/74360564): This is implementation defined behavior, but is - // currently respected by all implementations. Change this if we ever decide - // to oficially document different behavior. for (int64 i = 0; i < rank; ++i) { start[i] = std::min( std::max(0, start[i]), diff --git a/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.cc b/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.cc index 1bd73fc793..27fbb11e2e 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.cc @@ -56,10 +56,6 @@ static Status EmitDynamicUpdateSliceInPlaceImpl( // Clamp the start index so that the update region fits in the operand. // start_index = clamp(start_index, 0, output_dim_size - update_dim_size) - - // TODO(b/74360564): This is implementation defined behavior, but is - // currently respected by all implementations. Change this if we ever decide - // to officially document different behavior. llvm::Value* max_bound = b->CreateSub(output_dim_size, update_dim_size); llvm::Value* zero = llvm::ConstantInt::get(start_index[i]->getType(), 0); start_index[i] = -- GitLab From a08e606825b3ccc220bb495f29fc9954f91cddf9 Mon Sep 17 00:00:00 2001 From: Michael Kuperstein Date: Wed, 25 Jul 2018 10:23:39 -0700 Subject: [PATCH 387/519] [XLA] Simplify trivial dynamic-slices regardless of the start indices. With clamping semantics, if the shapes match, the indices are always clamped to zero. PiperOrigin-RevId: 206013929 --- .../xla/service/algebraic_simplifier.cc | 23 +++++-------------- .../xla/service/algebraic_simplifier_test.cc | 8 +++---- 2 files changed, 10 insertions(+), 21 deletions(-) diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier.cc b/tensorflow/compiler/xla/service/algebraic_simplifier.cc index 26a8a67601..505c0e8dff 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier.cc @@ -1769,13 +1769,12 @@ Status AlgebraicSimplifierVisitor::HandleSlice(HloInstruction* slice) { Status AlgebraicSimplifierVisitor::HandleDynamicSlice( HloInstruction* dynamic_slice) { auto operand = dynamic_slice->mutable_operand(0); - auto start_indices = dynamic_slice->operand(1); if (ShapeUtil::IsScalar(dynamic_slice->shape())) { return ReplaceInstruction(dynamic_slice, operand); } - // DynamicSlice where operand has the same size as the output and - // start_indices are all zero is simply equal to operand. - if (IsAll(start_indices, 0) && SameShape(operand, dynamic_slice)) { + // DynamicSlice where operand has the same size as the output is simply equal + // to operand. + if (SameShape(operand, dynamic_slice)) { return ReplaceInstruction(dynamic_slice, operand); } return Status::OK(); @@ -1784,20 +1783,10 @@ Status AlgebraicSimplifierVisitor::HandleDynamicSlice( Status AlgebraicSimplifierVisitor::HandleDynamicUpdateSlice( HloInstruction* dynamic_update_slice) { auto update = dynamic_update_slice->mutable_operand(1); - auto start_indices = dynamic_update_slice->operand(2); - // DynamicUpdateSlice on a scalar just passes through the update argument. - if (ShapeUtil::IsScalar(dynamic_update_slice->shape())) { - return ReplaceInstruction(dynamic_update_slice, update); - } - // DynamicUpdateSlice where operand and update have the same size and - // start_indices are all zero is simply equal to update. - // - // (We require start_indices to be all zero because we want this optimization - // not to affect the visible behavior of this op even when the indices are out - // of range. Currently dynamic-update-slice wraps out-of-range indices, so - // we can only remove the op if its indices never wrap.) - if (IsAll(start_indices, 0) && SameShape(dynamic_update_slice, update)) { + // DynamicUpdateSlice where operand and update have the same size is simply + // equal to update. + if (SameShape(dynamic_update_slice, update)) { return ReplaceInstruction(dynamic_update_slice, update); } diff --git a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc index ddf0a513c0..8b81b4c97e 100644 --- a/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc +++ b/tensorflow/compiler/xla/service/algebraic_simplifier_test.cc @@ -2489,8 +2489,8 @@ TEST_F(AlgebraicSimplifierTest, TrivialDynamicSlice) { shape, builder.AddInstruction( HloInstruction::CreateParameter(0, shape, "slice_from")), - builder.AddInstruction(HloInstruction::CreateConstant( - LiteralUtil::CreateR1({0, 0, 0}))), + builder.AddInstruction(HloInstruction::CreateParameter( + 1, ShapeUtil::MakeShape(U32, {3}), "slice_indices")), /*slice_sizes=*/{10, 100, 1000})); auto computation = module().AddEntryComputation(builder.Build()); @@ -2523,8 +2523,8 @@ TEST_F(AlgebraicSimplifierTest, TrivialDynamicUpdateSlice) { builder.AddInstruction( HloInstruction::CreateParameter(2, slice_shape, "to_update")), slice, - builder.AddInstruction(HloInstruction::CreateConstant( - LiteralUtil::CreateR1({0, 0, 0}))))); + builder.AddInstruction(HloInstruction::CreateParameter( + 3, ShapeUtil::MakeShape(U32, {3}), "update_indices")))); auto computation = module().AddEntryComputation(builder.Build()); AlgebraicSimplifier simplifier(/*is_layout_sensitive=*/false, -- GitLab From 06b269aa33a1156b157d91c67866ec08caf9a22b Mon Sep 17 00:00:00 2001 From: Guangda Lai Date: Wed, 25 Jul 2018 10:37:27 -0700 Subject: [PATCH 388/519] Reorder the dependencies to make sure trt appears before tflite, so the build will resolve the flatbuffer symbols used by trt within the trt library. PiperOrigin-RevId: 206016450 --- tensorflow/contrib/BUILD | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/BUILD b/tensorflow/contrib/BUILD index a173c51879..6a4e252b44 100644 --- a/tensorflow/contrib/BUILD +++ b/tensorflow/contrib/BUILD @@ -130,8 +130,12 @@ py_library( "//tensorflow/contrib/bigtable", # depends on bigtable "//tensorflow/contrib/cloud:cloud_py", # doesn't compile on Windows "//tensorflow/contrib/ffmpeg:ffmpeg_ops_py", - "//tensorflow/contrib/lite/python:lite", # unix dependency, need to fix code + # TODO(aaroey): tensorrt dependency has to appear before tflite so the + # build can resolve its flatbuffers symbols within the tensorrt library. + # This is an issue with the tensorrt static library and will be fixed by + # the next tensorrt release, so fix the order here after that. "//tensorflow/contrib/tensorrt:init_py", # doesn't compile on windows + "//tensorflow/contrib/lite/python:lite", # unix dependency, need to fix code ]), ) -- GitLab From 07f7c112129e9d4c782ab29bfd2bd2e51ed7eb64 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 10:49:23 -0700 Subject: [PATCH 389/519] Changing the dataset link PiperOrigin-RevId: 206018486 --- .../generative_examples/text_generation.ipynb | 336 +++++++++--------- 1 file changed, 168 insertions(+), 168 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb b/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb index 6be09f98df..b173f856c6 100644 --- a/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb +++ b/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb @@ -1,31 +1,11 @@ { - "nbformat": 4, - "nbformat_minor": 0, - "metadata": { - "colab": { - "name": "text_generation.ipynb", - "version": "0.3.2", - "views": {}, - "default_view": {}, - "provenance": [], - "private_outputs": true, - "collapsed_sections": [], - "toc_visible": true - }, - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "accelerator": "GPU" - }, "cells": [ { + "cell_type": "markdown", "metadata": { - "id": "hcD2nPQvPOFM", - "colab_type": "text" + "colab_type": "text", + "id": "hcD2nPQvPOFM" }, - "cell_type": "markdown", "source": [ "##### Copyright 2018 The TensorFlow Authors.\n", "\n", @@ -33,19 +13,19 @@ "\n", "# Text Generation using a RNN\n", "\n", - "
\n", - "\n", - " Run in Google Colab \n", - "\n", - "View source on Github
" + "\u003ctable class=\"tfo-notebook-buttons\" align=\"left\"\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb\"\u003e\n", + " \u003cimg src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" /\u003eRun in Google Colab\u003c/a\u003e \n", + "\u003c/td\u003e\u003ctd\u003e\n", + "\u003ca target=\"_blank\" href=\"https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/eager/python/examples/generative_examples/text_generation.ipynb\"\u003e\u003cimg width=32px src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" /\u003eView source on Github\u003c/a\u003e\u003c/td\u003e\u003c/table\u003e" ] }, { + "cell_type": "markdown", "metadata": { - "id": "BwpJ5IffzRG6", - "colab_type": "text" + "colab_type": "text", + "id": "BwpJ5IffzRG6" }, - "cell_type": "markdown", "source": [ "This notebook demonstrates how to generate text using an RNN using [tf.keras](https://www.tensorflow.org/programmers_guide/keras) and [eager execution](https://www.tensorflow.org/programmers_guide/eager). If you like, you can write a similar [model](https://github.com/fchollet/deep-learning-with-python-notebooks/blob/master/8.1-text-generation-with-lstm.ipynb) using less code. Here, we show a lower-level impementation that's useful to understand as prework before diving in to deeper examples in a similar, like [Neural Machine Translation with Attention](https://colab.research.google.com/github/tensorflow/tensorflow/blob/master/tensorflow/contrib/eager/python/examples/nmt_with_attention/nmt_with_attention.ipynb).\n", "\n", @@ -102,58 +82,60 @@ ] }, { + "cell_type": "markdown", "metadata": { - "id": "R3p22DBDsaCA", - "colab_type": "text" + "colab_type": "text", + "id": "R3p22DBDsaCA" }, - "cell_type": "markdown", "source": [ "## Install unidecode library\n", "A helpful library to convert unicode to ASCII." ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "wZ6LOM12wKGH", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "wZ6LOM12wKGH" }, - "cell_type": "code", + "outputs": [], "source": [ "!pip install unidecode" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "WGyKZj3bzf9p", - "colab_type": "text" + "colab_type": "text", + "id": "WGyKZj3bzf9p" }, - "cell_type": "markdown", "source": [ "## Import tensorflow and enable eager execution." ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "yG_n40gFzf9s", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "yG_n40gFzf9s" }, - "cell_type": "code", + "outputs": [], "source": [ - "# Import TensorFlow >= 1.9 and enable eager execution\n", + "# Import TensorFlow \u003e= 1.9 and enable eager execution\n", "import tensorflow as tf\n", "\n", "# Note: Once you enable eager execution, it cannot be disabled. \n", @@ -164,16 +146,14 @@ "import random\n", "import unidecode\n", "import time" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "EHDoRoc5PKWz", - "colab_type": "text" + "colab_type": "text", + "id": "EHDoRoc5PKWz" }, - "cell_type": "markdown", "source": [ "## Download the dataset\n", "\n", @@ -182,76 +162,78 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "pD_55cOxLkAb", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "pD_55cOxLkAb" }, - "cell_type": "code", + "outputs": [], "source": [ - "path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/yashkatariya/shakespeare.txt')" - ], - "execution_count": 0, - "outputs": [] + "path_to_file = tf.keras.utils.get_file('shakespeare.txt', 'https://storage.googleapis.com/download.tensorflow.org/data/shakespeare.txt')" + ] }, { + "cell_type": "markdown", "metadata": { - "id": "UHjdCjDuSvX_", - "colab_type": "text" + "colab_type": "text", + "id": "UHjdCjDuSvX_" }, - "cell_type": "markdown", "source": [ "## Read the dataset\n", "\n" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "-E5JvY3wzf94", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "-E5JvY3wzf94" }, - "cell_type": "code", + "outputs": [], "source": [ "text = unidecode.unidecode(open(path_to_file).read())\n", "# length of text is the number of characters in it\n", "print (len(text))" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "Il9ww98izf-D", - "colab_type": "text" + "colab_type": "text", + "id": "Il9ww98izf-D" }, - "cell_type": "markdown", "source": [ "Creating dictionaries to map from characters to their indices and vice-versa, which will be used to vectorize the inputs" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "IalZLbvOzf-F", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "IalZLbvOzf-F" }, - "cell_type": "code", + "outputs": [], "source": [ "# unique contains all the unique characters in the file\n", "unique = sorted(set(text))\n", @@ -259,22 +241,22 @@ "# creating a mapping from unique characters to indices\n", "char2idx = {u:i for i, u in enumerate(unique)}\n", "idx2char = {i:u for i, u in enumerate(unique)}" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "1v_qUYfAzf-I", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "1v_qUYfAzf-I" }, - "cell_type": "code", + "outputs": [], "source": [ "# setting the maximum length sentence we want for a single input in characters\n", "max_length = 100\n", @@ -293,16 +275,14 @@ "\n", "# buffer size to shuffle our dataset\n", "BUFFER_SIZE = 10000" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "LFjSVAlWzf-N", - "colab_type": "text" + "colab_type": "text", + "id": "LFjSVAlWzf-N" }, - "cell_type": "markdown", "source": [ "## Creating the input and output tensors\n", "\n", @@ -319,17 +299,19 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "0UHJDA39zf-O", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "0UHJDA39zf-O" }, - "cell_type": "code", + "outputs": [], "source": [ "input_text = []\n", "target_text = []\n", @@ -343,45 +325,43 @@ " \n", "print (np.array(input_text).shape)\n", "print (np.array(target_text).shape)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "MJdfPmdqzf-R", - "colab_type": "text" + "colab_type": "text", + "id": "MJdfPmdqzf-R" }, - "cell_type": "markdown", "source": [ "## Creating batches and shuffling them using tf.data" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "p2pGotuNzf-S", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "p2pGotuNzf-S" }, - "cell_type": "code", + "outputs": [], "source": [ "dataset = tf.data.Dataset.from_tensor_slices((input_text, target_text)).shuffle(BUFFER_SIZE)\n", "dataset = dataset.apply(tf.contrib.data.batch_and_drop_remainder(BATCH_SIZE))" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "m8gPwEjRzf-Z", - "colab_type": "text" + "colab_type": "text", + "id": "m8gPwEjRzf-Z" }, - "cell_type": "markdown", "source": [ "## Creating the model\n", "\n", @@ -393,17 +373,19 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "P3KTiiInzf-a", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "P3KTiiInzf-a" }, - "cell_type": "code", + "outputs": [], "source": [ "class Model(tf.keras.Model):\n", " def __init__(self, vocab_size, embedding_dim, units, batch_size):\n", @@ -447,66 +429,64 @@ " x = self.fc(output)\n", "\n", " return x, states" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "trpqTWyvk0nr", - "colab_type": "text" + "colab_type": "text", + "id": "trpqTWyvk0nr" }, - "cell_type": "markdown", "source": [ "## Call the model and set the optimizer and the loss function" ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "7t2XrzEOzf-e", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "7t2XrzEOzf-e" }, - "cell_type": "code", + "outputs": [], "source": [ "model = Model(vocab_size, embedding_dim, units, BATCH_SIZE)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "dkjWIATszf-h", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "dkjWIATszf-h" }, - "cell_type": "code", + "outputs": [], "source": [ "optimizer = tf.train.AdamOptimizer()\n", "\n", "# using sparse_softmax_cross_entropy so that we don't have to create one-hot vectors\n", "def loss_function(real, preds):\n", " return tf.losses.sparse_softmax_cross_entropy(labels=real, logits=preds)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "lPrP0XMUzf-p", - "colab_type": "text" + "colab_type": "text", + "id": "lPrP0XMUzf-p" }, - "cell_type": "markdown", "source": [ "## Train the model\n", "\n", @@ -531,17 +511,19 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "d4tSNwymzf-q", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "d4tSNwymzf-q" }, - "cell_type": "code", + "outputs": [], "source": [ "# Training step\n", "\n", @@ -574,16 +556,14 @@ " \n", " print ('Epoch {} Loss {:.4f}'.format(epoch+1, loss))\n", " print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "DjGz1tDkzf-u", - "colab_type": "text" + "colab_type": "text", + "id": "DjGz1tDkzf-u" }, - "cell_type": "markdown", "source": [ "## Predicting using our trained model\n", "\n", @@ -601,17 +581,19 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "WvuwZBX5Ogfd", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "WvuwZBX5Ogfd" }, - "cell_type": "code", + "outputs": [], "source": [ "# Evaluation step(generating text using the model learned)\n", "\n", @@ -648,16 +630,14 @@ " text_generated += idx2char[predicted_id]\n", "\n", "print (start_string + text_generated)" - ], - "execution_count": 0, - "outputs": [] + ] }, { + "cell_type": "markdown", "metadata": { - "id": "AM2Uma_-yVIq", - "colab_type": "text" + "colab_type": "text", + "id": "AM2Uma_-yVIq" }, - "cell_type": "markdown", "source": [ "## Next steps\n", "\n", @@ -668,22 +648,42 @@ ] }, { + "cell_type": "code", + "execution_count": 0, "metadata": { - "id": "gtEd86sX5cB2", - "colab_type": "code", "colab": { "autoexec": { "startup": false, "wait_interval": 0 } - } + }, + "colab_type": "code", + "id": "gtEd86sX5cB2" }, - "cell_type": "code", + "outputs": [], "source": [ "" - ], - "execution_count": 0, - "outputs": [] + ] } - ] + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "collapsed_sections": [], + "default_view": {}, + "name": "text_generation.ipynb", + "private_outputs": true, + "provenance": [], + "toc_visible": true, + "version": "0.3.2", + "views": {} + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "nbformat": 4, + "nbformat_minor": 0 } -- GitLab From 402bb7353289013520fa99a38559319bc7b3d845 Mon Sep 17 00:00:00 2001 From: Toby Boyd Date: Wed, 25 Jul 2018 10:57:12 -0700 Subject: [PATCH 390/519] Remove install of NCCL 2.x NCCL 2.x already exists in the base docker from NVIDIA. Installing just the runtime for some reasons results in the header file being removed from /usr/include/nccl.h. PiperOrigin-RevId: 206019917 --- tensorflow/tools/ci_build/Dockerfile.gpu | 4 ---- 1 file changed, 4 deletions(-) diff --git a/tensorflow/tools/ci_build/Dockerfile.gpu b/tensorflow/tools/ci_build/Dockerfile.gpu index 33d0425918..383f9545c9 100644 --- a/tensorflow/tools/ci_build/Dockerfile.gpu +++ b/tensorflow/tools/ci_build/Dockerfile.gpu @@ -15,10 +15,6 @@ 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 -# Install NCCL -RUN apt-get update && apt-get install -y --no-install-recommends \ - libnccl2=2.2.13-1+cuda9.0 - RUN /install/install_pip_packages.sh RUN /install/install_bazel.sh RUN /install/install_golang.sh -- GitLab From ebe8a6fba27a357117d0ba154197b02d6a8b4ffb Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 11:00:31 -0700 Subject: [PATCH 391/519] Make NNAPI delegation support more ops. PiperOrigin-RevId: 206020501 --- .../lite/delegates/nnapi/nnapi_delegate.cc | 211 +++- .../delegates/nnapi/nnapi_delegate_test.cc | 914 +++++++++++++++++- 2 files changed, 1120 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc index 0c7f6d3125..60855eb8ed 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate.cc @@ -436,7 +436,6 @@ class NNAPIDelegateKernel { } break; case kTfLiteBuiltinSqueeze: - // Squeeze requires NNAPI1.1. if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) { return [](TfLiteContext* context, NNAPIOpBuilder* builder, TfLiteNode* node) -> ANeuralNetworksOperationType { @@ -464,9 +463,215 @@ class NNAPIDelegateKernel { return ANEURALNETWORKS_L2_NORMALIZATION; }; } + case kTfLiteBuiltinLocalResponseNormalization: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + node->builtin_data); + builder->AddScalarInt32Operand(builtin->radius); + builder->AddScalarFloat32Operand(builtin->bias); + builder->AddScalarFloat32Operand(builtin->alpha); + builder->AddScalarFloat32Operand(builtin->beta); + return ANEURALNETWORKS_LOCAL_RESPONSE_NORMALIZATION; + }; + } else { + // TODO(miaowang): clean-up code and return early in the unsupported + // case. + return nullptr; + } + break; + case kTfLiteBuiltinLshProjection: + if (version == 1) { + // NNAPI does not support sparse projection correctly (b/111751836). + if (reinterpret_cast(node->builtin_data) + ->type == kTfLiteLshProjectionSparse) { + return nullptr; + } + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + node->builtin_data); + builder->AddScalarInt32Operand(builtin->type); + return ANEURALNETWORKS_LSH_PROJECTION; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinConcatenation: + if (version == 1 && + reinterpret_cast(node->builtin_data) + ->activation == kTfLiteActNone) { + if (context->tensors[node->inputs->data[0]].type == kTfLiteUInt8) { + // NNAPI only support concatenating quantized tensor of the same + // scale and offset. + auto first_param = context->tensors[node->inputs->data[0]].params; + for (int i = 0; i < node->inputs->size; i++) { + auto curr_param = context->tensors[node->inputs->data[i]].params; + if (curr_param.scale != first_param.scale || + curr_param.zero_point != first_param.zero_point) { + return nullptr; + } + } + } + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = reinterpret_cast( + node->builtin_data); + builder->AddScalarInt32Operand(builtin->axis); + return ANEURALNETWORKS_CONCATENATION; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinDequantize: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_DEQUANTIZE; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinFloor: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_FLOOR; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinRelu: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_RELU; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinReluN1To1: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_RELU1; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinRelu6: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_RELU6; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinLogistic: + if (version == 1) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_LOGISTIC; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinTanh: + // TODO(miaowang): add additional checks for the parameters. + if (version == 1 && + context->tensors[node->inputs->data[0]].type == kTfLiteFloat32) { + // NNAPI only support float tanh. + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_TANH; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinSub: + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11 && + context->tensors[node->inputs->data[0]].type == kTfLiteFloat32) { + // NNAPI only support float sub. + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_SUB; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinDiv: + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11 && + context->tensors[node->inputs->data[0]].type == kTfLiteFloat32) { + // NNAPI only support float div. + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->activation); + return ANEURALNETWORKS_DIV; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinPad: + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11 && + node->inputs->size == 2 && + context->tensors[node->inputs->data[0]].type == kTfLiteFloat32) { + // NNAPI does not support specifying the padding value. + // NNAPI pads physical zero for quantized tensors, so only delegate + // float pad to NNAPI. + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_PAD; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinSpaceToBatchNd: + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + return ANEURALNETWORKS_SPACE_TO_BATCH_ND; + }; + } else { + return nullptr; + } + break; + case kTfLiteBuiltinStridedSlice: + if (version == 1 && kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) { + return [](TfLiteContext* context, NNAPIOpBuilder* builder, + TfLiteNode* node) -> ANeuralNetworksOperationType { + auto builtin = + reinterpret_cast(node->builtin_data); + builder->AddScalarInt32Operand(builtin->begin_mask); + builder->AddScalarInt32Operand(builtin->end_mask); + builder->AddScalarInt32Operand(builtin->shrink_axis_mask); + return ANEURALNETWORKS_STRIDED_SLICE; + }; + } else { + return nullptr; + } + break; case kTfLiteBuiltinTranspose: - // Transpose requires NNAPI1.1. Also note that the permutation input - // tensor value dictates the output dimensions. + // Note that the permutation input tensor value dictates the output + // dimensions. // TODO(b/110888333): Support dynamically-sized tensors in delegates. if ((version == 1) && (kAndroidSdkVersion >= kMinSdkVersionForNNAPI11) && diff --git a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc index baf8046f9b..b7b159c59f 100644 --- a/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc +++ b/tensorflow/contrib/lite/delegates/nnapi/nnapi_delegate_test.cc @@ -82,7 +82,7 @@ TEST(NNAPIDelegate, AddWithRelu) { EXPECT_THAT(m.GetOutput(), ElementsAreArray({0.0, 0.4, 1.0, 1.3})); } -class FloatMulOpModel : public SingleOpModel { +class FloatMulOpModel : public SingleOpModelWithNNAPI { public: FloatMulOpModel(const TensorData& input1, const TensorData& input2, const TensorData& output, @@ -184,7 +184,7 @@ TEST(NNAPIDelegate, L2PoolWithNoActivation) { EXPECT_THAT(m.GetOutput(), ElementsAreArray({3.5, 6.5})); } -class BaseConvolutionOpModel : public SingleOpModel { +class BaseConvolutionOpModel : public SingleOpModelWithNNAPI { public: BaseConvolutionOpModel( const TensorData& input, const TensorData& filter, @@ -713,6 +713,916 @@ TEST(NNAPIDelegate, TransposeSimpleTest) { 2, 6, 10, 14, 18, 22, 3, 7, 11, 15, 19, 23})); } +class FloatSubOpModel : public SingleOpModelWithNNAPI { + public: + FloatSubOpModel(const TensorData& input1, const TensorData& input2, + const TensorData& output, + ActivationFunctionType activation_type) { + input1_ = AddInput(input1); + input2_ = AddInput(input2); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_SUB, BuiltinOptions_SubOptions, + CreateMulOptions(builder_, activation_type).Union()); + BuildInterpreter({GetShape(input1_), GetShape(input2_)}); + } + + int input1() { return input1_; } + int input2() { return input2_; } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input1_; + int input2_; + int output_; +}; + +TEST(NNAPIDelegate, SubWithNoActivation) { + FloatSubOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-2.0, 0.2, 0.7, 0.8}); + m.PopulateTensor(m.input2(), {0.1, 0.2, 0.3, 0.5}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-2.1, 0.0, 0.4, 0.3}))); +} + +class FloatDivOpModel : public SingleOpModelWithNNAPI { + public: + FloatDivOpModel(const TensorData& input1, const TensorData& input2, + const TensorData& output, + ActivationFunctionType activation_type) { + input1_ = AddInput(input1); + input2_ = AddInput(input2); + output_ = AddOutput(output); + SetBuiltinOp(BuiltinOperator_DIV, BuiltinOptions_DivOptions, + CreateMulOptions(builder_, activation_type).Union()); + BuildInterpreter({GetShape(input1_), GetShape(input2_)}); + } + + int input1() { return input1_; } + int input2() { return input2_; } + + std::vector GetOutput() { return ExtractVector(output_); } + + protected: + int input1_; + int input2_; + int output_; +}; + +TEST(NNAPIDelegate, DivWithNoActivation) { + FloatDivOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {1, 2, 2, 1}}, + {TensorType_FLOAT32, {}}, ActivationFunctionType_NONE); + m.PopulateTensor(m.input1(), {-2.0, 0.2, 0.8, 0.8}); + m.PopulateTensor(m.input2(), {0.1, 0.2, 0.4, 0.2}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({-20, 1, 2, 4}))); +} + +class BaseConcatenationOpModel : public SingleOpModelWithNNAPI { + public: + BaseConcatenationOpModel() {} + BaseConcatenationOpModel(const TensorData& input_template, int axis, + int num_inputs) { + std::vector> all_input_shapes; + for (int i = 0; i < num_inputs; ++i) { + all_input_shapes.push_back(input_template.shape); + AddInput(input_template); + } + output_ = AddOutput({input_template.type, /*shape=*/{}, input_template.min, + input_template.max}); + SetBuiltinOp( + BuiltinOperator_CONCATENATION, BuiltinOptions_ConcatenationOptions, + CreateConcatenationOptions(builder_, axis, ActivationFunctionType_NONE) + .Union()); + BuildInterpreter(all_input_shapes); + } + + protected: + int output_; +}; + +class ConcatenationOpModel : public BaseConcatenationOpModel { + public: + using BaseConcatenationOpModel::BaseConcatenationOpModel; + void SetInput(int index, std::initializer_list data) { + PopulateTensor(index, data); + } + std::vector GetOutput() { return ExtractVector(output_); } +}; + +TEST(NNAPIDelegate, ConcatenationThreeDimensionalOneInput) { + ConcatenationOpModel m0({TensorType_FLOAT32, {2, 1, 2}}, /*axis=*/1, + /*num_inputs=*/1); + m0.SetInput(0, {1.0f, 3.0f, 4.0f, 7.0f}); + m0.Invoke(); + EXPECT_THAT(m0.GetOutput(), ElementsAreArray({1, 3, 4, 7})); +} + +TEST(NNAPIDelegate, ConcatenationFourInputs) { + ConcatenationOpModel m0({TensorType_FLOAT32, {2, 1, 2}}, /*axis=*/2, + /*num_inputs=*/4); + m0.SetInput(0, {1.0f, 3.0f, 4.0f, 7.0f}); + m0.SetInput(1, {1.1f, 3.1f, 4.1f, 7.1f}); + m0.SetInput(2, {1.2f, 3.2f, 4.2f, 7.2f}); + m0.SetInput(3, {1.3f, 3.3f, 4.3f, 7.3f}); + m0.Invoke(); + EXPECT_THAT(m0.GetOutput(), + ElementsAreArray({ + 1.0f, 3.0f, 1.1f, 3.1f, 1.2f, 3.2f, 1.3f, 3.3f, // + 4.0f, 7.0f, 4.1f, 7.1f, 4.2f, 7.2f, 4.3f, 7.3f, // + })); +} + +class QuantizedConcatenationOpModel : public BaseConcatenationOpModel { + public: + using BaseConcatenationOpModel::BaseConcatenationOpModel; + QuantizedConcatenationOpModel(const std::vector& input_template, + int axis, int num_inputs, + const TensorData& output_template) { + std::vector> all_input_shapes; + CHECK_EQ(input_template.size(), num_inputs); + for (int i = 0; i < num_inputs; ++i) { + all_input_shapes.push_back(input_template[i].shape); + AddInput(input_template[i]); + } + output_ = AddOutput({output_template.type, /*shape=*/{}, + output_template.min, output_template.max}); + SetBuiltinOp( + BuiltinOperator_CONCATENATION, BuiltinOptions_ConcatenationOptions, + CreateConcatenationOptions(builder_, axis, ActivationFunctionType_NONE) + .Union()); + BuildInterpreter(all_input_shapes); + } + void SetInput(int index, std::initializer_list data) { + QuantizeAndPopulate(index, data); + } + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetDequantizedOutput() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } +}; + +TEST(NNAPIDelegate, ConcatenationFourInputsQuantized) { + QuantizedConcatenationOpModel m0({TensorType_UINT8, {2, 1, 2}, -12.7, 12.8}, + /*axis=*/2, + /*num_inputs=*/4); + + m0.SetInput(0, {1.0f, 3.0f, 4.0f, 7.0f}); + m0.SetInput(1, {1.1f, 3.1f, 4.1f, 7.1f}); + m0.SetInput(2, {1.2f, 3.2f, 4.2f, 7.2f}); + m0.SetInput(3, {1.3f, 3.3f, 4.3f, 7.3f}); + m0.Invoke(); + EXPECT_THAT(m0.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({ + 1.0f, 3.0f, 1.1f, 3.1f, 1.2f, 3.2f, 1.3f, 3.3f, // + 4.0f, 7.0f, 4.1f, 7.1f, 4.2f, 7.2f, 4.3f, 7.3f, // + }))); + EXPECT_THAT(m0.GetOutput(), ElementsAreArray({ + 137, 157, 138, 158, 139, 159, 140, 160, // + 167, 197, 168, 198, 169, 199, 170, 200, // + })); +} + +TEST(NNAPIDelegate, ConcatenationFourInputsQuantizedMixedRange) { + QuantizedConcatenationOpModel m0({{TensorType_UINT8, {2, 1, 2}, -10.7, 10.8}, + {TensorType_UINT8, {2, 1, 2}, 0, 12.8}, + {TensorType_UINT8, {2, 1, 2}, -11, 11.8}, + {TensorType_UINT8, {2, 1, 2}, 0, 7.4}}, + /*axis=*/2, /*num_inputs=*/4, + {TensorType_UINT8, {2, 1, 2}, -12.7, 12.8}); + + m0.SetInput(0, {1.0f, 3.0f, 4.0f, 7.0f}); + m0.SetInput(1, {1.1f, 3.1f, 4.1f, 7.1f}); + m0.SetInput(2, {1.2f, 3.2f, 4.2f, 7.2f}); + m0.SetInput(3, {1.3f, 3.3f, 4.3f, 7.3f}); + m0.Invoke(); + EXPECT_THAT(m0.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear({ + 1.0f, 3.0f, 1.1f, 3.1f, 1.2f, 3.2f, 1.3f, 3.3f, // + 4.0f, 7.0f, 4.1f, 7.1f, 4.2f, 7.2f, 4.3f, 7.3f, // + }))); + EXPECT_THAT(m0.GetOutput(), ElementsAreArray({ + 137, 157, 138, 158, 139, 159, 140, 160, // + 167, 197, 168, 198, 169, 199, 170, 200, // + })); +} + +class DequantizeOpModel : public SingleOpModelWithNNAPI { + public: + DequantizeOpModel(std::initializer_list shape, float min, float max) { + input_ = AddInput({TensorType_UINT8, shape, min, max}); + output_ = AddOutput({TensorType_FLOAT32, shape}); + SetBuiltinOp(BuiltinOperator_DEQUANTIZE, BuiltinOptions_DequantizeOptions, + CreateDequantizeOptions(builder_).Union()); + + BuildInterpreter({GetShape(input_)}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + private: + int input_; + int output_; +}; + +TEST(NNAPIDelegate, DequantizeFourDimensional) { + DequantizeOpModel m({2, 5}, -63.5, 64); + + m.SetInput({0, 1, 2, 3, 4, 251, 252, 253, 254, 255}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {-63.5, -63, -62.5, -62, -61.5, 62, 62.5, 63, 63.5, 64}))); +} + +class FloorOpModel : public SingleOpModelWithNNAPI { + public: + FloorOpModel(std::initializer_list input_shape, TensorType input_type) { + input_ = AddInput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_FLOOR, BuiltinOptions_NONE, 0); + BuildInterpreter({ + input_shape, + }); + } + + int input() { return input_; } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int output_; +}; + +TEST(NNAPIDelegate, FloorSingleDim) { + FloorOpModel model({2}, TensorType_FLOAT32); + model.PopulateTensor(model.input(), {8.5, 0.0}); + model.Invoke(); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({8, 0})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2})); +} + +TEST(NNAPIDelegate, FloorMultiDims) { + FloorOpModel model({2, 1, 1, 5}, TensorType_FLOAT32); + model.PopulateTensor(model.input(), { + 0.0001, + 8.0001, + 0.9999, + 9.9999, + 0.5, + -0.0001, + -8.0001, + -0.9999, + -9.9999, + -0.5, + }); + model.Invoke(); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({0, 8, 0, 9, 0, -1, -9, -1, -10, -1})); + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 1, 1, 5})); +} + +class LocalResponseNormOpModel : public SingleOpModelWithNNAPI { + public: + LocalResponseNormOpModel(std::initializer_list input_shape, int radius, + float bias, float alpha, float beta) { + input_ = AddInput(TensorType_FLOAT32); + output_ = AddOutput(TensorType_FLOAT32); + SetBuiltinOp(BuiltinOperator_LOCAL_RESPONSE_NORMALIZATION, + BuiltinOptions_LocalResponseNormalizationOptions, + CreateLocalResponseNormalizationOptions(builder_, radius, bias, + alpha, beta) + .Union()); + BuildInterpreter({input_shape}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + + private: + int input_; + int output_; +}; + +TEST(NNAPIDelegate, LocalResponseNormSameAsL2Norm) { + LocalResponseNormOpModel m({1, 1, 1, 6}, /*radius=*/20, /*bias=*/0.0, + /*alpha=*/1.0, /*beta=*/0.5); + m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + m.Invoke(); + // The result is every input divided by 2. + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-0.55, 0.3, 0.35, 0.6, -0.35, 0.05}))); +} + +TEST(NNAPIDelegate, LocalResponseNormWithAlpha) { + LocalResponseNormOpModel m({1, 1, 1, 6}, /*radius=*/20, /*bias=*/0.0, + /*alpha=*/4.0, /*beta=*/0.5); + m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + m.Invoke(); + // The result is every input divided by 3. + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear( + {-0.275, 0.15, 0.175, 0.3, -0.175, 0.025}))); +} + +TEST(NNAPIDelegate, LocalResponseNormWithBias) { + LocalResponseNormOpModel m({1, 1, 1, 6}, /*radius=*/20, /*bias=*/9.0, + /*alpha=*/4.0, /*beta=*/0.5); + m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + m.Invoke(); + // The result is every input divided by 5. + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear({-0.22, 0.12, 0.14, 0.24, -0.14, 0.02}))); +} + +TEST(NNAPIDelegate, LocalResponseNormSmallRadius) { + LocalResponseNormOpModel m({1, 1, 1, 6}, /*radius=*/2, /*bias=*/9.0, + /*alpha=*/4.0, /*beta=*/0.5); + m.SetInput({-1.1, 0.6, 0.7, 1.2, -0.7, 0.1}); + m.Invoke(); + EXPECT_THAT( + m.GetOutput(), + ElementsAreArray(ArrayFloatNear( + {-0.264926, 0.125109, 0.140112, 0.267261, -0.161788, 0.0244266}))); +} + +class LSHProjectionOpModel : public SingleOpModelWithNNAPI { + public: + LSHProjectionOpModel(LSHProjectionType type, + std::initializer_list hash_shape, + std::initializer_list input_shape, + std::initializer_list weight_shape) { + hash_ = AddInput(TensorType_FLOAT32); + input_ = AddInput(TensorType_INT32); + if (weight_shape.size() > 0) { + weight_ = AddInput(TensorType_FLOAT32); + } + output_ = AddOutput(TensorType_INT32); + + SetBuiltinOp(BuiltinOperator_LSH_PROJECTION, + BuiltinOptions_LSHProjectionOptions, + CreateLSHProjectionOptions(builder_, type).Union()); + if (weight_shape.size() > 0) { + BuildInterpreter({hash_shape, input_shape, weight_shape}); + } else { + BuildInterpreter({hash_shape, input_shape}); + } + + output_size_ = 1; + for (int i : hash_shape) { + output_size_ *= i; + if (type == LSHProjectionType_SPARSE) { + break; + } + } + } + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetHash(std::initializer_list data) { + PopulateTensor(hash_, data); + } + + void SetWeight(std::initializer_list f) { PopulateTensor(weight_, f); } + + std::vector GetOutput() { return ExtractVector(output_); } + + private: + int input_; + int hash_; + int weight_; + int output_; + + int output_size_; +}; + +TEST(NNAPIDelegate, LSHProjectionDense1DInputs) { + LSHProjectionOpModel m(LSHProjectionType_DENSE, {3, 2}, {5}, {5}); + + m.SetInput({12345, 54321, 67890, 9876, -12345678}); + m.SetHash({0.123, 0.456, -0.321, 1.234, 5.678, -4.321}); + m.SetWeight({1.0, 1.0, 1.0, 1.0, 1.0}); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAre(0, 0, 0, 1, 0, 0)); +} + +TEST(NNAPIDelegate, LSHProjectionSparse1DInputs) { + LSHProjectionOpModel m(LSHProjectionType_SPARSE, {3, 2}, {5}, {}); + + m.SetInput({12345, 54321, 67890, 9876, -12345678}); + m.SetHash({0.123, 0.456, -0.321, 1.234, 5.678, -4.321}); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAre(0 + 0, 4 + 1, 8 + 0)); +} + +TEST(NNAPIDelegate, LSHProjectionSparse3DInputs) { + LSHProjectionOpModel m(LSHProjectionType_SPARSE, {3, 2}, {5, 2, 2}, {5}); + + m.SetInput({1234, 2345, 3456, 1234, 4567, 5678, 6789, 4567, 7891, 8912, + 9123, 7890, -987, -876, -765, -987, -543, -432, -321, -543}); + m.SetHash({0.123, 0.456, -0.321, 1.234, 5.678, -4.321}); + m.SetWeight({0.12, 0.34, 0.56, 0.67, 0.78}); + + m.Invoke(); + + EXPECT_THAT(m.GetOutput(), ElementsAre(0 + 2, 4 + 1, 8 + 1)); +} + +class BaseActivationsOpModel : public SingleOpModelWithNNAPI { + public: + // Most activations don't take any options, so this constructor works for + // them. + BaseActivationsOpModel(BuiltinOperator type, TensorData input) { + input_ = AddInput(input); + if (input.type == TensorType_UINT8) { + output_ = AddOutput({input.type, {}, 0, 0, 1. / 256}); + } else { + output_ = AddOutput({input.type, {}}); + } + SetBuiltinOp(type, BuiltinOptions_NONE, 0); + 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_; +}; + +class FloatActivationsOpModel : public BaseActivationsOpModel { + public: + using BaseActivationsOpModel::BaseActivationsOpModel; + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + std::vector GetOutput() { return ExtractVector(output_); } +}; + +const float kQuantizedTolerance = 2 * (1. / 256); + +class QuantizedActivationsOpModel : public BaseActivationsOpModel { + public: + using BaseActivationsOpModel::BaseActivationsOpModel; + + template + void SetInput(std::initializer_list data) { + QuantizeAndPopulate(input_, data); + } + template + + std::vector GetOutput() { + return ExtractVector(output_); + } + template + std::vector GetDequantizedOutput() { + return Dequantize(ExtractVector(output_), GetScale(output_), + GetZeroPoint(output_)); + } +}; + +TEST(NNAPIDelegate, Relu) { + FloatActivationsOpModel m(BuiltinOperator_RELU, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 0, 0, 2, 4, // + 3, 0, 10, 1, // + })); +} + +TEST(NNAPIDelegate, Relu1) { + FloatActivationsOpModel m(BuiltinOperator_RELU_N1_TO_1, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); + m.SetInput({ + 0.0, -0.6, 0.2, -0.4, // + 0.3, -2.0, 1.1, -0.1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 0.0, -0.6, 0.2, -0.4, // + 0.3, -1.0, 1.0, -0.1, // + })); +} + +TEST(NNAPIDelegate, Relu6) { + FloatActivationsOpModel m(BuiltinOperator_RELU6, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 0, 0, 2, 4, // + 3, 0, 6, 1, // + })); +} + +TEST(NNAPIDelegate, Tanh) { + FloatActivationsOpModel m(BuiltinOperator_TANH, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 0, -0.9999877, 0.9640275, 0.999329, // + 0.99505475, -0.9640275, 1, 0.7615941, // + }))); +} + +TEST(NNAPIDelegate, LogisticFloat) { + FloatActivationsOpModel m(BuiltinOperator_LOGISTIC, + /*input=*/{TensorType_FLOAT32, {1, 2, 4, 1}}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 0.5, 0.002473, 0.880797, 0.982014, // + 0.952574, 0.119203, 0.999955, 0.731059, // + }))); +} + +TEST(NNAPIDelegate, LogisticQuantized) { + QuantizedActivationsOpModel m( + BuiltinOperator_LOGISTIC, + /*input=*/{TensorType_UINT8, {1, 2, 4, 1}, -10, 10}); + m.SetInput({ + 0, -6, 2, 4, // + 3, -2, 10, 1, // + }); + m.Invoke(); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(ArrayFloatNear( + { + 0.5, 0.002473, 0.880797, 0.982014, // + 0.952574, 0.119203, 0.999955, 0.731059, // + }, + kQuantizedTolerance))); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({128, 1, 227, 251, 244, 32, 255, 188})); +} + +#if 0 +class ResizeBilinearOpModel : public SingleOpModelWithNNAPI { + public: + ResizeBilinearOpModel(const TensorData& input, + std::initializer_list size_data = {}) { + bool const_size = size_data.size() != 0; + input_ = AddInput(input); + if (const_size) { + size_ = AddConstInput(TensorType_INT32, size_data, {2}); + } else { + size_ = AddInput({TensorType_INT32, {2}}); + } + output_ = AddOutput(input.type); + SetBuiltinOp(BuiltinOperator_RESIZE_BILINEAR, + BuiltinOptions_ResizeBilinearOptions, + CreateResizeBilinearOptions(builder_).Union()); + if (const_size) { + BuildInterpreter({GetShape(input_)}); + } else { + BuildInterpreter({GetShape(input_), GetShape(size_)}); + } + } + + template + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + void SetSize(std::initializer_list data) { PopulateTensor(size_, data); } + + template + std::vector GetOutput() { + return ExtractVector(output_); + } + + private: + int input_; + int size_; + int output_; +}; + +TEST(NNAPIDelegate, ResizeBilinearHorizontal) { + ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 1, 2, 1}}); + m.SetInput({3, 6}); + m.SetSize({1, 3}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 5, 6}))); + + ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 1, 2, 1}}, {1, 3}); + const_m.SetInput({3, 6}); + const_m.Invoke(); + EXPECT_THAT(const_m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 5, 6}))); +} + +TEST(NNAPIDelegate, ResizeBilinearVertical) { + ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 2, 1, 1}}); + m.SetInput({3, 9}); + m.SetSize({3, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 7, 9}))); + + ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 2, 1, 1}}, {3, 1}); + const_m.SetInput({3, 9}); + const_m.Invoke(); + EXPECT_THAT(const_m.GetOutput(), + ElementsAreArray(ArrayFloatNear({3, 7, 9}))); +} + +TEST(NNAPIDelegate, ResizeBilinearTwoDimensional) { + ResizeBilinearOpModel m({TensorType_FLOAT32, {1, 2, 2, 1}}); + m.SetInput({ + 3, 6, // + 9, 12 // + }); + m.SetSize({3, 3}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + }))); + + ResizeBilinearOpModel const_m({TensorType_FLOAT32, {1, 2, 2, 1}}, {3, 3}); + const_m.SetInput({ + 3, 6, // + 9, 12 // + }); + const_m.Invoke(); + EXPECT_THAT(const_m.GetOutput(), ElementsAreArray(ArrayFloatNear({ + 3, 5, 6, // + 7, 9, 10, // + 9, 11, 12, // + }))); +} +#endif + +template +class PadOpModel : public SingleOpModelWithNNAPI { + public: + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetQuantizedInput(std::initializer_list data) { + QuantizeAndPopulate(input_, data); + } + + void SetQuantizedPadValue(float data) { + QuantizeAndPopulate(constant_values_, {data}); + } + + void SetPaddings(std::initializer_list paddings) { + PopulateTensor(paddings_, paddings); + } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + std::vector GetDequantizedOutput() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } + + protected: + int input_; + int output_; + int paddings_; + int constant_values_; +}; + +class PadOpConstModel : public PadOpModel { + public: + PadOpConstModel(const TensorData& input, + std::initializer_list paddings_shape, + std::initializer_list paddings, + const TensorData& output) { + input_ = AddInput(input); + paddings_ = AddConstInput(TensorType_INT32, paddings, paddings_shape); + output_ = AddOutput(output); + + SetBuiltinOp(BuiltinOperator_PAD, BuiltinOptions_PadOptions, + CreatePadOptions(builder_).Union()); + BuildInterpreter({input.shape}); + } +}; + +TEST(NNAPIDelegate, PadAdvancedConstTest) { + PadOpConstModel m({TensorType_FLOAT32, {1, 2, 3, 1}}, {4, 2}, + {0, 0, 0, 2, 1, 3, 0, 0}, {TensorType_FLOAT32}); + m.SetInput({1, 2, 3, 4, 5, 6}); + m.Invoke(); + EXPECT_THAT(m.GetOutput(), + ElementsAreArray({0, 1, 2, 3, 0, 0, 0, 0, 4, 5, 6, 0, 0, 0, + 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0})); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 4, 7, 1})); +} + +class SpaceToBatchNDOpModel : public SingleOpModelWithNNAPI { + public: + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + + void SetBlockShape(std::initializer_list data) { + PopulateTensor(block_shape_, data); + } + + void SetPaddings(std::initializer_list data) { + PopulateTensor(paddings_, data); + } + + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + protected: + int input_; + int block_shape_; + int paddings_; + int output_; +}; + +class SpaceToBatchNDOpConstModel : public SpaceToBatchNDOpModel { + public: + SpaceToBatchNDOpConstModel(std::initializer_list input_shape, + std::initializer_list block_shape, + std::initializer_list paddings) { + input_ = AddInput(TensorType_FLOAT32); + block_shape_ = AddConstInput(TensorType_INT32, block_shape, {2}); + paddings_ = AddConstInput(TensorType_INT32, paddings, {2, 2}); + output_ = AddOutput(TensorType_FLOAT32); + + SetBuiltinOp(BuiltinOperator_SPACE_TO_BATCH_ND, + BuiltinOptions_SpaceToBatchNDOptions, + CreateSpaceToBatchNDOptions(builder_).Union()); + BuildInterpreter({input_shape}); + } +}; + +TEST(NNAPIDelegate, SpaceToBatchNDSimpleConstTest) { + SpaceToBatchNDOpConstModel m({1, 4, 4, 1}, {2, 2}, {0, 0, 0, 0}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 2, 2, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 3, 9, 11, 2, 4, 10, 12, 5, 7, + 13, 15, 6, 8, 14, 16})); +} + +TEST(NNAPIDelegate, SpaceToBatchNDMultipleInputBatchesConstTest) { + SpaceToBatchNDOpConstModel m({2, 2, 4, 1}, {2, 2}, {0, 0, 0, 0}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({8, 1, 2, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1, 3, 9, 11, 2, 4, 10, 12, 5, 7, + 13, 15, 6, 8, 14, 16})); +} + +TEST(NNAPIDelegate, SpaceToBatchNDSimplePaddingConstTest) { + SpaceToBatchNDOpConstModel m({1, 5, 2, 1}, {3, 2}, {1, 0, 2, 0}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 2, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 0, 0, 0, 5, 0, 0, 0, 6, 0, 1, 0, 7, + 0, 2, 0, 8, 0, 3, 0, 9, 0, 4, 0, 10, + })); +} + +TEST(NNAPIDelegate, SpaceToBatchNDComplexPaddingConstTest) { + SpaceToBatchNDOpConstModel m({1, 4, 2, 1}, {3, 2}, {1, 1, 2, 4}); + m.SetInput({1, 2, 3, 4, 5, 6, 7, 8}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 4, 1})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({ + 0, 0, 0, 0, 0, 5, 0, 0, 0, 0, 0, 0, 0, 6, 0, 0, + 0, 1, 0, 0, 0, 7, 0, 0, 0, 2, 0, 0, 0, 8, 0, 0, + 0, 3, 0, 0, 0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 0, 0, + })); +} + +template +class StridedSliceOpModel : public SingleOpModelWithNNAPI { + public: + StridedSliceOpModel(std::initializer_list input_shape, + std::initializer_list begin_shape, + std::initializer_list end_shape, + std::initializer_list strides_shape, int begin_mask, + int end_mask, int ellipsis_mask, int new_axis_mask, + int shrink_axis_mask) { + input_ = AddInput(tensor_input_type); + begin_ = AddInput(TensorType_INT32); + end_ = AddInput(TensorType_INT32); + strides_ = AddInput(TensorType_INT32); + output_ = AddOutput(tensor_input_type); + SetBuiltinOp( + BuiltinOperator_STRIDED_SLICE, BuiltinOptions_StridedSliceOptions, + CreateStridedSliceOptions(builder_, begin_mask, end_mask, ellipsis_mask, + new_axis_mask, shrink_axis_mask) + .Union()); + BuildInterpreter({input_shape, begin_shape, end_shape, strides_shape}); + } + + void SetInput(std::initializer_list data) { + PopulateTensor(input_, data); + } + void SetBegin(std::initializer_list data) { + PopulateTensor(begin_, data); + } + void SetEnd(std::initializer_list data) { + PopulateTensor(end_, data); + } + void SetStrides(std::initializer_list data) { + PopulateTensor(strides_, data); + } + + std::vector GetOutput() { + return ExtractVector(output_); + } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int input_; + int begin_; + int end_; + int strides_; + int output_; +}; + +TEST(NNAPIDelegate, StridedSliceIn2D) { + StridedSliceOpModel<> m({2, 3}, {2}, {2}, {2}, 0, 0, 0, 0, 0); + m.SetInput({1, 2, 3, 4, 5, 6}); + m.SetBegin({1, 0}); + m.SetEnd({2, 2}); + m.SetStrides({1, 1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({1, 2})); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({4, 5})); +} + +TEST(NNAPIDelegate, StridedSliceIn2D_ShrinkAxis_NegativeSlice) { + // This is equivalent to tf.range(4)[:, tf.newaxis][-2, -1]. + StridedSliceOpModel<> m({4, 1}, {2}, {2}, {2}, 0, 0, 0, 0, 3); + m.SetInput({0, 1, 2, 3}); + m.SetBegin({-2, -1}); + m.SetEnd({-1, 0}); + m.SetStrides({1, 1}); + + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({2})); +} + +TEST(NNAPIDelegate, StridedSliceIn2D_ShrinkAxisMask) { + StridedSliceOpModel<> m({2, 3}, {2}, {2}, {2}, 0, 0, 0, 0, 3); + m.SetInput({1, 2, 3, 4, 5, 6}); + m.SetBegin({0, 0}); + m.SetEnd({1, 1}); + m.SetStrides({1, 1}); + m.Invoke(); + EXPECT_TRUE(m.GetOutputShape().empty()); + EXPECT_THAT(m.GetOutput(), ElementsAreArray({1})); +} + } // namespace } // namespace tflite -- GitLab From 0bc512505957e3685305b6a850f222c6eed88c7d Mon Sep 17 00:00:00 2001 From: Guangda Lai Date: Wed, 25 Jul 2018 11:02:43 -0700 Subject: [PATCH 392/519] Enable TensorRT build. PiperOrigin-RevId: 206020981 --- tensorflow/contrib/tensorrt/BUILD | 8 +++++--- tensorflow/contrib/tensorrt/convert/utils.cc | 2 +- tensorflow/contrib/tensorrt/tensorrt_test.cc | 9 +++++++++ .../tensorrt/test/tf_trt_integration_test_base.py | 1 + 4 files changed, 16 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index 2fe1f2c242..5889fd5aaf 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -37,7 +37,9 @@ tf_cuda_cc_test( "nomac", ], deps = [ + "//tensorflow/core:gpu_init", "//tensorflow/core:lib", + "//tensorflow/core:stream_executor", "//tensorflow/core:test", "//tensorflow/core:test_main", ] + if_tensorrt([ @@ -384,12 +386,12 @@ cuda_py_tests( "test/base_test.py", # "test/batch_matmul_test.py", # "test/biasadd_matmul_test.py", - "test/binary_tensor_weight_broadcast_test.py", - "test/concatenation_test.py", + # "test/binary_tensor_weight_broadcast_test.py", # Blocked by trt4 installation + # "test/concatenation_test.py", # Blocked by trt4 installation "test/const_broadcast_test.py", "test/multi_connection_neighbor_engine_test.py", "test/neighboring_engine_test.py", - "test/unary_test.py", + # "test/unary_test.py", # Blocked by trt4 installation # "test/vgg_block_nchw_test.py", # "test/vgg_block_test.py", ], diff --git a/tensorflow/contrib/tensorrt/convert/utils.cc b/tensorflow/contrib/tensorrt/convert/utils.cc index 24591cf84b..17857cf4d0 100644 --- a/tensorflow/contrib/tensorrt/convert/utils.cc +++ b/tensorflow/contrib/tensorrt/convert/utils.cc @@ -24,7 +24,7 @@ bool IsGoogleTensorRTEnabled() { // safely write code that uses tensorrt conditionally. E.g. if it does not // check for for tensorrt, and user mistakenly uses tensorrt, they will just // crash and burn. -#ifdef GOOGLE_TENSORRT +#if GOOGLE_CUDA && GOOGLE_TENSORRT return true; #else return false; diff --git a/tensorflow/contrib/tensorrt/tensorrt_test.cc b/tensorflow/contrib/tensorrt/tensorrt_test.cc index 3712a9a6fe..769982c645 100644 --- a/tensorflow/contrib/tensorrt/tensorrt_test.cc +++ b/tensorflow/contrib/tensorrt/tensorrt_test.cc @@ -13,7 +13,9 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include "tensorflow/core/common_runtime/gpu/gpu_init.h" #include "tensorflow/core/platform/logging.h" +#include "tensorflow/core/platform/stream_executor.h" #include "tensorflow/core/platform/test.h" #if GOOGLE_CUDA @@ -130,6 +132,13 @@ void Execute(nvinfer1::IExecutionContext* context, const float* input, } TEST(TensorrtTest, BasicFunctions) { + // Handle the case where the test is run on machine with no gpu available. + if (CHECK_NOTNULL(GPUMachineManager())->VisibleDeviceCount() <= 0) { + LOG(WARNING) << "No gpu device available, probably not being run on a gpu " + "machine. Skipping..."; + return; + } + // Create the network model. nvinfer1::IHostMemory* model = CreateNetwork(); // Use the model to create an engine and then an execution context. diff --git a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py index 60b8eb6e81..bb7f5a77f0 100644 --- a/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py +++ b/tensorflow/contrib/tensorrt/test/tf_trt_integration_test_base.py @@ -107,6 +107,7 @@ class TfTrtIntegrationTestBase(test_util.TensorFlowTestCase): graph_options = config_pb2.GraphOptions() gpu_options = config_pb2.GPUOptions() + gpu_options.allow_growth = True if trt_convert.get_linked_tensorrt_version()[0] == 3: gpu_options.per_process_gpu_memory_fraction = 0.50 -- GitLab From a5285d999af961367437d72285f67c4a5a2878d4 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Wed, 25 Jul 2018 11:08:39 -0700 Subject: [PATCH 393/519] [XLA:GPU] Use a fast approximation for tanh Just reuse the CPU implementation, which in turn is derived from Eigen. It claims to be accurate within +-1% which is good enough for fast math. Refactor the CPU implementation into a common file and remove the VectorSupportLibrary dependency (it's not needed). PiperOrigin-RevId: 206022260 --- tensorflow/compiler/xla/service/cpu/BUILD | 1 + .../xla/service/cpu/llvm_ir_runtime.cc | 39 ++---------- tensorflow/compiler/xla/service/gpu/BUILD | 1 + .../xla/service/gpu/elemental_ir_emitter.cc | 11 ++++ tensorflow/compiler/xla/service/llvm_ir/BUILD | 10 ++++ .../compiler/xla/service/llvm_ir/math_ops.cc | 59 +++++++++++++++++++ .../compiler/xla/service/llvm_ir/math_ops.h | 32 ++++++++++ tensorflow/compiler/xla/tests/half_test.cc | 3 +- 8 files changed, 120 insertions(+), 36 deletions(-) create mode 100644 tensorflow/compiler/xla/service/llvm_ir/math_ops.cc create mode 100644 tensorflow/compiler/xla/service/llvm_ir/math_ops.h diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index ace9f96cfb..71f7f985d0 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -444,6 +444,7 @@ cc_library( deps = [ ":vector_support_library", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", + "//tensorflow/compiler/xla/service/llvm_ir:math_ops", "//tensorflow/core:lib", "@llvm//:core", "@llvm//:transform_utils", diff --git a/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc b/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc index ec0498e04e..cef5e57b0b 100644 --- a/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc +++ b/tensorflow/compiler/xla/service/cpu/llvm_ir_runtime.cc @@ -21,6 +21,7 @@ limitations under the License. #include "llvm/IR/Verifier.h" #include "llvm/Transforms/Utils/Cloning.h" #include "tensorflow/compiler/xla/service/cpu/vector_support_library.h" +#include "tensorflow/compiler/xla/service/llvm_ir/math_ops.h" #include "tensorflow/core/lib/core/casts.h" #include "tensorflow/core/platform/logging.h" @@ -54,44 +55,12 @@ llvm::Function* EmitVectorF32TanhIfNeeded(llvm::Module* module, llvm::IRBuilder<> b(vector_tanh_body); llvm::FastMathFlags fast_math_flags; - fast_math_flags.setFast(); + fast_math_flags.setFast(enable_fast_math); b.setFastMathFlags(fast_math_flags); - VectorSupportLibrary vsl(F32, vector_width, &b, "tanh_f32"); - llvm::Value* input = &*vector_tanh_function->arg_begin(); - CHECK_EQ(input->getType(), vsl.vector_type()); - - // This implements the same rational interpolant as implemented in Eigen3. - llvm::Value* input_clamped = - vsl.Clamp(input, /*low=*/GetIeeeF32(-9.0), /*high=*/GetIeeeF32(9.0)); - - std::array numerator_coeffs{ - -2.76076847742355e-16f, 2.00018790482477e-13f, -8.60467152213735e-11f, - 5.12229709037114e-08f, 1.48572235717979e-05f, 6.37261928875436e-04f, - 4.89352455891786e-03f}; - - std::array denominator_coeffs{ - 1.19825839466702e-06f, 1.18534705686654e-04f, 2.26843463243900e-03f, - 4.89352518554385e-03f}; - - llvm::Value* input_squared = vsl.Mul(input_clamped, input_clamped); - llvm::Value* numerator = vsl.SplatFloat(GetIeeeF32(numerator_coeffs[0])); - for (int i = 1; i < numerator_coeffs.size(); i++) { - numerator = - vsl.MulAdd(input_squared, numerator, GetIeeeF32(numerator_coeffs[i])); - } - - numerator = vsl.Mul(input_clamped, numerator); - - llvm::Value* denominator = vsl.SplatFloat(GetIeeeF32(denominator_coeffs[0])); - for (int i = 1; i < denominator_coeffs.size(); i++) { - denominator = vsl.MulAdd(input_squared, denominator, - GetIeeeF32(denominator_coeffs[i])); - } - - llvm::Value* result = vsl.Div(numerator, denominator); - b.CreateRet(result); + CHECK_EQ(vector_width, input->getType()->getVectorNumElements()); + b.CreateRet(llvm_ir::EmitFastTanh(&b, input)); DCHECK(!llvm::verifyFunction(*vector_tanh_function)); return vector_tanh_function; diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 08429c5b4d..6f1e766d1c 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -217,6 +217,7 @@ cc_library( "//tensorflow/compiler/xla/service/llvm_ir:llvm_loop", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/compiler/xla/service/llvm_ir:loop_emitter", + "//tensorflow/compiler/xla/service/llvm_ir:math_ops", "//tensorflow/core:lib", "@llvm//:core", "@llvm//:support", diff --git a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc index b97a627d9b..cc38db27e2 100644 --- a/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/elemental_ir_emitter.cc @@ -35,6 +35,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" +#include "tensorflow/compiler/xla/service/llvm_ir/math_ops.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" @@ -277,6 +278,16 @@ StatusOr GpuElementalIrEmitter::EmitFloatUnaryOp( PrimitiveType output_type = op->shape().element_type(); switch (op->opcode()) { case HloOpcode::kTanh: + // If we don't care much about precision, emit a fast approximation of + // tanh. + if (hlo_module_config_.debug_options().xla_enable_fast_math()) { + // Upcast F16 to F32 if necessary. + llvm::Type* type = + input_type == F16 ? b_->getFloatTy() : operand_value->getType(); + llvm::Value* input = b_->CreateFPCast(operand_value, type); + llvm::Value* fast_tanh = llvm_ir::EmitFastTanh(b_, input); + return b_->CreateFPCast(fast_tanh, operand_value->getType()); + } return EmitLibdeviceMathCall("__nv_tanh", {operand_value}, {input_type}, output_type); default: diff --git a/tensorflow/compiler/xla/service/llvm_ir/BUILD b/tensorflow/compiler/xla/service/llvm_ir/BUILD index 0573304912..309a186e58 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/BUILD +++ b/tensorflow/compiler/xla/service/llvm_ir/BUILD @@ -223,3 +223,13 @@ cc_library( "@llvm//:core", ], ) + +cc_library( + name = "math_ops", + srcs = ["math_ops.cc"], + hdrs = ["math_ops.h"], + deps = [ + ":llvm_util", + "@llvm//:core", + ], +) diff --git a/tensorflow/compiler/xla/service/llvm_ir/math_ops.cc b/tensorflow/compiler/xla/service/llvm_ir/math_ops.cc new file mode 100644 index 0000000000..0e115cdabf --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/math_ops.cc @@ -0,0 +1,59 @@ +/* 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/llvm_ir/math_ops.h" +#include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" + +namespace xla { +namespace llvm_ir { + +llvm::Value* EmitFastTanh(llvm::IRBuilder<>* b, llvm::Value* input) { + llvm::Type* type = input->getType(); + + // Clamp the input to [-9, 9]. + llvm::Value* input_clamped = llvm_ir::EmitFloatMin( + llvm_ir::EmitFloatMax(input, llvm::ConstantFP::get(type, -9.0), b), + llvm::ConstantFP::get(type, 9.0), b); + + static constexpr std::array numerator_coeffs{ + -2.76076847742355e-16f, 2.00018790482477e-13f, -8.60467152213735e-11f, + 5.12229709037114e-08f, 1.48572235717979e-05f, 6.37261928875436e-04f, + 4.89352455891786e-03f}; + + static constexpr std::array denominator_coeffs{ + 1.19825839466702e-06f, 1.18534705686654e-04f, 2.26843463243900e-03f, + 4.89352518554385e-03f}; + + llvm::Value* input_squared = b->CreateFMul(input_clamped, input_clamped); + llvm::Value* numerator = llvm::ConstantFP::get(type, numerator_coeffs[0]); + for (int i = 1; i < numerator_coeffs.size(); i++) { + numerator = b->CreateFAdd(b->CreateFMul(input_squared, numerator), + llvm::ConstantFP::get(type, numerator_coeffs[i])); + } + + numerator = b->CreateFMul(input_clamped, numerator); + + llvm::Value* denominator = llvm::ConstantFP::get(type, denominator_coeffs[0]); + for (int i = 1; i < denominator_coeffs.size(); i++) { + denominator = + b->CreateFAdd(b->CreateFMul(input_squared, denominator), + llvm::ConstantFP::get(type, denominator_coeffs[i])); + } + + return b->CreateFDiv(numerator, denominator); +} + +} // namespace llvm_ir +} // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/math_ops.h b/tensorflow/compiler/xla/service/llvm_ir/math_ops.h new file mode 100644 index 0000000000..6c8bc3a076 --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/math_ops.h @@ -0,0 +1,32 @@ +/* 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_LLVM_IR_MATH_OPS_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_MATH_OPS_H_ + +#include "llvm/IR/IRBuilder.h" +#include "llvm/IR/Value.h" + +namespace xla { +namespace llvm_ir { + +// Emits an approximation of tanh. The implementation uses the same rational +// interpolant as implemented in Eigen3. +llvm::Value* EmitFastTanh(llvm::IRBuilder<>* b, llvm::Value* input); + +} // namespace llvm_ir +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_MATH_OPS_H_ diff --git a/tensorflow/compiler/xla/tests/half_test.cc b/tensorflow/compiler/xla/tests/half_test.cc index 73a47eda72..249a4b2493 100644 --- a/tensorflow/compiler/xla/tests/half_test.cc +++ b/tensorflow/compiler/xla/tests/half_test.cc @@ -48,7 +48,8 @@ class UnaryOpTest : public HalfTestBase, public ::testing::WithParamInterface {}; XLA_TEST_P(UnaryOpTest, Ops) { - std::vector x({half(1.4), half(-2.3), half(3.2), half(-4.1)}); + std::vector x({half(1.4), half(-2.3), half(3.2), half(-4.1), half(9.0), + half(42.0), half(-9.0), half(-100.0)}); XlaBuilder builder(TestName()); XlaOp x_opnd; auto x_data = CreateR1Parameter(x, /*parameter_number=*/0, "x", -- GitLab From e99262f6ec4a9ea8921d390945dbf0cd90d58d34 Mon Sep 17 00:00:00 2001 From: Skye Wanderman-Milne Date: Wed, 25 Jul 2018 11:12:06 -0700 Subject: [PATCH 394/519] Fix importing a while loop into a cond. PiperOrigin-RevId: 206022821 --- tensorflow/python/framework/importer_test.py | 41 ++++++++++++++++++++ tensorflow/python/ops/control_flow_ops.py | 29 +++++++++++--- tensorflow/python/ops/control_flow_util.py | 8 ++++ 3 files changed, 73 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/framework/importer_test.py b/tensorflow/python/framework/importer_test.py index c5a54470d2..7182c28666 100644 --- a/tensorflow/python/framework/importer_test.py +++ b/tensorflow/python/framework/importer_test.py @@ -30,6 +30,7 @@ from tensorflow.python.framework import dtypes from tensorflow.python.framework import function from tensorflow.python.framework import importer from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import test_ops # pylint: disable=unused-import from tensorflow.python.framework import versions from tensorflow.python.ops import array_ops @@ -419,6 +420,46 @@ class ImportGraphDefTest(test.TestCase): with self.test_session() as sess: self.assertEqual(sess.run(imported_r), 10) + def testImportWhileLoopInCond(self): + # Produce GraphDef containing while loop. + graph = ops.Graph() + with graph.as_default(): + r = control_flow_ops.while_loop(lambda i: i < 10, lambda i: i + 1, [0]) + graph_def = graph.as_graph_def() + + # Import the GraphDef inside a cond and make sure it runs. + with ops.Graph().as_default(): + + def ImportFn(): + return importer.import_graph_def(graph_def, return_elements=[r.name])[0] + + pred = array_ops.placeholder(dtypes.bool) + out = control_flow_ops.cond(pred, ImportFn, + lambda: constant_op.constant(1)) + with self.test_session() as sess: + self.assertEqual(sess.run(out, {pred: True}), 10) + self.assertEqual(sess.run(out, {pred: False}), 1) + + def testImportWhileLoopInWhileLoop(self): + self.skipTest("b/111757448") + # Produce GraphDef containing while loop. + graph = ops.Graph() + with graph.as_default(): + r = control_flow_ops.while_loop(lambda i: i < 10, lambda i: i + 1, [0]) + graph_def = graph.as_graph_def() + + # Import the GraphDef inside another loop and make sure it runs. + with ops.Graph().as_default(): + + def ImportFn(_): + return importer.import_graph_def(graph_def, return_elements=[r.name])[0] + + out = control_flow_ops.while_loop( + lambda i: i < 2, ImportFn, [0], + shape_invariants=[tensor_shape.TensorShape(None)]) + with self.test_session() as sess: + self.assertEqual(sess.run(out), 10) + def testTypeMismatchInGraphDef(self): # TODO(skyewm): improve error message error_msg = ("Input 0 of node import/B was passed int32 from import/A:0 " diff --git a/tensorflow/python/ops/control_flow_ops.py b/tensorflow/python/ops/control_flow_ops.py index 888075ba2e..aeac61c005 100644 --- a/tensorflow/python/ops/control_flow_ops.py +++ b/tensorflow/python/ops/control_flow_ops.py @@ -1817,15 +1817,34 @@ class CondContext(ControlFlowContext): def _AddOpInternal(self, op): """Add `op` to the current context.""" if not op.inputs: - # Remove any external control dependency on this op + # If we're in a while loop, remove any control inputs from outside the + # loop. self._RemoveExternalControlEdges(op) - # pylint: disable=protected-access - op._add_control_input(self._pivot.op) - # pylint: enable=protected-access + + if not any(util.OpInContext(input_op, self) + for input_op in op.control_inputs): + # pylint: disable=protected-access + op._add_control_input(self._pivot.op) + # pylint: enable=protected-access else: + # Make each input to 'op' available in this CondContext. If an input is + # already part of this context there's nothing to do, but if it's + # external, AddValue() will handle adding the appropriate Switch node and + # other bookkeeping. for index in range(len(op.inputs)): x = op.inputs[index] - real_x = self.AddValue(x) + if op.type == "Merge" and x.op.type == "NextIteration": + # Edge case: if we're importing a while loop inside this CondContext, + # AddValue() will not correctly handle the NextIteration inputs to + # Merge node. The problem is that the NextIteration should also be + # part of this context, but if we're importing it won't have been + # processed and added to the context yet, so AddValue() will try to + # add a Switch which results in an invalid graph. Instead, we use the + # NextIteration input as-is here, and it will eventually be added to + # the context via AddOp(). + real_x = x + else: + real_x = self.AddValue(x) if real_x != x: # pylint: disable=protected-access op._update_input(index, real_x) diff --git a/tensorflow/python/ops/control_flow_util.py b/tensorflow/python/ops/control_flow_util.py index 7a18986c5b..72c074ed1a 100644 --- a/tensorflow/python/ops/control_flow_util.py +++ b/tensorflow/python/ops/control_flow_util.py @@ -214,6 +214,14 @@ def IsContainingContext(ctxt, maybe_containing_ctxt): return True +def OpInContext(op, ctxt): + return IsContainingContext(op._get_control_flow_context(), ctxt) # pylint: disable=protected-access + + +def TensorInContext(tensor, ctxt): + return OpInContext(tensor.op, ctxt) + + def CheckInputFromValidContext(op, input_op): """Returns whether `input_op` can be used from `op`s context. -- GitLab From 972920262107f5900abd79611e9432ddc6cd810b Mon Sep 17 00:00:00 2001 From: Asim Shankar Date: Wed, 25 Jul 2018 11:20:00 -0700 Subject: [PATCH 395/519] Implement __index__ in EagerTensor. This makes range(t) for an integer, scalar Tensor work in Python 3. PiperOrigin-RevId: 206024230 --- tensorflow/python/eager/ops_test.py | 4 ++++ tensorflow/python/framework/ops.py | 5 ++++- 2 files changed, 8 insertions(+), 1 deletion(-) diff --git a/tensorflow/python/eager/ops_test.py b/tensorflow/python/eager/ops_test.py index fc76ede4c5..17a090d526 100644 --- a/tensorflow/python/eager/ops_test.py +++ b/tensorflow/python/eager/ops_test.py @@ -370,6 +370,10 @@ class OpsTest(test_util.TensorFlowTestCase): with self.assertRaises(TypeError): float(x) + def testRange(self): + x = constant_op.constant(2) + self.assertEqual([0, 1], list(range(x))) + def testFormatString(self): x = constant_op.constant(3.1415) self.assertEqual('3.14', '{:.2f}'.format(x)) diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index 6a5c44e4d9..0fd028ebf0 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -709,7 +709,7 @@ class _EagerTensorBase(Tensor): raise ValueError("Resource handles are not convertible to numpy.") return self._cpu_nograd()._numpy() # pylint: disable=protected-access - # __int__ and __float__ may copy the tensor to CPU and + # __int__, __float__ and __index__ may copy the tensor to CPU and # only work for scalars; values are cast as per numpy. def __int__(self): return int(self.numpy()) @@ -717,6 +717,9 @@ class _EagerTensorBase(Tensor): def __float__(self): return float(self.numpy()) + def __index__(self): + return int(self.numpy()) + def __array__(self, dtype=None): return np.array(self.numpy(), dtype=dtype) -- GitLab From 96c76b296768852dac94aaf006beab2e637cbbb6 Mon Sep 17 00:00:00 2001 From: Karmel Allison Date: Wed, 25 Jul 2018 11:33:12 -0700 Subject: [PATCH 396/519] The SavedModel legacy_init_op and main_op are functionally equivalent. Here, we remove duplicated code paths by mapping legacy_init_op into main_op in the SavedModelBuilder, and we deprecate the legacy_init_op arg. Note that the loader will still look for both, so old SavedModels will still load without trouble. PiperOrigin-RevId: 206026743 --- tensorflow/cc/saved_model/loader.cc | 39 ++-------- tensorflow/python/saved_model/builder_impl.py | 76 ++++++++++--------- tensorflow/python/saved_model/loader_impl.py | 42 +++------- .../python/saved_model/saved_model_test.py | 28 +++++-- 4 files changed, 79 insertions(+), 106 deletions(-) diff --git a/tensorflow/cc/saved_model/loader.cc b/tensorflow/cc/saved_model/loader.cc index 07807ed2f3..d47b025743 100644 --- a/tensorflow/cc/saved_model/loader.cc +++ b/tensorflow/cc/saved_model/loader.cc @@ -86,10 +86,11 @@ bool HasMainOp(const MetaGraphDef& meta_graph_def) { Status RunMainOp(const RunOptions& run_options, const string& export_dir, const MetaGraphDef& meta_graph_def, const std::vector& asset_file_defs, - Session* session) { - LOG(INFO) << "Running MainOp on SavedModel bundle."; + Session* session, const string& main_op_key) { + LOG(INFO) << "Running MainOp with key " << main_op_key + << " on SavedModel bundle."; const auto& collection_def_map = meta_graph_def.collection_def(); - const auto main_op_it = collection_def_map.find(kSavedModelMainOpKey); + const auto main_op_it = collection_def_map.find(main_op_key); if (main_op_it != collection_def_map.end()) { if (main_op_it->second.node_list().value_size() != 1) { return errors::FailedPrecondition( @@ -141,30 +142,6 @@ Status RunRestore(const RunOptions& run_options, const string& export_dir, nullptr /* outputs */, &run_metadata); } -Status RunLegacyInitOp(const RunOptions& run_options, const string& export_dir, - const MetaGraphDef& meta_graph_def, - const std::vector& asset_file_defs, - Session* session) { - LOG(INFO) << "Running LegacyInitOp on SavedModel bundle."; - const auto& collection_def_map = meta_graph_def.collection_def(); - const auto init_op_it = collection_def_map.find(kSavedModelLegacyInitOpKey); - if (init_op_it != collection_def_map.end()) { - if (init_op_it->second.node_list().value_size() != 1) { - return errors::FailedPrecondition(strings::StrCat( - "Expected exactly one serving init op in : ", export_dir)); - } - std::vector> inputs; - AddAssetsTensorsToInputs(export_dir, asset_file_defs, &inputs); - RunMetadata run_metadata; - const StringPiece legacy_init_op_name = - init_op_it->second.node_list().value(0); - return session->Run(run_options, inputs, {}, - {legacy_init_op_name.ToString()}, nullptr /* outputs */, - &run_metadata); - } - return Status::OK(); -} - Status GetAssetFileDefs(const MetaGraphDef& meta_graph_def, std::vector* asset_file_defs) { const auto& collection_def_map = meta_graph_def.collection_def(); @@ -204,11 +181,11 @@ Status LoadSavedModelInternal(const SessionOptions& session_options, if (HasMainOp(bundle->meta_graph_def)) { TF_RETURN_IF_ERROR(RunMainOp(run_options, export_dir, bundle->meta_graph_def, asset_file_defs, - bundle->session.get())); + bundle->session.get(), kSavedModelMainOpKey)); } else { - TF_RETURN_IF_ERROR(RunLegacyInitOp(run_options, export_dir, - bundle->meta_graph_def, asset_file_defs, - bundle->session.get())); + TF_RETURN_IF_ERROR(RunMainOp( + run_options, export_dir, bundle->meta_graph_def, asset_file_defs, + bundle->session.get(), kSavedModelLegacyInitOpKey)); } return Status::OK(); } diff --git a/tensorflow/python/saved_model/builder_impl.py b/tensorflow/python/saved_model/builder_impl.py index e58be804c2..8c985a7c2f 100644 --- a/tensorflow/python/saved_model/builder_impl.py +++ b/tensorflow/python/saved_model/builder_impl.py @@ -34,6 +34,7 @@ from tensorflow.python.platform import tf_logging from tensorflow.python.saved_model import constants from tensorflow.python.training import saver as tf_saver from tensorflow.python.util import compat +from tensorflow.python.util.deprecation import deprecated_args from tensorflow.python.util.tf_export import tf_export @@ -133,39 +134,32 @@ class SavedModelBuilder(object): tf_logging.info("Assets written to: %s", compat.as_text(assets_destination_dir)) - def _maybe_add_legacy_init_op(self, legacy_init_op=None): - """Add legacy init op to the SavedModel. + def _maybe_add_main_op(self, main_op): + """Adds main op to the SavedModel. Args: - legacy_init_op: Optional legacy init op to support backward compatibility. + main_op: Main op to run as part of graph initialization. If None, no + main op will be added to the graph. Raises: - TypeError if legacy init op is not of type `Operation`. - AssertionError if the graph already contains one or more legacy init ops. + TypeError: if main op is provided but is not of type `Operation`. + ValueError: if the Graph already contains an init op. """ - if legacy_init_op is not None: - if not isinstance(legacy_init_op, ops.Operation): - raise TypeError("legacy_init_op needs to be an Operation: %r" % - legacy_init_op) - if ops.get_collection(constants.LEGACY_INIT_OP_KEY): - raise AssertionError( - "graph already contains one or more legacy init ops under the " - "collection {}.".format(constants.LEGACY_INIT_OP_KEY)) - ops.add_to_collection(constants.LEGACY_INIT_OP_KEY, legacy_init_op) - - def _add_main_op(self, main_op): - """Add main op to the SavedModel. + if main_op is None: + return - Args: - main_op: Main op to run as part of graph initialization. + if not isinstance(main_op, ops.Operation): + raise TypeError("main_op needs to be an Operation: %r" % main_op) - Raises: - TypeError if main op is not of type `Operation`. - """ - if main_op is not None: - if not isinstance(main_op, ops.Operation): - raise TypeError("main_op needs to be an Operation: %r" % main_op) - ops.add_to_collection(constants.MAIN_OP_KEY, main_op) + # Validate that no other init ops have been added to this graph already. + # We check main_op and legacy_init_op for thoroughness and explicitness. + for init_op_key in (constants.MAIN_OP_KEY, constants.LEGACY_INIT_OP_KEY): + if ops.get_collection(init_op_key): + raise ValueError( + "Graph already contains one or more main ops under the " + "collection {}.".format(init_op_key)) + + ops.add_to_collection(constants.MAIN_OP_KEY, main_op) def _add_train_op(self, train_op): """Add train op to the SavedModel. @@ -257,16 +251,12 @@ class SavedModelBuilder(object): self._validate_tensor_info(outputs[outputs_key]) def _add_collections( - self, assets_collection, legacy_init_op, main_op, train_op): + self, assets_collection, main_op, train_op): """Add asset and op collections to be saved.""" # Save asset files and write them to disk, if any. self._save_and_write_assets(assets_collection) - if main_op is None: - # Add legacy init op to the SavedModel. - self._maybe_add_legacy_init_op(legacy_init_op) - else: - self._add_main_op(main_op) + self._maybe_add_main_op(main_op) self._add_train_op(train_op) @@ -282,6 +272,9 @@ class SavedModelBuilder(object): allow_empty=True) return saver + @deprecated_args(None, + "Pass your op to the equivalent parameter main_op instead.", + "legacy_init_op") def add_meta_graph(self, tags, signature_def_map=None, @@ -306,7 +299,7 @@ class SavedModelBuilder(object): that this collection should be a subset of the assets saved as part of the first meta graph in the SavedModel. legacy_init_op: Legacy support for op or group of ops to execute after the - restore op upon a load. + restore op upon a load. Deprecated; please use main_op instead. clear_devices: Set to true if the device info on the default graph should be cleared. main_op: Op or group of ops to execute when the graph is loaded. Note @@ -333,8 +326,12 @@ class SavedModelBuilder(object): # properly populated. self._validate_signature_def_map(signature_def_map) + # legacy_init_op is deprecated, and going away in TF 2.0. + # Re-mapping to main_op, as treatment is identical regardless. + main_op = main_op or legacy_init_op + # Add assets and ops - self._add_collections(assets_collection, legacy_init_op, main_op, None) + self._add_collections(assets_collection, main_op, None) saver = self._maybe_create_saver(saver) @@ -351,6 +348,9 @@ class SavedModelBuilder(object): # Tag the meta graph def and add it to the SavedModel. self._tag_and_add_meta_graph(meta_graph_def, tags, signature_def_map) + @deprecated_args(None, + "Pass your op to the equivalent parameter main_op instead.", + "legacy_init_op") def add_meta_graph_and_variables(self, sess, tags, @@ -378,7 +378,7 @@ class SavedModelBuilder(object): def. assets_collection: Assets collection to be saved with SavedModel. legacy_init_op: Legacy support for op or group of ops to execute after the - restore op upon a load. + restore op upon a load. Deprecated; please use main_op instead. clear_devices: Set to true if the device info on the default graph should be cleared. main_op: Op or group of ops to execute when the graph is loaded. Note @@ -402,8 +402,12 @@ class SavedModelBuilder(object): # properly populated. self._validate_signature_def_map(signature_def_map) + # legacy_init_op is deprecated, and going away in TF 2.0. + # Re-mapping to main_op, as treatment is identical regardless. + main_op = main_op or legacy_init_op + # Add assets and ops - self._add_collections(assets_collection, legacy_init_op, main_op, None) + self._add_collections(assets_collection, main_op, None) # Create the variables sub-directory, if it does not exist. variables_dir = os.path.join( diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index e5f649fdab..fb70c91c29 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -116,11 +116,14 @@ def _get_asset_tensors(export_dir, meta_graph_def_to_load, import_scope=None): return asset_tensor_dict -def _get_main_op_tensor(meta_graph_def_to_load): +def _get_main_op_tensor( + meta_graph_def_to_load, init_op_key=constants.MAIN_OP_KEY): """Gets the main op tensor, if one exists. Args: meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded. + init_op_key: name of collection to check; should be one of MAIN_OP_KEY + or the deprecated LEGACY_INIT_OP_KEY Returns: The main op tensor, if it exists and `None` otherwise. @@ -131,38 +134,15 @@ def _get_main_op_tensor(meta_graph_def_to_load): """ collection_def = meta_graph_def_to_load.collection_def main_op_tensor = None - if constants.MAIN_OP_KEY in collection_def: - main_ops = collection_def[constants.MAIN_OP_KEY].node_list.value + if init_op_key in collection_def: + main_ops = collection_def[init_op_key].node_list.value if len(main_ops) != 1: - raise RuntimeError("Expected exactly one SavedModel main op.") - main_op_tensor = ops.get_collection(constants.MAIN_OP_KEY)[0] + raise RuntimeError("Expected exactly one SavedModel main op. " + "Found: {}".format(main_ops)) + main_op_tensor = ops.get_collection(init_op_key)[0] return main_op_tensor -def _get_legacy_init_op_tensor(meta_graph_def_to_load): - """Gets the legacy init op tensor, if one exists. - - Args: - meta_graph_def_to_load: The meta graph def from the SavedModel to be loaded. - - Returns: - The legacy init op tensor, if it exists and `None` otherwise. - - Raises: - RuntimeError: If the collection def corresponding to the legacy init op key - has other than exactly one tensor. - """ - collection_def = meta_graph_def_to_load.collection_def - legacy_init_op_tensor = None - if constants.LEGACY_INIT_OP_KEY in collection_def: - legacy_init_ops = collection_def[ - constants.LEGACY_INIT_OP_KEY].node_list.value - if len(legacy_init_ops) != 1: - raise RuntimeError("Expected exactly one legacy serving init op.") - legacy_init_op_tensor = ops.get_collection(constants.LEGACY_INIT_OP_KEY)[0] - return legacy_init_op_tensor - - @tf_export("saved_model.loader.maybe_saved_model_directory") def maybe_saved_model_directory(export_dir): """Checks whether the provided export directory could contain a SavedModel. @@ -340,8 +320,8 @@ class SavedModelLoader(object): self._export_dir, meta_graph_def, import_scope=import_scope) main_op_tensor = ( - _get_main_op_tensor(meta_graph_def) or - (_get_legacy_init_op_tensor(meta_graph_def))) + _get_main_op_tensor(meta_graph_def, constants.MAIN_OP_KEY) or + _get_main_op_tensor(meta_graph_def, constants.LEGACY_INIT_OP_KEY)) if main_op_tensor is not None: sess.run(fetches=[main_op_tensor], feed_dict=asset_tensors_dictionary) diff --git a/tensorflow/python/saved_model/saved_model_test.py b/tensorflow/python/saved_model/saved_model_test.py index fb4732aca2..00b669fc97 100644 --- a/tensorflow/python/saved_model/saved_model_test.py +++ b/tensorflow/python/saved_model/saved_model_test.py @@ -846,9 +846,19 @@ class SavedModelTest(test.TestCase): def testLegacyInitOpWithNonEmptyCollection(self): export_dir = self._get_export_dir( "test_legacy_init_op_with_non_empty_collection") + self._testInitOpsWithNonEmptyCollection( + export_dir, constants.LEGACY_INIT_OP_KEY) + + def testMainOpWithNonEmptyCollection(self): + export_dir = self._get_export_dir( + "test_main_op_with_non_empty_collection") + self._testInitOpsWithNonEmptyCollection(export_dir, constants.MAIN_OP_KEY) + + def _testInitOpsWithNonEmptyCollection(self, export_dir, key): builder = saved_model_builder.SavedModelBuilder(export_dir) - with self.test_session(graph=ops.Graph()) as sess: + g = ops.Graph() + with self.test_session(graph=g) as sess: # Initialize variable `v1` to 1. v1 = variables.Variable(1, name="v1") ops.add_to_collection("v", v1) @@ -857,19 +867,21 @@ class SavedModelTest(test.TestCase): v2 = variables.Variable(42, name="v2", trainable=False, collections=[]) ops.add_to_collection("v", v2) - # Set up an assignment op to be run as part of the legacy_init_op. + # Set up an assignment op to be run as part of the init op. assign_v2 = state_ops.assign(v2, v1) - legacy_init_op = control_flow_ops.group(assign_v2, name="legacy_init_op") + init_op = control_flow_ops.group(assign_v2, name="init_op") sess.run(variables.global_variables_initializer()) - ops.add_to_collection(constants.LEGACY_INIT_OP_KEY, - control_flow_ops.no_op()) - # AssertionError should be raised since the LEGACY_INIT_OP_KEY collection + ops.add_to_collection(key, control_flow_ops.no_op()) + # ValueError should be raised since the LEGACY_INIT_OP_KEY collection # is not empty and we don't support multiple init ops. - with self.assertRaises(AssertionError): + with self.assertRaisesRegexp(ValueError, "Graph already contains"): builder.add_meta_graph_and_variables( - sess, ["foo"], legacy_init_op=legacy_init_op) + sess, ["foo"], legacy_init_op=init_op) + # We shouldn't be able to add as MAIN_OP, either. + with self.assertRaisesRegexp(ValueError, "Graph already contains"): + builder.add_meta_graph_and_variables(sess, ["foo"], main_op=init_op) def testTrainOp(self): export_dir = self._get_export_dir("test_train_op") -- GitLab From 8c6782ec4ed12dcdda1fdf8cb45ba8afbf62a61f Mon Sep 17 00:00:00 2001 From: Wesley Qian Date: Wed, 25 Jul 2018 11:34:39 -0700 Subject: [PATCH 397/519] Update test for StarGANModel to conform the original GANModel test. PiperOrigin-RevId: 206027004 --- tensorflow/contrib/gan/python/train_test.py | 57 +++++++++++++++++++++ 1 file changed, 57 insertions(+) diff --git a/tensorflow/contrib/gan/python/train_test.py b/tensorflow/contrib/gan/python/train_test.py index fa52e9cca1..df8e0041a9 100644 --- a/tensorflow/contrib/gan/python/train_test.py +++ b/tensorflow/contrib/gan/python/train_test.py @@ -114,6 +114,12 @@ def stargan_generator_model(inputs, _): return variable_scope.get_variable('dummy_g', initializer=0.5) * inputs +class StarGANGenerator(object): + + def __call__(self, inputs, _): + return stargan_generator_model(inputs, _) + + def stargan_discriminator_model(inputs, num_domains): """Differentiable dummy discriminator for StarGAN.""" @@ -130,6 +136,12 @@ def stargan_discriminator_model(inputs, num_domains): return output_src, output_cls +class StarGANDiscriminator(object): + + def __call__(self, inputs, num_domains): + return stargan_discriminator_model(inputs, num_domains) + + def get_gan_model(): # TODO(joelshor): Find a better way of creating a variable scope. with variable_scope.variable_scope('generator') as gen_scope: @@ -272,6 +284,49 @@ def create_callable_cyclegan_model(): data_y=array_ops.ones([1, 2])) +def get_stargan_model(): + """Similar to get_gan_model().""" + # TODO(joelshor): Find a better way of creating a variable scope. + with variable_scope.variable_scope('generator') as gen_scope: + pass + with variable_scope.variable_scope('discriminator') as dis_scope: + pass + return namedtuples.StarGANModel( + input_data=array_ops.ones([1, 2, 2, 3]), + input_data_domain_label=array_ops.ones([1, 2]), + generated_data=array_ops.ones([1, 2, 2, 3]), + generated_data_domain_target=array_ops.ones([1, 2]), + reconstructed_data=array_ops.ones([1, 2, 2, 3]), + discriminator_input_data_source_predication=array_ops.ones([1]), + discriminator_generated_data_source_predication=array_ops.ones([1]), + discriminator_input_data_domain_predication=array_ops.ones([1, 2]), + discriminator_generated_data_domain_predication=array_ops.ones([1, 2]), + generator_variables=None, + generator_scope=gen_scope, + generator_fn=stargan_generator_model, + discriminator_variables=None, + discriminator_scope=dis_scope, + discriminator_fn=stargan_discriminator_model) + + +def get_callable_stargan_model(): + model = get_stargan_model() + return model._replace( + generator_fn=StarGANGenerator(), discriminator_fn=StarGANDiscriminator()) + + +def create_stargan_model(): + return train.stargan_model( + stargan_generator_model, stargan_discriminator_model, + array_ops.ones([1, 2, 2, 3]), array_ops.ones([1, 2])) + + +def create_callable_stargan_model(): + return train.stargan_model(StarGANGenerator(), StarGANDiscriminator(), + array_ops.ones([1, 2, 2, 3]), + array_ops.ones([1, 2])) + + def get_sync_optimizer(): return sync_replicas_optimizer.SyncReplicasOptimizer( gradient_descent.GradientDescentOptimizer(learning_rate=1.0), @@ -292,6 +347,8 @@ class GANModelTest(test.TestCase, parameterized.TestCase): ('cyclegan', get_cyclegan_model, namedtuples.CycleGANModel), ('callable_cyclegan', get_callable_cyclegan_model, namedtuples.CycleGANModel), + ('stargan', get_stargan_model, namedtuples.StarGANModel), + ('callabel_stargan', get_callable_stargan_model, namedtuples.StarGANModel) ) def test_output_type(self, create_fn, expected_tuple_type): """Test that output type is as expected.""" -- GitLab From 9a67bbb8830ed95c339289480cc2074fe28baf4e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 11:51:16 -0700 Subject: [PATCH 398/519] New container for remote builds with all the newest pip packages defined in tensorflow/tools/ci_build/install/install_pip_packages.sh PiperOrigin-RevId: 206030031 --- third_party/toolchains/BUILD | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third_party/toolchains/BUILD b/third_party/toolchains/BUILD index fc3183a754..ec1006fe23 100644 --- a/third_party/toolchains/BUILD +++ b/third_party/toolchains/BUILD @@ -17,6 +17,6 @@ platform( remote_execution_properties = """ properties: { name: "container-image" - value:"docker://gcr.io/asci-toolchain/nosla-ubuntu16_04-tf@sha256:800a7b68cabef15419695c188ed33ed70adf678c2371b97b236f3ae26c38274d" + value:"docker://gcr.io/asci-toolchain/nosla-ubuntu16_04-tf@sha256:495a025ed5e273cfa5d53357ef93ac20500c008994e0be106c509f51555fb93c" }""", ) -- GitLab From ea896e3457fecb5accc653425a5251d7aaea4642 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Wed, 25 Jul 2018 11:55:37 -0700 Subject: [PATCH 399/519] [TF:XLA] Bump open source llvm revision to r337934 PiperOrigin-RevId: 206030804 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 062beb9348..314169fc19 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -485,11 +485,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/35ffbe6bcf3b755f30633d834534a892b4c5fb29.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/35ffbe6bcf3b755f30633d834534a892b4c5fb29.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/a9364fc18506373b10922802983f76229cc1f371.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/a9364fc18506373b10922802983f76229cc1f371.tar.gz", ], - sha256 = "42b3924b56339bb953b587f3e55788c8fefa51068756e6ac2ee4aed9c187bbb8", - strip_prefix = "llvm-35ffbe6bcf3b755f30633d834534a892b4c5fb29", + sha256 = "5d727fedfbb805a44a671db8f3fbaa09dbe5177a5c1cc0635fd61c324e6409f2", + strip_prefix = "llvm-a9364fc18506373b10922802983f76229cc1f371", build_file = clean_dep("//third_party/llvm:llvm.autogenerated.BUILD"), ) -- GitLab From d0d12963008ca3e4a63f6f55432fd353ed0b6be6 Mon Sep 17 00:00:00 2001 From: Jie Date: Wed, 25 Jul 2018 12:07:43 -0700 Subject: [PATCH 400/519] unit test added for memory alignment --- tensorflow/contrib/tensorrt/BUILD | 1 + .../tensorrt/test/memory_alignment_test.py | 78 +++++++++++++++++++ 2 files changed, 79 insertions(+) create mode 100644 tensorflow/contrib/tensorrt/test/memory_alignment_test.py diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index 2fe1f2c242..e5138c2ca0 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -392,6 +392,7 @@ cuda_py_tests( "test/unary_test.py", # "test/vgg_block_nchw_test.py", # "test/vgg_block_test.py", + "test/memory_alignment_test.py", ], additional_deps = [ ":tf_trt_integration_test_base", diff --git a/tensorflow/contrib/tensorrt/test/memory_alignment_test.py b/tensorflow/contrib/tensorrt/test/memory_alignment_test.py new file mode 100644 index 0000000000..227a0ee6ae --- /dev/null +++ b/tensorflow/contrib/tensorrt/test/memory_alignment_test.py @@ -0,0 +1,78 @@ +# 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. +# ============================================================================== +"""Model script to test TF-TensorRT integration.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import numpy as np + +from tensorflow.contrib.tensorrt.test import tf_trt_integration_test_base as trt_test +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 nn +from tensorflow.python.platform import test + + +class MemoryAlignmentTest(trt_test.TfTrtIntegrationTestBase): + + def GetParams(self): + """Testing conversion of BatchMatMul in TF-TRT conversion.""" + dtype = dtypes.float32 + input_name = "input" + input_dims = [2, 15, 15, 3] + g = ops.Graph() + with g.as_default(): + inp = array_ops.placeholder( + dtype=dtype, + shape=[None] + input_dims[1:], + name=input_name) + with g.device("/GPU:0"): + e1 = constant_op.constant( + np.random.randn(1, 1, 3, 5), + name="kernel_1", + dtype=dtype) + e2 = constant_op.constant( + np.random.randn(1, 1, 5, 10), + name="kernel_2", + dtype=dtype) + conv = nn.conv2d( + input=inp, + filter=e1, + strides=[1, 1, 1, 1], + padding="VALID", + name="conv") + out = nn.conv2d( + input=conv, + filter=e2, + strides=[1, 1, 1, 1], + padding="VALID", + name="conv_2") + array_ops.squeeze(out, name=self.output_name) + return trt_test.TfTrtIntegrationTestParams( + gdef=g.as_graph_def(), + input_names=[input_name], + input_dims=[input_dims], + num_expected_engines=1, + expected_output_dims=(2, 15, 15, 10), + allclose_atol=1.e-02, + allclose_rtol=1.e-02) + + +if __name__ == "__main__": + test.main() -- GitLab From 8688a251608b03bd0aead0b6275d6cf21144ea23 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 11:56:47 -0700 Subject: [PATCH 401/519] Adding an opportunity to skip the check for input tensor placements. PiperOrigin-RevId: 206031015 --- .../boosted_trees/python/training/functions/gbdt_batch.py | 3 --- 1 file changed, 3 deletions(-) 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 643d8d2498..e08b230f46 100644 --- a/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py +++ b/tensorflow/contrib/boosted_trees/python/training/functions/gbdt_batch.py @@ -520,9 +520,6 @@ class GradientBoostedDecisionTreeModel(object): if not input_deps: raise ValueError("No input tensors for prediction.") - if any(i.device != input_deps[0].device for i in input_deps): - raise ValueError("All input tensors should be on the same device.") - # Get most current model stamp. ensemble_stamp = model_ops.tree_ensemble_stamp_token(self._ensemble_handle) -- GitLab From c46418aa28d754cce838a12a10a29e1b87328ed0 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Wed, 25 Jul 2018 12:04:19 -0700 Subject: [PATCH 402/519] Add missing space in error message PiperOrigin-RevId: 206032199 --- tensorflow/compiler/xla/service/hlo_verifier.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/hlo_verifier.cc b/tensorflow/compiler/xla/service/hlo_verifier.cc index c80c1e0e7d..25fa319faf 100644 --- a/tensorflow/compiler/xla/service/hlo_verifier.cc +++ b/tensorflow/compiler/xla/service/hlo_verifier.cc @@ -119,7 +119,7 @@ Status CheckIsTokenOperand(const HloInstruction* instruction, const HloInstruction* token = instruction->operand(operand_no); if (!ShapeUtil::Equal(token->shape(), ShapeUtil::MakeTokenShape())) { return InternalError( - "Expected operand %lld to be token-shaped, actual shape is" + "Expected operand %lld to be token-shaped, actual shape is " "%s:\n%s", operand_no, ShapeUtil::HumanString(token->shape()).c_str(), instruction->ToString().c_str()); -- GitLab From e29d089f29e705747346501d7011eb2f5b5c3351 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Wed, 25 Jul 2018 12:16:24 -0700 Subject: [PATCH 403/519] Disable broken autograph integration tests. PiperOrigin-RevId: 206034196 --- .../contrib/autograph/examples/integration_tests/BUILD | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/tensorflow/contrib/autograph/examples/integration_tests/BUILD b/tensorflow/contrib/autograph/examples/integration_tests/BUILD index 2a4a0f75e7..d22d39c83b 100644 --- a/tensorflow/contrib/autograph/examples/integration_tests/BUILD +++ b/tensorflow/contrib/autograph/examples/integration_tests/BUILD @@ -22,6 +22,11 @@ py_test( "keras_test.py", ], srcs_version = "PY2AND3", + tags = [ + "manual", + "no_oss", + "notap", + ], deps = [ "//tensorflow:tensorflow_py", ], @@ -33,6 +38,11 @@ py_test( "list_literals_test.py", ], srcs_version = "PY2AND3", + tags = [ + "manual", + "no_oss", + "notap", + ], deps = [ "//tensorflow:tensorflow_py", ], -- GitLab From 856e2bf7af3ae28a93ede51423c368f7b7e04c8a Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Wed, 25 Jul 2018 12:22:07 -0700 Subject: [PATCH 404/519] Fix incorrect Load/Store context value in template expansions for attributes. PiperOrigin-RevId: 206034999 --- tensorflow/contrib/autograph/pyct/templates.py | 4 ++-- tensorflow/contrib/autograph/pyct/templates_test.py | 13 +++++++++++++ 2 files changed, 15 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/autograph/pyct/templates.py b/tensorflow/contrib/autograph/pyct/templates.py index 72d1d3b269..5831d57ceb 100644 --- a/tensorflow/contrib/autograph/pyct/templates.py +++ b/tensorflow/contrib/autograph/pyct/templates.py @@ -140,8 +140,8 @@ class ReplaceTransformer(gast.NodeTransformer): def _set_inner_child_context(self, node, ctx): if isinstance(node, gast.Attribute): - self._set_inner_child_context(node.value, ctx) - node.ctx = gast.Load() + self._set_inner_child_context(node.value, gast.Load()) + node.ctx = ctx elif isinstance(node, gast.Tuple): for e in node.elts: self._set_inner_child_context(e, ctx) diff --git a/tensorflow/contrib/autograph/pyct/templates_test.py b/tensorflow/contrib/autograph/pyct/templates_test.py index a8bbc5a4de..77e8ff62fd 100644 --- a/tensorflow/contrib/autograph/pyct/templates_test.py +++ b/tensorflow/contrib/autograph/pyct/templates_test.py @@ -97,6 +97,19 @@ class TemplatesTest(test.TestCase): with self.assertRaises(ValueError): templates.replace(template, foo=1) + def test_replace_attribute_context(self): + template = """ + def test_fn(foo): + foo = 0 + """ + + node = templates.replace( + template, + foo=parser.parse_expression('a.b.c'))[0] + self.assertIsInstance(node.body[0].targets[0].ctx, gast.Store) + self.assertIsInstance(node.body[0].targets[0].value.ctx, gast.Load) + self.assertIsInstance(node.body[0].targets[0].value.value.ctx, gast.Load) + def test_replace_call_keyword(self): template = """ def test_fn(): -- GitLab From 73ebfd645aed051cc702d5ef4ef3ff208677a2f8 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Wed, 25 Jul 2018 13:03:19 -0700 Subject: [PATCH 405/519] Add a transformer correction for AST consistency. Replacing the value of an Expr node with an Assign or a list of nodes removes the need for the Expr node itself. PiperOrigin-RevId: 206040750 --- .../contrib/autograph/pyct/transformer.py | 17 +++++++++++++++++ 1 file changed, 17 insertions(+) diff --git a/tensorflow/contrib/autograph/pyct/transformer.py b/tensorflow/contrib/autograph/pyct/transformer.py index 3e8906823e..969ca12244 100644 --- a/tensorflow/contrib/autograph/pyct/transformer.py +++ b/tensorflow/contrib/autograph/pyct/transformer.py @@ -421,10 +421,13 @@ class Base(gast.NodeTransformer): source_file = self.entity_info.source_file did_enter_function = False local_scope_size_at_entry = len(self._local_scope_state) + processing_expr_node = False try: if isinstance(node, (gast.FunctionDef, gast.ClassDef, gast.Lambda)): did_enter_function = True + elif isinstance(node, gast.Expr): + processing_expr_node = True if did_enter_function: self._enclosing_entities.append(node) @@ -433,9 +436,23 @@ class Base(gast.NodeTransformer): self._lineno = node.lineno self._col_offset = node.col_offset + if processing_expr_node: + entry_expr_value = node.value + if not anno.hasanno(node, anno.Basic.SKIP_PROCESSING): result = super(Base, self).visit(node) + # Adjust for consistency: replacing the value of an Expr with + # an Assign node removes the need for the Expr node. + if processing_expr_node: + if isinstance(result, gast.Expr) and result.value != entry_expr_value: + # When the replacement is a list, it is assumed that the list came + # from a template that contained a number of statements, which + # themselves are standalone and don't require an enclosing Expr. + if isinstance(result.value, + (list, tuple, gast.Assign, gast.AugAssign)): + result = result.value + # On exception, the local scope integrity is not guaranteed. if did_enter_function: self._enclosing_entities.pop() -- GitLab From f0b189f3f24f5032642ff71338c1be66fbd446b5 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 13:11:27 -0700 Subject: [PATCH 406/519] internal change. PiperOrigin-RevId: 206042355 --- tensorflow/contrib/lite/testing/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index 8a2705950d..098f029f13 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -339,6 +339,7 @@ tf_cc_test( ], tags = [ "no_cuda_on_cpu_tap", + "no_oss", # needs test data "tflite_not_portable", ], deps = [ -- GitLab From 45aa4e968ff743eea697c9fbc8778c33b2926a6a Mon Sep 17 00:00:00 2001 From: Piotr Padlewski Date: Wed, 25 Jul 2018 13:37:16 -0700 Subject: [PATCH 407/519] MutableGraphView and other graph utils MutableGraphView was implemented so that the view could be updated when new nodes are added or connections changed. The current passes do not require it only because they do not do any optimization on already optimized nodes, but optimizations like MapFusion require it. PiperOrigin-RevId: 206046420 --- tensorflow/core/framework/function_testlib.cc | 7 +- tensorflow/core/framework/function_testlib.h | 2 +- tensorflow/core/grappler/BUILD | 30 +++ tensorflow/core/grappler/graph_view.cc | 37 +-- tensorflow/core/grappler/graph_view.h | 20 +- .../core/grappler/mutable_graph_view.cc | 72 ++++++ tensorflow/core/grappler/mutable_graph_view.h | 56 +++++ .../core/grappler/mutable_graph_view_test.cc | 94 ++++++++ .../optimizers/arithmetic_optimizer.cc | 9 +- .../grappler/optimizers/constant_folding.cc | 15 +- .../core/grappler/optimizers/data/BUILD | 14 +- .../grappler/optimizers/data/graph_utils.cc | 173 ++++++++------ .../grappler/optimizers/data/graph_utils.h | 77 ++++-- .../optimizers/data/graph_utils_test.cc | 221 +++++++++++------- .../optimizers/data/map_and_batch_fusion.cc | 128 +++++----- .../data/map_and_batch_fusion_test.cc | 164 ++++++------- .../optimizers/data/noop_elimination.cc | 9 +- .../optimizers/data/noop_elimination_test.cc | 95 ++++---- .../data/shuffle_and_repeat_fusion.cc | 67 +++--- .../data/shuffle_and_repeat_fusion_test.cc | 74 +++--- .../optimizers/dependency_optimizer.cc | 13 +- .../grappler/optimizers/loop_optimizer.cc | 14 +- tensorflow/core/grappler/utils.cc | 42 +++- tensorflow/core/grappler/utils.h | 10 + tensorflow/core/grappler/utils_test.cc | 6 +- 25 files changed, 907 insertions(+), 542 deletions(-) create mode 100644 tensorflow/core/grappler/mutable_graph_view.cc create mode 100644 tensorflow/core/grappler/mutable_graph_view.h create mode 100644 tensorflow/core/grappler/mutable_graph_view_test.cc diff --git a/tensorflow/core/framework/function_testlib.cc b/tensorflow/core/framework/function_testlib.cc index 2b5a0fe1bb..a8eecc1a63 100644 --- a/tensorflow/core/framework/function_testlib.cc +++ b/tensorflow/core/framework/function_testlib.cc @@ -45,13 +45,12 @@ GraphDef GDef(gtl::ArraySlice nodes, } // Helper to construct a NodeDef. -NodeDef NDef(const string& name, const string& op, - gtl::ArraySlice inputs, +NodeDef NDef(StringPiece name, StringPiece op, gtl::ArraySlice inputs, gtl::ArraySlice> attrs, const string& device) { NodeDef n; - n.set_name(name); - n.set_op(op); + n.set_name(name.ToString()); + n.set_op(op.ToString()); for (const auto& in : inputs) n.add_input(in); n.set_device(device); for (auto na : attrs) n.mutable_attr()->insert({na.first, na.second.proto}); diff --git a/tensorflow/core/framework/function_testlib.h b/tensorflow/core/framework/function_testlib.h index b67c5cb1ab..8cf3c6a680 100644 --- a/tensorflow/core/framework/function_testlib.h +++ b/tensorflow/core/framework/function_testlib.h @@ -48,7 +48,7 @@ class Attrs { // Helper to construct a NodeDef. NodeDef NDef( - const string& name, const string& op, gtl::ArraySlice inputs, + StringPiece name, StringPiece op, gtl::ArraySlice inputs, gtl::ArraySlice> attrs = {}, const string& device = ""); diff --git a/tensorflow/core/grappler/BUILD b/tensorflow/core/grappler/BUILD index 9dcc6765f5..7c6fe56e1f 100644 --- a/tensorflow/core/grappler/BUILD +++ b/tensorflow/core/grappler/BUILD @@ -33,6 +33,7 @@ tf_cc_test( name = "utils_test", srcs = ["utils_test.cc"], deps = [ + ":grappler_item", ":utils", "//tensorflow/cc:cc_ops", "//tensorflow/core:all_kernels", @@ -151,3 +152,32 @@ tf_cc_test( "//tensorflow/core/grappler/inputs:trivial_test_graph_input_yielder", ], ) + +cc_library( + name = "mutable_graph_view", + srcs = [ + "mutable_graph_view.cc", + ], + hdrs = ["mutable_graph_view.h"], + visibility = ["//visibility:public"], + deps = [ + ":graph_view", + ":grappler_item", + ":utils", + "//tensorflow/core:lib", + "//tensorflow/core:protos_all_cc", + ], +) + +tf_cc_test( + name = "mutable_graph_view_test", + srcs = ["mutable_graph_view_test.cc"], + deps = [ + ":grappler_item", + ":mutable_graph_view", + "//tensorflow/cc:cc_ops", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core/grappler/inputs:trivial_test_graph_input_yielder", + ], +) diff --git a/tensorflow/core/grappler/graph_view.cc b/tensorflow/core/grappler/graph_view.cc index 3e448216f9..7998f0a902 100644 --- a/tensorflow/core/grappler/graph_view.cc +++ b/tensorflow/core/grappler/graph_view.cc @@ -22,28 +22,33 @@ namespace grappler { GraphView::GraphView(GraphDef* graph) : graph_(graph) { for (int i = 0; i < graph_->node_size(); i++) { auto node = graph_->mutable_node(i); - auto rslt = nodes_.insert(std::make_pair(node->name(), node)); + auto result = nodes_.emplace(node->name(), node); // Check that the graph doesn't contain multiple nodes with the same name. - CHECK(rslt.second) << "Non unique node name detected: " << node->name(); + CHECK(result.second) << "Non unique node name detected: " << node->name(); } + for (NodeDef& node : *graph_->mutable_node()) { - for (int i = 0; i < node.input_size(); ++i) { - OutputPort fanin; - string fanin_name = ParseNodeName(node.input(i), &fanin.port_id); - fanin.node = nodes_[fanin_name]; + AddFanouts(&node); + } +} - InputPort input; - input.node = &node; - if (fanin.port_id < 0) { - input.port_id = -1; - } else { - input.port_id = i; - num_regular_outputs_[fanin.node] = - std::max(num_regular_outputs_[fanin.node], fanin.port_id); - } +void GraphView::AddFanouts(NodeDef* node) { + for (int i = 0; i < node->input_size(); ++i) { + OutputPort fanin; + string fanin_name = ParseNodeName(node->input(i), &fanin.port_id); + fanin.node = nodes_[fanin_name]; - fanouts_[fanin].insert(input); + InputPort input; + input.node = node; + if (fanin.port_id < 0) { + input.port_id = -1; + } else { + input.port_id = i; + num_regular_outputs_[fanin.node] = + std::max(num_regular_outputs_[fanin.node], fanin.port_id); } + + fanouts_[fanin].insert(input); } } diff --git a/tensorflow/core/grappler/graph_view.h b/tensorflow/core/grappler/graph_view.h index 584cb9048b..050789d2e2 100644 --- a/tensorflow/core/grappler/graph_view.h +++ b/tensorflow/core/grappler/graph_view.h @@ -29,8 +29,11 @@ namespace grappler { class GraphView { public: struct Port { - Port() : node(nullptr), port_id(-1) {} + Port() = default; Port(NodeDef* n, int port) : node(n), port_id(port) {} + + // TODO(prazek): ports should keep the constness of GraphView. The only way + // to modify graph through the view should be using MutableGraphView. NodeDef* node = nullptr; int port_id = -1; @@ -111,13 +114,22 @@ class GraphView { std::unordered_set GetFaninEdges( const NodeDef& node, bool include_controlling_edges) const; + protected: + // Add fanout to every `node` input. + void AddFanouts(NodeDef* node); + std::unordered_map* MutableNodes() { return &nodes_; } + GraphDef* MutableGraph() { return graph_; } + + using FanoutsMapType = + std::unordered_map, + HashPort>; + FanoutsMapType* MutableFanouts() { return &fanouts_; } + private: GraphDef* graph_; std::unordered_map nodes_; std::unordered_set empty_set_; - std::unordered_map, - HashPort> - fanouts_; + FanoutsMapType fanouts_; std::unordered_map num_regular_outputs_; }; diff --git a/tensorflow/core/grappler/mutable_graph_view.cc b/tensorflow/core/grappler/mutable_graph_view.cc new file mode 100644 index 0000000000..6abafe11a2 --- /dev/null +++ b/tensorflow/core/grappler/mutable_graph_view.cc @@ -0,0 +1,72 @@ +/* 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/mutable_graph_view.h" +#include "tensorflow/core/grappler/utils.h" + +namespace tensorflow { +namespace grappler { + +NodeDef* MutableGraphView::AddNode(NodeDef&& node) { + auto* node_in_graph = GetGraph()->add_node(); + *node_in_graph = std::move(node); + + auto result = MutableNodes()->emplace(node_in_graph->name(), node_in_graph); + // Check that the graph doesn't contain multiple nodes with the same name. + CHECK(result.second) << "Non unique node name detected: " + << node_in_graph->name(); + AddFanouts(node_in_graph); + return node_in_graph; +} + +void MutableGraphView::ReplaceInput(const NodeDef& old_input, + const NodeDef& new_input, + const int output_port_id) { + GraphView::OutputPort output_port = + GetOutputPort(old_input.name(), output_port_id); + auto fanout = GetFanout(output_port); + for (auto& input_port : fanout) { + input_port.node->set_input(input_port.port_id, new_input.name()); + AddFanouts(input_port.node); + } +} + +void MutableGraphView::DeleteNodes(const std::set& nodes_to_delete) { + for (const string& node_name_to_delete : nodes_to_delete) + RemoveFanouts(MutableNodes()->at(node_name_to_delete)); + for (const string& node_name_to_delete : nodes_to_delete) + MutableNodes()->erase(node_name_to_delete); + EraseNodesFromGraph(nodes_to_delete, GetGraph()); +} + +void MutableGraphView::RemoveFanouts(NodeDef* node) { + for (int i = 0; i < node->input_size(); ++i) { + OutputPort fanin; + string fanin_name = ParseNodeName(node->input(i), &fanin.port_id); + fanin.node = (*MutableNodes())[fanin_name]; + + InputPort input; + input.node = node; + if (fanin.port_id < 0) + input.port_id = -1; + else + input.port_id = i; + + (*MutableFanouts())[fanin].erase(input); + } +} + +} // end namespace grappler +} // end namespace tensorflow diff --git a/tensorflow/core/grappler/mutable_graph_view.h b/tensorflow/core/grappler/mutable_graph_view.h new file mode 100644 index 0000000000..105eb972e8 --- /dev/null +++ b/tensorflow/core/grappler/mutable_graph_view.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_CORE_GRAPPLER_MUTABLE_GRAPH_VIEW_H_ +#define TENSORFLOW_CORE_GRAPPLER_MUTABLE_GRAPH_VIEW_H_ + +#include "tensorflow/core/grappler/graph_view.h" + +namespace tensorflow { +namespace grappler { + +// A utility class to simplify the traversal of a GraphDef that, unlike +// GraphView, supports updating the graph. Note that you should not modify the +// graph separately, because the view will get out of sync. +class MutableGraphView : public GraphView { + public: + using GraphView::GraphView; + + GraphDef* GetGraph() { return MutableGraph(); } + // Adds a new node to graph and updates the view. + NodeDef* AddNode(NodeDef&& node); + + // Replaces the input for the output nodes of 'old_input' with a port + // `output_port_id` with 'new_input'. + // + // E.g: We have 2 nodes that use 'bar' node outputs as inputs: + // foo(bar:0, bar:1), foo2(other:0, bar:0) + // Calling ReplaceInput(bar, new, 0) changes every occurrence of bar:0 for + // new:0. Result: + // foo(new:0, bar:1), foo2(other:0, new:0) + void ReplaceInput(const NodeDef& old_input, const NodeDef& new_input, + int output_port_id = 0); + + // Deletes nodes from the graph. + void DeleteNodes(const std::set& nodes_to_delete); + + private: + void RemoveFanouts(NodeDef* node); +}; + +} // end namespace grappler +} // end namespace tensorflow + +#endif // TENSORFLOW_CORE_GRAPPLER_MUTABLE_GRAPH_VIEW_H_ diff --git a/tensorflow/core/grappler/mutable_graph_view_test.cc b/tensorflow/core/grappler/mutable_graph_view_test.cc new file mode 100644 index 0000000000..f09dfb8271 --- /dev/null +++ b/tensorflow/core/grappler/mutable_graph_view_test.cc @@ -0,0 +1,94 @@ +/* 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/mutable_graph_view.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/platform/test.h" + +namespace tensorflow { +namespace grappler { +namespace { + +TEST(MutableGraphViewTest, AddAndReplaceInput) { + // This outputs simple graph like: + // x + // / \ + // Square Square_1 + // | \ / | + // | \/ | + // | /\ | + // | / \ | + // AddN AddN_1 + // \ / + // y + TrivialTestGraphInputYielder fake_input(2, 2, 2, false, {"/CPU:0", "/GPU:0"}); + GrapplerItem item; + CHECK(fake_input.NextItem(&item)); + + GraphDef new_graph = item.graph; + MutableGraphView graph(&new_graph); + + GraphView::InputPort input = graph.GetInputPort("AddN", 0); + EXPECT_EQ("AddN", input.node->name()); + EXPECT_EQ(0, input.port_id); + GraphView::OutputPort fanin = graph.GetRegularFanin(input); + EXPECT_EQ("Square", fanin.node->name()); + EXPECT_EQ(0, fanin.port_id); + + auto find_child_with_name = [&graph](string output_port_name, + string input_name) { + GraphView::OutputPort output_port = + graph.GetOutputPort(output_port_name, 0); + auto fanout = graph.GetFanout(output_port); + for (auto& input_port : fanout) { + if (input_port.node->name() == input_name) return true; + } + return false; + }; + + EXPECT_FALSE(find_child_with_name("Square", "new_node")); + + NodeDef new_node = *input.node; + new_node.set_name("new_node"); + + EXPECT_EQ(graph.GetNode("new_node"), nullptr); + NodeDef* node_in_graph = graph.AddNode(std::move(new_node)); + EXPECT_NE(graph.GetNode("new_node"), nullptr); + + graph.ReplaceInput(*input.node, *node_in_graph); + EXPECT_TRUE(find_child_with_name("Square", "new_node")); + EXPECT_TRUE(find_child_with_name("new_node", "y")); +} + +TEST(MutableGraphViewTest, DeleteNodes) { + // Outputs simple graph as described in first test. + TrivialTestGraphInputYielder fake_input(2, 2, 2, false, {"/CPU:0", "/GPU:0"}); + GrapplerItem item; + CHECK(fake_input.NextItem(&item)); + + GraphDef new_graph = item.graph; + MutableGraphView graph(&new_graph); + + EXPECT_NE(graph.GetNode("AddN"), nullptr); + graph.DeleteNodes({"AddN"}); + + EXPECT_EQ(graph.GetNode("AddN"), nullptr); +} + +} // namespace +} // namespace grappler +} // namespace tensorflow diff --git a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc index 97862d1ed0..3ab2211694 100644 --- a/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/arithmetic_optimizer.cc @@ -3007,14 +3007,7 @@ void ArithmeticOptimizer::DedupComputations() { // Delete duplicates if (fetch_nodes_known_ && !duplicates.empty()) { - int last = optimized_graph_->node_size() - 1; - for (auto it = duplicates.rbegin(); it != duplicates.rend(); ++it) { - int index = *it; - optimized_graph_->mutable_node()->SwapElements(index, last); - last--; - } - optimized_graph_->mutable_node()->DeleteSubrange(last + 1, - duplicates.size()); + EraseNodesFromGraph(duplicates, optimized_graph_); // Rebuild the NodeMap which was invalidated by the node swapping above. node_map_.reset(new NodeMap(optimized_graph_)); } diff --git a/tensorflow/core/grappler/optimizers/constant_folding.cc b/tensorflow/core/grappler/optimizers/constant_folding.cc index 76c928f995..f016fae3a5 100644 --- a/tensorflow/core/grappler/optimizers/constant_folding.cc +++ b/tensorflow/core/grappler/optimizers/constant_folding.cc @@ -1305,17 +1305,12 @@ Status ConstantFolding::FoldGraph(GraphDef* output) { } // Delete the newly created nodes that don't feed anything. - int last = output->node_size() - 1; - for (int i = output->node_size() - 1; i >= 0; --i) { - const NodeDef& node = output->node(i); - auto fanout = node_map_->GetOutputs(node.name()); - if (fanout.empty()) { - output->mutable_node()->SwapElements(i, last); - last--; - } + std::vector nodes_to_delete; + for (int i = 0; i < output->node_size(); i++) { + auto fanout = node_map_->GetOutputs(output->node(i).name()); + if (fanout.empty()) nodes_to_delete.push_back(i); } - output->mutable_node()->DeleteSubrange(last + 1, - output->node_size() - last - 1); + EraseNodesFromGraph(std::move(nodes_to_delete), output); for (const auto& node : graph_->node()) { // If no fetch nodes is provided, we conservatively diff --git a/tensorflow/core/grappler/optimizers/data/BUILD b/tensorflow/core/grappler/optimizers/data/BUILD index c8946c499c..db96a81be8 100644 --- a/tensorflow/core/grappler/optimizers/data/BUILD +++ b/tensorflow/core/grappler/optimizers/data/BUILD @@ -46,7 +46,7 @@ cc_library( deps = [ "//tensorflow/core:framework", "//tensorflow/core:lib", - "//tensorflow/core/grappler:graph_view", + "//tensorflow/core/grappler:mutable_graph_view", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:utils", ] + tf_protos_all(), @@ -58,8 +58,14 @@ tf_cc_test( visibility = ["//visibility:public"], deps = [ ":graph_utils", + "//tensorflow/core:framework", "//tensorflow/core:test", "//tensorflow/core:test_main", + "//tensorflow/core:testlib", + "//tensorflow/core/grappler:grappler_item", + "//tensorflow/core/grappler/optimizers:custom_graph_optimizer", + "//tensorflow/core/grappler/optimizers:custom_graph_optimizer_registry", + "//tensorflow/core/kernels:cast_op", ], ) @@ -73,7 +79,7 @@ cc_library( deps = [ ":graph_utils", "//tensorflow/core:lib", - "//tensorflow/core/grappler:graph_view", + "//tensorflow/core/grappler:mutable_graph_view", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:op_types", "//tensorflow/core/grappler:utils", @@ -107,7 +113,7 @@ cc_library( deps = [ ":graph_utils", "//tensorflow/core:lib", - "//tensorflow/core/grappler:graph_view", + "//tensorflow/core/grappler:mutable_graph_view", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:op_types", "//tensorflow/core/grappler:utils", @@ -141,7 +147,7 @@ cc_library( deps = [ ":graph_utils", "//tensorflow/core:lib", - "//tensorflow/core/grappler:graph_view", + "//tensorflow/core/grappler:mutable_graph_view", "//tensorflow/core/grappler:grappler_item", "//tensorflow/core/grappler:op_types", "//tensorflow/core/grappler:utils", diff --git a/tensorflow/core/grappler/optimizers/data/graph_utils.cc b/tensorflow/core/grappler/optimizers/data/graph_utils.cc index ea5f450009..6ce6533369 100644 --- a/tensorflow/core/grappler/optimizers/data/graph_utils.cc +++ b/tensorflow/core/grappler/optimizers/data/graph_utils.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/core/grappler/optimizers/data/graph_utils.h" #include "tensorflow/core/framework/device_base.h" -#include "tensorflow/core/grappler/graph_view.h" +#include "tensorflow/core/framework/op_def.pb.h" #include "tensorflow/core/util/ptr_util.h" namespace tensorflow { @@ -26,14 +26,12 @@ namespace { constexpr char kConstOpName[] = "Const"; -int FindNodeWithPredicate(const std::function& predicate, - const GraphDef& graph) { - for (int i = 0; i < graph.node_size(); ++i) { - if (predicate(graph.node(i))) { - return i; - } - } - return -1; +template +int GetElementIdxWithPredicate(const Predicate& predicate, + const Collection& collection) { + auto it = std::find_if(collection.begin(), collection.end(), predicate); + if (it == collection.end()) return -1; + return std::distance(collection.begin(), it); } std::vector CreateNameIndex(const GraphDef& graph) { @@ -62,13 +60,14 @@ std::vector CreateInputIndex(const NodeDef& node) { return index; } -Status AddScalarConstNodeHelper( +NodeDef* AddScalarConstNodeHelper( DataType dtype, const std::function& add_value, - GraphDef* graph, NodeDef** result) { - NodeDef* node = graph->add_node(); - node->set_op(kConstOpName); - SetUniqueName(kConstOpName, graph, node); - (*node->mutable_attr())["dtype"].set_type(dtype); + MutableGraphView* graph) { + NodeDef node; + node.set_op(kConstOpName); + SetUniqueGraphNodeName(kConstOpName, graph->GetGraph(), &node); + + (*node.mutable_attr())["dtype"].set_type(dtype); std::unique_ptr tensor = tensorflow::MakeUnique(); std::unique_ptr tensor_shape = @@ -76,75 +75,69 @@ Status AddScalarConstNodeHelper( tensor->set_allocated_tensor_shape(tensor_shape.release()); tensor->set_dtype(dtype); add_value(tensor.get()); - (*node->mutable_attr())["value"].set_allocated_tensor(tensor.release()); - *result = node; - return Status::OK(); + (*node.mutable_attr())["value"].set_allocated_tensor(tensor.release()); + + return graph->AddNode(std::move(node)); } } // namespace -Status AddNode(const string& name, const string& op, - const std::vector& inputs, - const std::vector>& attributes, - GraphDef* graph, NodeDef** result) { - NodeDef* node = graph->add_node(); +NodeDef* AddNode(const string& name, const string& op, + const std::vector& inputs, + const std::vector>& attributes, + MutableGraphView* graph) { + NodeDef node; if (!name.empty()) { - node->set_name(name); + node.set_name(name); } else { - SetUniqueName(op, graph, node); + SetUniqueGraphNodeName(op, graph->GetGraph(), &node); } - node->set_op(op); + node.set_op(op); for (const string& input : inputs) { - node->add_input(input); + node.add_input(input); } for (auto attr : attributes) { - (*node->mutable_attr())[attr.first] = attr.second; + (*node.mutable_attr())[attr.first] = attr.second; } - *result = node; - return Status::OK(); + return graph->AddNode(std::move(node)); } template <> -Status AddScalarConstNode(bool v, GraphDef* graph, NodeDef** result) { +NodeDef* AddScalarConstNode(bool v, MutableGraphView* graph) { return AddScalarConstNodeHelper( - DT_BOOL, [v](TensorProto* proto) { proto->add_bool_val(v); }, graph, - result); + DT_BOOL, [v](TensorProto* proto) { proto->add_bool_val(v); }, graph); } template <> -Status AddScalarConstNode(double v, GraphDef* graph, NodeDef** result) { +NodeDef* AddScalarConstNode(double v, MutableGraphView* graph) { return AddScalarConstNodeHelper( - DT_DOUBLE, [v](TensorProto* proto) { proto->add_double_val(v); }, graph, - result); + DT_DOUBLE, [v](TensorProto* proto) { proto->add_double_val(v); }, graph); } template <> -Status AddScalarConstNode(float v, GraphDef* graph, NodeDef** result) { +NodeDef* AddScalarConstNode(float v, MutableGraphView* graph) { return AddScalarConstNodeHelper( - DT_FLOAT, [v](TensorProto* proto) { proto->add_float_val(v); }, graph, - result); + DT_FLOAT, [v](TensorProto* proto) { proto->add_float_val(v); }, graph); } template <> -Status AddScalarConstNode(int v, GraphDef* graph, NodeDef** result) { +NodeDef* AddScalarConstNode(int v, MutableGraphView* graph) { return AddScalarConstNodeHelper( - DT_INT32, [v](TensorProto* proto) { proto->add_int_val(v); }, graph, - result); + DT_INT32, [v](TensorProto* proto) { proto->add_int_val(v); }, graph); } template <> -Status AddScalarConstNode(int64 v, GraphDef* graph, NodeDef** result) { +NodeDef* AddScalarConstNode(int64 v, MutableGraphView* graph) { return AddScalarConstNodeHelper( - DT_INT64, [v](TensorProto* proto) { proto->add_int64_val(v); }, graph, - result); + DT_INT64, [v](TensorProto* proto) { proto->add_int64_val(v); }, graph); } template <> -Status AddScalarConstNode(StringPiece v, GraphDef* graph, NodeDef** result) { +NodeDef* AddScalarConstNode(StringPiece v, MutableGraphView* graph) { return AddScalarConstNodeHelper( DT_STRING, [v](TensorProto* proto) { proto->add_string_val(v.data(), v.size()); }, - graph, result); + graph); } bool Compare(const GraphDef& g1, const GraphDef& g2) { @@ -177,52 +170,82 @@ bool Compare(const GraphDef& g1, const GraphDef& g2) { return true; } -bool ContainsNodeWithName(const string& name, const GraphDef& graph) { - return FindNodeWithName(name, graph) != -1; +bool ContainsGraphNodeWithName(const string& name, const GraphDef& graph) { + return FindGraphNodeWithName(name, graph) != -1; } bool ContainsNodeWithOp(const string& op, const GraphDef& graph) { return FindNodeWithOp(op, graph) != -1; } -Status DeleteNodes(const std::set& nodes_to_delete, GraphDef* graph) { - int last = graph->node_size() - 1; - for (int i = graph->node_size() - 1; i >= 0; --i) { - const NodeDef& node = graph->node(i); - if (nodes_to_delete.find(node.name()) != nodes_to_delete.end()) { - graph->mutable_node()->SwapElements(i, last); - last--; - } - } - graph->mutable_node()->DeleteSubrange(last + 1, - graph->node_size() - last - 1); - return Status::OK(); +bool ContainsGraphFunctionWithName(const string& name, + const FunctionDefLibrary& library) { + return FindGraphFunctionWithName(name, library) != -1; } -int FindNodeWithName(const string& name, const GraphDef& graph) { - return FindNodeWithPredicate( - [name](const NodeDef& node) { return node.name() == name; }, graph); +bool ContainsFunctionNodeWithName(const string& name, + const FunctionDef& function) { + return FindFunctionNodeWithName(name, function) != -1; +} + +int FindGraphNodeWithName(const string& name, const GraphDef& graph) { + return GetElementIdxWithPredicate( + [&name](const NodeDef& node) { return node.name() == name; }, + graph.node()); } int FindNodeWithOp(const string& op, const GraphDef& graph) { - return FindNodeWithPredicate( - [op](const NodeDef& node) { return node.op() == op; }, graph); + return GetElementIdxWithPredicate( + [&op](const NodeDef& node) { return node.op() == op; }, graph.node()); } -void SetUniqueName(const string& op, GraphDef* graph, NodeDef* node) { +int FindGraphFunctionWithName(const string& name, + const FunctionDefLibrary& library) { + return GetElementIdxWithPredicate( + [&name](const FunctionDef& function) { + return function.signature().name() == name; + }, + library.function()); +} + +int FindFunctionNodeWithName(const string& name, const FunctionDef& function) { + return GetElementIdxWithPredicate( + [&name](const NodeDef& node) { return node.name() == name; }, + function.node_def()); +} + +void SetUniqueGraphNodeName(const string& prefix, GraphDef* graph, + NodeDef* node) { + string name = prefix; int id = graph->node_size(); - while (ContainsNodeWithName(strings::StrCat(op, "/_", id), *graph)) { + while (ContainsGraphNodeWithName(name, *graph)) { + name = strings::StrCat(prefix, "/_", id); ++id; } - node->set_name(strings::StrCat(op, "/_", id)); + node->set_name(std::move(name)); } -void ReplaceInput(const NodeDef& old_input, const NodeDef& new_input, - GraphView* graph) { - GraphView::OutputPort output_port = graph->GetOutputPort(old_input.name(), 0); - auto fanout = graph->GetFanout(output_port); - for (auto& input_port : fanout) - input_port.node->set_input(0, new_input.name()); +void SetUniqueFunctionNodeName(const string& prefix, FunctionDef* function, + NodeDef* node) { + string name = prefix; + int id = function->node_def_size(); + while (ContainsFunctionNodeWithName(name, *function)) { + name = strings::StrCat(prefix, "/_", id); + ++id; + } + node->set_name(std::move(name)); +} + +void SetUniqueGraphFunctionName(const string& prefix, + FunctionDefLibrary* library, + FunctionDef* function) { + string name = prefix; + int id = library->function_size(); + while (ContainsGraphFunctionWithName(name, *library)) { + name = strings::StrCat(prefix, "/_", id); + ++id; + } + function->mutable_signature()->set_name(name); } } // end namespace graph_utils diff --git a/tensorflow/core/grappler/optimizers/data/graph_utils.h b/tensorflow/core/grappler/optimizers/data/graph_utils.h index 1cb0f0c81d..0847748802 100644 --- a/tensorflow/core/grappler/optimizers/data/graph_utils.h +++ b/tensorflow/core/grappler/optimizers/data/graph_utils.h @@ -17,12 +17,13 @@ limitations under the License. #define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_DATA_GRAPH_UTILS_H_ #include "tensorflow/core/framework/attr_value.pb.h" +#include "tensorflow/core/framework/function.pb.h" #include "tensorflow/core/framework/graph.pb.h" #include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/framework/tensor.pb.h" #include "tensorflow/core/framework/tensor_shape.pb.h" #include "tensorflow/core/framework/types.h" -#include "tensorflow/core/grappler/graph_view.h" +#include "tensorflow/core/grappler/mutable_graph_view.h" #include "tensorflow/core/grappler/utils.h" #include "tensorflow/core/lib/core/errors.h" @@ -31,57 +32,83 @@ namespace grappler { namespace graph_utils { // Adds a node to the graph. -Status AddNode(const string& name, const string& op, - const std::vector& inputs, - const std::vector>& attributes, - GraphDef* graph, NodeDef** result); +NodeDef* AddNode(const string& name, const string& op, + const std::vector& inputs, + const std::vector>& attributes, + MutableGraphView* graph); // Adds a Const node with the given value to the graph. template -Status AddScalarConstNode(T v, GraphDef* graph, NodeDef** result) { - return errors::Unimplemented("Type %s is not supported.", - DataTypeToEnum::value); +NodeDef* AddScalarConstNode(T v, MutableGraphView* graph) { + // is_same is an idiomatic hack for making it compile if not instantiated. + // Replacing with false will result in a compile-time error. + static_assert(!std::is_same::value, + "Invalid specialization of this method for type T."); + return {}; } + template <> -Status AddScalarConstNode(bool v, GraphDef* graph, NodeDef** result); +NodeDef* AddScalarConstNode(bool v, MutableGraphView* graph); template <> -Status AddScalarConstNode(double v, GraphDef* graph, NodeDef** result); +NodeDef* AddScalarConstNode(double v, MutableGraphView* graph); template <> -Status AddScalarConstNode(float v, GraphDef* graph, NodeDef** result); +NodeDef* AddScalarConstNode(float v, MutableGraphView* graph); template <> -Status AddScalarConstNode(int v, GraphDef* graph, NodeDef** result); +NodeDef* AddScalarConstNode(int v, MutableGraphView* graph); template <> -Status AddScalarConstNode(int64 v, GraphDef* graph, NodeDef** result); +NodeDef* AddScalarConstNode(int64 v, MutableGraphView* graph); template <> -Status AddScalarConstNode(StringPiece v, GraphDef* graph, NodeDef** result); +NodeDef* AddScalarConstNode(StringPiece v, MutableGraphView* graph); // Checks whether the two graphs are the same. bool Compare(const GraphDef& g1, const GraphDef& g2); // Checks whether the graph contains a node with the given name. -bool ContainsNodeWithName(const string& name, const GraphDef& graph); +bool ContainsGraphNodeWithName(const string& name, const GraphDef& graph); + +// Checks whether the library contains a function with the given name. +bool ContainsGraphFunctionWithName(const string& name, + const FunctionDefLibrary& library); + +// Checks whether the function contains a node with the given name. +bool ContainsFunctionNodeWithName(const string& name, + const FunctionDef& function); // Checks whether the graph contains a node with the given op. bool ContainsNodeWithOp(const string& op, const GraphDef& graph); -// Deletes nodes from the graph. -Status DeleteNodes(const std::set& nodes_to_delete, GraphDef* graph); - // Returns the index of the node with the given name or -1 if the node does // not exist. -int FindNodeWithName(const string& name, const GraphDef& graph); +int FindGraphNodeWithName(const string& name, const GraphDef& graph); + +// Returns the index of the function with the given name or -1 if the function +// does not exist. +int FindGraphFunctionWithName(const string& name, + const FunctionDefLibrary& library); + +// Returns the index of the function node with the given name or -1 if the +// function node does not exist. +int FindFunctionNodeWithName(const string& name, const FunctionDef& function); // Returns the index of a node with the given op or -1 if no such node // exists. int FindNodeWithOp(const string& op, const GraphDef& graph); -// Sets the node name using the op name as a prefix while guaranteeing the name +// Sets the node name using `prefix` as a prefix while guaranteeing the name // is unique across the graph. -void SetUniqueName(const string& op, GraphDef* graph, NodeDef* node); - -// Replaces the input for the output nodes of 'old_input' with 'new_input'. -void ReplaceInput(const NodeDef& old_input, const NodeDef& new_input, - GraphView* graph); +void SetUniqueGraphNodeName(const string& prefix, GraphDef* graph, + NodeDef* node); + +// Sets the function node name using the `prefix` as a prefix while guaranteeing +// the name is unique across the functions nodes. +void SetUniqueFunctionNodeName(const string& prefix, FunctionDef* function, + NodeDef* node); + +// Sets the node name using the `prefix` name as a prefix while guaranteeing the +// name is unique across the graph. +void SetUniqueGraphFunctionName(const string& prefix, + FunctionDefLibrary* library, + FunctionDef* function); } // end namespace graph_utils } // end namespace grappler diff --git a/tensorflow/core/grappler/optimizers/data/graph_utils_test.cc b/tensorflow/core/grappler/optimizers/data/graph_utils_test.cc index d723d73b7a..59ed79ab8f 100644 --- a/tensorflow/core/grappler/optimizers/data/graph_utils_test.cc +++ b/tensorflow/core/grappler/optimizers/data/graph_utils_test.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/core/grappler/optimizers/data/graph_utils.h" +#include "tensorflow/core/framework/function_testlib.h" #include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" @@ -24,149 +25,191 @@ namespace graph_utils { namespace { TEST(GraphUtilsTest, AddScalarConstNodeBool) { - GraphDef graph; - NodeDef* bool_node; - TF_EXPECT_OK(AddScalarConstNode(true, &graph, &bool_node)); - EXPECT_TRUE(ContainsNodeWithName(bool_node->name(), graph)); + GraphDef graph_def; + MutableGraphView graph(&graph_def); + NodeDef* bool_node = AddScalarConstNode(true, &graph); + EXPECT_TRUE(ContainsGraphNodeWithName(bool_node->name(), *graph.GetGraph())); EXPECT_EQ(bool_node->attr().at("value").tensor().bool_val(0), true); } TEST(GraphUtilsTest, AddScalarConstNodeDouble) { - GraphDef graph; - NodeDef* double_node; - TF_EXPECT_OK(AddScalarConstNode(3.14, &graph, &double_node)); - EXPECT_TRUE(ContainsNodeWithName(double_node->name(), graph)); + GraphDef graph_def; + MutableGraphView graph(&graph_def); + NodeDef* double_node = AddScalarConstNode(3.14, &graph); + EXPECT_TRUE( + ContainsGraphNodeWithName(double_node->name(), *graph.GetGraph())); EXPECT_FLOAT_EQ(double_node->attr().at("value").tensor().double_val(0), 3.14); } TEST(GraphUtilsTest, AddScalarConstNodeFloat) { - GraphDef graph; - NodeDef* float_node; - TF_EXPECT_OK(AddScalarConstNode(3.14, &graph, &float_node)); - EXPECT_TRUE(ContainsNodeWithName(float_node->name(), graph)); + GraphDef graph_def; + MutableGraphView graph(&graph_def); + NodeDef* float_node = AddScalarConstNode(3.14, &graph); + EXPECT_TRUE(ContainsGraphNodeWithName(float_node->name(), *graph.GetGraph())); EXPECT_FLOAT_EQ(float_node->attr().at("value").tensor().float_val(0), 3.14); } TEST(GraphUtilsTest, AddScalarConstNodeInt) { - GraphDef graph; - NodeDef* int_node; - TF_EXPECT_OK(AddScalarConstNode(42, &graph, &int_node)); - EXPECT_TRUE(ContainsNodeWithName(int_node->name(), graph)); + GraphDef graph_def; + MutableGraphView graph(&graph_def); + NodeDef* int_node = AddScalarConstNode(42, &graph); + EXPECT_TRUE(ContainsGraphNodeWithName(int_node->name(), *graph.GetGraph())); EXPECT_EQ(int_node->attr().at("value").tensor().int_val(0), 42); } TEST(GraphUtilsTest, AddScalarConstNodeInt64) { - GraphDef graph; - NodeDef* int64_node; - TF_EXPECT_OK(AddScalarConstNode(42, &graph, &int64_node)); - EXPECT_TRUE(ContainsNodeWithName(int64_node->name(), graph)); + GraphDef graph_def; + MutableGraphView graph(&graph_def); + NodeDef* int64_node = AddScalarConstNode(42, &graph); + EXPECT_TRUE(ContainsGraphNodeWithName(int64_node->name(), *graph.GetGraph())); EXPECT_EQ(int64_node->attr().at("value").tensor().int64_val(0), 42); } TEST(GraphUtilsTest, AddScalarConstNodeString) { - GraphDef graph; - NodeDef* string_node; - TF_EXPECT_OK(AddScalarConstNode("hello", &graph, &string_node)); - EXPECT_TRUE(ContainsNodeWithName(string_node->name(), graph)); + GraphDef graph_def; + MutableGraphView graph(&graph_def); + NodeDef* string_node = AddScalarConstNode("hello", &graph); + EXPECT_TRUE( + ContainsGraphNodeWithName(string_node->name(), *graph.GetGraph())); EXPECT_EQ(string_node->attr().at("value").tensor().string_val(0), "hello"); } TEST(GraphUtilsTest, Compare) { - GraphDef graphA; - GraphDef graphB; - EXPECT_TRUE(Compare(graphA, graphB)); + GraphDef graph_def_a; + MutableGraphView graph_a(&graph_def_a); + GraphDef graph_def_b; + MutableGraphView graph_b(&graph_def_b); - NodeDef* nodeA; - TF_EXPECT_OK(AddNode("A", "OpA", {}, {}, &graphA, &nodeA)); - NodeDef* nodeB; - TF_EXPECT_OK(AddNode("B", "OpB", {"A"}, {}, &graphA, &nodeB)); - EXPECT_FALSE(Compare(graphA, graphB)); + EXPECT_TRUE(Compare(graph_def_a, graph_def_b)); - graphB.mutable_node()->CopyFrom(graphA.node()); - EXPECT_TRUE(Compare(graphA, graphB)); + AddNode("A", "OpA", {}, {}, &graph_a); + AddNode("B", "OpB", {"A"}, {}, &graph_a); + EXPECT_FALSE(Compare(graph_def_a, graph_def_b)); + + graph_def_b.mutable_node()->CopyFrom(graph_def_a.node()); + EXPECT_TRUE(Compare(graph_def_a, graph_def_b)); } -TEST(GraphUtilsTest, ContainsNodeWithName) { - GraphDef graph; - EXPECT_TRUE(!ContainsNodeWithName("A", graph)); +TEST(GraphUtilsTest, ContainsGraphNodeWithName) { + GraphDef graph_def; + MutableGraphView graph(&graph_def); + EXPECT_TRUE(!ContainsGraphNodeWithName("A", *graph.GetGraph())); - NodeDef* node; - TF_EXPECT_OK(AddNode("A", "OpA", {}, {}, &graph, &node)); - EXPECT_TRUE(ContainsNodeWithName("A", graph)); + AddNode("A", "OpA", {}, {}, &graph); + EXPECT_TRUE(ContainsGraphNodeWithName("A", *graph.GetGraph())); - TF_EXPECT_OK(DeleteNodes({"A"}, &graph)); - EXPECT_TRUE(!ContainsNodeWithName("A", graph)); + graph.DeleteNodes({"A"}); + EXPECT_TRUE(!ContainsGraphNodeWithName("A", *graph.GetGraph())); } -TEST(GraphUtilsTest, ContainsNodeWithOp) { - GraphDef graph; - EXPECT_TRUE(!ContainsNodeWithOp("OpA", graph)); +TEST(GraphUtilsTest, ContainsGraphFunctionWithName) { + FunctionDefLibrary library; + EXPECT_FALSE(ContainsGraphFunctionWithName("new_function", library)); + FunctionDef* new_function = library.add_function(); + SetUniqueGraphFunctionName("new_function", &library, new_function); - NodeDef* node; - TF_EXPECT_OK(AddNode("A", "OpA", {}, {}, &graph, &node)); - EXPECT_TRUE(ContainsNodeWithOp("OpA", graph)); + EXPECT_TRUE( + ContainsGraphFunctionWithName(new_function->signature().name(), library)); +} - TF_EXPECT_OK(DeleteNodes({"A"}, &graph)); - EXPECT_TRUE(!ContainsNodeWithOp("OpA", graph)); +TEST(GraphUtilsTest, ContainsFunctionNodeWithName) { + FunctionDef function = test::function::XTimesTwo(); + EXPECT_FALSE(ContainsFunctionNodeWithName( + "weird_name_that_should_not_be_there", function)); + EXPECT_TRUE(ContainsFunctionNodeWithName("two", function)); } -TEST(GraphUtilsTest, FindNodeWithName) { - GraphDef graph; - EXPECT_EQ(FindNodeWithName("A", graph), -1); +TEST(GraphUtilsTest, ContainsNodeWithOp) { + GraphDef graph_def; + MutableGraphView graph(&graph_def); + EXPECT_TRUE(!ContainsNodeWithOp("OpA", *graph.GetGraph())); - NodeDef* node; - TF_EXPECT_OK(AddNode("A", "OpA", {}, {}, &graph, &node)); - EXPECT_NE(FindNodeWithName("A", graph), -1); + AddNode("A", "OpA", {}, {}, &graph); + EXPECT_TRUE(ContainsNodeWithOp("OpA", *graph.GetGraph())); - TF_EXPECT_OK(DeleteNodes({"A"}, &graph)); - EXPECT_EQ(FindNodeWithName("A", graph), -1); + graph.DeleteNodes({"A"}); + EXPECT_TRUE(!ContainsNodeWithOp("OpA", *graph.GetGraph())); } -TEST(GraphUtilsTest, FindNodeWithOp) { - GraphDef graph; - EXPECT_EQ(FindNodeWithOp("OpA", graph), -1); +TEST(GraphUtilsTest, FindGraphNodeWithName) { + GraphDef graph_def; + MutableGraphView graph(&graph_def); + EXPECT_EQ(FindGraphNodeWithName("A", *graph.GetGraph()), -1); - NodeDef* node; - TF_EXPECT_OK(AddNode("A", "OpA", {}, {}, &graph, &node)); - EXPECT_NE(FindNodeWithOp("OpA", graph), -1); + AddNode("A", "OpA", {}, {}, &graph); + EXPECT_NE(FindGraphNodeWithName("A", *graph.GetGraph()), -1); - TF_EXPECT_OK(DeleteNodes({"A"}, &graph)); - EXPECT_EQ(FindNodeWithOp("OpA", graph), -1); + graph.DeleteNodes({"A"}); + EXPECT_EQ(FindGraphNodeWithName("A", *graph.GetGraph()), -1); } -TEST(GraphUtilsTest, SetUniqueName) { - GraphDef graph; +TEST(GraphUtilsTest, FindFunctionWithName) { + FunctionDef function = test::function::XTimesTwo(); + EXPECT_EQ( + FindFunctionNodeWithName("weird_name_that_should_not_be_there", function), + -1); + EXPECT_NE(FindFunctionNodeWithName("two", function), -1); +} - NodeDef* node1; - TF_EXPECT_OK(AddNode("", "A", {}, {}, &graph, &node1)); - NodeDef* node2; - TF_EXPECT_OK(AddNode("", "A", {}, {}, &graph, &node2)); - EXPECT_NE(node1->name(), node2->name()); +TEST(GraphUtilsTest, FindGraphFunctionWithName) { + FunctionDefLibrary library; + EXPECT_EQ(FindGraphFunctionWithName("new_function", library), -1); + FunctionDef* new_function = library.add_function(); + SetUniqueGraphFunctionName("new_function", &library, new_function); - TF_EXPECT_OK(DeleteNodes({node1->name()}, &graph)); - NodeDef* node3; - TF_EXPECT_OK(AddNode("", "A", {}, {}, &graph, &node3)); - EXPECT_NE(node2->name(), node3->name()); + EXPECT_NE( + FindGraphFunctionWithName(new_function->signature().name(), library), -1); } -TEST(GraphUtilsTest, ReplaceInput) { - GraphDef graph; +TEST(GraphUtilsTest, FindNodeWithOp) { + GraphDef graph_def; + MutableGraphView graph(&graph_def); + EXPECT_EQ(FindNodeWithOp("OpA", *graph.GetGraph()), -1); + + AddNode("A", "OpA", {}, {}, &graph); + EXPECT_NE(FindNodeWithOp("OpA", *graph.GetGraph()), -1); + + graph.DeleteNodes({"A"}); + EXPECT_EQ(FindNodeWithOp("OpA", *graph.GetGraph()), -1); +} - NodeDef* node1; - TF_EXPECT_OK(AddNode("", "A", {}, {}, &graph, &node1)); +TEST(GraphUtilsTest, SetUniqueGraphNodeName) { + GraphDef graph_def; + MutableGraphView graph(&graph_def); - NodeDef* node2; - TF_EXPECT_OK(AddNode("", "A", {node1->name()}, {}, &graph, &node2)); + NodeDef* node1 = AddNode("", "A", {}, {}, &graph); + NodeDef* node2 = AddNode("", "A", {}, {}, &graph); + EXPECT_NE(node1->name(), node2->name()); - NodeDef* node3; - TF_EXPECT_OK(AddNode("", "A", {node2->name()}, {}, &graph, &node3)); + graph.DeleteNodes({node1->name()}); + NodeDef* node3 = AddNode("", "A", {}, {}, &graph); + EXPECT_NE(node2->name(), node3->name()); +} - EXPECT_EQ(node3->input(0), node2->name()); +TEST(GraphUtilsTest, SetUniqueFunctionNodeName) { + FunctionDef function = test::function::XTimesTwo(); + NodeDef node; + SetUniqueFunctionNodeName("abc", &function, &node); + for (const NodeDef& function_node : function.node_def()) { + EXPECT_NE(node.name(), function_node.name()); + } + auto* new_node = function.add_node_def(); + *new_node = node; + + NodeDef other; + SetUniqueFunctionNodeName("abc", &function, &other); + EXPECT_NE(other.name(), new_node->name()); +} - GraphView view(&graph); - ReplaceInput(*node2, *node1, &view); +TEST(GraphUtilsTest, SetUniqueGraphFunctionName) { + FunctionDefLibrary library; + FunctionDef* new_function = library.add_function(); + SetUniqueGraphFunctionName("new_function", &library, new_function); - EXPECT_EQ(node3->input(0), node1->name()); + FunctionDef* other_function = library.add_function(); + SetUniqueGraphFunctionName("new_function", &library, other_function); + EXPECT_NE(new_function->signature().name(), + other_function->signature().name()); } } // namespace diff --git a/tensorflow/core/grappler/optimizers/data/map_and_batch_fusion.cc b/tensorflow/core/grappler/optimizers/data/map_and_batch_fusion.cc index eac665bd92..3ce238a30a 100644 --- a/tensorflow/core/grappler/optimizers/data/map_and_batch_fusion.cc +++ b/tensorflow/core/grappler/optimizers/data/map_and_batch_fusion.cc @@ -18,8 +18,8 @@ limitations under the License. #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/grappler/clusters/cluster.h" -#include "tensorflow/core/grappler/graph_view.h" #include "tensorflow/core/grappler/grappler_item.h" +#include "tensorflow/core/grappler/mutable_graph_view.h" #include "tensorflow/core/grappler/op_types.h" #include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h" #include "tensorflow/core/grappler/optimizers/data/graph_utils.h" @@ -32,12 +32,70 @@ namespace { constexpr char kFusedOpName[] = "MapAndBatchDatasetV2"; +NodeDef make_map_and_batch_node(const NodeDef& map_node, + const NodeDef& batch_node, + MutableGraphView* graph) { + NodeDef new_node; + new_node.set_op(kFusedOpName); + graph_utils::SetUniqueGraphNodeName(kFusedOpName, graph->GetGraph(), + &new_node); + + // Set the `input` input argument. + new_node.add_input(map_node.input(0)); + + // Set the `other_arguments` input arguments. + int num_other_args; + if (map_node.op() == "ParallelMapDataset") { + num_other_args = map_node.input_size() - 2; + } else { + num_other_args = map_node.input_size() - 1; + } + for (int i = 0; i < num_other_args; i++) { + new_node.add_input(map_node.input(i + 1)); + } + + // Set the `batch_size` input argument. + new_node.add_input(batch_node.input(1)); + + // Set the `num_parallel_calls` input argument. + if (map_node.op() == "ParallelMapDataset") { + // The type of the `num_parallel_calls` argument in ParallelMapDataset + // and MapAndBatchDataset is different (int32 and int64 respectively) + // so we cannot reuse the same Const node and thus create a new one. + NodeDef* v = graph->GetNode(map_node.input(map_node.input_size() - 1)); + NodeDef* tmp = graph_utils::AddScalarConstNode( + v->attr().at("value").tensor().int_val(0), graph); + new_node.add_input(tmp->name()); + } else { + NodeDef* tmp = graph_utils::AddScalarConstNode(1, graph); + new_node.add_input(tmp->name()); + } + + // Set the `drop_remainder` input argument. + if (batch_node.op() == "BatchDatasetV2") { + new_node.add_input(batch_node.input(2)); + } else { + NodeDef* tmp = graph_utils::AddScalarConstNode(false, graph); + new_node.add_input(tmp->name()); + } + + // Set `f` and `Targuments` attributes. + for (auto key : {"f", "Targuments"}) { + (*new_node.mutable_attr())[key] = map_node.attr().at(key); + } + // Set `output_types` and `output_shapes` attributes. + for (auto key : {"output_shapes", "output_types"}) { + (*new_node.mutable_attr())[key] = batch_node.attr().at(key); + } + return new_node; +} + } // namespace Status MapAndBatchFusion::Optimize(Cluster* cluster, const GrapplerItem& item, GraphDef* output) { *output = item.graph; - GraphView graph(output); + MutableGraphView graph(output); std::set nodes_to_delete; for (const NodeDef& node : item.graph.node()) { if (node.op() != "BatchDataset" && node.op() != "BatchDatasetV2") { @@ -45,79 +103,25 @@ Status MapAndBatchFusion::Optimize(Cluster* cluster, const GrapplerItem& item, } // Use a more descriptive variable name now that we know the node type. - const NodeDef batch_node(node); + const NodeDef& batch_node = node; GraphView::InputPort input_port = graph.GetInputPort(batch_node.name(), 0); NodeDef* node2 = graph.GetRegularFanin(input_port).node; if (node2->op() != "MapDataset" && node2->op() != "ParallelMapDataset") { continue; } - - NodeDef* new_node = output->add_node(); - new_node->set_op(kFusedOpName); - graph_utils::SetUniqueName(kFusedOpName, output, new_node); - // Use a more descriptive variable name now that we know the node type. NodeDef* map_node = node2; - // Set the `input` input argument. - new_node->add_input(map_node->input(0)); - - // Set the `other_arguments` input arguments. - int num_other_args; - if (map_node->op() == "ParallelMapDataset") { - num_other_args = map_node->input_size() - 2; - } else { - num_other_args = map_node->input_size() - 1; - } - for (int i = 0; i < num_other_args; i++) { - new_node->add_input(map_node->input(i + 1)); - } - // Set the `batch_size` input argument. - new_node->add_input(batch_node.input(1)); - - // Set the `num_parallel_calls` input argument. - if (map_node->op() == "ParallelMapDataset") { - // The type of the `num_parallel_calls` argument in ParallelMapDataset - // and MapAndBatchDataset is different (int32 and int64 respectively) - // so we cannot reuse the same Const node and thus create a new one. - NodeDef* v = graph.GetNode(map_node->input(map_node->input_size() - 1)); - NodeDef* tmp; - TF_RETURN_IF_ERROR(graph_utils::AddScalarConstNode( - v->attr().at("value").tensor().int_val(0), output, &tmp)); - new_node->add_input(tmp->name()); - } else { - NodeDef* tmp; - TF_RETURN_IF_ERROR( - graph_utils::AddScalarConstNode(1, output, &tmp)); - new_node->add_input(tmp->name()); - } - - // Set the `drop_remainder` input argument. - if (batch_node.op() == "BatchDatasetV2") { - new_node->add_input(batch_node.input(2)); - } else { - NodeDef* tmp; - TF_RETURN_IF_ERROR( - graph_utils::AddScalarConstNode(false, output, &tmp)); - new_node->add_input(tmp->name()); - } - - // Set `f` and `Targuments` attributes. - for (auto key : {"f", "Targuments"}) { - (*new_node->mutable_attr())[key] = map_node->attr().at(key); - } - // Set `output_types` and `output_shapes` attributes. - for (auto key : {"output_shapes", "output_types"}) { - (*new_node->mutable_attr())[key] = batch_node.attr().at(key); - } + auto* new_node = + graph.AddNode(make_map_and_batch_node(*map_node, batch_node, &graph)); + graph.ReplaceInput(batch_node, *new_node); // Mark the `Map` and `Batch` nodes for removal. nodes_to_delete.insert(map_node->name()); nodes_to_delete.insert(batch_node.name()); - - graph_utils::ReplaceInput(batch_node, *new_node, &graph); } - TF_RETURN_IF_ERROR(graph_utils::DeleteNodes(nodes_to_delete, output)); + + graph.DeleteNodes(nodes_to_delete); return Status::OK(); } diff --git a/tensorflow/core/grappler/optimizers/data/map_and_batch_fusion_test.cc b/tensorflow/core/grappler/optimizers/data/map_and_batch_fusion_test.cc index 3c1d8d5359..a46c504ac4 100644 --- a/tensorflow/core/grappler/optimizers/data/map_and_batch_fusion_test.cc +++ b/tensorflow/core/grappler/optimizers/data/map_and_batch_fusion_test.cc @@ -27,25 +27,21 @@ namespace { TEST(MapAndBatchFusionTest, FuseMapAndBatchNodesIntoOne) { GrapplerItem item; - GraphDef *graph = &item.graph; - NodeDef *start_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(0, graph, &start_node)); - NodeDef *stop_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(10, graph, &stop_node)); - NodeDef *step_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(1, graph, &step_node)); + MutableGraphView graph(&item.graph); + + NodeDef *start_node = graph_utils::AddScalarConstNode(0, &graph); + NodeDef *stop_node = graph_utils::AddScalarConstNode(10, &graph); + NodeDef *step_node = graph_utils::AddScalarConstNode(1, &graph); std::vector range_inputs(3); range_inputs[0] = start_node->name(); range_inputs[1] = stop_node->name(); range_inputs[2] = step_node->name(); std::vector> range_attrs; - NodeDef *range_node; - TF_ASSERT_OK(graph_utils::AddNode("", "RangeDataset", range_inputs, - range_attrs, graph, &range_node)); - NodeDef *captured_input_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode( - "hello", graph, &captured_input_node)); + NodeDef *range_node = graph_utils::AddNode("", "RangeDataset", range_inputs, + range_attrs, &graph); + NodeDef *captured_input_node = + graph_utils::AddScalarConstNode("hello", &graph); NodeDef *map_node; { @@ -59,13 +55,11 @@ TEST(MapAndBatchFusionTest, FuseMapAndBatchNodesIntoOne) { AttrValue args_attr; SetAttrValue("Targuments", &args_attr); map_attrs[1] = std::make_pair("Targuments", args_attr); - TF_ASSERT_OK(graph_utils::AddNode("", "MapDataset", map_inputs, map_attrs, - graph, &map_node)); + map_node = + graph_utils::AddNode("", "MapDataset", map_inputs, map_attrs, &graph); } - NodeDef *batch_size_node; - TF_ASSERT_OK( - graph_utils::AddScalarConstNode(5, graph, &batch_size_node)); + NodeDef *batch_size_node = graph_utils::AddScalarConstNode(5, &graph); NodeDef *batch_node; { std::vector batch_inputs(2); @@ -78,16 +72,18 @@ TEST(MapAndBatchFusionTest, FuseMapAndBatchNodesIntoOne) { AttrValue types_attr; SetAttrValue("output_types", &types_attr); batch_attrs[1] = std::make_pair("output_types", types_attr); - TF_ASSERT_OK(graph_utils::AddNode("", "BatchDataset", batch_inputs, - batch_attrs, graph, &batch_node)); + batch_node = graph_utils::AddNode("", "BatchDataset", batch_inputs, + batch_attrs, &graph); } MapAndBatchFusion optimizer; GraphDef output; TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output)); - EXPECT_FALSE(graph_utils::ContainsNodeWithName(map_node->name(), output)); - EXPECT_FALSE(graph_utils::ContainsNodeWithName(batch_node->name(), output)); + EXPECT_FALSE( + graph_utils::ContainsGraphNodeWithName(map_node->name(), output)); + EXPECT_FALSE( + graph_utils::ContainsGraphNodeWithName(batch_node->name(), output)); EXPECT_TRUE(graph_utils::ContainsNodeWithOp("MapAndBatchDatasetV2", output)); NodeDef map_and_batch_node = output.node(graph_utils::FindNodeWithOp("MapAndBatchDatasetV2", output)); @@ -96,11 +92,11 @@ TEST(MapAndBatchFusionTest, FuseMapAndBatchNodesIntoOne) { EXPECT_EQ(map_and_batch_node.input(1), map_node->input(1)); EXPECT_EQ(map_and_batch_node.input(2), batch_node->input(1)); NodeDef num_parallel_calls_node = output.node( - graph_utils::FindNodeWithName(map_and_batch_node.input(3), output)); + graph_utils::FindGraphNodeWithName(map_and_batch_node.input(3), output)); EXPECT_EQ(num_parallel_calls_node.attr().at("value").tensor().int64_val(0), 1); NodeDef drop_remainder_node = output.node( - graph_utils::FindNodeWithName(map_and_batch_node.input(4), output)); + graph_utils::FindGraphNodeWithName(map_and_batch_node.input(4), output)); EXPECT_EQ(drop_remainder_node.attr().at("value").tensor().bool_val(0), false); EXPECT_TRUE(AreAttrValuesEqual(map_and_batch_node.attr().at("f"), map_node->attr().at("f"))); @@ -114,25 +110,20 @@ TEST(MapAndBatchFusionTest, FuseMapAndBatchNodesIntoOne) { TEST(MapAndBatchFusionTest, FuseMapAndBatchV2NodesIntoOne) { GrapplerItem item; - GraphDef *graph = &item.graph; - NodeDef *start_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(0, graph, &start_node)); - NodeDef *stop_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(10, graph, &stop_node)); - NodeDef *step_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(1, graph, &step_node)); + MutableGraphView graph(&item.graph); + NodeDef *start_node = graph_utils::AddScalarConstNode(0, &graph); + NodeDef *stop_node = graph_utils::AddScalarConstNode(10, &graph); + NodeDef *step_node = graph_utils::AddScalarConstNode(1, &graph); std::vector range_inputs(3); range_inputs[0] = start_node->name(); range_inputs[1] = stop_node->name(); range_inputs[2] = step_node->name(); std::vector> range_attrs; - NodeDef *range_node; - TF_ASSERT_OK(graph_utils::AddNode("", "RangeDataset", range_inputs, - range_attrs, graph, &range_node)); - NodeDef *captured_input_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode( - "hello", graph, &captured_input_node)); + NodeDef *range_node = graph_utils::AddNode("", "RangeDataset", range_inputs, + range_attrs, &graph); + NodeDef *captured_input_node = + graph_utils::AddScalarConstNode("hello", &graph); NodeDef *map_node; { @@ -146,16 +137,13 @@ TEST(MapAndBatchFusionTest, FuseMapAndBatchV2NodesIntoOne) { AttrValue args_attr; SetAttrValue("Targuments", &args_attr); map_attrs[1] = std::make_pair("Targuments", args_attr); - TF_ASSERT_OK(graph_utils::AddNode("", "MapDataset", map_inputs, map_attrs, - graph, &map_node)); + map_node = + graph_utils::AddNode("", "MapDataset", map_inputs, map_attrs, &graph); } - NodeDef *batch_size_node; - TF_ASSERT_OK( - graph_utils::AddScalarConstNode(5, graph, &batch_size_node)); - NodeDef *drop_remainder_node; - TF_ASSERT_OK( - graph_utils::AddScalarConstNode(true, graph, &drop_remainder_node)); + NodeDef *batch_size_node = graph_utils::AddScalarConstNode(5, &graph); + NodeDef *drop_remainder_node = + graph_utils::AddScalarConstNode(true, &graph); NodeDef *batch_node; { std::vector batch_inputs(3); @@ -169,16 +157,18 @@ TEST(MapAndBatchFusionTest, FuseMapAndBatchV2NodesIntoOne) { AttrValue types_attr; SetAttrValue("output_types", &types_attr); batch_attrs[1] = std::make_pair("output_types", types_attr); - TF_ASSERT_OK(graph_utils::AddNode("", "BatchDatasetV2", batch_inputs, - batch_attrs, graph, &batch_node)); + batch_node = graph_utils::AddNode("", "BatchDatasetV2", batch_inputs, + batch_attrs, &graph); } MapAndBatchFusion optimizer; GraphDef output; TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output)); - EXPECT_FALSE(graph_utils::ContainsNodeWithName(map_node->name(), output)); - EXPECT_FALSE(graph_utils::ContainsNodeWithName(batch_node->name(), output)); + EXPECT_FALSE( + graph_utils::ContainsGraphNodeWithName(map_node->name(), output)); + EXPECT_FALSE( + graph_utils::ContainsGraphNodeWithName(batch_node->name(), output)); EXPECT_TRUE(graph_utils::ContainsNodeWithOp("MapAndBatchDatasetV2", output)); NodeDef map_and_batch_node = output.node(graph_utils::FindNodeWithOp("MapAndBatchDatasetV2", output)); @@ -187,7 +177,7 @@ TEST(MapAndBatchFusionTest, FuseMapAndBatchV2NodesIntoOne) { EXPECT_EQ(map_and_batch_node.input(1), map_node->input(1)); EXPECT_EQ(map_and_batch_node.input(2), batch_node->input(1)); NodeDef num_parallel_calls_node = output.node( - graph_utils::FindNodeWithName(map_and_batch_node.input(3), output)); + graph_utils::FindGraphNodeWithName(map_and_batch_node.input(3), output)); EXPECT_EQ(num_parallel_calls_node.attr().at("value").tensor().int64_val(0), 1); EXPECT_EQ(map_and_batch_node.input(4), batch_node->input(2)); @@ -203,28 +193,22 @@ TEST(MapAndBatchFusionTest, FuseMapAndBatchV2NodesIntoOne) { TEST(MapAndBatchFusionTest, FuseParallelMapAndBatchNodesIntoOne) { GrapplerItem item; - GraphDef *graph = &item.graph; - NodeDef *start_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(0, graph, &start_node)); - NodeDef *stop_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(10, graph, &stop_node)); - NodeDef *step_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(1, graph, &step_node)); + MutableGraphView graph(&item.graph); + NodeDef *start_node = graph_utils::AddScalarConstNode(0, &graph); + NodeDef *stop_node = graph_utils::AddScalarConstNode(10, &graph); + NodeDef *step_node = graph_utils::AddScalarConstNode(1, &graph); std::vector range_inputs(3); range_inputs[0] = start_node->name(); range_inputs[1] = stop_node->name(); range_inputs[2] = step_node->name(); std::vector> range_attrs; - NodeDef *range_node; - TF_ASSERT_OK(graph_utils::AddNode("", "RangeDataset", range_inputs, - range_attrs, graph, &range_node)); - NodeDef *captured_input_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode( - "hello", graph, &captured_input_node)); - NodeDef *num_parallel_calls_node; - TF_ASSERT_OK( - graph_utils::AddScalarConstNode(2, graph, &num_parallel_calls_node)); + NodeDef *range_node = graph_utils::AddNode("", "RangeDataset", range_inputs, + range_attrs, &graph); + NodeDef *captured_input_node = + graph_utils::AddScalarConstNode("hello", &graph); + NodeDef *num_parallel_calls_node = + graph_utils::AddScalarConstNode(2, &graph); NodeDef *map_node; { @@ -239,13 +223,11 @@ TEST(MapAndBatchFusionTest, FuseParallelMapAndBatchNodesIntoOne) { AttrValue args_attr; SetAttrValue("Targuments", &args_attr); map_attrs[1] = std::make_pair("Targuments", args_attr); - TF_ASSERT_OK(graph_utils::AddNode("", "ParallelMapDataset", map_inputs, - map_attrs, graph, &map_node)); + map_node = graph_utils::AddNode("", "ParallelMapDataset", map_inputs, + map_attrs, &graph); } - NodeDef *batch_size_node; - TF_ASSERT_OK( - graph_utils::AddScalarConstNode(5, graph, &batch_size_node)); + NodeDef *batch_size_node = graph_utils::AddScalarConstNode(5, &graph); NodeDef *batch_node; { std::vector batch_inputs(2); @@ -258,16 +240,18 @@ TEST(MapAndBatchFusionTest, FuseParallelMapAndBatchNodesIntoOne) { AttrValue types_attr; SetAttrValue("output_types", &types_attr); batch_attrs[1] = std::make_pair("output_types", types_attr); - TF_ASSERT_OK(graph_utils::AddNode("", "BatchDataset", batch_inputs, - batch_attrs, graph, &batch_node)); + batch_node = graph_utils::AddNode("", "BatchDataset", batch_inputs, + batch_attrs, &graph); } MapAndBatchFusion optimizer; GraphDef output; TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output)); - EXPECT_FALSE(graph_utils::ContainsNodeWithName(map_node->name(), output)); - EXPECT_FALSE(graph_utils::ContainsNodeWithName(batch_node->name(), output)); + EXPECT_FALSE( + graph_utils::ContainsGraphNodeWithName(map_node->name(), output)); + EXPECT_FALSE( + graph_utils::ContainsGraphNodeWithName(batch_node->name(), output)); EXPECT_TRUE(graph_utils::ContainsNodeWithOp("MapAndBatchDatasetV2", output)); NodeDef map_and_batch_node = output.node(graph_utils::FindNodeWithOp("MapAndBatchDatasetV2", output)); @@ -276,11 +260,11 @@ TEST(MapAndBatchFusionTest, FuseParallelMapAndBatchNodesIntoOne) { EXPECT_EQ(map_and_batch_node.input(1), map_node->input(1)); EXPECT_EQ(map_and_batch_node.input(2), batch_node->input(1)); NodeDef num_parallel_calls_node2 = output.node( - graph_utils::FindNodeWithName(map_and_batch_node.input(3), output)); + graph_utils::FindGraphNodeWithName(map_and_batch_node.input(3), output)); EXPECT_EQ(num_parallel_calls_node2.attr().at("value").tensor().int64_val(0), 2); NodeDef drop_remainder_node = output.node( - graph_utils::FindNodeWithName(map_and_batch_node.input(4), output)); + graph_utils::FindGraphNodeWithName(map_and_batch_node.input(4), output)); EXPECT_EQ(drop_remainder_node.attr().at("value").tensor().bool_val(0), false); EXPECT_TRUE(AreAttrValuesEqual(map_and_batch_node.attr().at("f"), map_node->attr().at("f"))); @@ -294,27 +278,21 @@ TEST(MapAndBatchFusionTest, FuseParallelMapAndBatchNodesIntoOne) { TEST(MapAndBatchFusionTest, NoChange) { GrapplerItem item; - GraphDef *graph = &item.graph; + MutableGraphView graph(&item.graph); - NodeDef *start_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(0, graph, &start_node)); - NodeDef *stop_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(10, graph, &stop_node)); - NodeDef *step_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(1, graph, &step_node)); + NodeDef *start_node = graph_utils::AddScalarConstNode(0, &graph); + NodeDef *stop_node = graph_utils::AddScalarConstNode(10, &graph); + NodeDef *step_node = graph_utils::AddScalarConstNode(1, &graph); std::vector range_inputs(3); range_inputs[0] = start_node->name(); range_inputs[1] = stop_node->name(); range_inputs[2] = step_node->name(); std::vector> range_attrs; - NodeDef *range_node; - TF_ASSERT_OK(graph_utils::AddNode("", "RangeDataset", range_inputs, - range_attrs, graph, &range_node)); + NodeDef *range_node = graph_utils::AddNode("", "RangeDataset", range_inputs, + range_attrs, &graph); - NodeDef *batch_size_node; - TF_ASSERT_OK( - graph_utils::AddScalarConstNode(5, graph, &batch_size_node)); + NodeDef *batch_size_node = graph_utils::AddScalarConstNode(5, &graph); std::vector batch_inputs(2); batch_inputs[0] = range_node->name(); batch_inputs[1] = batch_size_node->name(); @@ -325,15 +303,13 @@ TEST(MapAndBatchFusionTest, NoChange) { AttrValue types_attr; SetAttrValue("output_types", &types_attr); batch_attrs[1] = std::make_pair("output_types", types_attr); - NodeDef *batch_node; - TF_ASSERT_OK(graph_utils::AddNode("", "BatchDataset", batch_inputs, - batch_attrs, graph, &batch_node)); + graph_utils::AddNode("", "BatchDataset", batch_inputs, batch_attrs, &graph); MapAndBatchFusion optimizer; GraphDef output; TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output)); - EXPECT_TRUE(graph_utils::Compare(*graph, output)); + EXPECT_TRUE(graph_utils::Compare(*graph.GetGraph(), output)); } } // namespace diff --git a/tensorflow/core/grappler/optimizers/data/noop_elimination.cc b/tensorflow/core/grappler/optimizers/data/noop_elimination.cc index 5670966367..55d57b3b97 100644 --- a/tensorflow/core/grappler/optimizers/data/noop_elimination.cc +++ b/tensorflow/core/grappler/optimizers/data/noop_elimination.cc @@ -18,8 +18,8 @@ limitations under the License. #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/grappler/clusters/cluster.h" -#include "tensorflow/core/grappler/graph_view.h" #include "tensorflow/core/grappler/grappler_item.h" +#include "tensorflow/core/grappler/mutable_graph_view.h" #include "tensorflow/core/grappler/op_types.h" #include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h" #include "tensorflow/core/grappler/optimizers/data/graph_utils.h" @@ -64,18 +64,19 @@ bool IsNoOp(const NodeDef& node, const GraphView& graph) { Status NoOpElimination::Optimize(Cluster* cluster, const GrapplerItem& item, GraphDef* output) { *output = item.graph; - GraphView graph(output); + MutableGraphView graph(output); std::set nodes_to_delete; for (const NodeDef& node : item.graph.node()) { if (!IsNoOp(node, graph)) continue; GraphView::InputPort input_port = graph.GetInputPort(node.name(), 0); NodeDef* const parent = graph.GetRegularFanin(input_port).node; - graph_utils::ReplaceInput(node, *parent, &graph); + graph.ReplaceInput(node, *parent); nodes_to_delete.insert(node.name()); } - TF_RETURN_IF_ERROR(graph_utils::DeleteNodes(nodes_to_delete, output)); + + graph.DeleteNodes(nodes_to_delete); return Status::OK(); } diff --git a/tensorflow/core/grappler/optimizers/data/noop_elimination_test.cc b/tensorflow/core/grappler/optimizers/data/noop_elimination_test.cc index 8628b16ea5..a6cc63edba 100644 --- a/tensorflow/core/grappler/optimizers/data/noop_elimination_test.cc +++ b/tensorflow/core/grappler/optimizers/data/noop_elimination_test.cc @@ -35,36 +35,32 @@ std::vector> GetCommonAttributes() { return commonAttributes; } -void MakeUnaryNode(GraphDef *graph, const std::string &node_type, int count, - string input_node, NodeDef **return_node) { - NodeDef *node_count; - TF_ASSERT_OK( - graph_utils::AddScalarConstNode(count, graph, &node_count)); - TF_ASSERT_OK(graph_utils::AddNode("", node_type, - {std::move(input_node), node_count->name()}, - GetCommonAttributes(), graph, return_node)); +NodeDef *MakeUnaryNode(const std::string &node_type, int count, + string input_node, MutableGraphView *graph) { + NodeDef *node_count = graph_utils::AddScalarConstNode(count, graph); + return graph_utils::AddNode("", node_type, + {std::move(input_node), node_count->name()}, + GetCommonAttributes(), graph); } -void MakeCacheNode(GraphDef *graph, string input_node, NodeDef **return_node) { - NodeDef *node_filename; - TF_ASSERT_OK( - graph_utils::AddScalarConstNode("", graph, &node_filename)); - TF_ASSERT_OK(graph_utils::AddNode( - "", "CacheDataset", {std::move(input_node), node_filename->name()}, - GetCommonAttributes(), graph, return_node)); +NodeDef *MakeCacheNode(string input_node, MutableGraphView *graph) { + NodeDef *node_filename = + graph_utils::AddScalarConstNode("", graph); + return graph_utils::AddNode("", "CacheDataset", + {std::move(input_node), node_filename->name()}, + GetCommonAttributes(), graph); } -void MakeRangeNode(GraphDef *graph, NodeDef **range_node) { - NodeDef *start_node, *stop_node, *step_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(0, graph, &start_node)); - TF_ASSERT_OK(graph_utils::AddScalarConstNode(10, graph, &stop_node)); - TF_ASSERT_OK(graph_utils::AddScalarConstNode(1, graph, &step_node)); +NodeDef *MakeRangeNode(MutableGraphView *graph) { + auto *start_node = graph_utils::AddScalarConstNode(0, graph); + auto *stop_node = graph_utils::AddScalarConstNode(10, graph); + auto *step_node = graph_utils::AddScalarConstNode(1, graph); std::vector range_inputs = {start_node->name(), stop_node->name(), step_node->name()}; - TF_ASSERT_OK(graph_utils::AddNode("", "RangeDataset", range_inputs, - GetCommonAttributes(), graph, range_node)); + return graph_utils::AddNode("", "RangeDataset", range_inputs, + GetCommonAttributes(), graph); } struct NoOpLastEliminationTest @@ -74,23 +70,22 @@ struct NoOpLastEliminationTest // transformations at the end of the pipeline. TEST_P(NoOpLastEliminationTest, EliminateLastNoOpNode) { GrapplerItem item; - GraphDef *graph = &item.graph; + MutableGraphView graph(&item.graph); const std::string &node_type = std::get<0>(GetParam()); const int node_count = std::get<1>(GetParam()); const bool should_keep_node = std::get<2>(GetParam()); - NodeDef *range_node; - MakeRangeNode(graph, &range_node); + NodeDef *range_node = MakeRangeNode(&graph); - NodeDef *node; - MakeUnaryNode(graph, node_type, node_count, range_node->name(), &node); + NodeDef *node = + MakeUnaryNode(node_type, node_count, range_node->name(), &graph); NoOpElimination optimizer; GraphDef output; TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output)); - EXPECT_EQ(graph_utils::ContainsNodeWithName(node->name(), output), + EXPECT_EQ(graph_utils::ContainsGraphNodeWithName(node->name(), output), should_keep_node); } @@ -113,30 +108,29 @@ struct NoOpMiddleEliminationTest // transformations int the middle of the pipeline. TEST_P(NoOpMiddleEliminationTest, EliminateMiddleNoOpNode) { GrapplerItem item; - GraphDef *graph = &item.graph; + MutableGraphView graph(&item.graph); const std::string &node_type = std::get<0>(GetParam()); const int node_count = std::get<1>(GetParam()); const bool should_keep_node = std::get<2>(GetParam()); - NodeDef *range_node; - MakeRangeNode(graph, &range_node); + NodeDef *range_node = MakeRangeNode(&graph); - NodeDef *node; - MakeUnaryNode(graph, node_type, node_count, range_node->name(), &node); + NodeDef *node = + MakeUnaryNode(node_type, node_count, range_node->name(), &graph); - NodeDef *cache_node; - MakeCacheNode(graph, node->name(), &cache_node); + NodeDef *cache_node = MakeCacheNode(node->name(), &graph); NoOpElimination optimizer; GraphDef output; TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output)); - EXPECT_EQ(graph_utils::ContainsNodeWithName(node->name(), output), + EXPECT_EQ(graph_utils::ContainsGraphNodeWithName(node->name(), output), should_keep_node); - EXPECT_TRUE(graph_utils::ContainsNodeWithName(cache_node->name(), output)); + EXPECT_TRUE( + graph_utils::ContainsGraphNodeWithName(cache_node->name(), output)); - NodeDef cache_node_out = - output.node(graph_utils::FindNodeWithName(cache_node->name(), output)); + NodeDef cache_node_out = output.node( + graph_utils::FindGraphNodeWithName(cache_node->name(), output)); EXPECT_EQ(cache_node_out.input_size(), 2); auto last_node_input = (should_keep_node ? node : range_node)->name(); @@ -162,41 +156,40 @@ struct NoOpMultipleEliminationTest : ::testing::TestWithParam {}; // multiple noop nodes. TEST_P(NoOpMultipleEliminationTest, EliminateMultipleNoOpNode) { GrapplerItem item; - GraphDef *graph = &item.graph; + MutableGraphView graph(&item.graph); static_assert(std::tuple_size::value == 2, "Make sure to include everything in the test"); const std::vector> noop_nodes = { std::get<0>(GetParam()), std::get<1>(GetParam())}; - NodeDef *range_node; - MakeRangeNode(graph, &range_node); + NodeDef *range_node = MakeRangeNode(&graph); NodeDef *previous = range_node; std::vector nodes_to_remove; nodes_to_remove.reserve(noop_nodes.size()); for (const auto &noop_node : noop_nodes) { - NodeDef *node; - MakeUnaryNode(graph, noop_node.first, noop_node.second, previous->name(), - &node); + NodeDef *node = MakeUnaryNode(noop_node.first, noop_node.second, + previous->name(), &graph); nodes_to_remove.push_back(node->name()); previous = node; } - NodeDef *cache_node; - MakeCacheNode(graph, previous->name(), &cache_node); + NodeDef *cache_node = MakeCacheNode(previous->name(), &graph); NoOpElimination optimizer; GraphDef output; TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output)); for (const auto &noop_node_name : nodes_to_remove) - EXPECT_FALSE(graph_utils::ContainsNodeWithName(noop_node_name, output)); + EXPECT_FALSE( + graph_utils::ContainsGraphNodeWithName(noop_node_name, output)); - EXPECT_TRUE(graph_utils::ContainsNodeWithName(cache_node->name(), output)); + EXPECT_TRUE( + graph_utils::ContainsGraphNodeWithName(cache_node->name(), output)); - NodeDef cache_node_out = - output.node(graph_utils::FindNodeWithName(cache_node->name(), output)); + NodeDef cache_node_out = output.node( + graph_utils::FindGraphNodeWithName(cache_node->name(), output)); EXPECT_EQ(cache_node_out.input_size(), 2); EXPECT_EQ(cache_node_out.input(0), range_node->name()); diff --git a/tensorflow/core/grappler/optimizers/data/shuffle_and_repeat_fusion.cc b/tensorflow/core/grappler/optimizers/data/shuffle_and_repeat_fusion.cc index 8332fb0b1e..7c7161c5b2 100644 --- a/tensorflow/core/grappler/optimizers/data/shuffle_and_repeat_fusion.cc +++ b/tensorflow/core/grappler/optimizers/data/shuffle_and_repeat_fusion.cc @@ -18,8 +18,8 @@ limitations under the License. #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/framework/node_def.pb.h" #include "tensorflow/core/grappler/clusters/cluster.h" -#include "tensorflow/core/grappler/graph_view.h" #include "tensorflow/core/grappler/grappler_item.h" +#include "tensorflow/core/grappler/mutable_graph_view.h" #include "tensorflow/core/grappler/op_types.h" #include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h" #include "tensorflow/core/grappler/optimizers/data/graph_utils.h" @@ -38,55 +38,62 @@ Status ShuffleAndRepeatFusion::Optimize(Cluster* cluster, const GrapplerItem& item, GraphDef* output) { *output = item.graph; - GraphView graph(output); + MutableGraphView graph(output); std::set nodes_to_delete; - for (const NodeDef& node : item.graph.node()) { - if (node.op() != "RepeatDataset") { - continue; - } - // Use a more descriptive variable name now that we know the node type. - const NodeDef repeat_node(node); - GraphView::InputPort input_port = graph.GetInputPort(repeat_node.name(), 0); - NodeDef* node2 = graph.GetRegularFanin(input_port).node; - if (node2->op() != "ShuffleDataset") { - continue; - } - - NodeDef* new_node = output->add_node(); - new_node->set_op(kFusedOpName); - graph_utils::SetUniqueName(kFusedOpName, output, new_node); - - // Use a more descriptive variable name now that we know the node type. - NodeDef* shuffle_node = node2; + auto make_shuffle_and_repeat_node = [&output](const NodeDef& shuffle_node, + const NodeDef& repeat_node) { + NodeDef new_node; + new_node.set_op(kFusedOpName); + graph_utils::SetUniqueGraphNodeName(kFusedOpName, output, &new_node); // Set the `input` input argument. - new_node->add_input(shuffle_node->input(0)); + new_node.add_input(shuffle_node.input(0)); // Set the `buffer_size` input argument. - new_node->add_input(shuffle_node->input(1)); + new_node.add_input(shuffle_node.input(1)); // Set the `seed` input argument. - new_node->add_input(shuffle_node->input(2)); + new_node.add_input(shuffle_node.input(2)); // Set the `seed2` input argument. - new_node->add_input(shuffle_node->input(3)); + new_node.add_input(shuffle_node.input(3)); // Set the `count` input argument. - new_node->add_input(repeat_node.input(1)); + new_node.add_input(repeat_node.input(1)); // Set `output_types` and `output_shapes` attributes. for (auto key : {"output_shapes", "output_types"}) { - (*new_node->mutable_attr())[key] = repeat_node.attr().at(key); + (*new_node.mutable_attr())[key] = repeat_node.attr().at(key); } + return new_node; + }; + + for (const NodeDef& node : item.graph.node()) { + if (node.op() != "RepeatDataset") { + continue; + } + + // Use a more descriptive variable name now that we know the node type. + const NodeDef& repeat_node = node; + GraphView::InputPort input_port = graph.GetInputPort(repeat_node.name(), 0); + NodeDef* node2 = graph.GetRegularFanin(input_port).node; + if (node2->op() != "ShuffleDataset") { + continue; + } + // Use a more descriptive variable name now that we know the node type. + const NodeDef& shuffle_node = *node2; + + NodeDef* shuffle_and_repeat_node = + graph.AddNode(make_shuffle_and_repeat_node(shuffle_node, repeat_node)); + graph.ReplaceInput(repeat_node, *shuffle_and_repeat_node); // Mark the `Shuffle` and `Repeat` nodes for removal. - nodes_to_delete.insert(shuffle_node->name()); + nodes_to_delete.insert(shuffle_node.name()); nodes_to_delete.insert(repeat_node.name()); - - graph_utils::ReplaceInput(repeat_node, *new_node, &graph); } - TF_RETURN_IF_ERROR(graph_utils::DeleteNodes(nodes_to_delete, output)); + + graph.DeleteNodes(nodes_to_delete); return Status::OK(); } diff --git a/tensorflow/core/grappler/optimizers/data/shuffle_and_repeat_fusion_test.cc b/tensorflow/core/grappler/optimizers/data/shuffle_and_repeat_fusion_test.cc index e89675efb7..a2e470e511 100644 --- a/tensorflow/core/grappler/optimizers/data/shuffle_and_repeat_fusion_test.cc +++ b/tensorflow/core/grappler/optimizers/data/shuffle_and_repeat_fusion_test.cc @@ -27,7 +27,7 @@ namespace { TEST(ShuffleAndRepeatFusionTest, FuseShuffleAndRepeatNodesIntoOne) { GrapplerItem item; - GraphDef *graph = &item.graph; + MutableGraphView graph(&item.graph); std::vector> common_attrs(2); AttrValue shapes_attr; @@ -37,52 +37,44 @@ TEST(ShuffleAndRepeatFusionTest, FuseShuffleAndRepeatNodesIntoOne) { SetAttrValue("output_types", &types_attr); common_attrs[1] = std::make_pair("output_types", types_attr); - NodeDef *start_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(0, graph, &start_node)); - NodeDef *stop_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(10, graph, &stop_node)); - NodeDef *step_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(1, graph, &step_node)); + NodeDef *start_node = graph_utils::AddScalarConstNode(0, &graph); + NodeDef *stop_node = graph_utils::AddScalarConstNode(10, &graph); + NodeDef *step_node = graph_utils::AddScalarConstNode(1, &graph); std::vector range_inputs(3); range_inputs[0] = start_node->name(); range_inputs[1] = stop_node->name(); range_inputs[2] = step_node->name(); - NodeDef *range_node; - TF_ASSERT_OK(graph_utils::AddNode("", "RangeDataset", range_inputs, - common_attrs, graph, &range_node)); - - NodeDef *buffer_size_node; - TF_ASSERT_OK( - graph_utils::AddScalarConstNode(128, graph, &buffer_size_node)); - NodeDef *seed_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(-1, graph, &seed_node)); - NodeDef *seed2_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(-1, graph, &seed2_node)); + NodeDef *range_node = graph_utils::AddNode("", "RangeDataset", range_inputs, + common_attrs, &graph); + + NodeDef *buffer_size_node = + graph_utils::AddScalarConstNode(128, &graph); + NodeDef *seed_node = graph_utils::AddScalarConstNode(-1, &graph); + NodeDef *seed2_node = graph_utils::AddScalarConstNode(-1, &graph); std::vector shuffle_inputs(4); shuffle_inputs[0] = range_node->name(); shuffle_inputs[1] = buffer_size_node->name(); shuffle_inputs[2] = seed_node->name(); shuffle_inputs[3] = seed2_node->name(); - NodeDef *shuffle_node; - TF_ASSERT_OK(graph_utils::AddNode("", "ShuffleDataset", shuffle_inputs, - common_attrs, graph, &shuffle_node)); + NodeDef *shuffle_node = graph_utils::AddNode( + "", "ShuffleDataset", shuffle_inputs, common_attrs, &graph); - NodeDef *count_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(-1, graph, &count_node)); + NodeDef *count_node = graph_utils::AddScalarConstNode(-1, &graph); std::vector repeat_inputs(2); repeat_inputs[0] = shuffle_node->name(); repeat_inputs[1] = count_node->name(); - NodeDef *repeat_node; - TF_ASSERT_OK(graph_utils::AddNode("", "RepeatDataset", repeat_inputs, - common_attrs, graph, &repeat_node)); + NodeDef *repeat_node = graph_utils::AddNode( + "", "RepeatDataset", repeat_inputs, common_attrs, &graph); ShuffleAndRepeatFusion optimizer; GraphDef output; TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output)); - EXPECT_FALSE(graph_utils::ContainsNodeWithName(shuffle_node->name(), output)); - EXPECT_FALSE(graph_utils::ContainsNodeWithName(repeat_node->name(), output)); + EXPECT_FALSE( + graph_utils::ContainsGraphNodeWithName(shuffle_node->name(), output)); + EXPECT_FALSE( + graph_utils::ContainsGraphNodeWithName(repeat_node->name(), output)); EXPECT_TRUE( graph_utils::ContainsNodeWithOp("ShuffleAndRepeatDataset", output)); NodeDef shuffle_and_repeat_node = output.node( @@ -103,7 +95,7 @@ TEST(ShuffleAndRepeatFusionTest, FuseShuffleAndRepeatNodesIntoOne) { TEST(ShuffleAndRepeatFusionTest, NoChange) { GrapplerItem item; - GraphDef *graph = &item.graph; + MutableGraphView graph(&item.graph); std::vector> common_attrs(2); AttrValue shapes_attr; @@ -113,35 +105,29 @@ TEST(ShuffleAndRepeatFusionTest, NoChange) { SetAttrValue("output_types", &types_attr); common_attrs[1] = std::make_pair("output_types", types_attr); - NodeDef *start_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(0, graph, &start_node)); - NodeDef *stop_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(10, graph, &stop_node)); - NodeDef *step_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(1, graph, &step_node)); + NodeDef *start_node = graph_utils::AddScalarConstNode(0, &graph); + NodeDef *stop_node = graph_utils::AddScalarConstNode(10, &graph); + NodeDef *step_node = graph_utils::AddScalarConstNode(1, &graph); std::vector range_inputs(3); range_inputs[0] = start_node->name(); range_inputs[1] = stop_node->name(); range_inputs[2] = step_node->name(); - NodeDef *range_node; - TF_ASSERT_OK(graph_utils::AddNode("", "RangeDataset", range_inputs, - common_attrs, graph, &range_node)); + NodeDef *range_node = graph_utils::AddNode("", "RangeDataset", range_inputs, + common_attrs, &graph); - NodeDef *count_node; - TF_ASSERT_OK(graph_utils::AddScalarConstNode(-1, graph, &count_node)); + NodeDef *count_node = graph_utils::AddScalarConstNode(-1, &graph); std::vector repeat_inputs(2); repeat_inputs[0] = range_node->name(); repeat_inputs[1] = count_node->name(); - NodeDef *repeat_node; - TF_ASSERT_OK(graph_utils::AddNode("", "RepeatDataset", repeat_inputs, - common_attrs, graph, &repeat_node)); + graph_utils::AddNode("", "RepeatDataset", repeat_inputs, common_attrs, + &graph); ShuffleAndRepeatFusion optimizer; GraphDef output; TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output)); - EXPECT_TRUE(graph_utils::Compare(*graph, output)); + EXPECT_TRUE(graph_utils::Compare(*graph.GetGraph(), output)); } } // namespace diff --git a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc index fdd82b9603..bb14ce310d 100644 --- a/tensorflow/core/grappler/optimizers/dependency_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/dependency_optimizer.cc @@ -24,6 +24,7 @@ limitations under the License. #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/topological_sort.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/stringpiece.h" @@ -53,16 +54,6 @@ bool RemoveInput(NodeDef* node, const string& input, NodeMap* node_map) { return removed_input; } -void DeleteNodes(const std::set& nodes_to_delete, GraphDef* graph) { - int last = graph->node_size() - 1; - for (auto it = nodes_to_delete.rbegin(); it != nodes_to_delete.rend(); ++it) { - const int index = *it; - graph->mutable_node()->SwapElements(index, last); - last--; - } - graph->mutable_node()->DeleteSubrange(last + 1, nodes_to_delete.size()); -} - } // namespace bool DependencyOptimizer::SafeToRemoveIdentity(const NodeDef& node) const { @@ -441,7 +432,7 @@ Status DependencyOptimizer::OptimizeDependencies() { if (fetch_nodes_known_) { VLOG(1) << "Deleted " << nodes_to_delete.size() << " out of " << optimized_graph_->node_size() << " nodes."; - DeleteNodes(nodes_to_delete, optimized_graph_); + EraseNodesFromGraph(nodes_to_delete, optimized_graph_); node_map_.reset(new NodeMap(optimized_graph_)); BuildNodeToIdx(); } diff --git a/tensorflow/core/grappler/optimizers/loop_optimizer.cc b/tensorflow/core/grappler/optimizers/loop_optimizer.cc index 9627ed7323..405778222a 100644 --- a/tensorflow/core/grappler/optimizers/loop_optimizer.cc +++ b/tensorflow/core/grappler/optimizers/loop_optimizer.cc @@ -616,15 +616,13 @@ Status RemoveDeadBranches(const std::unordered_set& nodes_to_preserve, } } - int last = optimized_graph->node_size() - 1; - for (int i = optimized_graph->node_size() - 1; i >= 0; --i) { - NodeDef* node = optimized_graph->mutable_node(i); - if (dead_nodes.find(node) != dead_nodes.end()) { - optimized_graph->mutable_node()->SwapElements(i, last); - last--; - } + std::vector nodes_idx_to_delete; + nodes_idx_to_delete.reserve(dead_nodes.size()); + for (int i = 0; i < optimized_graph->node_size(); ++i) { + if (dead_nodes.count(&optimized_graph->node(i))) + nodes_idx_to_delete.push_back(i); } - optimized_graph->mutable_node()->DeleteSubrange(last + 1, dead_nodes.size()); + EraseNodesFromGraph(std::move(nodes_idx_to_delete), optimized_graph); for (const auto& itr : dead_merge_inputs) { NodeDef* dead_node = itr.first; diff --git a/tensorflow/core/grappler/utils.cc b/tensorflow/core/grappler/utils.cc index c8e63f95e1..153785d3b4 100644 --- a/tensorflow/core/grappler/utils.cc +++ b/tensorflow/core/grappler/utils.cc @@ -13,7 +13,10 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ +#include "tensorflow/core/grappler/utils.h" + #include +#include #include #include "tensorflow/core/framework/attr_value.pb.h" @@ -21,7 +24,6 @@ limitations under the License. #include "tensorflow/core/framework/op.h" #include "tensorflow/core/framework/op_def.pb.h" #include "tensorflow/core/framework/types.h" -#include "tensorflow/core/grappler/utils.h" #include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/lib/strings/scanner.h" #include "tensorflow/core/lib/strings/strcat.h" @@ -354,13 +356,51 @@ void DedupControlInputs(NodeDef* node) { } namespace { + +template +void EraseNodesFromGraphImpl(const UniqueContainer& nodes_to_delete, + GraphDef* graph) { + static_assert(std::is_same::value, + "Need to pass container of ints"); + + int last = graph->node_size() - 1; + for (auto it = nodes_to_delete.rbegin(); it != nodes_to_delete.rend(); ++it) { + const int index = *it; + graph->mutable_node()->SwapElements(index, last); + last--; + } + graph->mutable_node()->DeleteSubrange(last + 1, nodes_to_delete.size()); +} + template inline void STLSortAndRemoveDuplicates(T* v) { std::sort(v->begin(), v->end()); v->erase(std::unique(v->begin(), v->end()), v->end()); } + } // namespace +void EraseNodesFromGraph(const std::set& nodes_to_delete, + GraphDef* graph) { + EraseNodesFromGraphImpl(nodes_to_delete, graph); +} + +void EraseNodesFromGraph(std::vector&& nodes_to_delete, GraphDef* graph) { + STLSortAndRemoveDuplicates(&nodes_to_delete); + EraseNodesFromGraphImpl(nodes_to_delete, graph); +} + +void EraseNodesFromGraph(const std::set& nodes_to_delete, + GraphDef* graph) { + std::vector nodes_idx_to_delete; + nodes_idx_to_delete.reserve(nodes_to_delete.size()); + for (int i = 0; i < graph->node_size(); ++i) { + if (nodes_to_delete.count(graph->node(i).name())) + nodes_idx_to_delete.push_back(i); + } + EraseNodesFromGraphImpl(nodes_idx_to_delete, graph); +} + Status SimpleGraphView::Initialize( const GraphDef& graph, const std::vector>* diff --git a/tensorflow/core/grappler/utils.h b/tensorflow/core/grappler/utils.h index 1c6fef59ea..b297caa8d4 100644 --- a/tensorflow/core/grappler/utils.h +++ b/tensorflow/core/grappler/utils.h @@ -209,6 +209,13 @@ void PermuteNodesInPlace(GraphDef* graph, std::vector* permutation, Status SetTensorValue(DataType dtype, int value, Tensor* tensor); +void EraseNodesFromGraph(const std::set& nodes_to_delete, GraphDef* graph); + +void EraseNodesFromGraph(std::vector&& nodes_to_delete, GraphDef* graph); + +void EraseNodesFromGraph(const std::set& nodes_to_delete, + GraphDef* graph); + class SimpleGraphView { public: // Build a graph view for the specified graphdef. @@ -237,6 +244,9 @@ class SimpleGraphView { DCHECK(it != name_to_index_.end()); return it == name_to_index_.end() ? -1 : it->second; } + inline const NodeDef& node(int node_idx) const { + return graph_->node(node_idx); + } inline const string& node_name(int node_idx) const { return index_to_name_[node_idx]; } diff --git a/tensorflow/core/grappler/utils_test.cc b/tensorflow/core/grappler/utils_test.cc index 49a1996d25..c6e035834c 100644 --- a/tensorflow/core/grappler/utils_test.cc +++ b/tensorflow/core/grappler/utils_test.cc @@ -16,7 +16,9 @@ limitations under the License. #include "tensorflow/core/grappler/utils.h" #include "tensorflow/cc/ops/standard_ops.h" #include "tensorflow/core/framework/node_def.pb.h" +#include "tensorflow/core/grappler/grappler_item.h" #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/lib/core/threadpool.h" #include "tensorflow/core/platform/env.h" #include "tensorflow/core/platform/notification.h" @@ -333,7 +335,9 @@ TEST_F(UtilsTest, NumNonControlOutputs) { EXPECT_EQ(1, NumNonControlDataOutputs(*add_node, node_map)); } -TEST_F(UtilsTest, DeleteNodes) {} +TEST_F(UtilsTest, DeleteNodes) { + // TODO(rmlarsen): write forgtten test. +} } // namespace } // namespace grappler -- GitLab From 9671996af3a17396440b099442eb6bff33607a54 Mon Sep 17 00:00:00 2001 From: Katherine Wu Date: Wed, 25 Jul 2018 13:50:34 -0700 Subject: [PATCH 408/519] Add estimator in contrib that loads its model function from a SavedModel. PiperOrigin-RevId: 206048542 --- tensorflow/contrib/estimator/BUILD | 41 ++ tensorflow/contrib/estimator/__init__.py | 5 + .../python/estimator/saved_model_estimator.py | 445 ++++++++++++++++++ .../estimator/saved_model_estimator_test.py | 369 +++++++++++++++ tensorflow/python/estimator/estimator.py | 59 ++- tensorflow/python/framework/importer.py | 2 +- tensorflow/python/framework/meta_graph.py | 68 ++- tensorflow/python/saved_model/loader_impl.py | 13 +- tensorflow/python/saved_model/loader_test.py | 19 +- tensorflow/python/training/saver.py | 28 +- 10 files changed, 1019 insertions(+), 30 deletions(-) create mode 100644 tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py create mode 100644 tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py diff --git a/tensorflow/contrib/estimator/BUILD b/tensorflow/contrib/estimator/BUILD index 1aa3df8d8d..349f48f7f7 100644 --- a/tensorflow/contrib/estimator/BUILD +++ b/tensorflow/contrib/estimator/BUILD @@ -28,6 +28,7 @@ py_library( ":multi_head", ":replicate_model_fn", ":rnn", + ":saved_model_estimator", "//tensorflow:tensorflow_py_no_contrib", ], ) @@ -465,3 +466,43 @@ py_test( "@absl_py//absl/testing:parameterized", ], ) + +py_library( + name = "saved_model_estimator", + srcs = ["python/estimator/saved_model_estimator.py"], + deps = [ + ":export", + "//tensorflow/python:framework_ops", + "//tensorflow/python:platform", + "//tensorflow/python:training", + "//tensorflow/python/estimator", + "//tensorflow/python/estimator:export", + "//tensorflow/python/estimator:model_fn", + "//tensorflow/python/saved_model", + ], +) + +py_test( + name = "saved_model_estimator_test", + size = "medium", + srcs = ["python/estimator/saved_model_estimator_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":export", + ":saved_model_estimator", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:framework_ops", + "//tensorflow/python:metrics", + "//tensorflow/python:platform", + "//tensorflow/python:state_ops", + "//tensorflow/python:training", + "//tensorflow/python:variables", + "//tensorflow/python/data/ops:dataset_ops", + "//tensorflow/python/estimator", + "//tensorflow/python/estimator:export_export", + "//tensorflow/python/estimator:export_output", + "//tensorflow/python/estimator:model_fn", + ], +) diff --git a/tensorflow/contrib/estimator/__init__.py b/tensorflow/contrib/estimator/__init__.py index 09fcfd66a1..e1453ae1d0 100644 --- a/tensorflow/contrib/estimator/__init__.py +++ b/tensorflow/contrib/estimator/__init__.py @@ -33,6 +33,8 @@ from tensorflow.contrib.estimator.python.estimator.logit_fns import * from tensorflow.contrib.estimator.python.estimator.multi_head import * from tensorflow.contrib.estimator.python.estimator.replicate_model_fn import * from tensorflow.contrib.estimator.python.estimator.rnn import * +from tensorflow.contrib.estimator.python.estimator.saved_model_estimator import * +from tensorflow.python.estimator.export.export import * from tensorflow.python.util.all_util import remove_undocumented # pylint: enable=unused-import,line-too-long,wildcard-import @@ -70,6 +72,9 @@ _allowed_symbols = [ 'stop_if_higher_hook', 'stop_if_no_increase_hook', 'stop_if_no_decrease_hook', + 'build_raw_supervised_input_receiver_fn', + 'build_supervised_input_receiver_fn_from_input_fn', + 'SavedModelEstimator' ] remove_undocumented(__name__, allowed_exception_list=_allowed_symbols) diff --git a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py new file mode 100644 index 0000000000..f3d0f6b047 --- /dev/null +++ b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py @@ -0,0 +1,445 @@ +# 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. +# ============================================================================== +"""Class that creates an Estimator from a SavedModel.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import six + +from tensorflow.python.estimator import estimator as estimator_lib +from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator.export import export as export_lib +from tensorflow.python.estimator.export import export_output +from tensorflow.python.framework import dtypes +from tensorflow.python.framework import ops +from tensorflow.python.framework import tensor_shape +from tensorflow.python.platform import tf_logging as logging +from tensorflow.python.saved_model import constants +from tensorflow.python.saved_model import loader_impl +from tensorflow.python.saved_model import signature_constants +from tensorflow.python.training import checkpoint_utils +from tensorflow.python.training import monitored_session +from tensorflow.python.training import training_util + + +class SavedModelEstimator(estimator_lib.Estimator): + """Create an Estimator from a SavedModel. + + Only SavedModels exported with + `tf.contrib.estimator.export_all_saved_models()` or + `tf.estimator.Estimator.export_savedmodel()` are supported for this class. + + Example with `tf.estimator.DNNClassifier`: + + **Step 1: Create and train DNNClassifier.** + ```python + feature1 = tf.feature_column.embedding_column( + tf.feature_column.categorical_column_with_vocabulary_list( + key='feature1', vocabulary_list=('green', 'yellow')), dimension=1) + feature2 = tf.feature_column.numeric_column(key='feature2', default_value=0.0) + + classifier = tf.estimator.DNNClassifier( + hidden_units=[4,2], feature_columns=[feature1, feature2]) + + def input_fn(): + features = {'feature1': tf.constant(['green', 'green', 'yellow']), + 'feature2': tf.constant([3.5, 4.2, 6.1])} + label = tf.constant([1., 0., 0.]) + return tf.data.Dataset.from_tensors((features, label)).repeat() + + classifier.train(input_fn=input_fn, steps=10) + ``` + + **Step 2: Export classifier.** + First, build functions that specify the expected inputs. + ```python + # During train and evaluation, both the features and labels should be defined. + supervised_input_receiver_fn = ( + tf.contrib.estimator.build_raw_supervised_input_receiver_fn( + {'feature1': tf.placeholder(dtype=tf.string, shape=[None]), + 'feature2': tf.placeholder(dtype=tf.float32, shape=[None])}, + tf.placeholder(dtype=tf.float32, shape=[None]))) + + # During predict mode, expect to receive a `tf.Example` proto, so a parsing + # function is used. + serving_input_receiver_fn = ( + tf.estimator.export.build_parsing_serving_input_receiver_fn( + tf.feature_column.make_parse_example_spec([feature1, feature2]))) + ``` + + Next, export the model as a SavedModel. A timestamped directory will be + created (for example `/tmp/export_all/1234567890`). + ```python + # Option 1: Save all modes (train, eval, predict) + export_dir = tf.contrib.estimator.export_all_saved_models( + classifier, '/tmp/export_all', + {tf.estimator.ModeKeys.TRAIN: supervised_input_receiver_fn, + tf.estimator.ModeKeys.EVAL: supervised_input_receiver_fn, + tf.estimator.ModeKeys.PREDICT: serving_input_receiver_fn}) + + # Option 2: Only export predict mode + export_dir = classifier.export_savedmodel( + '/tmp/export_predict', serving_input_receiver_fn) + ``` + + **Step 3: Create a SavedModelEstimator from the exported SavedModel.** + ```python + est = tf.contrib.estimator.SavedModelEstimator(export_dir) + + # If all modes were exported, you can immediately evaluate and predict, or + # continue training. Otherwise only predict is available. + eval_results = est.evaluate(input_fn=input_fn, steps=1) + print(eval_results) + + est.train(input_fn=input_fn, steps=20) + + def predict_input_fn(): + example = example_pb2.Example() + example.features.feature['feature1'].bytes_list.value.extend(['yellow']) + example.features.feature['feature2'].float_list.value.extend([1.]) + return {'inputs':tf.constant([example.SerializeToString()])} + + predictions = est.predict(predict_input_fn) + print(next(predictions)) + ``` + """ + + def __init__(self, saved_model_dir, model_dir=None): + """Initialize a SavedModelEstimator. + + The SavedModelEstimator loads its model function and variable values from + the graphs defined in the SavedModel. There is no option to pass in + `RunConfig` or `params` arguments, because the model function graph is + defined statically in the SavedModel. + + Args: + saved_model_dir: Directory containing SavedModel protobuf and subfolders. + model_dir: Directory to save new checkpoints during training. + + Raises: + NotImplementedError: If a DistributionStrategy is defined in the config. + Unless the SavedModelEstimator is subclassed, this shouldn't happen. + """ + checkpoint = estimator_lib._get_saved_model_ckpt(saved_model_dir) # pylint: disable=protected-access + vars_to_warm_start = [name for name, _ in + checkpoint_utils.list_variables(checkpoint)] + warm_start_settings = estimator_lib.WarmStartSettings( + ckpt_to_initialize_from=checkpoint, + vars_to_warm_start=vars_to_warm_start) + + super(SavedModelEstimator, self).__init__( + model_fn=self._model_fn_from_saved_model, model_dir=model_dir, + warm_start_from=warm_start_settings) + if self._distribution is not None: + raise NotImplementedError( + 'SavedModelEstimator currently does not support ' + 'DistributionStrategy.') + self.saved_model_dir = saved_model_dir + self.saved_model_loader = loader_impl.SavedModelLoader(saved_model_dir) + self._available_modes = self._extract_available_modes() + + def _extract_available_modes(self): + """Return list of modes found in SavedModel.""" + available_modes = [] + logging.info('Checking available modes for SavedModelEstimator.') + for mode in [model_fn_lib.ModeKeys.TRAIN, model_fn_lib.ModeKeys.EVAL, + model_fn_lib.ModeKeys.PREDICT]: + try: + self._get_meta_graph_def_for_mode(mode) + except RuntimeError: + logging.warning('%s mode not found in SavedModel.' % mode) + continue + + if self._get_signature_def_for_mode(mode) is not None: + available_modes.append(mode) + + logging.info('Available modes for Estimator: %s' % available_modes) + return available_modes + + def _validate_mode(self, mode): + """Make sure that mode can be run using the SavedModel.""" + if mode not in self._available_modes: + raise RuntimeError('%s mode is not available in the SavedModel. Use ' + 'saved_model_cli to check that the Metagraph for this ' + 'mode has been exported.' % mode) + + def _get_meta_graph_def_for_mode(self, mode): + tags = model_fn_lib.EXPORT_TAG_MAP[mode] + return self.saved_model_loader.get_meta_graph_def_from_tags(tags) + + def _get_signature_def_for_mode(self, mode): + meta_graph_def = self._get_meta_graph_def_for_mode(mode) + sig_def_key = (signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY + if mode == model_fn_lib.ModeKeys.PREDICT else mode) + if sig_def_key not in meta_graph_def.signature_def: + logging.warning('Metagraph for mode %s was found, but SignatureDef with' + ' key \"%s\" is missing.' % (mode, sig_def_key)) + return None + return meta_graph_def.signature_def[sig_def_key] + + def _create_and_assert_global_step(self, graph): + # Do nothing here. The global step variable will be created/loaded from the + # SavedModel. If a global step variable were created here, the result + # will be two duplicate global step variables, causing issues during + # the warm-start phase. + # Due to the global variable being created in the model function, this may + # cause issues when running DistributionStrategy. Thus, DistributionStrategy + # is not yet supported with SavedModelEstimator. + return None + + def _model_fn_from_saved_model(self, features, labels, mode): + """Load a SavedModel graph and return an EstimatorSpec.""" + # TODO(kathywu): Model function loads placeholders from the graph. Calling + # export_all_saved_models creates another placeholder for the inputs, on top + # of the original placeholders. There should be a way to avoid this. + self._validate_mode(mode) + + g = ops.get_default_graph() + if training_util.get_global_step(g) is not None: + raise RuntimeError( + 'Graph must not contain a global step tensor before the SavedModel is' + ' loaded. Please make sure that the input function does not create a ' + 'global step.') + + # Extract SignatureDef for information about the input and output tensors. + signature_def = self._get_signature_def_for_mode(mode) + + # Generate input map for replacing the inputs in the SavedModel graph with + # the provided features and labels. + input_map = _generate_input_map(signature_def, features, labels) + + # Create a list of the names of output tensors. When the graph is loaded, + # names of the output tensors may be remapped. This ensures that the correct + # tensors are returned in the EstimatorSpec. + output_tensor_names = [ + value.name for value in six.itervalues(signature_def.outputs)] + + # Load the graph. `output_tensors` contains output `Tensors` in the same + # same order as the `output_tensor_names` list. + tags = model_fn_lib.EXPORT_TAG_MAP[mode] + _, output_tensors = self.saved_model_loader.load_graph( + g, tags, input_map=input_map, return_elements=output_tensor_names) + + # Create a scaffold from the MetaGraphDef that contains ops to initialize + # the graph. This should mirror the steps from _add_meta_graph_for_mode(), + # which creates a MetaGraphDef from the EstimatorSpec's scaffold. + scaffold = monitored_session.Scaffold( + local_init_op=loader_impl._get_main_op_tensor( # pylint: disable=protected-access + self._get_meta_graph_def_for_mode(mode))) + + # Ensure that a global step tensor has been created. + global_step_tensor = training_util.get_global_step(g) + training_util.assert_global_step(global_step_tensor) + + # Extract values to return in the EstimatorSpec. + output_map = dict(zip(output_tensor_names, output_tensors)) + outputs = {key: output_map[value.name] + for key, value in six.iteritems(signature_def.outputs)} + + loss, predictions, metrics = _validate_and_extract_outputs( + mode, outputs, signature_def.method_name) + + train_op = ops.get_collection(constants.TRAIN_OP_KEY) + if len(train_op) > 1: + raise RuntimeError('Multiple ops found in the train_op collection.') + train_op = None if not train_op else train_op[0] + + _clear_saved_model_collections() + return model_fn_lib.EstimatorSpec( + scaffold=scaffold, + mode=mode, + loss=loss, + train_op=train_op, + predictions=predictions, + eval_metric_ops=metrics) + + +def _clear_saved_model_collections(): + """Clear collections that are expected empty when exporting a SavedModel. + + The SavedModel builder uses these collections to track ops necessary to + restore the graph state. These collections are expected to be empty before + MetaGraphs are added to the builder. + """ + del ops.get_collection_ref(constants.ASSETS_KEY)[:] + del ops.get_collection_ref(constants.LEGACY_INIT_OP_KEY)[:] + del ops.get_collection_ref(constants.MAIN_OP_KEY)[:] + del ops.get_collection_ref(constants.TRAIN_OP_KEY)[:] + + +def _generate_input_map(signature_def, features, labels): + """Return dict mapping an input tensor name to a feature or label tensor. + + Args: + signature_def: SignatureDef loaded from SavedModel + features: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or + `SparseTensor`, specifying the features to be passed to the model. + labels: A `Tensor`, `SparseTensor`, or dict of string to `Tensor` or + `SparseTensor`, specifying the labels to be passed to the model. May be + `None`. + + Returns: + dict mapping string names of inputs to features or labels tensors + + Raises: + ValueError: if SignatureDef inputs are not completely mapped by the input + features and labels. + """ + # pylint: disable=protected-access + if not isinstance(features, dict): + features = {export_lib._SINGLE_FEATURE_DEFAULT_NAME: features} + if labels is not None and not isinstance(labels, dict): + labels = {export_lib._SINGLE_LABEL_DEFAULT_NAME: labels} + # pylint: enable=protected-access + + inputs = signature_def.inputs + input_map = {} + for key, tensor_info in six.iteritems(inputs): + input_name = tensor_info.name + if ':' in input_name: + input_name = input_name[:input_name.find(':')] + + # When tensors are used as control inputs for operations, their names are + # prepended with a '^' character in the GraphDef. To handle possible control + # flow edge cases, control input names must be included in the input map. + control_dependency_name = '^' + input_name + + if key in features: + _check_same_dtype_and_shape(features[key], tensor_info, key) + input_map[input_name] = input_map[control_dependency_name] = features[key] + elif labels is not None and key in labels: + _check_same_dtype_and_shape(labels[key], tensor_info, key) + input_map[input_name] = input_map[control_dependency_name] = labels[key] + else: + raise ValueError( + 'Key \"%s\" not found in features or labels passed in to the model ' + 'function. All required keys: %s' % (key, inputs.keys())) + + return input_map + + +def _check_same_dtype_and_shape(tensor, tensor_info, name): + """Validate that tensor has the same properties as the TensorInfo proto. + + Args: + tensor: a `Tensor` object. + tensor_info: a `TensorInfo` proto. + name: Name of the input (to identify Tensor if an error is raised). + + Raises: + ValueError: If the tensor shape or dtype don't match the TensorInfo + """ + dtype_error = (tensor.dtype != dtypes.DType(tensor_info.dtype)) + shape_error = not tensor.shape.is_compatible_with(tensor_info.tensor_shape) + + if dtype_error or shape_error: + msg = 'Tensor shape and/or dtype validation failed for input %s:' % name + if dtype_error: + msg += ('\n\tExpected dtype: %s, Got: %s' + % (dtypes.DType(tensor_info.dtype), tensor.dtype)) + if shape_error: + msg += ('\n\tExpected shape: %s, Got: %s' + % (tensor_shape.TensorShape(tensor_info.tensor_shape), + tensor.shape)) + + raise ValueError(msg) + + +def _extract_eval_metrics(output_dict): + """Return a eval metric dict extracted from the output_dict. + + Eval metrics consist of a value tensor and an update op. Both must be in the + passed-in tensor dictionary for an eval metric to be added to the returned + dictionary. + + Args: + output_dict: a dict that maps strings to tensors. + + Returns: + dict mapping strings to (value, update_op) tuples. + """ + # pylint: disable=protected-access + metric_ops = {} + separator_char = export_output._SupervisedOutput._SEPARATOR_CHAR + + for key, tensor in six.iteritems(output_dict): + split_key = key.split(separator_char) + + # The metric name may contain the separator character, so recreate its name. + metric_name = separator_char.join(split_key[:-1]) + + if split_key[0] == export_output._SupervisedOutput.METRICS_NAME: + # If the key ends with the value suffix, and there is a corresponding + # key ending with the update_op suffix, then add tensors to metrics dict. + if split_key[-1] == export_output._SupervisedOutput.METRIC_VALUE_SUFFIX: + update_op = ''.join( + [metric_name, separator_char, + export_output._SupervisedOutput.METRIC_UPDATE_SUFFIX]) + if update_op in output_dict: + update_op_tensor = output_dict[update_op] + metric_ops[metric_name] = (tensor, update_op_tensor) + + # pylint: enable=protected-access + return metric_ops + + +def _validate_and_extract_outputs(mode, output_dict, method_name): + """Extract values from SignatureDef output dictionary. + + Args: + mode: One of the modes enumerated in `tf.estimator.ModeKeys`. + output_dict: dict of string SignatureDef keys to `Tensor`. + method_name: Method name of the SignatureDef as a string. + + Returns: + Tuple of ( + loss: `Tensor` object, + predictions: dictionary mapping string keys to `Tensor` objects, + metrics: dictionary mapping string keys to a tuple of two `Tensor` objects + ) + + Raises: + RuntimeError: raised if SignatureDef has an invalid method name for the mode + """ + # pylint: disable=protected-access + loss, predictions, metrics = None, None, None + + if mode == model_fn_lib.ModeKeys.PREDICT: + predictions = output_dict + else: + # Validate that the SignatureDef's method name matches the expected name for + # the given mode. + expected_method_name = signature_constants.SUPERVISED_TRAIN_METHOD_NAME + if mode == model_fn_lib.ModeKeys.EVAL: + expected_method_name = signature_constants.SUPERVISED_EVAL_METHOD_NAME + if method_name != expected_method_name: + raise RuntimeError( + 'Invalid SignatureDef method name for mode %s.\n\tExpected: %s\n\t' + 'Got: %s\nPlease ensure that the SavedModel was exported with ' + '`tf.contrib.estimator.export_all_saved_models()`.' % + (mode, expected_method_name, method_name)) + + # Extract loss, metrics and predictions from the output dict. + loss = output_dict[export_output._SupervisedOutput.LOSS_NAME] + metrics = _extract_eval_metrics(output_dict) + predictions = { + key: value for key, value in six.iteritems(output_dict) + if key.split(export_output._SupervisedOutput._SEPARATOR_CHAR)[0] == ( + export_output._SupervisedOutput.PREDICTIONS_NAME)} + + # pylint: enable=protected-access + return loss, predictions, metrics diff --git a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py new file mode 100644 index 0000000000..718da1367c --- /dev/null +++ b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator_test.py @@ -0,0 +1,369 @@ +# 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 SavedModelEstimator.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import shutil +import tempfile + +from tensorflow.contrib.estimator.python.estimator import export as contrib_export +from tensorflow.contrib.estimator.python.estimator import saved_model_estimator +from tensorflow.python.data.ops import dataset_ops +from tensorflow.python.estimator import estimator +from tensorflow.python.estimator import model_fn as model_fn_lib +from tensorflow.python.estimator.export import export +from tensorflow.python.estimator.export import export_output +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 control_flow_ops +from tensorflow.python.ops import metrics as metrics_lib +from tensorflow.python.ops import state_ops +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import monitored_session +from tensorflow.python.training import training + + +def dummy_input_fn(): + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([[1], [-2]], dtype=dtypes.int64)}, + constant_op.constant([[4], [-3]], dtype=dtypes.float32))).repeat() + + +def dummy_input_fn_features_only(): + return dataset_ops.Dataset.from_tensors( + {'x': constant_op.constant([[5], [6]], dtype=dtypes.int64)}).repeat() + + +def dummy_supervised_receiver_fn(): + feature_spec = { + 'x': array_ops.placeholder( + dtype=dtypes.int64, shape=(2, 1), name='feature_x'), + } + label_spec = array_ops.placeholder( + dtype=dtypes.float32, shape=[2, 1], name='truth') + return export.build_raw_supervised_input_receiver_fn( + feature_spec, label_spec) + + +def dummy_serving_receiver_fn(): + feature_spec = {'x': array_ops.placeholder( + dtype=dtypes.int64, shape=(2, 1), name='feature_x'),} + return export.build_raw_serving_input_receiver_fn(feature_spec) + + +def model_fn_diff_modes(features, labels, mode): + _, _ = features, labels + v = variables.Variable(21, name='some_var') + train_op = None + loss = constant_op.constant(104) + if mode == model_fn_lib.ModeKeys.TRAIN: + loss = constant_op.constant(105) + predictions = constant_op.constant([501]) + train_op = control_flow_ops.group( + state_ops.assign_add(training.get_global_step(), 1), + state_ops.assign_add(v, 3)) + elif mode == model_fn_lib.ModeKeys.EVAL: + loss = constant_op.constant(106) + predictions = constant_op.constant([502]) + else: + loss = constant_op.constant(107) + predictions = constant_op.constant([503]) + return model_fn_lib.EstimatorSpec( + mode, + loss=loss, + train_op=train_op, + eval_metric_ops={ + 'abs_err': metrics_lib.mean_absolute_error( + constant_op.constant(0), predictions)}, + predictions=predictions) + + +class SavedModelEstimatorTest(test.TestCase): + + def setUp(self): + self.tmpdirs = [] + + def tearDown(self): + for tmpdir in self.tmpdirs: + # gfile.DeleteRecursively fails in the windows cmake test, so use shutil. + shutil.rmtree(tmpdir, ignore_errors=True) + self.tmpdirs = [] + + def _get_tmp_dir(self): + tmpdir = tempfile.mkdtemp() + self.tmpdirs.append(tmpdir) + return tmpdir + + def _export_estimator(self, train=True, evaluate=True, predict=True, + model_fn=model_fn_diff_modes): + est = estimator.Estimator(model_fn, self._get_tmp_dir()) + est.train(input_fn=dummy_input_fn, steps=10) + + input_receiver_fn_map = {} + if train: + input_receiver_fn_map[model_fn_lib.ModeKeys.TRAIN] = ( + dummy_supervised_receiver_fn()) + if evaluate: + input_receiver_fn_map[model_fn_lib.ModeKeys.EVAL] = ( + dummy_supervised_receiver_fn()) + if predict: + input_receiver_fn_map[model_fn_lib.ModeKeys.PREDICT] = ( + dummy_serving_receiver_fn()) + + export_base_path = self._get_tmp_dir() + export_dir = contrib_export.export_all_saved_models( + est, export_base_path, input_receiver_fn_map) + return export_dir + + def test_load_all_modes(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + sme.train(input_fn=dummy_input_fn, steps=1) + sme.train(input_fn=dummy_input_fn, steps=2) + self.assertEqual(13, sme.get_variable_value('global_step')) + self.assertEqual(60, sme.get_variable_value('some_var')) + + eval_results = sme.evaluate(dummy_input_fn, steps=5) + + self.assertEqual(13, eval_results['global_step']) + self.assertEqual(106, eval_results['loss']) + self.assertEqual(502, eval_results['metrics/abs_err']) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_load_all_modes_no_train(self): + """Ensure that all functions can be used without requiring a ckpt.""" + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + eval_results = sme.evaluate(dummy_input_fn, steps=5) + self.assertEqual(10, eval_results['global_step']) + self.assertEqual(106, eval_results['loss']) + self.assertEqual(502, eval_results['metrics/abs_err']) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_partial_exported_estimator(self): + sme1 = saved_model_estimator.SavedModelEstimator( + self._export_estimator(train=False, predict=False), self._get_tmp_dir()) + sme1.evaluate(dummy_input_fn, steps=5) + with self.assertRaisesRegexp(RuntimeError, 'train mode is not available'): + sme1.train(input_fn=dummy_input_fn, steps=1) + with self.assertRaisesRegexp(RuntimeError, 'infer mode is not available'): + next(sme1.predict(dummy_input_fn_features_only)) + + sme2 = saved_model_estimator.SavedModelEstimator( + self._export_estimator(evaluate=False), self._get_tmp_dir()) + sme2.train(input_fn=dummy_input_fn, steps=1) + next(sme2.predict(dummy_input_fn_features_only)) + with self.assertRaisesRegexp(RuntimeError, 'eval mode is not available'): + sme2.evaluate(dummy_input_fn, steps=5) + + def test_with_incorrect_input(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + + def bad_shape_input_fn(): + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([1, 2], dtype=dtypes.int64)}, + constant_op.constant([1, 2], dtype=dtypes.float32))) + + with self.assertRaisesRegexp(ValueError, 'Expected shape'): + sme.train(bad_shape_input_fn, steps=1) + + def bad_dtype_input_fn(): + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([[1], [1]], dtype=dtypes.int32)}, + constant_op.constant([[1], [1]], dtype=dtypes.int64))) + + with self.assertRaisesRegexp(ValueError, 'Expected dtype'): + sme.train(bad_dtype_input_fn, steps=1) + + def test_input_fn_with_global_step(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + + def bad_input_fn(): + training.get_or_create_global_step() + return dataset_ops.Dataset.from_tensors(( + {'x': constant_op.constant([[1], [1]], dtype=dtypes.int64)}, + constant_op.constant([[1], [1]], dtype=dtypes.float32))) + + with self.assertRaisesRegexp(RuntimeError, + 'Graph must not contain a global step tensor'): + sme.train(bad_input_fn, steps=1) + + def test_re_export_saved_model_serving_only(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + sme.train(dummy_input_fn, steps=3) + self.assertEqual(13, sme.get_variable_value('global_step')) + self.assertEqual(60, sme.get_variable_value('some_var')) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + # Export SavedModel, and test that the variable and prediction values are + # the same. + sme_export_dir = sme.export_savedmodel( + self._get_tmp_dir(), dummy_serving_receiver_fn()) + + sme2 = saved_model_estimator.SavedModelEstimator( + sme_export_dir, self._get_tmp_dir()) + self.assertEqual(60, sme.get_variable_value('some_var')) + self.assertEqual(13, sme.get_variable_value('global_step')) + + predictions = next(sme2.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_re_export_saved_model(self): + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(), self._get_tmp_dir()) + self.assertDictEqual( + {'loss': 106, 'metrics/abs_err': 502, 'global_step': 10}, + sme.evaluate(dummy_input_fn, steps=1)) + + sme.train(dummy_input_fn, steps=3) + self.assertDictEqual( + {'loss': 106, 'metrics/abs_err': 502, 'global_step': 13}, + sme.evaluate(dummy_input_fn, steps=1)) + self.assertEqual(60, sme.get_variable_value('some_var')) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + # Export SavedModel for all modes + input_receiver_fn_map = { + model_fn_lib.ModeKeys.TRAIN: dummy_supervised_receiver_fn(), + model_fn_lib.ModeKeys.EVAL: dummy_supervised_receiver_fn(), + model_fn_lib.ModeKeys.PREDICT: dummy_serving_receiver_fn()} + sme_export_dir = contrib_export.export_all_saved_models( + sme, self._get_tmp_dir(), input_receiver_fn_map) + + sme2 = saved_model_estimator.SavedModelEstimator( + sme_export_dir, self._get_tmp_dir()) + self.assertDictEqual( + {'loss': 106, 'metrics/abs_err': 502, 'global_step': 13}, + sme.evaluate(dummy_input_fn, steps=1)) + self.assertEqual(60, sme.get_variable_value('some_var')) + + sme.train(dummy_input_fn, steps=7) + self.assertEqual(20, sme.get_variable_value('global_step')) + + predictions = next(sme2.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'output': 503}, predictions) + + def test_load_saved_model_from_serving_only(self): + def model_fn(features, labels, mode): + _, _ = features, labels + return model_fn_lib.EstimatorSpec( + mode, + loss=constant_op.constant([103]), + train_op=state_ops.assign_add(training.get_global_step(), 1), + predictions=constant_op.constant([502]), + export_outputs={'test': export_output.ClassificationOutput( + constant_op.constant([[32.]]))}) + + est = estimator.Estimator(model_fn, self._get_tmp_dir()) + est.train(input_fn=dummy_input_fn, steps=10) + + def serving_input_receiver_fn(): + return export.ServingInputReceiver( + {'test-features': constant_op.constant([[1], [1]])}, + array_ops.placeholder(dtype=dtypes.string)) + + export_dir = est.export_savedmodel( + self._get_tmp_dir(), serving_input_receiver_fn) + + sme = saved_model_estimator.SavedModelEstimator( + export_dir, self._get_tmp_dir()) + + def input_fn(): + return {'inputs': constant_op.constant('someinputstr')} + + prediction = next(sme.predict(input_fn)) + self.assertDictEqual({'scores': 32}, prediction) + + def test_with_local_init_op(self): + def model_fn(features, labels, mode): + _, _ = features, labels + v = variables.Variable(21, name='some_var') + scaffold = monitored_session.Scaffold( + local_init_op=state_ops.assign_add(v, -3).op + ) + return model_fn_lib.EstimatorSpec( + mode, + scaffold=scaffold, + train_op=state_ops.assign_add(training.get_global_step(), 1), + loss=array_ops.identity(v)) + export_dir = self._export_estimator(predict=False, model_fn=model_fn) + sme = saved_model_estimator.SavedModelEstimator( + export_dir, self._get_tmp_dir()) + + eval_results1 = sme.evaluate(dummy_input_fn, steps=2) + self.assertEqual(15, eval_results1['loss']) + + sme.train(dummy_input_fn, steps=1) + self.assertEqual(15, sme.get_variable_value('some_var')) + + eval_results2 = sme.evaluate(dummy_input_fn, steps=5) + self.assertEqual(12, eval_results2['loss']) + + def test_with_working_input_fn(self): + def model_fn(features, labels, mode): + loss = None + if labels is not None: + loss = labels[0][0] + labels[1][0] + return model_fn_lib.EstimatorSpec( + mode, + loss=loss, + train_op=state_ops.assign_add(training.get_global_step(), 1), + predictions={'features_0': array_ops.identity([features['x'][0][0]]), + 'features_1': array_ops.identity([features['x'][1][0]])}) + + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(model_fn=model_fn), self._get_tmp_dir()) + eval_results = sme.evaluate(dummy_input_fn, steps=1) + self.assertEqual(1, eval_results['loss']) + + predictions = next(sme.predict(dummy_input_fn_features_only)) + self.assertDictEqual({'features_0': 5, 'features_1': 6}, predictions) + + def test_control_dependency(self): + # Control dependencies are saved with "^" appended to the start of the input + # name. The input map must include control dependencies as well. + def model_fn(features, labels, mode): + _ = labels + with ops.control_dependencies([features['x']]): + loss = features['x'][1][0] + return model_fn_lib.EstimatorSpec( + mode, + loss=loss, + train_op=state_ops.assign_add(training.get_global_step(), 1)) + sme = saved_model_estimator.SavedModelEstimator( + self._export_estimator(train=False, predict=False, model_fn=model_fn), + self._get_tmp_dir()) + sme.evaluate(dummy_input_fn, steps=1) # Should run without error + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/estimator/estimator.py b/tensorflow/python/estimator/estimator.py index 915ceeb98b..cc5a61b54e 100644 --- a/tensorflow/python/estimator/estimator.py +++ b/tensorflow/python/estimator/estimator.py @@ -572,13 +572,14 @@ class Estimator(object): return allowed_overrides = set([ - '_call_input_fn', '_create_global_step', + '_call_input_fn', '_call_model_fn', '_convert_train_steps_to_hooks', '_convert_eval_steps_to_hooks', + '_create_global_step', '_create_and_assert_global_step', '_tf_api_names', '_tf_api_names_v1', '_estimator_api_names', '_estimator_api_names_v1', '_estimator_api_constants', '_estimator_api_constants_v1', '_validate_features_in_predict_input', - '_call_model_fn', '_add_meta_graph_for_mode' + '_add_meta_graph_for_mode' ]) estimator_members = set([m for m in Estimator.__dict__.keys() if not m.startswith('__')]) @@ -905,9 +906,10 @@ class Estimator(object): with tf_session.Session(config=self._session_config) as session: - local_init_op = ( - estimator_spec.scaffold.local_init_op or - monitored_session.Scaffold.default_local_init_op()) + if estimator_spec.scaffold.local_init_op is not None: + local_init_op = estimator_spec.scaffold.local_init_op + else: + local_init_op = monitored_session.Scaffold.default_local_init_op() # This saver will be used both for restoring variables now, # and in saving out the metagraph below. This ensures that any @@ -1159,13 +1161,19 @@ class Estimator(object): with ops.Graph().as_default() as g, g.device(self._device_fn): random_seed.set_random_seed(self._config.tf_random_seed) global_step_tensor = self._create_and_assert_global_step(g) - training_util._get_or_create_global_step_read() # pylint: disable=protected-access + + # Skip creating a read variable if _create_and_assert_global_step + # returns None (e.g. tf.contrib.estimator.SavedModelEstimator). + if global_step_tensor is not None: + training_util._get_or_create_global_step_read(g) # pylint: disable=protected-access + features, labels, input_hooks = ( self._get_features_and_labels_from_input_fn( input_fn, model_fn_lib.ModeKeys.TRAIN)) worker_hooks.extend(input_hooks) estimator_spec = self._call_model_fn( features, labels, model_fn_lib.ModeKeys.TRAIN, self.config) + global_step_tensor = training_util.get_global_step(g) return self._train_with_estimator_spec(estimator_spec, worker_hooks, hooks, global_step_tensor, saving_listeners) @@ -1452,13 +1460,13 @@ class Estimator(object): def _evaluate_build_graph(self, input_fn, hooks=None, checkpoint_path=None): """Builds the graph and related hooks to run evaluation.""" random_seed.set_random_seed(self._config.tf_random_seed) - global_step_tensor = self._create_and_assert_global_step( - ops.get_default_graph()) + self._create_and_assert_global_step(ops.get_default_graph()) features, labels, input_hooks = ( self._get_features_and_labels_from_input_fn(input_fn, model_fn_lib.ModeKeys.EVAL)) estimator_spec = self._call_model_fn( features, labels, model_fn_lib.ModeKeys.EVAL, self.config) + global_step_tensor = training_util.get_global_step(ops.get_default_graph()) # Call to warm_start has to be after model_fn is called. self._maybe_warm_start(checkpoint_path) @@ -1484,7 +1492,21 @@ class Estimator(object): all_hooks.extend(hooks) all_hooks.extend(list(estimator_spec.evaluation_hooks or [])) - return estimator_spec.scaffold, update_op, eval_dict, all_hooks + # New local variables have been added, so update the estimator spec's + # local init op if it was defined. + scaffold = estimator_spec.scaffold + if estimator_spec.scaffold and estimator_spec.scaffold.local_init_op: + # Ensure that eval step has been created before updating local init op. + evaluation._get_or_create_eval_step() # pylint: disable=protected-access + + scaffold = monitored_session.Scaffold( + local_init_op=control_flow_ops.group( + estimator_spec.scaffold.local_init_op, + monitored_session.Scaffold.default_local_init_op()), + copy_from_scaffold=scaffold + ) + + return scaffold, update_op, eval_dict, all_hooks def _evaluate_run(self, checkpoint_path, scaffold, update_op, eval_dict, all_hooks, output_dir): @@ -1915,6 +1937,19 @@ class WarmStartSettings( ) +def _get_saved_model_ckpt(saved_model_dir): + """Return path to variables checkpoint in a SavedModel directory.""" + if not gfile.Exists( + os.path.join(compat.as_bytes(saved_model_dir), + compat.as_bytes('variables/variables.index'))): + raise ValueError('Directory provided has an invalid SavedModel format: %s' + % saved_model_dir) + return os.path.join( + compat.as_bytes(saved_model_dir), + compat.as_bytes('{}/{}'.format(constants.VARIABLES_DIRECTORY, + constants.VARIABLES_FILENAME))) + + def _get_default_warm_start_settings(warm_start_from): """Returns default WarmStartSettings. @@ -1938,10 +1973,8 @@ def _get_default_warm_start_settings(warm_start_from): if gfile.Exists(os.path.join(compat.as_bytes(warm_start_from), compat.as_bytes('variables/variables.index'))): logging.info('Warm-starting from a SavedModel') - return WarmStartSettings(ckpt_to_initialize_from=os.path.join( - compat.as_bytes(warm_start_from), - compat.as_bytes('{}/{}'.format(constants.VARIABLES_DIRECTORY, - constants.VARIABLES_FILENAME)))) + return WarmStartSettings( + ckpt_to_initialize_from=_get_saved_model_ckpt(warm_start_from)) return WarmStartSettings(ckpt_to_initialize_from=warm_start_from) elif isinstance(warm_start_from, WarmStartSettings): return warm_start_from diff --git a/tensorflow/python/framework/importer.py b/tensorflow/python/framework/importer.py index 699d2b70d1..687bfebd43 100644 --- a/tensorflow/python/framework/importer.py +++ b/tensorflow/python/framework/importer.py @@ -205,7 +205,7 @@ def _PopulateTFImportGraphDefOptions(options, prefix, input_map, for input_src, input_dst in input_map.items(): input_src = compat.as_str(input_src) if input_src.startswith('^'): - src_name = compat.as_bytes(input_src[1:]) + src_name = compat.as_str(input_src[1:]) dst_op = input_dst._as_tf_output().oper # pylint: disable=protected-access c_api.TF_ImportGraphDefOptionsRemapControlDependency( options, src_name, dst_op) diff --git a/tensorflow/python/framework/meta_graph.py b/tensorflow/python/framework/meta_graph.py index 923e76fc9c..33631282bd 100644 --- a/tensorflow/python/framework/meta_graph.py +++ b/tensorflow/python/framework/meta_graph.py @@ -696,6 +696,67 @@ def import_scoped_meta_graph(meta_graph_or_file, Raises: ValueError: If the graph_def contains unbound inputs. """ + return import_scoped_meta_graph_with_return_elements( + meta_graph_or_file, clear_devices, graph, import_scope, input_map, + unbound_inputs_col_name, restore_collections_predicate)[0] + + +def import_scoped_meta_graph_with_return_elements( + meta_graph_or_file, + clear_devices=False, + graph=None, + import_scope=None, + input_map=None, + unbound_inputs_col_name="unbound_inputs", + restore_collections_predicate=(lambda key: True), + return_elements=None): + """Imports graph from `MetaGraphDef` and returns vars and return elements. + + This function takes a `MetaGraphDef` protocol buffer as input. If + the argument is a file containing a `MetaGraphDef` protocol buffer , + it constructs a protocol buffer from the file content. The function + then adds all the nodes from the `graph_def` field to the + current graph, recreates the desired collections, and returns a dictionary of + all the Variables imported into the name scope. + + In combination with `export_scoped_meta_graph()`, this function can be used to + + * Serialize a graph along with other Python objects such as `QueueRunner`, + `Variable` into a `MetaGraphDef`. + + * Restart training from a saved graph and checkpoints. + + * Run inference from a saved graph and checkpoints. + + Args: + meta_graph_or_file: `MetaGraphDef` protocol buffer or filename (including + the path) containing a `MetaGraphDef`. + clear_devices: Boolean which controls whether to clear device information + from graph_def. Default false. + graph: The `Graph` to import into. If `None`, use the default graph. + import_scope: Optional `string`. Name scope into which to import the + subgraph. If `None`, the graph is imported to the root name scope. + input_map: A dictionary mapping input names (as strings) in `graph_def` to + `Tensor` objects. The values of the named input tensors in the imported + graph will be re-mapped to the respective `Tensor` values. + unbound_inputs_col_name: Collection name for looking up unbound inputs. + restore_collections_predicate: a predicate on collection names. A collection + named c (i.e whose key is c) will be restored iff + 1) `restore_collections_predicate(c)` is True, and + 2) `c != unbound_inputs_col_name`. + return_elements: A list of strings containing operation names in the + `MetaGraphDef` that will be returned as `Operation` objects; and/or + tensor names in `MetaGraphDef` that will be returned as `Tensor` objects. + + Returns: + A tuple of ( + dictionary of all the `Variables` imported into the name scope, + list of `Operation` or `Tensor` objects from the `return_elements` list). + + Raises: + ValueError: If the graph_def contains unbound inputs. + + """ if context.executing_eagerly(): raise ValueError("Exporting/importing meta graphs is not supported when " "eager execution is enabled.") @@ -737,11 +798,12 @@ def import_scoped_meta_graph(meta_graph_or_file, scope_to_prepend_to_names = graph.unique_name( import_scope or "", mark_as_used=False) - importer.import_graph_def( + imported_return_elements = importer.import_graph_def( input_graph_def, name=(import_scope or scope_to_prepend_to_names), input_map=input_map, - producer_op_list=producer_op_list) + producer_op_list=producer_op_list, + return_elements=return_elements) # Restores all the other collections. variable_objects = {} @@ -806,7 +868,7 @@ def import_scoped_meta_graph(meta_graph_or_file, for v in variables: var_list[ops.strip_name_scope(v.name, scope_to_prepend_to_names)] = v - return var_list + return var_list, imported_return_elements def export_scoped_meta_graph(filename=None, diff --git a/tensorflow/python/saved_model/loader_impl.py b/tensorflow/python/saved_model/loader_impl.py index fb70c91c29..16077f52fa 100644 --- a/tensorflow/python/saved_model/loader_impl.py +++ b/tensorflow/python/saved_model/loader_impl.py @@ -264,12 +264,15 @@ class SavedModelLoader(object): **saver_kwargs: keyword arguments to pass to tf.train.import_meta_graph. Returns: - Saver defined by the MetaGraph, which can be used to restore the variable - values. + A tuple of + * Saver defined by the MetaGraph, which can be used to restore the + variable values. + * List of `Operation`/`Tensor` objects returned from + `tf.import_graph_def` (may be `None`). """ meta_graph_def = self.get_meta_graph_def_from_tags(tags) with graph.as_default(): - return tf_saver.import_meta_graph( + return tf_saver._import_meta_graph_with_return_elements( # pylint: disable=protected-access meta_graph_def, import_scope=import_scope, **saver_kwargs) def restore_variables(self, sess, saver, import_scope=None): @@ -341,8 +344,8 @@ class SavedModelLoader(object): `MetagraphDef` proto of the graph that was loaded. """ with sess.graph.as_default(): - saver = self.load_graph(sess.graph, tags, import_scope, - **saver_kwargs) + saver, _ = self.load_graph(sess.graph, tags, import_scope, + **saver_kwargs) self.restore_variables(sess, saver, import_scope) self.run_init_ops(sess, tags, import_scope) return self.get_meta_graph_def_from_tags(tags) diff --git a/tensorflow/python/saved_model/loader_test.py b/tensorflow/python/saved_model/loader_test.py index ce18859f6b..9a0b276a4b 100644 --- a/tensorflow/python/saved_model/loader_test.py +++ b/tensorflow/python/saved_model/loader_test.py @@ -111,7 +111,8 @@ class SavedModelLoaderTest(test.TestCase): def test_load_with_import_scope(self): loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) with self.test_session(graph=ops.Graph()) as sess: - saver = loader.load_graph(sess.graph, ["foo_graph"], import_scope="baz") + saver, _ = loader.load_graph( + sess.graph, ["foo_graph"], import_scope="baz") # The default saver should not work when the import scope is set. with self.assertRaises(errors.NotFoundError): @@ -149,7 +150,7 @@ class SavedModelLoaderTest(test.TestCase): def test_run_init_op(self): loader = loader_impl.SavedModelLoader(SAVED_MODEL_WITH_MAIN_OP) graph = ops.Graph() - saver = loader.load_graph(graph, ["foo_graph"]) + saver, _ = loader.load_graph(graph, ["foo_graph"]) with self.test_session(graph=graph) as sess: loader.restore_variables(sess, saver) self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) @@ -203,7 +204,7 @@ class SavedModelLoaderTest(test.TestCase): loader = loader_impl.SavedModelLoader(path) with self.test_session(graph=ops.Graph()) as sess: - saver = loader.load_graph(sess.graph, ["foo_graph"]) + saver, _ = loader.load_graph(sess.graph, ["foo_graph"]) self.assertFalse(variables._all_saveable_objects()) self.assertIsNotNone(saver) @@ -212,6 +213,18 @@ class SavedModelLoaderTest(test.TestCase): self.assertEqual(5, sess.graph.get_tensor_by_name("x:0").eval()) self.assertEqual(11, sess.graph.get_tensor_by_name("y:0").eval()) + def test_load_saved_model_graph_with_return_elements(self): + """Ensure that the correct elements are returned.""" + loader = loader_impl.SavedModelLoader(SIMPLE_ADD_SAVED_MODEL) + graph = ops.Graph() + _, ret = loader.load_graph(graph, ["foo_graph"], + return_elements=["y:0", "x:0"]) + + self.assertEqual(graph.get_tensor_by_name("y:0"), ret[0]) + self.assertEqual(graph.get_tensor_by_name("x:0"), ret[1]) + + with self.assertRaisesRegexp(ValueError, "not found in graph"): + loader.load_graph(graph, ["foo_graph"], return_elements=["z:0"]) if __name__ == "__main__": test.main() diff --git a/tensorflow/python/training/saver.py b/tensorflow/python/training/saver.py index 60885e9292..c80cdf03be 100644 --- a/tensorflow/python/training/saver.py +++ b/tensorflow/python/training/saver.py @@ -1925,6 +1925,14 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False, execution is enabled. @end_compatibility """ # pylint: disable=g-doc-exception + return _import_meta_graph_with_return_elements( + meta_graph_or_file, clear_devices, import_scope, **kwargs)[0] + + +def _import_meta_graph_with_return_elements( + meta_graph_or_file, clear_devices=False, import_scope=None, + return_elements=None, **kwargs): + """Import MetaGraph, and return both a saver and returned elements.""" if context.executing_eagerly(): raise RuntimeError("Exporting/importing meta graphs is not supported when " "eager execution is enabled. No graph exists when eager " @@ -1934,12 +1942,22 @@ def import_meta_graph(meta_graph_or_file, clear_devices=False, else: meta_graph_def = meta_graph_or_file - imported_vars = meta_graph.import_scoped_meta_graph( - meta_graph_def, - clear_devices=clear_devices, - import_scope=import_scope, - **kwargs) + imported_vars, imported_return_elements = ( + meta_graph.import_scoped_meta_graph_with_return_elements( + meta_graph_def, + clear_devices=clear_devices, + import_scope=import_scope, + return_elements=return_elements, + **kwargs)) + + saver = _create_saver_from_imported_meta_graph( + meta_graph_def, import_scope, imported_vars) + return saver, imported_return_elements + +def _create_saver_from_imported_meta_graph( + meta_graph_def, import_scope, imported_vars): + """Return a saver for restoring variable values to an imported MetaGraph.""" if meta_graph_def.HasField("saver_def"): # Infer the scope that is prepended by `import_scoped_meta_graph`. scope = import_scope -- GitLab From d5f3478dd405c6f49bada873db6418e633bf7466 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Wed, 25 Jul 2018 14:06:08 -0700 Subject: [PATCH 409/519] Fix inconsistency in generated AST. The argument should be not an Index node, but an expression, like Num. PiperOrigin-RevId: 206051230 --- tensorflow/contrib/autograph/converters/slices.py | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/autograph/converters/slices.py b/tensorflow/contrib/autograph/converters/slices.py index 9cfa066672..c527f98613 100644 --- a/tensorflow/contrib/autograph/converters/slices.py +++ b/tensorflow/contrib/autograph/converters/slices.py @@ -36,12 +36,14 @@ class SliceTransformer(converter.Base): def _process_single_assignment(self, target, value): if not isinstance(target, gast.Subscript): return None + if not isinstance(target.slice, gast.Index): + return None template = """ target = ag__.set_item(target, key, item) """ return templates.replace( - template, target=target.value, key=target.slice, item=value) + template, target=target.value, key=target.slice.value, item=value) def visit_Assign(self, node): node = self.generic_visit(node) @@ -76,7 +78,7 @@ class SliceTransformer(converter.Base): opts=ag__.GetItemOpts(element_dtype=dtype)) """ return templates.replace_as_expression( - template, target=node.value, key=node.slice, dtype=dtype) + template, target=node.value, key=node.slice.value, dtype=dtype) def transform(node, ctx): -- GitLab From 7bbd69fd16d4e97afd417786c1ee2fff27d92703 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Wed, 25 Jul 2018 14:14:46 -0700 Subject: [PATCH 410/519] Disable failing prefetching_ops_test. PiperOrigin-RevId: 206052752 --- tensorflow/contrib/data/python/kernel_tests/BUILD | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index 036dc795bb..3759ba8d5a 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -234,7 +234,12 @@ cuda_py_test( "//tensorflow/python/data/ops:dataset_ops", "//tensorflow/python/data/ops:iterator_ops", ], - tags = ["no_windows_gpu"], + tags = [ + "manual", + "no_oss", + "no_windows_gpu" + + "notap", + ], ) py_test( -- GitLab From 438c8e2b0a164233347f3f1614ebeef6482f1650 Mon Sep 17 00:00:00 2001 From: Mark Heffernan Date: Wed, 25 Jul 2018 14:29:54 -0700 Subject: [PATCH 411/519] Move xla_computation.* from xla/client/xla_client up to xla/client. Plan is to move everything in xla/client/xla_client up to xla/client and remove the directory. No functional change. PiperOrigin-RevId: 206055680 --- tensorflow/compiler/aot/BUILD | 1 + tensorflow/compiler/aot/compile.cc | 1 + tensorflow/compiler/tf2xla/BUILD | 6 +- tensorflow/compiler/tf2xla/kernels/BUILD | 2 + .../compiler/tf2xla/kernels/pooling_ops.cc | 1 + .../kernels/quantize_and_dequantize_op.cc | 1 + .../tf2xla/kernels/reduce_window_op.cc | 1 + .../tf2xla/kernels/reduction_ops_common.cc | 1 + .../compiler/tf2xla/kernels/scan_ops.cc | 1 + .../compiler/tf2xla/kernels/softmax_op.cc | 1 + .../compiler/tf2xla/kernels/while_op.cc | 1 + tensorflow/compiler/tf2xla/lib/BUILD | 14 +- tensorflow/compiler/tf2xla/lib/batch_dot.h | 2 +- tensorflow/compiler/tf2xla/lib/cholesky.h | 2 +- tensorflow/compiler/tf2xla/lib/scatter.h | 2 +- .../compiler/tf2xla/lib/triangular_solve.cc | 1 + .../compiler/tf2xla/lib/triangular_solve.h | 2 +- tensorflow/compiler/tf2xla/lib/util.h | 2 +- tensorflow/compiler/tf2xla/lib/while_loop.h | 2 +- tensorflow/compiler/tf2xla/tf2xla.cc | 1 + tensorflow/compiler/tf2xla/tf2xla.h | 2 +- tensorflow/compiler/tf2xla/tf2xla_test.cc | 1 + tensorflow/compiler/tf2xla/xla_compiler.cc | 1 + tensorflow/compiler/tf2xla/xla_compiler.h | 1 + tensorflow/compiler/tf2xla/xla_context.cc | 1 + tensorflow/compiler/tf2xla/xla_context.h | 2 +- tensorflow/compiler/tf2xla/xla_helpers.cc | 1 + .../tf2xla/xla_jit_compiled_cpu_function.cc | 1 + tensorflow/compiler/tf2xla/xla_op_kernel.cc | 1 + tensorflow/compiler/tf2xla/xla_op_kernel.h | 1 + tensorflow/compiler/xla/client/BUILD | 20 ++- tensorflow/compiler/xla/client/client.cc | 1 + tensorflow/compiler/xla/client/client.h | 2 +- .../compiler/xla/client/compile_only_client.h | 2 +- tensorflow/compiler/xla/client/lib/BUILD | 4 +- .../compiler/xla/client/lib/arithmetic.cc | 2 +- .../compiler/xla/client/lib/arithmetic.h | 2 +- tensorflow/compiler/xla/client/lib/testing.cc | 1 + tensorflow/compiler/xla/client/lib/testing.h | 2 +- .../compiler/xla/client/local_client.cc | 1 + tensorflow/compiler/xla/client/local_client.h | 2 +- .../compiler/xla/client/xla_client/BUILD | 15 +-- .../xla/client/xla_client/xla_builder.cc | 1 + .../xla/client/xla_client/xla_builder.h | 2 +- .../xla/client/xla_client/xla_builder_test.cc | 1 + .../{xla_client => }/xla_computation.cc | 2 +- .../client/{xla_client => }/xla_computation.h | 6 +- tensorflow/compiler/xla/python/BUILD | 2 +- .../xla/python/local_computation_builder.cc | 1 + .../xla/python/local_computation_builder.h | 2 +- tensorflow/compiler/xla/service/BUILD | 4 +- tensorflow/compiler/xla/service/cpu/BUILD | 2 +- .../xla/service/cpu/sample_harness.cc | 2 +- .../compiler/xla/service/cpu/tests/BUILD | 2 +- .../xla/service/cpu/tests/cpu_infeed_test.cc | 2 +- .../xla/service/hlo_cost_analysis_test.cc | 2 +- .../compiler/xla/service/local_service.cc | 1 + .../compiler/xla/service/local_service.h | 2 +- tensorflow/compiler/xla/tests/BUILD | 120 +++++++++--------- .../tests/bad_rng_shape_validation_test.cc | 2 +- .../xla/tests/batch_normalization_test.cc | 2 +- tensorflow/compiler/xla/tests/call_test.cc | 2 +- .../xla/tests/client_library_test_base.cc | 1 + .../xla/tests/client_library_test_base.h | 2 +- tensorflow/compiler/xla/tests/client_test.cc | 2 +- .../xla/tests/compilation_cache_test.cc | 2 +- .../xla/tests/compute_constant_test.cc | 2 +- tensorflow/compiler/xla/tests/concat_test.cc | 2 +- .../compiler/xla/tests/conditional_test.cc | 2 +- .../compiler/xla/tests/deallocation_test.cc | 2 +- .../xla/tests/deconstruct_tuple_test.cc | 2 +- .../xla/tests/execution_profile_test.cc | 2 +- .../xla/tests/gather_operation_test.cc | 1 + .../xla/tests/local_client_aot_test_helper.cc | 2 +- .../xla/tests/local_client_test_base.cc | 1 + .../xla/tests/local_client_test_base.h | 2 +- tensorflow/compiler/xla/tests/map_test.cc | 2 +- .../xla/tests/matrix_ops_simple_test.cc | 2 +- tensorflow/compiler/xla/tests/pad_test.cc | 1 + tensorflow/compiler/xla/tests/params_test.cc | 2 +- tensorflow/compiler/xla/tests/reduce_test.cc | 2 +- .../compiler/xla/tests/reduce_window_test.cc | 2 +- tensorflow/compiler/xla/tests/replay_test.cc | 2 +- tensorflow/compiler/xla/tests/reshape_test.cc | 2 +- .../xla/tests/scalar_computations_test.cc | 2 +- .../xla/tests/select_and_scatter_test.cc | 2 +- tensorflow/compiler/xla/tests/tuple_test.cc | 2 +- .../xla/tests/vector_ops_simple_test.cc | 2 +- tensorflow/compiler/xla/tests/while_test.cc | 2 +- .../xla/tests/xla_hlo_profile_test.cc | 2 +- tensorflow/compiler/xla/tools/BUILD | 5 + .../tools/dumped_computation_to_graphviz.cc | 1 + .../dumped_computation_to_operation_list.cc | 1 + .../xla/tools/dumped_computation_to_text.cc | 1 + .../dumped_computation_to_tf_graphdef.cc | 1 + .../compiler/xla/tools/replay_computation.cc | 1 + 96 files changed, 191 insertions(+), 142 deletions(-) rename tensorflow/compiler/xla/client/{xla_client => }/xla_computation.cc (94%) rename tensorflow/compiler/xla/client/{xla_client => }/xla_computation.h (90%) diff --git a/tensorflow/compiler/aot/BUILD b/tensorflow/compiler/aot/BUILD index 2119c8ec47..fef8b8d4d4 100644 --- a/tensorflow/compiler/aot/BUILD +++ b/tensorflow/compiler/aot/BUILD @@ -68,6 +68,7 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:compile_only_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:compiler", "//tensorflow/compiler/xla/service/cpu:cpu_compiler", "//tensorflow/core:core_cpu_internal", diff --git a/tensorflow/compiler/aot/compile.cc b/tensorflow/compiler/aot/compile.cc index bbc35da2ef..2b5f97b34c 100644 --- a/tensorflow/compiler/aot/compile.cc +++ b/tensorflow/compiler/aot/compile.cc @@ -25,6 +25,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/tf2xla_util.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/compile_only_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/util.h" diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index ff002d15b0..881624fff8 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -81,7 +81,7 @@ cc_library( "//tensorflow/compiler/tf2xla/kernels:xla_cpu_only_ops", "//tensorflow/compiler/tf2xla/kernels:xla_ops", "//tensorflow/compiler/xla/client", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", @@ -119,6 +119,7 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:cpu_plugin", "//tensorflow/compiler/xla/service/cpu:cpu_executable", "//tensorflow/core:lib", @@ -172,11 +173,11 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:numeric", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/legacy_flags:parse_flags_from_env", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", @@ -294,6 +295,7 @@ tf_cc_test( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:cpu_plugin", "//tensorflow/core:framework", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index d88a34dfd9..7f3e32d96d 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -123,6 +123,7 @@ tf_kernel_library( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:math", @@ -164,6 +165,7 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/ops:xla_ops", "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index 12d9cb9bac..2a4c0cab4b 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc b/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc index e88221e4f4..2e632e185d 100644 --- a/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/platform/macros.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc b/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc index 76bd1e62aa..23ac45beb7 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc index ed1d1c6610..bb8dd3ac90 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" diff --git a/tensorflow/compiler/tf2xla/kernels/scan_ops.cc b/tensorflow/compiler/tf2xla/kernels/scan_ops.cc index 27ab3e1bf5..56f237d588 100644 --- a/tensorflow/compiler/tf2xla/kernels/scan_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/scan_ops.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/partial_tensor_shape.h" diff --git a/tensorflow/compiler/tf2xla/kernels/softmax_op.cc b/tensorflow/compiler/tf2xla/kernels/softmax_op.cc index a71fbcd901..60c6a5d349 100644 --- a/tensorflow/compiler/tf2xla/kernels/softmax_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/softmax_op.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc index 009fdd81b2..c653a11029 100644 --- a/tensorflow/compiler/tf2xla/kernels/while_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD index 30039e256a..e35a457f09 100644 --- a/tensorflow/compiler/tf2xla/lib/BUILD +++ b/tensorflow/compiler/tf2xla/lib/BUILD @@ -25,8 +25,8 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -44,9 +44,9 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -78,12 +78,12 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:math", "//tensorflow/compiler/xla/client/lib:numeric", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -100,9 +100,9 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -119,10 +119,10 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:numeric", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -162,8 +162,8 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -200,8 +200,8 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.h b/tensorflow/compiler/tf2xla/lib/batch_dot.h index d07a9486f1..dbba5eaf26 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.h +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_LIB_BATCH_DOT_H_ #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.h b/tensorflow/compiler/tf2xla/lib/cholesky.h index 0f6e0e9d15..bc1b0ed82f 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.h +++ b/tensorflow/compiler/tf2xla/lib/cholesky.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_LIB_CHOLESKY_H_ #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/scatter.h b/tensorflow/compiler/tf2xla/lib/scatter.h index 87309e10ed..452fda565d 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.h +++ b/tensorflow/compiler/tf2xla/lib/scatter.h @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc index 75c0ad7f7e..05dad759df 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.h b/tensorflow/compiler/tf2xla/lib/triangular_solve.h index 2dce620ba8..9c4314e275 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.h +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_LIB_TRIANGULAR_SOLVE_H_ #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/util.h b/tensorflow/compiler/tf2xla/lib/util.h index 6cb6c088e9..a139873d32 100644 --- a/tensorflow/compiler/tf2xla/lib/util.h +++ b/tensorflow/compiler/tf2xla/lib/util.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_LIB_UTIL_H_ #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/lib/gtl/array_slice.h" diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.h b/tensorflow/compiler/tf2xla/lib/while_loop.h index 5b6684c995..69cc70bfaf 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.h +++ b/tensorflow/compiler/tf2xla/lib/while_loop.h @@ -20,7 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/array_slice.h" diff --git a/tensorflow/compiler/tf2xla/tf2xla.cc b/tensorflow/compiler/tf2xla/tf2xla.cc index ac768b206e..48568c825b 100644 --- a/tensorflow/compiler/tf2xla/tf2xla.cc +++ b/tensorflow/compiler/tf2xla/tf2xla.cc @@ -27,6 +27,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/tf2xla_util.h" #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/graph.pb.h" diff --git a/tensorflow/compiler/tf2xla/tf2xla.h b/tensorflow/compiler/tf2xla/tf2xla.h index d02fc56c5b..432a12a516 100644 --- a/tensorflow/compiler/tf2xla/tf2xla.h +++ b/tensorflow/compiler/tf2xla/tf2xla.h @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/tf2xla.pb.h" #include "tensorflow/compiler/xla/client/client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/framework/graph.pb.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/tf2xla_test.cc b/tensorflow/compiler/tf2xla/tf2xla_test.cc index f0b30dcf4e..56f7045a98 100644 --- a/tensorflow/compiler/tf2xla/tf2xla_test.cc +++ b/tensorflow/compiler/tf2xla/tf2xla_test.cc @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/tf2xla.pb.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc index cb47581e36..678e209cf6 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler.cc @@ -29,6 +29,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/executor.h" #include "tensorflow/core/common_runtime/function.h" diff --git a/tensorflow/compiler/tf2xla/xla_compiler.h b/tensorflow/compiler/tf2xla/xla_compiler.h index 079c99797e..acc64d99d3 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.h +++ b/tensorflow/compiler/tf2xla/xla_compiler.h @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/device_mgr.h" diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc index 0dea366476..2836cb3df3 100644 --- a/tensorflow/compiler/tf2xla/xla_context.cc +++ b/tensorflow/compiler/tf2xla/xla_context.cc @@ -26,6 +26,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h index 38d8cd653c..beee7d48e8 100644 --- a/tensorflow/compiler/tf2xla/xla_context.h +++ b/tensorflow/compiler/tf2xla/xla_context.h @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc index 4d1b3b1a13..225da16807 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.cc +++ b/tensorflow/compiler/tf2xla/xla_helpers.cc @@ -27,6 +27,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/core/status.h" diff --git a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc index 9e17756b27..00ccfb1c78 100644 --- a/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc +++ b/tensorflow/compiler/tf2xla/xla_jit_compiled_cpu_function.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiled_cpu_function.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/cpu/cpu_executable.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc index e8eafb3819..38ec559576 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/common_runtime/dma_helper.h" diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.h b/tensorflow/compiler/tf2xla/xla_op_kernel.h index 6203cffd80..71990b57d9 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.h +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.h @@ -18,6 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/platform/macros.h" diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD index 25666cad40..c5b352b30f 100644 --- a/tensorflow/compiler/xla/client/BUILD +++ b/tensorflow/compiler/xla/client/BUILD @@ -1,6 +1,8 @@ # Description: # XLA client libraries. +load("//tools/build_defs:cc_public_library.bzl", "cc_public_library") + licenses(["notice"]) # Apache 2.0 package(default_visibility = [":friends"]) @@ -64,6 +66,7 @@ cc_library( hdrs = ["client.h"], deps = [ ":global_data", + ":xla_computation", "//tensorflow/compiler/xla:execution_options_util", "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:service_interface", @@ -73,7 +76,6 @@ cc_library( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla:xla_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/core:lib", @@ -100,12 +102,12 @@ cc_library( deps = [ ":client", ":executable_build_options", + ":xla_computation", "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:backend", "//tensorflow/compiler/xla/service:compiler", "//tensorflow/compiler/xla/service:device_memory_allocator", @@ -126,11 +128,11 @@ cc_library( hdrs = ["compile_only_client.h"], deps = [ ":client", + ":xla_computation", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:compile_only_service", "//tensorflow/compiler/xla/service:compiler", "//tensorflow/core:stream_executor_no_cuda", @@ -174,3 +176,15 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", ], ) + +cc_public_library( + name = "xla_computation", + srcs = ["xla_computation.cc"], + hdrs = ["xla_computation.h"], + deps = [ + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo_proto", + ], +) diff --git a/tensorflow/compiler/xla/client/client.cc b/tensorflow/compiler/xla/client/client.cc index 8e54311bad..d0ce5e8a6a 100644 --- a/tensorflow/compiler/xla/client/client.cc +++ b/tensorflow/compiler/xla/client/client.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" #include "tensorflow/compiler/xla/literal.h" diff --git a/tensorflow/compiler/xla/client/client.h b/tensorflow/compiler/xla/client/client.h index d751e183dd..be50cebfcc 100644 --- a/tensorflow/compiler/xla/client/client.h +++ b/tensorflow/compiler/xla/client/client.h @@ -20,7 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/global_data.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service_interface.h" diff --git a/tensorflow/compiler/xla/client/compile_only_client.h b/tensorflow/compiler/xla/client/compile_only_client.h index 332c965036..a551edeab0 100644 --- a/tensorflow/compiler/xla/client/compile_only_client.h +++ b/tensorflow/compiler/xla/client/compile_only_client.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_XLA_CLIENT_COMPILE_ONLY_CLIENT_H_ #include "tensorflow/compiler/xla/client/client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/compile_only_service.h" #include "tensorflow/compiler/xla/service/compiler.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/client/lib/BUILD b/tensorflow/compiler/xla/client/lib/BUILD index 77ba474cf6..45506986c8 100644 --- a/tensorflow/compiler/xla/client/lib/BUILD +++ b/tensorflow/compiler/xla/client/lib/BUILD @@ -29,8 +29,8 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) @@ -150,8 +150,8 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client:global_data", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/core:lib", ], diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.cc b/tensorflow/compiler/xla/client/lib/arithmetic.cc index de1d785e19..1872925aba 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.cc +++ b/tensorflow/compiler/xla/client/lib/arithmetic.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/types.h" diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.h b/tensorflow/compiler/xla/client/lib/arithmetic.h index 8367e09450..80d3f8b95a 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.h +++ b/tensorflow/compiler/xla/client/lib/arithmetic.h @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/xla_data.pb.h" namespace xla { diff --git a/tensorflow/compiler/xla/client/lib/testing.cc b/tensorflow/compiler/xla/client/lib/testing.cc index 534c509868..2de65016dd 100644 --- a/tensorflow/compiler/xla/client/lib/testing.cc +++ b/tensorflow/compiler/xla/client/lib/testing.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/testing.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/client/lib/testing.h b/tensorflow/compiler/xla/client/lib/testing.h index dc613099e2..03695ce2a3 100644 --- a/tensorflow/compiler/xla/client/lib/testing.h +++ b/tensorflow/compiler/xla/client/lib/testing.h @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/global_data.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/xla_data.pb.h" namespace xla { diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc index 5f9710914b..035ee9bf4c 100644 --- a/tensorflow/compiler/xla/client/local_client.cc +++ b/tensorflow/compiler/xla/client/local_client.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include "llvm/ADT/Triple.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/backend.h" #include "tensorflow/compiler/xla/service/service_executable_run_options.h" diff --git a/tensorflow/compiler/xla/client/local_client.h b/tensorflow/compiler/xla/client/local_client.h index 4d9e0d7cd9..ae23809261 100644 --- a/tensorflow/compiler/xla/client/local_client.h +++ b/tensorflow/compiler/xla/client/local_client.h @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/executable_build_options.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/executable_run_options.h" #include "tensorflow/compiler/xla/service/compiler.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" diff --git a/tensorflow/compiler/xla/client/xla_client/BUILD b/tensorflow/compiler/xla/client/xla_client/BUILD index 763653c685..a7168e731b 100644 --- a/tensorflow/compiler/xla/client/xla_client/BUILD +++ b/tensorflow/compiler/xla/client/xla_client/BUILD @@ -23,25 +23,12 @@ filegroup( load("//tensorflow:tensorflow.bzl", "tf_cc_test") -cc_library( - name = "xla_computation", - srcs = ["xla_computation.cc"], - hdrs = ["xla_computation.h"], - deps = [ - "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla:util", - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/service:hlo_proto", - ], -) - cc_library( name = "xla_builder", srcs = ["xla_builder.cc"], hdrs = ["xla_builder.h"], visibility = ["//visibility:public"], deps = [ - ":xla_computation", "//tensorflow/compiler/xla:execution_options_util", "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", @@ -53,6 +40,7 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:padding", "//tensorflow/compiler/xla/client:sharding_builder", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/compiler/xla/service:shape_inference", @@ -71,6 +59,7 @@ tf_cc_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_matchers", diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc index a9a4b3bc5d..152335e22a 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.cc @@ -22,6 +22,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/sharding_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/shape_inference.h" diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.h b/tensorflow/compiler/xla/client/xla_client/xla_builder.h index 8359d936b7..980e84e40c 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.h +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.h @@ -22,7 +22,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc b/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc index 3b8beb2c78..b4a5aedfb1 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc @@ -17,6 +17,7 @@ limitations under the License. #include +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" #include "tensorflow/compiler/xla/service/hlo_matchers.h" #include "tensorflow/compiler/xla/service/hlo_module.h" diff --git a/tensorflow/compiler/xla/client/xla_client/xla_computation.cc b/tensorflow/compiler/xla/client/xla_computation.cc similarity index 94% rename from tensorflow/compiler/xla/client/xla_client/xla_computation.cc rename to tensorflow/compiler/xla/client/xla_computation.cc index 72e3935696..3543d41fc2 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_computation.cc +++ b/tensorflow/compiler/xla/client/xla_computation.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include diff --git a/tensorflow/compiler/xla/client/xla_client/xla_computation.h b/tensorflow/compiler/xla/client/xla_computation.h similarity index 90% rename from tensorflow/compiler/xla/client/xla_client/xla_computation.h rename to tensorflow/compiler/xla/client/xla_computation.h index 0ffba208b1..71598ef8b2 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_computation.h +++ b/tensorflow/compiler/xla/client/xla_computation.h @@ -13,8 +13,8 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_COMPUTATION_H_ -#define TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_COMPUTATION_H_ +#ifndef TENSORFLOW_COMPILER_XLA_CLIENT_XLA_COMPUTATION_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_XLA_COMPUTATION_H_ #include @@ -64,4 +64,4 @@ class XlaComputation { } // namespace xla -#endif // TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_COMPUTATION_H_ +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_XLA_COMPUTATION_H_ diff --git a/tensorflow/compiler/xla/python/BUILD b/tensorflow/compiler/xla/python/BUILD index fe346f9956..e26e35eb11 100644 --- a/tensorflow/compiler/xla/python/BUILD +++ b/tensorflow/compiler/xla/python/BUILD @@ -53,9 +53,9 @@ cc_library( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:executable_build_options", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:math", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/core:framework_lite", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index 8aefc4cd5e..fbcf0f1969 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -16,6 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/python/local_computation_builder.h" #include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/executable_run_options.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/util.h" diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index dd9e2fbe72..57da7e53d5 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/executable_build_options.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/gtl/array_slice.h" diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index cba7883fde..2305dd4318 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -642,7 +642,7 @@ cc_library( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:executable_build_options", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", ], @@ -1663,8 +1663,8 @@ tf_cc_test( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 71f7f985d0..bcac65ecda 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -363,8 +363,8 @@ tf_cc_binary( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/core:lib", ], ) diff --git a/tensorflow/compiler/xla/service/cpu/sample_harness.cc b/tensorflow/compiler/xla/service/cpu/sample_harness.cc index d9e8dcaed9..eb83432f57 100644 --- a/tensorflow/compiler/xla/service/cpu/sample_harness.cc +++ b/tensorflow/compiler/xla/service/cpu/sample_harness.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" diff --git a/tensorflow/compiler/xla/service/cpu/tests/BUILD b/tensorflow/compiler/xla/service/cpu/tests/BUILD index b4c33e2f6c..e6d25680b5 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/BUILD +++ b/tensorflow/compiler/xla/service/cpu/tests/BUILD @@ -135,9 +135,9 @@ tf_cc_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:cpu_plugin", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc index 0d45918d09..be3fae5161 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc index 9fd0363f57..b2241cd423 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/local_service.h" #include "tensorflow/compiler/xla/service/service.h" diff --git a/tensorflow/compiler/xla/service/local_service.cc b/tensorflow/compiler/xla/service/local_service.cc index 53efc30c36..5e02096ee5 100644 --- a/tensorflow/compiler/xla/service/local_service.cc +++ b/tensorflow/compiler/xla/service/local_service.cc @@ -20,6 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/executable_build_options.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/backend.h" diff --git a/tensorflow/compiler/xla/service/local_service.h b/tensorflow/compiler/xla/service/local_service.h index 39d6734c3f..8f707ea904 100644 --- a/tensorflow/compiler/xla/service/local_service.h +++ b/tensorflow/compiler/xla/service/local_service.h @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/executable_build_options.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/backend.h" #include "tensorflow/compiler/xla/service/compiler.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index e840067056..200cafbe9c 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -154,8 +154,8 @@ tf_cc_binary( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/client:client_library", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service/cpu:cpu_compiler", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/core:lib", @@ -192,8 +192,8 @@ cc_library( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:interpreter_plugin", # reference backend "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -262,7 +262,7 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service:computation_placer", "//tensorflow/compiler/xla/service:device_memory_allocator", "//tensorflow/compiler/xla/service:local_service", @@ -290,8 +290,8 @@ xla_test( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -314,8 +314,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", @@ -334,8 +334,8 @@ xla_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", @@ -356,9 +356,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -376,8 +376,8 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:test_utils", @@ -395,8 +395,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -419,9 +419,9 @@ xla_test( "//tensorflow/compiler/xla:xla_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -445,8 +445,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -464,9 +464,9 @@ xla_test( deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", @@ -483,8 +483,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -501,8 +501,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -519,9 +519,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -543,8 +543,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -562,8 +562,8 @@ xla_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -586,8 +586,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -612,8 +612,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -681,8 +681,8 @@ xla_test( "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -702,6 +702,7 @@ xla_test( "//tensorflow/compiler/xla:execution_options_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -725,8 +726,8 @@ xla_test( "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -749,8 +750,8 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -773,8 +774,8 @@ xla_test( "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -838,8 +839,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -862,8 +863,8 @@ xla_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -891,10 +892,10 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:math", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -924,9 +925,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -950,8 +951,8 @@ xla_test( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -991,8 +992,8 @@ xla_test( "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array3d", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1044,8 +1045,8 @@ xla_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1065,9 +1066,9 @@ xla_test( "//tensorflow/compiler/xla:array3d", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1096,9 +1097,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1123,9 +1124,9 @@ xla_test_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1164,9 +1165,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1241,8 +1242,8 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1283,8 +1284,8 @@ xla_test( "//tensorflow/compiler/xla:array4d", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1306,8 +1307,8 @@ xla_test( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1327,6 +1328,7 @@ xla_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", @@ -1345,8 +1347,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1362,8 +1364,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1387,8 +1389,8 @@ xla_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -1408,8 +1410,8 @@ xla_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -1438,8 +1440,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1481,9 +1483,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1507,8 +1509,8 @@ xla_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1527,8 +1529,8 @@ xla_test( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1549,8 +1551,8 @@ xla_test( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -1593,8 +1595,8 @@ xla_test( "//tensorflow/compiler/xla:xla_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -1612,8 +1614,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1635,8 +1637,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1656,8 +1658,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -1673,8 +1675,8 @@ xla_test( deps = [ ":client_library_test_base", "//tensorflow/compiler/xla/client:global_data", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", ], @@ -1687,8 +1689,8 @@ xla_test( deps = [ ":client_library_test_base", "//tensorflow/compiler/xla/client:global_data", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", ], @@ -1708,8 +1710,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1793,8 +1795,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/service:hlo_runner", @@ -1821,8 +1823,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_runner", "//tensorflow/compiler/xla/service:platform_util", @@ -1858,8 +1860,8 @@ xla_test( "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:local_service", "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1886,8 +1888,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:device_memory_allocator", "//tensorflow/compiler/xla/service:local_service", "//tensorflow/compiler/xla/service:platform_util", @@ -1954,8 +1956,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -2053,8 +2055,8 @@ xla_test( ":local_client_test_base", ":test_utils", "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", - "//tensorflow/compiler/xla/client/xla_client:xla_computation", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", diff --git a/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc b/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc index 8c227df7f0..71dbe4f0b6 100644 --- a/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc +++ b/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc index 6a024798f9..033382708a 100644 --- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc +++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" diff --git a/tensorflow/compiler/xla/tests/call_test.cc b/tensorflow/compiler/xla/tests/call_test.cc index 2086e38b91..05c1c361bb 100644 --- a/tensorflow/compiler/xla/tests/call_test.cc +++ b/tensorflow/compiler/xla/tests/call_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc index 7a2e70d39f..515c0201d1 100644 --- a/tensorflow/compiler/xla/tests/client_library_test_base.cc +++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h index f0f7ff1ea0..edc1ba8a57 100644 --- a/tensorflow/compiler/xla/tests/client_library_test_base.h +++ b/tensorflow/compiler/xla/tests/client_library_test_base.h @@ -27,7 +27,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" diff --git a/tensorflow/compiler/xla/tests/client_test.cc b/tensorflow/compiler/xla/tests/client_test.cc index 6ce2f844a3..f97008bee2 100644 --- a/tensorflow/compiler/xla/tests/client_test.cc +++ b/tensorflow/compiler/xla/tests/client_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/compilation_cache_test.cc b/tensorflow/compiler/xla/tests/compilation_cache_test.cc index ff38246286..2b407ed263 100644 --- a/tensorflow/compiler/xla/tests/compilation_cache_test.cc +++ b/tensorflow/compiler/xla/tests/compilation_cache_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/compute_constant_test.cc b/tensorflow/compiler/xla/tests/compute_constant_test.cc index 64bf8b3b38..672fb06de6 100644 --- a/tensorflow/compiler/xla/tests/compute_constant_test.cc +++ b/tensorflow/compiler/xla/tests/compute_constant_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/concat_test.cc b/tensorflow/compiler/xla/tests/concat_test.cc index 9f288634c0..e63d2480b6 100644 --- a/tensorflow/compiler/xla/tests/concat_test.cc +++ b/tensorflow/compiler/xla/tests/concat_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" diff --git a/tensorflow/compiler/xla/tests/conditional_test.cc b/tensorflow/compiler/xla/tests/conditional_test.cc index 369663de15..d9d42bf061 100644 --- a/tensorflow/compiler/xla/tests/conditional_test.cc +++ b/tensorflow/compiler/xla/tests/conditional_test.cc @@ -14,7 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.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" diff --git a/tensorflow/compiler/xla/tests/deallocation_test.cc b/tensorflow/compiler/xla/tests/deallocation_test.cc index d4b3aac85b..062b8cb8c4 100644 --- a/tensorflow/compiler/xla/tests/deallocation_test.cc +++ b/tensorflow/compiler/xla/tests/deallocation_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/test_helpers.h" diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc index a6a233e71a..6795130cd1 100644 --- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc +++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/execution_profile_test.cc b/tensorflow/compiler/xla/tests/execution_profile_test.cc index ebba13c5b3..e2c145b795 100644 --- a/tensorflow/compiler/xla/tests/execution_profile_test.cc +++ b/tensorflow/compiler/xla/tests/execution_profile_test.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" #include "tensorflow/core/platform/test.h" diff --git a/tensorflow/compiler/xla/tests/gather_operation_test.cc b/tensorflow/compiler/xla/tests/gather_operation_test.cc index c5ca64fa3f..2008d69237 100644 --- a/tensorflow/compiler/xla/tests/gather_operation_test.cc +++ b/tensorflow/compiler/xla/tests/gather_operation_test.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc index 70612e7c49..0b44090702 100644 --- a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc +++ b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc @@ -22,7 +22,7 @@ limitations under the License. #include "llvm/ADT/Triple.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/types.h" diff --git a/tensorflow/compiler/xla/tests/local_client_test_base.cc b/tensorflow/compiler/xla/tests/local_client_test_base.cc index c31ba0e713..eaddf756db 100644 --- a/tensorflow/compiler/xla/tests/local_client_test_base.cc +++ b/tensorflow/compiler/xla/tests/local_client_test_base.cc @@ -20,6 +20,7 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/local_client_test_base.h b/tensorflow/compiler/xla/tests/local_client_test_base.h index 258226523d..b4477e9a6b 100644 --- a/tensorflow/compiler/xla/tests/local_client_test_base.h +++ b/tensorflow/compiler/xla/tests/local_client_test_base.h @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" #include "tensorflow/compiler/xla/service/local_service.h" #include "tensorflow/compiler/xla/service/platform_util.h" diff --git a/tensorflow/compiler/xla/tests/map_test.cc b/tensorflow/compiler/xla/tests/map_test.cc index 7ddc636931..34bcaef513 100644 --- a/tensorflow/compiler/xla/tests/map_test.cc +++ b/tensorflow/compiler/xla/tests/map_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc index 069b8a881f..4fca90af77 100644 --- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc +++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/reference_util.h" diff --git a/tensorflow/compiler/xla/tests/pad_test.cc b/tensorflow/compiler/xla/tests/pad_test.cc index e428fa9b5e..d8c17202f2 100644 --- a/tensorflow/compiler/xla/tests/pad_test.cc +++ b/tensorflow/compiler/xla/tests/pad_test.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" diff --git a/tensorflow/compiler/xla/tests/params_test.cc b/tensorflow/compiler/xla/tests/params_test.cc index 8ba1d11b33..bf3b5f2b65 100644 --- a/tensorflow/compiler/xla/tests/params_test.cc +++ b/tensorflow/compiler/xla/tests/params_test.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/reduce_test.cc b/tensorflow/compiler/xla/tests/reduce_test.cc index e4a8ddf86a..638b0825a1 100644 --- a/tensorflow/compiler/xla/tests/reduce_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_test.cc @@ -38,7 +38,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/reference_util.h" diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc index c2681f70f7..161b74a5c8 100644 --- a/tensorflow/compiler/xla/tests/reduce_window_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc @@ -25,7 +25,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" diff --git a/tensorflow/compiler/xla/tests/replay_test.cc b/tensorflow/compiler/xla/tests/replay_test.cc index d544968648..f026ad6c42 100644 --- a/tensorflow/compiler/xla/tests/replay_test.cc +++ b/tensorflow/compiler/xla/tests/replay_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/protobuf_util.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" diff --git a/tensorflow/compiler/xla/tests/reshape_test.cc b/tensorflow/compiler/xla/tests/reshape_test.cc index 46d91711a5..a6e985293a 100644 --- a/tensorflow/compiler/xla/tests/reshape_test.cc +++ b/tensorflow/compiler/xla/tests/reshape_test.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/reference_util.h" diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc index 3b603c0d31..5a3bcaf086 100644 --- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc +++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc index b1f1e69d3c..ceb795219a 100644 --- a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc +++ b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc index a517007591..ad46eaa1c3 100644 --- a/tensorflow/compiler/xla/tests/tuple_test.cc +++ b/tensorflow/compiler/xla/tests/tuple_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc index 79bae22dac..cacbe83b86 100644 --- a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc +++ b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test_helpers.h" diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc index 29befef92e..0a39778002 100644 --- a/tensorflow/compiler/xla/tests/while_test.cc +++ b/tensorflow/compiler/xla/tests/while_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/platform_util.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc index c000ff4dc8..7a75e5102c 100644 --- a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc +++ b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/platform_util.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tools/BUILD b/tensorflow/compiler/xla/tools/BUILD index 55501827f2..d7cabbe876 100644 --- a/tensorflow/compiler/xla/tools/BUILD +++ b/tensorflow/compiler/xla/tools/BUILD @@ -37,6 +37,7 @@ cc_library( "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", "//tensorflow/compiler/xla/service", "//tensorflow/compiler/xla/service:hlo_proto", @@ -84,6 +85,7 @@ cc_library( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:testing", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/service:hlo_proto", @@ -164,6 +166,7 @@ tf_cc_binary( "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service", "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/compiler/xla/service:interpreter_plugin", @@ -181,6 +184,7 @@ tf_cc_binary( "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/service", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_proto", @@ -198,6 +202,7 @@ tf_cc_binary( "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", "//tensorflow/compiler/xla/service", "//tensorflow/compiler/xla/service:hlo_graph_dumper", diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc index befb554537..f20dcef382 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_graphviz.cc @@ -31,6 +31,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/service.h" diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc index cfb8f37487..f0af0580c1 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_operation_list.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor_with_default.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/service.h" diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc index 5dd5150be3..f03e1b1f96 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_text.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/service.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc index a5dce20456..dc5c106d02 100644 --- a/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc +++ b/tensorflow/compiler/xla/tools/dumped_computation_to_tf_graphdef.cc @@ -29,6 +29,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/legacy_flags/debug_options_flags.h" #include "tensorflow/compiler/xla/service/hlo.pb.h" #include "tensorflow/compiler/xla/service/service.h" diff --git a/tensorflow/compiler/xla/tools/replay_computation.cc b/tensorflow/compiler/xla/tools/replay_computation.cc index 854e797ec2..3bb2f3c000 100644 --- a/tensorflow/compiler/xla/tools/replay_computation.cc +++ b/tensorflow/compiler/xla/tools/replay_computation.cc @@ -42,6 +42,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/lib/testing.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/gpu/infeed_manager.h" -- GitLab From f0bf6c5191d224f229808f4b321158d890a481e0 Mon Sep 17 00:00:00 2001 From: James Qin Date: Wed, 25 Jul 2018 14:44:18 -0700 Subject: [PATCH 412/519] Minor change for better error msg in eager input type checking PiperOrigin-RevId: 206058281 --- tensorflow/core/common_runtime/eager/execute.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/core/common_runtime/eager/execute.cc b/tensorflow/core/common_runtime/eager/execute.cc index 7ea78b63d9..0c0fbc729c 100644 --- a/tensorflow/core/common_runtime/eager/execute.cc +++ b/tensorflow/core/common_runtime/eager/execute.cc @@ -175,7 +175,7 @@ Status ValidateInputTypeAndPlacement(EagerContext* ctx, Device* op_device, tensorflow::TensorHandle* handle = op->Inputs()[i]; if (handle->dtype != kernel->input_type(i)) { return errors::InvalidArgument( - "cannot compute ", op->Name(), " as input #", i, + "cannot compute ", op->Name(), " as input #", i, "(zero-based)", " was expected to be a ", DataTypeString(kernel->input_type(i)), " tensor but is a ", DataTypeString(handle->dtype), " tensor"); } -- GitLab From 411a722626ddf98072d2e778835cbeb666db631f Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Wed, 25 Jul 2018 14:44:29 -0700 Subject: [PATCH 413/519] Re-enable the integration tests on TAP, and only disable the failing windows environment. PiperOrigin-RevId: 206058315 --- .../autograph/examples/integration_tests/BUILD | 12 ++---------- 1 file changed, 2 insertions(+), 10 deletions(-) diff --git a/tensorflow/contrib/autograph/examples/integration_tests/BUILD b/tensorflow/contrib/autograph/examples/integration_tests/BUILD index d22d39c83b..d20c17b63b 100644 --- a/tensorflow/contrib/autograph/examples/integration_tests/BUILD +++ b/tensorflow/contrib/autograph/examples/integration_tests/BUILD @@ -22,11 +22,7 @@ py_test( "keras_test.py", ], srcs_version = "PY2AND3", - tags = [ - "manual", - "no_oss", - "notap", - ], + tags = ["no_windows"], deps = [ "//tensorflow:tensorflow_py", ], @@ -38,11 +34,7 @@ py_test( "list_literals_test.py", ], srcs_version = "PY2AND3", - tags = [ - "manual", - "no_oss", - "notap", - ], + tags = ["no_windows"], deps = [ "//tensorflow:tensorflow_py", ], -- GitLab From 89bcc80f1b62c5d87486f074b569a750336c24a1 Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Wed, 25 Jul 2018 15:17:36 -0700 Subject: [PATCH 414/519] Also disable tf_driver_test and generate_testspec_test. PiperOrigin-RevId: 206063891 --- tensorflow/contrib/lite/testing/BUILD | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/lite/testing/BUILD b/tensorflow/contrib/lite/testing/BUILD index 098f029f13..a788d41ba7 100644 --- a/tensorflow/contrib/lite/testing/BUILD +++ b/tensorflow/contrib/lite/testing/BUILD @@ -257,6 +257,7 @@ cc_test( srcs = ["tf_driver_test.cc"], data = ["//tensorflow/contrib/lite:testdata/multi_add.pb"], tags = [ + "no_oss", "tflite_not_portable", ], deps = [ @@ -283,6 +284,7 @@ cc_test( size = "small", srcs = ["generate_testspec_test.cc"], tags = [ + "no_oss", "tflite_not_portable", ], deps = [ -- GitLab From cca3a5abd897fe78657da3ae192bf9e582c53295 Mon Sep 17 00:00:00 2001 From: Piotr Padlewski Date: Wed, 25 Jul 2018 15:51:39 -0700 Subject: [PATCH 415/519] [tf.data] Adding optimization for fusing consecutive `map(...)` transformations. This optimization pass fuses maps together e.g: map(x: x*x).map(x: x+1) can be fused to: map(x: x*x+1) We can see 10-x speedup on simple pipeline containing 50 consecutive maps of identity. PiperOrigin-RevId: 206069355 --- .../contrib/data/python/kernel_tests/BUILD | 2 + .../kernel_tests/map_dataset_op_test.py | 52 +++- .../kernel_tests/optimize_dataset_op_test.py | 51 +++- .../core/grappler/optimizers/data/BUILD | 38 +++ .../grappler/optimizers/data/map_fusion.cc | 261 ++++++++++++++++++ .../grappler/optimizers/data/map_fusion.h | 47 ++++ .../optimizers/data/map_fusion_test.cc | 90 ++++++ 7 files changed, 536 insertions(+), 5 deletions(-) create mode 100644 tensorflow/core/grappler/optimizers/data/map_fusion.cc create mode 100644 tensorflow/core/grappler/optimizers/data/map_fusion.h create mode 100644 tensorflow/core/grappler/optimizers/data/map_fusion_test.cc diff --git a/tensorflow/contrib/data/python/kernel_tests/BUILD b/tensorflow/contrib/data/python/kernel_tests/BUILD index 3759ba8d5a..2de1a79d28 100644 --- a/tensorflow/contrib/data/python/kernel_tests/BUILD +++ b/tensorflow/contrib/data/python/kernel_tests/BUILD @@ -192,6 +192,7 @@ py_test( deps = [ "//tensorflow/contrib/data/python/ops:batching", "//tensorflow/contrib/data/python/ops:error_ops", + "//tensorflow/contrib/data/python/ops:optimization", "//tensorflow/python:array_ops", "//tensorflow/python:client_testlib", "//tensorflow/python:errors", @@ -213,6 +214,7 @@ py_test( "//tensorflow/python:client_testlib", "//tensorflow/python:errors", "//tensorflow/python/data/ops:dataset_ops", + "@absl_py//absl/testing:parameterized", ], ) diff --git a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py index b7025f3802..48adc98e9a 100644 --- a/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/map_dataset_op_test.py @@ -26,6 +26,7 @@ import numpy as np from tensorflow.contrib.data.python.ops import batching from tensorflow.contrib.data.python.ops import error_ops +from tensorflow.contrib.data.python.ops import optimization from tensorflow.core.protobuf import config_pb2 from tensorflow.python.client import session from tensorflow.python.data.ops import dataset_ops @@ -82,15 +83,17 @@ class MapDatasetTest(test.TestCase): def write_string_to_file(value, filename): with open(filename, "w") as f: f.write(value) - filenames = [os.path.join(self.get_temp_dir(), "file_%d.txt" % i) - for i in range(5)] + + filenames = [ + os.path.join(self.get_temp_dir(), "file_%d.txt" % i) for i in range(5) + ] for filename in filenames: write_string_to_file(filename, filename) dataset = ( dataset_ops.Dataset.from_tensor_slices(filenames).map( - io_ops.read_file, num_parallel_calls=2).prefetch(2).apply( - error_ops.ignore_errors())) + io_ops.read_file, + num_parallel_calls=2).prefetch(2).apply(error_ops.ignore_errors())) iterator = dataset.make_initializable_iterator() init_op = iterator.initializer get_next = iterator.get_next() @@ -264,5 +267,46 @@ class MapDatasetBenchmark(test.Benchmark): benchmark("Transformation parallelism evaluation", par_num_calls_series) benchmark("Threadpool size evaluation", par_inter_op_series) + # This benchmark compares the performance of pipeline with multiple chained + # maps with and without map fusion. + def benchmarkChainOfMaps(self): + chain_lengths = [0, 1, 2, 5, 10, 20, 50] + for chain_length in chain_lengths: + self._benchmarkChainOfMaps(chain_length, False) + self._benchmarkChainOfMaps(chain_length, True) + + def _benchmarkChainOfMaps(self, chain_length, optimize_dataset): + with ops.Graph().as_default(): + dataset = dataset_ops.Dataset.from_tensors(0).repeat(None) + for _ in range(chain_length): + dataset = dataset.map(lambda x: x) + if optimize_dataset: + dataset = dataset.apply(optimization.optimize(["map_fusion"])) + + iterator = dataset.make_one_shot_iterator() + next_element = iterator.get_next() + + with session.Session() as sess: + for _ in range(5): + sess.run(next_element.op) + deltas = [] + for _ in range(100): + start = time.time() + for _ in range(100): + sess.run(next_element.op) + end = time.time() + deltas.append(end - start) + + median_wall_time = np.median(deltas) / 100 + opt_mark = "opt" if optimize_dataset else "no-opt" + print("Map dataset {} chain length: {} Median wall time: {}".format( + opt_mark, chain_length, median_wall_time)) + self.report_benchmark( + iters=1000, + wall_time=median_wall_time, + name="benchmark_map_dataset_chain_latency_{}_{}".format( + opt_mark, chain_length)) + + if __name__ == "__main__": test.main() diff --git a/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py b/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py index cfef40e192..d8156dc9c7 100644 --- a/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py +++ b/tensorflow/contrib/data/python/kernel_tests/optimize_dataset_op_test.py @@ -17,13 +17,15 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function +from absl.testing import parameterized + from tensorflow.contrib.data.python.ops import optimization from tensorflow.python.data.ops import dataset_ops from tensorflow.python.framework import errors from tensorflow.python.platform import test -class OptimizeDatasetTest(test.TestCase): +class OptimizeDatasetTest(test.TestCase, parameterized.TestCase): def testAssertSuffix(self): dataset = dataset_ops.Dataset.from_tensors(0).apply( @@ -110,6 +112,53 @@ class OptimizeDatasetTest(test.TestCase): "Function .* is not defined."): sess.run(get_next) + @staticmethod + def map_functions(): + identity = lambda x: x + increment = lambda x: x + 1 + + def increment_and_square(x): + y = x + 1 + return y * y + + functions = [identity, increment, increment_and_square] + tests = [] + + for fun1 in functions: + for fun2 in functions: + tests.append(([fun1, fun2],)) + for fun3 in functions: + tests.append(([fun1, fun2, fun3],)) + + swap = lambda x, n: (n, x) + tests.append(([lambda x: (x, 42), swap],)) + tests.append(([lambda x: (x, 42), swap, swap],)) + return tuple(tests) + + @parameterized.parameters(*map_functions.__func__()) + def testMapFusion(self, functions): + dataset = dataset_ops.Dataset.range(5).apply( + optimization.assert_next(["Map", "Prefetch"])) + for function in functions: + dataset = dataset.map(function) + + dataset = dataset.prefetch(0).apply(optimization.optimize(["map_fusion"])) + iterator = dataset.make_one_shot_iterator() + get_next = iterator.get_next() + with self.test_session() as sess: + for x in range(5): + result = sess.run(get_next) + r = x + for function in functions: + if isinstance(r, tuple): + r = function(*r) # Pass tuple as multiple arguments. + else: + r = function(r) + self.assertAllEqual(r, result) + + with self.assertRaises(errors.OutOfRangeError): + sess.run(get_next) + if __name__ == "__main__": test.main() diff --git a/tensorflow/core/grappler/optimizers/data/BUILD b/tensorflow/core/grappler/optimizers/data/BUILD index db96a81be8..d7ac58c99d 100644 --- a/tensorflow/core/grappler/optimizers/data/BUILD +++ b/tensorflow/core/grappler/optimizers/data/BUILD @@ -103,6 +103,43 @@ tf_cc_test( ], ) +cc_library( + name = "map_fusion", + srcs = ["map_fusion.cc"], + hdrs = [ + "map_fusion.h", + ], + visibility = ["//visibility:public"], + deps = [ + ":graph_utils", + "//tensorflow/core/grappler:mutable_graph_view", + "//tensorflow/core:lib", + "//tensorflow/core/grappler:grappler_item", + "//tensorflow/core/grappler:op_types", + "//tensorflow/core/grappler:utils", + "//tensorflow/core/grappler/clusters:cluster", + "//tensorflow/core/kernels:cast_op", + "//tensorflow/core/grappler/utils:topological_sort", + "//tensorflow/core/grappler/optimizers:custom_graph_optimizer", + "//tensorflow/core/grappler/optimizers:custom_graph_optimizer_registry", + ] + tf_protos_all(), +) + +tf_cc_test( + name = "map_fusion_test", + srcs = ["map_fusion_test.cc"], + visibility = ["//visibility:public"], + deps = [ + ":graph_utils", + ":map_fusion", + "//tensorflow/core:framework", + "//tensorflow/core:test", + "//tensorflow/core:test_main", + "//tensorflow/core:testlib", + "//tensorflow/core/grappler:grappler_item", + ], +) + cc_library( name = "noop_elimination", srcs = ["noop_elimination.cc"], @@ -177,6 +214,7 @@ cc_library( deps = [ ":function_rename", ":map_and_batch_fusion", + ":map_fusion", ":noop_elimination", ":shuffle_and_repeat_fusion", ], diff --git a/tensorflow/core/grappler/optimizers/data/map_fusion.cc b/tensorflow/core/grappler/optimizers/data/map_fusion.cc new file mode 100644 index 0000000000..707f4a3407 --- /dev/null +++ b/tensorflow/core/grappler/optimizers/data/map_fusion.cc @@ -0,0 +1,261 @@ +/* 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/data/map_fusion.h" + +#include "tensorflow/core/framework/attr_value.pb.h" +#include "tensorflow/core/framework/node_def.pb.h" +#include "tensorflow/core/grappler/clusters/cluster.h" +#include "tensorflow/core/grappler/grappler_item.h" +#include "tensorflow/core/grappler/mutable_graph_view.h" +#include "tensorflow/core/grappler/op_types.h" +#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.h" +#include "tensorflow/core/grappler/optimizers/data/graph_utils.h" +#include "tensorflow/core/grappler/utils.h" +#include "tensorflow/core/grappler/utils/topological_sort.h" +#include "tensorflow/core/platform/protobuf.h" + +namespace tensorflow { +namespace grappler { +namespace { + +// Sets basic function parameters and copies attributes from parent and map +// node. +NodeDef MakeFusedNode(const NodeDef& parent_map_node, const NodeDef& map_node, + const FunctionDef& fused_function, + MutableGraphView* graph) { + NodeDef fused_node; + graph_utils::SetUniqueGraphNodeName("fused_map", graph->GetGraph(), + &fused_node); + + fused_node.set_op("MapDataset"); + fused_node.add_input(parent_map_node.input(0)); + + auto copy_attribute = [](const string& attribute_name, const NodeDef& from, + NodeDef* to) { + (*to->mutable_attr())[attribute_name] = from.attr().at(attribute_name); + }; + + auto attr = parent_map_node.attr().at("f"); + *attr.mutable_func()->mutable_name() = fused_function.signature().name(); + (*fused_node.mutable_attr())["f"] = std::move(attr); + + copy_attribute("Targuments", parent_map_node, &fused_node); + + for (auto key : {"output_shapes", "output_types"}) + copy_attribute(key, map_node, &fused_node); + + return fused_node; +} + +string ParseNodeConnection(const string& name) { + // If input/output node name has semicolon, take the prefix. Otherwise take + // the whole string. + return name.substr(0, name.find(':')); +} + +string ParseOutputNode(const string& name) { + return name.substr(name.find(':'), string::npos); +} + +const string& GetOutputNode(const FunctionDef& parent_function, + int output_idx) { + const auto& ret_output_name = + parent_function.signature().output_arg(output_idx).name(); + return parent_function.ret().at(ret_output_name); +} + +// Nodes that will be added to the function can have the same name as the nodes +// from parent function. We need to rename them and the connections of the +// inputs that refer to them. +void RenameFunctionNodes(FunctionDef* fused_function, + protobuf::RepeatedPtrField* nodes_to_fuse) { + std::unordered_map changed_node_names; + for (NodeDef& function_node : *nodes_to_fuse) { + string name_before = function_node.name(); + graph_utils::SetUniqueFunctionNodeName(name_before, fused_function, + &function_node); + if (name_before != function_node.name()) + changed_node_names[name_before] = function_node.name(); + } + + auto update_name = [&changed_node_names](string* input) { + string input_node = ParseNodeConnection(*input); + if (changed_node_names.count(input_node) == 0) return; + const string& new_node_name = changed_node_names.at(input_node); + *input = new_node_name + ParseOutputNode(*input); + }; + + for (NodeDef& function_node : *nodes_to_fuse) { + for (string& input : *function_node.mutable_input()) { + update_name(&input); + } + } + + for (auto& ret : *fused_function->mutable_ret()) update_name(&ret.second); +} + +// This function adds new nodes and changes their input to the output nodes +// of parent function. +void FuseFunctionNodes(const FunctionDef& parent_function, + const FunctionDef& function, + protobuf::RepeatedPtrField* nodes_to_fuse) { + const auto number_of_outputs = parent_function.signature().output_arg_size(); + CHECK(number_of_outputs == function.signature().input_arg_size()) + << "The number of input arguments of function " + << function.signature().name() + << " should be the same as the number of output arguments of function " + << parent_function.signature().name() << "."; + + for (int output_idx = 0; output_idx < number_of_outputs; output_idx++) { + const string& output = GetOutputNode(parent_function, output_idx); + + const auto& input_node_name = + function.signature().input_arg(output_idx).name(); + + for (NodeDef& function_node : *nodes_to_fuse) { + for (auto& node_input : *function_node.mutable_input()) { + auto parsed_name = ParseNodeConnection(node_input); + if (parsed_name != input_node_name) continue; + + node_input = output; + } + } + } +} + +// This function looks for direct edges from input to return and rewrites +// them to the coresponding input of the return of parent_function. +void FuseReturns(const FunctionDef& parent_function, + const FunctionDef& function, FunctionDef* fused_function) { + const auto number_of_inputs = function.signature().input_arg_size(); + + for (auto& ret : *fused_function->mutable_ret()) { + auto return_input = ParseNodeConnection(ret.second); + for (int input_idx = 0; input_idx < number_of_inputs; input_idx++) { + const auto& input_arg = function.signature().input_arg(input_idx); + if (return_input != input_arg.name()) continue; + + ret.second = GetOutputNode(parent_function, input_idx); + } + } +} + +// This function produces new function that is a result of fusion of +// `parent_function` with `function`. +FunctionDef* FuseFunctions(const FunctionDef& parent_function, + const FunctionDef& function, + FunctionDefLibrary* library) { + FunctionDef* fused_function = library->add_function(); + graph_utils::SetUniqueGraphFunctionName("fused_function", library, + fused_function); + + // Copy input signature from parent function. + *fused_function->mutable_signature()->mutable_input_arg() = + parent_function.signature().input_arg(); + + fused_function->mutable_node_def()->CopyFrom(parent_function.node_def()); + // This code assumes functions does not have any attributes. If this is + // not the case, we need to merge attributes and fix name conflicts. + CHECK(parent_function.attr_size() == 0 && function.attr_size() == 0 && + "Functions with attributes are currently not supported"); + + // Copy the returns and output signature from the second node. + auto nodes_to_fuse = function.node_def(); + fused_function->mutable_signature()->mutable_output_arg()->CopyFrom( + function.signature().output_arg()); + *fused_function->mutable_ret() = function.ret(); + + RenameFunctionNodes(fused_function, &nodes_to_fuse); + FuseFunctionNodes(parent_function, function, &nodes_to_fuse); + FuseReturns(parent_function, function, fused_function); + + // Copy transformed nodes from the second function. + fused_function->mutable_node_def()->MergeFrom(nodes_to_fuse); + + return fused_function; +} + +} // namespace + +Status MapFusion::Optimize(Cluster* cluster, const GrapplerItem& item, + GraphDef* output) { + GraphDef sorted_old_graph = item.graph; + TF_RETURN_IF_ERROR(TopologicalSort(&sorted_old_graph)); + *output = sorted_old_graph; + + MutableGraphView graph(output); + std::set nodes_to_delete; + FunctionLibraryDefinition function_library(OpRegistry::Global(), + item.graph.library()); + + auto get_map_node = [](const NodeDef& node) -> const NodeDef* { + // TODO(prazek): we could also handle ParallelMapDataset and + // MapAndBatchDataset. + if (node.op() == "MapDataset") return &node; + return nullptr; + }; + + auto get_fused_function = [&function_library, &output]( + const NodeDef* parent_map_node, + const NodeDef* map_node) { + const auto& parent_fun = parent_map_node->attr().at("f"); + const FunctionDef* parent_func = + function_library.Find(parent_fun.func().name()); + const auto& fun = map_node->attr().at("f"); + const FunctionDef* func = function_library.Find(fun.func().name()); + + return FuseFunctions(*parent_func, *func, output->mutable_library()); + }; + + for (const NodeDef& node : sorted_old_graph.node()) { + const NodeDef* map_node = get_map_node(node); + if (!map_node) continue; + + GraphView::InputPort input_port = graph.GetInputPort(map_node->name(), 0); + const NodeDef* parent_map_node = + get_map_node(*graph.GetRegularFanin(input_port).node); + if (!parent_map_node) continue; + + const auto* fused_function = get_fused_function(parent_map_node, map_node); + const auto* fused_maps_node = graph.AddNode( + MakeFusedNode(*parent_map_node, *map_node, *fused_function, &graph)); + + graph.ReplaceInput(*map_node, *fused_maps_node); + + // TODO(prazek): we should run some optimizations on the fused map + // functions, or make sure that optimization passes run after map + // fusion. + TF_RETURN_IF_ERROR(function_library.AddFunctionDef(*fused_function)); + + // TODO(prazek): we could also remove map functions from library if they + // are not used anymore. + nodes_to_delete.insert(parent_map_node->name()); + nodes_to_delete.insert(map_node->name()); + } + + graph.DeleteNodes(nodes_to_delete); + return Status::OK(); +} + +void MapFusion::Feedback(Cluster* cluster, const GrapplerItem& item, + const GraphDef& optimize_output, double result) { + // no-op +} + +REGISTER_GRAPH_OPTIMIZER_AS(MapFusion, "map_fusion"); + +} // end namespace grappler +} // end namespace tensorflow diff --git a/tensorflow/core/grappler/optimizers/data/map_fusion.h b/tensorflow/core/grappler/optimizers/data/map_fusion.h new file mode 100644 index 0000000000..a6a06592b8 --- /dev/null +++ b/tensorflow/core/grappler/optimizers/data/map_fusion.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_CORE_GRAPPLER_OPTIMIZERS_DATA_MAP_FUSION_H_ +#define TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_DATA_MAP_FUSION_H_ + +#include "tensorflow/core/grappler/optimizers/custom_graph_optimizer.h" + +namespace tensorflow { +namespace grappler { + +// This optimization fuses map transformations by merging their map functions. +class MapFusion : public CustomGraphOptimizer { + public: + MapFusion() = default; + ~MapFusion() override = default; + + string name() const override { return "map_fusion"; }; + + Status Init( + const tensorflow::RewriterConfig_CustomGraphOptimizer* config) override { + return Status::OK(); + } + + Status Optimize(Cluster* cluster, const GrapplerItem& item, + GraphDef* output) override; + + void Feedback(Cluster* cluster, const GrapplerItem& item, + const GraphDef& optimize_output, double result) override; +}; + +} // end namespace grappler +} // end namespace tensorflow + +#endif // TENSORFLOW_CORE_GRAPPLER_OPTIMIZERS_DATA_MAP_FUSION_H_ diff --git a/tensorflow/core/grappler/optimizers/data/map_fusion_test.cc b/tensorflow/core/grappler/optimizers/data/map_fusion_test.cc new file mode 100644 index 0000000000..df6c19dc7c --- /dev/null +++ b/tensorflow/core/grappler/optimizers/data/map_fusion_test.cc @@ -0,0 +1,90 @@ +/* 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/data/map_fusion.h" + +#include "tensorflow/core/framework/attr_value_util.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/optimizers/data/graph_utils.h" + +#include "tensorflow/core/lib/core/status_test_util.h" +#include "tensorflow/core/platform/test.h" + +namespace tensorflow { +namespace grappler { +namespace { + +NodeDef MakeMapNode(StringPiece name, StringPiece input_node_name) { + return test::function::NDef( + name, "MapDataset", {input_node_name.ToString()}, + {{"f", FunctionDefHelper::FunctionRef("XTimesTwo")}, + {"Targuments", {}}, + {"output_shapes", {}}, + {"output_types", {}}}); +} + +TEST(MapFusionTest, FuseTwoMapNodesIntoOne) { + using test::function::NDef; + GrapplerItem item; + item.graph = test::function::GDef( + {NDef("start", "Const", {}, {{"value", 0}, {"dtype", DT_INT32}}), + NDef("stop", "Const", {}, {{"value", 10}, {"dtype", DT_INT32}}), + NDef("step", "Const", {}, {{"value", 1}, {"dtype", DT_INT32}}), + NDef("range", "RangeDataset", {"start", "stop", "step"}, {}), + MakeMapNode("map1", "range"), MakeMapNode("map2", "map1")}, + // FunctionLib + { + test::function::XTimesTwo(), + }); + + MapFusion optimizer; + GraphDef output; + TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output)); + EXPECT_TRUE(graph_utils::ContainsNodeWithOp("MapDataset", output)); + EXPECT_FALSE(graph_utils::ContainsGraphNodeWithName("map1", output)); + EXPECT_FALSE(graph_utils::ContainsGraphNodeWithName("map2", output)); +} + +TEST(MapFusionTest, FuseThreeNodesIntoOne) { + using test::function::NDef; + GrapplerItem item; + item.graph = test::function::GDef( + {NDef("start", "Const", {}, {{"value", 0}, {"dtype", DT_INT32}}), + NDef("stop", "Const", {}, {{"value", 10}, {"dtype", DT_INT32}}), + NDef("step", "Const", {}, {{"value", 1}, {"dtype", DT_INT32}}), + NDef("filename", "Const", {}, {{"value", ""}, {"dtype", DT_STRING}}), + NDef("range", "RangeDataset", {"start", "stop", "step"}, {}), + MakeMapNode("map1", "range"), MakeMapNode("map2", "map1"), + MakeMapNode("map3", "map2"), + NDef("cache", "CacheDataset", {"map3", "filename"}, {})}, + // FunctionLib + { + test::function::XTimesTwo(), + }); + + MapFusion optimizer; + GraphDef output; + TF_ASSERT_OK(optimizer.Optimize(nullptr, item, &output)); + EXPECT_TRUE(graph_utils::ContainsNodeWithOp("MapDataset", output)); + EXPECT_FALSE(graph_utils::ContainsGraphNodeWithName("map1", output)); + EXPECT_FALSE(graph_utils::ContainsGraphNodeWithName("map2", output)); + EXPECT_FALSE(graph_utils::ContainsGraphNodeWithName("map3", output)); +} + +} // namespace +} // namespace grappler +} // namespace tensorflow -- GitLab From 7ff06d70e5fbe1f28034541a56b17a02f8735b20 Mon Sep 17 00:00:00 2001 From: Bixia Zheng Date: Wed, 25 Jul 2018 15:53:42 -0700 Subject: [PATCH 416/519] [XLA:CPU/GPU] Implement the parallel Philox random number generation algorithm. Implement the RNG elemental ir generator using the Philox algorithm. To ensure multiple execution of the same RNG hlo instruction rarely produce the same result, we increment a global variable with the number of random numbers generated by the RNG hlo each time the hlo is executed and use the value of the global variable to construct the seed for the RNG algorithm. Modify the GPU backend to generate a parallel loop to execute the Philox algorithm. The CPU backend still uses a sequential loop to perform Philox random number generation, and we will need to enhance the ParallelTaskAssignment pass to change this. Remove the old PCG RNG algorithm for the CPU and GPU backends. PiperOrigin-RevId: 206069733 --- .../compiler/xla/service/cpu/ir_emitter.cc | 17 + .../compiler/xla/service/cpu/ir_emitter.h | 1 + .../xla/service/elemental_ir_emitter.cc | 398 +++++++++++------- .../xla/service/elemental_ir_emitter.h | 11 +- .../compiler/xla/service/gpu/ir_emitter.cc | 17 - .../compiler/xla/service/gpu/ir_emitter.h | 1 - .../xla/service/gpu/ir_emitter_unnested.cc | 42 +- .../xla/service/gpu/stream_assignment.cc | 70 +-- .../compiler/xla/service/llvm_ir/ir_array.cc | 1 + .../compiler/xla/service/llvm_ir/llvm_util.cc | 51 +++ .../compiler/xla/service/llvm_ir/llvm_util.h | 21 + 11 files changed, 426 insertions(+), 204 deletions(-) diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index d4ac35a604..9d9d3e04a9 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -2506,6 +2506,23 @@ Status IrEmitter::HandleIota(HloInstruction* iota) { return Unimplemented("Iota is not implemented on CPU."); } +Status IrEmitter::HandleRng(HloInstruction* rng) { + ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator; + for (const HloInstruction* operand : rng->operands()) { + operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) { + return GetIrArrayFor(operand).EmitReadArrayElement(index, &b_); + }; + } + + CpuElementalIrEmitter elemental_emitter(hlo_module_config_, this, module_); + TF_RETURN_IF_ERROR(EmitTargetElementLoop( + rng, elemental_emitter.MakeElementGenerator(rng, operand_to_generator))); + + llvm_ir::IncrementVariableForPhiloxRngState(1, module_, &b_); + + return Status::OK(); +} + Status IrEmitter::FinishVisit(HloInstruction* root) { // When this method is called, we should have already emitted an IR value for // the root (return) op. The IR value holds the address of the buffer holding diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h index 4e928ffadc..cf7fa05b20 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h @@ -149,6 +149,7 @@ class IrEmitter : public DfsHloVisitorWithDefault { Status HandleConditional(HloInstruction* conditional) override; Status HandleAfterAll(HloInstruction* gen_token) override; Status HandleIota(HloInstruction* iota) override; + Status HandleRng(HloInstruction* rng) override; Status FinishVisit(HloInstruction* root) override; Status Preprocess(HloInstruction* hlo) override; diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc index 47ed6162ed..f883eb828c 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.cc +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.cc @@ -1223,168 +1223,254 @@ llvm_ir::IrArray::Index ElementalIrEmitter::ElementwiseSourceIndex( return source_index; } -llvm_ir::ElementGenerator ElementalIrEmitter::MakeRngElementGenerator( +StatusOr ElementalIrEmitter::ConvertValueForDistribution( const HloInstruction* hlo, - const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator) - const { - PrimitiveType param_prim_type = hlo->operand(0)->shape().element_type(); - llvm::Type* param_ir_type = - llvm_ir::PrimitiveTypeToIrType(param_prim_type, module_); - - // Same values as PCG library - // https://github.com/imneme/pcg-c/blob/master/include/pcg_variants.h - llvm::Value* multiplier = - b_->getInt(llvm::APInt(128, {0x4385DF649FCCF645, 0x2360ED051FC65DA4})); - llvm::Value* increment = - b_->getInt(llvm::APInt(128, {0x14057B7EF767814F, 0x5851F42D4C957F2D})); - - auto random_value_from_hlo = [hlo]() { - const HloModule* module = - hlo->IsFused() ? hlo->parent()->FusionInstruction()->parent()->parent() - : hlo->parent()->parent(); - return module->RandomNew64(); - }; + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index, llvm::Value* raw_value) const { + TF_ASSIGN_OR_RETURN(llvm::Value * a_or_mean, + operand_to_generator.at(hlo->operand(0))(index)); + TF_ASSIGN_OR_RETURN(llvm::Value * b_or_sigma, + operand_to_generator.at(hlo->operand(1))(index)); + PrimitiveType elem_prim_ty = hlo->shape().element_type(); + llvm::Type* elem_ir_ty = + llvm_ir::PrimitiveTypeToIrType(elem_prim_ty, module_); + llvm::Type* raw_value_ty = raw_value->getType(); + + // Convert raw integer to float in range [0, 1) if the element is a float. + llvm::Value* elem_value = raw_value; + if (elem_ir_ty->isFloatingPointTy()) { + elem_value = b_->CreateUIToFP(elem_value, elem_ir_ty); + unsigned raw_value_size_in_bits = raw_value_ty->getPrimitiveSizeInBits(); + CHECK(raw_value_size_in_bits == 32 || raw_value_size_in_bits == 64); + elem_value = b_->CreateFDiv( + elem_value, + llvm::ConstantFP::get(elem_ir_ty, + raw_value_size_in_bits == 64 ? 0x1p64 : 0x1p32)); + } + + // Convert the value for the requested distribution. + switch (hlo->random_distribution()) { + case RNG_UNIFORM: { + if (elem_ir_ty->isFloatingPointTy()) { + return b_->CreateFAdd( + b_->CreateFMul(b_->CreateFSub(b_or_sigma, a_or_mean), elem_value), + a_or_mean); + } else { + // To generate a uniform random value in [a, b) from a raw random sample + // in range [0, 2^N), we let range = b - a and return + // (a + raw_value % range). If range is not a power of 2, raw values + // larger than (2^N - 2^N % range) are biased toward results in + // [a, a + (limit % range)). An unbiased algorithm would need to drop + // raw values and re-sample, but we don't do this because re-sampling in + // an efficient way is complex, and it's not clear that users need it. + // In particular, if one thread in a GPU warp needs to re-sample, we pay + // the same cost as if the whole warp were to re-sample. So an + // efficient re-sampling implementation on GPU would need to do + // nontrivial work to share entropy between threads in the warp. + auto range = b_->CreateSub(b_or_sigma, a_or_mean); + return b_->CreateAdd(a_or_mean, b_->CreateURem(elem_value, range)); + } + } + case RNG_NORMAL: { + TF_ASSIGN_OR_RETURN( + llvm::Value * r, + EmitErfcInv(elem_prim_ty, + b_->CreateFMul(llvm::ConstantFP::get(elem_ir_ty, 2.0), + elem_value))); + return b_->CreateFAdd(b_->CreateFMul(r, b_or_sigma), a_or_mean); + } + default: + return InvalidArgument( + "unhandled distribution %s", + RandomDistribution_Name(hlo->random_distribution()).c_str()); + } +} + +namespace { + +// Checks that the primitive type is supported by the elemental IR emitter for +// Philox RNG and returns the number of elements in each 128 bit sample of the +// Philox RNG algorithm. +int32 GetNumberOfElementsPerPhiloxRngSample(PrimitiveType elem_prim_ty) { + // Calculate the number of elements, that is the number of random numbers, in + // a 128 bit sample. + switch (elem_prim_ty) { + case U32: + case S32: + case F32: + // The algorithm uses 32 bits to generate values for F16. + case F16: + return 4; + case U64: + case F64: + return 2; + default: + // BF16 is converted to F16 by the hlo pass HloElementTypeConverter. + // Other data types are not supported by XLA random operation. + LOG(FATAL) << "Unrecognized primitive type for RNG " << elem_prim_ty; + } + return 0; +} + +// Calculates the four uint32 values for the 128-bit Philox sample. +std::array CalculateSampleValues( + llvm::Value* sample_idx, llvm::Value* hlo_random_value, + llvm::Value* global_random_number, llvm::Value* rng_state, + llvm::IRBuilder<>* b) { + llvm::Type* index_ty = sample_idx->getType(); + + std::array counter_values; + + // Use the sample index to initialize counter[0] and counter[1]. + unsigned index_ty_size_in_bits = index_ty->getPrimitiveSizeInBits(); + CHECK(index_ty_size_in_bits == 32 || index_ty_size_in_bits == 64); + if (index_ty_size_in_bits == 32) { + counter_values[0] = sample_idx; + counter_values[1] = b->getInt32(0); + } else { + std::tie(counter_values[0], counter_values[1]) = + llvm_ir::SplitInt64ToInt32s(b, sample_idx); + } + + // Xor the global state variable with the global random number seed and use + // the result to initialize counter[2] and counter[3]. + std::tie(counter_values[2], counter_values[3]) = llvm_ir::SplitInt64ToInt32s( + b, b->CreateXor(rng_state, global_random_number)); - // Seed each RNG emitter with a new 64-bit seed from the HloModule. If the - // compilation order is deterministic (i.e., RandomNew64 invocation order is - // deterministic), then the order of RNG is deterministic for a given seed and - // hence tests will be deterministic. - // If the user provides a global seed instruction then we only use 64-bits of - // the host's random number generator to seed the 128 bit value with the other - // 64-bits is due to a user specified global seed instruction. - // Create a GlobalVariable to maintain state between invocations. There is a - // bug in NVPTX with GlobalVariable and 128 bit values, so using 2 64-bit + // The algorithm uses a 64 bit key, which is also interpreted as two uint32 // values. - llvm::GlobalVariable* state_ptr0 = new llvm::GlobalVariable( - /*M=*/*module_, - /*Ty=*/b_->getInt64Ty(), - /*isConstant=*/false, - /*Linkage=*/llvm::GlobalValue::PrivateLinkage, - /*Initializer=*/b_->getInt64(random_value_from_hlo()), - /*Name=*/"state_ptr0"); - - // When the module config seed is 0, the expected result of a prng is a random - // value. Instead of using the random_value_from_hlo, we need a global random - // value as the graph seed. This is because if we use random_value_from_hlo - // here, then for a newly built hlo graph, it always gives the same number. - uint64 graph_seed = hlo_module_config_.seed() != 0 ? hlo_module_config_.seed() - : GlobalRandomValue(); - llvm::GlobalVariable* state_ptr1 = new llvm::GlobalVariable( - /*M=*/*module_, - /*Ty=*/b_->getInt64Ty(), - /*isConstant=*/false, - /*Linkage=*/llvm::GlobalValue::PrivateLinkage, - /*Initializer=*/b_->getInt64(graph_seed), - /*Name=*/"state_ptr1"); - - // We want each thread to use its own stream, so we modify the increment per - // thread. We want the increment to remain odd, so we shift the thread id left - // 1 and add it to the increment. - increment = b_->CreateAdd(increment, b_->CreateShl(EmitThreadId(), 1)); - - // PCG-XSL-RR algorithm - // http://www.pcg-random.org/pdf/toms-oneill-pcg-family-v1.02.pdf - // state = multiplier * state + increment - // return uint64_t(state ^ (state >> 64))) >>> (state >> 122) - // where ">>>" is bitwise rotation - auto get_next_i64 = [=]() { - llvm::Value* state0 = b_->CreateZExtOrTrunc( - b_->CreateLoad(state_ptr0, "state0"), b_->getInt128Ty()); - llvm::Value* state1 = b_->CreateShl( - b_->CreateZExtOrTrunc(b_->CreateLoad(state_ptr1, "state1"), - b_->getInt128Ty()), - 64); - llvm::Value* state = b_->CreateOr(state0, state1); - llvm::Value* updated = - b_->CreateAdd(b_->CreateMul(state, multiplier), increment); - b_->CreateStore(b_->CreateTrunc(updated, b_->getInt64Ty()), state_ptr0); - b_->CreateStore( - b_->CreateTrunc(b_->CreateLShr(updated, 64), b_->getInt64Ty()), - state_ptr1); - - return llvm_ir::CreateRor( - b_->CreateTrunc(b_->CreateXor(state, b_->CreateLShr(state, 64)), - b_->getInt64Ty()), - b_->CreateTrunc(b_->CreateLShr(state, 122), b_->getInt64Ty()), b_); - }; + llvm::Value* key_values[2]; + + // Use a module random number to initialize the key. + std::tie(key_values[0], key_values[1]) = + llvm_ir::SplitInt64ToInt32s(b, hlo_random_value); + + // Prepare the constants used in the Philox RNG Algorithm. + llvm::Value* philoxW32A = b->getInt32(0x9E3779B9); + llvm::Value* philoxW32B = b->getInt32(0xBB67AE85); + llvm::Value* philoxM4xW32A = b->getInt32(0xD2511F53); + llvm::Value* philoxM4xW32B = b->getInt32(0xCD9E8D57); + + // Compute the 128 bit value for the current sample by repeating the + // single round computation and key raising computation for ten times. + for (int round = 0; round < 10; ++round) { + // A single round of computation of the counter values is as follows: + // MultiplyHighLow(kPhiloxM4x32A, counter[0], &lo0, &hi0); + // MultiplyHighLow(kPhiloxM4x32B, counter[2], &lo1, &hi1); + // counter[0] = hi1 ^ counter[1] ^ key[0]; + // counter[1] = lo1; + // counter[2] = hi0 ^ counter[3] ^ key[1]; + // counter[3] = lo0; + llvm::Value* lo0; + llvm::Value* hi0; + std::tie(lo0, hi0) = + llvm_ir::UMulLowHigh32(b, philoxM4xW32A, counter_values[0]); + llvm::Value* lo1; + llvm::Value* hi1; + std::tie(lo1, hi1) = + llvm_ir::UMulLowHigh32(b, philoxM4xW32B, counter_values[2]); + counter_values[0] = + b->CreateXor(hi1, b->CreateXor(counter_values[1], key_values[0])); + counter_values[1] = lo1; + counter_values[2] = + b->CreateXor(hi0, b->CreateXor(counter_values[3], key_values[1])); + counter_values[3] = lo0; + key_values[0] = b->CreateAdd(key_values[0], philoxW32A); + key_values[1] = b->CreateAdd(key_values[1], philoxW32B); + } - auto get_next_uniform_float = [=]() { - return b_->CreateFDiv(b_->CreateUIToFP(get_next_i64(), param_ir_type), - llvm::ConstantFP::get(param_ir_type, 0x1p64)); - }; + return counter_values; +} + +} // namespace +// Implements the Philox algorithm to generate random numbers in parallel. +// Salmon et al. SC 2011. Parallel random numbers: as easy as 1, 2, 3. +// http://www.thesalmons.org/john/random123/papers/random123sc11.pdf +// +// The paper presents a few variants of the Philox algorithm, we picked the +// 4x32_10 version of the algorithm for the following reasons: +// . 4x32 uses 32-bit multiplication which is fast on GPUs. +// . The authors recommend the 10-round variant, and TensorFlow also uses it. +// +// Precondition: the RNG instruction is not fused. +llvm_ir::ElementGenerator ElementalIrEmitter::MakePhiloxRngElementGenerator( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator) + const { + VLOG(3) << "Using philox RNG algorithm"; + CHECK(!hlo->IsFused()); + // A random number generated by the per module random number generator. + // This ensures that each RNG HLO generates a different random sequence. + llvm::Value* hlo_random_value = b_->getInt64(hlo->GetModule()->RandomNew64()); + // A value specified by the configuration or generated by a global random + // number generator. + llvm::Value* global_random_number = + b_->getInt64(hlo_module_config_.seed() != 0 ? hlo_module_config_.seed() + : GlobalRandomValue()); + + int elems_per_sample = + GetNumberOfElementsPerPhiloxRngSample(hlo->shape().element_type()); + + // Allocate stack storage for the 128 bit sample as four int32. + llvm::Type* int32_ty = b_->getInt32Ty(); + llvm::Value* sample_address = llvm_ir::EmitAllocaAtFunctionEntryWithCount( + int32_ty, /*element_count=*/b_->getInt32(4), "sample", b_); + + // Load the global state variable for the Philox RNG algorithm. + llvm::GlobalVariable* rng_state_ptr = + llvm_ir::GetOrCreateVariableForPhiloxRngState(module_, b_); + llvm::Value* rng_state = b_->CreateLoad(rng_state_ptr, "rng_state_value"); + + // Build and return the elemental IR generator to generate a random value for + // the element corresponding to the current thread. + // + // This elemental IR generator computes one sample with multiple random + // numbers but only returns one random number. As a result, neighboring + // threads may calculate the same sample unnecessarily. However, if the + // kernel containing the RNG hlo is unrolled, LLVM is able to optimize away + // the duplicated computation of the same sample. In particular, if the unroll + // factor is a multiplier of elems_per_sample, LLVM is able to completely + // remove such duplicated computation. If the unroll factor is a non-trivial + // factor of elems_per_sample, LLVM can only partially remove such duplicated + // computation. return [=](const llvm_ir::IrArray::Index& index) -> StatusOr { - switch (hlo->random_distribution()) { - case RNG_UNIFORM: { - TF_ASSIGN_OR_RETURN(llvm::Value * p, - operand_to_generator.at(hlo->operand(0))(index)); - TF_ASSIGN_OR_RETURN(llvm::Value * q, - operand_to_generator.at(hlo->operand(1))(index)); - if (primitive_util::IsFloatingPointType(param_prim_type)) { - return b_->CreateFAdd( - b_->CreateFMul(b_->CreateFSub(q, p), get_next_uniform_float()), - p); - } else { - auto r = b_->CreateSub(q, p); - auto leading_zeros = llvm_ir::EmitCallToIntrinsic( - llvm::Intrinsic::ctlz, {r, b_->getInt1(true)}, {param_ir_type}, - b_); - auto in_block = b_->GetInsertBlock(); - - // A terminator should be present iff we're emitting code - // into the middle (as opposed to the end) of a basic block. - CHECK_EQ(b_->GetInsertPoint() == in_block->end(), - in_block->getTerminator() == nullptr); - - llvm::BasicBlock* body_block; - llvm::BasicBlock* out_block; - - if (b_->GetInsertPoint() == in_block->end()) { - body_block = - llvm_ir::CreateBasicBlock(nullptr, IrName(hlo, "rng_body"), b_); - out_block = - llvm_ir::CreateBasicBlock(nullptr, IrName(hlo, "rng_out"), b_); - llvm::BranchInst::Create(body_block, in_block); - } else { - body_block = - in_block->splitBasicBlock(b_->GetInsertPoint(), "rng_body"); - out_block = - body_block->splitBasicBlock(b_->GetInsertPoint(), "rng_out"); - body_block->getTerminator()->eraseFromParent(); - } - - SetToFirstInsertPoint(body_block, b_); - auto random = b_->CreateAnd( - b_->CreateZExtOrTrunc(get_next_i64(), param_ir_type), - b_->CreateLShr(llvm::ConstantInt::get(param_ir_type, ~0), - leading_zeros)); - llvm::BranchInst::Create(out_block, body_block, - b_->CreateICmpULT(random, r), body_block); - SetToFirstInsertPoint(out_block, b_); - return b_->CreateAdd( - p, b_->CreateSelect(b_->CreateICmpEQ(p, q), - llvm::ConstantInt::get(param_ir_type, 0), - random)); - } - } - case RNG_NORMAL: { - TF_ASSIGN_OR_RETURN(llvm::Value * m, - operand_to_generator.at(hlo->operand(0))(index)); - TF_ASSIGN_OR_RETURN(llvm::Value * s, - operand_to_generator.at(hlo->operand(1))(index)); - TF_ASSIGN_OR_RETURN( - llvm::Value * r, - EmitErfcInv( - param_prim_type, - b_->CreateFMul(llvm::ConstantFP::get(param_ir_type, 2.0), - get_next_uniform_float()))); - return b_->CreateFAdd(b_->CreateFMul(r, s), m); - } - default: - return InvalidArgument( - "unhandled distribution %s", - RandomDistribution_Name(hlo->random_distribution()).c_str()); + llvm::Type* index_ty = index.GetType(); + // Calculate the linear element index. + llvm::Value* elem_idx = index.linear(); + if (elem_idx == nullptr) { + elem_idx = index.Linearize(AsInt64Slice(hlo->shape().dimensions()), b_); } + + // Calculate the index for the 128 bit sample and the offset of the current + // element within the sample. + llvm::Value* elems_per_sample_value = + llvm::ConstantInt::get(index_ty, elems_per_sample); + llvm::Value* sample_idx = b_->CreateUDiv(elem_idx, elems_per_sample_value); + llvm::Value* elem_offset = b_->CreateURem(elem_idx, elems_per_sample_value); + + std::array counter_values = CalculateSampleValues( + sample_idx, hlo_random_value, global_random_number, rng_state, b_); + + // Store the four counter_values into the sample_address alloca so we can + // load the elem_offset'th one below. + for (int idx = 0; idx < 4; ++idx) { + b_->CreateStore(counter_values[idx], + b_->CreateInBoundsGEP(sample_address, b_->getInt32(idx))); + } + + llvm::Type* int64_ty = b_->getInt64Ty(); + CHECK(elems_per_sample == 2 || elems_per_sample == 4); + llvm::Type* raw_value_ty = elems_per_sample == 2 ? int64_ty : int32_ty; + // Retrieve the raw value for the current element from the current sample. + llvm::Value* raw_elem_value = b_->CreateLoad( + b_->CreateInBoundsGEP( + b_->CreatePointerCast(sample_address, raw_value_ty->getPointerTo()), + elem_offset), + "raw_elem_value"); + + return ConvertValueForDistribution(hlo, operand_to_generator, index, + raw_elem_value); }; } @@ -2034,7 +2120,7 @@ llvm_ir::ElementGenerator ElementalIrEmitter::MakeElementGenerator( hlo->shape(), hlo->operand(0)->shape(), hlo->dimensions(), b_)); }; case HloOpcode::kRng: - return MakeRngElementGenerator(hlo, operand_to_generator); + return MakePhiloxRngElementGenerator(hlo, operand_to_generator); case HloOpcode::kPad: return [this, hlo, &operand_to_generator]( const IrArray::Index& padded_index) -> StatusOr { diff --git a/tensorflow/compiler/xla/service/elemental_ir_emitter.h b/tensorflow/compiler/xla/service/elemental_ir_emitter.h index deba6bea0a..fcb34557a5 100644 --- a/tensorflow/compiler/xla/service/elemental_ir_emitter.h +++ b/tensorflow/compiler/xla/service/elemental_ir_emitter.h @@ -193,10 +193,17 @@ class ElementalIrEmitter { const HloModuleConfig& hlo_module_config_; private: - // Returns a ElementGenerator for a RNG HloInstruction. - llvm_ir::ElementGenerator MakeRngElementGenerator( + // Returns a ElementGenerator for an RNG HloInstruction using the Philox + // random number generation algorithm. + llvm_ir::ElementGenerator MakePhiloxRngElementGenerator( const HloInstruction* hlo, const HloToElementGeneratorMap& operand_to_generator) const; + // Converts the raw value generated by a random number generation algorithm + // to the distribution requested by the RNG HloInstruction. + StatusOr ConvertValueForDistribution( + const HloInstruction* hlo, + const ElementalIrEmitter::HloToElementGeneratorMap& operand_to_generator, + const llvm_ir::IrArray::Index& index, llvm::Value* raw_value) const; }; } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index f95541cba4..973848c336 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -716,23 +716,6 @@ Status IrEmitter::HandleOutfeed(HloInstruction*) { return Unimplemented("Outfeed is not supported on GPU."); } -Status IrEmitter::HandleRng(HloInstruction* random) { - ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator; - for (const HloInstruction* operand : random->operands()) { - operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) { - return GetIrArray(*operand, *random).EmitReadArrayElement(index, &b_); - }; - } - // Emits a single-threaded loop because the loop body generated by the element - // generator for Rng can't be parallelized (b/32333178). - return llvm_ir::LoopEmitter( - GpuElementalIrEmitter(hlo_module_config_, module_, &b_, - GetNestedComputer()) - .MakeElementGenerator(random, operand_to_generator), - GetIrArray(*random, *random), &b_) - .EmitLoop(IrName(random)); -} - Status IrEmitter::HandleBatchNormInference(HloInstruction*) { return Unimplemented( "The GPU backend does not implement BatchNormInference directly. It " diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.h b/tensorflow/compiler/xla/service/gpu/ir_emitter.h index e89967a378..80e2a203ac 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.h @@ -91,7 +91,6 @@ class IrEmitter : public DfsHloVisitorWithDefault { Status HandleFusion(HloInstruction* fusion) override; Status HandleCall(HloInstruction* call) override; Status HandleCustomCall(HloInstruction* custom_call) override; - Status HandleRng(HloInstruction* random) override; Status HandleBatchNormInference(HloInstruction* batch_norm) override; Status HandleBatchNormTraining(HloInstruction* batch_norm) override; Status HandleBatchNormGrad(HloInstruction* batch_norm) override; diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 1f31a7f36b..45a9807560 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -2006,10 +2006,44 @@ Status IrEmitterUnnested::HandleWhile(HloInstruction* xla_while) { return Status::OK(); } -Status IrEmitterUnnested::HandleRng(HloInstruction* random) { - thunk_sequence_->push_back( - BuildKernelThunk(random, /*implements_whole_instruction=*/true)); - return IrEmitter::HandleRng(random); +Status IrEmitterUnnested::HandleRng(HloInstruction* rng) { + // Build the kernel to generate the random numbers. + // + // Unroll the kernel so that the duplicated computation that calculates the + // 128 bit sample can be optimized away by LLVM. + thunk_sequence_->emplace_back( + BuildKernelThunk(rng, /*implements_whole_instruction=*/false, + ComputeMaxUnrollFactor(rng))); + ElementalIrEmitter::HloToElementGeneratorMap operand_to_generator; + for (const HloInstruction* operand : rng->operands()) { + operand_to_generator[operand] = [=](const llvm_ir::IrArray::Index& index) { + return GetIrArray(*operand, *rng).EmitReadArrayElement(index, &b_); + }; + } + TF_RETURN_IF_ERROR(EmitTargetElementLoop( + *rng, GpuElementalIrEmitter(hlo_module_config_, module_, &b_, + GetNestedComputer()) + .MakeElementGenerator(rng, operand_to_generator))); + std::unique_ptr rng_thunk = std::move(thunk_sequence_->back()); + thunk_sequence_->pop_back(); + + // Emit a kernel to increment the global state for Philox RNG algorithm. + thunk_sequence_->emplace_back( + BuildKernelThunk(rng, /*implements_whole_instruction=*/false)); + llvm_ir::IncrementVariableForPhiloxRngState(1, module_, &b_); + std::unique_ptr increment_seed_thunk = + std::move(thunk_sequence_->back()); + thunk_sequence_->pop_back(); + + // Build the SequentialThunk for the RNG hlo. + std::vector> thunks; + thunks.reserve(2); + thunks.push_back(std::move(rng_thunk)); + thunks.push_back(std::move(increment_seed_thunk)); + thunk_sequence_->emplace_back( + MakeUnique(std::move(thunks), rng)); + + return Status::OK(); } Status IrEmitterUnnested::HandleSelect(HloInstruction* select) { diff --git a/tensorflow/compiler/xla/service/gpu/stream_assignment.cc b/tensorflow/compiler/xla/service/gpu/stream_assignment.cc index e4cfc6999f..0806dd5161 100644 --- a/tensorflow/compiler/xla/service/gpu/stream_assignment.cc +++ b/tensorflow/compiler/xla/service/gpu/stream_assignment.cc @@ -33,13 +33,13 @@ int StreamAssignment::StreamNumberForHlo(const HloInstruction& hlo) const { } void StreamAssignment::AssignStreamToHlo(const HloInstruction* hlo, - int stream_no) { - CHECK_GE(stream_no, 0); - if (stream_no >= stream_count_) { - stream_count_ = stream_no + 1; + int stream_num) { + CHECK_GE(stream_num, 0); + if (stream_num >= stream_count_) { + stream_count_ = stream_num + 1; } - InsertOrDie(&hlo_to_stream_number_, hlo, stream_no); - VLOG(2) << "Assign stream #" << stream_no << " to " << hlo->ToString(); + InsertOrDie(&hlo_to_stream_number_, hlo, stream_num); + VLOG(2) << "Assign stream #" << stream_num << " to " << hlo->ToString(); } namespace { @@ -51,6 +51,12 @@ bool CanRunConcurrently(const HloInstruction& a, const HloInstruction& b, return !reachability.IsConnected(&a, &b); } +constexpr int kInvalidStreamNum = -1; +// Returns true iff `stream_num` is an invalid stream number. +inline bool IsStreamNumValid(int stream_num) { + return stream_num != kInvalidStreamNum; +} + // Returns which existing stream to assign to `hlo`, or -1 if a stream is not // needed. `stream_assignment` is the existing stream assignment for all // instructions topologically before `hlo`. `seen_gemms` contains all GEMMs that @@ -62,7 +68,7 @@ int ComputeStreamToAssign( if (hlo.opcode() == HloOpcode::kParameter || hlo.opcode() == HloOpcode::kConstant) { // kParameter and kConstant do not need a thunk. - return -1; + return kInvalidStreamNum; } if (hlo.GetModule() @@ -75,17 +81,17 @@ int ComputeStreamToAssign( if (!ImplementedAsGemm(hlo)) { // If `hlo` is not implemented as a GEMM, keep it close to its operands to // avoid excessive synchronization. - int stream_no = -1; + int stream_num = -1; for (const auto* operand : hlo.operands()) { if (stream_assignment.HasStreamAssigned(*operand)) { - stream_no = - std::max(stream_no, stream_assignment.StreamNumberForHlo(*operand)); + stream_num = std::max(stream_num, + stream_assignment.StreamNumberForHlo(*operand)); } } - if (stream_no == -1) { - stream_no = 0; + if (!IsStreamNumValid(stream_num)) { + stream_num = 0; } - return stream_no; + return stream_num; } // Assign different streams to concurrent GEMMs. The code below uses a @@ -94,17 +100,17 @@ int ComputeStreamToAssign( // `hlo` a different stream. std::set forbidden_stream_numbers; for (const auto* seen_gemm : seen_gemms) { - int stream_no = stream_assignment.StreamNumberForHlo(*seen_gemm); - if (!forbidden_stream_numbers.count(stream_no) && + int stream_num = stream_assignment.StreamNumberForHlo(*seen_gemm); + if (!forbidden_stream_numbers.count(stream_num) && CanRunConcurrently(*seen_gemm, hlo, reachability)) { - forbidden_stream_numbers.insert(stream_no); + forbidden_stream_numbers.insert(stream_num); } } - for (int stream_no = 0; stream_no < stream_assignment.StreamCount(); - ++stream_no) { - if (!forbidden_stream_numbers.count(stream_no)) { - return stream_no; + for (int stream_num = 0; stream_num < stream_assignment.StreamCount(); + ++stream_num) { + if (!forbidden_stream_numbers.count(stream_num)) { + return stream_num; } } return stream_assignment.StreamCount(); @@ -118,11 +124,27 @@ std::unique_ptr AssignStreams(const HloModule& module) { std::unique_ptr reachability = computation.ComputeReachability(); std::vector seen_gemms; + // The execution of different RNG Hlo instructions in the same module updates + // a common global variable. To avoid a race condition, we simply assign all + // RNG kernels to the same stream to make them run sequentially. + // + // TODO(b/111791052): If we remove such a common variable, we will need to + // clean up the code here. + int stream_num_for_rng = kInvalidStreamNum; for (const auto* hlo : computation.MakeInstructionPostOrder()) { - int stream_no = ComputeStreamToAssign(*hlo, *stream_assignment, - *reachability, seen_gemms); - if (stream_no != -1) { - stream_assignment->AssignStreamToHlo(hlo, stream_no); + // If we ever enable fusion of RNG instructions, we will need to extend this + // code to look inside a fused instruction. + int stream_num = (hlo->opcode() == HloOpcode::kRng && + IsStreamNumValid(stream_num_for_rng)) + ? stream_num_for_rng + : ComputeStreamToAssign(*hlo, *stream_assignment, + *reachability, seen_gemms); + if (IsStreamNumValid(stream_num)) { + stream_assignment->AssignStreamToHlo(hlo, stream_num); + if (hlo->opcode() == HloOpcode::kRng && + !IsStreamNumValid(stream_num_for_rng)) { + stream_num_for_rng = stream_num; + } } if (ImplementedAsGemm(*hlo)) { seen_gemms.push_back(hlo); diff --git a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc index 7a9170f379..2b6caee6aa 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/ir_array.cc @@ -327,6 +327,7 @@ llvm::Value* IrArray::Index::Linearize( llvm::IRBuilder<>* builder) const { // Each dimension is multiplied by the product of the sizes of all // earlier dimensions and added to the accumulator logical_linear_index. + CHECK_EQ(size(), dimensions.size()); llvm::Value* logical_linear_index = GetConstantWithIndexType(0); int64 multiplier = 1; for (ssize_t i = size() - 1; i >= 0; --i) { diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc index e4f65bd427..e6126881af 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.cc @@ -657,5 +657,56 @@ void InitializeLLVMCommandLineOptions(const HloModuleConfig& config) { } } +std::pair UMulLowHigh32(llvm::IRBuilder<>* b, + llvm::Value* src0, + llvm::Value* src1) { + CHECK_EQ(src0->getType()->getPrimitiveSizeInBits(), 32); + CHECK_EQ(src1->getType()->getPrimitiveSizeInBits(), 32); + llvm::Type* int64_ty = b->getInt64Ty(); + src0 = b->CreateZExt(src0, int64_ty); + src1 = b->CreateZExt(src1, int64_ty); + return SplitInt64ToInt32s(b, b->CreateMul(src0, src1)); +} + +std::pair SplitInt64ToInt32s( + llvm::IRBuilder<>* b, llvm::Value* value_64bits) { + CHECK_EQ(value_64bits->getType()->getPrimitiveSizeInBits(), 64); + llvm::Type* int32_ty = b->getInt32Ty(); + llvm::Value* low_32bits = b->CreateTrunc(value_64bits, int32_ty); + llvm::Value* high_32bits = + b->CreateTrunc(b->CreateLShr(value_64bits, 32), int32_ty); + return std::make_pair(low_32bits, high_32bits); +} + +llvm::GlobalVariable* GetOrCreateVariableForPhiloxRngState( + llvm::Module* module, llvm::IRBuilder<>* b) { + static const char* kPhiloxRngStateVariableName = "philox_rng_state"; + llvm::GlobalVariable* state_ptr = + module->getNamedGlobal(kPhiloxRngStateVariableName); + if (!state_ptr) { + state_ptr = new llvm::GlobalVariable( + /*M=*/*module, + /*Ty=*/b->getInt64Ty(), + /*isConstant=*/false, + /*Linkage=*/llvm::GlobalValue::PrivateLinkage, + /*Initializer=*/b->getInt64(0), + /*Name=*/kPhiloxRngStateVariableName); + } + return state_ptr; +} + +void IncrementVariableForPhiloxRngState(int64 value, llvm::Module* module, + llvm::IRBuilder<>* builder) { + llvm::GlobalVariable* state_ptr = + GetOrCreateVariableForPhiloxRngState(module, builder); + llvm::Value* state_value_old = builder->CreateLoad(state_ptr, "load_state"); + // If the 64-bit value overflows, we use the wraparound value. This should + // be fine in practice as we only add one to the value each time when a RNG is + // executed. + llvm::Value* state_value_new = builder->CreateAdd( + state_value_old, builder->getInt64(value), "inc_state"); + builder->CreateStore(state_value_new, state_ptr); +} + } // namespace llvm_ir } // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h index d8746ffe01..0958398534 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h +++ b/tensorflow/compiler/xla/service/llvm_ir/llvm_util.h @@ -292,6 +292,27 @@ llvm::Function* CreateFunction(llvm::FunctionType* function_type, // don't start with xla_ to LLVM. void InitializeLLVMCommandLineOptions(const HloModuleConfig& config); +// Zero-extends two 32-bit values to 64 bits, multiplies them, and returns the +// result as a pair of (low 32 bits, high 32 bits). +std::pair UMulLowHigh32(llvm::IRBuilder<>* b, + llvm::Value* src0, + llvm::Value* src1); +// Splits the 64-bit integer value into its high and low 32 bits. +std::pair SplitInt64ToInt32s( + llvm::IRBuilder<>* b, llvm::Value* value_64bits); + +// Checks whether a global variable is already created to represent a +// state passed between RNG calls implemented with Philox algorithm. If not, +// creates such a variable. Returns the global variable. +llvm::GlobalVariable* GetOrCreateVariableForPhiloxRngState( + llvm::Module* module, llvm::IRBuilder<>* b); + +// Adds a value to the global state variable each time when a RNG hlo is +// executed. The value of this global state variable is added to the seed +// of the Philox RNG algorithm so that calling the same RNG Hlo multiple times +// should rarely produce the same result. +void IncrementVariableForPhiloxRngState(int64 value, llvm::Module* module, + llvm::IRBuilder<>* b); } // namespace llvm_ir } // namespace xla -- GitLab From ece4f8e7e83b8f90d0b5ccdc6d14aa686bb2f0d7 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 16:16:59 -0700 Subject: [PATCH 417/519] [XLA] Correctly make xla_computation public. PiperOrigin-RevId: 206073510 --- tensorflow/compiler/xla/client/BUILD | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD index c5b352b30f..289d3f552a 100644 --- a/tensorflow/compiler/xla/client/BUILD +++ b/tensorflow/compiler/xla/client/BUILD @@ -1,8 +1,6 @@ # Description: # XLA client libraries. -load("//tools/build_defs:cc_public_library.bzl", "cc_public_library") - licenses(["notice"]) # Apache 2.0 package(default_visibility = [":friends"]) @@ -177,10 +175,11 @@ cc_library( ], ) -cc_public_library( +cc_library( name = "xla_computation", srcs = ["xla_computation.cc"], hdrs = ["xla_computation.h"], + visibility = ["//visibility:public"], deps = [ "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:util", -- GitLab From 19e4d68cf1402b6594793b815f8041c04d9ce32d Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Wed, 25 Jul 2018 16:28:05 -0700 Subject: [PATCH 418/519] [XLA:GPU] Remember to execute non-root outfeed instructions in nested computations PiperOrigin-RevId: 206075141 --- .../xla/service/gpu/ir_emitter_unnested.cc | 10 +- tensorflow/compiler/xla/tests/BUILD | 10 ++ .../outfeed_in_nested_computation_test.cc | 168 ++++++++++++++++++ 3 files changed, 183 insertions(+), 5 deletions(-) create mode 100644 tensorflow/compiler/xla/tests/outfeed_in_nested_computation_test.cc diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 45a9807560..db6a4e6f30 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -2753,13 +2753,13 @@ std::unique_ptr IrEmitterUnnested::BuildWhileThunk( HloComputation* condition = hlo->while_condition(); IrEmitterUnnested ir_emitter_condition(hlo_module_config_, condition, ir_emitter_context_); - TF_CHECK_OK(condition->root_instruction()->Accept(&ir_emitter_condition)); + TF_CHECK_OK(condition->Accept(&ir_emitter_condition)); // Generate thunk sequence for while 'body'. HloComputation* body = hlo->while_body(); IrEmitterUnnested ir_emitter_body(hlo_module_config_, body, ir_emitter_context_); - TF_CHECK_OK(body->root_instruction()->Accept(&ir_emitter_body)); + TF_CHECK_OK(body->Accept(&ir_emitter_body)); return MakeUnique( GetAllocationSlice(*condition->root_instruction()), // cond result @@ -2777,7 +2777,7 @@ std::unique_ptr IrEmitterUnnested::BuildForThunk( HloComputation* body = hlo->while_body(); IrEmitterUnnested ir_emitter_body(hlo_module_config_, body, ir_emitter_context_); - TF_CHECK_OK(body->root_instruction()->Accept(&ir_emitter_body)); + TF_CHECK_OK(body->Accept(&ir_emitter_body)); return MakeUnique(loop_limit, ir_emitter_body.ConsumeThunkSequence(), hlo); @@ -2793,12 +2793,12 @@ std::unique_ptr IrEmitterUnnested::BuildConditionalThunk( HloComputation* true_computation = hlo->true_computation(); IrEmitterUnnested ir_emitter_true(hlo_module_config_, true_computation, ir_emitter_context_); - TF_CHECK_OK(true_computation->root_instruction()->Accept(&ir_emitter_true)); + TF_CHECK_OK(true_computation->Accept(&ir_emitter_true)); HloComputation* false_computation = hlo->false_computation(); IrEmitterUnnested ir_emitter_false(hlo_module_config_, false_computation, ir_emitter_context_); - TF_CHECK_OK(false_computation->root_instruction()->Accept(&ir_emitter_false)); + TF_CHECK_OK(false_computation->Accept(&ir_emitter_false)); return MakeUnique( GetAllocationSlice(*hlo->operand(0)), diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 200cafbe9c..099431d949 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -1905,6 +1905,16 @@ xla_test( ], ) +xla_test( + name = "outfeed_in_nested_computation_test", + srcs = ["outfeed_in_nested_computation_test.cc"], + deps = [ + "//tensorflow/compiler/xla/tests:local_client_test_base", + "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:test", + ], +) + tf_cc_test( name = "hlo_metadata_test", srcs = [ diff --git a/tensorflow/compiler/xla/tests/outfeed_in_nested_computation_test.cc b/tensorflow/compiler/xla/tests/outfeed_in_nested_computation_test.cc new file mode 100644 index 0000000000..cea7006526 --- /dev/null +++ b/tensorflow/compiler/xla/tests/outfeed_in_nested_computation_test.cc @@ -0,0 +1,168 @@ +/* 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/tests/local_client_test_base.h" +#include "tensorflow/core/lib/core/status_test_util.h" + +namespace xla { +namespace { + +// Tests that ensure outfeed instructions that are contained in nested +// computations in non-root positions are executed. + +class LocalClientExecuteTest : public LocalClientTestBase {}; + +TEST_F(LocalClientExecuteTest, OutfeedInWhile) { + XlaBuilder b(TestName()); + + Shape state_tuple_array_shape = ShapeUtil::MakeShape(xla::S32, {10, 5}); + Shape int_shape = ShapeUtil::MakeShape(xla::S32, {}); + Shape state_tuple_shape = + ShapeUtil::MakeTupleShape({int_shape, state_tuple_array_shape}); + Shape xfeed_shape = ShapeUtil::MakeShape(xla::S32, {2}); + + XlaOp some_buffer = Broadcast(ConstantR0(&b, 0), {10, 5}); + XlaOp num_iter = Infeed(&b, int_shape); + XlaOp init_tuple = Tuple(&b, {num_iter, some_buffer}); + + TF_ASSERT_OK_AND_ASSIGN(XlaComputation loop_cond, [&] { + // Condition: iteration variable > 0 + XlaBuilder cond_builder("loop_condition"); + XlaOp state_tuple = Parameter(&cond_builder, 0, state_tuple_shape, "state"); + XlaOp loop_counter = GetTupleElement(state_tuple, 0); + Outfeed(loop_counter, int_shape, ""); + Gt(loop_counter, ConstantR0(&cond_builder, 0)); + return cond_builder.Build(); + }()); + + TF_ASSERT_OK_AND_ASSIGN(XlaComputation loop_body, [&] { + XlaBuilder body_builder("loop_body"); + XlaOp state_tuple = Parameter(&body_builder, 0, state_tuple_shape, "state"); + XlaOp loop_counter = GetTupleElement(state_tuple, 0); + XlaOp buffer_inside = GetTupleElement(state_tuple, 1); + + // Read some stuff from Infeed. + XlaOp some_input = Infeed(&body_builder, xfeed_shape); + XlaOp sum = Add(some_input, Broadcast(loop_counter, {2})); + Outfeed(sum, xfeed_shape, ""); + + XlaOp iter_left = Sub(loop_counter, ConstantR0(&body_builder, 1)); + + Tuple(&body_builder, {iter_left, buffer_inside}); + return body_builder.Build(); + }()); + + // Build loop. + XlaOp result_tuple = While(loop_cond, loop_body, init_tuple); + GetTupleElement(result_tuple, 0); + TF_ASSERT_OK_AND_ASSIGN(XlaComputation computation, b.Build()); + + std::unique_ptr comp_result; + std::unique_ptr thread( + tensorflow::Env::Default()->StartThread( + tensorflow::ThreadOptions(), "execute_thread", [&] { + comp_result = local_client_->ExecuteAndTransfer(computation, {}) + .ConsumeValueOrDie(); + })); + + VLOG(1) << "Transferring trip count to computation"; + // Transfer number of iterations to Infeed. + TF_ASSERT_OK( + local_client_->TransferToInfeed(*LiteralUtil::CreateR0(1))); + + // Pick up value from outfeed + { + VLOG(1) << "Reading from condition outfeed"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr r, + local_client_->TransferFromOutfeed(&int_shape)); + EXPECT_EQ(r->Get({}), 1); + } + + VLOG(1) << "Writing data to infeed"; + // Transfer some stuff to Infeed for use inside of loop. + TF_ASSERT_OK(local_client_->TransferToInfeed( + *LiteralUtil::CreateR1({10, 20}))); + + // Pick up value from outfeed + { + VLOG(1) << "Reading from body outfeed"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr r, + local_client_->TransferFromOutfeed(&xfeed_shape)); + EXPECT_EQ(r->Get({0}), 11); + EXPECT_EQ(r->Get({1}), 21); + } + + { + VLOG(1) << "Reading from condition outfeed"; + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr r, + local_client_->TransferFromOutfeed(&int_shape)); + EXPECT_EQ(r->Get({}), 0); + } + + // Joins the thread + thread.reset(); + + EXPECT_EQ(comp_result->Get({}), 0); +} + +TEST_F(LocalClientExecuteTest, OutfeedInConditional) { + XlaBuilder b(TestName()); + + Shape condition_shape = ShapeUtil::MakeShape(xla::PRED, {}); + Shape result_shape = ShapeUtil::MakeShape(xla::PRED, {}); + + TF_ASSERT_OK_AND_ASSIGN(XlaComputation true_computation, [&] { + XlaBuilder inner_builder("true_computation"); + XlaOp param = Parameter(&inner_builder, 0, result_shape, "param"); + Outfeed(param, result_shape, ""); + Or(param, param); + return inner_builder.Build(); + }()); + + TF_ASSERT_OK_AND_ASSIGN(XlaComputation false_computation, [&] { + XlaBuilder inner_builder("false_computation"); + Parameter(&inner_builder, 0, result_shape, "param"); + return inner_builder.Build(); + }()); + + XlaOp pred = Infeed(&b, condition_shape); + Conditional(/*predicate=*/pred, /*true_operand=*/pred, + /*true_computation=*/true_computation, /*false_operand=*/pred, + /*false_computation=*/false_computation); + + TF_ASSERT_OK_AND_ASSIGN(XlaComputation computation, b.Build()); + + std::unique_ptr comp_result; + std::unique_ptr thread( + tensorflow::Env::Default()->StartThread( + tensorflow::ThreadOptions(), "execute_thread", [&] { + comp_result = local_client_->ExecuteAndTransfer(computation, {}) + .ConsumeValueOrDie(); + })); + + TF_ASSERT_OK( + local_client_->TransferToInfeed(*LiteralUtil::CreateR0(true))); + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr r, + local_client_->TransferFromOutfeed(&result_shape)); + + EXPECT_EQ(r->Get({}), true); + + // Join the thread + thread.reset(); +} + +} // namespace +} // namespace xla -- GitLab From 1e5854fb157a6be7a6a2b4bad97bce50c264e678 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 16:42:00 -0700 Subject: [PATCH 419/519] Adds a mechanism for adaptive shared batch scheduler that the earliest closed batches are scheduled immediately if batch_thread_pool_ has an idle thead. This helps to prevent overloads when traffic suddenly increase from very low to high. PiperOrigin-RevId: 206077249 --- .../adaptive_shared_batch_scheduler.h | 57 ++++++++++++++++--- 1 file changed, 50 insertions(+), 7 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 b77c14d012..656b6ced6d 100644 --- a/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h +++ b/tensorflow/core/kernels/batching_util/adaptive_shared_batch_scheduler.h @@ -147,13 +147,21 @@ class AdaptiveSharedBatchScheduler // Tracks processing latency and adjusts in_flight_batches_limit to minimize. void CallbackWrapper(const internal::ASBSBatch* batch, - BatchProcessor callback); + BatchProcessor callback, bool is_express); // Schedules batch if in_flight_batches_limit_ is not met. void MaybeScheduleNextBatch() EXCLUSIVE_LOCKS_REQUIRED(mu_); + // Schedules the earliest closed batch in batches_ + // if batch_thread_pool_ has an idle thead. + // Batches scheduled this way are called express batches. + // Express batches are not limited by in_flight_batches_limit_, and + // their latencies will not affect in_flight_batches_limit_. + void MaybeScheduleClosedBatch() EXCLUSIVE_LOCKS_REQUIRED(mu_); + // Notifies scheduler of non-empty batch which is eligible for processing. - void AddBatch(const internal::ASBSBatch* batch); + void AddBatch(const internal::ASBSBatch* batch, + bool also_schedule_closed_batch); // Removes queue from scheduler. void RemoveQueue(const internal::ASBSQueue* queue); @@ -180,8 +188,10 @@ class AdaptiveSharedBatchScheduler // results in an actual cap of 3 80% of the time, and 4 20% of the time. double in_flight_batches_limit_ GUARDED_BY(mu_); - // Number of batches currently being processed. + // Number of regular batches currently being processed. int64 in_flight_batches_ GUARDED_BY(mu_) = 0; + // Number of express batches currently being processed. + int64 in_flight_express_batches_ GUARDED_BY(mu_) = 0; // RNG engine and distribution. std::default_random_engine rand_engine_; @@ -363,10 +373,14 @@ Status AdaptiveSharedBatchScheduler::AddQueue( template void AdaptiveSharedBatchScheduler::AddBatch( - const internal::ASBSBatch* batch) { + const internal::ASBSBatch* batch, + bool also_schedule_closed_batch) { mutex_lock l(mu_); batches_.push_back(batch); MaybeScheduleNextBatch(); + if (also_schedule_closed_batch) { + MaybeScheduleClosedBatch(); + } } template @@ -407,19 +421,45 @@ void AdaptiveSharedBatchScheduler::MaybeScheduleNextBatch() { batch->queue()->ReleaseBatch(batch); batch_thread_pool_->Schedule( std::bind(&AdaptiveSharedBatchScheduler::CallbackWrapper, this, - batch, queues_and_callbacks_[batch->queue()])); + batch, queues_and_callbacks_[batch->queue()], false)); in_flight_batches_++; } +template +void AdaptiveSharedBatchScheduler::MaybeScheduleClosedBatch() { + if (in_flight_batches_ + in_flight_express_batches_ >= + options_.num_batch_threads) { + return; + } + for (auto it = batches_.begin(); it != batches_.end(); it++) { + if ((*it)->IsClosed()) { + const internal::ASBSBatch* batch = *it; + batches_.erase(it); + batch->queue()->ReleaseBatch(batch); + batch_thread_pool_->Schedule( + std::bind(&AdaptiveSharedBatchScheduler::CallbackWrapper, + this, batch, queues_and_callbacks_[batch->queue()], true)); + in_flight_express_batches_++; + return; + } + } +} + template void AdaptiveSharedBatchScheduler::CallbackWrapper( const internal::ASBSBatch* batch, - AdaptiveSharedBatchScheduler::BatchProcessor callback) { + AdaptiveSharedBatchScheduler::BatchProcessor callback, + bool is_express) { int64 start_time = batch->creation_time_micros(); callback(std::unique_ptr>( const_cast*>(batch))); int64 end_time = GetEnv()->NowMicros(); mutex_lock l(mu_); + if (is_express) { + in_flight_express_batches_--; + MaybeScheduleClosedBatch(); + return; + } in_flight_batches_--; batch_count_++; batch_latency_sum_ += end_time - start_time; @@ -496,6 +536,7 @@ Status ASBSQueue::Schedule(std::unique_ptr* task) { " is larger than maximum batch size ", options_.max_batch_size); } + bool is_old_batch_closed = false; { mutex_lock l(mu_); // Current batch is full, create another if allowed. @@ -505,6 +546,7 @@ Status ASBSQueue::Schedule(std::unique_ptr* task) { return errors::Unavailable("The batch scheduling queue is full"); } current_batch_->Close(); + is_old_batch_closed = true; current_batch_ = nullptr; } if (!current_batch_) { @@ -516,7 +558,8 @@ Status ASBSQueue::Schedule(std::unique_ptr* task) { num_enqueued_tasks_++; } // AddBatch must be called outside of lock, since it may call ReleaseBatch. - if (new_batch != nullptr) scheduler_->AddBatch(new_batch); + if (new_batch != nullptr) + scheduler_->AddBatch(new_batch, is_old_batch_closed); return Status::OK(); } -- GitLab From 773ee806ceb1feab77ac02f424814eb4ab503170 Mon Sep 17 00:00:00 2001 From: Rohan Jain Date: Wed, 25 Jul 2018 16:50:39 -0700 Subject: [PATCH 420/519] Fixing bug in MultiDeviceIterator where we were using OP_REQUIRES_OK in an AsyncOpKernel. Replacing it with OP_REQUIRES_OK_ASYNC. PiperOrigin-RevId: 206078496 --- tensorflow/contrib/data/kernels/prefetching_kernels.cc | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/tensorflow/contrib/data/kernels/prefetching_kernels.cc b/tensorflow/contrib/data/kernels/prefetching_kernels.cc index 6edc61b2c2..32f03ca683 100644 --- a/tensorflow/contrib/data/kernels/prefetching_kernels.cc +++ b/tensorflow/contrib/data/kernels/prefetching_kernels.cc @@ -791,16 +791,17 @@ class MultiDeviceIteratorGetNextFromShardOp : public AsyncOpKernel { void ComputeAsync(OpKernelContext* ctx, DoneCallback done) override { const Tensor* tensor_shard_num; - OP_REQUIRES_OK(ctx, ctx->input("shard_num", &tensor_shard_num)); + OP_REQUIRES_OK_ASYNC(ctx, ctx->input("shard_num", &tensor_shard_num), done); int32 shard_num = tensor_shard_num->scalar()(); const Tensor* tensor_incarnation_id; - OP_REQUIRES_OK(ctx, ctx->input("incarnation_id", &tensor_incarnation_id)); + OP_REQUIRES_OK_ASYNC( + ctx, ctx->input("incarnation_id", &tensor_incarnation_id), done); int64 incarnation_id = tensor_incarnation_id->scalar()(); MultiDeviceIterator* iterator; - OP_REQUIRES_OK(ctx, - LookupResource(ctx, HandleFromInput(ctx, 0), &iterator)); + OP_REQUIRES_OK_ASYNC( + ctx, LookupResource(ctx, HandleFromInput(ctx, 0), &iterator), done); thread_pool_->Schedule(std::bind( [ctx, iterator, shard_num, incarnation_id](DoneCallback done) { std::vector components; -- GitLab From 3f966669c7198b17f677405a2f26d87bafb28f8b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 17:05:57 -0700 Subject: [PATCH 421/519] Improve the documentation and error checking for invalid values of epsilon in weighted quantile stream. PiperOrigin-RevId: 206080755 --- tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc | 5 +++++ .../boosted_trees/lib/quantiles/weighted_quantiles_stream.h | 5 +++++ 2 files changed, 10 insertions(+) diff --git a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc index 0b28f81e7c..5b4be2f258 100644 --- a/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc +++ b/tensorflow/contrib/boosted_trees/kernels/quantile_ops.cc @@ -241,6 +241,11 @@ class CreateQuantileAccumulatorOp : public OpKernel { // other exceptions. If one already exists, it unrefs the new one. const Tensor* stamp_token_t; OP_REQUIRES_OK(context, context->input(kStampTokenName, &stamp_token_t)); + // An epsilon value of zero could cause perfoamance issues and is therefore, + // disallowed. + OP_REQUIRES( + context, epsilon_ > 0, + errors::InvalidArgument("An epsilon value of zero is not allowed.")); auto result = new QuantileStreamResource(epsilon_, num_quantiles_, max_elements_, generate_quantiles_, stamp_token_t->scalar()()); diff --git a/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h b/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h index c120dd8a6c..f19e5116f5 100644 --- a/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h +++ b/tensorflow/contrib/boosted_trees/lib/quantiles/weighted_quantiles_stream.h @@ -58,6 +58,8 @@ namespace quantiles { // Compute: O(n * log(1/eps * log(eps * n))). // Memory: O(1/eps * log^2(eps * n)) <- for one worker streaming through the // entire dataset. +// An epsilon value of zero would make the algorithm extremely inefficent and +// therefore, is disallowed. template > class WeightedQuantilesStream { @@ -69,6 +71,9 @@ class WeightedQuantilesStream { explicit WeightedQuantilesStream(double eps, int64 max_elements) : eps_(eps), buffer_(1LL, 2LL), finalized_(false) { + // See the class documentation. An epsilon value of zero could cause + // perfoamance issues. + QCHECK(eps > 0) << "An epsilon value of zero is not allowed."; std::tie(max_levels_, block_size_) = GetQuantileSpecs(eps, max_elements); buffer_ = Buffer(block_size_, max_elements); summary_levels_.reserve(max_levels_); -- GitLab From 70d9a489537a5a3c5fe85e75dd52bdc479966992 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Wed, 25 Jul 2018 17:12:04 -0700 Subject: [PATCH 422/519] Add ImageNet TFRecords reading boilerplate. PiperOrigin-RevId: 206081636 --- .../eager/python/examples/revnet/BUILD | 21 ++ .../python/examples/revnet/imagenet_input.py | 230 ++++++++++++++++++ .../examples/revnet/resnet_preprocessing.py | 190 +++++++++++++++ 3 files changed, 441 insertions(+) create mode 100644 tensorflow/contrib/eager/python/examples/revnet/imagenet_input.py create mode 100644 tensorflow/contrib/eager/python/examples/revnet/resnet_preprocessing.py diff --git a/tensorflow/contrib/eager/python/examples/revnet/BUILD b/tensorflow/contrib/eager/python/examples/revnet/BUILD index 3316dc1114..4f0d46b1ba 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/BUILD +++ b/tensorflow/contrib/eager/python/examples/revnet/BUILD @@ -43,6 +43,27 @@ py_library( ], ) +py_library( + name = "resnet_preprocessing", + srcs = ["resnet_preprocessing.py"], + srcs_version = "PY2AND3", + tags = ["local"], + deps = [ + "//tensorflow:tensorflow_py", + ], +) + +py_library( + name = "imagenet_input", + srcs = ["imagenet_input.py"], + srcs_version = "PY2AND3", + tags = ["local"], + deps = [ + ":resnet_preprocessing", + "//tensorflow:tensorflow_py", + ], +) + # Tests cuda_py_test( name = "ops_test", diff --git a/tensorflow/contrib/eager/python/examples/revnet/imagenet_input.py b/tensorflow/contrib/eager/python/examples/revnet/imagenet_input.py new file mode 100644 index 0000000000..e81351b1b1 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/imagenet_input.py @@ -0,0 +1,230 @@ +# 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. +# ============================================================================== +"""Efficient ImageNet input pipeline using tf.data.Dataset.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import functools +import os + +import tensorflow as tf + +from tensorflow.contrib.eager.python.examples.revnet import resnet_preprocessing + + +def image_serving_input_fn(): + """Serving input fn for raw images.""" + + def _preprocess_image(image_bytes): + """Preprocess a single raw image.""" + image = resnet_preprocessing.preprocess_image( + image_bytes=image_bytes, is_training=False) + return image + + image_bytes_list = tf.placeholder( + shape=[None], + dtype=tf.string, + ) + images = tf.map_fn( + _preprocess_image, image_bytes_list, back_prop=False, dtype=tf.float32) + return tf.estimator.export.ServingInputReceiver( + images, {'image_bytes': image_bytes_list}) + + +class ImageNetInput(object): + """Generates ImageNet input_fn for training or evaluation. + + The training data is assumed to be in TFRecord format with keys as specified + in the dataset_parser below, sharded across 1024 files, named sequentially: + train-00000-of-01024 + train-00001-of-01024 + ... + train-01023-of-01024 + + The validation data is in the same format but sharded in 128 files. + + The format of the data required is created by the script at: + https://github.com/tensorflow/tpu/blob/master/tools/datasets/imagenet_to_gcs.py + + Args: + is_training: `bool` for whether the input is for training + data_dir: `str` for the directory of the training and validation data; + if 'null' (the literal string 'null', not None), then construct a null + pipeline, consisting of empty images. + use_bfloat16: If True, use bfloat16 precision; else use float32. + transpose_input: 'bool' for whether to use the double transpose trick + num_cores: `int` for the number of TPU cores + """ + + def __init__(self, is_training, + use_bfloat16, + data_dir, + num_cores=8, + num_parallel_calls=64, + image_size=224, + transpose_input=False, + cache=False): + self.image_preprocessing_fn = resnet_preprocessing.preprocess_image + self.is_training = is_training + self.use_bfloat16 = use_bfloat16 + self.data_dir = data_dir + self.num_cores = num_cores + self.num_parallel_calls = num_parallel_calls + if self.data_dir == 'null' or self.data_dir == '': + self.data_dir = None + self.transpose_input = transpose_input + self.image_size = image_size + self.cache = cache + + def set_shapes(self, batch_size, images, labels): + """Statically set the batch_size dimension.""" + if self.transpose_input: + images.set_shape(images.get_shape().merge_with( + tf.TensorShape([None, None, None, batch_size]))) + labels.set_shape(labels.get_shape().merge_with( + tf.TensorShape([batch_size]))) + else: + images.set_shape(images.get_shape().merge_with( + tf.TensorShape([batch_size, None, None, None]))) + labels.set_shape(labels.get_shape().merge_with( + tf.TensorShape([batch_size]))) + + return images, labels + + def dataset_parser(self, value): + """Parse an ImageNet record from a serialized string Tensor.""" + keys_to_features = { + 'image/encoded': tf.FixedLenFeature((), tf.string, ''), + 'image/format': tf.FixedLenFeature((), tf.string, 'jpeg'), + 'image/class/label': tf.FixedLenFeature([], tf.int64, -1), + 'image/class/text': tf.FixedLenFeature([], tf.string, ''), + 'image/object/bbox/xmin': tf.VarLenFeature(dtype=tf.float32), + 'image/object/bbox/ymin': tf.VarLenFeature(dtype=tf.float32), + 'image/object/bbox/xmax': tf.VarLenFeature(dtype=tf.float32), + 'image/object/bbox/ymax': tf.VarLenFeature(dtype=tf.float32), + 'image/object/class/label': tf.VarLenFeature(dtype=tf.int64), + } + + parsed = tf.parse_single_example(value, keys_to_features) + image_bytes = tf.reshape(parsed['image/encoded'], shape=[]) + + image = self.image_preprocessing_fn( + image_bytes=image_bytes, + is_training=self.is_training, + image_size=self.image_size, + use_bfloat16=self.use_bfloat16) + + # Subtract one so that labels are in [0, 1000). + label = tf.cast( + tf.reshape(parsed['image/class/label'], shape=[]), dtype=tf.int32) - 1 + + return image, label + + def input_fn(self, params): + """Input function which provides a single batch for train or eval. + + Args: + params: `dict` of parameters passed from the `TPUEstimator`. + `params['batch_size']` is always provided and should be used as the + effective batch size. + + Returns: + A `tf.data.Dataset` object. + """ + if self.data_dir is None: + tf.logging.info('Using fake input.') + return self.input_fn_null(params) + + # Retrieves the batch size for the current shard. The # of shards is + # computed according to the input pipeline deployment. See + # tf.contrib.tpu.RunConfig for details. + batch_size = params['batch_size'] + + # Shuffle the filenames to ensure better randomization. + file_pattern = os.path.join( + self.data_dir, 'train-*' if self.is_training else 'validation-*') + dataset = tf.data.Dataset.list_files(file_pattern, shuffle=self.is_training) + + if self.is_training and not self.cache: + dataset = dataset.repeat() + + def fetch_dataset(filename): + buffer_size = 8 * 1024 * 1024 # 8 MiB per file + dataset = tf.data.TFRecordDataset(filename, buffer_size=buffer_size) + return dataset + + # Read the data from disk in parallel + dataset = dataset.apply( + tf.contrib.data.parallel_interleave( + fetch_dataset, cycle_length=self.num_parallel_calls, sloppy=True)) + if self.cache: + dataset = dataset.cache().apply( + tf.contrib.data.shuffle_and_repeat(1024 * 16)) + else: + dataset = dataset.shuffle(1024) + + # Use the fused map-and-batch operation. + # + # For XLA, we must used fixed shapes. Because we repeat the source training + # dataset indefinitely, we can use `drop_remainder=True` to get fixed-size + # batches without dropping any training examples. + # + # When evaluating, `drop_remainder=True` prevents accidentally evaluating + # the same image twice by dropping the final batch if it is less than a full + # batch size. As long as this validation is done with consistent batch size, + # exactly the same images will be used. + dataset = dataset.apply( + tf.contrib.data.map_and_batch( + self.dataset_parser, batch_size=batch_size, + num_parallel_batches=self.num_cores, drop_remainder=True)) + + # Transpose for performance on TPU + if self.transpose_input: + dataset = dataset.map( + lambda images, labels: (tf.transpose(images, [1, 2, 3, 0]), labels), + num_parallel_calls=self.num_cores) + + # Assign static batch size dimension + dataset = dataset.map(functools.partial(self.set_shapes, batch_size)) + + # Prefetch overlaps in-feed with training + dataset = dataset.prefetch(tf.contrib.data.AUTOTUNE) + return dataset + + def input_fn_null(self, params): + """Input function which provides null (black) images.""" + batch_size = params['batch_size'] + dataset = tf.data.Dataset.range(1).repeat().map(self._get_null_input) + dataset = dataset.prefetch(batch_size) + + dataset = dataset.apply( + tf.contrib.data.batch_and_drop_remainder(batch_size)) + if self.transpose_input: + dataset = dataset.map( + lambda images, labels: (tf.transpose(images, [1, 2, 3, 0]), labels), + num_parallel_calls=8) + + dataset = dataset.map(functools.partial(self.set_shapes, batch_size)) + + dataset = dataset.prefetch(32) # Prefetch overlaps in-feed with training + tf.logging.info('Input dataset: %s', str(dataset)) + return dataset + + def _get_null_input(self, _): + null_image = tf.zeros([224, 224, 3], tf.bfloat16 + if self.use_bfloat16 else tf.float32) + return (null_image, tf.constant(0, tf.int32)) diff --git a/tensorflow/contrib/eager/python/examples/revnet/resnet_preprocessing.py b/tensorflow/contrib/eager/python/examples/revnet/resnet_preprocessing.py new file mode 100644 index 0000000000..21a1ab85d4 --- /dev/null +++ b/tensorflow/contrib/eager/python/examples/revnet/resnet_preprocessing.py @@ -0,0 +1,190 @@ +# 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. +# ============================================================================== +"""ImageNet preprocessing for ResNet.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import tensorflow as tf + +IMAGE_SIZE = 224 +CROP_PADDING = 32 + + +def distorted_bounding_box_crop(image_bytes, + bbox, + min_object_covered=0.1, + aspect_ratio_range=(0.75, 1.33), + area_range=(0.05, 1.0), + max_attempts=100, + scope=None): + """Generates cropped_image using one of the bboxes randomly distorted. + + See `tf.image.sample_distorted_bounding_box` for more documentation. + + Args: + image_bytes: `Tensor` of binary image data. + bbox: `Tensor` of bounding boxes arranged `[1, num_boxes, coords]` + where each coordinate is [0, 1) and the coordinates are arranged + as `[ymin, xmin, ymax, xmax]`. If num_boxes is 0 then use the whole + image. + min_object_covered: An optional `float`. Defaults to `0.1`. The cropped + area of the image must contain at least this fraction of any bounding + box supplied. + aspect_ratio_range: An optional list of `float`s. The cropped area of the + image must have an aspect ratio = width / height within this range. + area_range: An optional list of `float`s. The cropped area of the image + must contain a fraction of the supplied image within in this range. + max_attempts: An optional `int`. Number of attempts at generating a cropped + region of the image of the specified constraints. After `max_attempts` + failures, return the entire image. + scope: Optional `str` for name scope. + Returns: + cropped image `Tensor` + """ + with tf.name_scope(scope, 'distorted_bounding_box_crop', [image_bytes, bbox]): + shape = tf.image.extract_jpeg_shape(image_bytes) + sample_distorted_bounding_box = tf.image.sample_distorted_bounding_box( + shape, + bounding_boxes=bbox, + min_object_covered=min_object_covered, + aspect_ratio_range=aspect_ratio_range, + area_range=area_range, + max_attempts=max_attempts, + use_image_if_no_bounding_boxes=True) + bbox_begin, bbox_size, _ = sample_distorted_bounding_box + + # Crop the image to the specified bounding box. + offset_y, offset_x, _ = tf.unstack(bbox_begin) + target_height, target_width, _ = tf.unstack(bbox_size) + crop_window = tf.stack([offset_y, offset_x, target_height, target_width]) + image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3) + + return image + + +def _at_least_x_are_equal(a, b, x): + """At least `x` of `a` and `b` `Tensors` are equal.""" + match = tf.equal(a, b) + match = tf.cast(match, tf.int32) + return tf.greater_equal(tf.reduce_sum(match), x) + + +def _decode_and_random_crop(image_bytes, image_size): + """Make a random crop of image_size.""" + bbox = tf.constant([0.0, 0.0, 1.0, 1.0], dtype=tf.float32, shape=[1, 1, 4]) + image = distorted_bounding_box_crop( + image_bytes, + bbox, + min_object_covered=0.1, + aspect_ratio_range=(3. / 4, 4. / 3.), + area_range=(0.08, 1.0), + max_attempts=10, + scope=None) + original_shape = tf.image.extract_jpeg_shape(image_bytes) + bad = _at_least_x_are_equal(original_shape, tf.shape(image), 3) + + image = tf.cond( + bad, + lambda: _decode_and_center_crop(image_bytes, image_size), + lambda: tf.image.resize_bicubic([image], # pylint: disable=g-long-lambda + [image_size, image_size])[0]) + + return image + + +def _decode_and_center_crop(image_bytes, image_size): + """Crops to center of image with padding then scales image_size.""" + shape = tf.image.extract_jpeg_shape(image_bytes) + image_height = shape[0] + image_width = shape[1] + + padded_center_crop_size = tf.cast( + ((image_size / (image_size + CROP_PADDING)) * + tf.cast(tf.minimum(image_height, image_width), tf.float32)), + tf.int32) + + offset_height = ((image_height - padded_center_crop_size) + 1) // 2 + offset_width = ((image_width - padded_center_crop_size) + 1) // 2 + crop_window = tf.stack([offset_height, offset_width, + padded_center_crop_size, padded_center_crop_size]) + image = tf.image.decode_and_crop_jpeg(image_bytes, crop_window, channels=3) + image = tf.image.resize_bicubic([image], [image_size, image_size])[0] + + return image + + +def _flip(image): + """Random horizontal image flip.""" + image = tf.image.random_flip_left_right(image) + return image + + +def preprocess_for_train(image_bytes, use_bfloat16, image_size=IMAGE_SIZE): + """Preprocesses the given image for evaluation. + + Args: + image_bytes: `Tensor` representing an image binary of arbitrary size. + use_bfloat16: `bool` for whether to use bfloat16. + image_size: image size. + + Returns: + A preprocessed image `Tensor`. + """ + image = _decode_and_random_crop(image_bytes, image_size) + image = _flip(image) + image = tf.reshape(image, [image_size, image_size, 3]) + image = tf.image.convert_image_dtype( + image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32) + return image + + +def preprocess_for_eval(image_bytes, use_bfloat16, image_size=IMAGE_SIZE): + """Preprocesses the given image for evaluation. + + Args: + image_bytes: `Tensor` representing an image binary of arbitrary size. + use_bfloat16: `bool` for whether to use bfloat16. + image_size: image size. + + Returns: + A preprocessed image `Tensor`. + """ + image = _decode_and_center_crop(image_bytes, image_size) + image = tf.reshape(image, [image_size, image_size, 3]) + image = tf.image.convert_image_dtype( + image, dtype=tf.bfloat16 if use_bfloat16 else tf.float32) + return image + + +def preprocess_image(image_bytes, + is_training=False, + use_bfloat16=False, + image_size=IMAGE_SIZE): + """Preprocesses the given image. + + Args: + image_bytes: `Tensor` representing an image binary of arbitrary size. + is_training: `bool` for whether the preprocessing is for training. + use_bfloat16: `bool` for whether to use bfloat16. + image_size: image size. + + Returns: + A preprocessed image `Tensor`. + """ + if is_training: + return preprocess_for_train(image_bytes, use_bfloat16, image_size) + else: + return preprocess_for_eval(image_bytes, use_bfloat16, image_size) -- GitLab From 59305b118a9ed56d733b414e8ee1a272dc66466a Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Wed, 25 Jul 2018 17:36:23 -0700 Subject: [PATCH 423/519] [XLA:GPU] Allow `dot`s inside of while loops (etc) to be lowered to cublas calls. Pretty bad bug. PiperOrigin-RevId: 206084428 --- tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc | 5 ----- tensorflow/compiler/xla/service/gpu/ir_emission_utils.h | 6 ++++++ 2 files changed, 6 insertions(+), 5 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc index 2799baab41..6352b330d1 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.cc @@ -81,11 +81,6 @@ bool DotImplementedAsGemm(const HloInstruction& dot) { } // namespace bool ImplementedAsGemm(const HloInstruction& hlo) { - // We can only do this if the HLO is unnested. - if (hlo.parent() != hlo.GetModule()->entry_computation()) { - return false; - } - // For certain types of Dot, we can call pre-canned BLAS gemm. if (hlo.opcode() == HloOpcode::kDot) { return DotImplementedAsGemm(hlo); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h index 9bb4c42b15..5d23a3d018 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emission_utils.h @@ -31,6 +31,12 @@ namespace gpu { constexpr int64 kWarpSize = 32; // Returns true if `hlo` will be implemented as a call to BLAS gemm. +// +// Precondition: `hlo` is in an "unnested context", meaning, it lives within the +// entry computation, within the either of a while loop's subcomputations, +// within any of a conditional's subcomputations, etc., but *does not* live +// within a reduce subcomputation, a map subcomputation, a fusion +// subcomputation, etc. It's OK if `hlo` *is* a fusion. bool ImplementedAsGemm(const HloInstruction& hlo); // A call to cuDNN for batch normalization is represented as CustomCall HLO with -- GitLab From b24037513f12a5812a21b7ea92ff904ee9ea6cd8 Mon Sep 17 00:00:00 2001 From: Dan Moldovan Date: Wed, 25 Jul 2018 18:01:50 -0700 Subject: [PATCH 424/519] Fix bug in parallel_walk, along with a few structural bugs that this fix revealed: 1. The conversion process was inconsistently packaging the final output into modules or lists. This CL uniformly uses a list of nodes as output from all *_to_graph functions. As a side effect, converter_testing.py asserts that the output is always a single node and extracts it, so there is no need for tests to unpack it any more. Modify the compiler to skip generating a source map by default. 2. The class converter was incorrectly saving the superclass value to the string 'object' instead of the symbol `object`. Additional refactoring that was caught along: Simplify the source mapping code, move it to origin_info.py, add tests and additional checks. Slightly simplify the error rewriting mechanism. PiperOrigin-RevId: 206087110 --- .../autograph/converters/asserts_test.py | 2 +- .../autograph/converters/directives_test.py | 6 +- .../converters/error_handlers_test.py | 6 +- .../autograph/converters/lists_test.py | 4 +- .../converters/side_effect_guards_test.py | 12 +- .../autograph/converters/slices_test.py | 6 +- .../autograph/core/converter_testing.py | 4 +- tensorflow/contrib/autograph/core/errors.py | 116 +++++++-------- .../contrib/autograph/core/errors_test.py | 108 ++++++-------- tensorflow/contrib/autograph/impl/api.py | 21 +-- .../contrib/autograph/impl/conversion.py | 34 ++--- .../contrib/autograph/impl/conversion_test.py | 29 ++-- tensorflow/contrib/autograph/pyct/BUILD | 10 ++ tensorflow/contrib/autograph/pyct/ast_util.py | 87 +++++++---- .../contrib/autograph/pyct/ast_util_test.py | 62 +++++--- tensorflow/contrib/autograph/pyct/cfg.py | 6 +- .../pyct/common_transformers/anf_test.py | 2 +- tensorflow/contrib/autograph/pyct/compiler.py | 127 +++++++--------- .../contrib/autograph/pyct/compiler_test.py | 2 +- .../contrib/autograph/pyct/origin_info.py | 137 ++++++++++++++---- .../autograph/pyct/origin_info_test.py | 101 +++++++++++++ tensorflow/contrib/autograph/pyct/parser.py | 1 + 22 files changed, 539 insertions(+), 344 deletions(-) create mode 100644 tensorflow/contrib/autograph/pyct/origin_info_test.py diff --git a/tensorflow/contrib/autograph/converters/asserts_test.py b/tensorflow/contrib/autograph/converters/asserts_test.py index 9c58ae3acc..38faba45df 100644 --- a/tensorflow/contrib/autograph/converters/asserts_test.py +++ b/tensorflow/contrib/autograph/converters/asserts_test.py @@ -35,7 +35,7 @@ class AssertsTest(converter_testing.TestCase): node, ctx = self.prepare(test_fn, {}) node = asserts.transform(node, ctx) - self.assertTrue(isinstance(node.body[0].body[0].value, gast.Call)) + self.assertTrue(isinstance(node.body[0].value, gast.Call)) if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/converters/directives_test.py b/tensorflow/contrib/autograph/converters/directives_test.py index 5f798a5b76..a573ba5850 100644 --- a/tensorflow/contrib/autograph/converters/directives_test.py +++ b/tensorflow/contrib/autograph/converters/directives_test.py @@ -38,7 +38,7 @@ class DirectivesTest(converter_testing.TestCase): node, ctx = self.prepare(test_fn, {'directives': directives}) node = directives_converter.transform(node, ctx) - def_, = anno.getanno(node.body[0].body[0].targets[0], + def_, = anno.getanno(node.body[0].targets[0], anno.Static.DEFINITIONS) d = def_.directives[directives.set_element_type] self.assertEqual(d['dtype'].s, 'a') @@ -52,7 +52,7 @@ class DirectivesTest(converter_testing.TestCase): node, ctx = self.prepare(test_fn, {'directives': directives}) node = directives_converter.transform(node, ctx) - def_, = anno.getanno(node.body[0].args.args[0], anno.Static.DEFINITIONS) + def_, = anno.getanno(node.args.args[0], anno.Static.DEFINITIONS) d = def_.directives[directives.set_element_type] self.assertEqual(d['dtype'].n, 1) self.assertEqual(d['shape'].n, 2) @@ -67,7 +67,7 @@ class DirectivesTest(converter_testing.TestCase): node, ctx = self.prepare(test_fn, {'directives': directives}) node = directives_converter.transform(node, ctx) - d = anno.getanno(node.body[0].body[1], AgAnno.DIRECTIVES) + d = anno.getanno(node.body[1], AgAnno.DIRECTIVES) d = d[directives.set_loop_options] self.assertEqual(d['parallel_iterations'].n, 10) self.assertEqual(d['back_prop'].id, 'a') diff --git a/tensorflow/contrib/autograph/converters/error_handlers_test.py b/tensorflow/contrib/autograph/converters/error_handlers_test.py index 878526c8b4..cd74e5f18f 100644 --- a/tensorflow/contrib/autograph/converters/error_handlers_test.py +++ b/tensorflow/contrib/autograph/converters/error_handlers_test.py @@ -34,11 +34,13 @@ class ErrorHandlersTest(converter_testing.TestCase): raise ValueError() node, ctx = self.prepare(test_fn, {}) - anno.setanno(node.body[0], anno.Basic.ORIGIN, - origin_info.OriginInfo('test_path', None, None, None, None)) + anno.setanno(node, anno.Basic.ORIGIN, + origin_info.OriginInfo(None, None, None)) node = error_handlers.transform(node, ctx) with self.compiled(node, {}) as result: with self.assertRaises(errors.GraphConstructionError): + # Here we just assert that the handler works. Its correctness is + # verified by errors_test.py. result.test_fn() def test_no_origin_annotation(self): diff --git a/tensorflow/contrib/autograph/converters/lists_test.py b/tensorflow/contrib/autograph/converters/lists_test.py index f906918ac0..996e99ee61 100644 --- a/tensorflow/contrib/autograph/converters/lists_test.py +++ b/tensorflow/contrib/autograph/converters/lists_test.py @@ -79,7 +79,7 @@ class ListTest(converter_testing.TestCase): ns = {'special_functions': special_functions} node, ctx = self.prepare(test_fn, ns) - def_, = anno.getanno(node.body[0].body[0].targets[0], + def_, = anno.getanno(node.body[0].targets[0], anno.Static.ORIG_DEFINITIONS) def_.directives[directives.set_element_type] = { 'dtype': parser.parse_expression('tf.int32'), @@ -114,7 +114,7 @@ class ListTest(converter_testing.TestCase): return tf.stack(l) node, ctx = self.prepare(test_fn, {}) - def_, = anno.getanno(node.body[0].body[0].targets[0], + def_, = anno.getanno(node.body[0].targets[0], anno.Static.ORIG_DEFINITIONS) def_.directives[directives.set_element_type] = { 'dtype': parser.parse_expression('tf.int32') diff --git a/tensorflow/contrib/autograph/converters/side_effect_guards_test.py b/tensorflow/contrib/autograph/converters/side_effect_guards_test.py index de1874321e..bee512abbc 100644 --- a/tensorflow/contrib/autograph/converters/side_effect_guards_test.py +++ b/tensorflow/contrib/autograph/converters/side_effect_guards_test.py @@ -43,7 +43,7 @@ class SideEffectGuardsTest(converter_testing.TestCase): node, ctx = self.prepare(test_fn, {}) node = side_effect_guards.transform(node, ctx) - self.assertEqual(len(node.body[0].body), 1) + self.assertEqual(len(node.body), 1) with self.compiled(node, {}, state_ops.assign) as result: with self.test_session() as sess: @@ -64,7 +64,7 @@ class SideEffectGuardsTest(converter_testing.TestCase): node, ctx = self.prepare(test_fn, {}) node = side_effect_guards.transform(node, ctx) - self.assertEqual(len(node.body[0].body), 1) + self.assertEqual(len(node.body), 1) with self.compiled(node, {}, state_ops.assign) as result: with self.test_session() as sess: @@ -84,7 +84,7 @@ class SideEffectGuardsTest(converter_testing.TestCase): node, ctx = self.prepare(test_fn, {}) node = side_effect_guards.transform(node, ctx) - self.assertEqual(len(node.body[0].body), 1) + self.assertEqual(len(node.body), 1) with self.compiled(node, {}, control_flow_ops.Assert) as result: with self.test_session() as sess: @@ -104,7 +104,7 @@ class SideEffectGuardsTest(converter_testing.TestCase): node, ctx = self.prepare(test_fn, {}) node = side_effect_guards.transform(node, ctx) - self.assertEqual(len(node.body[0].body), 1) + self.assertEqual(len(node.body), 1) with self.compiled(node, {}, state_ops.assign_add) as result: with self.test_session() as sess: @@ -125,7 +125,7 @@ class SideEffectGuardsTest(converter_testing.TestCase): node, ctx = self.prepare(test_fn, {}) node = side_effect_guards.transform(node, ctx) - self.assertEqual(len(node.body[0].body[0].body), 1) + self.assertEqual(len(node.body[0].body), 1) with self.compiled(node, {}, state_ops.assign, ops.name_scope) as result: with self.test_session() as sess: @@ -147,7 +147,7 @@ class SideEffectGuardsTest(converter_testing.TestCase): node, ctx = self.prepare(test_fn, {}) node = side_effect_guards.transform(node, ctx) - self.assertEqual(len(node.body[0].body), 1) + self.assertEqual(len(node.body), 1) with self.compiled(node, {}, state_ops.assign, state_ops.assign_add) as result: diff --git a/tensorflow/contrib/autograph/converters/slices_test.py b/tensorflow/contrib/autograph/converters/slices_test.py index 3c0f81e8bc..c822d53a4a 100644 --- a/tensorflow/contrib/autograph/converters/slices_test.py +++ b/tensorflow/contrib/autograph/converters/slices_test.py @@ -38,7 +38,7 @@ class SliceTest(converter_testing.TestCase): return l[1] node, ctx = self.prepare(test_fn, {}) - def_, = anno.getanno(node.body[0].args.args[0], anno.Static.DEFINITIONS) + def_, = anno.getanno(node.args.args[0], anno.Static.DEFINITIONS) def_.directives[directives.set_element_type] = { 'dtype': parser.parse_expression('tf.int32') } @@ -59,11 +59,11 @@ class SliceTest(converter_testing.TestCase): return l[1] node, ctx = self.prepare(test_fn, {}) - def_, = anno.getanno(node.body[0].args.args[0], anno.Static.DEFINITIONS) + def_, = anno.getanno(node.args.args[0], anno.Static.DEFINITIONS) def_.directives[directives.set_element_type] = { 'dtype': parser.parse_expression('tf.int32') } - def_, = anno.getanno(node.body[0].body[0].body[0].targets[0], + def_, = anno.getanno(node.body[0].body[0].targets[0], anno.Static.DEFINITIONS) def_.directives[directives.set_element_type] = { 'dtype': parser.parse_expression('tf.float32') diff --git a/tensorflow/contrib/autograph/core/converter_testing.py b/tensorflow/contrib/autograph/core/converter_testing.py index 2025e32817..5ee2c3fffd 100644 --- a/tensorflow/contrib/autograph/core/converter_testing.py +++ b/tensorflow/contrib/autograph/core/converter_testing.py @@ -94,7 +94,8 @@ class TestCase(test.TestCase): return 7 try: - result, source = compiler.ast_to_object(node) + result, source = compiler.ast_to_object(node, include_source_map=True) + result.tf = self.make_fake_mod('fake_tf', *symbols) fake_ag = self.make_fake_mod('fake_ag', converted_call) fake_ag.__dict__.update(operators.__dict__) @@ -144,6 +145,7 @@ class TestCase(test.TestCase): recursive=True, autograph_decorators=()): node, source = parser.parse_entity(test_fn) + node = node.body[0] if namer is None: namer = FakeNamer() program_ctx = converter.ProgramContext( diff --git a/tensorflow/contrib/autograph/core/errors.py b/tensorflow/contrib/autograph/core/errors.py index e58745337a..c219b372c1 100644 --- a/tensorflow/contrib/autograph/core/errors.py +++ b/tensorflow/contrib/autograph/core/errors.py @@ -31,11 +31,14 @@ import logging import sys import traceback -from tensorflow.contrib.autograph.pyct.origin_info import CodeLocation +from tensorflow.contrib.autograph.pyct import origin_info from tensorflow.python.framework import errors_impl from tensorflow.python.util import tf_inspect +# TODO(mdan): Add a superclass common to all errors. + + class GraphConstructionError(Exception): """Error for graph construction errors from AutoGraph generated code.""" @@ -65,27 +68,35 @@ class TfRuntimeError(Exception): return message + ''.join(traceback.format_list(self.custom_traceback)) -def _rewrite_frame(source_map, cleaned_traceback, stack_frame_indices): - """Rewrites the stack frames at the given indices using the given source map. +def _rewrite_tb(source_map, tb, filter_function_name=None): + """Rewrites code references in a traceback. Args: - source_map: Dict[CodeLocation, OriginInfo], a mapping between the user and - AG generated code. - cleaned_traceback: List[Tuple[text, text, text, text]], the current - traceback. - stack_frame_indices: Iterable[Int], frame indices to possibly rewrite if - there are matching source mapping keys. - + source_map: Dict[origin_info.LineLocation, origin_info.OriginInfo], mapping + locations to their origin + tb: List[Tuple[Text, Text, Text, Text]], consistent with + traceback.extract_tb + filter_function_name: Optional[Text], allows restricting restricts the + frames to rewrite to a particular function name Returns: - None + List[Tuple[Text, Text, Text, Text]], the rewritten traceback """ - for frame_index in stack_frame_indices: - # (file_path, line number, function name, code) - file_path, line_number, _, _ = cleaned_traceback[frame_index] - source_map_key = CodeLocation(file_path=file_path, line_number=line_number) - found_mapping = source_map_key in source_map - if found_mapping: - cleaned_traceback[frame_index] = source_map[source_map_key].as_frame() + new_tb = [] + for frame in tb: + filename, lineno, function_name, _ = frame + loc = origin_info.LineLocation(filename, lineno) + origin = source_map.get(loc) + # TODO(mdan): We shouldn't need the function name at all. + # filename + lineno should be sufficient, even if there are multiple source + # maps. + if origin is not None: + if filter_function_name == function_name or filter_function_name is None: + new_tb.append(origin.as_frame()) + else: + new_tb.append(frame) + else: + new_tb.append(frame) + return new_tb # TODO(znado): Make more robust to name changes in the rewriting logic. @@ -98,18 +109,20 @@ def _remove_rewrite_frames(tb): return cleaned_tb +# TODO(mdan): rename to raise_* def rewrite_graph_construction_error(source_map): """Rewrites errors raised by non-AG APIs inside AG generated code. - Meant to be called from the try/except block inside each AutoGraph generated - function. Only rewrites the traceback frames corresponding to the function - that this is called from. When we raise a GraphConstructionError at the end - it is then caught by calling functions, where they can be responsible for - rewriting their own frames. + This is called from the except handler inside an AutoGraph generated function + (that is, during exception handling). Only rewrites the frames corresponding + to the function that this is called from, so each function is responsible + to call this to have its own frames rewritten. + + This function always raises an error. Args: - source_map: Dict[CodeLocation, OriginInfo], a mapping between the user and - AG generated code. + source_map: Dict[origin_info.Location, origin_info.OriginInfo], the source + map belonging to the calling function Raises: GraphConstructionError: The rewritten underlying error. @@ -120,31 +133,19 @@ def rewrite_graph_construction_error(source_map): assert original_error is not None try: _, _, _, func_name, _, _ = tf_inspect.stack()[1] - # The latest function call is added to the beginning of a traceback, but - # when rewriting the traceback of multiple function calls the previous - # functions' except blocks may have already rewritten their own frames so - # we want to copy over all of the previous frames. We may have rewritten - # previous frames only if the error is a GraphConstructionError. if isinstance(original_error, GraphConstructionError): + # TODO(mdan): This is incomplete. + # The error might have bubbled through a non-converted function. cleaned_traceback = traceback.extract_tb(e_traceback) previous_traceback = original_error.custom_traceback cleaned_traceback = [cleaned_traceback[0]] + previous_traceback else: cleaned_traceback = traceback.extract_tb(e_traceback) - cleaned_traceback = _remove_rewrite_frames(cleaned_traceback) - - current_frame_indices = [] - # This code is meant to be called from the try/except block that wraps a - # function body. Here we look for all frames that came from the function - # that this wraps, look for any matching line numbers in the source - # mapping, and then rewrite them if matches are found. - for fi, frame in enumerate(cleaned_traceback): - _, _, frame_func_name, _ = frame - if frame_func_name == func_name: - current_frame_indices.append(fi) - break - if current_frame_indices: - _rewrite_frame(source_map, cleaned_traceback, current_frame_indices) + + # Remove the frame corresponding to this function call. + cleaned_traceback = cleaned_traceback[1:] + + cleaned_traceback = _rewrite_tb(source_map, cleaned_traceback, func_name) if isinstance(original_error, GraphConstructionError): original_error.custom_traceback = cleaned_traceback @@ -153,6 +154,7 @@ def rewrite_graph_construction_error(source_map): new_error = GraphConstructionError(original_error, cleaned_traceback) except Exception: logging.exception('Error while rewriting AutoGraph error:') + # TODO(mdan): Should reraise here, removing the top frame as well. raise original_error else: raise new_error @@ -161,18 +163,17 @@ def rewrite_graph_construction_error(source_map): del e_traceback +# TODO(mdan): This should be consistent with rewrite_graph_construction_error +# Both should either raise or return. def rewrite_tf_runtime_error(error, source_map): """Rewrites TensorFlow runtime errors raised by ops created in AG code. Args: - error: error_impl.OpError, an TensorFlow error that will have its traceback - rewritten. - source_map: Dict[CodeLocation, OriginInfo], a mapping between the user and - AG generated code. + error: tf.OpError + source_map: Dict[origin_info.LineLocation, origin_info.OriginInfo] Returns: - A TfRuntimeError with a traceback rewritten according to the given - source mapping. + TfRuntimeError, the rewritten underlying error. """ # Check for cases where we leave a user method and re-enter it in the # traceback. This is done by looking at the function names when the @@ -198,15 +199,16 @@ def rewrite_tf_runtime_error(error, source_map): # The source map keys are (file_path, line_number) so get the set of all user # file_paths. try: - all_user_files = set(k.file_path for k in source_map) + all_user_files = set(loc.filename for loc in source_map) cleaned_traceback = [] last_user_frame_index = None last_user_user_file_path = None last_user_user_fn_name = None + # TODO(mdan): Simplify this logic. for fi, frame in enumerate(error.op.traceback): - frame_file_path, frame_line_number, _, _ = frame - src_map_key = CodeLocation( - file_path=frame_file_path, line_number=frame_line_number) + frame_file_path, lineno, _, _ = frame + lineno -= 1 # Frame line numbers are 1-based. + src_map_key = origin_info.LineLocation(frame_file_path, lineno) if frame_file_path in all_user_files: if src_map_key in source_map: original_fn_name = source_map[src_map_key].function_name @@ -223,8 +225,8 @@ def rewrite_tf_runtime_error(error, source_map): last_user_user_file_path = frame_file_path cleaned_traceback.append(frame) - for fi in range(len(cleaned_traceback)): - _rewrite_frame(source_map, cleaned_traceback, [fi]) + cleaned_traceback = _rewrite_tb(source_map, cleaned_traceback) + op_name = error.op.name op_message = error.message rewritten_error = TfRuntimeError(op_name, op_message, cleaned_traceback) @@ -263,7 +265,7 @@ def improved_errors(converted_function): ValueError: If converted_function is not generated by AutoGraph """ if (getattr(converted_function, 'ag_source_map', None) is None or - not converted_function.ag_source_map): + not isinstance(converted_function.ag_source_map, dict)): raise ValueError( 'converted_function must be the result of an autograph.to_graph call') try: diff --git a/tensorflow/contrib/autograph/core/errors_test.py b/tensorflow/contrib/autograph/core/errors_test.py index 7be54563a1..c0e2c74e47 100644 --- a/tensorflow/contrib/autograph/core/errors_test.py +++ b/tensorflow/contrib/autograph/core/errors_test.py @@ -28,88 +28,76 @@ from tensorflow.python.util import tf_inspect def zero_div(): - return array_ops.constant(10, dtype=dtypes.int32) // 0 + x = array_ops.constant(10, dtype=dtypes.int32) + return x // 0 def zero_div_caller(): - a = zero_div() + 2 - return a + return zero_div() class RuntimeErrorsTest(test.TestCase): - def setUp(self): - self._fake_origin = origin_info.OriginInfo('new file', 'new func', 96, 0, - 'print("hello world!")') - - def test_error_replacement(self): - _, zero_div_lineno = tf_inspect.getsourcelines(zero_div) - src_map = { - errors.CodeLocation( - file_path=__file__, line_number=zero_div_lineno + 1): - self._fake_origin - } + def fake_origin(self, function, line_offset): + _, lineno = tf_inspect.getsourcelines(function) + filename = tf_inspect.getsourcefile(function) + lineno += line_offset + loc = origin_info.LineLocation(filename, lineno) + origin = origin_info.OriginInfo(loc, 'test_function_name', 'test_code') + return loc, origin + + def test_improved_errors_basic(self): + loc, origin = self.fake_origin(zero_div, 2) + zero_div_caller.ag_source_map = {loc: origin} + + ops = zero_div_caller() with self.assertRaises(errors.TfRuntimeError) as cm: - z = zero_div_caller() - zero_div_caller.ag_source_map = src_map with errors.improved_errors(zero_div_caller): with self.test_session() as sess: - sess.run(z) - expected = cm.exception - current_traceback = expected.custom_traceback - for frame in current_traceback: - self.assertNotEqual('zero_div', frame[2]) - self.assertTrue( - any(self._fake_origin.as_frame() == frame - for frame in current_traceback)) - - def test_error_not_found(self): - src_map = { - errors.CodeLocation(file_path=__file__, line_number=-1): - self._fake_origin - } + sess.run(ops) + + for frame in cm.exception.custom_traceback: + _, _, function_name, _ = frame + self.assertNotEqual('zero_div', function_name) + self.assertIn(origin.as_frame(), set(cm.exception.custom_traceback)) + + def test_improved_errors_no_matching_lineno(self): + loc, origin = self.fake_origin(zero_div, -1) + zero_div_caller.ag_source_map = {loc: origin} + + ops = zero_div_caller() with self.assertRaises(errors.TfRuntimeError) as cm: - z = zero_div_caller() - zero_div_caller.ag_source_map = src_map with errors.improved_errors(zero_div_caller): with self.test_session() as sess: - sess.run(z) - expected = cm.exception - current_traceback = expected.custom_traceback - self.assertTrue(any('zero_div' in frame[2] for frame in current_traceback)) - for frame in current_traceback: - self.assertNotEqual(frame, self._fake_origin.as_frame()) - - def test_rewriting_error(self): - _, zero_div_lineno = tf_inspect.getsourcelines(zero_div) - src_map = { - errors.CodeLocation( - file_path=__file__, line_number=zero_div_lineno + 1): - None - } - with self.assertRaisesRegexp(tf_errors.InvalidArgumentError, - 'Integer division by zero'): - z = zero_div_caller() - zero_div_caller.ag_source_map = src_map + sess.run(ops) + + all_function_names = set() + for frame in cm.exception.custom_traceback: + _, _, function_name, _ = frame + all_function_names.add(function_name) + self.assertNotEqual('test_function_name', function_name) + self.assertIn('zero_div', all_function_names) + + def test_improved_errors_failures(self): + loc, _ = self.fake_origin(zero_div, 2) + zero_div_caller.ag_source_map = {loc: 'bogus object'} + + ops = zero_div_caller() + with self.assertRaises(tf_errors.InvalidArgumentError): with errors.improved_errors(zero_div_caller): with self.test_session() as sess: - sess.run(z) + sess.run(ops) - def test_no_ag_source_map(self): + def test_improved_errors_validation(self): with self.assertRaisesRegexp( ValueError, 'converted_function must be the result of an autograph.to_graph call'): - with errors.improved_errors(None): - pass - - def test_bad_ag_source_map(self): + errors.improved_errors(zero_div).__enter__() with self.assertRaisesRegexp( ValueError, 'converted_function must be the result of an autograph.to_graph call'): - src_map = None - zero_div_caller.ag_source_map = src_map - with errors.improved_errors(None): - pass + zero_div_caller.ag_source_map = 'not a dict' + errors.improved_errors(zero_div_caller).__enter__() if __name__ == '__main__': diff --git a/tensorflow/contrib/autograph/impl/api.py b/tensorflow/contrib/autograph/impl/api.py index f7fe3de5da..ee71f4f9ac 100644 --- a/tensorflow/contrib/autograph/impl/api.py +++ b/tensorflow/contrib/autograph/impl/api.py @@ -23,7 +23,6 @@ from functools import wraps from enum import Enum # pylint:disable=g-bad-import-order -import gast import six # pylint:enable=g-bad-import-order @@ -245,19 +244,21 @@ def to_graph(e, _, name, namespace = conversion.entity_to_graph(e, program_ctx, arg_values, arg_types) - module = gast.Module([]) + nodes = [] for dep in reversed(program_ctx.dependency_cache.values()): - module.body.append(dep) - compiled_node, compiled_src = compiler.ast_to_object( - module, source_prefix=program_ctx.required_imports) + nodes.extend(dep) + compiled_module, compiled_src = compiler.ast_to_object( + nodes, + source_prefix=program_ctx.required_imports, + include_source_map=True) # The compiled code should see everything the entry entity saw. # TODO(mdan): This might not work well if the call tree spans modules? for key, val in namespace.items(): # Avoid overwriting entities that have been transformed. - if key not in compiled_node.__dict__: - compiled_node.__dict__[key] = val - compiled_fn = getattr(compiled_node, name) + if key not in compiled_module.__dict__: + compiled_module.__dict__[key] = val + compiled_fn = getattr(compiled_module, name) # Need this so the source_mapping attribute is available for the context # manager to access for runtime errors. @@ -270,7 +271,7 @@ def to_graph(e, '"%s", which is reserved for AutoGraph.' % (compiled_fn, source_map_attribute_name)) setattr(compiled_fn, source_map_attribute_name, - compiled_node.__dict__['ag_source_map__']) + compiled_module.__dict__['ag_source_map__']) if verbose: logging.info('Compiled output of %s:\n\n%s\n', e, compiled_src) @@ -308,7 +309,7 @@ def to_code(e, conversion.entity_to_graph(e, program_ctx, arg_values, arg_types) code = '\n'.join( - compiler.ast_to_source(dep, indentation)[0] + compiler.ast_to_source(dep, indentation) for dep in reversed(tuple(six.itervalues(program_ctx.dependency_cache)))) return program_ctx.required_imports + '\n\n' + code diff --git a/tensorflow/contrib/autograph/impl/conversion.py b/tensorflow/contrib/autograph/impl/conversion.py index 7bd0ba3f2d..57ec739a80 100644 --- a/tensorflow/contrib/autograph/impl/conversion.py +++ b/tensorflow/contrib/autograph/impl/conversion.py @@ -164,7 +164,7 @@ def class_to_graph(c, program_ctx): class_namespace = namespace else: class_namespace.update(namespace) - converted_members[m] = node + converted_members[m] = node[0] namer = program_ctx.new_namer(class_namespace) class_name = namer.compiled_class_name(c.__name__, c) @@ -175,10 +175,10 @@ def class_to_graph(c, program_ctx): # program_ctx.update_name_map(namer)). output_nodes = [] renames = {} - bases = [] + base_names = [] for base in c.__bases__: if isinstance(object, base): - bases.append('object') + base_names.append('object') continue if is_whitelisted_for_graph(base): alias = namer.new_symbol(base.__name__, ()) @@ -190,28 +190,28 @@ def class_to_graph(c, program_ctx): else: # This will trigger a conversion into a class with this name. alias = namer.compiled_class_name(base.__name__, base) - bases.append(alias) + base_names.append(alias) renames[qual_names.QN(base.__name__)] = qual_names.QN(alias) program_ctx.update_name_map(namer) # Generate the definition of the converted class. - output_nodes.append( - gast.ClassDef( - class_name, - bases=bases, - keywords=[], - body=list(converted_members.values()), - decorator_list=[])) - node = gast.Module(output_nodes) - + bases = [gast.Name(n, gast.Load(), None) for n in base_names] + class_def = gast.ClassDef( + class_name, + bases=bases, + keywords=[], + body=list(converted_members.values()), + decorator_list=[]) # Make a final pass to replace references to the class or its base classes. # Most commonly, this occurs when making super().__init__() calls. # TODO(mdan): Making direct references to superclass' superclass will fail. - node = qual_names.resolve(node) + class_def = qual_names.resolve(class_def) renames[qual_names.QN(c.__name__)] = qual_names.QN(class_name) - node = ast_util.rename_symbols(node, renames) + class_def = ast_util.rename_symbols(class_def, renames) + + output_nodes.append(class_def) - return node, class_name, class_namespace + return output_nodes, class_name, class_namespace def _add_reserved_symbol(namespace, name, entity): @@ -279,7 +279,7 @@ def function_to_graph(f, program_ctx.update_name_map(namer) # TODO(mdan): Use this at compilation. - return node, new_name, namespace + return (node,), new_name, namespace def node_to_graph(node, context, rewrite_errors=True): diff --git a/tensorflow/contrib/autograph/impl/conversion_test.py b/tensorflow/contrib/autograph/impl/conversion_test.py index 207225a1ac..bfc51365a3 100644 --- a/tensorflow/contrib/autograph/impl/conversion_test.py +++ b/tensorflow/contrib/autograph/impl/conversion_test.py @@ -60,10 +60,11 @@ class ConversionTest(test.TestCase): return a + b program_ctx = self._simple_program_ctx() - ast, name, ns = conversion.entity_to_graph(f, program_ctx, None, None) - self.assertTrue(isinstance(ast, gast.FunctionDef), ast) + nodes, name, ns = conversion.entity_to_graph(f, program_ctx, None, None) + fn_node, = nodes + self.assertIsInstance(fn_node, gast.FunctionDef) self.assertEqual('tf__f', name) - self.assertTrue(ns['b'] is b) + self.assertIs(ns['b'], b) def test_entity_to_graph_call_tree(self): @@ -78,14 +79,11 @@ class ConversionTest(test.TestCase): self.assertTrue(f in program_ctx.dependency_cache) self.assertTrue(g in program_ctx.dependency_cache) - self.assertEqual('tf__f', program_ctx.dependency_cache[f].name) - # need one extra .body[0] in order to step past the try/except wrapper that - # is added automatically, the other for the with tf.name_scope('f') that is - # added automatically - self.assertEqual( - 'tf__g', - program_ctx.dependency_cache[f].body[0].body[0].body[0].value.func.id) - self.assertEqual('tf__g', program_ctx.dependency_cache[g].name) + f_node = program_ctx.dependency_cache[f][0] + g_node = program_ctx.dependency_cache[g][0] + self.assertEqual('tf__f', f_node.name) + self.assertEqual('tf__g', f_node.body[0].body[0].body[0].value.func.id) + self.assertEqual('tf__g', g_node.name) def test_entity_to_graph_class_hierarchy(self): @@ -118,9 +116,9 @@ class ConversionTest(test.TestCase): self.assertTrue(TestBase in program_ctx.dependency_cache) self.assertTrue(TestSubclass in program_ctx.dependency_cache) self.assertEqual('TfTestBase', - program_ctx.dependency_cache[TestBase].body[-1].name) + program_ctx.dependency_cache[TestBase][-1].name) self.assertEqual('TfTestSubclass', - program_ctx.dependency_cache[TestSubclass].body[-1].name) + program_ctx.dependency_cache[TestSubclass][-1].name) def test_entity_to_graph_class_hierarchy_whitelisted(self): @@ -139,10 +137,9 @@ class ConversionTest(test.TestCase): self.assertTrue(TestSubclass in program_ctx.dependency_cache) self.assertFalse(training.Model in program_ctx.dependency_cache) self.assertEqual( - 'Model', - program_ctx.dependency_cache[TestSubclass].body[0].names[0].name) + 'Model', program_ctx.dependency_cache[TestSubclass][0].names[0].name) self.assertEqual('TfTestSubclass', - program_ctx.dependency_cache[TestSubclass].body[-1].name) + program_ctx.dependency_cache[TestSubclass][-1].name) def test_entity_to_graph_lambda(self): f = lambda a: a diff --git a/tensorflow/contrib/autograph/pyct/BUILD b/tensorflow/contrib/autograph/pyct/BUILD index f77a6ab392..ddadc6b96e 100644 --- a/tensorflow/contrib/autograph/pyct/BUILD +++ b/tensorflow/contrib/autograph/pyct/BUILD @@ -99,6 +99,16 @@ py_test( ], ) +py_test( + name = "origin_info_test", + srcs = ["origin_info_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":pyct", + "//tensorflow/python:client_testlib", + ], +) + py_test( name = "parser_test", srcs = ["parser_test.py"], diff --git a/tensorflow/contrib/autograph/pyct/ast_util.py b/tensorflow/contrib/autograph/pyct/ast_util.py index 86e3f56a64..d7453b0781 100644 --- a/tensorflow/contrib/autograph/pyct/ast_util.py +++ b/tensorflow/contrib/autograph/pyct/ast_util.py @@ -20,7 +20,6 @@ from __future__ import print_function import ast -import collections import gast from tensorflow.contrib.autograph.pyct import anno @@ -185,6 +184,7 @@ class PatternMatcher(gast.NodeVisitor): if v != p: return self.no_match() + def matches(node, pattern): """Basic pattern matcher for AST. @@ -253,30 +253,61 @@ def apply_to_single_assignments(targets, values, apply_fn): apply_fn(target, values) -def iter_fields(node): - for field in sorted(node._fields): - try: - yield getattr(node, field) - except AttributeError: - pass - - -def iter_child_nodes(node): - for field in iter_fields(node): - if isinstance(field, gast.AST): - yield field - elif isinstance(field, list): - for item in field: - if isinstance(item, gast.AST): - yield item - - -def parallel_walk(node_a, node_b): - todo_a = collections.deque([node_a]) - todo_b = collections.deque([node_b]) - while todo_a and todo_b: - node_a = todo_a.popleft() - node_b = todo_b.popleft() - todo_a.extend(iter_child_nodes(node_a)) - todo_b.extend(iter_child_nodes(node_b)) - yield node_a, node_b +def parallel_walk(node, other): + """Walks two ASTs in parallel. + + The two trees must have identical structure. + + Args: + node: Union[ast.AST, Iterable[ast.AST]] + other: Union[ast.AST, Iterable[ast.AST]] + Yields: + Tuple[ast.AST, ast.AST] + Raises: + ValueError: if the two trees don't have identical structure. + """ + if isinstance(node, (list, tuple)): + node_stack = list(node) + else: + node_stack = [node] + + if isinstance(other, (list, tuple)): + other_stack = list(other) + else: + other_stack = [other] + + while node_stack and other_stack: + assert len(node_stack) == len(other_stack) + n = node_stack.pop() + o = other_stack.pop() + + if (not isinstance(n, (ast.AST, gast.AST)) or + not isinstance(o, (ast.AST, gast.AST)) or + n.__class__.__name__ != o.__class__.__name__): + raise ValueError('inconsistent nodes: {} and {}'.format(n, o)) + + yield n, o + + for f in n._fields: + n_child = getattr(n, f, None) + o_child = getattr(o, f, None) + if f.startswith('__') or n_child is None or o_child is None: + continue + + if isinstance(n_child, (list, tuple)): + if (not isinstance(o_child, (list, tuple)) or + len(n_child) != len(o_child)): + raise ValueError( + 'inconsistent values for field {}: {} and {}'.format( + f, n_child, o_child)) + node_stack.extend(n_child) + other_stack.extend(o_child) + + elif isinstance(n_child, (gast.AST, ast.AST)): + node_stack.append(n_child) + other_stack.append(o_child) + + elif n_child != o_child: + raise ValueError( + 'inconsistent values for field {}: {} and {}'.format( + f, n_child, o_child)) diff --git a/tensorflow/contrib/autograph/pyct/ast_util_test.py b/tensorflow/contrib/autograph/pyct/ast_util_test.py index 981e398b93..2293c89720 100644 --- a/tensorflow/contrib/autograph/pyct/ast_util_test.py +++ b/tensorflow/contrib/autograph/pyct/ast_util_test.py @@ -44,7 +44,7 @@ class AstUtilTest(test.TestCase): node, {qual_names.QN('a'): qual_names.QN('renamed_a')}) self.assertIsInstance(node.body[0].value.left.id, str) - source, _ = compiler.ast_to_source(node) + source = compiler.ast_to_source(node) self.assertEqual(source.strip(), 'renamed_a + b') def test_rename_symbols_attributes(self): @@ -54,7 +54,7 @@ class AstUtilTest(test.TestCase): node = ast_util.rename_symbols( node, {qual_names.from_str('b.c'): qual_names.QN('renamed_b_c')}) - source, _ = compiler.ast_to_source(node) + source = compiler.ast_to_source(node) self.assertEqual(source.strip(), 'renamed_b_c = renamed_b_c.d') def test_rename_symbols_annotations(self): @@ -97,10 +97,10 @@ class AstUtilTest(test.TestCase): d = ast_util.keywords_to_dict(keywords) # Make sure we generate a usable dict node by attaching it to a variable and # compiling everything. - output = parser.parse_str('b = 3') - output.body += (ast.Assign([ast.Name(id='d', ctx=ast.Store())], d),) - result, _ = compiler.ast_to_object(output) - self.assertDictEqual(result.d, {'a': 3, 'c': 1, 'd': 'e'}) + node = parser.parse_str('def f(b): pass').body[0] + node.body.append(ast.Return(d)) + result, _ = compiler.ast_to_object(node) + self.assertDictEqual(result.f(3), {'a': 3, 'c': 1, 'd': 'e'}) def assertMatch(self, target_str, pattern_str): node = parser.parse_expression(target_str) @@ -130,8 +130,8 @@ class AstUtilTest(test.TestCase): 'super(Bar, _).__init__(_)') def _mock_apply_fn(self, target, source): - target, _ = compiler.ast_to_source(target) - source, _ = compiler.ast_to_source(source) + target = compiler.ast_to_source(target) + source = compiler.ast_to_source(source) self._invocation_counts[(target.strip(), source.strip())] += 1 def test_apply_to_single_assignments_dynamic_unpack(self): @@ -157,24 +157,40 @@ class AstUtilTest(test.TestCase): }) def test_parallel_walk(self): - ret = ast.Return( - ast.BinOp( - op=ast.Add(), - left=ast.Name(id='a', ctx=ast.Load()), - right=ast.Num(1))) - node = ast.FunctionDef( - name='f', - args=ast.arguments( - args=[ast.Name(id='a', ctx=ast.Param())], - vararg=None, - kwarg=None, - defaults=[]), - body=[ret], - decorator_list=[], - returns=None) + node = parser.parse_str( + textwrap.dedent(""" + def f(a): + return a + 1 + """)) for child_a, child_b in ast_util.parallel_walk(node, node): self.assertEqual(child_a, child_b) + def test_parallel_walk_inconsistent_trees(self): + node_1 = parser.parse_str( + textwrap.dedent(""" + def f(a): + return a + 1 + """)) + node_2 = parser.parse_str( + textwrap.dedent(""" + def f(a): + return a + (a * 2) + """)) + node_3 = parser.parse_str( + textwrap.dedent(""" + def f(a): + return a + 2 + """)) + with self.assertRaises(ValueError): + for _ in ast_util.parallel_walk(node_1, node_2): + pass + # There is not particular reason to reject trees that differ only in the + # value of a constant. + # TODO(mdan): This should probably be allowed. + with self.assertRaises(ValueError): + for _ in ast_util.parallel_walk(node_1, node_3): + pass + if __name__ == '__main__': test.main() diff --git a/tensorflow/contrib/autograph/pyct/cfg.py b/tensorflow/contrib/autograph/pyct/cfg.py index 25fec7fd53..ba51dcf285 100644 --- a/tensorflow/contrib/autograph/pyct/cfg.py +++ b/tensorflow/contrib/autograph/pyct/cfg.py @@ -67,10 +67,8 @@ class Node(object): if isinstance(self.ast_node, gast.FunctionDef): return 'def %s' % self.ast_node.name elif isinstance(self.ast_node, gast.withitem): - source, _ = compiler.ast_to_source(self.ast_node.context_expr) - return source.strip() - source, _ = compiler.ast_to_source(self.ast_node) - return source.strip() + return compiler.ast_to_source(self.ast_node.context_expr).strip() + return compiler.ast_to_source(self.ast_node).strip() class Graph( diff --git a/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py b/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py index 81983a5ecb..aefbc69d8c 100644 --- a/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py +++ b/tensorflow/contrib/autograph/pyct/common_transformers/anf_test.py @@ -43,7 +43,7 @@ class AnfTransformerTest(test.TestCase): return a node, _ = parser.parse_entity(test_function) - node = anf.transform(node, self._simple_source_info()) + node = anf.transform(node.body[0], self._simple_source_info()) result, _ = compiler.ast_to_object(node) self.assertEqual(test_function(), result.test_function()) diff --git a/tensorflow/contrib/autograph/pyct/compiler.py b/tensorflow/contrib/autograph/pyct/compiler.py index c90a5e89c2..f9cee10962 100644 --- a/tensorflow/contrib/autograph/pyct/compiler.py +++ b/tensorflow/contrib/autograph/pyct/compiler.py @@ -30,44 +30,7 @@ import tempfile import astor import gast -from tensorflow.contrib.autograph.pyct import anno -from tensorflow.contrib.autograph.pyct import ast_util from tensorflow.contrib.autograph.pyct import origin_info -from tensorflow.contrib.autograph.pyct import parser - - -def _build_source_map(node, code): - """Return the Python objects represented by given AST. - - Compiling the AST code this way ensures that the source code is readable by - e.g. `pdb` or `inspect`. - - Args: - node: An AST node of the original generated code, before the source code is - generated. - code: The string representation of the source code for the newly generated - code. - - Returns: - Dict[CodeLocation, OriginInfo], a mapping between the user and AutoGraph - generated code. - """ - # After we have the final generated code we reparse it to get the final line - # numbers. Then we walk through the generated and original ASTs in parallel - # to build the mapping between the user and generated code. - new_node = parser.parse_str(code) - origin_info.resolve(new_node, code) - source_mapping = {} - for before, after in ast_util.parallel_walk(node, new_node): - # Need both checks because if origin information is ever copied over to new - # nodes then we need to rely on the fact that only the original user code - # has the origin annotation. - if (anno.hasanno(before, anno.Basic.ORIGIN) and - anno.hasanno(after, anno.Basic.ORIGIN)): - source_info = anno.getanno(before, anno.Basic.ORIGIN) - new_line_number = anno.getanno(after, anno.Basic.ORIGIN).line_number - source_mapping[new_line_number] = source_info - return source_mapping def ast_to_source(node, indentation=' '): @@ -81,24 +44,28 @@ def ast_to_source(node, indentation=' '): code: The source code generated from the AST object source_mapping: A mapping between the user and AutoGraph generated code. """ - original_node = node - if isinstance(node, gast.AST): - node = gast.gast_to_ast(node) + if not isinstance(node, (list, tuple)): + node = (node,) generator = astor.codegen.SourceGenerator(indentation, False, astor.string_repr.pretty_string) - generator.visit(node) - generator.result.append('\n') + + for n in node: + if isinstance(n, gast.AST): + n = gast.gast_to_ast(n) + generator.visit(n) + generator.result.append('\n') + # In some versions of Python, literals may appear as actual values. This # ensures everything is string. code = map(str, generator.result) code = astor.source_repr.pretty_source(code).lstrip() - source_mapping = _build_source_map(original_node, code) - return code, source_mapping + return code -def ast_to_object(node, +def ast_to_object(nodes, indentation=' ', + include_source_map=False, source_prefix=None, delete_on_exit=True): """Return the Python objects represented by given AST. @@ -107,42 +74,46 @@ def ast_to_object(node, e.g. `pdb` or `inspect`. Args: - node: The code to compile, as an AST object. - indentation: The string to use for indentation. - source_prefix: Optional string to print as-is into the source file. - delete_on_exit: Whether to delete the temporary file used for compilation on - exit. + nodes: Union[ast.AST, Iterable[ast.AST]], the code to compile, as an AST + object. + indentation: Text, the string to use for indentation. + include_source_map: bool, whether to attach a source map to the compiled + object. Also see origin_info.py. + source_prefix: Optional[Text], string to print as-is into the source file. + delete_on_exit: bool, whether to delete the temporary file used for + compilation on exit. Returns: - compiled_node: A module object containing the compiled source code. + compiled_nodes: A module object containing the compiled source code. source: The source code of the compiled object Raises: ValueError: If ag_source_map__ is already in the namespace of the compiled - node. + nodes. """ - # code_source_mapping does not yet include the offsets from import statements. - source, code_source_mapping = ast_to_source(node, indentation=indentation) + if not isinstance(nodes, (list, tuple)): + nodes = (nodes,) + + source = ast_to_source(nodes, indentation=indentation) + + if source_prefix: + source = source_prefix + '\n' + source with tempfile.NamedTemporaryFile(mode='w', suffix='.py', delete=False) as f: - # TODO(znado): move into an _offset_source_map() helper function. - # Need to offset the generated line numbers by the number of import lines. - if source_prefix: - num_import_lines = source_prefix.count('\n') + 1 - else: - num_import_lines = 0 - source_mapping = {} - for line_number, original_position in code_source_mapping.items(): - source_map_key = origin_info.CodeLocation( - file_path=f.name, line_number=line_number + num_import_lines) - source_mapping[source_map_key] = original_position module_name = os.path.basename(f.name[:-3]) - if source_prefix: - f.write(source_prefix) - f.write('\n') f.write(source) + + if isinstance(nodes, (list, tuple)): + indices = range(-len(nodes), 0) + else: + indices = (-1,) + + if include_source_map: + source_map = origin_info.source_map(nodes, source, f.name, indices) + + # TODO(mdan): Try flush() and delete=False instead. if delete_on_exit: atexit.register(lambda: os.remove(f.name)) - compiled_node = imp.load_source(module_name, f.name) + compiled_nodes = imp.load_source(module_name, f.name) # TODO(znado): Clean this up so we don't need to attach it to the namespace. # TODO(znado): This does not work for classes because their methods share a @@ -158,11 +129,13 @@ def ast_to_object(node, # is hard, and this cleanly fixes the # issues encountered with nested functions because this is attached to the # outermost one. - source_map_name = 'ag_source_map__' - if source_map_name in compiled_node.__dict__: - raise ValueError('cannot convert %s because is has namespace attribute ' - '"%s", which is reserved for AutoGraph.' % - (compiled_node, source_map_name)) - compiled_node.__dict__[source_map_name] = source_mapping - - return compiled_node, source + if include_source_map: + # TODO(mdan): This name should be decided by the caller. + source_map_name = 'ag_source_map__' + if source_map_name in compiled_nodes.__dict__: + raise ValueError('cannot convert %s because is has namespace attribute ' + '"%s", which is reserved for AutoGraph.' % + (compiled_nodes, source_map_name)) + compiled_nodes.__dict__[source_map_name] = source_map + + return compiled_nodes, source diff --git a/tensorflow/contrib/autograph/pyct/compiler_test.py b/tensorflow/contrib/autograph/pyct/compiler_test.py index e29fa9324c..cf783da6a3 100644 --- a/tensorflow/contrib/autograph/pyct/compiler_test.py +++ b/tensorflow/contrib/autograph/pyct/compiler_test.py @@ -59,7 +59,7 @@ class CompilerTest(test.TestCase): value=gast.Str('c')) ]) - source, _ = compiler.ast_to_source(node, indentation=' ') + source = compiler.ast_to_source(node, indentation=' ') self.assertEqual( textwrap.dedent(""" if 1: diff --git a/tensorflow/contrib/autograph/pyct/origin_info.py b/tensorflow/contrib/autograph/pyct/origin_info.py index 614e346634..1aad2f47df 100644 --- a/tensorflow/contrib/autograph/pyct/origin_info.py +++ b/tensorflow/contrib/autograph/pyct/origin_info.py @@ -22,49 +22,115 @@ import collections import gast from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import ast_util +from tensorflow.contrib.autograph.pyct import parser from tensorflow.python.util import tf_inspect -class CodeLocation( - collections.namedtuple('CodeLocation', ('file_path', 'line_number'))): - """Location of a line of code. +class LineLocation( + collections.namedtuple('LineLocation', ('filename', 'lineno'))): + """Similar to Location, but without column information. Attributes: - file_path: text, the full path to the file containing the code. - line_number: Int, the 1-based line number of the code in its file. + filename: Text + lineno: int, 1-based """ pass +class Location( + collections.namedtuple('Location', ('filename', 'lineno', 'col_offset'))): + """Encodes code location information. + + Attributes: + filename: Text + lineno: int, 1-based + col_offset: int + """ + + @property + def line_loc(self): + return LineLocation(self.filename, self.lineno) + + class OriginInfo( - collections.namedtuple('OriginInfo', - ('file_path', 'function_name', 'line_number', - 'column_offset', 'source_code_line'))): + collections.namedtuple( + 'OriginInfo', + ('loc', 'function_name', 'source_code_line'))): """Container for information about the source code before conversion. - Instances of this class contain information about the source code that - transformed code originated from. Examples include: - * line number - * file name - * original user code + Attributes: + loc: Location + function_name: Optional[Text] + source_code_line: Text """ def as_frame(self): - """Makes a traceback frame tuple. - - Returns: - A tuple of (file_path, line_number, function_name, source_code_line). - """ - return (self.file_path, self.line_number, self.function_name, + """Returns a 4-tuple consistent with the return of traceback.extract_tb.""" + return (self.loc.filename, self.loc.lineno, self.function_name, self.source_code_line) +# TODO(mdan): This source map should be a class - easier to refer to. +def source_map(nodes, code, filename, indices_in_code): + """Creates a source map between an annotated AST and the code it compiles to. + + Args: + nodes: Iterable[ast.AST, ...] + code: Text + filename: Optional[Text] + indices_in_code: Union[int, Iterable[int, ...]], the positions at which + nodes appear in code. The parser always returns a module when parsing + code. This argument indicates the position in that module's body at + which the corresponding of node should appear. + + Returns: + Dict[CodeLocation, OriginInfo], mapping locations in code to locations + indicated by origin annotations in node. + """ + reparsed_nodes = parser.parse_str(code) + reparsed_nodes = [reparsed_nodes.body[i] for i in indices_in_code] + + resolve(reparsed_nodes, code) + result = {} + + for before, after in ast_util.parallel_walk(nodes, reparsed_nodes): + # Note: generated code might not be mapped back to its origin. + # TODO(mdan): Generated code should always be mapped to something. + origin_info = anno.getanno(before, anno.Basic.ORIGIN, default=None) + final_info = anno.getanno(after, anno.Basic.ORIGIN, default=None) + if origin_info is None or final_info is None: + continue + + line_loc = LineLocation(filename, final_info.loc.lineno) + + existing_origin = result.get(line_loc) + if existing_origin is not None: + # Overlaps may exist because of child nodes, but almost never to + # different line locations. Exception make decorated functions, where + # both lines are mapped to the same line in the AST. + + # Line overlaps: keep bottom node. + if existing_origin.loc.line_loc == origin_info.loc.line_loc: + if existing_origin.loc.lineno >= origin_info.loc.lineno: + continue + + # In case of overlaps, keep the leftmost node. + if existing_origin.loc.col_offset <= origin_info.loc.col_offset: + continue + + result[line_loc] = origin_info + + return result + + # TODO(znado): Consider refactoring this into a Visitor. -def resolve(node, source, function=None): +# TODO(mdan): Does this work correctly with inner functions? +def resolve(nodes, source, function=None): """Adds an origin information to all nodes inside the body of function. Args: - node: The AST node for the function whose body nodes will be annotated. + nodes: Union[ast.AST, Iterable[ast.AST, ...]] source: Text, the source code string for the function whose body nodes will be annotated. function: Callable, the function that will have all nodes inside of it @@ -76,25 +142,32 @@ def resolve(node, source, function=None): A tuple of the AST node for function and a String containing its source code. """ + if not isinstance(nodes, (list, tuple)): + nodes = (nodes,) + if function: _, function_lineno = tf_inspect.getsourcelines(function) function_filepath = tf_inspect.getsourcefile(function) else: function_lineno = None function_filepath = None + source_lines = source.split('\n') - for n in gast.walk(node): - if hasattr(n, 'lineno'): - # n.lineno is relative to the start of the enclosing function, so need to - # offset it by the line of the function. - source_code_line = source_lines[n.lineno - 1] + for node in nodes: + for n in gast.walk(node): + if not hasattr(n, 'lineno'): + continue + + lineno_in_body = n.lineno + + source_code_line = source_lines[lineno_in_body - 1] if function: - source_lineno = n.lineno + function_lineno - 1 + source_lineno = function_lineno + lineno_in_body function_name = function.__name__ else: - source_lineno = n.lineno + source_lineno = lineno_in_body function_name = None - anno.setanno( - n, anno.Basic.ORIGIN, - OriginInfo(function_filepath, function_name, source_lineno, - n.col_offset, source_code_line)) + + location = Location(function_filepath, source_lineno, n.col_offset) + origin = OriginInfo(location, function_name, source_code_line) + anno.setanno(n, anno.Basic.ORIGIN, origin) diff --git a/tensorflow/contrib/autograph/pyct/origin_info_test.py b/tensorflow/contrib/autograph/pyct/origin_info_test.py new file mode 100644 index 0000000000..6d7d8b1622 --- /dev/null +++ b/tensorflow/contrib/autograph/pyct/origin_info_test.py @@ -0,0 +1,101 @@ +# 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 origin_info module.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +from tensorflow.contrib.autograph.pyct import anno +from tensorflow.contrib.autograph.pyct import compiler +from tensorflow.contrib.autograph.pyct import origin_info +from tensorflow.contrib.autograph.pyct import parser +from tensorflow.python.platform import test + + +class OriginInfoTest(test.TestCase): + + def test_source_map(self): + + def test_fn(x): + if x > 0: + x += 1 + return x + + node, source = parser.parse_entity(test_fn) + fn_node = node.body[0] + origin_info.resolve(fn_node, source) + + # Insert a traced line. + new_node = parser.parse_str('x = abs(x)').body[0] + anno.copyanno(fn_node.body[0], new_node, anno.Basic.ORIGIN) + fn_node.body.insert(0, new_node) + + # Insert an untraced line. + fn_node.body.insert(0, parser.parse_str('x = 0').body[0]) + + modified_source = compiler.ast_to_source(fn_node) + + source_map = origin_info.source_map(fn_node, modified_source, + 'test_filename', [0]) + + loc = origin_info.LineLocation('test_filename', 1) + origin = source_map[loc] + self.assertEqual(origin.source_code_line, 'def test_fn(x):') + self.assertEqual(origin.loc.lineno, 1) + + # The untraced line, inserted second. + loc = origin_info.LineLocation('test_filename', 2) + self.assertFalse(loc in source_map) + + # The traced line, inserted first. + loc = origin_info.LineLocation('test_filename', 3) + origin = source_map[loc] + self.assertEqual(origin.source_code_line, ' if x > 0:') + self.assertEqual(origin.loc.lineno, 2) + + loc = origin_info.LineLocation('test_filename', 4) + origin = source_map[loc] + self.assertEqual(origin.source_code_line, ' if x > 0:') + self.assertEqual(origin.loc.lineno, 2) + + def test_resolve(self): + + def test_fn(x): + """Docstring.""" + return x # comment + + node, source = parser.parse_entity(test_fn) + fn_node = node.body[0] + origin_info.resolve(fn_node, source) + + origin = anno.getanno(fn_node, anno.Basic.ORIGIN) + self.assertEqual(origin.loc.lineno, 1) + self.assertEqual(origin.loc.col_offset, 0) + self.assertEqual(origin.source_code_line, 'def test_fn(x):') + + origin = anno.getanno(fn_node.body[0], anno.Basic.ORIGIN) + self.assertEqual(origin.loc.lineno, 2) + self.assertEqual(origin.loc.col_offset, 2) + self.assertEqual(origin.source_code_line, ' """Docstring."""') + + origin = anno.getanno(fn_node.body[1], anno.Basic.ORIGIN) + self.assertEqual(origin.loc.lineno, 3) + self.assertEqual(origin.loc.col_offset, 2) + self.assertEqual(origin.source_code_line, ' return x # comment') + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/contrib/autograph/pyct/parser.py b/tensorflow/contrib/autograph/pyct/parser.py index c961efa892..112ed46a1e 100644 --- a/tensorflow/contrib/autograph/pyct/parser.py +++ b/tensorflow/contrib/autograph/pyct/parser.py @@ -37,6 +37,7 @@ def parse_entity(entity): def parse_str(src): """Returns the AST of given piece of code.""" + # TODO(mdan): This should exclude the module things are autowrapped in. return gast.parse(src) -- GitLab From 5d92abe1e426b85ef549a8ce811628d708f7d914 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Wed, 25 Jul 2018 18:32:48 -0700 Subject: [PATCH 425/519] [XLA:GPU] Clarify HeuristicLayoutAssignment function. No functional change. PiperOrigin-RevId: 206090291 --- .../xla/service/gpu/gpu_layout_assignment.cc | 41 +++++++++++-------- 1 file changed, 24 insertions(+), 17 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc index 09ef62c87f..8786bb6262 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc @@ -52,31 +52,38 @@ HeuristicLayoutAssignment(const HloInstruction* instr, // W <=> X // // Therefore kOutputInputYX and kBatchDepthYX mean NCHW. + // + // If you have trouble keeping these straight, consider that all that matters + // is the location of the channel dim: Is it major (NCHW), or minor (NHWC)? + + constexpr auto kAllNCHW = + std::make_tuple(DataLayout::kBatchDepthYX, FilterLayout::kOutputInputYX, + DataLayout::kBatchDepthYX); + constexpr auto kAllNHWC = + std::make_tuple(DataLayout::kBatchYXDepth, FilterLayout::kOutputYXInput, + DataLayout::kBatchYXDepth); - // As of today, our empirical evidence is that cudnn 7.0 is faster on V100 x - // fp16 with the mostly-NHWC layout. The heuristic may change as cudnn version - // changes, as well as the hardware updates. + // If we're not Volta or not fp16, the decision is easy: Use NCHW. if (!(instr->operand(0)->shape().element_type() == xla::PrimitiveType::F16 && IsVoltaOrLater(*stream_executor))) { - return std::make_tuple(DataLayout::kBatchDepthYX, - FilterLayout::kOutputInputYX, - DataLayout::kBatchDepthYX); + return kAllNCHW; } + VLOG(2) << "Using heuristic to figure out layouts for " << instr->ToString(); - // For BackwardInput that has stride, full NHWC layouts run significantly - // slower than (NHWC, NCHW, NCHW) or (NHWC, NCHW, NHWC). - // - // TODO(timshen): more closely compare (NHWC, NCHW, NCHW) and (NHWC, NCHW, - // NHWC). + + // Empirically we've found with Volta and cudnn 7 that backward-input convs + // with stride are significantly faster with input in NHWC and filter/output + // in NCHW. if (instr->custom_call_target() == kCudnnConvBackwardInputCallTarget && window_util::HasStride(instr->window())) { - return std::make_tuple(DataLayout::kBatchYXDepth, - FilterLayout::kOutputInputYX, - DataLayout::kBatchDepthYX); + return std::make_tuple(DataLayout::kBatchYXDepth, // NHWC + FilterLayout::kOutputInputYX, // NCHW + DataLayout::kBatchDepthYX // NCHW + ); } - return std::make_tuple(DataLayout::kBatchYXDepth, - FilterLayout::kOutputYXInput, - DataLayout::kBatchYXDepth); + + // For other Volta f16 convolutions, use NHWC. + return kAllNHWC; } // Adds layout constraints on the cudnn custom-call instruction. The layout -- GitLab From 15b155e929f2eb3e30c1194fa9afc1ea40e330a4 Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Wed, 25 Jul 2018 20:58:13 -0700 Subject: [PATCH 426/519] Replace generic Pool with StreamPool, and discard failed streams. We have a Pool in XLA that maintains a freelist of Streams, to avoid the overhead of repeatedly allocating new Streams. Streams have a monotonic state machine; if a stream encounters any error, it will remain in an error state forever. The functional change in this CL is to ensure that streams which have encountered an error are deleted, rather than being put back on the pool. Without this change, a previously failed stream will be put back on the pool, only to cause the next usage of the stream to trivially fail. I've chosen to replace the generic templatized Pool with a concrete StreamPool, since this makes the logic more straightforward to reason about. Also note that the only existing usage of Pool is to hold streams. The functional change is in stream_pool.cc; most of everything else is mechanical updates. PiperOrigin-RevId: 206100631 --- tensorflow/compiler/jit/BUILD | 1 + tensorflow/compiler/jit/xla_device.h | 7 +- tensorflow/compiler/xla/client/BUILD | 1 + .../compiler/xla/client/local_client.cc | 7 +- tensorflow/compiler/xla/service/BUILD | 21 +-- tensorflow/compiler/xla/service/backend.cc | 17 +-- tensorflow/compiler/xla/service/backend.h | 14 +- .../compiler/xla/service/execution_tracker.cc | 9 +- .../compiler/xla/service/execution_tracker.h | 8 +- tensorflow/compiler/xla/service/gpu/BUILD | 3 +- .../xla/service/gpu/gpu_executable.cc | 2 +- .../xla/service/gpu/hlo_execution_profiler.cc | 11 +- .../xla/service/gpu/hlo_execution_profiler.h | 12 +- tensorflow/compiler/xla/service/pool.h | 84 ----------- tensorflow/compiler/xla/service/pool_test.cc | 40 ------ tensorflow/compiler/xla/service/service.cc | 11 +- .../service/service_executable_run_options.h | 7 +- .../compiler/xla/service/stream_pool.cc | 56 ++++++++ tensorflow/compiler/xla/service/stream_pool.h | 64 +++++++++ .../compiler/xla/service/stream_pool_test.cc | 136 ++++++++++++++++++ tensorflow/compiler/xla/tests/BUILD | 2 + .../xla/tests/transfer_manager_test.cc | 3 +- .../xla/tests/xla_hlo_profile_test.cc | 3 +- 23 files changed, 329 insertions(+), 190 deletions(-) delete mode 100644 tensorflow/compiler/xla/service/pool.h delete mode 100644 tensorflow/compiler/xla/service/pool_test.cc create mode 100644 tensorflow/compiler/xla/service/stream_pool.cc create mode 100644 tensorflow/compiler/xla/service/stream_pool.h create mode 100644 tensorflow/compiler/xla/service/stream_pool_test.cc diff --git a/tensorflow/compiler/jit/BUILD b/tensorflow/compiler/jit/BUILD index 9174a67cc6..e34347b9d4 100644 --- a/tensorflow/compiler/jit/BUILD +++ b/tensorflow/compiler/jit/BUILD @@ -166,6 +166,7 @@ cc_library( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", diff --git a/tensorflow/compiler/jit/xla_device.h b/tensorflow/compiler/jit/xla_device.h index fccdb14368..4a5942fbd7 100644 --- a/tensorflow/compiler/jit/xla_device.h +++ b/tensorflow/compiler/jit/xla_device.h @@ -29,6 +29,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/local_client.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/core/common_runtime/device_factory.h" #include "tensorflow/core/common_runtime/local_device.h" #include "tensorflow/core/framework/allocator.h" @@ -153,17 +154,17 @@ class XlaDevice : public LocalDevice { // stream are executed on the device. Operations include data // copying back and forth between CPU and the device, and // computations enqueued by XLA. - xla::Backend::StreamPtr stream_; + xla::StreamPool::Ptr stream_; // If true, only stream_ is valid and all computation and transfers use // stream_. If false, computation is performed by stream_ and transfers are // performed by host_to_device/device_to_host_stream. bool use_multiple_streams_; // If use_multiple_streams_, host to device transfers are performed using this // stream. - xla::Backend::StreamPtr host_to_device_stream_; + xla::StreamPool::Ptr host_to_device_stream_; // If use_multiple_streams_, device to host transfers are performed using this // stream. - xla::Backend::StreamPtr device_to_host_stream_; + xla::StreamPool::Ptr device_to_host_stream_; // Must we use XLA's transfer manager for correct host<->device transfers? if // false, we can use ThenMemcpy() instead. bool transfer_as_literal_; diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD index 289d3f552a..ef166f8638 100644 --- a/tensorflow/compiler/xla/client/BUILD +++ b/tensorflow/compiler/xla/client/BUILD @@ -114,6 +114,7 @@ cc_library( "//tensorflow/compiler/xla/service:local_service", "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/compiler/xla/service:source_map_util", + "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", "@llvm//:support", diff --git a/tensorflow/compiler/xla/client/local_client.cc b/tensorflow/compiler/xla/client/local_client.cc index 035ee9bf4c..e7250e11d5 100644 --- a/tensorflow/compiler/xla/client/local_client.cc +++ b/tensorflow/compiler/xla/client/local_client.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/backend.h" #include "tensorflow/compiler/xla/service/service_executable_run_options.h" #include "tensorflow/compiler/xla/service/source_map_util.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/status_macros.h" using xla::source_map_util::InvalidParameterArgument; @@ -30,8 +31,8 @@ using xla::source_map_util::InvalidParameterArgument; namespace xla { namespace { -StatusOr BorrowStreamForDevice(int device_ordinal, - Backend* backend) { +StatusOr BorrowStreamForDevice(int device_ordinal, + Backend* backend) { if (device_ordinal < 0) { device_ordinal = backend->default_device_ordinal(); } @@ -142,7 +143,7 @@ StatusOr LocalExecutable::Run( TF_RETURN_IF_ERROR( ValidateExecutionOptions(arguments, run_options, *backend_)); - Backend::StreamPtr stream; + StreamPool::Ptr stream; if (run_options.stream() == nullptr) { // NB! The lifetime of `stream` needs to match the lifetime of // `actual_options` (otherwise we will end up using a returned stream in diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 2305dd4318..6c140caab7 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -564,7 +564,7 @@ cc_library( ":computation_placer", ":device_memory_allocator", ":platform_util", - ":pool", + ":stream_pool", ":transfer_manager", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", @@ -598,6 +598,7 @@ cc_library( ":hlo_proto_util", ":platform_util", ":source_map_util", + ":stream_pool", ":transfer_manager", "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:execution_options_util", @@ -751,8 +752,8 @@ cc_library( ":hlo_execution_profile", ":hlo_graph_dumper", ":hlo_proto", - ":pool", ":shaped_buffer", + ":stream_pool", "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:status", "//tensorflow/compiler/xla:status_macros", @@ -838,7 +839,7 @@ cc_library( hdrs = ["execution_tracker.h"], deps = [ ":backend", - ":pool", + ":stream_pool", "//tensorflow/compiler/xla:executable_run_options", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", @@ -2715,21 +2716,25 @@ tf_cc_test( ) cc_library( - name = "pool", - hdrs = ["pool.h"], + name = "stream_pool", + srcs = ["stream_pool.cc"], + hdrs = ["stream_pool.h"], deps = [ + "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", "//tensorflow/core:lib", + "//tensorflow/core:stream_executor_no_cuda", ], ) tf_cc_test( - name = "pool_test", - srcs = ["pool_test.cc"], + name = "stream_pool_test", + srcs = ["stream_pool_test.cc"], deps = [ - ":pool", + ":stream_pool", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/tests:xla_internal_test_main", + "//tensorflow/core:stream_executor_no_cuda", ], ) diff --git a/tensorflow/compiler/xla/service/backend.cc b/tensorflow/compiler/xla/service/backend.cc index 349b32451a..d12be3e007 100644 --- a/tensorflow/compiler/xla/service/backend.cc +++ b/tensorflow/compiler/xla/service/backend.cc @@ -96,24 +96,19 @@ Backend::CreateDefaultBackend() { return CreateBackend(backend_options); } -StatusOr Backend::BorrowStream(int device_ordinal) { - TF_ASSIGN_OR_RETURN(auto exec, stream_executor(device_ordinal)); - return BorrowStream(exec); +StatusOr Backend::BorrowStream(int device_ordinal) { + TF_ASSIGN_OR_RETURN(auto executor, stream_executor(device_ordinal)); + return BorrowStream(executor); } -StatusOr Backend::BorrowStream( - se::StreamExecutor* executor) { +StatusOr Backend::BorrowStream(se::StreamExecutor* executor) { tensorflow::mutex_lock l(mu_); if (0 == stream_pools_.count(executor)) { stream_pools_.emplace(std::piecewise_construct, std::forward_as_tuple(executor), - std::forward_as_tuple([executor]() { - auto stream = MakeUnique(executor); - stream->Init(); - return stream; - })); + std::forward_as_tuple()); } - return stream_pools_.at(executor).Allocate(); + return stream_pools_.at(executor).BorrowStream(executor); } Backend::Backend( diff --git a/tensorflow/compiler/xla/service/backend.h b/tensorflow/compiler/xla/service/backend.h index 6546602473..1bc3796fa4 100644 --- a/tensorflow/compiler/xla/service/backend.h +++ b/tensorflow/compiler/xla/service/backend.h @@ -24,7 +24,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/compiler.h" #include "tensorflow/compiler/xla/service/computation_placer.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" -#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/service/transfer_manager.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" @@ -63,11 +63,9 @@ class BackendOptions { // // It also offers a pooling API for creation/use of initialized streams: // -// StreamPtr stream = backend->BorrowStream().ConsumeValueOrDie(); +// StreamPool::Ptr stream = backend->BorrowStream().ConsumeValueOrDie(); class Backend { public: - using StreamPtr = Pool::SmartPtr; - // Creates a new backend. static StatusOr> CreateBackend( const BackendOptions& options); @@ -114,13 +112,13 @@ class Backend { // Borrows a stream for use by the caller, either by grabbing it from an // internal pool, or by constructing/initializating it, and returns the result // to the caller. - StatusOr BorrowStream(int device_ordinal); - StatusOr BorrowStream(se::StreamExecutor* executor); + StatusOr BorrowStream(int device_ordinal); + StatusOr BorrowStream(se::StreamExecutor* executor); // Returns a function to borrow a stream, as `BorrowStream` above does. // Purely for convenience, the caller could rather make this anonymous // function itself. - std::function(int)> StreamBorrower() { + std::function(int)> StreamBorrower() { return [this](int device_ordinal) { return BorrowStream(device_ordinal); }; } @@ -169,7 +167,7 @@ class Backend { tensorflow::mutex mu_; // Mapping from stream executor to stream pools, used by `BorrowStream` above. - std::map> stream_pools_ GUARDED_BY(mu_); + std::map stream_pools_ GUARDED_BY(mu_); // The default memory allocator to use. std::unique_ptr memory_allocator_; diff --git a/tensorflow/compiler/xla/service/execution_tracker.cc b/tensorflow/compiler/xla/service/execution_tracker.cc index 6794cfe297..228c3fac95 100644 --- a/tensorflow/compiler/xla/service/execution_tracker.cc +++ b/tensorflow/compiler/xla/service/execution_tracker.cc @@ -25,7 +25,7 @@ limitations under the License. namespace xla { AsyncExecution::AsyncExecution(Backend* backend, - std::vector streams, + std::vector streams, const ExecutionProfile& profile, GlobalDataHandle result) : backend_(CHECK_NOTNULL(backend)), @@ -46,9 +46,10 @@ Status AsyncExecution::BlockUntilDone() const { ExecutionTracker::ExecutionTracker() : next_handle_(1) {} -ExecutionHandle ExecutionTracker::Register( - Backend* backend, std::vector streams, - const ExecutionProfile& profile, GlobalDataHandle result) { +ExecutionHandle ExecutionTracker::Register(Backend* backend, + std::vector streams, + const ExecutionProfile& profile, + GlobalDataHandle result) { tensorflow::mutex_lock lock(execution_mutex_); int64 handle = next_handle_++; auto inserted = handle_to_execution_.emplace( diff --git a/tensorflow/compiler/xla/service/execution_tracker.h b/tensorflow/compiler/xla/service/execution_tracker.h index 4458152dd9..4e9b9f883e 100644 --- a/tensorflow/compiler/xla/service/execution_tracker.h +++ b/tensorflow/compiler/xla/service/execution_tracker.h @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/executable_run_options.h" #include "tensorflow/compiler/xla/service/backend.h" -#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/compiler/xla/xla_data.pb.h" @@ -40,7 +40,7 @@ namespace xla { // the stream when destructed. class AsyncExecution { public: - AsyncExecution(Backend* backend, std::vector streams, + AsyncExecution(Backend* backend, std::vector streams, const ExecutionProfile& profile, GlobalDataHandle result); Status BlockUntilDone() const; @@ -54,7 +54,7 @@ class AsyncExecution { Backend* backend_; // Stream on which the execution is launched. - std::vector streams_; + std::vector streams_; // Profile object of the execution to be returned to the user. ExecutionProfile profile_; @@ -72,7 +72,7 @@ class ExecutionTracker { // Registers an execution with its backend, streams, and data handle to the // execution result. Returns a handle for the registered execution. ExecutionHandle Register(Backend* backend, - std::vector stream, + std::vector stream, const ExecutionProfile& profile, GlobalDataHandle data); diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 6f1e766d1c..4c21811698 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -248,7 +248,7 @@ cc_library( deps = [ "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_execution_profile", - "//tensorflow/compiler/xla/service:pool", + "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/core:lib", "//tensorflow/core:ptr_util", "//tensorflow/core:stream_executor_no_cuda", @@ -321,6 +321,7 @@ cc_library( "//tensorflow/compiler/xla/service:hlo_execution_profile", "//tensorflow/compiler/xla/service:logical_buffer", "//tensorflow/compiler/xla/service:shaped_buffer", + "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/compiler/xla/service:transfer_manager", "//tensorflow/compiler/xla/service:tuple_points_to_analysis", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index 0cad2958c7..9767836cd6 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -84,7 +84,7 @@ Status GpuExecutable::ExecuteThunks( } // Stream 0 indicates `main_stream` and substreams start from stream 1. - std::vector::SmartPtr> sub_streams; + std::vector sub_streams; sub_streams.reserve(thunk_schedule_->StreamCount() - 1); while (sub_streams.size() + 1 < thunk_schedule_->StreamCount()) { sub_streams.emplace_back(); diff --git a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc index 19420e590d..1722676930 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_execution_profile.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" -#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" #include "tensorflow/core/util/ptr_util.h" @@ -37,10 +37,9 @@ void InitAndStartTimer(std::stack>* timers, stream->InitTimer(timers->top().get()).ThenStartTimer(timers->top().get()); } -uint64 GetCyclesTaken( - std::stack>* timers, - const std::vector::SmartPtr>& sub_streams, - se::Stream* stream, double clock_rate_ghz) { +uint64 GetCyclesTaken(std::stack>* timers, + const std::vector& sub_streams, + se::Stream* stream, double clock_rate_ghz) { CHECK_GT(timers->size(), 0); stream->ThenWaitFor(&sub_streams); stream->ThenStopTimer(timers->top().get()); @@ -53,7 +52,7 @@ uint64 GetCyclesTaken( HloExecutionProfiler::HloExecutionProfiler( bool do_profile, HloExecutionProfile* profile, se::Stream* stream, - const std::vector::SmartPtr>& sub_streams, + const std::vector& sub_streams, const HloComputation* computation) : do_profile_(do_profile), profile_(profile), diff --git a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h index 6654850bef..80cde75f2b 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h +++ b/tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h @@ -24,7 +24,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_computation.h" #include "tensorflow/compiler/xla/service/hlo_execution_profile.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" -#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" namespace xla { @@ -38,10 +38,10 @@ class ScopedInstructionProfiler; class HloExecutionProfiler { public: // If profiling is enabled, start an execution timer running. - explicit HloExecutionProfiler( - bool do_profile, HloExecutionProfile* profile, se::Stream* stream, - const std::vector::SmartPtr>& sub_streams, - const HloComputation* computation); + explicit HloExecutionProfiler(bool do_profile, HloExecutionProfile* profile, + se::Stream* stream, + const std::vector& sub_streams, + const HloComputation* computation); // If profiling is enabled, sets the total cycle count on the profile from the // execution timer. @@ -72,7 +72,7 @@ class HloExecutionProfiler { double clock_rate_ghz_; HloExecutionProfile* profile_; se::Stream* stream_; - const std::vector::SmartPtr>& sub_streams_; + const std::vector& sub_streams_; const HloComputation* computation_; std::stack> timers_; // Contains the HLO instructions for which we are currently measuring the diff --git a/tensorflow/compiler/xla/service/pool.h b/tensorflow/compiler/xla/service/pool.h deleted file mode 100644 index 8e710ebb6d..0000000000 --- a/tensorflow/compiler/xla/service/pool.h +++ /dev/null @@ -1,84 +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. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_XLA_POOL_H_ -#define TENSORFLOW_COMPILER_XLA_POOL_H_ - -#include -#include - -#include "tensorflow/compiler/xla/ptr_util.h" -#include "tensorflow/core/platform/mutex.h" - -namespace xla { - -// Pool of values, which are created as needed and destroyed when the `Pool` is -// destroyed -template -class Pool { - public: - struct Deleter { - void operator()(T* ptr) { pool->Deallocate(ptr); } - - Pool* pool; - }; - - // A pointer to a taken element of a `Pool` which returns it to the pool on - // destruction - using SmartPtr = std::unique_ptr; - - // Constructs a `Pool` with given factory function, which need not be - // thread-safe. - explicit Pool(std::function()> factory) - : factory_(factory) {} - - explicit Pool() : Pool([]() { return MakeUnique(); }) {} - - // Returns a pointer to a value in the pool, creating a new value if none is - // free. The returned smart pointer returns the element to the pool on - // destruction. - // - // This method is thread-safe. - SmartPtr Allocate() { - tensorflow::mutex_lock lock(mu_); - T* ptr; - if (!xs_.empty()) { - ptr = std::move(xs_.back()).release(); - xs_.pop_back(); - } else { - ptr = factory_().release(); - } - Deleter del = {this}; - return std::unique_ptr(ptr, del); - } - - private: - // Puts a pointer to a value back into the pool, leaving it free for future - // use. - // - // This method is thread-safe. - void Deallocate(T* ptr) { - tensorflow::mutex_lock lock(mu_); - xs_.push_back(std::unique_ptr(ptr)); - } - - const std::function()> factory_ GUARDED_BY(mu_); - std::vector> xs_ GUARDED_BY(mu_); - tensorflow::mutex mu_; -}; - -} // namespace xla - -#endif // TENSORFLOW_COMPILER_XLA_POOL_H_ diff --git a/tensorflow/compiler/xla/service/pool_test.cc b/tensorflow/compiler/xla/service/pool_test.cc deleted file mode 100644 index 8c4fe258e3..0000000000 --- a/tensorflow/compiler/xla/service/pool_test.cc +++ /dev/null @@ -1,40 +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/service/pool.h" - -#include "tensorflow/compiler/xla/test_helpers.h" - -namespace xla { -namespace { - -using PoolTest = ::testing::Test; - -TEST_F(PoolTest, Test) { - Pool pool; - - { - auto ptr = pool.Allocate(); - EXPECT_NE(nullptr, ptr.get()); - *ptr = 5; - } - - auto ptr = pool.Allocate(); - EXPECT_NE(nullptr, ptr.get()); - EXPECT_EQ(5, *ptr); -} - -} // namespace -} // namespace xla diff --git a/tensorflow/compiler/xla/service/service.cc b/tensorflow/compiler/xla/service/service.cc index 636013cbb5..ce070bc5b6 100644 --- a/tensorflow/compiler/xla/service/service.cc +++ b/tensorflow/compiler/xla/service/service.cc @@ -37,6 +37,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_proto_util.h" #include "tensorflow/compiler/xla/service/platform_util.h" #include "tensorflow/compiler/xla/service/source_map_util.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/service/transfer_manager.h" #include "tensorflow/compiler/xla/shape_layout.h" #include "tensorflow/compiler/xla/shape_util.h" @@ -376,7 +377,7 @@ Service::ExecuteParallelAndRegisterResult( ExecutionProfile* profile) { // Streams where the computation are launched, so we can wait on the streams // to complete. - std::vector::SmartPtr> streams; + std::vector streams; std::vector> timers; // Global data handles for the computation results, one for each computation. @@ -403,7 +404,7 @@ Service::ExecuteParallelAndRegisterResult( CHECK_EQ(replicas.size(), arguments[i].size()); std::vector result_buffers; for (int64 replica = 0; replica < replicas.size(); ++replica) { - TF_ASSIGN_OR_RETURN(Pool::SmartPtr stream, + TF_ASSIGN_OR_RETURN(StreamPool::Ptr stream, backend->BorrowStream(replicas[replica])); streams.push_back(std::move(stream)); @@ -515,13 +516,13 @@ StatusOr Service::ExecuteAndRegisterResult( arguments, Backend* backend, const string& result_tag, ExecutionProfile* profile) { // Set up streams. - std::vector::SmartPtr> streams; + std::vector streams; TF_ASSIGN_OR_RETURN(auto replicas, Replicas(*backend, SingleComputationDeviceHandle())); TF_RET_CHECK(!replicas.empty()); for (se::StreamExecutor* executor : replicas) { - TF_ASSIGN_OR_RETURN(Pool::SmartPtr stream, + TF_ASSIGN_OR_RETURN(StreamPool::Ptr stream, backend->BorrowStream(executor)); streams.push_back(std::move(stream)); } @@ -533,7 +534,7 @@ StatusOr Service::ExecuteAndRegisterResult( // Set up run options. std::vector run_options; - for (const Pool::SmartPtr& stream : streams) { + for (const StreamPool::Ptr& stream : streams) { ExecutableRunOptions options; options.set_stream(stream.get()); options.set_device_ordinal(stream->parent()->device_ordinal()); diff --git a/tensorflow/compiler/xla/service/service_executable_run_options.h b/tensorflow/compiler/xla/service/service_executable_run_options.h index 7f3910cdb0..dbfed628bf 100644 --- a/tensorflow/compiler/xla/service/service_executable_run_options.h +++ b/tensorflow/compiler/xla/service/service_executable_run_options.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_XLA_SERVICE_SERVICE_EXECUTABLE_RUN_OPTIONS_H_ #include "tensorflow/compiler/xla/executable_run_options.h" -#include "tensorflow/compiler/xla/service/pool.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/stream_executor/stream_executor.h" @@ -27,8 +27,7 @@ namespace xla { // data, now only a stream cache for GPU backend. class ServiceExecutableRunOptions { public: - using StreamBorrower = - std::function::SmartPtr>(int)>; + using StreamBorrower = std::function(int)>; ServiceExecutableRunOptions() : ServiceExecutableRunOptions(ExecutableRunOptions()) {} @@ -51,7 +50,7 @@ class ServiceExecutableRunOptions { // Borrows a stream and returns a smart pointer which returns the stream on // destruction. - StatusOr::SmartPtr> BorrowStream(int device_ordinal) const { + StatusOr BorrowStream(int device_ordinal) const { return borrow_stream_ ? borrow_stream_(device_ordinal) : Status(tensorflow::error::UNIMPLEMENTED, "No stream cache"); diff --git a/tensorflow/compiler/xla/service/stream_pool.cc b/tensorflow/compiler/xla/service/stream_pool.cc new file mode 100644 index 0000000000..92bb21b816 --- /dev/null +++ b/tensorflow/compiler/xla/service/stream_pool.cc @@ -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. +==============================================================================*/ + +#include "tensorflow/compiler/xla/service/stream_pool.h" + +#include "tensorflow/compiler/xla/ptr_util.h" + +namespace xla { + +StreamPool::Ptr StreamPool::BorrowStream(se::StreamExecutor* executor) { + std::unique_ptr stream; + { + tensorflow::mutex_lock lock(mu_); + if (!streams_.empty()) { + // Re-use an existing stream from the pool. + stream = std::move(streams_.back()); + streams_.pop_back(); + } + } + + if (!stream) { + // Create a new stream. + stream = MakeUnique(executor); + stream->Init(); + } + + // Return the stream wrapped in Ptr, which has our special deleter semantics. + PtrDeleter deleter = {this}; + return Ptr(stream.release(), deleter); +} + +void StreamPool::ReturnStream(se::Stream* stream) { + if (stream->ok()) { + tensorflow::mutex_lock lock(mu_); + streams_.emplace_back(stream); + } else { + // If the stream has encountered any errors, all subsequent + // operations on it will fail. So just delete the stream, and rely + // on new streams to be created in the future. + delete stream; + } +} + +} // namespace xla diff --git a/tensorflow/compiler/xla/service/stream_pool.h b/tensorflow/compiler/xla/service/stream_pool.h new file mode 100644 index 0000000000..7221d323a6 --- /dev/null +++ b/tensorflow/compiler/xla/service/stream_pool.h @@ -0,0 +1,64 @@ +/* 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_STREAM_POOL_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_STREAM_POOL_H_ + +#include +#include + +#include "tensorflow/compiler/xla/types.h" +#include "tensorflow/core/platform/mutex.h" +#include "tensorflow/core/platform/stream_executor_no_cuda.h" + +namespace xla { + +// Pool of stream_executor::Streams, which are created as needed and +// destroyed when the pool is destroyed. +class StreamPool { + public: + struct PtrDeleter { + void operator()(se::Stream* stream) { pool->ReturnStream(stream); } + StreamPool* pool; + }; + + // Stream pointer type returned by BorrowStream, which returns the + // stream to the pool on destruction. + using Ptr = std::unique_ptr; + + StreamPool() {} + + // Returns a pointer to a stream in the pool, creating a new stream + // if none are available in the pool. The returned smart pointer + // returns the stream to the pool on destruction. + // + // This method is thread-safe. + Ptr BorrowStream(se::StreamExecutor* executor); + + private: + // Puts a pointer to a stream back into the pool, leaving it free + // for future use. Streams that have previously encountered errors + // are deleted, and not returned to the pool. + // + // This method is thread-safe. + void ReturnStream(se::Stream* stream); + + tensorflow::mutex mu_; + std::vector> streams_ GUARDED_BY(mu_); +}; + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_STREAM_POOL_H_ diff --git a/tensorflow/compiler/xla/service/stream_pool_test.cc b/tensorflow/compiler/xla/service/stream_pool_test.cc new file mode 100644 index 0000000000..aaf5c37b0d --- /dev/null +++ b/tensorflow/compiler/xla/service/stream_pool_test.cc @@ -0,0 +1,136 @@ +/* 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/stream_pool.h" + +#include + +#include "tensorflow/compiler/xla/test_helpers.h" +#include "tensorflow/core/platform/stream_executor_no_cuda.h" + +namespace xla { +namespace { + +class StreamPoolTest : public ::testing::Test { + protected: + std::unique_ptr NewStreamExecutor() { + se::Platform* platform = + se::MultiPlatformManager::PlatformWithName("Host").ConsumeValueOrDie(); + se::StreamExecutorConfig config(/*ordinal=*/0); + return platform->GetUncachedExecutor(config).ConsumeValueOrDie(); + } +}; + +TEST_F(StreamPoolTest, EmptyPool) { StreamPool pool; } + +TEST_F(StreamPoolTest, OneStreamPool) { + std::unique_ptr executor = NewStreamExecutor(); + StreamPool pool; + + // Borrow and return a stream. + StreamPool::Ptr stream1 = pool.BorrowStream(executor.get()); + se::Stream* stream1_ptr = stream1.get(); + EXPECT_TRUE(stream1->ok()); + stream1 = nullptr; + + // Borrow and return another stream. + StreamPool::Ptr stream2 = pool.BorrowStream(executor.get()); + se::Stream* stream2_ptr = stream2.get(); + EXPECT_TRUE(stream2->ok()); + stream2 = nullptr; + + // The underlying streams should be the same, since stream1 was the + // only stream available in the pool when stream2 was borrowed. + EXPECT_EQ(stream1_ptr, stream2_ptr); +} + +TEST_F(StreamPoolTest, TwoStreamPool) { + std::unique_ptr executor = NewStreamExecutor(); + StreamPool pool; + + // Borrow two streams. + StreamPool::Ptr stream1 = pool.BorrowStream(executor.get()); + se::Stream* stream1_ptr = stream1.get(); + EXPECT_TRUE(stream1->ok()); + StreamPool::Ptr stream2 = pool.BorrowStream(executor.get()); + se::Stream* stream2_ptr = stream2.get(); + EXPECT_TRUE(stream2->ok()); + + // The underlying streams should be different, since we haven't + // returned either of them yet. + EXPECT_NE(stream1_ptr, stream2_ptr); + + // Return stream1 and borrow stream3. + stream1 = nullptr; + StreamPool::Ptr stream3 = pool.BorrowStream(executor.get()); + se::Stream* stream3_ptr = stream3.get(); + EXPECT_TRUE(stream3->ok()); + + // stream1 and stream3 should be the same. + EXPECT_EQ(stream1_ptr, stream3_ptr); + EXPECT_NE(stream2_ptr, stream3_ptr); + + // Return stream2, and borrow stream4. + stream2 = nullptr; + StreamPool::Ptr stream4 = pool.BorrowStream(executor.get()); + se::Stream* stream4_ptr = stream4.get(); + EXPECT_TRUE(stream4->ok()); + + // Stream2 and stream4 should be the same. + EXPECT_EQ(stream2_ptr, stream4_ptr); + EXPECT_NE(stream3_ptr, stream4_ptr); +} + +TEST_F(StreamPoolTest, BadStreamDiscarded) { + std::unique_ptr executor = NewStreamExecutor(); + StreamPool pool; + + // Borrow a stream. + StreamPool::Ptr stream1 = pool.BorrowStream(executor.get()); + EXPECT_TRUE(stream1->ok()); + + // Force an error on the stream; here we call a method that requires + // DNN support, which we know the Host platform doesn't support. + stream1->ThenDepthConcatenate({}, {}, nullptr); + EXPECT_FALSE(stream1->ok()); + + // Return stream1 and borrow stream2. + stream1 = nullptr; + StreamPool::Ptr stream2 = pool.BorrowStream(executor.get()); + se::Stream* stream2_ptr = stream2.get(); + EXPECT_TRUE(stream2->ok()); + + // The underlying streams should be different. They would have been + // the same, but since we forced an error on stream1, it cannot be + // put back into the pool. Sadly we can't just check: + // EXPECT_NE(stream1_ptr, stream2_ptr); + // + // The above should hold logically, but it may fail if the new + // stream instance allocated for stream2 happens to reside in the + // same memory address as stream1, which has been deleted. + // + // The check that stream2->ok() serves as a good-enough check. + + // Return stream2 and borrow stream3. The previous error on stream1 + // has no effect on these streams, and they are the same. + stream2 = nullptr; + StreamPool::Ptr stream3 = pool.BorrowStream(executor.get()); + se::Stream* stream3_ptr = stream3.get(); + EXPECT_TRUE(stream3->ok()); + EXPECT_EQ(stream2_ptr, stream3_ptr); +} + +} // namespace +} // namespace xla diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 099431d949..39dec5b2e0 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -379,6 +379,7 @@ xla_test( "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:platform_util", + "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/core:lib", @@ -2013,6 +2014,7 @@ xla_test( "//tensorflow/compiler/xla/service:device_memory_allocator", "//tensorflow/compiler/xla/service:generic_transfer_manager", "//tensorflow/compiler/xla/service:shaped_buffer", + "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/core:lib", "//tensorflow/core:stream_executor_no_cuda", "//tensorflow/core:test", diff --git a/tensorflow/compiler/xla/tests/transfer_manager_test.cc b/tensorflow/compiler/xla/tests/transfer_manager_test.cc index 0f86b7f20f..125513ddfd 100644 --- a/tensorflow/compiler/xla/tests/transfer_manager_test.cc +++ b/tensorflow/compiler/xla/tests/transfer_manager_test.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/device_memory_allocator.h" #include "tensorflow/compiler/xla/service/generic_transfer_manager.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" @@ -60,7 +61,7 @@ class TransferManagerTest : public LocalClientTestBase { } protected: - Backend::StreamPtr stream_ptr_; + StreamPool::Ptr stream_ptr_; se::Stream* stream_; private: diff --git a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc index 7a75e5102c..10c51a1c84 100644 --- a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc +++ b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc @@ -22,6 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/platform_util.h" +#include "tensorflow/compiler/xla/service/stream_pool.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" @@ -133,7 +134,7 @@ void ExecuteAndFetchProfile(string* profile_output, LocalClient* client, DeviceMemoryAllocator* allocator = backend->memory_allocator(); auto* transfer_manager = backend->transfer_manager(); TF_ASSERT_OK_AND_ASSIGN( - Backend::StreamPtr stream_ptr, + StreamPool::Ptr stream_ptr, backend->BorrowStream(backend->default_device_ordinal())); TF_ASSERT_OK_AND_ASSIGN( -- GitLab From dea6f2a4b671e1b9316797acdcc1c20e218c75d3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 22:08:46 -0700 Subject: [PATCH 427/519] [XLA] The first step to incrementally move client/xla_client/* to client/. PiperOrigin-RevId: 206105815 --- tensorflow/compiler/xla/client/BUILD | 44 + .../client/{xla_client => }/xla_builder.cc | 2 +- tensorflow/compiler/xla/client/xla_builder.h | 2241 +++++++++++++++++ .../{xla_client => }/xla_builder_test.cc | 2 +- .../compiler/xla/client/xla_client/BUILD | 37 +- .../xla/client/xla_client/xla_builder.h | 2222 +--------------- .../docs_src/performance/xla/broadcasting.md | 2 +- 7 files changed, 2290 insertions(+), 2260 deletions(-) rename tensorflow/compiler/xla/client/{xla_client => }/xla_builder.cc (99%) create mode 100644 tensorflow/compiler/xla/client/xla_builder.h rename tensorflow/compiler/xla/client/{xla_client => }/xla_builder_test.cc (99%) diff --git a/tensorflow/compiler/xla/client/BUILD b/tensorflow/compiler/xla/client/BUILD index ef166f8638..ad3fcee05b 100644 --- a/tensorflow/compiler/xla/client/BUILD +++ b/tensorflow/compiler/xla/client/BUILD @@ -188,3 +188,47 @@ cc_library( "//tensorflow/compiler/xla/service:hlo_proto", ], ) + +cc_library( + name = "xla_builder", + srcs = ["xla_builder.cc"], + hdrs = ["xla_builder.h"], + visibility = ["//visibility:public"], + deps = [ + ":padding", + ":sharding_builder", + ":xla_computation", + "//tensorflow/compiler/xla:execution_options_util", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:literal_util", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:types", + "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_proto", + "//tensorflow/compiler/xla/service:shape_inference", + "//tensorflow/core:lib", + ], +) + +tf_cc_test( + name = "xla_builder_test", + srcs = ["xla_builder_test.cc"], + deps = [ + ":xla_builder", + ":xla_computation", + "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla:status_macros", + "//tensorflow/compiler/xla:test", + "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", + "//tensorflow/compiler/xla/service:hlo", + "//tensorflow/compiler/xla/service:hlo_matchers", + "//tensorflow/core:test", + ], +) diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc b/tensorflow/compiler/xla/client/xla_builder.cc similarity index 99% rename from tensorflow/compiler/xla/client/xla_client/xla_builder.cc rename to tensorflow/compiler/xla/client/xla_builder.cc index 152335e22a..53be5a79c2 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.cc +++ b/tensorflow/compiler/xla/client/xla_builder.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include #include diff --git a/tensorflow/compiler/xla/client/xla_builder.h b/tensorflow/compiler/xla/client/xla_builder.h new file mode 100644 index 0000000000..ae331407d6 --- /dev/null +++ b/tensorflow/compiler/xla/client/xla_builder.h @@ -0,0 +1,2241 @@ +/* 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_CLIENT_XLA_BUILDER_H_ +#define TENSORFLOW_COMPILER_XLA_CLIENT_XLA_BUILDER_H_ + +#include +#include +#include +#include + +#include "tensorflow/compiler/xla/client/padding.h" +#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/literal.h" +#include "tensorflow/compiler/xla/literal_util.h" +#include "tensorflow/compiler/xla/service/hlo.pb.h" +#include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/shape_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" +#include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/flatset.h" +#include "tensorflow/core/platform/macros.h" +#include "tensorflow/core/platform/stacktrace.h" +#include "tensorflow/core/platform/types.h" + +namespace xla { + +class XlaBuilder; + +// This represents an instruction that has been enqueued using the XlaBuilder. +// This is used to pass to subsequent computations that depends upon the +// instruction as an operand. +class XlaOp { + public: + XlaOp() : handle_(-1), builder_(nullptr) { + static_assert(std::is_trivially_destructible::value, + "XlaOp should be trivially destructible"); + } + ~XlaOp() = default; + + // Precondition: !IsUninitialized(). + // + // It's very common to do foo.builder()->bar(). Without this precondition, if + // foo.builder() is null, the call to bar will segfault at some point possibly + // deep in the callstack when we finally dereference `this`. The precondition + // lets us avoid this tricky-to-debug problem. + XlaBuilder* builder() const { + CHECK(builder_ != nullptr); + return builder_; + } + + // Returns true if the XlaOp represents valid, non-erroneous value. + bool valid() const { return handle_ >= 0; } + + // Returns true if the XlaOp was created by the XlaOp() constructor and + // not returned by a builder. + bool IsUninitialized() const { return builder_ == nullptr; } + + bool IsIdenticalTo(const XlaOp& rhs) const { + return handle_ == rhs.handle_ && builder_ == rhs.builder_; + } + + friend std::ostream& operator<<(std::ostream& out, const XlaOp& op) { + out << op.handle(); + return out; + } + + private: + explicit XlaOp(XlaBuilder* builder) : handle_(-1), builder_(builder) {} + XlaOp(int64 handle, XlaBuilder* builder) + : handle_(handle), builder_(builder) {} + + int64 handle() const { return handle_; } + + friend class XlaBuilder; + + // < 0 means "invalid handle". + int64 handle_; + + // Not owned. Non-null for any handle returned by XlaBuilder, even if the + // handle is invalid. + XlaBuilder* builder_; +}; + +// Arithmetic operator overloads for the XlaOp type. +XlaOp operator-(const XlaOp& x); +XlaOp operator+(const XlaOp& x, const XlaOp& y); +XlaOp operator-(const XlaOp& x, const XlaOp& y); +XlaOp operator*(const XlaOp& x, const XlaOp& y); +XlaOp operator/(const XlaOp& x, const XlaOp& y); +XlaOp operator%(const XlaOp& x, const XlaOp& y); + +// Bitwise operator overloads for the XlaOp type. +XlaOp operator~(const XlaOp& x); +XlaOp operator&(const XlaOp& x, const XlaOp& y); +XlaOp operator|(const XlaOp& x, const XlaOp& y); +XlaOp operator^(const XlaOp& x, const XlaOp& y); +XlaOp operator<<(const XlaOp& x, const XlaOp& y); +// Performs a right arithmetic shift if 'x' is a signed type, otherwise performs +// a right logical shift. +XlaOp operator>>(const XlaOp& x, const XlaOp& y); + +// We don't overload the relational operators (==, !=, <, <=, >, >=) because the +// semantics might be surprising since their result types are usually 'bool'. +// Further programmers may expect == to be a structural equality. +// We also choose not to overload any of the mutating operators (e.g., +=, -=) +// because the semantics might be misleading — XLA computations are immutable. + +// A convenient interface for building up computations. +// +// Thread-compatible. +class XlaBuilder { + public: + // computation_name: name to use for the built computation. + XlaBuilder(const string& computation_name); + + XlaBuilder(const XlaBuilder&) = delete; + XlaBuilder& operator=(const XlaBuilder&) = delete; + + ~XlaBuilder(); + + // Returns the computation name. + const string& name() const { return name_; } + + // Sets OpMetadata that will be added to all instructions until cleared. + // + // OpMetadata is often applied to a series of XLA HLO instructions. As a + // result, OpMetadata is set on the Computation Builder. All subsequent + // instructions generated via this Computation Builder will have the same + // OpMetadata attached until a call to ClearOpMetadata. + void SetOpMetadata(const OpMetadata& metadata) { metadata_ = metadata; } + + // Clears the HloMetadata state. + void ClearOpMetadata() { metadata_.Clear(); } + + // Sets an OpSharding that will be attached to all instructions until cleared. + void SetSharding(const OpSharding& sharding) { sharding_ = sharding; } + + // Clears the sharding. Ops will be sharded according to the default placement + // policy. + void ClearSharding() { sharding_ = tensorflow::gtl::nullopt; } + + // Returns the OpSharding that will be attached to all instructions. + const tensorflow::gtl::optional& sharding() const { + return sharding_; + } + + // Sets the builder to a mode where it will die immediately when an error is + // encountered, rather than producing it in a deferred fashion when Build() is + // called (which is the default). + void set_die_immediately_on_error(bool enabled) { + die_immediately_on_error_ = enabled; + } + + // Default dimension numbers used for a 2D convolution. + static constexpr int64 kConvBatchDimension = 0; + static constexpr int64 kConvFeatureDimension = 1; + static constexpr int64 kConvFirstSpatialDimension = 2; + static constexpr int64 kConvSecondSpatialDimension = 3; + static constexpr int64 kConvKernelOutputDimension = 0; + static constexpr int64 kConvKernelInputDimension = 1; + static constexpr int64 kConvKernelFirstSpatialDimension = 2; + static constexpr int64 kConvKernelSecondSpatialDimension = 3; + + // Creates a default ConvolutionDimensionNumbers. For a 2D convolution, for + // the input operand {batch, feature, height, width} = {0, 1, 2, 3} and for + // the kernel operand + // {output_feature, input_feature, height, width} = {0, 1, 2, 3}. + static ConvolutionDimensionNumbers CreateDefaultConvDimensionNumbers( + int num_spatial_dims = 2); + + // Returns an error if the convolution dimension numbers have conflicts. + static Status Validate(const ConvolutionDimensionNumbers& dnum); + + // Returns a new XlaBuilder whose resultant Computation is used only by this + // XlaBuilder. The sub-XlaBuilder has the same die_immediately_on_error + // behavior as the parent. + std::unique_ptr CreateSubBuilder(const string& computation_name); + + // Builds the computation with the requested operations, or returns a non-ok + // status. Note that all ops that have been enqueued will be moved to the + // computation being returned. + StatusOr Build(); + + // Builds the computation with the requested operations, or notes an error in + // the parent XlaBuilder and returns an empty computation if building failed. + // This function is intended to be used where the returned XlaComputation is + // only used by the parent XlaBuilder and hence further operation on the + // returned XlaComputation will simply be error'ed out if an error occurred + // while building this computation. If the built computation is to be used by + // a XlaBuilder other than the parent XlaBuilder then Build() should be used + // instead. + XlaComputation BuildAndNoteError(); + + // Returns a subgraph that roots on the given root. If the root is not a + // compile-time constant (see `IsConstant`), returns an error. + // + // This will copy the needed ops/computations to the subgraph. + StatusOr BuildConstantSubGraph(const XlaOp& root_op) const; + + // Returns the first error that was encountered while building the + // computation. When an error is encountered, by default we return a vacuous + // XlaOp and inform the user of the error that occurred while + // building the computation when they make a final call to Build(). + // + // See also set_die_immediately_on_error(). + Status first_error() const { return first_error_; } + + // Returns the shape of the given op. + StatusOr GetShape(const XlaOp& op) const; + + // Returns the (inferred) result for the current computation's shape. + StatusOr GetProgramShape() const; + + // Reports an error to the builder, by + // * storing it internally and capturing a backtrace if it's the first error + // (this deferred value will be produced on the call to + // Build()/GetShape()/...) + // * dying if die_immediately_on_error_ is true. + // Returns an XlaOp with an invalid handle but a valid builder. This value can + // be returned in place of a value in APIs that return an XlaOp. + XlaOp ReportError(const Status& error); + + // A helper function that converts a StatusOr into an XlaOp. + // If the Status was an error, reports the error to builder and returns an + // invalid XlaOp handle. + XlaOp ReportErrorOrReturn(const StatusOr& op); + + // A helper function that runs a function that returns a StatusOr and + // returns an XlaOp. + XlaOp ReportErrorOrReturn(const std::function()>& op_creator); + + // Returns true if 'operand' is a compile-time constant. A compile-time + // constant does not depend on any parameters, or on stateful operators such + // as `RngNormal` or `Infeed`. + // + // This tests whether a computation is a compile-time constant without + // evaluating the computation. + StatusOr IsConstant(const XlaOp& operand) const; + + private: + // Enqueues a "retrieve parameter value" instruction for a parameter that was + // passed to the computation. + XlaOp Parameter(int64 parameter_number, const Shape& shape, + const string& name); + + // Enqueues a constant with the value of the given literal onto the + // computation. + XlaOp ConstantLiteral(const LiteralSlice& literal); + + // Enqueues a constant onto the computation. Methods are templated on the + // native host type (NativeT) which corresponds to a specific XLA + // PrimitiveType as given in the following table: + // + // Native Type PrimitiveType + // ----------------------------- + // bool PRED + // int32 S32 + // int64 S64 + // uint32 U32 + // uint64 U64 + // float F32 + // double F64 + // + // Note: not all primitive types defined in xla_data.proto have a + // corresponding native type yet. + template + XlaOp ConstantR0(NativeT value); + template + XlaOp ConstantR1(tensorflow::gtl::ArraySlice values); + XlaOp ConstantR1(const tensorflow::core::Bitmap& values); + template + XlaOp ConstantR2( + std::initializer_list> values); + template + XlaOp ConstantFromArrayWithLayout(const Array& values, + const Layout& layout); + template + XlaOp ConstantFromArray(const Array& values); + template + XlaOp ConstantR2FromArray2DWithLayout(const Array2D& values, + const Layout& layout); + template + XlaOp ConstantR2FromArray2D(const Array2D& values); + template + XlaOp ConstantR3FromArray3DWithLayout(const Array3D& values, + const Layout& layout); + template + XlaOp ConstantR3FromArray3D(const Array3D& values); + template + XlaOp ConstantR4FromArray4DWithLayout(const Array4D& values, + const Layout& layout); + template + XlaOp ConstantR4FromArray4D(const Array4D& values); + + // Enqueues a rank one constant (vector) onto the computation. The vector has + // size 'length' and every element has the value 'value'. + template + XlaOp ConstantR1(int64 length, NativeT value); + + // Adds dimensions to an array by duplicating the data in the array. + // + // The new dimensions are inserted on the left, i.e. if + // broadcast_sizes has values {a0, ..., aN} and the operand shape + // has dimensions {b0, ..., bM} then the shape of the output has + // dimensions {a0, ..., aN, b0, ..., bM}. + // + // The new dimensions index into copies of the operand, i.e. + // + // output[i0, ..., iN, j0, ..., jM] = operand[j0, ..., jM] + XlaOp Broadcast(const XlaOp& operand, + tensorflow::gtl::ArraySlice broadcast_sizes); + + // Performs in-dimension-style broadcast. + // + // Operand specifies the input to be broadcast. "shape" is expected output + // shape. "broadcast_dimensions" are the dimensions to be broadcasting into. + // Dimension numbers in broadcast_dimensions map to individual dimensions + // of the operand, and specify what dimension of the output shape they + // should be broadcast. + // e.g. + // Say operand = [1, 2], i.e., a 1D tensor with 2 elements. + // and dimension of shape is [2,2]. + // Specifying {1} as brodcast_dimension will generate output + // [1 , 2] + // [1 , 2] + // On the other hand, specifying {0} as broadcast_dimension + // will generate output + // [1 , 1] + // [2 , 2] + XlaOp BroadcastInDim( + const XlaOp& operand, const Shape& shape, + const tensorflow::gtl::ArraySlice broadcast_dimensions); + + // Enqueues a pad operation onto the computation that pads the given value on + // the edges as well as between the elements of the input. padding_config + // specifies the padding amount for each dimension. + XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, + const PaddingConfig& padding_config); + + // Enqueues an operation onto the computation that flattens the operand based + // on the dimension order (major/slowest-varying to minor/fastest-varying) + // given, followed by reshaping it into the shape with the given dimension + // sizes (also major to minor). Conceptually, this is a limited form of + // "shape casting". + XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice new_sizes); + + // Enqueues an operation onto the computation that collapses the operand, from + // first to last dimension (C order), then reshapes it to the given dimension + // sizes. Conceptually, this is a limited form of "shape casting". + XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice new_sizes); + + // Wrapper for Reshape. + // Enqueues an operation to collapse the provided dimensions; e.g. an + // operand with dimensions {x=256, y=2, z=2, p=32} can be collapsed to + // {x=1024, y=32} by collapsing dims {0, 1, 2}. Collapsing dimensions must + // be a consecutive, in-order subsequence of the operand dimensions. + // + // Note that collapsing a single dimension does nothing: + // + // {256} collapsing {0} => {256} + // {1} collapsing {0} => {1} + // + // Collapsing multiple dimensions produces a single result dimension: + // + // {256, 2} collapsing {0,1} => {512} + // {256, 2, 3} collapsing {0,1} => {512, 3} + // + // This could potentially cause data to be moved -- it provides a more + // structured form of reshaping than an arbitrary Reshape operation. + XlaOp Collapse(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + + // Enqueues a slice operation onto the computation that slices the operand + // from the start indices to the limit indices; e.g. + // + // x + // [ 0 1 2 3 ] + // y [ 4 5 6 7 ] => slice(start={1, 1}, limit={2, 3}) => [ 5 6 ] + // [ 8 9 a b ] + // + // Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D + // range notation. + // The strides parameter determines the stride over the slice + XlaOp Slice(const XlaOp& operand, + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides); + + // Enqueues a slice operation in a given dimension, taking all other + // dimensions as they are; e.g. if dimno is 1 from start_index 2 to + // limit_index 4 by 1, and the shape is f32[7,8,9], this call is short-hand + // for: + // + // array[:, 2:4:1, :] + XlaOp SliceInDim(const XlaOp& operand, int64 start_index, int64 limit_index, + int64 stride, int64 dimno); + + // Enqueues a slice operation onto the computation that slices the 'operand' + // from dynamic start indices which are passed in 'start_indices'. + // The size of the slice in each dimension is passed in 'slice_sizes', + // which specify the end point of exclusive slice intervals in each + // dimension [start, start + size). + // The shape of 'start_indices' must be rank == 1, with dimension size + // equal to the rank of the 'operand'. + // Slice index calculations are computed modulo input dimension sizes to + // prevent dynamic start indices from generating out-of-bound array accesses. + XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, + tensorflow::gtl::ArraySlice slice_sizes); + + // Enqueues a dynamic update slice operation onto the computation, which + // updates a slice of 'operand' with 'update' at dynamic 'start_indices'. + // The shape of 'update' determines the shape of the slice of 'operand' + // which is updated. + // The indices specified in 'start_indices' specify the offset of the slice + // of 'operand' which is updated. + // + // update = {10, 11} // calculated at runtime. + // [1 2 3] start = {1, 1} // calculated at runtime. [1 2 3 ] + // [4 5 6] => DynamicUpdateslice(data, update, start) => [4 10 11] + // [7 8 9] [7 8 9 ] + // + // The shape of 'start_indices' must be rank == 1, with dimension size + // equal to the rank of the 'operand'. + // Slice index calculations are computed modulo update dimension sizes to + // prevent dynamic start indices from generating out-of-bound array accesses. + XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, + const XlaOp& start_indices); + + // Enqueues a concatenate instruction onto the computation. 'operands' must + // have >= 1 entry. + XlaOp ConcatInDim(tensorflow::gtl::ArraySlice operands, + int64 dimension); + + // Enqueue a tracing operation onto the computation; the computation will emit + // a logging message with the operand. + void Trace(const string& tag, const XlaOp& operand); + + // Enqueues a conditional-move-like select operation onto the computation; + // predicated on pred, selects between on_true and on_false. + XlaOp Select(const XlaOp& pred, const XlaOp& on_true, const XlaOp& on_false); + + // Enqueues a tuple-creation instruction onto the computation. + XlaOp Tuple(tensorflow::gtl::ArraySlice elements); + + // Enqueues a tuple-element-get instruction onto the computation. + XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); + + // Enqueues an equal-to comparison instruction onto the computation. + XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a not-equal comparison instruction onto the computation. + XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a greater-or-equal comparison instruction onto the computation. + XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a greater-than comparison instruction onto the computation. + XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a less-than comparison instruction onto the computation. + XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a less-or-equal comparison instruction onto the computation. + XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a dot instruction onto the computation. + XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); + + // Enqueues a general dot instruction onto the computation. + XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, + const DotDimensionNumbers& dimension_numbers); + + // Enqueues a convolution instruction onto the computation, which uses the + // default convolution dimension numbers. + XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + Padding padding); + + // Enqueues a convolution instruction onto the computation, with the caller + // provided padding configuration in the format returned by MakePadding(). + XlaOp ConvWithGeneralPadding( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + + // Enqueues a convolution instruction onto the computation, with the caller + // provided dimension numbers configuration. + XlaOp ConvWithGeneralDimensions( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const ConvolutionDimensionNumbers& dimension_numbers); + + // Enqueues a convolution instruction onto the computation, with the caller + // provided padding configuration as well as the dimension numbers. + XlaOp ConvGeneral( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const ConvolutionDimensionNumbers& dimension_numbers); + + // Enqueues a convolution instruction onto the computation, with the caller + // provided padding configuration, dilation factors and dimension numbers. + XlaOp ConvGeneralDilated( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + tensorflow::gtl::ArraySlice lhs_dilation, + tensorflow::gtl::ArraySlice rhs_dilation, + const ConvolutionDimensionNumbers& dimension_numbers); + + // Enqueues an FFT instruction onto the computation, of the given type and + // with the given FFT length. + XlaOp Fft(const XlaOp& operand, FftType fft_type, + tensorflow::gtl::ArraySlice fft_length); + + // Enqueues an infeed instruction onto the computation, which writes data of + // the given shape to the infeed buffer of the device. + XlaOp Infeed(const Shape& shape, const string& config = ""); + XlaOp InfeedWithToken(const XlaOp& token, const Shape& shape, + const string& config = ""); + + // Enqueues an outfeed instruction onto the computation. This instruction + // generates outgoing data transfers for the given data. + // + // shape_with_layout communicates the laid out shape that we want to outfeed + // -- if !ShapeUtil::Compatible(GetShape(operand), shape_with_layout) an error + // will occur. + void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, + const string& outfeed_config); + XlaOp OutfeedWithToken(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, + const string& outfeed_config); + + // Enqueues a call instruction onto the computation. + XlaOp Call(const XlaComputation& computation, + tensorflow::gtl::ArraySlice operands); + + // Enqueues a custom call instruction onto the computation. + // During code generation, a call instruction is emitted which targets a + // symbol with the name |call_target_name|. The |operands| are passed to the + // call instruction. |shape| is the resultant shape. + XlaOp CustomCall(const string& call_target_name, + tensorflow::gtl::ArraySlice 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. + XlaOp HostCompute(tensorflow::gtl::ArraySlice 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 + // (see g3doc for more details). + + // Enqueues a complex compose instruction onto the computation. + XlaOp Complex(const XlaOp& real, const XlaOp& imag, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a complex conjugate instruction onto the computation. + XlaOp Conj(const XlaOp& operand); + + // Enqueues an add instruction onto the computation. + XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a subtract instruction onto the computation. + XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a multiply instruction onto the computation. + XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a divide instruction onto the computation. + XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a remainder instruction onto the computation. + XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a max instruction onto the computation. + XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues a min instruction onto the computation. + XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Element-wise logical operators + XlaOp And(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + XlaOp Not(const XlaOp& operand); + + XlaOp ShiftLeft(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + XlaOp ShiftRightArithmetic( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + XlaOp ShiftRightLogical( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Reduces an array among the provided dimensions, given "computation" as a + // reduction operator. + XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions_to_reduce); + + // Convenience wrapper around the above that reduces all the dimensions in the + // operand shape. + XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation); + + // Enqueues a windowed reduce instruction onto the computation. + XlaOp ReduceWindow(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding); + + // As ReduceWindow(), but the padding is given in the format + // returned by MakePadding(). + XlaOp ReduceWindowWithGeneralPadding( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + + // Returns the sum of the operand value within each subgroup of replicas. All + // replicas supply one input to the sum and all replicas receive the resulting + // sum for each subgroup. + XlaOp CrossReplicaSum( + const XlaOp& operand, + tensorflow::gtl::ArraySlice replica_group_ids = {}); + + // Enqueues an operation that do an AllReduce of the operand cross cores. Here + // AllReduce means doing a reduction on the input operand cross cores and then + // broadcasting the reduction result to those cores. The reduction function is + // defined by `computation`, which should be a commutative computation on + // scalars, e.g., add, min, or max. The way that AllReduce is applied is + // configured by: + // + // - `replica_group_ids`: maps replica ids to subgroup ids. If empty, all + // replicas belong to one group. Allreduce will be applied within subgroups. + // For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, + // replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. + // + // - `channel_id`: for Allreduce nodes from different models, if they have the + // same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be + // applied cross models. + // + // TODO(b/79737069): Rename this to AllReduce when it's ready to use. + XlaOp CrossReplicaSum( + const XlaOp& operand, const XlaComputation& computation, + tensorflow::gtl::ArraySlice replica_group_ids = {}, + const tensorflow::gtl::optional& channel_id = + tensorflow::gtl::nullopt); + + // Enqueues an operation that scatters the `source` array to the selected + // indices of each window. + XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding, const XlaOp& source, + const XlaOp& init_value, + const XlaComputation& scatter); + + // As SelectAndScatter(), but the padding is given in the format + // returned by MakePadding(). + XlaOp SelectAndScatterWithGeneralPadding( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter); + + // Enqueues an abs instruction onto the computation. + XlaOp Abs(const XlaOp& operand); + + // Enqueues a atan2 instruction onto the computation. + XlaOp Atan2(const XlaOp& y, const XlaOp& x, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues an exp instruction onto the computation. + XlaOp Exp(const XlaOp& operand); + + // Enqueues an expm1 instruction onto the computation. + XlaOp Expm1(const XlaOp& operand); + + // Enqueues a floor instruction onto the computation. + XlaOp Floor(const XlaOp& operand); + + // Enqueues a ceil instruction onto the computation. + XlaOp Ceil(const XlaOp& operand); + + // Enqueues a round instruction onto the computation, rounding to nearest even + // with half-way cases rounding away from zero. + XlaOp Round(const XlaOp& operand); + + // Enqueues an log instruction (natural logarithm) onto the computation. + XlaOp Log(const XlaOp& operand); + + // Enqueues an log1p instruction (log(x+1)) onto the computation. + XlaOp Log1p(const XlaOp& operand); + + // Enqueues a sign instruction onto the computation. + XlaOp Sign(const XlaOp& operand); + + // Enqueues a count leading zeros instruction onto the computation. + XlaOp Clz(const XlaOp& operand); + + // Enqueues a cosine instruction onto the computation. + XlaOp Cos(const XlaOp& operand); + + // Enqueues a sine instruction onto the computation. + XlaOp Sin(const XlaOp& operand); + + // Enqueues a tanh instruction onto the computation. + XlaOp Tanh(const XlaOp& operand); + + // Enqueues a real-part instruction onto the computation. + XlaOp Real(const XlaOp& operand); + + // Enqueues an imaginary-part instruction onto the computation. + XlaOp Imag(const XlaOp& operand); + + // Enqueues a lhs^rhs computation onto the computation. + XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + + // Enqueues an operator that tests if the operand's values are finite, i.e., + // not Inf or NaN. Defined only for floating-point types. Returns an array of + // booleans with the same shape where entries are true iff the corresponding + // entry was NaN. + XlaOp IsFinite(const XlaOp& operand); + + // Enqueues a convert instruction onto the computation that changes the + // element type of the operand array to primitive_type. + XlaOp ConvertElementType(const XlaOp& operand, + PrimitiveType new_element_type); + + // Enqueues a no-op instruction onto the computation that changes + // the element type of the operand array to primitive_type. The + // bit-widths of the source and destination element types must be + // identical. + XlaOp BitcastConvertType(const XlaOp& operand, + PrimitiveType new_element_type); + + // Enqueues a negate instruction onto the computation. + XlaOp Neg(const XlaOp& operand); + + // Enqueues a transpose instruction onto the computation. + XlaOp Transpose(const XlaOp& operand, + tensorflow::gtl::ArraySlice permutation); + + // Enqueues a reverse instruction onto the computation. The order of the + // elements in the given dimensions is reversed (i.e., the element at index i + // is moved to index dimension_size - 1 - i). + XlaOp Rev(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + + // Enqueues a sort (as increasing order) instruction onto the computation. + // If only keys are provided: + // * If the keys are an rank-1 tensor (an array), the result is a sorted array + // of keys, in ascending order. + // * If the keys have higher rank, the keys are sorted along the provided + // dimension. For example, for a rank-2 tensor (a matrix) of keys, a dimension + // value of 0 will indepenently sort every column, and a dimension value of 1 + // will independently sort each row. If no dimension number is provided, then + // the last dimension is chosen by default. + // + // If both keys and values are provided: + // * The keys and the values must tensors with the same dimensions. The + // element types of the tensors may be different. + // * The result is a tuple that consists of a sorted tensor of keys (along the + // provided dimension, as above) as the first element, and a tensor with their + // corresponding values as the second element. + XlaOp Sort(XlaOp keys, + tensorflow::gtl::optional values = tensorflow::gtl::nullopt, + int64 dimension = -1); + + // Enqueues a clamp instruction onto the computation. + XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); + + // Enqueues a map instruction onto the computation. + XlaOp Map(tensorflow::gtl::ArraySlice operands, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice static_operands = {}); + + // Enqueues a N(mu, sigma) random number generation instruction onto the + // computation. + XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, const Shape& shape); + + // Enqueues a U(a, b) random number generation instruction onto the + // computation. Returns values in the semi-open interval [a, b). + XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); + + // Enqueues a while node onto the computation. + XlaOp While(const XlaComputation& condition, const XlaComputation& body, + const XlaOp& init); + + // Enqueues a conditional node onto the computation. + XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, + const XlaComputation& true_computation, + const XlaOp& false_operand, + const XlaComputation& false_computation); + + // Enqueues a ReducePrecision node onto the computation. + XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, + const int mantissa_bits); + + // Enqueues a Gather node onto the computation. + XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, + const GatherDimensionNumbers& dimension_numbers, + tensorflow::gtl::ArraySlice window_bounds); + + // Enqueues a Send node onto the computation for device-to-device + // communication, to send the given operand to a Recv instruction that shares + // the same channel handle. + void Send(const XlaOp& operand, const ChannelHandle& handle); + XlaOp SendWithToken(const XlaOp& operand, const XlaOp& token, + const ChannelHandle& handle); + + // Enqueues a Send node which sends data to the host. + XlaOp SendToHost(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, const ChannelHandle& handle); + + // Enqueues a Recv node which receives data from the host. + XlaOp RecvFromHost(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + + // Enqueues an AfterAll operation with no operands producing a token-shaped + // value. + XlaOp CreateToken(); + + // Enqueues an AfterAll operation with no operands producing a token-shaped + // value. + XlaOp AfterAll(tensorflow::gtl::ArraySlice tokens); + + // Enqueues a Recv node onto the computation. The data comes from a Send + // instruction that shares the same channel handle and its shape must + // be the same as the given shape. + XlaOp Recv(const Shape& shape, const ChannelHandle& handle); + XlaOp RecvWithToken(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + + // Normalizes operand across spatial and batch dimensions for each feature. + // + // Returns a tuple (normalized, batch_mean, batch_var) where `normalized` + // is the normalized result and batch_mean and batch_var are the mean and + // variance, respectively, across batch for the operand. + XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, float epsilon, + int64 feature_index); + + // Normalizes operand across spatial and batch dimensions for each feature. + // + // `BatchNormInference` is equivalent to calling `BatchNormTraining` without + // computing `mean` and `variance` for each batch inside the operation. It + // uses the input `mean` and `variance` instead as estimated values. The + // purpose of this op is to reduce latency in inference, hence the name + // `BatchNormInference`. + // + // The output has the same shape as `operand`, and contains the normalized + // values for each batch. + XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, const XlaOp& mean, + const XlaOp& variance, float epsilon, + int64 feature_index); + + // Calculates the gradients of a batch norm op. + // + // The inputs `batch_mean` and `batch_var` represent the mean and variance + // across the batch. + // + // Returns a tuple of three elements: + // - grad_operand: Gradient with respect to input `operand` + // - grad_offset: Gradient with respect to input `offset` + // - grad_scale: Gradient with respect to input `scale` + XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, + const XlaOp& batch_mean, const XlaOp& batch_var, + const XlaOp& grad_output, float epsilon, + int64 feature_index); + + StatusOr AddInstruction( + HloInstructionProto&& instr, HloOpcode opcode, + tensorflow::gtl::ArraySlice operands = {}); + + void AddCalledComputation(const XlaComputation& computation, + HloInstructionProto* instr); + + StatusOr LookUpInstruction(const XlaOp& op) const; + + // Internal helper method that does the building for an arbitrary unary op. + XlaOp UnaryOp(HloOpcode unop, const XlaOp& operand); + + // Internal helper method that does the building for an arbitrary binary op. + // broadcast_dimensions specifies which dimensions to use for broadcasting + // when the operation is between tensors of different ranks. + XlaOp BinaryOp(HloOpcode binop, const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + + // Internal helper method that does the building for an arbitrary ternary op. + XlaOp TernaryOp(HloOpcode triop, const XlaOp& lhs, const XlaOp& rhs, + const XlaOp& ehs); + + XlaOp RngOp(RandomDistribution distribution, + tensorflow::gtl::ArraySlice parameters, + const Shape& shape); + + StatusOr InDimBroadcast( + const Shape& shape, const XlaOp& operand, + tensorflow::gtl::ArraySlice broadcast_dimensions); + + // Internal helper method that creates a sequence of instructions that + // performs an explicit broadcast of the operand to the target shape. + StatusOr AddBroadcastSequence(const Shape& output_shape, + const XlaOp& operand); + + // Internal helper method for creating a Reshape op with the already inferred + // shape. + StatusOr Reshape(const Shape& shape, const XlaOp& operand); + + // Returns the (inferred) result for the program shape for the current + // computation and fills the root_id in the pointer. + StatusOr GetProgramShape(int64* root_id) const; + + // Returns shapes for the operands. + StatusOr> GetOperandShapes( + tensorflow::gtl::ArraySlice operands) const; + + // A visitor which checks whether an operation is a compile-time constant, + // meaning that it doesn't depend on any parameters, or on any stateful + // operation such as `RngNormal` or `Infeed`. The visitor walks the + // computation starting at a given operation and sets is_constant to false iff + // a parameter or stateful operation is encountered. + void IsConstantVisitor(const int64 op_handle, std::set* visited, + bool* is_constant) const; + + // Checks bounds for convolution parameters. + Status VerifyConvolution( + const Shape& lhs_shape, const Shape& rhs_shape, + const ConvolutionDimensionNumbers& dimension_numbers) const; + + // Helper function for creating a Window proto from user-supplied data. + // Returns error if the user-supplied data was invalid. + StatusOr MakeWindow( + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + tensorflow::gtl::ArraySlice lhs_dilation, + tensorflow::gtl::ArraySlice rhs_dilation) const; + + string name_; // Name to use for the built computation. + + // The first error encountered while building the computation. + // This is OK until the first error is encountered. + Status first_error_; + + // The saved stack trace from the point at which the first error occurred. + tensorflow::SavedStackTrace first_error_backtrace_; + + // The instructions of this computation. + std::vector instructions_; + + // The embedded computations used by this computation. Each computation was + // the entry computation of some XlaComputation, the key is the unique id of + // that XlaComputation. + std::map embedded_; + + // The unique parameter numbers. + tensorflow::gtl::FlatSet parameter_numbers_; + + // The metadata to attach to each op. This is structured as a "modal"-like + // operation, in order to simplify client code (and not sprinkle this metadata + // throughout the TensorFlow op kernel implementations). + OpMetadata metadata_; + + // Sharding for this operator. This is structured as a "model"-like operation, + // in order to simplify client code, similar to metadata_. + tensorflow::gtl::optional sharding_; + + // Mode bit that indicates whether to die when a first error is encountered. + bool die_immediately_on_error_ = false; + + XlaBuilder* parent_builder_{nullptr}; + + friend XlaOp Parameter(XlaBuilder* builder, int64 parameter_number, + const Shape& shape, const string& name); + friend XlaOp ConstantLiteral(XlaBuilder* builder, + const LiteralSlice& literal); + template + friend XlaOp ConstantR0(XlaBuilder* builder, NativeT value); + template + friend XlaOp ConstantR1(XlaBuilder* builder, + tensorflow::gtl::ArraySlice values); + friend XlaOp ConstantR1(XlaBuilder* builder, + const tensorflow::core::Bitmap& values); + template + friend XlaOp ConstantR2( + XlaBuilder* builder, + std::initializer_list> values); + template + friend XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, + const Array& values, + const Layout& layout); + template + friend XlaOp ConstantFromArray(XlaBuilder* builder, + const Array& values); + template + friend XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, + const Array2D& values, + const Layout& layout); + template + friend XlaOp ConstantR2FromArray2D(XlaBuilder* builder, + const Array2D& values); + template + friend XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, + const Array3D& values, + const Layout& layout); + template + friend XlaOp ConstantR3FromArray3D(XlaBuilder* builder, + const Array3D& values); + template + friend XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, + const Array4D& values, + const Layout& layout); + template + friend XlaOp ConstantR4FromArray4D(XlaBuilder* builder, + const Array4D& values); + + template + friend XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value); + + friend XlaOp Broadcast(const XlaOp& operand, + tensorflow::gtl::ArraySlice broadcast_sizes); + + friend XlaOp BroadcastInDim( + const XlaOp& operand, const Shape& shape, + const tensorflow::gtl::ArraySlice broadcast_dimensions); + + friend XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, + const PaddingConfig& padding_config); + + friend XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice new_sizes); + + friend XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice new_sizes); + + friend XlaOp Collapse(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + + friend XlaOp Slice(const XlaOp& operand, + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides); + + friend XlaOp SliceInDim(const XlaOp& operand, int64 start_index, + int64 limit_index, int64 stride, int64 dimno); + + friend XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, + tensorflow::gtl::ArraySlice slice_sizes); + + friend XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, + const XlaOp& start_indices); + + friend XlaOp ConcatInDim(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + int64 dimension); + + friend void Trace(const string& tag, const XlaOp& operand); + + friend XlaOp Select(const XlaOp& pred, const XlaOp& on_true, + const XlaOp& on_false); + friend XlaOp Tuple(XlaBuilder* builder, + tensorflow::gtl::ArraySlice elements); + friend XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); + friend XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); + friend XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, + const DotDimensionNumbers& dimension_numbers); + friend XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + Padding padding); + friend XlaOp ConvWithGeneralPadding( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + friend XlaOp ConvWithGeneralDimensions( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const ConvolutionDimensionNumbers& dimension_numbers); + friend XlaOp ConvGeneral( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const ConvolutionDimensionNumbers& dimension_numbers); + friend XlaOp ConvGeneralDilated( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + tensorflow::gtl::ArraySlice lhs_dilation, + tensorflow::gtl::ArraySlice rhs_dilation, + const ConvolutionDimensionNumbers& dimension_numbers); + friend XlaOp Fft(const XlaOp& operand, FftType fft_type, + tensorflow::gtl::ArraySlice fft_length); + friend XlaOp Infeed(XlaBuilder* builder, const Shape& shape, + const string& config); + friend void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, + const string& outfeed_config); + friend XlaOp Call(XlaBuilder* builder, const XlaComputation& computation, + tensorflow::gtl::ArraySlice operands); + friend XlaOp CustomCall(XlaBuilder* builder, const string& call_target_name, + tensorflow::gtl::ArraySlice operands, + const Shape& shape); + friend XlaOp HostCompute(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + const string& channel_name, int64 cost_estimate_ns, + const Shape& shape); + friend XlaOp Complex(const XlaOp& real, const XlaOp& imag, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Conj(const XlaOp& operand); + friend XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp And(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Not(const XlaOp& operand); + friend XlaOp ShiftLeft( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp ShiftRightArithmetic( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp ShiftRightLogical( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions_to_reduce); + friend XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation); + friend XlaOp ReduceWindow( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, Padding padding); + friend XlaOp ReduceWindowWithGeneralPadding( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + friend XlaOp CrossReplicaSum( + const XlaOp& operand, + tensorflow::gtl::ArraySlice replica_group_ids); + friend XlaOp CrossReplicaSum( + const XlaOp& operand, const XlaComputation& computation, + tensorflow::gtl::ArraySlice replica_group_ids, + const tensorflow::gtl::optional& channel_id); + friend XlaOp SelectAndScatter( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter); + friend XlaOp SelectAndScatterWithGeneralPadding( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter); + friend XlaOp Abs(const XlaOp& operand); + friend XlaOp Atan2(const XlaOp& y, const XlaOp& x, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp Exp(const XlaOp& operand); + friend XlaOp Expm1(const XlaOp& operand); + friend XlaOp Floor(const XlaOp& operand); + friend XlaOp Ceil(const XlaOp& operand); + friend XlaOp Round(const XlaOp& operand); + friend XlaOp Log(const XlaOp& operand); + friend XlaOp Log1p(const XlaOp& operand); + friend XlaOp Sign(const XlaOp& operand); + friend XlaOp Clz(const XlaOp& operand); + friend XlaOp Cos(const XlaOp& operand); + friend XlaOp Sin(const XlaOp& operand); + friend XlaOp Tanh(const XlaOp& operand); + friend XlaOp Real(const XlaOp& operand); + friend XlaOp Imag(const XlaOp& operand); + friend XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions); + friend XlaOp IsFinite(const XlaOp& operand); + // TODO(b/64798317): Finish CPU & GPU implementation, then replace xla::Iota + // in xla/client/lib/numeric.h with this (renamed to xla::Iota). + friend XlaOp IotaGen(XlaBuilder* builder, PrimitiveType type, int64 size); + friend XlaOp ConvertElementType(const XlaOp& operand, + PrimitiveType new_element_type); + friend XlaOp BitcastConvertType(const XlaOp& operand, + PrimitiveType new_element_type); + friend XlaOp Neg(const XlaOp& operand); + friend XlaOp Transpose(const XlaOp& operand, + tensorflow::gtl::ArraySlice permutation); + friend XlaOp Rev(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + friend XlaOp Sort(XlaOp keys, tensorflow::gtl::optional values, + int64 dimension); + friend XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); + friend XlaOp Map(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice static_operands); + friend XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, + const Shape& shape); + friend XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); + friend XlaOp While(const XlaComputation& condition, + const XlaComputation& body, const XlaOp& init); + friend XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, + const XlaComputation& true_computation, + const XlaOp& false_operand, + const XlaComputation& false_computation); + friend XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, + const int mantissa_bits); + friend XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, + const GatherDimensionNumbers& dimension_numbers, + tensorflow::gtl::ArraySlice window_bounds); + friend void Send(const XlaOp& operand, const ChannelHandle& handle); + friend XlaOp Recv(XlaBuilder* builder, const Shape& shape, + const ChannelHandle& handle); + friend XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, float epsilon, + int64 feature_index); + friend XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, const XlaOp& mean, + const XlaOp& variance, float epsilon, + int64 feature_index); + friend XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, + const XlaOp& batch_mean, const XlaOp& batch_var, + const XlaOp& grad_output, float epsilon, + int64 feature_index); + friend XlaOp SendWithToken(const XlaOp& operand, const XlaOp& token, + const ChannelHandle& handle); + friend XlaOp RecvWithToken(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + friend XlaOp SendToHost(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, + const ChannelHandle& handle); + friend XlaOp RecvFromHost(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + friend XlaOp InfeedWithToken(const XlaOp& token, const Shape& shape, + const string& config); + friend XlaOp OutfeedWithToken(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, + const string& outfeed_config); + friend XlaOp CreateToken(XlaBuilder* builder); + friend XlaOp AfterAll(XlaBuilder* builder, + tensorflow::gtl::ArraySlice tokens); +}; + +// RAII-style object: sets the current sharding assignment in builder on +// construction, and sets back to the previous assignment on destruction. +class XlaScopedShardingAssignment { + public: + XlaScopedShardingAssignment(xla::XlaBuilder* builder, + tensorflow::gtl::optional sharding) + : builder_(builder), prev_sharding_(builder->sharding()) { + SetSharding(sharding); + } + + XlaScopedShardingAssignment(const XlaScopedShardingAssignment&) = delete; + XlaScopedShardingAssignment& operator=(const XlaScopedShardingAssignment&) = + delete; + + ~XlaScopedShardingAssignment() { SetSharding(prev_sharding_); } + + private: + void SetSharding(const tensorflow::gtl::optional& sharding) { + if (sharding.has_value()) { + builder_->SetSharding(sharding.value()); + } else { + builder_->ClearSharding(); + } + } + + xla::XlaBuilder* const builder_; + tensorflow::gtl::optional prev_sharding_; +}; + +// Free functions for building XlaOps. The intention is that these will +// become the public API for building XlaOps rather than calling methods on +// XlaBuilder directly. + +// Enqueues a "retrieve parameter value" instruction for a parameter that was +// passed to the computation. +XlaOp Parameter(XlaBuilder* builder, int64 parameter_number, const Shape& shape, + const string& name); + +// Enqueues a constant with the value of the given literal onto the +// computation. +XlaOp ConstantLiteral(XlaBuilder* builder, const LiteralSlice& literal); + +// Enqueues a constant onto the computation. Methods are templated on the +// native host type (NativeT) which corresponds to a specific XLA +// PrimitiveType as given in the following table: +// +// Native Type PrimitiveType +// ----------------------------- +// bool PRED +// int32 S32 +// int64 S64 +// uint32 U32 +// uint64 U64 +// float F32 +// double F64 +// +// Note: not all primitive types defined in xla_data.proto have a +// corresponding native type yet. +template +XlaOp ConstantR0(XlaBuilder* builder, NativeT value); +template +XlaOp ConstantR1(XlaBuilder* builder, + tensorflow::gtl::ArraySlice values); +XlaOp ConstantR1(XlaBuilder* builder, const tensorflow::core::Bitmap& values); +template +XlaOp ConstantR2(XlaBuilder* builder, + std::initializer_list> values); +template +XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, + const Array& values, + const Layout& layout); +template +XlaOp ConstantFromArray(XlaBuilder* builder, const Array& values); +template +XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, + const Array2D& values, + const Layout& layout); +template +XlaOp ConstantR2FromArray2D(XlaBuilder* builder, + const Array2D& values); +template +XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, + const Array3D& values, + const Layout& layout); +template +XlaOp ConstantR3FromArray3D(XlaBuilder* builder, + const Array3D& values); +template +XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, + const Array4D& values, + const Layout& layout); +template +XlaOp ConstantR4FromArray4D(XlaBuilder* builder, + const Array4D& values); + +// Enqueues a rank one constant (XlaBuilder* builder, vector) onto the +// computation. The vector has size 'length' and every element has the value +// 'value'. +template +XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value); + +// Adds dimensions to an array by duplicating the data in the array. +// +// The new dimensions are inserted on the left, i.e. if +// broadcast_sizes has values {a0, ..., aN} and the operand shape +// has dimensions {b0, ..., bM} then the shape of the output has +// dimensions {a0, ..., aN, b0, ..., bM}. +// +// The new dimensions index into copies of the operand, i.e. +// +// output[i0, ..., iN, j0, ..., jM] = operand[j0, ..., jM] +XlaOp Broadcast(const XlaOp& operand, + tensorflow::gtl::ArraySlice broadcast_sizes); + +// Performs in-dimension-style broadcast. +// +// Operand specifies the input to be broadcast. "shape" is expected output +// shape. "broadcast_dimensions" are the dimensions to be broadcasting into. +// Dimension numbers in broadcast_dimensions map to individual dimensions +// of the operand, and specify what dimension of the output shape they +// should be broadcast. +// e.g. +// Say operand = [1, 2], i.e., a 1D tensor with 2 elements. +// and dimension of shape is [2,2]. +// Specifying {1} as brodcast_dimension will generate output +// [1 , 2] +// [1 , 2] +// On the other hand, specifying {0} as broadcast_dimension +// will generate output +// [1 , 1] +// [2 , 2] +XlaOp BroadcastInDim( + const XlaOp& operand, const Shape& shape, + const tensorflow::gtl::ArraySlice broadcast_dimensions); + +// Enqueues a pad operation onto the computation that pads the given value on +// the edges as well as between the elements of the input. padding_config +// specifies the padding amount for each dimension. +XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, + const PaddingConfig& padding_config); + +// Enqueues an operation onto the computation that flattens the operand based +// on the dimension order (major/slowest-varying to minor/fastest-varying) +// given, followed by reshaping it into the shape with the given dimension +// sizes (also major to minor). Conceptually, this is a limited form of +// "shape casting". +XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice new_sizes); + +// Enqueues an operation onto the computation that collapses the operand, from +// first to last dimension (C order), then reshapes it to the given dimension +// sizes. Conceptually, this is a limited form of "shape casting". +XlaOp Reshape(const XlaOp& operand, + tensorflow::gtl::ArraySlice new_sizes); + +// Wrapper for Reshape. +// Enqueues an operation to collapse the provided dimensions; e.g. an +// operand with dimensions {x=256, y=2, z=2, p=32} can be collapsed to +// {x=1024, y=32} by collapsing dims {0, 1, 2}. Collapsing dimensions must +// be a consecutive, in-order subsequence of the operand dimensions. +// +// Note that collapsing a single dimension does nothing: +// +// {256} collapsing {0} => {256} +// {1} collapsing {0} => {1} +// +// Collapsing multiple dimensions produces a single result dimension: +// +// {256, 2} collapsing {0,1} => {512} +// {256, 2, 3} collapsing {0,1} => {512, 3} +// +// This could potentially cause data to be moved -- it provides a more +// structured form of reshaping than an arbitrary Reshape operation. +XlaOp Collapse(const XlaOp& operand, + tensorflow::gtl::ArraySlice dimensions); + +// Enqueues a slice operation onto the computation that slices the operand +// from the start indices to the limit indices; e.g. +// +// x +// [ 0 1 2 3 ] +// y [ 4 5 6 7 ] => slice(start={1, 1}, limit={2, 3}) => [ 5 6 ] +// [ 8 9 a b ] +// +// Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D +// range notation. +// The strides parameter determines the stride over the slice +XlaOp Slice(const XlaOp& operand, + tensorflow::gtl::ArraySlice start_indices, + tensorflow::gtl::ArraySlice limit_indices, + tensorflow::gtl::ArraySlice strides); + +// Enqueues a slice operation in a given dimension, taking all other +// dimensions as they are; e.g. if dimno is 1 from start_index 2 to +// limit_index 4 by 1, and the shape is f32[7,8,9], this call is short-hand +// for: +// +// array[:, 2:4:1, :] +XlaOp SliceInDim(const XlaOp& operand, int64 start_index, int64 limit_index, + int64 stride, int64 dimno); + +// Enqueues a slice operation onto the computation that slices the 'operand' +// from dynamic start indices which are passed in 'start_indices'. +// The size of the slice in each dimension is passed in 'slice_sizes', +// which specify the end point of exclusive slice intervals in each +// dimension [start, start + size). +// The shape of 'start_indices' must be rank == 1, with dimension size +// equal to the rank of the 'operand'. +// Slice index calculations are computed modulo input dimension sizes to +// prevent dynamic start indices from generating out-of-bound array accesses. +XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, + tensorflow::gtl::ArraySlice slice_sizes); + +// Enqueues a dynamic update slice operation onto the computation, which +// updates a slice of 'operand' with 'update' at dynamic 'start_indices'. +// The shape of 'update' determines the shape of the slice of 'operand' +// which is updated. +// The indices specified in 'start_indices' specify the offset of the slice +// of 'operand' which is updated. +// +// update = {10, 11} // calculated at runtime. +// [1 2 3] start = {1, 1} // calculated at runtime. [1 2 3 ] +// [4 5 6] => DynamicUpdateslice(data, update, start) => [4 10 11] +// [7 8 9] [7 8 9 ] +// +// The shape of 'start_indices' must be rank == 1, with dimension size +// equal to the rank of the 'operand'. +// Slice index calculations are computed modulo update dimension sizes to +// prevent dynamic start indices from generating out-of-bound array accesses. +XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, + const XlaOp& start_indices); + +// Enqueues a concatenate instruction onto the computation. 'operands' must +// have >= 1 entry. +XlaOp ConcatInDim(XlaBuilder* builder, + tensorflow::gtl::ArraySlice operands, int64 dimension); + +// Enqueue a tracing operation onto the computation; the computation will emit +// a logging message with the operand. +void Trace(const string& tag, const XlaOp& operand); + +// Enqueues a conditional-move-like select operation onto the computation; +// predicated on pred, selects between on_true and on_false. +XlaOp Select(const XlaOp& pred, const XlaOp& on_true, const XlaOp& on_false); + +// Enqueues a tuple-creation instruction onto the computation. +XlaOp Tuple(XlaBuilder* builder, tensorflow::gtl::ArraySlice elements); + +// Enqueues a tuple-element-get instruction onto the computation. +XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); + +// Enqueues an equal-to comparison instruction onto the computation. +XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a not-equal comparison instruction onto the computation. +XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a greater-or-equal comparison instruction onto the computation. +XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a greater-than comparison instruction onto the computation. +XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a less-than comparison instruction onto the computation. +XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a less-or-equal comparison instruction onto the computation. +XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a dot instruction onto the computation. +XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); + +// Enqueues a general dot instruction onto the computation. +XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, + const DotDimensionNumbers& dimension_numbers); + +// Enqueues a convolution instruction onto the computation, which uses the +// default convolution dimension numbers. +XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided padding configuration in the format returned by MakePadding(). +XlaOp ConvWithGeneralPadding( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided dimension numbers configuration. +XlaOp ConvWithGeneralDimensions( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, Padding padding, + const ConvolutionDimensionNumbers& dimension_numbers); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided padding configuration as well as the dimension numbers. +XlaOp ConvGeneral(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const ConvolutionDimensionNumbers& dimension_numbers); + +// Enqueues a convolution instruction onto the computation, with the caller +// provided padding configuration, dilation factors and dimension numbers. +XlaOp ConvGeneralDilated( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + tensorflow::gtl::ArraySlice lhs_dilation, + tensorflow::gtl::ArraySlice rhs_dilation, + const ConvolutionDimensionNumbers& dimension_numbers); + +// Enqueues an FFT instruction onto the computation, of the given type and +// with the given FFT length. +XlaOp Fft(const XlaOp& operand, FftType fft_type, + tensorflow::gtl::ArraySlice fft_length); + +// Enqueues an infeed instruction onto the computation, which writes data of +// the given shape to the infeed buffer of the device. +XlaOp Infeed(XlaBuilder* builder, const Shape& shape, + const string& config = ""); + +// Variant of Infeed which takes a token-shaped operand and produces a +// two-element tuple containing the data value and a token-shaped value. +// Tokens are used for ordering side-effecting operations. +// TODO(b/110532604): Replace all uses of the non-token form with this variant. +XlaOp InfeedWithToken(const XlaOp& token, const Shape& shape, + const string& config = ""); + +// Enqueues an outfeed instruction onto the computation. This instruction +// generates outgoing data transfers for the given data. +// +// shape_with_layout communicates the laid out shape that we want to outfeed +// -- if !ShapeUtil::Compatible(GetShape(operand), shape_with_layout) an error +// will occur. +void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, + const string& outfeed_config); + +// Variant of Outfeed which takes a token-shaped operand and produces a +// token-shaped value. Tokens are used for ordering side-effecting operations. +// TODO(b/110532604): Replace all uses of the non-token form with this variant. +XlaOp OutfeedWithToken(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, + const string& outfeed_config); + +// Enqueues a call instruction onto the computation. +XlaOp Call(XlaBuilder* builder, const XlaComputation& computation, + tensorflow::gtl::ArraySlice operands); + +// Enqueues a custom call instruction onto the computation. +// During code generation, a call instruction is emitted which targets a +// symbol with the name |call_target_name|. The |operands| are passed to the +// call instruction. |shape| is the resultant shape. +XlaOp CustomCall(XlaBuilder* builder, const string& call_target_name, + tensorflow::gtl::ArraySlice 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. +XlaOp HostCompute(XlaBuilder* builder, + tensorflow::gtl::ArraySlice 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 +// (see g3doc for more details). + +// Enqueues a complex compose instruction onto the computation. +XlaOp Complex(const XlaOp& real, const XlaOp& imag, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a complex conjugate instruction onto the computation. +XlaOp Conj(const XlaOp& operand); + +// Enqueues an add instruction onto the computation. +XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a subtract instruction onto the computation. +XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a multiply instruction onto the computation. +XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a divide instruction onto the computation. +XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a remainder instruction onto the computation. +XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a max instruction onto the computation. +XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues a min instruction onto the computation. +XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Element-wise logical operators +XlaOp And(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +XlaOp Not(const XlaOp& operand); + +XlaOp ShiftLeft(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); +XlaOp ShiftRightArithmetic( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); +XlaOp ShiftRightLogical( + const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Reduces an array among the provided dimensions, given "computation" as a +// reduction operator. +XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions_to_reduce); + +// Convenience wrapper around the above that reduces all the dimensions in the +// operand shape. +XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation); + +// Enqueues a windowed reduce instruction onto the computation. +XlaOp ReduceWindow(const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding); + +// As ReduceWindow(), but the padding is given in the format +// returned by MakePadding(). +XlaOp ReduceWindowWithGeneralPadding( + const XlaOp& operand, const XlaOp& init_value, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding); + +// Returns the sum of the operand value within each subgroup of replicas. All +// replicas supply one input to the sum and all replicas receive the resulting +// sum for each subgroup. +XlaOp CrossReplicaSum( + const XlaOp& operand, + tensorflow::gtl::ArraySlice replica_group_ids = {}); + +// Enqueues an operation that do an AllReduce of the operand cross cores. Here +// AllReduce means doing a reduction on the input operand cross cores and then +// broadcasting the reduction result to those cores. The reduction function is +// defined by `computation`, which should be a commutative computation on +// scalars, e.g., add, min, or max. The way that AllReduce is applied is +// configured by: +// +// - `replica_group_ids`: maps replica ids to subgroup ids. If empty, all +// replicas belong to one group. Allreduce will be applied within subgroups. +// For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, +// replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. +// +// - `channel_id`: for Allreduce nodes from different models, if they have the +// same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be +// applied cross models. +// +// TODO(b/79737069): Rename this to AllReduce when it's ready to use. +XlaOp CrossReplicaSum(const XlaOp& operand, const XlaComputation& computation, + tensorflow::gtl::ArraySlice replica_group_ids = {}, + const tensorflow::gtl::optional& + channel_id = tensorflow::gtl::nullopt); + +// Enqueues an operation that scatters the `source` array to the selected +// indices of each window. +XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + Padding padding, const XlaOp& source, + const XlaOp& init_value, const XlaComputation& scatter); + +// As SelectAndScatter(), but the padding is given in the format +// returned by MakePadding(). +XlaOp SelectAndScatterWithGeneralPadding( + const XlaOp& operand, const XlaComputation& select, + tensorflow::gtl::ArraySlice window_dimensions, + tensorflow::gtl::ArraySlice window_strides, + tensorflow::gtl::ArraySlice> padding, + const XlaOp& source, const XlaOp& init_value, + const XlaComputation& scatter); + +// Enqueues an abs instruction onto the computation. +XlaOp Abs(const XlaOp& operand); + +// Enqueues a atan2 instruction onto the computation. +XlaOp Atan2(const XlaOp& y, const XlaOp& x, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues an exp instruction onto the computation. +XlaOp Exp(const XlaOp& operand); + +// Enqueues an expm1 instruction onto the computation. +XlaOp Expm1(const XlaOp& operand); + +// Enqueues a floor instruction onto the computation. +XlaOp Floor(const XlaOp& operand); + +// Enqueues a ceil instruction onto the computation. +XlaOp Ceil(const XlaOp& operand); + +// Enqueues a round instruction onto the computation, rounding to nearest even +// with half-way cases rounding away from zero. +XlaOp Round(const XlaOp& operand); + +// Enqueues an log instruction (natural logarithm) onto the computation. +XlaOp Log(const XlaOp& operand); + +// Enqueues an log1p instruction (log(x+1)) onto the computation. +XlaOp Log1p(const XlaOp& operand); + +// Enqueues a sign instruction onto the computation. +XlaOp Sign(const XlaOp& operand); + +// Enqueues a count leading zeros instruction onto the computation. +XlaOp Clz(const XlaOp& operand); + +// Enqueues a cosine instruction onto the computation. +XlaOp Cos(const XlaOp& operand); + +// Enqueues a sine instruction onto the computation. +XlaOp Sin(const XlaOp& operand); + +// Enqueues a tanh instruction onto the computation. +XlaOp Tanh(const XlaOp& operand); + +// Enqueues a real-part instruction onto the computation. +XlaOp Real(const XlaOp& operand); + +// Enqueues an imaginary-part instruction onto the computation. +XlaOp Imag(const XlaOp& operand); + +// Enqueues a lhs^rhs computation onto the computation. +XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, + tensorflow::gtl::ArraySlice broadcast_dimensions = {}); + +// Enqueues an operator that tests if the operand's values are finite, i.e., +// not Inf or NaN. Defined only for floating-point types. Returns an array of +// booleans with the same shape where entries are true iff the corresponding +// entry was NaN. +XlaOp IsFinite(const XlaOp& operand); + +// Enqueues a convert instruction onto the computation that changes the +// element type of the operand array to primitive_type. +XlaOp ConvertElementType(const XlaOp& operand, PrimitiveType new_element_type); + +// Enqueues a no-op instruction onto the computation that changes +// the element type of the operand array to primitive_type. The +// bit-widths of the source and destination element types must be +// identical. +XlaOp BitcastConvertType(const XlaOp& operand, PrimitiveType new_element_type); + +// Enqueues a negate instruction onto the computation. +XlaOp Neg(const XlaOp& operand); + +// Enqueues a transpose instruction onto the computation. +XlaOp Transpose(const XlaOp& operand, + tensorflow::gtl::ArraySlice permutation); + +// Enqueues a reverse instruction onto the computation. The order of the +// elements in the given dimensions is reversed (i.e., the element at index i +// is moved to index dimension_size - 1 - i). +XlaOp Rev(const XlaOp& operand, tensorflow::gtl::ArraySlice dimensions); + +// Enqueues a sort (as increasing order) instruction onto the computation. +// If only keys are provided: +// * If the keys are an rank-1 tensor (an array), the result is a sorted array +// of keys, in ascending order. +// * If the keys have higher rank, the keys are sorted along the provided +// dimension. For example, for a rank-2 tensor (a matrix) of keys, a dimension +// value of 0 will indepenently sort every column, and a dimension value of 1 +// will independently sort each row. If no dimension number is provided, then +// the last dimension is chosen by default. +// +// If both keys and values are provided: +// * The keys and the values must tensors with the same dimensions. The +// element types of the tensors may be different. +// * The result is a tuple that consists of a sorted tensor of keys (along the +// provided dimension, as above) as the first element, and a tensor with their +// corresponding values as the second element. +XlaOp Sort(XlaOp keys, + tensorflow::gtl::optional values = tensorflow::gtl::nullopt, + int64 dimension = -1); + +// Enqueues a clamp instruction onto the computation. +XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); + +// Enqueues a map instruction onto the computation. +XlaOp Map(XlaBuilder* builder, tensorflow::gtl::ArraySlice operands, + const XlaComputation& computation, + tensorflow::gtl::ArraySlice dimensions, + tensorflow::gtl::ArraySlice static_operands = {}); + +// Enqueues a N(mu, sigma) random number generation instruction onto the +// computation. +XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, const Shape& shape); + +// Enqueues a U(a, b) random number generation instruction onto the +// computation. Returns values in the semi-open interval [a, b). +XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); + +// Enqueues a while node onto the computation. +XlaOp While(const XlaComputation& condition, const XlaComputation& body, + const XlaOp& init); + +// Enqueues a conditional node onto the computation. +XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, + const XlaComputation& true_computation, + const XlaOp& false_operand, + const XlaComputation& false_computation); + +// Enqueues a ReducePrecision node onto the computation. +XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, + const int mantissa_bits); + +// Enqueues a Gather node onto the computation. +XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, + const GatherDimensionNumbers& dimension_numbers, + tensorflow::gtl::ArraySlice window_bounds); + +// Enqueues a Send node onto the computation for device-to-device +// communication. This operation sends the given operand to +// a Recv instruction in a different computation that shares the same channel +// handle. +void Send(const XlaOp& operand, const ChannelHandle& handle); + +// Variant of Send which takes a token-shaped operand and produces a +// token-shaped value. Tokens are used for ordering side-effecting operations. +// TODO(b/110532604): Replace all uses of the non-token form with this variant. +XlaOp SendWithToken(const XlaOp& operand, const XlaOp& token, + const ChannelHandle& handle); + +// Enqueues a Recv node onto the computation for device-to-device +// communication. The data comes from a Send instruction in a different +// computation that shares the same channel handle and its shape must be the +// same as the given shape. +XlaOp Recv(XlaBuilder* builder, const Shape& shape, + const ChannelHandle& handle); + +// Variant of Recv which takes a token-shaped operand and produces a two-element +// tuple containing the data value and a token-shaped value. Tokens are used +// for ordering side-effecting operations. +// TODO(b/110532604): Replace all uses of the non-token form with this variant. +XlaOp RecvWithToken(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + +// Enqueues a Send node which transfers data from the device to the host. The +// 'shape_with_layout' argument defines the layout of the data transferred; its +// shape must be compatible with the shape of the operand. The operand must be +// array-shaped. +// TODO(b/111544877): Support tuple shapes. +XlaOp SendToHost(const XlaOp& operand, const XlaOp& token, + const Shape& shape_with_layout, const ChannelHandle& handle); + +// Enqueues a Recv node which transfers data from the host to the device. The +// given shape must contain a layout and must be an array. +// TODO(b/111544877): Support tuple shapes. +XlaOp RecvFromHost(const XlaOp& token, const Shape& shape, + const ChannelHandle& handle); + +// Enqueues an operation (AfterAll) with no operands that produces a +// token-shaped value. Tokens are used for ordering side-effecting operations. +// This is a separate method from AfterAll to facility the removal of +// operand-less AfterAll instructions. +// TODO(b/110532604): Remove this function when all tokens are derived from a +// single token generated or passed into the entry computation. +XlaOp CreateToken(XlaBuilder* builder); + +// Enqueues an AfterAll instruction which produces a token-shaped value and +// takes a variadic number of token-shaped operands. The number of operands must +// be greater than zero. Used for joining tokens. +XlaOp AfterAll(XlaBuilder* builder, tensorflow::gtl::ArraySlice tokens); + +// Normalizes operand across spatial and batch dimensions for each feature. +// +// Returns a tuple (normalized, batch_mean, batch_var) where `normalized` +// is the normalized result and batch_mean and batch_var are the mean and +// variance, respectively, across batch for the operand. +XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, float epsilon, + int64 feature_index); + +// Normalizes operand across spatial and batch dimensions for each feature. +// +// `BatchNormInference` is equivalent to calling `BatchNormTraining` without +// computing `mean` and `variance` for each batch inside the operation. It +// uses the input `mean` and `variance` instead as estimated values. The +// purpose of this op is to reduce latency in inference, hence the name +// `BatchNormInference`. +// +// The output has the same shape as `operand`, and contains the normalized +// values for each batch. +XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, + const XlaOp& offset, const XlaOp& mean, + const XlaOp& variance, float epsilon, + int64 feature_index); + +// Calculates the gradients of a batch norm op. +// +// The inputs `batch_mean` and `batch_var` represent the mean and variance +// across the batch. +// +// Returns a tuple of three elements: +// - grad_operand: Gradient with respect to input `operand` +// - grad_offset: Gradient with respect to input `offset` +// - grad_scale: Gradient with respect to input `scale` +XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, + const XlaOp& batch_mean, const XlaOp& batch_var, + const XlaOp& grad_output, float epsilon, + int64 feature_index); + +// Implementation details below this point. + +template +XlaOp XlaBuilder::ConstantR0(NativeT value) { + return ConstantLiteral(*LiteralUtil::CreateR0(value)); +} + +template +XlaOp XlaBuilder::ConstantR1(tensorflow::gtl::ArraySlice values) { + return ConstantLiteral(*LiteralUtil::CreateR1(values)); +} + +template +XlaOp XlaBuilder::ConstantR1(int64 length, NativeT value) { + Literal literal(ShapeUtil::MakeShape( + primitive_util::NativeToPrimitiveType(), {length})); + literal.PopulateWithValue(value); + return ConstantLiteral(literal); +} + +inline XlaOp XlaBuilder::ConstantR1(const tensorflow::core::Bitmap& values) { + return ConstantLiteral(*LiteralUtil::CreateR1(values)); +} + +template +XlaOp XlaBuilder::ConstantR2( + std::initializer_list> values) { + return ConstantLiteral(*LiteralUtil::CreateR2(values)); +} + +template +XlaOp XlaBuilder::ConstantFromArrayWithLayout(const Array& values, + const Layout& layout) { + return ConstantLiteral( + *LiteralUtil::CreateFromArrayWithLayout(values, layout)); +} + +template +XlaOp XlaBuilder::ConstantFromArray(const Array& values) { + return ConstantLiteral(*LiteralUtil::CreateFromArray(values)); +} + +template +XlaOp XlaBuilder::ConstantR2FromArray2DWithLayout( + const Array2D& values, const Layout& layout) { + return ConstantLiteral( + *LiteralUtil::CreateFromArrayWithLayout(values, layout)); +} + +template +XlaOp XlaBuilder::ConstantR2FromArray2D(const Array2D& values) { + return ConstantLiteral(*LiteralUtil::CreateR2FromArray2D(values)); +} + +template +XlaOp XlaBuilder::ConstantR3FromArray3DWithLayout( + const Array3D& values, const Layout& layout) { + return ConstantLiteral( + *LiteralUtil::CreateR3FromArray3DWithLayout(values, layout)); +} + +template +XlaOp XlaBuilder::ConstantR3FromArray3D(const Array3D& values) { + return ConstantFromArray(values); +} + +template +XlaOp XlaBuilder::ConstantR4FromArray4DWithLayout( + const Array4D& values, const Layout& layout) { + return ConstantFromArrayWithLayout(values, layout); +} + +template +XlaOp XlaBuilder::ConstantR4FromArray4D(const Array4D& values) { + return ConstantFromArray(values); +} + +// Free function template implementations. + +template +XlaOp ConstantR0(XlaBuilder* builder, NativeT value) { + return ConstantLiteral(builder, *LiteralUtil::CreateR0(value)); +} + +template +XlaOp ConstantR1(XlaBuilder* builder, + tensorflow::gtl::ArraySlice values) { + return ConstantLiteral(builder, *LiteralUtil::CreateR1(values)); +} + +template +XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value) { + Literal literal(ShapeUtil::MakeShape( + primitive_util::NativeToPrimitiveType(), {length})); + literal.PopulateWithValue(value); + return ConstantLiteral(builder, literal); +} + +inline XlaOp ConstantR1(XlaBuilder* builder, + const tensorflow::core::Bitmap& values) { + return ConstantLiteral(builder, *LiteralUtil::CreateR1(values)); +} + +template +XlaOp ConstantR2(XlaBuilder* builder, + std::initializer_list> values) { + return ConstantLiteral(builder, *LiteralUtil::CreateR2(values)); +} + +template +XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, + const Array& values, + const Layout& layout) { + return ConstantLiteral( + builder, + *LiteralUtil::CreateFromArrayWithLayout(values, layout)); +} + +template +XlaOp ConstantFromArray(XlaBuilder* builder, const Array& values) { + return ConstantLiteral(builder, + *LiteralUtil::CreateFromArray(values)); +} + +template +XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, + const Array2D& values, + const Layout& layout) { + return ConstantLiteral( + builder, + *LiteralUtil::CreateFromArrayWithLayout(values, layout)); +} + +template +XlaOp ConstantR2FromArray2D(XlaBuilder* builder, + const Array2D& values) { + return ConstantLiteral(builder, + *LiteralUtil::CreateR2FromArray2D(values)); +} + +template +XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, + const Array3D& values, + const Layout& layout) { + return ConstantLiteral( + builder, + *LiteralUtil::CreateR3FromArray3DWithLayout(values, layout)); +} + +template +XlaOp ConstantR3FromArray3D(XlaBuilder* builder, + const Array3D& values) { + return ConstantFromArray(builder, values); +} + +template +XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, + const Array4D& values, + const Layout& layout) { + return ConstantFromArrayWithLayout(builder, values, layout); +} + +template +XlaOp ConstantR4FromArray4D(XlaBuilder* builder, + const Array4D& values) { + return ConstantFromArray(builder, values); +} + +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_CLIENT_XLA_BUILDER_H_ diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc b/tensorflow/compiler/xla/client/xla_builder_test.cc similarity index 99% rename from tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc rename to tensorflow/compiler/xla/client/xla_builder_test.cc index b4a5aedfb1..28a207b137 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder_test.cc +++ b/tensorflow/compiler/xla/client/xla_builder_test.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include diff --git a/tensorflow/compiler/xla/client/xla_client/BUILD b/tensorflow/compiler/xla/client/xla_client/BUILD index a7168e731b..2e131dbad2 100644 --- a/tensorflow/compiler/xla/client/xla_client/BUILD +++ b/tensorflow/compiler/xla/client/xla_client/BUILD @@ -25,44 +25,9 @@ load("//tensorflow:tensorflow.bzl", "tf_cc_test") cc_library( name = "xla_builder", - srcs = ["xla_builder.cc"], hdrs = ["xla_builder.h"], visibility = ["//visibility:public"], deps = [ - "//tensorflow/compiler/xla:execution_options_util", - "//tensorflow/compiler/xla:literal", - "//tensorflow/compiler/xla:literal_util", - "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla:statusor", - "//tensorflow/compiler/xla:types", - "//tensorflow/compiler/xla:util", - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client:padding", - "//tensorflow/compiler/xla/client:sharding_builder", - "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/service:hlo", - "//tensorflow/compiler/xla/service:hlo_proto", - "//tensorflow/compiler/xla/service:shape_inference", - "//tensorflow/core:lib", - ], -) - -tf_cc_test( - name = "xla_builder_test", - srcs = ["xla_builder_test.cc"], - deps = [ - ":xla_builder", - "//tensorflow/compiler/xla:literal", - "//tensorflow/compiler/xla:shape_util", - "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla:test", - "//tensorflow/compiler/xla:test_helpers", - "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/legacy_flags:debug_options_flags", - "//tensorflow/compiler/xla/service:hlo", - "//tensorflow/compiler/xla/service:hlo_matchers", - "//tensorflow/core:test", + "//tensorflow/compiler/xla/client:xla_builder", ], ) diff --git a/tensorflow/compiler/xla/client/xla_client/xla_builder.h b/tensorflow/compiler/xla/client/xla_client/xla_builder.h index 980e84e40c..ce2a8afd4c 100644 --- a/tensorflow/compiler/xla/client/xla_client/xla_builder.h +++ b/tensorflow/compiler/xla/client/xla_client/xla_builder.h @@ -16,2226 +16,6 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ #define TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ -#include -#include -#include -#include - -#include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_computation.h" -#include "tensorflow/compiler/xla/literal.h" -#include "tensorflow/compiler/xla/literal_util.h" -#include "tensorflow/compiler/xla/service/hlo.pb.h" -#include "tensorflow/compiler/xla/service/hlo_opcode.h" -#include "tensorflow/compiler/xla/shape_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" -#include "tensorflow/core/lib/core/stringpiece.h" -#include "tensorflow/core/lib/gtl/array_slice.h" -#include "tensorflow/core/lib/gtl/flatset.h" -#include "tensorflow/core/platform/macros.h" -#include "tensorflow/core/platform/stacktrace.h" -#include "tensorflow/core/platform/types.h" - -namespace xla { - -class XlaBuilder; - -// This represents an instruction that has been enqueued using the XlaBuilder. -// This is used to pass to subsequent computations that depends upon the -// instruction as an operand. -class XlaOp { - public: - XlaOp() : handle_(-1), builder_(nullptr) { - static_assert(std::is_trivially_destructible::value, - "XlaOp should be trivially destructible"); - } - ~XlaOp() = default; - - // Precondition: !IsUninitialized(). - // - // It's very common to do foo.builder()->bar(). Without this precondition, if - // foo.builder() is null, the call to bar will segfault at some point possibly - // deep in the callstack when we finally dereference `this`. The precondition - // lets us avoid this tricky-to-debug problem. - XlaBuilder* builder() const { - CHECK(builder_ != nullptr); - return builder_; - } - - // Returns true if the XlaOp represents valid, non-erroneous value. - bool valid() const { return handle_ >= 0; } - - // Returns true if the XlaOp was created by the XlaOp() constructor and - // not returned by a builder. - bool IsUninitialized() const { return builder_ == nullptr; } - - bool IsIdenticalTo(const XlaOp& rhs) const { - return handle_ == rhs.handle_ && builder_ == rhs.builder_; - } - - friend std::ostream& operator<<(std::ostream& out, const XlaOp& op) { - out << op.handle(); - return out; - } - - private: - explicit XlaOp(XlaBuilder* builder) : handle_(-1), builder_(builder) {} - XlaOp(int64 handle, XlaBuilder* builder) - : handle_(handle), builder_(builder) {} - - int64 handle() const { return handle_; } - - friend class XlaBuilder; - - // < 0 means "invalid handle". - int64 handle_; - - // Not owned. Non-null for any handle returned by XlaBuilder, even if the - // handle is invalid. - XlaBuilder* builder_; -}; - -// Arithmetic operator overloads for the XlaOp type. -XlaOp operator-(const XlaOp& x); -XlaOp operator+(const XlaOp& x, const XlaOp& y); -XlaOp operator-(const XlaOp& x, const XlaOp& y); -XlaOp operator*(const XlaOp& x, const XlaOp& y); -XlaOp operator/(const XlaOp& x, const XlaOp& y); -XlaOp operator%(const XlaOp& x, const XlaOp& y); - -// Bitwise operator overloads for the XlaOp type. -XlaOp operator~(const XlaOp& x); -XlaOp operator&(const XlaOp& x, const XlaOp& y); -XlaOp operator|(const XlaOp& x, const XlaOp& y); -XlaOp operator^(const XlaOp& x, const XlaOp& y); -XlaOp operator<<(const XlaOp& x, const XlaOp& y); -// Performs a right arithmetic shift if 'x' is a signed type, otherwise performs -// a right logical shift. -XlaOp operator>>(const XlaOp& x, const XlaOp& y); - -// We don't overload the relational operators (==, !=, <, <=, >, >=) because the -// semantics might be surprising since their result types are usually 'bool'. -// Further programmers may expect == to be a structural equality. -// We also choose not to overload any of the mutating operators (e.g., +=, -=) -// because the semantics might be misleading — XLA computations are immutable. - -// A convenient interface for building up computations. -// -// Thread-compatible. -class XlaBuilder { - public: - // computation_name: name to use for the built computation. - XlaBuilder(const string& computation_name); - - XlaBuilder(const XlaBuilder&) = delete; - XlaBuilder& operator=(const XlaBuilder&) = delete; - - ~XlaBuilder(); - - // Returns the computation name. - const string& name() const { return name_; } - - // Sets OpMetadata that will be added to all instructions until cleared. - // - // OpMetadata is often applied to a series of XLA HLO instructions. As a - // result, OpMetadata is set on the Computation Builder. All subsequent - // instructions generated via this Computation Builder will have the same - // OpMetadata attached until a call to ClearOpMetadata. - void SetOpMetadata(const OpMetadata& metadata) { metadata_ = metadata; } - - // Clears the HloMetadata state. - void ClearOpMetadata() { metadata_.Clear(); } - - // Sets an OpSharding that will be attached to all instructions until cleared. - void SetSharding(const OpSharding& sharding) { sharding_ = sharding; } - - // Clears the sharding. Ops will be sharded according to the default placement - // policy. - void ClearSharding() { sharding_ = tensorflow::gtl::nullopt; } - - // Returns the OpSharding that will be attached to all instructions. - const tensorflow::gtl::optional& sharding() const { - return sharding_; - } - - // Sets the builder to a mode where it will die immediately when an error is - // encountered, rather than producing it in a deferred fashion when Build() is - // called (which is the default). - void set_die_immediately_on_error(bool enabled) { - die_immediately_on_error_ = enabled; - } - - // Default dimension numbers used for a 2D convolution. - static constexpr int64 kConvBatchDimension = 0; - static constexpr int64 kConvFeatureDimension = 1; - static constexpr int64 kConvFirstSpatialDimension = 2; - static constexpr int64 kConvSecondSpatialDimension = 3; - static constexpr int64 kConvKernelOutputDimension = 0; - static constexpr int64 kConvKernelInputDimension = 1; - static constexpr int64 kConvKernelFirstSpatialDimension = 2; - static constexpr int64 kConvKernelSecondSpatialDimension = 3; - - // Creates a default ConvolutionDimensionNumbers. For a 2D convolution, for - // the input operand {batch, feature, height, width} = {0, 1, 2, 3} and for - // the kernel operand - // {output_feature, input_feature, height, width} = {0, 1, 2, 3}. - static ConvolutionDimensionNumbers CreateDefaultConvDimensionNumbers( - int num_spatial_dims = 2); - - // Returns an error if the convolution dimension numbers have conflicts. - static Status Validate(const ConvolutionDimensionNumbers& dnum); - - // Returns a new XlaBuilder whose resultant Computation is used only by this - // XlaBuilder. The sub-XlaBuilder has the same die_immediately_on_error - // behavior as the parent. - std::unique_ptr CreateSubBuilder(const string& computation_name); - - // Builds the computation with the requested operations, or returns a non-ok - // status. Note that all ops that have been enqueued will be moved to the - // computation being returned. - StatusOr Build(); - - // Builds the computation with the requested operations, or notes an error in - // the parent XlaBuilder and returns an empty computation if building failed. - // This function is intended to be used where the returned XlaComputation is - // only used by the parent XlaBuilder and hence further operation on the - // returned XlaComputation will simply be error'ed out if an error occurred - // while building this computation. If the built computation is to be used by - // a XlaBuilder other than the parent XlaBuilder then Build() should be used - // instead. - XlaComputation BuildAndNoteError(); - - // Returns a subgraph that roots on the given root. If the root is not a - // compile-time constant (see `IsConstant`), returns an error. - // - // This will copy the needed ops/computations to the subgraph. - StatusOr BuildConstantSubGraph(const XlaOp& root_op) const; - - // Returns the first error that was encountered while building the - // computation. When an error is encountered, by default we return a vacuous - // XlaOp and inform the user of the error that occurred while - // building the computation when they make a final call to Build(). - // - // See also set_die_immediately_on_error(). - Status first_error() const { return first_error_; } - - // Returns the shape of the given op. - StatusOr GetShape(const XlaOp& op) const; - - // Returns the (inferred) result for the current computation's shape. - StatusOr GetProgramShape() const; - - // Reports an error to the builder, by - // * storing it internally and capturing a backtrace if it's the first error - // (this deferred value will be produced on the call to - // Build()/GetShape()/...) - // * dying if die_immediately_on_error_ is true. - // Returns an XlaOp with an invalid handle but a valid builder. This value can - // be returned in place of a value in APIs that return an XlaOp. - XlaOp ReportError(const Status& error); - - // A helper function that converts a StatusOr into an XlaOp. - // If the Status was an error, reports the error to builder and returns an - // invalid XlaOp handle. - XlaOp ReportErrorOrReturn(const StatusOr& op); - - // A helper function that runs a function that returns a StatusOr and - // returns an XlaOp. - XlaOp ReportErrorOrReturn(const std::function()>& op_creator); - - // Returns true if 'operand' is a compile-time constant. A compile-time - // constant does not depend on any parameters, or on stateful operators such - // as `RngNormal` or `Infeed`. - // - // This tests whether a computation is a compile-time constant without - // evaluating the computation. - StatusOr IsConstant(const XlaOp& operand) const; - - private: - // Enqueues a "retrieve parameter value" instruction for a parameter that was - // passed to the computation. - XlaOp Parameter(int64 parameter_number, const Shape& shape, - const string& name); - - // Enqueues a constant with the value of the given literal onto the - // computation. - XlaOp ConstantLiteral(const LiteralSlice& literal); - - // Enqueues a constant onto the computation. Methods are templated on the - // native host type (NativeT) which corresponds to a specific XLA - // PrimitiveType as given in the following table: - // - // Native Type PrimitiveType - // ----------------------------- - // bool PRED - // int32 S32 - // int64 S64 - // uint32 U32 - // uint64 U64 - // float F32 - // double F64 - // - // Note: not all primitive types defined in xla_data.proto have a - // corresponding native type yet. - template - XlaOp ConstantR0(NativeT value); - template - XlaOp ConstantR1(tensorflow::gtl::ArraySlice values); - XlaOp ConstantR1(const tensorflow::core::Bitmap& values); - template - XlaOp ConstantR2( - std::initializer_list> values); - template - XlaOp ConstantFromArrayWithLayout(const Array& values, - const Layout& layout); - template - XlaOp ConstantFromArray(const Array& values); - template - XlaOp ConstantR2FromArray2DWithLayout(const Array2D& values, - const Layout& layout); - template - XlaOp ConstantR2FromArray2D(const Array2D& values); - template - XlaOp ConstantR3FromArray3DWithLayout(const Array3D& values, - const Layout& layout); - template - XlaOp ConstantR3FromArray3D(const Array3D& values); - template - XlaOp ConstantR4FromArray4DWithLayout(const Array4D& values, - const Layout& layout); - template - XlaOp ConstantR4FromArray4D(const Array4D& values); - - // Enqueues a rank one constant (vector) onto the computation. The vector has - // size 'length' and every element has the value 'value'. - template - XlaOp ConstantR1(int64 length, NativeT value); - - // Adds dimensions to an array by duplicating the data in the array. - // - // The new dimensions are inserted on the left, i.e. if - // broadcast_sizes has values {a0, ..., aN} and the operand shape - // has dimensions {b0, ..., bM} then the shape of the output has - // dimensions {a0, ..., aN, b0, ..., bM}. - // - // The new dimensions index into copies of the operand, i.e. - // - // output[i0, ..., iN, j0, ..., jM] = operand[j0, ..., jM] - XlaOp Broadcast(const XlaOp& operand, - tensorflow::gtl::ArraySlice broadcast_sizes); - - // Performs in-dimension-style broadcast. - // - // Operand specifies the input to be broadcast. "shape" is expected output - // shape. "broadcast_dimensions" are the dimensions to be broadcasting into. - // Dimension numbers in broadcast_dimensions map to individual dimensions - // of the operand, and specify what dimension of the output shape they - // should be broadcast. - // e.g. - // Say operand = [1, 2], i.e., a 1D tensor with 2 elements. - // and dimension of shape is [2,2]. - // Specifying {1} as brodcast_dimension will generate output - // [1 , 2] - // [1 , 2] - // On the other hand, specifying {0} as broadcast_dimension - // will generate output - // [1 , 1] - // [2 , 2] - XlaOp BroadcastInDim( - const XlaOp& operand, const Shape& shape, - const tensorflow::gtl::ArraySlice broadcast_dimensions); - - // Enqueues a pad operation onto the computation that pads the given value on - // the edges as well as between the elements of the input. padding_config - // specifies the padding amount for each dimension. - XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, - const PaddingConfig& padding_config); - - // Enqueues an operation onto the computation that flattens the operand based - // on the dimension order (major/slowest-varying to minor/fastest-varying) - // given, followed by reshaping it into the shape with the given dimension - // sizes (also major to minor). Conceptually, this is a limited form of - // "shape casting". - XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice new_sizes); - - // Enqueues an operation onto the computation that collapses the operand, from - // first to last dimension (C order), then reshapes it to the given dimension - // sizes. Conceptually, this is a limited form of "shape casting". - XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice new_sizes); - - // Wrapper for Reshape. - // Enqueues an operation to collapse the provided dimensions; e.g. an - // operand with dimensions {x=256, y=2, z=2, p=32} can be collapsed to - // {x=1024, y=32} by collapsing dims {0, 1, 2}. Collapsing dimensions must - // be a consecutive, in-order subsequence of the operand dimensions. - // - // Note that collapsing a single dimension does nothing: - // - // {256} collapsing {0} => {256} - // {1} collapsing {0} => {1} - // - // Collapsing multiple dimensions produces a single result dimension: - // - // {256, 2} collapsing {0,1} => {512} - // {256, 2, 3} collapsing {0,1} => {512, 3} - // - // This could potentially cause data to be moved -- it provides a more - // structured form of reshaping than an arbitrary Reshape operation. - XlaOp Collapse(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions); - - // Enqueues a slice operation onto the computation that slices the operand - // from the start indices to the limit indices; e.g. - // - // x - // [ 0 1 2 3 ] - // y [ 4 5 6 7 ] => slice(start={1, 1}, limit={2, 3}) => [ 5 6 ] - // [ 8 9 a b ] - // - // Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D - // range notation. - // The strides parameter determines the stride over the slice - XlaOp Slice(const XlaOp& operand, - tensorflow::gtl::ArraySlice start_indices, - tensorflow::gtl::ArraySlice limit_indices, - tensorflow::gtl::ArraySlice strides); - - // Enqueues a slice operation in a given dimension, taking all other - // dimensions as they are; e.g. if dimno is 1 from start_index 2 to - // limit_index 4 by 1, and the shape is f32[7,8,9], this call is short-hand - // for: - // - // array[:, 2:4:1, :] - XlaOp SliceInDim(const XlaOp& operand, int64 start_index, int64 limit_index, - int64 stride, int64 dimno); - - // Enqueues a slice operation onto the computation that slices the 'operand' - // from dynamic start indices which are passed in 'start_indices'. - // The size of the slice in each dimension is passed in 'slice_sizes', - // which specify the end point of exclusive slice intervals in each - // dimension [start, start + size). - // The shape of 'start_indices' must be rank == 1, with dimension size - // equal to the rank of the 'operand'. - // Slice index calculations are computed modulo input dimension sizes to - // prevent dynamic start indices from generating out-of-bound array accesses. - XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, - tensorflow::gtl::ArraySlice slice_sizes); - - // Enqueues a dynamic update slice operation onto the computation, which - // updates a slice of 'operand' with 'update' at dynamic 'start_indices'. - // The shape of 'update' determines the shape of the slice of 'operand' - // which is updated. - // The indices specified in 'start_indices' specify the offset of the slice - // of 'operand' which is updated. - // - // update = {10, 11} // calculated at runtime. - // [1 2 3] start = {1, 1} // calculated at runtime. [1 2 3 ] - // [4 5 6] => DynamicUpdateslice(data, update, start) => [4 10 11] - // [7 8 9] [7 8 9 ] - // - // The shape of 'start_indices' must be rank == 1, with dimension size - // equal to the rank of the 'operand'. - // Slice index calculations are computed modulo update dimension sizes to - // prevent dynamic start indices from generating out-of-bound array accesses. - XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, - const XlaOp& start_indices); - - // Enqueues a concatenate instruction onto the computation. 'operands' must - // have >= 1 entry. - XlaOp ConcatInDim(tensorflow::gtl::ArraySlice operands, - int64 dimension); - - // Enqueue a tracing operation onto the computation; the computation will emit - // a logging message with the operand. - void Trace(const string& tag, const XlaOp& operand); - - // Enqueues a conditional-move-like select operation onto the computation; - // predicated on pred, selects between on_true and on_false. - XlaOp Select(const XlaOp& pred, const XlaOp& on_true, const XlaOp& on_false); - - // Enqueues a tuple-creation instruction onto the computation. - XlaOp Tuple(tensorflow::gtl::ArraySlice elements); - - // Enqueues a tuple-element-get instruction onto the computation. - XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); - - // Enqueues an equal-to comparison instruction onto the computation. - XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a not-equal comparison instruction onto the computation. - XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a greater-or-equal comparison instruction onto the computation. - XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a greater-than comparison instruction onto the computation. - XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a less-than comparison instruction onto the computation. - XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a less-or-equal comparison instruction onto the computation. - XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a dot instruction onto the computation. - XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); - - // Enqueues a general dot instruction onto the computation. - XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, - const DotDimensionNumbers& dimension_numbers); - - // Enqueues a convolution instruction onto the computation, which uses the - // default convolution dimension numbers. - XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - Padding padding); - - // Enqueues a convolution instruction onto the computation, with the caller - // provided padding configuration in the format returned by MakePadding(). - XlaOp ConvWithGeneralPadding( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - - // Enqueues a convolution instruction onto the computation, with the caller - // provided dimension numbers configuration. - XlaOp ConvWithGeneralDimensions( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, Padding padding, - const ConvolutionDimensionNumbers& dimension_numbers); - - // Enqueues a convolution instruction onto the computation, with the caller - // provided padding configuration as well as the dimension numbers. - XlaOp ConvGeneral( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const ConvolutionDimensionNumbers& dimension_numbers); - - // Enqueues a convolution instruction onto the computation, with the caller - // provided padding configuration, dilation factors and dimension numbers. - XlaOp ConvGeneralDilated( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - tensorflow::gtl::ArraySlice lhs_dilation, - tensorflow::gtl::ArraySlice rhs_dilation, - const ConvolutionDimensionNumbers& dimension_numbers); - - // Enqueues an FFT instruction onto the computation, of the given type and - // with the given FFT length. - XlaOp Fft(const XlaOp& operand, FftType fft_type, - tensorflow::gtl::ArraySlice fft_length); - - // Enqueues an infeed instruction onto the computation, which writes data of - // the given shape to the infeed buffer of the device. - XlaOp Infeed(const Shape& shape, const string& config = ""); - XlaOp InfeedWithToken(const XlaOp& token, const Shape& shape, - const string& config = ""); - - // Enqueues an outfeed instruction onto the computation. This instruction - // generates outgoing data transfers for the given data. - // - // shape_with_layout communicates the laid out shape that we want to outfeed - // -- if !ShapeUtil::Compatible(GetShape(operand), shape_with_layout) an error - // will occur. - void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, - const string& outfeed_config); - XlaOp OutfeedWithToken(const XlaOp& operand, const XlaOp& token, - const Shape& shape_with_layout, - const string& outfeed_config); - - // Enqueues a call instruction onto the computation. - XlaOp Call(const XlaComputation& computation, - tensorflow::gtl::ArraySlice operands); - - // Enqueues a custom call instruction onto the computation. - // During code generation, a call instruction is emitted which targets a - // symbol with the name |call_target_name|. The |operands| are passed to the - // call instruction. |shape| is the resultant shape. - XlaOp CustomCall(const string& call_target_name, - tensorflow::gtl::ArraySlice 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. - XlaOp HostCompute(tensorflow::gtl::ArraySlice 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 - // (see g3doc for more details). - - // Enqueues a complex compose instruction onto the computation. - XlaOp Complex(const XlaOp& real, const XlaOp& imag, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a complex conjugate instruction onto the computation. - XlaOp Conj(const XlaOp& operand); - - // Enqueues an add instruction onto the computation. - XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a subtract instruction onto the computation. - XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a multiply instruction onto the computation. - XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a divide instruction onto the computation. - XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a remainder instruction onto the computation. - XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a max instruction onto the computation. - XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues a min instruction onto the computation. - XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Element-wise logical operators - XlaOp And(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - XlaOp Not(const XlaOp& operand); - - XlaOp ShiftLeft(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - XlaOp ShiftRightArithmetic( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - XlaOp ShiftRightLogical( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Reduces an array among the provided dimensions, given "computation" as a - // reduction operator. - XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions_to_reduce); - - // Convenience wrapper around the above that reduces all the dimensions in the - // operand shape. - XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation); - - // Enqueues a windowed reduce instruction onto the computation. - XlaOp ReduceWindow(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - Padding padding); - - // As ReduceWindow(), but the padding is given in the format - // returned by MakePadding(). - XlaOp ReduceWindowWithGeneralPadding( - const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - - // Returns the sum of the operand value within each subgroup of replicas. All - // replicas supply one input to the sum and all replicas receive the resulting - // sum for each subgroup. - XlaOp CrossReplicaSum( - const XlaOp& operand, - tensorflow::gtl::ArraySlice replica_group_ids = {}); - - // Enqueues an operation that do an AllReduce of the operand cross cores. Here - // AllReduce means doing a reduction on the input operand cross cores and then - // broadcasting the reduction result to those cores. The reduction function is - // defined by `computation`, which should be a commutative computation on - // scalars, e.g., add, min, or max. The way that AllReduce is applied is - // configured by: - // - // - `replica_group_ids`: maps replica ids to subgroup ids. If empty, all - // replicas belong to one group. Allreduce will be applied within subgroups. - // For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, - // replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. - // - // - `channel_id`: for Allreduce nodes from different models, if they have the - // same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be - // applied cross models. - // - // TODO(b/79737069): Rename this to AllReduce when it's ready to use. - XlaOp CrossReplicaSum( - const XlaOp& operand, const XlaComputation& computation, - tensorflow::gtl::ArraySlice replica_group_ids = {}, - const tensorflow::gtl::optional& channel_id = - tensorflow::gtl::nullopt); - - // Enqueues an operation that scatters the `source` array to the selected - // indices of each window. - XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - Padding padding, const XlaOp& source, - const XlaOp& init_value, - const XlaComputation& scatter); - - // As SelectAndScatter(), but the padding is given in the format - // returned by MakePadding(). - XlaOp SelectAndScatterWithGeneralPadding( - const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const XlaOp& source, const XlaOp& init_value, - const XlaComputation& scatter); - - // Enqueues an abs instruction onto the computation. - XlaOp Abs(const XlaOp& operand); - - // Enqueues a atan2 instruction onto the computation. - XlaOp Atan2(const XlaOp& y, const XlaOp& x, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues an exp instruction onto the computation. - XlaOp Exp(const XlaOp& operand); - - // Enqueues an expm1 instruction onto the computation. - XlaOp Expm1(const XlaOp& operand); - - // Enqueues a floor instruction onto the computation. - XlaOp Floor(const XlaOp& operand); - - // Enqueues a ceil instruction onto the computation. - XlaOp Ceil(const XlaOp& operand); - - // Enqueues a round instruction onto the computation, rounding to nearest even - // with half-way cases rounding away from zero. - XlaOp Round(const XlaOp& operand); - - // Enqueues an log instruction (natural logarithm) onto the computation. - XlaOp Log(const XlaOp& operand); - - // Enqueues an log1p instruction (log(x+1)) onto the computation. - XlaOp Log1p(const XlaOp& operand); - - // Enqueues a sign instruction onto the computation. - XlaOp Sign(const XlaOp& operand); - - // Enqueues a count leading zeros instruction onto the computation. - XlaOp Clz(const XlaOp& operand); - - // Enqueues a cosine instruction onto the computation. - XlaOp Cos(const XlaOp& operand); - - // Enqueues a sine instruction onto the computation. - XlaOp Sin(const XlaOp& operand); - - // Enqueues a tanh instruction onto the computation. - XlaOp Tanh(const XlaOp& operand); - - // Enqueues a real-part instruction onto the computation. - XlaOp Real(const XlaOp& operand); - - // Enqueues an imaginary-part instruction onto the computation. - XlaOp Imag(const XlaOp& operand); - - // Enqueues a lhs^rhs computation onto the computation. - XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - - // Enqueues an operator that tests if the operand's values are finite, i.e., - // not Inf or NaN. Defined only for floating-point types. Returns an array of - // booleans with the same shape where entries are true iff the corresponding - // entry was NaN. - XlaOp IsFinite(const XlaOp& operand); - - // Enqueues a convert instruction onto the computation that changes the - // element type of the operand array to primitive_type. - XlaOp ConvertElementType(const XlaOp& operand, - PrimitiveType new_element_type); - - // Enqueues a no-op instruction onto the computation that changes - // the element type of the operand array to primitive_type. The - // bit-widths of the source and destination element types must be - // identical. - XlaOp BitcastConvertType(const XlaOp& operand, - PrimitiveType new_element_type); - - // Enqueues a negate instruction onto the computation. - XlaOp Neg(const XlaOp& operand); - - // Enqueues a transpose instruction onto the computation. - XlaOp Transpose(const XlaOp& operand, - tensorflow::gtl::ArraySlice permutation); - - // Enqueues a reverse instruction onto the computation. The order of the - // elements in the given dimensions is reversed (i.e., the element at index i - // is moved to index dimension_size - 1 - i). - XlaOp Rev(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions); - - // Enqueues a sort (as increasing order) instruction onto the computation. - // If only keys are provided: - // * If the keys are an rank-1 tensor (an array), the result is a sorted array - // of keys, in ascending order. - // * If the keys have higher rank, the keys are sorted along the provided - // dimension. For example, for a rank-2 tensor (a matrix) of keys, a dimension - // value of 0 will indepenently sort every column, and a dimension value of 1 - // will independently sort each row. If no dimension number is provided, then - // the last dimension is chosen by default. - // - // If both keys and values are provided: - // * The keys and the values must tensors with the same dimensions. The - // element types of the tensors may be different. - // * The result is a tuple that consists of a sorted tensor of keys (along the - // provided dimension, as above) as the first element, and a tensor with their - // corresponding values as the second element. - XlaOp Sort(XlaOp keys, - tensorflow::gtl::optional values = tensorflow::gtl::nullopt, - int64 dimension = -1); - - // Enqueues a clamp instruction onto the computation. - XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); - - // Enqueues a map instruction onto the computation. - XlaOp Map(tensorflow::gtl::ArraySlice operands, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice static_operands = {}); - - // Enqueues a N(mu, sigma) random number generation instruction onto the - // computation. - XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, const Shape& shape); - - // Enqueues a U(a, b) random number generation instruction onto the - // computation. Returns values in the semi-open interval [a, b). - XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); - - // Enqueues a while node onto the computation. - XlaOp While(const XlaComputation& condition, const XlaComputation& body, - const XlaOp& init); - - // Enqueues a conditional node onto the computation. - XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, - const XlaComputation& true_computation, - const XlaOp& false_operand, - const XlaComputation& false_computation); - - // Enqueues a ReducePrecision node onto the computation. - XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, - const int mantissa_bits); - - // Enqueues a Gather node onto the computation. - XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, - const GatherDimensionNumbers& dimension_numbers, - tensorflow::gtl::ArraySlice window_bounds); - - // Enqueues a Send node onto the computation for device-to-device - // communication, to send the given operand to a Recv instruction that shares - // the same channel handle. - void Send(const XlaOp& operand, const ChannelHandle& handle); - XlaOp SendWithToken(const XlaOp& operand, const XlaOp& token, - const ChannelHandle& handle); - - // Enqueues a Send node which sends data to the host. - XlaOp SendToHost(const XlaOp& operand, const XlaOp& token, - const Shape& shape_with_layout, const ChannelHandle& handle); - - // Enqueues a Recv node which receives data from the host. - XlaOp RecvFromHost(const XlaOp& token, const Shape& shape, - const ChannelHandle& handle); - - // Enqueues an AfterAll operation with no operands producing a token-shaped - // value. - XlaOp CreateToken(); - - // Enqueues an AfterAll operation with no operands producing a token-shaped - // value. - XlaOp AfterAll(tensorflow::gtl::ArraySlice tokens); - - // Enqueues a Recv node onto the computation. The data comes from a Send - // instruction that shares the same channel handle and its shape must - // be the same as the given shape. - XlaOp Recv(const Shape& shape, const ChannelHandle& handle); - XlaOp RecvWithToken(const XlaOp& token, const Shape& shape, - const ChannelHandle& handle); - - // Normalizes operand across spatial and batch dimensions for each feature. - // - // Returns a tuple (normalized, batch_mean, batch_var) where `normalized` - // is the normalized result and batch_mean and batch_var are the mean and - // variance, respectively, across batch for the operand. - XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, float epsilon, - int64 feature_index); - - // Normalizes operand across spatial and batch dimensions for each feature. - // - // `BatchNormInference` is equivalent to calling `BatchNormTraining` without - // computing `mean` and `variance` for each batch inside the operation. It - // uses the input `mean` and `variance` instead as estimated values. The - // purpose of this op is to reduce latency in inference, hence the name - // `BatchNormInference`. - // - // The output has the same shape as `operand`, and contains the normalized - // values for each batch. - XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, const XlaOp& mean, - const XlaOp& variance, float epsilon, - int64 feature_index); - - // Calculates the gradients of a batch norm op. - // - // The inputs `batch_mean` and `batch_var` represent the mean and variance - // across the batch. - // - // Returns a tuple of three elements: - // - grad_operand: Gradient with respect to input `operand` - // - grad_offset: Gradient with respect to input `offset` - // - grad_scale: Gradient with respect to input `scale` - XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, - const XlaOp& batch_mean, const XlaOp& batch_var, - const XlaOp& grad_output, float epsilon, - int64 feature_index); - - StatusOr AddInstruction( - HloInstructionProto&& instr, HloOpcode opcode, - tensorflow::gtl::ArraySlice operands = {}); - - void AddCalledComputation(const XlaComputation& computation, - HloInstructionProto* instr); - - StatusOr LookUpInstruction(const XlaOp& op) const; - - // Internal helper method that does the building for an arbitrary unary op. - XlaOp UnaryOp(HloOpcode unop, const XlaOp& operand); - - // Internal helper method that does the building for an arbitrary binary op. - // broadcast_dimensions specifies which dimensions to use for broadcasting - // when the operation is between tensors of different ranks. - XlaOp BinaryOp(HloOpcode binop, const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - - // Internal helper method that does the building for an arbitrary ternary op. - XlaOp TernaryOp(HloOpcode triop, const XlaOp& lhs, const XlaOp& rhs, - const XlaOp& ehs); - - XlaOp RngOp(RandomDistribution distribution, - tensorflow::gtl::ArraySlice parameters, - const Shape& shape); - - StatusOr InDimBroadcast( - const Shape& shape, const XlaOp& operand, - tensorflow::gtl::ArraySlice broadcast_dimensions); - - // Internal helper method that creates a sequence of instructions that - // performs an explicit broadcast of the operand to the target shape. - StatusOr AddBroadcastSequence(const Shape& output_shape, - const XlaOp& operand); - - // Internal helper method for creating a Reshape op with the already inferred - // shape. - StatusOr Reshape(const Shape& shape, const XlaOp& operand); - - // Returns the (inferred) result for the program shape for the current - // computation and fills the root_id in the pointer. - StatusOr GetProgramShape(int64* root_id) const; - - // Returns shapes for the operands. - StatusOr> GetOperandShapes( - tensorflow::gtl::ArraySlice operands) const; - - // A visitor which checks whether an operation is a compile-time constant, - // meaning that it doesn't depend on any parameters, or on any stateful - // operation such as `RngNormal` or `Infeed`. The visitor walks the - // computation starting at a given operation and sets is_constant to false iff - // a parameter or stateful operation is encountered. - void IsConstantVisitor(const int64 op_handle, std::set* visited, - bool* is_constant) const; - - // Checks bounds for convolution parameters. - Status VerifyConvolution( - const Shape& lhs_shape, const Shape& rhs_shape, - const ConvolutionDimensionNumbers& dimension_numbers) const; - - // Helper function for creating a Window proto from user-supplied data. - // Returns error if the user-supplied data was invalid. - StatusOr MakeWindow( - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - tensorflow::gtl::ArraySlice lhs_dilation, - tensorflow::gtl::ArraySlice rhs_dilation) const; - - string name_; // Name to use for the built computation. - - // The first error encountered while building the computation. - // This is OK until the first error is encountered. - Status first_error_; - - // The saved stack trace from the point at which the first error occurred. - tensorflow::SavedStackTrace first_error_backtrace_; - - // The instructions of this computation. - std::vector instructions_; - - // The embedded computations used by this computation. Each computation was - // the entry computation of some XlaComputation, the key is the unique id of - // that XlaComputation. - std::map embedded_; - - // The unique parameter numbers. - tensorflow::gtl::FlatSet parameter_numbers_; - - // The metadata to attach to each op. This is structured as a "modal"-like - // operation, in order to simplify client code (and not sprinkle this metadata - // throughout the TensorFlow op kernel implementations). - OpMetadata metadata_; - - // Sharding for this operator. This is structured as a "model"-like operation, - // in order to simplify client code, similar to metadata_. - tensorflow::gtl::optional sharding_; - - // Mode bit that indicates whether to die when a first error is encountered. - bool die_immediately_on_error_ = false; - - XlaBuilder* parent_builder_{nullptr}; - - friend XlaOp Parameter(XlaBuilder* builder, int64 parameter_number, - const Shape& shape, const string& name); - friend XlaOp ConstantLiteral(XlaBuilder* builder, - const LiteralSlice& literal); - template - friend XlaOp ConstantR0(XlaBuilder* builder, NativeT value); - template - friend XlaOp ConstantR1(XlaBuilder* builder, - tensorflow::gtl::ArraySlice values); - friend XlaOp ConstantR1(XlaBuilder* builder, - const tensorflow::core::Bitmap& values); - template - friend XlaOp ConstantR2( - XlaBuilder* builder, - std::initializer_list> values); - template - friend XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, - const Array& values, - const Layout& layout); - template - friend XlaOp ConstantFromArray(XlaBuilder* builder, - const Array& values); - template - friend XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, - const Array2D& values, - const Layout& layout); - template - friend XlaOp ConstantR2FromArray2D(XlaBuilder* builder, - const Array2D& values); - template - friend XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, - const Array3D& values, - const Layout& layout); - template - friend XlaOp ConstantR3FromArray3D(XlaBuilder* builder, - const Array3D& values); - template - friend XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, - const Array4D& values, - const Layout& layout); - template - friend XlaOp ConstantR4FromArray4D(XlaBuilder* builder, - const Array4D& values); - - template - friend XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value); - - friend XlaOp Broadcast(const XlaOp& operand, - tensorflow::gtl::ArraySlice broadcast_sizes); - - friend XlaOp BroadcastInDim( - const XlaOp& operand, const Shape& shape, - const tensorflow::gtl::ArraySlice broadcast_dimensions); - - friend XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, - const PaddingConfig& padding_config); - - friend XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice new_sizes); - - friend XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice new_sizes); - - friend XlaOp Collapse(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions); - - friend XlaOp Slice(const XlaOp& operand, - tensorflow::gtl::ArraySlice start_indices, - tensorflow::gtl::ArraySlice limit_indices, - tensorflow::gtl::ArraySlice strides); - - friend XlaOp SliceInDim(const XlaOp& operand, int64 start_index, - int64 limit_index, int64 stride, int64 dimno); - - friend XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, - tensorflow::gtl::ArraySlice slice_sizes); - - friend XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, - const XlaOp& start_indices); - - friend XlaOp ConcatInDim(XlaBuilder* builder, - tensorflow::gtl::ArraySlice operands, - int64 dimension); - - friend void Trace(const string& tag, const XlaOp& operand); - - friend XlaOp Select(const XlaOp& pred, const XlaOp& on_true, - const XlaOp& on_false); - friend XlaOp Tuple(XlaBuilder* builder, - tensorflow::gtl::ArraySlice elements); - friend XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); - friend XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); - friend XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, - const DotDimensionNumbers& dimension_numbers); - friend XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - Padding padding); - friend XlaOp ConvWithGeneralPadding( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - friend XlaOp ConvWithGeneralDimensions( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, Padding padding, - const ConvolutionDimensionNumbers& dimension_numbers); - friend XlaOp ConvGeneral( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const ConvolutionDimensionNumbers& dimension_numbers); - friend XlaOp ConvGeneralDilated( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - tensorflow::gtl::ArraySlice lhs_dilation, - tensorflow::gtl::ArraySlice rhs_dilation, - const ConvolutionDimensionNumbers& dimension_numbers); - friend XlaOp Fft(const XlaOp& operand, FftType fft_type, - tensorflow::gtl::ArraySlice fft_length); - friend XlaOp Infeed(XlaBuilder* builder, const Shape& shape, - const string& config); - friend void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, - const string& outfeed_config); - friend XlaOp Call(XlaBuilder* builder, const XlaComputation& computation, - tensorflow::gtl::ArraySlice operands); - friend XlaOp CustomCall(XlaBuilder* builder, const string& call_target_name, - tensorflow::gtl::ArraySlice operands, - const Shape& shape); - friend XlaOp HostCompute(XlaBuilder* builder, - tensorflow::gtl::ArraySlice operands, - const string& channel_name, int64 cost_estimate_ns, - const Shape& shape); - friend XlaOp Complex(const XlaOp& real, const XlaOp& imag, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Conj(const XlaOp& operand); - friend XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp And(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Not(const XlaOp& operand); - friend XlaOp ShiftLeft( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp ShiftRightArithmetic( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp ShiftRightLogical( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions_to_reduce); - friend XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation); - friend XlaOp ReduceWindow( - const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, Padding padding); - friend XlaOp ReduceWindowWithGeneralPadding( - const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - friend XlaOp CrossReplicaSum( - const XlaOp& operand, - tensorflow::gtl::ArraySlice replica_group_ids); - friend XlaOp CrossReplicaSum( - const XlaOp& operand, const XlaComputation& computation, - tensorflow::gtl::ArraySlice replica_group_ids, - const tensorflow::gtl::optional& channel_id); - friend XlaOp SelectAndScatter( - const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, Padding padding, - const XlaOp& source, const XlaOp& init_value, - const XlaComputation& scatter); - friend XlaOp SelectAndScatterWithGeneralPadding( - const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const XlaOp& source, const XlaOp& init_value, - const XlaComputation& scatter); - friend XlaOp Abs(const XlaOp& operand); - friend XlaOp Atan2(const XlaOp& y, const XlaOp& x, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp Exp(const XlaOp& operand); - friend XlaOp Expm1(const XlaOp& operand); - friend XlaOp Floor(const XlaOp& operand); - friend XlaOp Ceil(const XlaOp& operand); - friend XlaOp Round(const XlaOp& operand); - friend XlaOp Log(const XlaOp& operand); - friend XlaOp Log1p(const XlaOp& operand); - friend XlaOp Sign(const XlaOp& operand); - friend XlaOp Clz(const XlaOp& operand); - friend XlaOp Cos(const XlaOp& operand); - friend XlaOp Sin(const XlaOp& operand); - friend XlaOp Tanh(const XlaOp& operand); - friend XlaOp Real(const XlaOp& operand); - friend XlaOp Imag(const XlaOp& operand); - friend XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions); - friend XlaOp IsFinite(const XlaOp& operand); - // TODO(b/64798317): Finish CPU & GPU implementation, then replace xla::Iota - // in xla/client/lib/numeric.h with this (renamed to xla::Iota). - friend XlaOp IotaGen(XlaBuilder* builder, PrimitiveType type, int64 size); - friend XlaOp ConvertElementType(const XlaOp& operand, - PrimitiveType new_element_type); - friend XlaOp BitcastConvertType(const XlaOp& operand, - PrimitiveType new_element_type); - friend XlaOp Neg(const XlaOp& operand); - friend XlaOp Transpose(const XlaOp& operand, - tensorflow::gtl::ArraySlice permutation); - friend XlaOp Rev(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions); - friend XlaOp Sort(XlaOp keys, tensorflow::gtl::optional values, - int64 dimension); - friend XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); - friend XlaOp Map(XlaBuilder* builder, - tensorflow::gtl::ArraySlice operands, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice static_operands); - friend XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, - const Shape& shape); - friend XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); - friend XlaOp While(const XlaComputation& condition, - const XlaComputation& body, const XlaOp& init); - friend XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, - const XlaComputation& true_computation, - const XlaOp& false_operand, - const XlaComputation& false_computation); - friend XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, - const int mantissa_bits); - friend XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, - const GatherDimensionNumbers& dimension_numbers, - tensorflow::gtl::ArraySlice window_bounds); - friend void Send(const XlaOp& operand, const ChannelHandle& handle); - friend XlaOp Recv(XlaBuilder* builder, const Shape& shape, - const ChannelHandle& handle); - friend XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, float epsilon, - int64 feature_index); - friend XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, const XlaOp& mean, - const XlaOp& variance, float epsilon, - int64 feature_index); - friend XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, - const XlaOp& batch_mean, const XlaOp& batch_var, - const XlaOp& grad_output, float epsilon, - int64 feature_index); - friend XlaOp SendWithToken(const XlaOp& operand, const XlaOp& token, - const ChannelHandle& handle); - friend XlaOp RecvWithToken(const XlaOp& token, const Shape& shape, - const ChannelHandle& handle); - friend XlaOp SendToHost(const XlaOp& operand, const XlaOp& token, - const Shape& shape_with_layout, - const ChannelHandle& handle); - friend XlaOp RecvFromHost(const XlaOp& token, const Shape& shape, - const ChannelHandle& handle); - friend XlaOp InfeedWithToken(const XlaOp& token, const Shape& shape, - const string& config); - friend XlaOp OutfeedWithToken(const XlaOp& operand, const XlaOp& token, - const Shape& shape_with_layout, - const string& outfeed_config); - friend XlaOp CreateToken(XlaBuilder* builder); - friend XlaOp AfterAll(XlaBuilder* builder, - tensorflow::gtl::ArraySlice tokens); -}; - -// RAII-style object: sets the current sharding assignment in builder on -// construction, and sets back to the previous assignment on destruction. -class XlaScopedShardingAssignment { - public: - XlaScopedShardingAssignment(xla::XlaBuilder* builder, - tensorflow::gtl::optional sharding) - : builder_(builder), prev_sharding_(builder->sharding()) { - SetSharding(sharding); - } - - XlaScopedShardingAssignment(const XlaScopedShardingAssignment&) = delete; - XlaScopedShardingAssignment& operator=(const XlaScopedShardingAssignment&) = - delete; - - ~XlaScopedShardingAssignment() { SetSharding(prev_sharding_); } - - private: - void SetSharding(const tensorflow::gtl::optional& sharding) { - if (sharding.has_value()) { - builder_->SetSharding(sharding.value()); - } else { - builder_->ClearSharding(); - } - } - - xla::XlaBuilder* const builder_; - tensorflow::gtl::optional prev_sharding_; -}; - -// Free functions for building XlaOps. The intention is that these will -// become the public API for building XlaOps rather than calling methods on -// XlaBuilder directly. - -// Enqueues a "retrieve parameter value" instruction for a parameter that was -// passed to the computation. -XlaOp Parameter(XlaBuilder* builder, int64 parameter_number, const Shape& shape, - const string& name); - -// Enqueues a constant with the value of the given literal onto the -// computation. -XlaOp ConstantLiteral(XlaBuilder* builder, const LiteralSlice& literal); - -// Enqueues a constant onto the computation. Methods are templated on the -// native host type (NativeT) which corresponds to a specific XLA -// PrimitiveType as given in the following table: -// -// Native Type PrimitiveType -// ----------------------------- -// bool PRED -// int32 S32 -// int64 S64 -// uint32 U32 -// uint64 U64 -// float F32 -// double F64 -// -// Note: not all primitive types defined in xla_data.proto have a -// corresponding native type yet. -template -XlaOp ConstantR0(XlaBuilder* builder, NativeT value); -template -XlaOp ConstantR1(XlaBuilder* builder, - tensorflow::gtl::ArraySlice values); -XlaOp ConstantR1(XlaBuilder* builder, const tensorflow::core::Bitmap& values); -template -XlaOp ConstantR2(XlaBuilder* builder, - std::initializer_list> values); -template -XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, - const Array& values, - const Layout& layout); -template -XlaOp ConstantFromArray(XlaBuilder* builder, const Array& values); -template -XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, - const Array2D& values, - const Layout& layout); -template -XlaOp ConstantR2FromArray2D(XlaBuilder* builder, - const Array2D& values); -template -XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, - const Array3D& values, - const Layout& layout); -template -XlaOp ConstantR3FromArray3D(XlaBuilder* builder, - const Array3D& values); -template -XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, - const Array4D& values, - const Layout& layout); -template -XlaOp ConstantR4FromArray4D(XlaBuilder* builder, - const Array4D& values); - -// Enqueues a rank one constant (XlaBuilder* builder, vector) onto the -// computation. The vector has size 'length' and every element has the value -// 'value'. -template -XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value); - -// Adds dimensions to an array by duplicating the data in the array. -// -// The new dimensions are inserted on the left, i.e. if -// broadcast_sizes has values {a0, ..., aN} and the operand shape -// has dimensions {b0, ..., bM} then the shape of the output has -// dimensions {a0, ..., aN, b0, ..., bM}. -// -// The new dimensions index into copies of the operand, i.e. -// -// output[i0, ..., iN, j0, ..., jM] = operand[j0, ..., jM] -XlaOp Broadcast(const XlaOp& operand, - tensorflow::gtl::ArraySlice broadcast_sizes); - -// Performs in-dimension-style broadcast. -// -// Operand specifies the input to be broadcast. "shape" is expected output -// shape. "broadcast_dimensions" are the dimensions to be broadcasting into. -// Dimension numbers in broadcast_dimensions map to individual dimensions -// of the operand, and specify what dimension of the output shape they -// should be broadcast. -// e.g. -// Say operand = [1, 2], i.e., a 1D tensor with 2 elements. -// and dimension of shape is [2,2]. -// Specifying {1} as brodcast_dimension will generate output -// [1 , 2] -// [1 , 2] -// On the other hand, specifying {0} as broadcast_dimension -// will generate output -// [1 , 1] -// [2 , 2] -XlaOp BroadcastInDim( - const XlaOp& operand, const Shape& shape, - const tensorflow::gtl::ArraySlice broadcast_dimensions); - -// Enqueues a pad operation onto the computation that pads the given value on -// the edges as well as between the elements of the input. padding_config -// specifies the padding amount for each dimension. -XlaOp Pad(const XlaOp& operand, const XlaOp& padding_value, - const PaddingConfig& padding_config); - -// Enqueues an operation onto the computation that flattens the operand based -// on the dimension order (major/slowest-varying to minor/fastest-varying) -// given, followed by reshaping it into the shape with the given dimension -// sizes (also major to minor). Conceptually, this is a limited form of -// "shape casting". -XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice new_sizes); - -// Enqueues an operation onto the computation that collapses the operand, from -// first to last dimension (C order), then reshapes it to the given dimension -// sizes. Conceptually, this is a limited form of "shape casting". -XlaOp Reshape(const XlaOp& operand, - tensorflow::gtl::ArraySlice new_sizes); - -// Wrapper for Reshape. -// Enqueues an operation to collapse the provided dimensions; e.g. an -// operand with dimensions {x=256, y=2, z=2, p=32} can be collapsed to -// {x=1024, y=32} by collapsing dims {0, 1, 2}. Collapsing dimensions must -// be a consecutive, in-order subsequence of the operand dimensions. -// -// Note that collapsing a single dimension does nothing: -// -// {256} collapsing {0} => {256} -// {1} collapsing {0} => {1} -// -// Collapsing multiple dimensions produces a single result dimension: -// -// {256, 2} collapsing {0,1} => {512} -// {256, 2, 3} collapsing {0,1} => {512, 3} -// -// This could potentially cause data to be moved -- it provides a more -// structured form of reshaping than an arbitrary Reshape operation. -XlaOp Collapse(const XlaOp& operand, - tensorflow::gtl::ArraySlice dimensions); - -// Enqueues a slice operation onto the computation that slices the operand -// from the start indices to the limit indices; e.g. -// -// x -// [ 0 1 2 3 ] -// y [ 4 5 6 7 ] => slice(start={1, 1}, limit={2, 3}) => [ 5 6 ] -// [ 8 9 a b ] -// -// Note that "limit" means up-to-but-not-including; i.e. [start, limit) in 1D -// range notation. -// The strides parameter determines the stride over the slice -XlaOp Slice(const XlaOp& operand, - tensorflow::gtl::ArraySlice start_indices, - tensorflow::gtl::ArraySlice limit_indices, - tensorflow::gtl::ArraySlice strides); - -// Enqueues a slice operation in a given dimension, taking all other -// dimensions as they are; e.g. if dimno is 1 from start_index 2 to -// limit_index 4 by 1, and the shape is f32[7,8,9], this call is short-hand -// for: -// -// array[:, 2:4:1, :] -XlaOp SliceInDim(const XlaOp& operand, int64 start_index, int64 limit_index, - int64 stride, int64 dimno); - -// Enqueues a slice operation onto the computation that slices the 'operand' -// from dynamic start indices which are passed in 'start_indices'. -// The size of the slice in each dimension is passed in 'slice_sizes', -// which specify the end point of exclusive slice intervals in each -// dimension [start, start + size). -// The shape of 'start_indices' must be rank == 1, with dimension size -// equal to the rank of the 'operand'. -// Slice index calculations are computed modulo input dimension sizes to -// prevent dynamic start indices from generating out-of-bound array accesses. -XlaOp DynamicSlice(const XlaOp& operand, const XlaOp& start_indices, - tensorflow::gtl::ArraySlice slice_sizes); - -// Enqueues a dynamic update slice operation onto the computation, which -// updates a slice of 'operand' with 'update' at dynamic 'start_indices'. -// The shape of 'update' determines the shape of the slice of 'operand' -// which is updated. -// The indices specified in 'start_indices' specify the offset of the slice -// of 'operand' which is updated. -// -// update = {10, 11} // calculated at runtime. -// [1 2 3] start = {1, 1} // calculated at runtime. [1 2 3 ] -// [4 5 6] => DynamicUpdateslice(data, update, start) => [4 10 11] -// [7 8 9] [7 8 9 ] -// -// The shape of 'start_indices' must be rank == 1, with dimension size -// equal to the rank of the 'operand'. -// Slice index calculations are computed modulo update dimension sizes to -// prevent dynamic start indices from generating out-of-bound array accesses. -XlaOp DynamicUpdateSlice(const XlaOp& operand, const XlaOp& update, - const XlaOp& start_indices); - -// Enqueues a concatenate instruction onto the computation. 'operands' must -// have >= 1 entry. -XlaOp ConcatInDim(XlaBuilder* builder, - tensorflow::gtl::ArraySlice operands, int64 dimension); - -// Enqueue a tracing operation onto the computation; the computation will emit -// a logging message with the operand. -void Trace(const string& tag, const XlaOp& operand); - -// Enqueues a conditional-move-like select operation onto the computation; -// predicated on pred, selects between on_true and on_false. -XlaOp Select(const XlaOp& pred, const XlaOp& on_true, const XlaOp& on_false); - -// Enqueues a tuple-creation instruction onto the computation. -XlaOp Tuple(XlaBuilder* builder, tensorflow::gtl::ArraySlice elements); - -// Enqueues a tuple-element-get instruction onto the computation. -XlaOp GetTupleElement(const XlaOp& tuple_data, int64 index); - -// Enqueues an equal-to comparison instruction onto the computation. -XlaOp Eq(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a not-equal comparison instruction onto the computation. -XlaOp Ne(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a greater-or-equal comparison instruction onto the computation. -XlaOp Ge(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a greater-than comparison instruction onto the computation. -XlaOp Gt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a less-than comparison instruction onto the computation. -XlaOp Lt(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a less-or-equal comparison instruction onto the computation. -XlaOp Le(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a dot instruction onto the computation. -XlaOp Dot(const XlaOp& lhs, const XlaOp& rhs); - -// Enqueues a general dot instruction onto the computation. -XlaOp DotGeneral(const XlaOp& lhs, const XlaOp& rhs, - const DotDimensionNumbers& dimension_numbers); - -// Enqueues a convolution instruction onto the computation, which uses the -// default convolution dimension numbers. -XlaOp Conv(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, Padding padding); - -// Enqueues a convolution instruction onto the computation, with the caller -// provided padding configuration in the format returned by MakePadding(). -XlaOp ConvWithGeneralPadding( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - -// Enqueues a convolution instruction onto the computation, with the caller -// provided dimension numbers configuration. -XlaOp ConvWithGeneralDimensions( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, Padding padding, - const ConvolutionDimensionNumbers& dimension_numbers); - -// Enqueues a convolution instruction onto the computation, with the caller -// provided padding configuration as well as the dimension numbers. -XlaOp ConvGeneral(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const ConvolutionDimensionNumbers& dimension_numbers); - -// Enqueues a convolution instruction onto the computation, with the caller -// provided padding configuration, dilation factors and dimension numbers. -XlaOp ConvGeneralDilated( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - tensorflow::gtl::ArraySlice lhs_dilation, - tensorflow::gtl::ArraySlice rhs_dilation, - const ConvolutionDimensionNumbers& dimension_numbers); - -// Enqueues an FFT instruction onto the computation, of the given type and -// with the given FFT length. -XlaOp Fft(const XlaOp& operand, FftType fft_type, - tensorflow::gtl::ArraySlice fft_length); - -// Enqueues an infeed instruction onto the computation, which writes data of -// the given shape to the infeed buffer of the device. -XlaOp Infeed(XlaBuilder* builder, const Shape& shape, - const string& config = ""); - -// Variant of Infeed which takes a token-shaped operand and produces a -// two-element tuple containing the data value and a token-shaped value. -// Tokens are used for ordering side-effecting operations. -// TODO(b/110532604): Replace all uses of the non-token form with this variant. -XlaOp InfeedWithToken(const XlaOp& token, const Shape& shape, - const string& config = ""); - -// Enqueues an outfeed instruction onto the computation. This instruction -// generates outgoing data transfers for the given data. -// -// shape_with_layout communicates the laid out shape that we want to outfeed -// -- if !ShapeUtil::Compatible(GetShape(operand), shape_with_layout) an error -// will occur. -void Outfeed(const XlaOp& operand, const Shape& shape_with_layout, - const string& outfeed_config); - -// Variant of Outfeed which takes a token-shaped operand and produces a -// token-shaped value. Tokens are used for ordering side-effecting operations. -// TODO(b/110532604): Replace all uses of the non-token form with this variant. -XlaOp OutfeedWithToken(const XlaOp& operand, const XlaOp& token, - const Shape& shape_with_layout, - const string& outfeed_config); - -// Enqueues a call instruction onto the computation. -XlaOp Call(XlaBuilder* builder, const XlaComputation& computation, - tensorflow::gtl::ArraySlice operands); - -// Enqueues a custom call instruction onto the computation. -// During code generation, a call instruction is emitted which targets a -// symbol with the name |call_target_name|. The |operands| are passed to the -// call instruction. |shape| is the resultant shape. -XlaOp CustomCall(XlaBuilder* builder, const string& call_target_name, - tensorflow::gtl::ArraySlice 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. -XlaOp HostCompute(XlaBuilder* builder, - tensorflow::gtl::ArraySlice 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 -// (see g3doc for more details). - -// Enqueues a complex compose instruction onto the computation. -XlaOp Complex(const XlaOp& real, const XlaOp& imag, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a complex conjugate instruction onto the computation. -XlaOp Conj(const XlaOp& operand); - -// Enqueues an add instruction onto the computation. -XlaOp Add(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a subtract instruction onto the computation. -XlaOp Sub(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a multiply instruction onto the computation. -XlaOp Mul(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a divide instruction onto the computation. -XlaOp Div(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a remainder instruction onto the computation. -XlaOp Rem(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a max instruction onto the computation. -XlaOp Max(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues a min instruction onto the computation. -XlaOp Min(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Element-wise logical operators -XlaOp And(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -XlaOp Or(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -XlaOp Xor(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -XlaOp Not(const XlaOp& operand); - -XlaOp ShiftLeft(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); -XlaOp ShiftRightArithmetic( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); -XlaOp ShiftRightLogical( - const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Reduces an array among the provided dimensions, given "computation" as a -// reduction operator. -XlaOp Reduce(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions_to_reduce); - -// Convenience wrapper around the above that reduces all the dimensions in the -// operand shape. -XlaOp ReduceAll(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation); - -// Enqueues a windowed reduce instruction onto the computation. -XlaOp ReduceWindow(const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - Padding padding); - -// As ReduceWindow(), but the padding is given in the format -// returned by MakePadding(). -XlaOp ReduceWindowWithGeneralPadding( - const XlaOp& operand, const XlaOp& init_value, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding); - -// Returns the sum of the operand value within each subgroup of replicas. All -// replicas supply one input to the sum and all replicas receive the resulting -// sum for each subgroup. -XlaOp CrossReplicaSum( - const XlaOp& operand, - tensorflow::gtl::ArraySlice replica_group_ids = {}); - -// Enqueues an operation that do an AllReduce of the operand cross cores. Here -// AllReduce means doing a reduction on the input operand cross cores and then -// broadcasting the reduction result to those cores. The reduction function is -// defined by `computation`, which should be a commutative computation on -// scalars, e.g., add, min, or max. The way that AllReduce is applied is -// configured by: -// -// - `replica_group_ids`: maps replica ids to subgroup ids. If empty, all -// replicas belong to one group. Allreduce will be applied within subgroups. -// For example, we have 4 replicas, then replica_group_ids={0,1,0,1} means, -// replica 0 and 2 are in subgroup 0, replica 1 and 3 are in subgroup 1. -// -// - `channel_id`: for Allreduce nodes from different models, if they have the -// same channel_id, they will be 'Allreduce'd. If empty, Allreduce will not be -// applied cross models. -// -// TODO(b/79737069): Rename this to AllReduce when it's ready to use. -XlaOp CrossReplicaSum(const XlaOp& operand, const XlaComputation& computation, - tensorflow::gtl::ArraySlice replica_group_ids = {}, - const tensorflow::gtl::optional& - channel_id = tensorflow::gtl::nullopt); - -// Enqueues an operation that scatters the `source` array to the selected -// indices of each window. -XlaOp SelectAndScatter(const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - Padding padding, const XlaOp& source, - const XlaOp& init_value, const XlaComputation& scatter); - -// As SelectAndScatter(), but the padding is given in the format -// returned by MakePadding(). -XlaOp SelectAndScatterWithGeneralPadding( - const XlaOp& operand, const XlaComputation& select, - tensorflow::gtl::ArraySlice window_dimensions, - tensorflow::gtl::ArraySlice window_strides, - tensorflow::gtl::ArraySlice> padding, - const XlaOp& source, const XlaOp& init_value, - const XlaComputation& scatter); - -// Enqueues an abs instruction onto the computation. -XlaOp Abs(const XlaOp& operand); - -// Enqueues a atan2 instruction onto the computation. -XlaOp Atan2(const XlaOp& y, const XlaOp& x, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues an exp instruction onto the computation. -XlaOp Exp(const XlaOp& operand); - -// Enqueues an expm1 instruction onto the computation. -XlaOp Expm1(const XlaOp& operand); - -// Enqueues a floor instruction onto the computation. -XlaOp Floor(const XlaOp& operand); - -// Enqueues a ceil instruction onto the computation. -XlaOp Ceil(const XlaOp& operand); - -// Enqueues a round instruction onto the computation, rounding to nearest even -// with half-way cases rounding away from zero. -XlaOp Round(const XlaOp& operand); - -// Enqueues an log instruction (natural logarithm) onto the computation. -XlaOp Log(const XlaOp& operand); - -// Enqueues an log1p instruction (log(x+1)) onto the computation. -XlaOp Log1p(const XlaOp& operand); - -// Enqueues a sign instruction onto the computation. -XlaOp Sign(const XlaOp& operand); - -// Enqueues a count leading zeros instruction onto the computation. -XlaOp Clz(const XlaOp& operand); - -// Enqueues a cosine instruction onto the computation. -XlaOp Cos(const XlaOp& operand); - -// Enqueues a sine instruction onto the computation. -XlaOp Sin(const XlaOp& operand); - -// Enqueues a tanh instruction onto the computation. -XlaOp Tanh(const XlaOp& operand); - -// Enqueues a real-part instruction onto the computation. -XlaOp Real(const XlaOp& operand); - -// Enqueues an imaginary-part instruction onto the computation. -XlaOp Imag(const XlaOp& operand); - -// Enqueues a lhs^rhs computation onto the computation. -XlaOp Pow(const XlaOp& lhs, const XlaOp& rhs, - tensorflow::gtl::ArraySlice broadcast_dimensions = {}); - -// Enqueues an operator that tests if the operand's values are finite, i.e., -// not Inf or NaN. Defined only for floating-point types. Returns an array of -// booleans with the same shape where entries are true iff the corresponding -// entry was NaN. -XlaOp IsFinite(const XlaOp& operand); - -// Enqueues a convert instruction onto the computation that changes the -// element type of the operand array to primitive_type. -XlaOp ConvertElementType(const XlaOp& operand, PrimitiveType new_element_type); - -// Enqueues a no-op instruction onto the computation that changes -// the element type of the operand array to primitive_type. The -// bit-widths of the source and destination element types must be -// identical. -XlaOp BitcastConvertType(const XlaOp& operand, PrimitiveType new_element_type); - -// Enqueues a negate instruction onto the computation. -XlaOp Neg(const XlaOp& operand); - -// Enqueues a transpose instruction onto the computation. -XlaOp Transpose(const XlaOp& operand, - tensorflow::gtl::ArraySlice permutation); - -// Enqueues a reverse instruction onto the computation. The order of the -// elements in the given dimensions is reversed (i.e., the element at index i -// is moved to index dimension_size - 1 - i). -XlaOp Rev(const XlaOp& operand, tensorflow::gtl::ArraySlice dimensions); - -// Enqueues a sort (as increasing order) instruction onto the computation. -// If only keys are provided: -// * If the keys are an rank-1 tensor (an array), the result is a sorted array -// of keys, in ascending order. -// * If the keys have higher rank, the keys are sorted along the provided -// dimension. For example, for a rank-2 tensor (a matrix) of keys, a dimension -// value of 0 will indepenently sort every column, and a dimension value of 1 -// will independently sort each row. If no dimension number is provided, then -// the last dimension is chosen by default. -// -// If both keys and values are provided: -// * The keys and the values must tensors with the same dimensions. The -// element types of the tensors may be different. -// * The result is a tuple that consists of a sorted tensor of keys (along the -// provided dimension, as above) as the first element, and a tensor with their -// corresponding values as the second element. -XlaOp Sort(XlaOp keys, - tensorflow::gtl::optional values = tensorflow::gtl::nullopt, - int64 dimension = -1); - -// Enqueues a clamp instruction onto the computation. -XlaOp Clamp(const XlaOp& min, const XlaOp& operand, const XlaOp& max); - -// Enqueues a map instruction onto the computation. -XlaOp Map(XlaBuilder* builder, tensorflow::gtl::ArraySlice operands, - const XlaComputation& computation, - tensorflow::gtl::ArraySlice dimensions, - tensorflow::gtl::ArraySlice static_operands = {}); - -// Enqueues a N(mu, sigma) random number generation instruction onto the -// computation. -XlaOp RngNormal(const XlaOp& mu, const XlaOp& sigma, const Shape& shape); - -// Enqueues a U(a, b) random number generation instruction onto the -// computation. Returns values in the semi-open interval [a, b). -XlaOp RngUniform(const XlaOp& a, const XlaOp& b, const Shape& shape); - -// Enqueues a while node onto the computation. -XlaOp While(const XlaComputation& condition, const XlaComputation& body, - const XlaOp& init); - -// Enqueues a conditional node onto the computation. -XlaOp Conditional(const XlaOp& predicate, const XlaOp& true_operand, - const XlaComputation& true_computation, - const XlaOp& false_operand, - const XlaComputation& false_computation); - -// Enqueues a ReducePrecision node onto the computation. -XlaOp ReducePrecision(const XlaOp& operand, const int exponent_bits, - const int mantissa_bits); - -// Enqueues a Gather node onto the computation. -XlaOp Gather(const XlaOp& input, const XlaOp& gather_indices, - const GatherDimensionNumbers& dimension_numbers, - tensorflow::gtl::ArraySlice window_bounds); - -// Enqueues a Send node onto the computation for device-to-device -// communication. This operation sends the given operand to -// a Recv instruction in a different computation that shares the same channel -// handle. -void Send(const XlaOp& operand, const ChannelHandle& handle); - -// Variant of Send which takes a token-shaped operand and produces a -// token-shaped value. Tokens are used for ordering side-effecting operations. -// TODO(b/110532604): Replace all uses of the non-token form with this variant. -XlaOp SendWithToken(const XlaOp& operand, const XlaOp& token, - const ChannelHandle& handle); - -// Enqueues a Recv node onto the computation for device-to-device -// communication. The data comes from a Send instruction in a different -// computation that shares the same channel handle and its shape must be the -// same as the given shape. -XlaOp Recv(XlaBuilder* builder, const Shape& shape, - const ChannelHandle& handle); - -// Variant of Recv which takes a token-shaped operand and produces a two-element -// tuple containing the data value and a token-shaped value. Tokens are used -// for ordering side-effecting operations. -// TODO(b/110532604): Replace all uses of the non-token form with this variant. -XlaOp RecvWithToken(const XlaOp& token, const Shape& shape, - const ChannelHandle& handle); - -// Enqueues a Send node which transfers data from the device to the host. The -// 'shape_with_layout' argument defines the layout of the data transferred; its -// shape must be compatible with the shape of the operand. The operand must be -// array-shaped. -// TODO(b/111544877): Support tuple shapes. -XlaOp SendToHost(const XlaOp& operand, const XlaOp& token, - const Shape& shape_with_layout, const ChannelHandle& handle); - -// Enqueues a Recv node which transfers data from the host to the device. The -// given shape must contain a layout and must be an array. -// TODO(b/111544877): Support tuple shapes. -XlaOp RecvFromHost(const XlaOp& token, const Shape& shape, - const ChannelHandle& handle); - -// Enqueues an operation (AfterAll) with no operands that produces a -// token-shaped value. Tokens are used for ordering side-effecting operations. -// This is a separate method from AfterAll to facility the removal of -// operand-less AfterAll instructions. -// TODO(b/110532604): Remove this function when all tokens are derived from a -// single token generated or passed into the entry computation. -XlaOp CreateToken(XlaBuilder* builder); - -// Enqueues an AfterAll instruction which produces a token-shaped value and -// takes a variadic number of token-shaped operands. The number of operands must -// be greater than zero. Used for joining tokens. -XlaOp AfterAll(XlaBuilder* builder, tensorflow::gtl::ArraySlice tokens); - -// Normalizes operand across spatial and batch dimensions for each feature. -// -// Returns a tuple (normalized, batch_mean, batch_var) where `normalized` -// is the normalized result and batch_mean and batch_var are the mean and -// variance, respectively, across batch for the operand. -XlaOp BatchNormTraining(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, float epsilon, - int64 feature_index); - -// Normalizes operand across spatial and batch dimensions for each feature. -// -// `BatchNormInference` is equivalent to calling `BatchNormTraining` without -// computing `mean` and `variance` for each batch inside the operation. It -// uses the input `mean` and `variance` instead as estimated values. The -// purpose of this op is to reduce latency in inference, hence the name -// `BatchNormInference`. -// -// The output has the same shape as `operand`, and contains the normalized -// values for each batch. -XlaOp BatchNormInference(const XlaOp& operand, const XlaOp& scale, - const XlaOp& offset, const XlaOp& mean, - const XlaOp& variance, float epsilon, - int64 feature_index); - -// Calculates the gradients of a batch norm op. -// -// The inputs `batch_mean` and `batch_var` represent the mean and variance -// across the batch. -// -// Returns a tuple of three elements: -// - grad_operand: Gradient with respect to input `operand` -// - grad_offset: Gradient with respect to input `offset` -// - grad_scale: Gradient with respect to input `scale` -XlaOp BatchNormGrad(const XlaOp& operand, const XlaOp& scale, - const XlaOp& batch_mean, const XlaOp& batch_var, - const XlaOp& grad_output, float epsilon, - int64 feature_index); - -// Implementation details below this point. - -template -XlaOp XlaBuilder::ConstantR0(NativeT value) { - return ConstantLiteral(*LiteralUtil::CreateR0(value)); -} - -template -XlaOp XlaBuilder::ConstantR1(tensorflow::gtl::ArraySlice values) { - return ConstantLiteral(*LiteralUtil::CreateR1(values)); -} - -template -XlaOp XlaBuilder::ConstantR1(int64 length, NativeT value) { - Literal literal(ShapeUtil::MakeShape( - primitive_util::NativeToPrimitiveType(), {length})); - literal.PopulateWithValue(value); - return ConstantLiteral(literal); -} - -inline XlaOp XlaBuilder::ConstantR1(const tensorflow::core::Bitmap& values) { - return ConstantLiteral(*LiteralUtil::CreateR1(values)); -} - -template -XlaOp XlaBuilder::ConstantR2( - std::initializer_list> values) { - return ConstantLiteral(*LiteralUtil::CreateR2(values)); -} - -template -XlaOp XlaBuilder::ConstantFromArrayWithLayout(const Array& values, - const Layout& layout) { - return ConstantLiteral( - *LiteralUtil::CreateFromArrayWithLayout(values, layout)); -} - -template -XlaOp XlaBuilder::ConstantFromArray(const Array& values) { - return ConstantLiteral(*LiteralUtil::CreateFromArray(values)); -} - -template -XlaOp XlaBuilder::ConstantR2FromArray2DWithLayout( - const Array2D& values, const Layout& layout) { - return ConstantLiteral( - *LiteralUtil::CreateFromArrayWithLayout(values, layout)); -} - -template -XlaOp XlaBuilder::ConstantR2FromArray2D(const Array2D& values) { - return ConstantLiteral(*LiteralUtil::CreateR2FromArray2D(values)); -} - -template -XlaOp XlaBuilder::ConstantR3FromArray3DWithLayout( - const Array3D& values, const Layout& layout) { - return ConstantLiteral( - *LiteralUtil::CreateR3FromArray3DWithLayout(values, layout)); -} - -template -XlaOp XlaBuilder::ConstantR3FromArray3D(const Array3D& values) { - return ConstantFromArray(values); -} - -template -XlaOp XlaBuilder::ConstantR4FromArray4DWithLayout( - const Array4D& values, const Layout& layout) { - return ConstantFromArrayWithLayout(values, layout); -} - -template -XlaOp XlaBuilder::ConstantR4FromArray4D(const Array4D& values) { - return ConstantFromArray(values); -} - -// Free function template implementations. - -template -XlaOp ConstantR0(XlaBuilder* builder, NativeT value) { - return ConstantLiteral(builder, *LiteralUtil::CreateR0(value)); -} - -template -XlaOp ConstantR1(XlaBuilder* builder, - tensorflow::gtl::ArraySlice values) { - return ConstantLiteral(builder, *LiteralUtil::CreateR1(values)); -} - -template -XlaOp ConstantR1(XlaBuilder* builder, int64 length, NativeT value) { - Literal literal(ShapeUtil::MakeShape( - primitive_util::NativeToPrimitiveType(), {length})); - literal.PopulateWithValue(value); - return ConstantLiteral(builder, literal); -} - -inline XlaOp ConstantR1(XlaBuilder* builder, - const tensorflow::core::Bitmap& values) { - return ConstantLiteral(builder, *LiteralUtil::CreateR1(values)); -} - -template -XlaOp ConstantR2(XlaBuilder* builder, - std::initializer_list> values) { - return ConstantLiteral(builder, *LiteralUtil::CreateR2(values)); -} - -template -XlaOp ConstantFromArrayWithLayout(XlaBuilder* builder, - const Array& values, - const Layout& layout) { - return ConstantLiteral( - builder, - *LiteralUtil::CreateFromArrayWithLayout(values, layout)); -} - -template -XlaOp ConstantFromArray(XlaBuilder* builder, const Array& values) { - return ConstantLiteral(builder, - *LiteralUtil::CreateFromArray(values)); -} - -template -XlaOp ConstantR2FromArray2DWithLayout(XlaBuilder* builder, - const Array2D& values, - const Layout& layout) { - return ConstantLiteral( - builder, - *LiteralUtil::CreateFromArrayWithLayout(values, layout)); -} - -template -XlaOp ConstantR2FromArray2D(XlaBuilder* builder, - const Array2D& values) { - return ConstantLiteral(builder, - *LiteralUtil::CreateR2FromArray2D(values)); -} - -template -XlaOp ConstantR3FromArray3DWithLayout(XlaBuilder* builder, - const Array3D& values, - const Layout& layout) { - return ConstantLiteral( - builder, - *LiteralUtil::CreateR3FromArray3DWithLayout(values, layout)); -} - -template -XlaOp ConstantR3FromArray3D(XlaBuilder* builder, - const Array3D& values) { - return ConstantFromArray(builder, values); -} - -template -XlaOp ConstantR4FromArray4DWithLayout(XlaBuilder* builder, - const Array4D& values, - const Layout& layout) { - return ConstantFromArrayWithLayout(builder, values, layout); -} - -template -XlaOp ConstantR4FromArray4D(XlaBuilder* builder, - const Array4D& values) { - return ConstantFromArray(builder, values); -} - -} // namespace xla +#include "tensorflow/compiler/xla/client/xla_builder.h" #endif // TENSORFLOW_COMPILER_XLA_CLIENT_XLA_CLIENT_XLA_BUILDER_H_ diff --git a/tensorflow/docs_src/performance/xla/broadcasting.md b/tensorflow/docs_src/performance/xla/broadcasting.md index eaa709c2f8..7018ded53f 100644 --- a/tensorflow/docs_src/performance/xla/broadcasting.md +++ b/tensorflow/docs_src/performance/xla/broadcasting.md @@ -99,7 +99,7 @@ dimensions 1 and 2 of the cuboid. This type of broadcast is used in the binary ops in `XlaBuilder`, if the `broadcast_dimensions` argument is given. For example, see -[XlaBuilder::Add](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.cc). +[XlaBuilder::Add](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.cc). In the XLA source code, this type of broadcasting is sometimes called "InDim" broadcasting. -- GitLab From 700a6698e634391cf96a314f378a8de973b49995 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 22:42:08 -0700 Subject: [PATCH 428/519] [XLA] Migrate tf2xla to include the builder in client/. This is a part of the work that incrementally moves client/xla_client/* to client/. PiperOrigin-RevId: 206107733 --- tensorflow/compiler/tf2xla/BUILD | 2 +- tensorflow/compiler/tf2xla/graph_compiler.cc | 2 +- tensorflow/compiler/tf2xla/kernels/BUILD | 8 ++++---- .../compiler/tf2xla/kernels/aggregate_ops.cc | 2 +- .../compiler/tf2xla/kernels/batch_norm_op.cc | 2 +- .../compiler/tf2xla/kernels/batchtospace_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/bias_ops.cc | 2 +- .../compiler/tf2xla/kernels/binary_ops.cc | 2 +- .../compiler/tf2xla/kernels/bucketize_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/cast_op.cc | 2 +- .../compiler/tf2xla/kernels/categorical_op.cc | 2 +- .../tf2xla/kernels/clip_by_value_op.cc | 2 +- .../compiler/tf2xla/kernels/concat_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/const_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/conv_ops.cc | 2 +- tensorflow/compiler/tf2xla/kernels/cross_op.cc | 2 +- .../compiler/tf2xla/kernels/cwise_ops.cc | 2 +- tensorflow/compiler/tf2xla/kernels/cwise_ops.h | 2 +- .../compiler/tf2xla/kernels/depthtospace_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/diag_op.cc | 2 +- .../tf2xla/kernels/dynamic_slice_ops.cc | 2 +- .../tf2xla/kernels/dynamic_stitch_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/elu_op.cc | 2 +- .../tf2xla/kernels/extract_image_patches_op.cc | 2 +- .../tf2xla/kernels/fake_quantize_ops.cc | 2 +- tensorflow/compiler/tf2xla/kernels/fft_ops.cc | 2 +- tensorflow/compiler/tf2xla/kernels/fill_op.cc | 2 +- .../compiler/tf2xla/kernels/gather_op.cc | 2 +- .../tf2xla/kernels/gather_op_helpers.h | 2 +- tensorflow/compiler/tf2xla/kernels/if_op.cc | 2 +- .../compiler/tf2xla/kernels/image_ops.cc | 2 +- .../tf2xla/kernels/image_resize_ops.cc | 2 +- .../compiler/tf2xla/kernels/l2loss_op.cc | 2 +- .../compiler/tf2xla/kernels/listdiff_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/lrn_ops.cc | 2 +- .../compiler/tf2xla/kernels/matmul_op.cc | 2 +- .../tf2xla/kernels/matrix_band_part_op.cc | 2 +- .../tf2xla/kernels/matrix_set_diag_op.cc | 2 +- .../compiler/tf2xla/kernels/mirror_pad_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/pack_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/pad_op.cc | 2 +- .../compiler/tf2xla/kernels/pooling_ops.cc | 2 +- .../kernels/quantize_and_dequantize_op.cc | 2 +- .../compiler/tf2xla/kernels/random_ops.cc | 2 +- .../tf2xla/kernels/reduce_window_op.cc | 2 +- .../compiler/tf2xla/kernels/reduction_ops.cc | 2 +- .../compiler/tf2xla/kernels/reduction_ops.h | 2 +- .../tf2xla/kernels/reduction_ops_common.cc | 2 +- tensorflow/compiler/tf2xla/kernels/relu_op.cc | 2 +- .../compiler/tf2xla/kernels/reshape_op.cc | 2 +- .../compiler/tf2xla/kernels/retval_op.cc | 2 +- .../compiler/tf2xla/kernels/reverse_op.cc | 2 +- .../tf2xla/kernels/reverse_sequence_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/scan_ops.cc | 2 +- .../compiler/tf2xla/kernels/scatter_nd_op.cc | 2 +- .../tf2xla/kernels/segment_reduction_ops.cc | 2 +- .../compiler/tf2xla/kernels/select_op.cc | 2 +- .../compiler/tf2xla/kernels/sendrecv_ops.cc | 2 +- tensorflow/compiler/tf2xla/kernels/shape_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/slice_op.cc | 2 +- .../compiler/tf2xla/kernels/softmax_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/sort_ops.cc | 2 +- .../compiler/tf2xla/kernels/spacetobatch_op.cc | 2 +- .../compiler/tf2xla/kernels/spacetodepth_op.cc | 2 +- tensorflow/compiler/tf2xla/kernels/split_op.cc | 2 +- .../tf2xla/kernels/stateless_random_ops.cc | 2 +- .../tf2xla/kernels/strided_slice_op.cc | 2 +- .../tf2xla/kernels/tensor_array_ops.cc | 2 +- tensorflow/compiler/tf2xla/kernels/tile_ops.cc | 2 +- tensorflow/compiler/tf2xla/kernels/topk_op.cc | 2 +- .../compiler/tf2xla/kernels/training_ops.cc | 2 +- .../compiler/tf2xla/kernels/transpose_op.cc | 2 +- .../compiler/tf2xla/kernels/unary_ops.cc | 2 +- .../compiler/tf2xla/kernels/unpack_op.cc | 2 +- .../compiler/tf2xla/kernels/variable_ops.cc | 2 +- tensorflow/compiler/tf2xla/kernels/while_op.cc | 2 +- tensorflow/compiler/tf2xla/lib/BUILD | 18 +++++++++--------- tensorflow/compiler/tf2xla/lib/batch_dot.cc | 2 +- tensorflow/compiler/tf2xla/lib/batch_dot.h | 2 +- tensorflow/compiler/tf2xla/lib/cholesky.cc | 2 +- tensorflow/compiler/tf2xla/lib/cholesky.h | 2 +- tensorflow/compiler/tf2xla/lib/qr.cc | 2 +- tensorflow/compiler/tf2xla/lib/qr.h | 2 +- tensorflow/compiler/tf2xla/lib/random.cc | 2 +- tensorflow/compiler/tf2xla/lib/random.h | 2 +- tensorflow/compiler/tf2xla/lib/scatter.cc | 2 +- tensorflow/compiler/tf2xla/lib/scatter.h | 2 +- .../compiler/tf2xla/lib/triangular_solve.cc | 2 +- .../compiler/tf2xla/lib/triangular_solve.h | 2 +- .../tf2xla/lib/triangular_solve_test.cc | 2 +- tensorflow/compiler/tf2xla/lib/util.cc | 2 +- tensorflow/compiler/tf2xla/lib/util.h | 2 +- tensorflow/compiler/tf2xla/lib/while_loop.cc | 2 +- tensorflow/compiler/tf2xla/lib/while_loop.h | 2 +- .../compiler/tf2xla/xla_compilation_device.cc | 2 +- .../compiler/tf2xla/xla_compilation_device.h | 2 +- tensorflow/compiler/tf2xla/xla_compiler.cc | 2 +- tensorflow/compiler/tf2xla/xla_context.cc | 2 +- tensorflow/compiler/tf2xla/xla_context.h | 2 +- tensorflow/compiler/tf2xla/xla_helpers.cc | 2 +- tensorflow/compiler/tf2xla/xla_helpers.h | 2 +- tensorflow/compiler/tf2xla/xla_op_kernel.cc | 2 +- tensorflow/compiler/tf2xla/xla_op_kernel.h | 2 +- tensorflow/compiler/tf2xla/xla_resource.cc | 2 +- tensorflow/compiler/tf2xla/xla_resource.h | 2 +- 105 files changed, 116 insertions(+), 116 deletions(-) diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index 881624fff8..a803f29160 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -173,11 +173,11 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:numeric", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/legacy_flags:parse_flags_from_env", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", diff --git a/tensorflow/compiler/tf2xla/graph_compiler.cc b/tensorflow/compiler/tf2xla/graph_compiler.cc index e1cea03865..e4fdf0a618 100644 --- a/tensorflow/compiler/tf2xla/graph_compiler.cc +++ b/tensorflow/compiler/tf2xla/graph_compiler.cc @@ -29,7 +29,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/executor.h" #include "tensorflow/core/common_runtime/function.h" diff --git a/tensorflow/compiler/tf2xla/kernels/BUILD b/tensorflow/compiler/tf2xla/kernels/BUILD index 7f3e32d96d..f96483d23d 100644 --- a/tensorflow/compiler/tf2xla/kernels/BUILD +++ b/tensorflow/compiler/tf2xla/kernels/BUILD @@ -123,13 +123,13 @@ tf_kernel_library( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:math", "//tensorflow/compiler/xla/client/lib:numeric", "//tensorflow/compiler/xla/client/lib:prng", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:image_ops_op_lib", "//tensorflow/core:lib", @@ -165,8 +165,8 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/ops:xla_ops", "//tensorflow/compiler/xla:literal", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", @@ -182,7 +182,7 @@ tf_kernel_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/tf2xla/ops:xla_ops", "//tensorflow/compiler/xla:literal", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", @@ -219,8 +219,8 @@ tf_kernel_library( "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla/client:client_library", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:framework", "//tensorflow/core:lib", "//tensorflow/core/kernels:argmax_op", diff --git a/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc b/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc index e335328280..41a453da80 100644 --- a/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/aggregate_ops.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc b/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc index c4af79281d..b3ad0aea84 100644 --- a/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batch_norm_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc index 26130fd9e7..48f2a005ab 100644 --- a/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/batchtospace_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/bias_ops.cc b/tensorflow/compiler/tf2xla/kernels/bias_ops.cc index e9b2c0b16d..41f540506b 100644 --- a/tensorflow/compiler/tf2xla/kernels/bias_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/bias_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/util/tensor_format.h" diff --git a/tensorflow/compiler/tf2xla/kernels/binary_ops.cc b/tensorflow/compiler/tf2xla/kernels/binary_ops.cc index d6d4ae8937..2c328102e0 100644 --- a/tensorflow/compiler/tf2xla/kernels/binary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/binary_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc b/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc index efbdb76eaa..5078f8662b 100644 --- a/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/bucketize_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/cast_op.cc b/tensorflow/compiler/tf2xla/kernels/cast_op.cc index 62eebf762b..8cc2479dd5 100644 --- a/tensorflow/compiler/tf2xla/kernels/cast_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/cast_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/core/framework/kernel_def_builder.h" diff --git a/tensorflow/compiler/tf2xla/kernels/categorical_op.cc b/tensorflow/compiler/tf2xla/kernels/categorical_op.cc index 1784e712b5..e7fef77edc 100644 --- a/tensorflow/compiler/tf2xla/kernels/categorical_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/categorical_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" diff --git a/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc b/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc index 4e6d33304c..547fe48046 100644 --- a/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/clip_by_value_op.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/tensor_shape.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/concat_op.cc b/tensorflow/compiler/tf2xla/kernels/concat_op.cc index e3a32a5c0e..f410605104 100644 --- a/tensorflow/compiler/tf2xla/kernels/concat_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/concat_op.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/const_op.cc b/tensorflow/compiler/tf2xla/kernels/const_op.cc index f4360d8c3f..da8cf3fc6f 100644 --- a/tensorflow/compiler/tf2xla/kernels/const_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/const_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/tensor.pb.h" diff --git a/tensorflow/compiler/tf2xla/kernels/conv_ops.cc b/tensorflow/compiler/tf2xla/kernels/conv_ops.cc index 48ac4867ed..5da7972397 100644 --- a/tensorflow/compiler/tf2xla/kernels/conv_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/conv_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/cross_op.cc b/tensorflow/compiler/tf2xla/kernels/cross_op.cc index 500a564f3f..db579a5b35 100644 --- a/tensorflow/compiler/tf2xla/kernels/cross_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/cross_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc b/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc index 9ff3e02228..ef1015552d 100644 --- a/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/cwise_ops.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/util/bcast.h" diff --git a/tensorflow/compiler/tf2xla/kernels/cwise_ops.h b/tensorflow/compiler/tf2xla/kernels/cwise_ops.h index 4f92dbc874..a5b870f8db 100644 --- a/tensorflow/compiler/tf2xla/kernels/cwise_ops.h +++ b/tensorflow/compiler/tf2xla/kernels/cwise_ops.h @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/util/bcast.h" diff --git a/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc b/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc index f314920025..12b0e38288 100644 --- a/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/depthtospace_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/diag_op.cc b/tensorflow/compiler/tf2xla/kernels/diag_op.cc index 22cda27567..ed44ad218b 100644 --- a/tensorflow/compiler/tf2xla/kernels/diag_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/diag_op.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc index 3b86ea34c9..a3389d5b90 100644 --- a/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/dynamic_slice_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/compiler/tf2xla/type_util.h" diff --git a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc index 958231505b..cb73053666 100644 --- a/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/dynamic_stitch_op.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/elu_op.cc b/tensorflow/compiler/tf2xla/kernels/elu_op.cc index 81f42e504e..5fdb1d972c 100644 --- a/tensorflow/compiler/tf2xla/kernels/elu_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/elu_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc b/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc index 65d42a302f..c68b0bfd79 100644 --- a/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/extract_image_patches_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc b/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc index 2fd1a34741..cdba6680de 100644 --- a/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/fake_quantize_ops.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/platform/macros.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/fft_ops.cc b/tensorflow/compiler/tf2xla/kernels/fft_ops.cc index b2b00e51e3..80bcef9663 100644 --- a/tensorflow/compiler/tf2xla/kernels/fft_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/fft_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/fill_op.cc b/tensorflow/compiler/tf2xla/kernels/fill_op.cc index 95faa1d058..54b21a2782 100644 --- a/tensorflow/compiler/tf2xla/kernels/fill_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/fill_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op.cc b/tensorflow/compiler/tf2xla/kernels/gather_op.cc index 5f041be5df..35de96e0aa 100644 --- a/tensorflow/compiler/tf2xla/kernels/gather_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/gather_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h b/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h index d898e43b85..92346283c3 100644 --- a/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h +++ b/tensorflow/compiler/tf2xla/kernels/gather_op_helpers.h @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/util/bcast.h" diff --git a/tensorflow/compiler/tf2xla/kernels/if_op.cc b/tensorflow/compiler/tf2xla/kernels/if_op.cc index e2160feba0..ceb2af756c 100644 --- a/tensorflow/compiler/tf2xla/kernels/if_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/if_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/image_ops.cc b/tensorflow/compiler/tf2xla/kernels/image_ops.cc index cb4caf7bcb..6e061ba278 100644 --- a/tensorflow/compiler/tf2xla/kernels/image_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/image_ops.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc b/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc index d6bf92fb3d..8d75624e74 100644 --- a/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/image_resize_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/lib/math/math_util.h" diff --git a/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc b/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc index 9e64711051..f028e361bc 100644 --- a/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/l2loss_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/kernels/no_op.h" diff --git a/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc b/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc index 2fb072f827..a11bbe918f 100644 --- a/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/listdiff_op.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/lib/core/errors.h" diff --git a/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc b/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc index dc934543cb..87ee2d3aed 100644 --- a/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/lrn_ops.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc index aa45b02551..6440770c29 100644 --- a/tensorflow/compiler/tf2xla/kernels/matmul_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matmul_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc index e06c87db7a..8dfd7de591 100644 --- a/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matrix_band_part_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/tensor_shape.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc b/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc index e2ab4b83cf..c0ca881ff8 100644 --- a/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/matrix_set_diag_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc index 529959dbd9..eedfc3c914 100644 --- a/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/mirror_pad_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/util/mirror_pad_mode.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/pack_op.cc b/tensorflow/compiler/tf2xla/kernels/pack_op.cc index 3aed47de26..a9b519d892 100644 --- a/tensorflow/compiler/tf2xla/kernels/pack_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/pack_op.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/pad_op.cc b/tensorflow/compiler/tf2xla/kernels/pad_op.cc index 89fd610bc6..e5937b56c1 100644 --- a/tensorflow/compiler/tf2xla/kernels/pad_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/pad_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc index 2a4c0cab4b..3d506e71e0 100644 --- a/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/pooling_ops.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/util.h" diff --git a/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc b/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc index 2e632e185d..6f4ed496a1 100644 --- a/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/quantize_and_dequantize_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/platform/macros.h" diff --git a/tensorflow/compiler/tf2xla/kernels/random_ops.cc b/tensorflow/compiler/tf2xla/kernels/random_ops.cc index 607cad798a..2da9340625 100644 --- a/tensorflow/compiler/tf2xla/kernels/random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/random_ops.cc @@ -27,7 +27,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" diff --git a/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc b/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc index 23ac45beb7..b11a4ce36d 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduce_window_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compiler.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/framework/function.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc index be7f2bce8c..0d260fa8fc 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops.h b/tensorflow/compiler/tf2xla/kernels/reduction_ops.h index 8333f9b288..466e79828d 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops.h +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops.h @@ -19,7 +19,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_KERNELS_REDUCTION_OPS_H_ #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc index bb8dd3ac90..b52f0a0ab6 100644 --- a/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc +++ b/tensorflow/compiler/tf2xla/kernels/reduction_ops_common.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" diff --git a/tensorflow/compiler/tf2xla/kernels/relu_op.cc b/tensorflow/compiler/tf2xla/kernels/relu_op.cc index f4b804e546..d35777ccb1 100644 --- a/tensorflow/compiler/tf2xla/kernels/relu_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/relu_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/kernels/cwise_ops.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/reshape_op.cc b/tensorflow/compiler/tf2xla/kernels/reshape_op.cc index 354fec9be7..121750a82a 100644 --- a/tensorflow/compiler/tf2xla/kernels/reshape_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reshape_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/retval_op.cc b/tensorflow/compiler/tf2xla/kernels/retval_op.cc index 5be70a4ded..1911e6ea36 100644 --- a/tensorflow/compiler/tf2xla/kernels/retval_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/retval_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/reverse_op.cc b/tensorflow/compiler/tf2xla/kernels/reverse_op.cc index ec15b4cc7a..d962ef4a5f 100644 --- a/tensorflow/compiler/tf2xla/kernels/reverse_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reverse_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc b/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc index c810456f94..03a50ef8a0 100644 --- a/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/reverse_sequence_op.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/tensor_shape.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/scan_ops.cc b/tensorflow/compiler/tf2xla/kernels/scan_ops.cc index 56f237d588..ab094d7dd1 100644 --- a/tensorflow/compiler/tf2xla/kernels/scan_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/scan_ops.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc b/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc index 14709bb6cb..f1f32699fe 100644 --- a/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/scatter_nd_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc index e2ac7da2c2..b22ecb7c6d 100644 --- a/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/segment_reduction_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/select_op.cc b/tensorflow/compiler/tf2xla/kernels/select_op.cc index 5c010c9df2..6ce50efb4a 100644 --- a/tensorflow/compiler/tf2xla/kernels/select_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/select_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/kernels/bounds_check.h" diff --git a/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc b/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc index 6281d6c653..a7f5a8f169 100644 --- a/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/sendrecv_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/shape_op.cc b/tensorflow/compiler/tf2xla/kernels/shape_op.cc index 5798823cd5..4e0cf99d8e 100644 --- a/tensorflow/compiler/tf2xla/kernels/shape_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/shape_op.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/kernels/bounds_check.h" diff --git a/tensorflow/compiler/tf2xla/kernels/slice_op.cc b/tensorflow/compiler/tf2xla/kernels/slice_op.cc index 1864584ade..6adc3c58de 100644 --- a/tensorflow/compiler/tf2xla/kernels/slice_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/slice_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/softmax_op.cc b/tensorflow/compiler/tf2xla/kernels/softmax_op.cc index 60c6a5d349..1d7a63dc31 100644 --- a/tensorflow/compiler/tf2xla/kernels/softmax_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/softmax_op.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/sort_ops.cc b/tensorflow/compiler/tf2xla/kernels/sort_ops.cc index faaf8964ff..aaeeae01cc 100644 --- a/tensorflow/compiler/tf2xla/kernels/sort_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/sort_ops.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc index 8a8525efa1..7327258c31 100644 --- a/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/spacetobatch_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { namespace { diff --git a/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc b/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc index 47d282fe9e..4493539fe3 100644 --- a/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/spacetodepth_op.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/util/tensor_format.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/split_op.cc b/tensorflow/compiler/tf2xla/kernels/split_op.cc index 242638f981..93fc14e9ef 100644 --- a/tensorflow/compiler/tf2xla/kernels/split_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/split_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc index cc4b13d3b9..5412e13547 100644 --- a/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/stateless_random_ops.cc @@ -24,7 +24,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" #include "tensorflow/compiler/xla/client/lib/prng.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/framework/tensor_shape.h" diff --git a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc index c2165ccd86..1062399d91 100644 --- a/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/strided_slice_op.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc index 26326f18b8..be1814d8e3 100644 --- a/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/tensor_array_ops.cc @@ -25,7 +25,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/tf2xla/xla_resource.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/partial_tensor_shape.h" diff --git a/tensorflow/compiler/tf2xla/kernels/tile_ops.cc b/tensorflow/compiler/tf2xla/kernels/tile_ops.cc index c9e5694262..1233a37565 100644 --- a/tensorflow/compiler/tf2xla/kernels/tile_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/tile_ops.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/numeric_op.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/kernels/topk_op.cc b/tensorflow/compiler/tf2xla/kernels/topk_op.cc index 82d4a69777..e73fb283b0 100644 --- a/tensorflow/compiler/tf2xla/kernels/topk_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/topk_op.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/training_ops.cc b/tensorflow/compiler/tf2xla/kernels/training_ops.cc index 98df730249..be5e911386 100644 --- a/tensorflow/compiler/tf2xla/kernels/training_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/training_ops.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/math.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/transpose_op.cc b/tensorflow/compiler/tf2xla/kernels/transpose_op.cc index 6c721c48fe..f9148b3942 100644 --- a/tensorflow/compiler/tf2xla/kernels/transpose_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/transpose_op.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/kernels/bounds_check.h" diff --git a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc index e6ec794cfd..0bdfc05726 100644 --- a/tensorflow/compiler/tf2xla/kernels/unary_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/unary_ops.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/math.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/kernel_def_builder.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc index f951127bb9..8671632976 100644 --- a/tensorflow/compiler/tf2xla/kernels/unpack_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/unpack_op.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/op_kernel.h" #include "tensorflow/core/framework/register_types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/variable_ops.cc b/tensorflow/compiler/tf2xla/kernels/variable_ops.cc index bb27b5d56f..2c92a585f5 100644 --- a/tensorflow/compiler/tf2xla/kernels/variable_ops.cc +++ b/tensorflow/compiler/tf2xla/kernels/variable_ops.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/kernel_def_builder.h" #include "tensorflow/core/framework/types.h" diff --git a/tensorflow/compiler/tf2xla/kernels/while_op.cc b/tensorflow/compiler/tf2xla/kernels/while_op.cc index c653a11029..1e8a376765 100644 --- a/tensorflow/compiler/tf2xla/kernels/while_op.cc +++ b/tensorflow/compiler/tf2xla/kernels/while_op.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/core/framework/function.h" diff --git a/tensorflow/compiler/tf2xla/lib/BUILD b/tensorflow/compiler/tf2xla/lib/BUILD index e35a457f09..cb7a40e23d 100644 --- a/tensorflow/compiler/tf2xla/lib/BUILD +++ b/tensorflow/compiler/tf2xla/lib/BUILD @@ -25,8 +25,8 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:lib", ], ) @@ -44,9 +44,9 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:constants", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:lib", ], ) @@ -59,9 +59,9 @@ cc_library( "//tensorflow/compiler/tf2xla:xla_compiler", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:math", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:protos_all_cc", ], ) @@ -78,12 +78,12 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:math", "//tensorflow/compiler/xla/client/lib:numeric", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:lib", ], ) @@ -100,9 +100,9 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:lib", ], ) @@ -119,10 +119,10 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:numeric", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:lib", ], ) @@ -142,7 +142,7 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -162,8 +162,8 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:util", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:lib", ], ) @@ -200,8 +200,8 @@ cc_library( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:lib", ], ) diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.cc b/tensorflow/compiler/tf2xla/lib/batch_dot.cc index 3c4eec081b..f666d22ea4 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.cc +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/tf2xla/lib/batch_dot.h b/tensorflow/compiler/tf2xla/lib/batch_dot.h index dbba5eaf26..8757b16a1c 100644 --- a/tensorflow/compiler/tf2xla/lib/batch_dot.h +++ b/tensorflow/compiler/tf2xla/lib/batch_dot.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_BATCH_DOT_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_BATCH_DOT_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.cc b/tensorflow/compiler/tf2xla/lib/cholesky.cc index 35b137aa2c..87d73eb3f0 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.cc +++ b/tensorflow/compiler/tf2xla/lib/cholesky.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/tf2xla/lib/cholesky.h b/tensorflow/compiler/tf2xla/lib/cholesky.h index bc1b0ed82f..1bef9bb166 100644 --- a/tensorflow/compiler/tf2xla/lib/cholesky.h +++ b/tensorflow/compiler/tf2xla/lib/cholesky.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_CHOLESKY_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_CHOLESKY_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/qr.cc b/tensorflow/compiler/tf2xla/lib/qr.cc index 9c8ac7af25..fc0c1ee838 100644 --- a/tensorflow/compiler/tf2xla/lib/qr.cc +++ b/tensorflow/compiler/tf2xla/lib/qr.cc @@ -25,7 +25,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/tf2xla/lib/qr.h b/tensorflow/compiler/tf2xla/lib/qr.h index 3aa6a9b075..abd2316ac9 100644 --- a/tensorflow/compiler/tf2xla/lib/qr.h +++ b/tensorflow/compiler/tf2xla/lib/qr.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_QR_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_QR_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/random.cc b/tensorflow/compiler/tf2xla/lib/random.cc index 8ff10fbd3f..5e7cf00ee5 100644 --- a/tensorflow/compiler/tf2xla/lib/random.cc +++ b/tensorflow/compiler/tf2xla/lib/random.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/math.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/status_macros.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/random.h b/tensorflow/compiler/tf2xla/lib/random.h index 2c573fd85b..59fc5d0433 100644 --- a/tensorflow/compiler/tf2xla/lib/random.h +++ b/tensorflow/compiler/tf2xla/lib/random.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_RANDOM_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_RANDOM_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/framework/types.pb.h" diff --git a/tensorflow/compiler/tf2xla/lib/scatter.cc b/tensorflow/compiler/tf2xla/lib/scatter.cc index 739032fef7..ba22eff73a 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.cc +++ b/tensorflow/compiler/tf2xla/lib/scatter.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/tf2xla/lib/scatter.h b/tensorflow/compiler/tf2xla/lib/scatter.h index 452fda565d..13a5f1b850 100644 --- a/tensorflow/compiler/tf2xla/lib/scatter.h +++ b/tensorflow/compiler/tf2xla/lib/scatter.h @@ -18,7 +18,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc index 05dad759df..04fa10108c 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/util.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve.h b/tensorflow/compiler/tf2xla/lib/triangular_solve.h index 9c4314e275..555760b7ef 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve.h +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_TRIANGULAR_SOLVE_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_TRIANGULAR_SOLVE_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc index a29496dec4..aeebf16028 100644 --- a/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc +++ b/tensorflow/compiler/tf2xla/lib/triangular_solve_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/array2d.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" diff --git a/tensorflow/compiler/tf2xla/lib/util.cc b/tensorflow/compiler/tf2xla/lib/util.cc index a6f5d346cb..8b5beba383 100644 --- a/tensorflow/compiler/tf2xla/lib/util.cc +++ b/tensorflow/compiler/tf2xla/lib/util.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/tf2xla/lib/util.h b/tensorflow/compiler/tf2xla/lib/util.h index a139873d32..b4905c9528 100644 --- a/tensorflow/compiler/tf2xla/lib/util.h +++ b/tensorflow/compiler/tf2xla/lib/util.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_TF2XLA_LIB_UTIL_H_ #define TENSORFLOW_COMPILER_TF2XLA_LIB_UTIL_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/lib/gtl/array_slice.h" diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.cc b/tensorflow/compiler/tf2xla/lib/while_loop.cc index 574e70ddee..d64394f140 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.cc +++ b/tensorflow/compiler/tf2xla/lib/while_loop.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/lib/while_loop.h" #include "tensorflow/compiler/tf2xla/lib/util.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/tf2xla/lib/while_loop.h b/tensorflow/compiler/tf2xla/lib/while_loop.h index 69cc70bfaf..9493b1f109 100644 --- a/tensorflow/compiler/tf2xla/lib/while_loop.h +++ b/tensorflow/compiler/tf2xla/lib/while_loop.h @@ -19,7 +19,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/core/lib/core/stringpiece.h" diff --git a/tensorflow/compiler/tf2xla/xla_compilation_device.cc b/tensorflow/compiler/tf2xla/xla_compilation_device.cc index fe7ec633ec..e89f473328 100644 --- a/tensorflow/compiler/tf2xla/xla_compilation_device.cc +++ b/tensorflow/compiler/tf2xla/xla_compilation_device.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/sharding_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/common_runtime/local_device.h" #include "tensorflow/core/framework/device_base.h" #include "tensorflow/core/platform/mem.h" diff --git a/tensorflow/compiler/tf2xla/xla_compilation_device.h b/tensorflow/compiler/tf2xla/xla_compilation_device.h index d0b9e34e16..a6e7882533 100644 --- a/tensorflow/compiler/tf2xla/xla_compilation_device.h +++ b/tensorflow/compiler/tf2xla/xla_compilation_device.h @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/tf2xla/xla_resource.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/common_runtime/local_device.h" #include "tensorflow/core/framework/device_base.h" diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc index 678e209cf6..59dcd0870b 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler.cc @@ -28,7 +28,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/core/common_runtime/device.h" #include "tensorflow/core/common_runtime/executor.h" diff --git a/tensorflow/compiler/tf2xla/xla_context.cc b/tensorflow/compiler/tf2xla/xla_context.cc index 2836cb3df3..b24e3aabbe 100644 --- a/tensorflow/compiler/tf2xla/xla_context.cc +++ b/tensorflow/compiler/tf2xla/xla_context.cc @@ -25,7 +25,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_helpers.h" #include "tensorflow/compiler/tf2xla/xla_op_kernel.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" diff --git a/tensorflow/compiler/tf2xla/xla_context.h b/tensorflow/compiler/tf2xla/xla_context.h index beee7d48e8..3db37afdba 100644 --- a/tensorflow/compiler/tf2xla/xla_context.h +++ b/tensorflow/compiler/tf2xla/xla_context.h @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/xla_compilation_device.h" #include "tensorflow/compiler/tf2xla/xla_compiler.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/tf2xla/xla_helpers.cc b/tensorflow/compiler/tf2xla/xla_helpers.cc index 225da16807..8efb3d55c8 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.cc +++ b/tensorflow/compiler/tf2xla/xla_helpers.cc @@ -26,7 +26,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/framework/tensor.h" diff --git a/tensorflow/compiler/tf2xla/xla_helpers.h b/tensorflow/compiler/tf2xla/xla_helpers.h index d6ca4ab934..e6522157a5 100644 --- a/tensorflow/compiler/tf2xla/xla_helpers.h +++ b/tensorflow/compiler/tf2xla/xla_helpers.h @@ -19,7 +19,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_XLA_HELPERS_H_ #include "tensorflow/compiler/tf2xla/xla_context.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/lib/gtl/array_slice.h" diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.cc b/tensorflow/compiler/tf2xla/xla_op_kernel.cc index 38ec559576..82028c8b9c 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.cc +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/shape_util.h" #include "tensorflow/compiler/tf2xla/type_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/status_macros.h" #include "tensorflow/core/common_runtime/dma_helper.h" diff --git a/tensorflow/compiler/tf2xla/xla_op_kernel.h b/tensorflow/compiler/tf2xla/xla_op_kernel.h index 71990b57d9..ac9dfe3369 100644 --- a/tensorflow/compiler/tf2xla/xla_op_kernel.h +++ b/tensorflow/compiler/tf2xla/xla_op_kernel.h @@ -17,7 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_TF2XLA_XLA_OP_KERNEL_H_ #include "tensorflow/compiler/tf2xla/xla_compiler.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/op_kernel.h" diff --git a/tensorflow/compiler/tf2xla/xla_resource.cc b/tensorflow/compiler/tf2xla/xla_resource.cc index baea814965..7928fa0347 100644 --- a/tensorflow/compiler/tf2xla/xla_resource.cc +++ b/tensorflow/compiler/tf2xla/xla_resource.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/tf2xla/sharding_util.h" #include "tensorflow/compiler/tf2xla/xla_context.h" #include "tensorflow/compiler/tf2xla/xla_helpers.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace tensorflow { diff --git a/tensorflow/compiler/tf2xla/xla_resource.h b/tensorflow/compiler/tf2xla/xla_resource.h index 4de18a7788..2438490be1 100644 --- a/tensorflow/compiler/tf2xla/xla_resource.h +++ b/tensorflow/compiler/tf2xla/xla_resource.h @@ -18,7 +18,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/framework/tensor_shape.h" #include "tensorflow/core/framework/types.pb.h" -- GitLab From 6d14f75b6d2aefc15f4093a2d47666b9496f173b Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 23:26:07 -0700 Subject: [PATCH 429/519] [XLA] Update xla document. The xla_builder and xla_computation have been moved to client/. PiperOrigin-RevId: 206110380 --- .../performance/xla/operation_semantics.md | 84 +++++++++---------- 1 file changed, 42 insertions(+), 42 deletions(-) diff --git a/tensorflow/docs_src/performance/xla/operation_semantics.md b/tensorflow/docs_src/performance/xla/operation_semantics.md index fe9afc4ecb..5f7482f90f 100644 --- a/tensorflow/docs_src/performance/xla/operation_semantics.md +++ b/tensorflow/docs_src/performance/xla/operation_semantics.md @@ -1,7 +1,7 @@ # Operation Semantics The following describes the semantics of operations defined in the -[`XlaBuilder`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) interface. Typically, these operations map one-to-one to operations defined in the RPC interface in [`xla_data.proto`](https://www.tensorflow.org/code/tensorflow/compiler/xla/xla_data.proto). @@ -16,7 +16,7 @@ and familiar names; for example a *vector* is a 1-dimensional array and a ## BatchNormGrad See also -[`XlaBuilder::BatchNormGrad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder::BatchNormGrad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) and [the original batch normalization paper](https://arxiv.org/abs/1502.03167) for a detailed description of the algorithm. @@ -80,7 +80,7 @@ The output type is a tuple of three handles: ## BatchNormInference See also -[`XlaBuilder::BatchNormInference`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder::BatchNormInference`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) and [the original batch normalization paper](https://arxiv.org/abs/1502.03167) for a detailed description of the algorithm. @@ -115,7 +115,7 @@ The output is an n-dimensional, normalized array with the same shape as input ## BatchNormTraining See also -[`XlaBuilder::BatchNormTraining`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder::BatchNormTraining`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) and [`the original batch normalization paper`](https://arxiv.org/abs/1502.03167) for a detailed description of the algorithm. @@ -167,7 +167,7 @@ spatial dimensions using the formulas above. ## BitcastConvertType See also -[`XlaBuilder::BitcastConvertType`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::BitcastConvertType`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). 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 @@ -189,7 +189,7 @@ and destination element types must not be tuples. ## Broadcast See also -[`XlaBuilder::Broadcast`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Broadcast`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Adds dimensions to an array by duplicating the data in the array. @@ -217,7 +217,7 @@ For example, if `operand` is a scalar `f32` with value `2.0f`, and ## Call See also -[`XlaBuilder::Call`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Call`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Invokes a computation with the given arguments. @@ -236,7 +236,7 @@ The arity and types of the `args` must match the parameters of the ## Clamp See also -[`XlaBuilder::Clamp`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Clamp`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Clamps an operand to within the range between a minimum and maximum value. @@ -269,7 +269,7 @@ Clamp(min, operand, max) = s32[3]{0, 5, 6}; ## Collapse See also -[`XlaBuilder::Collapse`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder::Collapse`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) and the @{tf.reshape} operation. Collapses dimensions of an array into one dimension. @@ -332,7 +332,7 @@ then v12 == f32[8x3] {{10, 11, 12}, ## Concatenate See also -[`XlaBuilder::ConcatInDim`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::ConcatInDim`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Concatenate composes an array from multiple array operands. The array is of the same rank as each of the input array operands (which must be of the same rank as @@ -388,7 +388,7 @@ Diagram: ## Conditional See also -[`XlaBuilder::Conditional`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Conditional`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Conditional(pred, true_operand, true_computation, false_operand, false_computation)` @@ -416,7 +416,7 @@ executed depending on the value of `pred`. ## Conv (convolution) See also -[`XlaBuilder::Conv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Conv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). As ConvWithGeneralPadding, but the padding is specified in a short-hand way as either SAME or VALID. SAME padding pads the input (`lhs`) with zeroes so that @@ -426,7 +426,7 @@ account. VALID padding simply means no padding. ## ConvWithGeneralPadding (convolution) See also -[`XlaBuilder::ConvWithGeneralPadding`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::ConvWithGeneralPadding`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Computes a convolution of the kind used in neural networks. Here, a convolution can be thought of as a n-dimensional window moving across a n-dimensional base @@ -538,7 +538,7 @@ for (b, oz, oy, ox) { // output coordinates ## ConvertElementType See also -[`XlaBuilder::ConvertElementType`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::ConvertElementType`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Similar to an element-wise `static_cast` in C++, performs an element-wise conversion operation from a data shape to a target shape. The dimensions must @@ -572,7 +572,7 @@ then b == f32[3]{0.0, 1.0, 2.0} ## CrossReplicaSum See also -[`XlaBuilder::CrossReplicaSum`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::CrossReplicaSum`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Computes a sum across replicas. @@ -607,7 +607,7 @@ than another. ## CustomCall See also -[`XlaBuilder::CustomCall`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::CustomCall`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Call a user-provided function within a computation. @@ -668,7 +668,7 @@ idempotent. ## Dot See also -[`XlaBuilder::Dot`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Dot`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Dot(lhs, rhs)` @@ -697,7 +697,7 @@ multiplications or matrix/matrix multiplications. ## DotGeneral See also -[`XlaBuilder::DotGeneral`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::DotGeneral`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `DotGeneral(lhs, rhs, dimension_numbers)` @@ -784,7 +784,7 @@ non-contracting/non-batch dimension. ## DynamicSlice See also -[`XlaBuilder::DynamicSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::DynamicSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). DynamicSlice extracts a sub-array from the input array at dynamic `start_indices`. The size of the slice in each dimension is passed in @@ -848,7 +848,7 @@ DynamicSlice(b, s, {2, 2}) produces: ## DynamicUpdateSlice See also -[`XlaBuilder::DynamicUpdateSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::DynamicUpdateSlice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). DynamicUpdateSlice generates a result which is the value of the input array `operand`, with a slice `update` overwritten at `start_indices`. @@ -920,7 +920,7 @@ DynamicUpdateSlice(b, u, s) produces: ## Element-wise binary arithmetic operations See also -[`XlaBuilder::Add`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Add`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). A set of element-wise binary arithmetic operations is supported. @@ -965,7 +965,7 @@ shapes of both operands. The semantics are described in detail on the ## Element-wise comparison operations See also -[`XlaBuilder::Eq`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Eq`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). A set of standard element-wise binary comparison operations is supported. Note that standard IEEE 754 floating-point comparison semantics apply when comparing @@ -1051,7 +1051,7 @@ potentially different runtime offset) of an input tensor into an output tensor. ### General Semantics See also -[`XlaBuilder::Gather`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Gather`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). For a more intuitive description, see the "Informal Description" section below. `gather(operand, gather_indices, output_window_dims, elided_window_dims, window_bounds, gather_dims_to_operand_dims)` @@ -1254,7 +1254,7 @@ concatenation of all these rows. ## GetTupleElement See also -[`XlaBuilder::GetTupleElement`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::GetTupleElement`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Indexes into a tuple with a compile-time-constant value. @@ -1275,7 +1275,7 @@ See also @{tf.tuple}. ## Infeed See also -[`XlaBuilder::Infeed`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Infeed`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Infeed(shape)` @@ -1327,7 +1327,7 @@ Arguments | Type | Semantics ## Map See also -[`XlaBuilder::Map`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Map`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Map(operands..., computation)` @@ -1356,7 +1356,7 @@ input arrays to produce the output array. ## Pad See also -[`XlaBuilder::Pad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Pad`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Pad(operand, padding_value, padding_config)` @@ -1395,7 +1395,7 @@ are all 0. The figure below shows examples of different `edge_padding` and ## Recv See also -[`XlaBuilder::Recv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Recv`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Recv(shape, channel_handle)` @@ -1429,7 +1429,7 @@ complete and returns the received data. ## Reduce See also -[`XlaBuilder::Reduce`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Reduce`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Applies a reduction function to an array. @@ -1546,7 +1546,7 @@ Reducing the 3D array over all its dimensions produces the scalar `84`. ## ReducePrecision See also -[`XlaBuilder::ReducePrecision`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::ReducePrecision`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Models the effect of converting floating-point values to a lower-precision format (such as IEEE-FP16) and back to the original format. The number of @@ -1577,7 +1577,7 @@ portion of the conversion is then simply a no-op. ## ReduceWindow See also -[`XlaBuilder::ReduceWindow`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::ReduceWindow`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Applies a reduction function to all elements in each window of the input multi-dimensional array, producing an output multi-dimensional array with the @@ -1660,7 +1660,7 @@ context of [`Reduce`](#reduce) for more details. ## Reshape See also -[`XlaBuilder::Reshape`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h) +[`XlaBuilder::Reshape`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h) and the [`Collapse`](#collapse) operation. Reshapes the dimensions of an array into a new configuration. @@ -1741,7 +1741,7 @@ Reshape(5, {}, {1,1}) == f32[1x1] {{5}}; ## Rev (reverse) See also -[`XlaBuilder::Rev`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Rev`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Rev(operand, dimensions)` @@ -1763,7 +1763,7 @@ the two window dimensions during the gradient computation in neural networks. ## RngNormal See also -[`XlaBuilder::RngNormal`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::RngNormal`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Constructs an output of a given shape with random numbers generated following the $$N(\mu, \sigma)$$ normal distribution. The parameters `mu` and `sigma`, and @@ -1783,7 +1783,7 @@ be scalar valued. ## RngUniform See also -[`XlaBuilder::RngUniform`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::RngUniform`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Constructs an output of a given shape with random numbers generated following the uniform distribution over the interval $$[a,b)$$. The parameters and output @@ -1804,7 +1804,7 @@ is implementation-defined. ## Select See also -[`XlaBuilder::Select`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Select`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Constructs an output array from elements of two input arrays, based on the values of a predicate array. @@ -1855,7 +1855,7 @@ the same shape!) then `pred` has to be a scalar of type `PRED`. ## SelectAndScatter See also -[`XlaBuilder::SelectAndScatter`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::SelectAndScatter`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). This operation can be considered as a composite operation that first computes `ReduceWindow` on the `operand` array to select an element from each window, and @@ -1935,7 +1935,7 @@ context of [`Reduce`](#reduce) for more details. ## Send See also -[`XlaBuilder::Send`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Send`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `Send(operand, channel_handle)` @@ -1990,7 +1990,7 @@ computations. For example, below schedules lead to deadlocks. ## Slice See also -[`XlaBuilder::Slice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Slice`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). Slicing extracts a sub-array from the input array. The sub-array is of the same rank as the input and contains the values inside a bounding box within the input @@ -2039,7 +2039,7 @@ Slice(b, {2, 1}, {4, 3}) produces: ## Sort See also -[`XlaBuilder::Sort`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Sort`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). There are two versions of the Sort instruction: a single-operand and a two-operand version. @@ -2099,7 +2099,7 @@ This is the same as Reshape(operand, permutation, ## Tuple See also -[`XlaBuilder::Tuple`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::Tuple`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). A tuple containing a variable number of data handles, each of which has its own shape. @@ -2118,7 +2118,7 @@ Tuples can be deconstructed (accessed) via the [`GetTupleElement`] ## While See also -[`XlaBuilder::While`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_client/xla_builder.h). +[`XlaBuilder::While`](https://www.tensorflow.org/code/tensorflow/compiler/xla/client/xla_builder.h). `While(condition, body, init)` -- GitLab From fae3a601193c9a9e565e0a3a2b82e9e849abe49d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Wed, 25 Jul 2018 23:42:29 -0700 Subject: [PATCH 430/519] [XLA] This is a step to incrementally move client/xla_client/* to client/. PiperOrigin-RevId: 206111380 --- tensorflow/compiler/xla/BUILD | 2 +- tensorflow/compiler/xla/client/lib/BUILD | 18 +-- .../compiler/xla/client/lib/arithmetic.cc | 2 +- .../compiler/xla/client/lib/arithmetic.h | 2 +- .../compiler/xla/client/lib/constants.h | 2 +- .../compiler/xla/client/lib/constants_test.cc | 2 +- tensorflow/compiler/xla/client/lib/math.h | 2 +- .../compiler/xla/client/lib/math_test.cc | 2 +- tensorflow/compiler/xla/client/lib/numeric.h | 2 +- .../compiler/xla/client/lib/numeric_test.cc | 2 +- tensorflow/compiler/xla/client/lib/prng.cc | 2 +- tensorflow/compiler/xla/client/lib/prng.h | 2 +- tensorflow/compiler/xla/client/lib/testing.cc | 3 +- tensorflow/compiler/xla/python/BUILD | 2 +- .../xla/python/local_computation_builder.cc | 3 +- .../xla/python/local_computation_builder.h | 2 +- tensorflow/compiler/xla/reference_util.cc | 2 +- tensorflow/compiler/xla/rpc/BUILD | 2 +- .../compiler/xla/rpc/grpc_client_test.cc | 2 +- tensorflow/compiler/xla/service/BUILD | 8 +- tensorflow/compiler/xla/service/cpu/BUILD | 2 +- .../xla/service/cpu/sample_harness.cc | 2 +- .../compiler/xla/service/cpu/tests/BUILD | 2 +- .../xla/service/cpu/tests/cpu_infeed_test.cc | 2 +- .../compiler/xla/service/gpu/tests/BUILD | 2 +- .../xla/service/gpu/tests/infeed_test.cc | 2 +- .../xla/service/hlo_cost_analysis_test.cc | 2 +- .../xla/service/hlo_evaluator_test.cc | 2 +- .../xla/service/transpose_folding_test.cc | 2 +- tensorflow/compiler/xla/tests/BUILD | 146 +++++++++--------- .../xla/tests/array_elementwise_ops_test.cc | 2 +- .../compiler/xla/tests/axpy_simple_test.cc | 2 +- .../tests/bad_rng_shape_validation_test.cc | 2 +- .../xla/tests/batch_normalization_test.cc | 2 +- .../compiler/xla/tests/bfloat16_test.cc | 2 +- .../compiler/xla/tests/binop_scaling_test.cc | 2 +- .../xla/tests/bitcast_convert_test.cc | 2 +- .../xla/tests/broadcast_simple_test.cc | 2 +- tensorflow/compiler/xla/tests/call_test.cc | 2 +- .../xla/tests/check_execution_arity_test.cc | 2 +- .../xla/tests/client_library_test_base.cc | 3 +- .../xla/tests/client_library_test_base.h | 2 +- tensorflow/compiler/xla/tests/client_test.cc | 2 +- .../xla/tests/compilation_cache_test.cc | 2 +- .../xla/tests/compute_constant_test.cc | 2 +- tensorflow/compiler/xla/tests/concat_test.cc | 2 +- .../compiler/xla/tests/conditional_test.cc | 2 +- .../compiler/xla/tests/constants_test.cc | 2 +- tensorflow/compiler/xla/tests/convert_test.cc | 2 +- .../convolution_dimension_numbers_test.cc | 2 +- .../compiler/xla/tests/convolution_test.cc | 2 +- .../xla/tests/convolution_variants_test.cc | 2 +- tensorflow/compiler/xla/tests/copy_test.cc | 2 +- .../compiler/xla/tests/custom_call_test.cc | 2 +- .../compiler/xla/tests/deallocation_test.cc | 2 +- .../xla/tests/deconstruct_tuple_test.cc | 2 +- .../compiler/xla/tests/deep_graph_test.cc | 2 +- .../compiler/xla/tests/dot_operation_test.cc | 2 +- .../compiler/xla/tests/dynamic_ops_test.cc | 2 +- .../xla/tests/execution_profile_test.cc | 2 +- .../exhaustive_f32_elementwise_op_test.cc | 2 +- .../compiler/xla/tests/floor_ceil_test.cc | 2 +- tensorflow/compiler/xla/tests/fmax_test.cc | 2 +- tensorflow/compiler/xla/tests/fusion_test.cc | 2 +- .../xla/tests/gather_operation_test.cc | 3 +- tensorflow/compiler/xla/tests/half_test.cc | 2 +- .../compiler/xla/tests/hlo_metadata_test.cc | 2 +- .../xla/tests/local_client_allocation_test.cc | 2 +- .../xla/tests/local_client_aot_test_helper.cc | 2 +- .../xla/tests/local_client_execute_test.cc | 2 +- tensorflow/compiler/xla/tests/log_test.cc | 2 +- tensorflow/compiler/xla/tests/map_test.cc | 2 +- .../xla/tests/matrix_ops_simple_test.cc | 2 +- .../xla/tests/multidimensional_slice_test.cc | 2 +- tensorflow/compiler/xla/tests/pad_test.cc | 3 +- tensorflow/compiler/xla/tests/params_test.cc | 2 +- tensorflow/compiler/xla/tests/pred_test.cc | 2 +- tensorflow/compiler/xla/tests/prng_test.cc | 2 +- .../xla/tests/query_inferred_shape_test.cc | 2 +- .../xla/tests/reduce_precision_test.cc | 2 +- tensorflow/compiler/xla/tests/reduce_test.cc | 2 +- .../compiler/xla/tests/reduce_window_test.cc | 2 +- tensorflow/compiler/xla/tests/replay_test.cc | 2 +- .../compiler/xla/tests/reshape_motion_test.cc | 2 +- tensorflow/compiler/xla/tests/reshape_test.cc | 2 +- tensorflow/compiler/xla/tests/reverse_test.cc | 2 +- .../xla/tests/scalar_computations_test.cc | 2 +- .../xla/tests/select_and_scatter_test.cc | 2 +- tensorflow/compiler/xla/tests/select_test.cc | 2 +- tensorflow/compiler/xla/tests/slice_test.cc | 2 +- .../compiler/xla/tests/test_utils_test.cc | 2 +- .../compiler/xla/tests/transpose_test.cc | 2 +- tensorflow/compiler/xla/tests/tuple_test.cc | 2 +- .../compiler/xla/tests/unary_op_test.cc | 2 +- .../xla/tests/vector_ops_reduce_test.cc | 2 +- .../xla/tests/vector_ops_simple_test.cc | 2 +- tensorflow/compiler/xla/tests/while_test.cc | 2 +- .../xla/tests/xla_hlo_profile_test.cc | 2 +- 98 files changed, 180 insertions(+), 187 deletions(-) diff --git a/tensorflow/compiler/xla/BUILD b/tensorflow/compiler/xla/BUILD index f1c383fd9e..fdf13bb18c 100644 --- a/tensorflow/compiler/xla/BUILD +++ b/tensorflow/compiler/xla/BUILD @@ -636,7 +636,7 @@ cc_library( ":window_util", ":xla_data_proto", "//tensorflow/compiler/xla/client:padding", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_evaluator", "//tensorflow/compiler/xla/service:shape_inference", diff --git a/tensorflow/compiler/xla/client/lib/BUILD b/tensorflow/compiler/xla/client/lib/BUILD index 45506986c8..789daf4728 100644 --- a/tensorflow/compiler/xla/client/lib/BUILD +++ b/tensorflow/compiler/xla/client/lib/BUILD @@ -29,8 +29,8 @@ cc_library( "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:lib", ], ) @@ -45,7 +45,7 @@ cc_library( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", ], ) @@ -58,7 +58,7 @@ xla_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], @@ -72,7 +72,7 @@ cc_library( ":constants", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:status_macros", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", ], ) @@ -86,7 +86,7 @@ xla_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], @@ -101,7 +101,7 @@ cc_library( ":constants", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/core:lib", ], ) @@ -115,7 +115,7 @@ xla_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], @@ -131,7 +131,7 @@ cc_library( ":numeric", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/core:lib", ], ) @@ -150,8 +150,8 @@ cc_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client", "//tensorflow/compiler/xla/client:global_data", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/core:lib", ], diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.cc b/tensorflow/compiler/xla/client/lib/arithmetic.cc index 1872925aba..9225b1acd6 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.cc +++ b/tensorflow/compiler/xla/client/lib/arithmetic.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/xla/client/lib/arithmetic.h b/tensorflow/compiler/xla/client/lib/arithmetic.h index 80d3f8b95a..632e8cc8bc 100644 --- a/tensorflow/compiler/xla/client/lib/arithmetic.h +++ b/tensorflow/compiler/xla/client/lib/arithmetic.h @@ -18,7 +18,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/xla/client/lib/constants.h b/tensorflow/compiler/xla/client/lib/constants.h index b47f5243f0..0c8a9b8cc0 100644 --- a/tensorflow/compiler/xla/client/lib/constants.h +++ b/tensorflow/compiler/xla/client/lib/constants.h @@ -18,7 +18,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/xla/client/lib/constants_test.cc b/tensorflow/compiler/xla/client/lib/constants_test.cc index f1e3439862..f4320f65c1 100644 --- a/tensorflow/compiler/xla/client/lib/constants_test.cc +++ b/tensorflow/compiler/xla/client/lib/constants_test.cc @@ -14,7 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/lib/constants.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" diff --git a/tensorflow/compiler/xla/client/lib/math.h b/tensorflow/compiler/xla/client/lib/math.h index d003d529cc..13db232556 100644 --- a/tensorflow/compiler/xla/client/lib/math.h +++ b/tensorflow/compiler/xla/client/lib/math.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_MATH_H_ #define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_MATH_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" namespace xla { diff --git a/tensorflow/compiler/xla/client/lib/math_test.cc b/tensorflow/compiler/xla/client/lib/math_test.cc index 1df287d7db..14c259a7fa 100644 --- a/tensorflow/compiler/xla/client/lib/math_test.cc +++ b/tensorflow/compiler/xla/client/lib/math_test.cc @@ -14,7 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/lib/math.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" diff --git a/tensorflow/compiler/xla/client/lib/numeric.h b/tensorflow/compiler/xla/client/lib/numeric.h index 212f658313..efd8cdc257 100644 --- a/tensorflow/compiler/xla/client/lib/numeric.h +++ b/tensorflow/compiler/xla/client/lib/numeric.h @@ -16,7 +16,7 @@ limitations under the License. #ifndef TENSORFLOW_COMPILER_XLA_CLIENT_LIB_NUMERIC_H_ #define TENSORFLOW_COMPILER_XLA_CLIENT_LIB_NUMERIC_H_ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/xla/client/lib/numeric_test.cc b/tensorflow/compiler/xla/client/lib/numeric_test.cc index f56cadc547..8a96ec68d2 100644 --- a/tensorflow/compiler/xla/client/lib/numeric_test.cc +++ b/tensorflow/compiler/xla/client/lib/numeric_test.cc @@ -14,7 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/test.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" diff --git a/tensorflow/compiler/xla/client/lib/prng.cc b/tensorflow/compiler/xla/client/lib/prng.cc index 299a6ac2b6..3a744148fb 100644 --- a/tensorflow/compiler/xla/client/lib/prng.cc +++ b/tensorflow/compiler/xla/client/lib/prng.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/constants.h" #include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/lib/numeric.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/util.h" #include "tensorflow/core/lib/core/casts.h" diff --git a/tensorflow/compiler/xla/client/lib/prng.h b/tensorflow/compiler/xla/client/lib/prng.h index ac86390239..ad000b1fa1 100644 --- a/tensorflow/compiler/xla/client/lib/prng.h +++ b/tensorflow/compiler/xla/client/lib/prng.h @@ -18,7 +18,7 @@ limitations under the License. #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/xla_data.pb.h" namespace xla { diff --git a/tensorflow/compiler/xla/client/lib/testing.cc b/tensorflow/compiler/xla/client/lib/testing.cc index 2de65016dd..b1a776b8b8 100644 --- a/tensorflow/compiler/xla/client/lib/testing.cc +++ b/tensorflow/compiler/xla/client/lib/testing.cc @@ -15,8 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/testing.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/python/BUILD b/tensorflow/compiler/xla/python/BUILD index e26e35eb11..c8f2d65c22 100644 --- a/tensorflow/compiler/xla/python/BUILD +++ b/tensorflow/compiler/xla/python/BUILD @@ -53,9 +53,9 @@ cc_library( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:executable_build_options", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:math", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/core:framework_lite", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/python/local_computation_builder.cc b/tensorflow/compiler/xla/python/local_computation_builder.cc index fbcf0f1969..434d78d78d 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.cc +++ b/tensorflow/compiler/xla/python/local_computation_builder.cc @@ -15,8 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/python/local_computation_builder.h" #include "tensorflow/compiler/xla/client/lib/math.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/executable_run_options.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/util.h" diff --git a/tensorflow/compiler/xla/python/local_computation_builder.h b/tensorflow/compiler/xla/python/local_computation_builder.h index 57da7e53d5..545aa63f9d 100644 --- a/tensorflow/compiler/xla/python/local_computation_builder.h +++ b/tensorflow/compiler/xla/python/local_computation_builder.h @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/executable_build_options.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" #include "tensorflow/compiler/xla/xla_data.pb.h" diff --git a/tensorflow/compiler/xla/reference_util.cc b/tensorflow/compiler/xla/reference_util.cc index 6397f1f479..a803520876 100644 --- a/tensorflow/compiler/xla/reference_util.cc +++ b/tensorflow/compiler/xla/reference_util.cc @@ -18,7 +18,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/service/cpu/runtime_single_threaded_matmul.h" #include "tensorflow/compiler/xla/service/hlo_evaluator.h" diff --git a/tensorflow/compiler/xla/rpc/BUILD b/tensorflow/compiler/xla/rpc/BUILD index 0b1cec1925..44b22a5586 100644 --- a/tensorflow/compiler/xla/rpc/BUILD +++ b/tensorflow/compiler/xla/rpc/BUILD @@ -56,7 +56,7 @@ tf_cc_test( ":grpc_stub", "//tensorflow:grpc++", "//tensorflow/compiler/xla/client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/core:framework_internal", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/rpc/grpc_client_test.cc b/tensorflow/compiler/xla/rpc/grpc_client_test.cc index 90efee50b4..6788676181 100644 --- a/tensorflow/compiler/xla/rpc/grpc_client_test.cc +++ b/tensorflow/compiler/xla/rpc/grpc_client_test.cc @@ -24,7 +24,7 @@ limitations under the License. #include "grpcpp/security/credentials.h" #include "tensorflow/compiler/xla/client/client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/rpc/grpc_stub.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/core/lib/io/path.h" diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index 6c140caab7..cfab7622c3 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -256,7 +256,7 @@ tf_cc_test( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo_element_type_converter", "//tensorflow/compiler/xla/tests:hlo_verified_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1664,8 +1664,8 @@ tf_cc_test( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -2671,7 +2671,7 @@ tf_cc_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/service/gpu:ir_emission_utils", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -2708,7 +2708,7 @@ tf_cc_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index bcac65ecda..6c997a068d 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -363,8 +363,8 @@ tf_cc_binary( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/core:lib", ], ) diff --git a/tensorflow/compiler/xla/service/cpu/sample_harness.cc b/tensorflow/compiler/xla/service/cpu/sample_harness.cc index eb83432f57..f227e4ae13 100644 --- a/tensorflow/compiler/xla/service/cpu/sample_harness.cc +++ b/tensorflow/compiler/xla/service/cpu/sample_harness.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/service/cpu/tests/BUILD b/tensorflow/compiler/xla/service/cpu/tests/BUILD index e6d25680b5..181cec3cdd 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/BUILD +++ b/tensorflow/compiler/xla/service/cpu/tests/BUILD @@ -135,9 +135,9 @@ tf_cc_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:cpu_plugin", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", diff --git a/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc b/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc index be3fae5161..c433bddc84 100644 --- a/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc +++ b/tensorflow/compiler/xla/service/cpu/tests/cpu_infeed_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/service/gpu/tests/BUILD b/tensorflow/compiler/xla/service/gpu/tests/BUILD index 686c3c16c9..4fad3f46cf 100644 --- a/tensorflow/compiler/xla/service/gpu/tests/BUILD +++ b/tensorflow/compiler/xla/service/gpu/tests/BUILD @@ -111,8 +111,8 @@ tf_cc_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/core:lib", diff --git a/tensorflow/compiler/xla/service/gpu/tests/infeed_test.cc b/tensorflow/compiler/xla/service/gpu/tests/infeed_test.cc index ba5cd2d84d..9072b30317 100644 --- a/tensorflow/compiler/xla/service/gpu/tests/infeed_test.cc +++ b/tensorflow/compiler/xla/service/gpu/tests/infeed_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/test_helpers.h" diff --git a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc index b2241cd423..2c854eea18 100644 --- a/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc +++ b/tensorflow/compiler/xla/service/hlo_cost_analysis_test.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/hlo_module.h" #include "tensorflow/compiler/xla/service/local_service.h" diff --git a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc index 5f575b24a1..cba72469ce 100644 --- a/tensorflow/compiler/xla/service/hlo_evaluator_test.cc +++ b/tensorflow/compiler/xla/service/hlo_evaluator_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" diff --git a/tensorflow/compiler/xla/service/transpose_folding_test.cc b/tensorflow/compiler/xla/service/transpose_folding_test.cc index 7051a4cf51..58f767e913 100644 --- a/tensorflow/compiler/xla/service/transpose_folding_test.cc +++ b/tensorflow/compiler/xla/service/transpose_folding_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" diff --git a/tensorflow/compiler/xla/tests/BUILD b/tensorflow/compiler/xla/tests/BUILD index 39dec5b2e0..42d52aee78 100644 --- a/tensorflow/compiler/xla/tests/BUILD +++ b/tensorflow/compiler/xla/tests/BUILD @@ -154,8 +154,8 @@ tf_cc_binary( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/client:client_library", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service/cpu:cpu_compiler", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/core:lib", @@ -192,8 +192,8 @@ cc_library( "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:interpreter_plugin", # reference backend "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -290,8 +290,8 @@ xla_test( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -314,8 +314,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", @@ -334,8 +334,8 @@ xla_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", @@ -356,9 +356,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -376,8 +376,8 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:platform_util", "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/compiler/xla/tests:client_library_test_base", @@ -396,8 +396,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -420,9 +420,9 @@ xla_test( "//tensorflow/compiler/xla:xla_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -446,8 +446,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -465,9 +465,9 @@ xla_test( deps = [ "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", @@ -484,8 +484,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -502,8 +502,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -520,9 +520,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -544,8 +544,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -563,8 +563,8 @@ xla_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -587,8 +587,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -613,8 +613,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -639,7 +639,7 @@ xla_test( deps = [ ":client_library_test_base", ":literal_test_util", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", ], @@ -659,7 +659,7 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:reduce_precision_insertion", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -682,8 +682,8 @@ xla_test( "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -703,8 +703,7 @@ xla_test( "//tensorflow/compiler/xla:execution_options_util", "//tensorflow/compiler/xla:status_macros", "//tensorflow/compiler/xla:test", - "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], @@ -727,8 +726,8 @@ xla_test( "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -751,8 +750,8 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -775,8 +774,8 @@ xla_test( "//tensorflow/compiler/xla:literal_util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -797,7 +796,7 @@ CONVOLUTION_TEST_DEPS = [ "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -840,8 +839,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -864,8 +863,8 @@ xla_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -893,10 +892,10 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:math", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -926,9 +925,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -952,8 +951,8 @@ xla_test( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -974,7 +973,7 @@ xla_test( "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -993,8 +992,8 @@ xla_test( "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array3d", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1015,7 +1014,7 @@ xla_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:computation_placer", "//tensorflow/compiler/xla/service:device_memory_allocator", "//tensorflow/compiler/xla/service:local_service", @@ -1046,8 +1045,8 @@ xla_test( "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1067,9 +1066,9 @@ xla_test( "//tensorflow/compiler/xla:array3d", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1098,9 +1097,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1125,9 +1124,9 @@ xla_test_library( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1166,9 +1165,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", "//tensorflow/compiler/xla/client:padding", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1189,7 +1188,7 @@ xla_test( "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/tests:hlo_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1243,8 +1242,8 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla:xla_data_proto", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1262,7 +1261,7 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service/cpu:custom_call_target_registry", "//tensorflow/compiler/xla/tests:client_library_test_base", @@ -1285,8 +1284,8 @@ xla_test( "//tensorflow/compiler/xla:array4d", "//tensorflow/compiler/xla:reference_util", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1308,8 +1307,8 @@ xla_test( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1329,9 +1328,8 @@ xla_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client:xla_computation", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1348,8 +1346,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1365,8 +1363,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1390,8 +1388,8 @@ xla_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -1411,8 +1409,8 @@ xla_test( "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:lib", @@ -1441,8 +1439,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1461,7 +1459,7 @@ xla_test( "//tensorflow/compiler/xla:array2d", "//tensorflow/compiler/xla:array4d", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1484,9 +1482,9 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", "//tensorflow/compiler/xla/client/lib:arithmetic", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1510,8 +1508,8 @@ xla_test( "//tensorflow/compiler/xla:test", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1530,8 +1528,8 @@ xla_test( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1552,8 +1550,8 @@ xla_test( "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:hlo_test_base", @@ -1575,7 +1573,7 @@ xla_test( "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1596,8 +1594,8 @@ xla_test( "//tensorflow/compiler/xla:xla_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -1615,8 +1613,8 @@ xla_test( ], deps = [ "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1638,8 +1636,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:global_data", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1659,8 +1657,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:test_utils", @@ -1676,8 +1674,8 @@ xla_test( deps = [ ":client_library_test_base", "//tensorflow/compiler/xla/client:global_data", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", ], @@ -1690,8 +1688,8 @@ xla_test( deps = [ ":client_library_test_base", "//tensorflow/compiler/xla/client:global_data", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", ], @@ -1711,8 +1709,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:hlo_proto", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1796,8 +1794,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/service:hlo_runner", @@ -1824,8 +1822,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:hlo_runner", "//tensorflow/compiler/xla/service:platform_util", @@ -1861,8 +1859,8 @@ xla_test( "//tensorflow/compiler/xla:literal", "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:local_service", "//tensorflow/compiler/xla/service:shaped_buffer", "//tensorflow/compiler/xla/tests:literal_test_util", @@ -1889,8 +1887,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:client_library", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:device_memory_allocator", "//tensorflow/compiler/xla/service:local_service", "//tensorflow/compiler/xla/service:platform_util", @@ -1925,7 +1923,7 @@ tf_cc_test( ":local_client_test_base", "//tensorflow/compiler/xla:test_helpers", "//tensorflow/compiler/xla/client:local_client", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/service:cpu_plugin", "//tensorflow/compiler/xla/service:local_service", "//tensorflow/core:test_main", @@ -1967,8 +1965,8 @@ xla_test( "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/compiler/xla/client:global_data", "//tensorflow/compiler/xla/client:local_client", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:literal_test_util", "//tensorflow/compiler/xla/tests:xla_internal_test_main", @@ -1981,7 +1979,7 @@ xla_test( name = "deep_graph_test", srcs = ["deep_graph_test.cc"], deps = [ - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/tests:client_library_test_base", "//tensorflow/compiler/xla/tests:xla_internal_test_main", ], @@ -2067,8 +2065,8 @@ xla_test( ":local_client_test_base", ":test_utils", "//tensorflow/compiler/xla:shape_util", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/compiler/xla/client:xla_computation", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", "//tensorflow/compiler/xla/service:hlo_parser", "//tensorflow/compiler/xla/tests:xla_internal_test_main", "//tensorflow/core:test", @@ -2089,7 +2087,7 @@ xla_test( ":client_library_test_base", ":literal_test_util", ":xla_internal_test_main", - "//tensorflow/compiler/xla/client/xla_client:xla_builder", + "//tensorflow/compiler/xla/client:xla_builder", "//tensorflow/core:lib", "//tensorflow/core:test", ], diff --git a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc index 3ae96fa1bc..74f2e36f82 100644 --- a/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc +++ b/tensorflow/compiler/xla/tests/array_elementwise_ops_test.cc @@ -24,7 +24,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/axpy_simple_test.cc b/tensorflow/compiler/xla/tests/axpy_simple_test.cc index 8d15b7841b..caeb0bf49a 100644 --- a/tensorflow/compiler/xla/tests/axpy_simple_test.cc +++ b/tensorflow/compiler/xla/tests/axpy_simple_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.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" diff --git a/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc b/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc index 71dbe4f0b6..af0b852239 100644 --- a/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc +++ b/tensorflow/compiler/xla/tests/bad_rng_shape_validation_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" diff --git a/tensorflow/compiler/xla/tests/batch_normalization_test.cc b/tensorflow/compiler/xla/tests/batch_normalization_test.cc index 033382708a..d372d1ca43 100644 --- a/tensorflow/compiler/xla/tests/batch_normalization_test.cc +++ b/tensorflow/compiler/xla/tests/batch_normalization_test.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/lib/math.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" diff --git a/tensorflow/compiler/xla/tests/bfloat16_test.cc b/tensorflow/compiler/xla/tests/bfloat16_test.cc index 747c82b502..6c20f654fe 100644 --- a/tensorflow/compiler/xla/tests/bfloat16_test.cc +++ b/tensorflow/compiler/xla/tests/bfloat16_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" diff --git a/tensorflow/compiler/xla/tests/binop_scaling_test.cc b/tensorflow/compiler/xla/tests/binop_scaling_test.cc index 20cb989751..0d7a3aa46a 100644 --- a/tensorflow/compiler/xla/tests/binop_scaling_test.cc +++ b/tensorflow/compiler/xla/tests/binop_scaling_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" diff --git a/tensorflow/compiler/xla/tests/bitcast_convert_test.cc b/tensorflow/compiler/xla/tests/bitcast_convert_test.cc index d531e8fa82..c6b5108fe9 100644 --- a/tensorflow/compiler/xla/tests/bitcast_convert_test.cc +++ b/tensorflow/compiler/xla/tests/bitcast_convert_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" diff --git a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc index 50dd574624..1d28e85b16 100644 --- a/tensorflow/compiler/xla/tests/broadcast_simple_test.cc +++ b/tensorflow/compiler/xla/tests/broadcast_simple_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/call_test.cc b/tensorflow/compiler/xla/tests/call_test.cc index 05c1c361bb..b1d18210ea 100644 --- a/tensorflow/compiler/xla/tests/call_test.cc +++ b/tensorflow/compiler/xla/tests/call_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" diff --git a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc index 0bc8facfe2..a4eb57fc7b 100644 --- a/tensorflow/compiler/xla/tests/check_execution_arity_test.cc +++ b/tensorflow/compiler/xla/tests/check_execution_arity_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.cc b/tensorflow/compiler/xla/tests/client_library_test_base.cc index 515c0201d1..59d917054b 100644 --- a/tensorflow/compiler/xla/tests/client_library_test_base.cc +++ b/tensorflow/compiler/xla/tests/client_library_test_base.cc @@ -19,8 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" diff --git a/tensorflow/compiler/xla/tests/client_library_test_base.h b/tensorflow/compiler/xla/tests/client_library_test_base.h index edc1ba8a57..4a6e8a3124 100644 --- a/tensorflow/compiler/xla/tests/client_library_test_base.h +++ b/tensorflow/compiler/xla/tests/client_library_test_base.h @@ -26,7 +26,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/global_data.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" diff --git a/tensorflow/compiler/xla/tests/client_test.cc b/tensorflow/compiler/xla/tests/client_test.cc index f97008bee2..c898dacf48 100644 --- a/tensorflow/compiler/xla/tests/client_test.cc +++ b/tensorflow/compiler/xla/tests/client_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/xla/tests/compilation_cache_test.cc b/tensorflow/compiler/xla/tests/compilation_cache_test.cc index 2b407ed263..7c52c9fbbb 100644 --- a/tensorflow/compiler/xla/tests/compilation_cache_test.cc +++ b/tensorflow/compiler/xla/tests/compilation_cache_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/compute_constant_test.cc b/tensorflow/compiler/xla/tests/compute_constant_test.cc index 672fb06de6..5a06d061f0 100644 --- a/tensorflow/compiler/xla/tests/compute_constant_test.cc +++ b/tensorflow/compiler/xla/tests/compute_constant_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/global_data.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" diff --git a/tensorflow/compiler/xla/tests/concat_test.cc b/tensorflow/compiler/xla/tests/concat_test.cc index e63d2480b6..be017477d8 100644 --- a/tensorflow/compiler/xla/tests/concat_test.cc +++ b/tensorflow/compiler/xla/tests/concat_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/conditional_test.cc b/tensorflow/compiler/xla/tests/conditional_test.cc index d9d42bf061..b27c1044ba 100644 --- a/tensorflow/compiler/xla/tests/conditional_test.cc +++ b/tensorflow/compiler/xla/tests/conditional_test.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" diff --git a/tensorflow/compiler/xla/tests/constants_test.cc b/tensorflow/compiler/xla/tests/constants_test.cc index 71d72a9828..4937574831 100644 --- a/tensorflow/compiler/xla/tests/constants_test.cc +++ b/tensorflow/compiler/xla/tests/constants_test.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" diff --git a/tensorflow/compiler/xla/tests/convert_test.cc b/tensorflow/compiler/xla/tests/convert_test.cc index 0fb6853e3f..1adc68cc48 100644 --- a/tensorflow/compiler/xla/tests/convert_test.cc +++ b/tensorflow/compiler/xla/tests/convert_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" diff --git a/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc index 944366410b..7b6bbc4f57 100644 --- a/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc +++ b/tensorflow/compiler/xla/tests/convolution_dimension_numbers_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/convolution_test.cc b/tensorflow/compiler/xla/tests/convolution_test.cc index a8b8f74ca9..5ed8122e00 100644 --- a/tensorflow/compiler/xla/tests/convolution_test.cc +++ b/tensorflow/compiler/xla/tests/convolution_test.cc @@ -23,7 +23,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" diff --git a/tensorflow/compiler/xla/tests/convolution_variants_test.cc b/tensorflow/compiler/xla/tests/convolution_variants_test.cc index 8792e7781b..6784c16715 100644 --- a/tensorflow/compiler/xla/tests/convolution_variants_test.cc +++ b/tensorflow/compiler/xla/tests/convolution_variants_test.cc @@ -27,7 +27,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" diff --git a/tensorflow/compiler/xla/tests/copy_test.cc b/tensorflow/compiler/xla/tests/copy_test.cc index 1dc6ff0f4f..5ef273e5a2 100644 --- a/tensorflow/compiler/xla/tests/copy_test.cc +++ b/tensorflow/compiler/xla/tests/copy_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/array2d.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/hlo_computation.h" diff --git a/tensorflow/compiler/xla/tests/custom_call_test.cc b/tensorflow/compiler/xla/tests/custom_call_test.cc index 90f3d1b874..13c777835e 100644 --- a/tensorflow/compiler/xla/tests/custom_call_test.cc +++ b/tensorflow/compiler/xla/tests/custom_call_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/service/cpu/custom_call_target_registry.h" diff --git a/tensorflow/compiler/xla/tests/deallocation_test.cc b/tensorflow/compiler/xla/tests/deallocation_test.cc index 062b8cb8c4..5f234f36a8 100644 --- a/tensorflow/compiler/xla/tests/deallocation_test.cc +++ b/tensorflow/compiler/xla/tests/deallocation_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" diff --git a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc index 6795130cd1..2db6503afa 100644 --- a/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc +++ b/tensorflow/compiler/xla/tests/deconstruct_tuple_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/deep_graph_test.cc b/tensorflow/compiler/xla/tests/deep_graph_test.cc index 810947ab01..3f3e8ab712 100644 --- a/tensorflow/compiler/xla/tests/deep_graph_test.cc +++ b/tensorflow/compiler/xla/tests/deep_graph_test.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" namespace xla { diff --git a/tensorflow/compiler/xla/tests/dot_operation_test.cc b/tensorflow/compiler/xla/tests/dot_operation_test.cc index d86fd7cc2d..cfd36abf47 100644 --- a/tensorflow/compiler/xla/tests/dot_operation_test.cc +++ b/tensorflow/compiler/xla/tests/dot_operation_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc index 88ac96d6b0..7f6f203a1b 100644 --- a/tensorflow/compiler/xla/tests/dynamic_ops_test.cc +++ b/tensorflow/compiler/xla/tests/dynamic_ops_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" #include "tensorflow/compiler/xla/service/local_service.h" diff --git a/tensorflow/compiler/xla/tests/execution_profile_test.cc b/tensorflow/compiler/xla/tests/execution_profile_test.cc index e2c145b795..5116e60ca6 100644 --- a/tensorflow/compiler/xla/tests/execution_profile_test.cc +++ b/tensorflow/compiler/xla/tests/execution_profile_test.cc @@ -14,7 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/global_data.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/test_macros.h" diff --git a/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc b/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc index 86bfaea4ef..bf1de02ba9 100644 --- a/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc +++ b/tensorflow/compiler/xla/tests/exhaustive_f32_elementwise_op_test.cc @@ -13,7 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.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" diff --git a/tensorflow/compiler/xla/tests/floor_ceil_test.cc b/tensorflow/compiler/xla/tests/floor_ceil_test.cc index 30dc639f11..39cc6c5927 100644 --- a/tensorflow/compiler/xla/tests/floor_ceil_test.cc +++ b/tensorflow/compiler/xla/tests/floor_ceil_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.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" diff --git a/tensorflow/compiler/xla/tests/fmax_test.cc b/tensorflow/compiler/xla/tests/fmax_test.cc index 0254ae1baa..c5bbbe778d 100644 --- a/tensorflow/compiler/xla/tests/fmax_test.cc +++ b/tensorflow/compiler/xla/tests/fmax_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" #include "tensorflow/core/platform/test.h" diff --git a/tensorflow/compiler/xla/tests/fusion_test.cc b/tensorflow/compiler/xla/tests/fusion_test.cc index 607bcdd51e..792be0d3fc 100644 --- a/tensorflow/compiler/xla/tests/fusion_test.cc +++ b/tensorflow/compiler/xla/tests/fusion_test.cc @@ -25,7 +25,7 @@ limitations under the License. #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/ptr_util.h" diff --git a/tensorflow/compiler/xla/tests/gather_operation_test.cc b/tensorflow/compiler/xla/tests/gather_operation_test.cc index 2008d69237..b77bece85a 100644 --- a/tensorflow/compiler/xla/tests/gather_operation_test.cc +++ b/tensorflow/compiler/xla/tests/gather_operation_test.cc @@ -13,8 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/execution_options_util.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/status_macros.h" diff --git a/tensorflow/compiler/xla/tests/half_test.cc b/tensorflow/compiler/xla/tests/half_test.cc index 249a4b2493..51450314b6 100644 --- a/tensorflow/compiler/xla/tests/half_test.cc +++ b/tensorflow/compiler/xla/tests/half_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test.h" diff --git a/tensorflow/compiler/xla/tests/hlo_metadata_test.cc b/tensorflow/compiler/xla/tests/hlo_metadata_test.cc index 4d82442f7e..5511190caf 100644 --- a/tensorflow/compiler/xla/tests/hlo_metadata_test.cc +++ b/tensorflow/compiler/xla/tests/hlo_metadata_test.cc @@ -14,7 +14,7 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/service/local_service.h" #include "tensorflow/compiler/xla/test_helpers.h" #include "tensorflow/compiler/xla/tests/local_client_test_base.h" diff --git a/tensorflow/compiler/xla/tests/local_client_allocation_test.cc b/tensorflow/compiler/xla/tests/local_client_allocation_test.cc index 0df50150ae..e2cd5bcc5a 100644 --- a/tensorflow/compiler/xla/tests/local_client_allocation_test.cc +++ b/tensorflow/compiler/xla/tests/local_client_allocation_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/local_service.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" diff --git a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc index 0b44090702..9e21c53569 100644 --- a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc +++ b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc @@ -21,7 +21,7 @@ limitations under the License. #include "llvm/ADT/Triple.h" #include "tensorflow/compiler/xla/client/client_library.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/service/cpu/cpu_compiler.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" diff --git a/tensorflow/compiler/xla/tests/local_client_execute_test.cc b/tensorflow/compiler/xla/tests/local_client_execute_test.cc index 5c3498c84c..1a823cf189 100644 --- a/tensorflow/compiler/xla/tests/local_client_execute_test.cc +++ b/tensorflow/compiler/xla/tests/local_client_execute_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/device_memory_allocator.h" diff --git a/tensorflow/compiler/xla/tests/log_test.cc b/tensorflow/compiler/xla/tests/log_test.cc index cdf70ee418..2d622242e6 100644 --- a/tensorflow/compiler/xla/tests/log_test.cc +++ b/tensorflow/compiler/xla/tests/log_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.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" diff --git a/tensorflow/compiler/xla/tests/map_test.cc b/tensorflow/compiler/xla/tests/map_test.cc index 34bcaef513..0732e195d4 100644 --- a/tensorflow/compiler/xla/tests/map_test.cc +++ b/tensorflow/compiler/xla/tests/map_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc index 4fca90af77..da8c42d465 100644 --- a/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc +++ b/tensorflow/compiler/xla/tests/matrix_ops_simple_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/ptr_util.h" diff --git a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc index e576f000ef..955dbef6dc 100644 --- a/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc +++ b/tensorflow/compiler/xla/tests/multidimensional_slice_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.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" diff --git a/tensorflow/compiler/xla/tests/pad_test.cc b/tensorflow/compiler/xla/tests/pad_test.cc index d8c17202f2..ca21b0b2ba 100644 --- a/tensorflow/compiler/xla/tests/pad_test.cc +++ b/tensorflow/compiler/xla/tests/pad_test.cc @@ -20,8 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" -#include "tensorflow/compiler/xla/client/xla_computation.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/ptr_util.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" diff --git a/tensorflow/compiler/xla/tests/params_test.cc b/tensorflow/compiler/xla/tests/params_test.cc index bf3b5f2b65..f6c762e7a4 100644 --- a/tensorflow/compiler/xla/tests/params_test.cc +++ b/tensorflow/compiler/xla/tests/params_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" diff --git a/tensorflow/compiler/xla/tests/pred_test.cc b/tensorflow/compiler/xla/tests/pred_test.cc index 5c351b2d11..2fc7f816b5 100644 --- a/tensorflow/compiler/xla/tests/pred_test.cc +++ b/tensorflow/compiler/xla/tests/pred_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/platform/test.h" diff --git a/tensorflow/compiler/xla/tests/prng_test.cc b/tensorflow/compiler/xla/tests/prng_test.cc index 3f98099be6..029af69573 100644 --- a/tensorflow/compiler/xla/tests/prng_test.cc +++ b/tensorflow/compiler/xla/tests/prng_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/primitive_util.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc b/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc index 526a38e8d1..fab2a65de1 100644 --- a/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc +++ b/tensorflow/compiler/xla/tests/query_inferred_shape_test.cc @@ -16,7 +16,7 @@ limitations under the License. #include #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/test_helpers.h" diff --git a/tensorflow/compiler/xla/tests/reduce_precision_test.cc b/tensorflow/compiler/xla/tests/reduce_precision_test.cc index 04c7f31646..531648fe3e 100644 --- a/tensorflow/compiler/xla/tests/reduce_precision_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_precision_test.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/reduce_precision_insertion.h" diff --git a/tensorflow/compiler/xla/tests/reduce_test.cc b/tensorflow/compiler/xla/tests/reduce_test.cc index 638b0825a1..2065271a7f 100644 --- a/tensorflow/compiler/xla/tests/reduce_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_test.cc @@ -37,7 +37,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal_util.h" diff --git a/tensorflow/compiler/xla/tests/reduce_window_test.cc b/tensorflow/compiler/xla/tests/reduce_window_test.cc index 161b74a5c8..1bd6fdab31 100644 --- a/tensorflow/compiler/xla/tests/reduce_window_test.cc +++ b/tensorflow/compiler/xla/tests/reduce_window_test.cc @@ -24,7 +24,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/replay_test.cc b/tensorflow/compiler/xla/tests/replay_test.cc index f026ad6c42..d891451381 100644 --- a/tensorflow/compiler/xla/tests/replay_test.cc +++ b/tensorflow/compiler/xla/tests/replay_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/protobuf_util.h" diff --git a/tensorflow/compiler/xla/tests/reshape_motion_test.cc b/tensorflow/compiler/xla/tests/reshape_motion_test.cc index 7c0389cfa3..368f5583c9 100644 --- a/tensorflow/compiler/xla/tests/reshape_motion_test.cc +++ b/tensorflow/compiler/xla/tests/reshape_motion_test.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/reference_util.h" diff --git a/tensorflow/compiler/xla/tests/reshape_test.cc b/tensorflow/compiler/xla/tests/reshape_test.cc index a6e985293a..382d1b1ae7 100644 --- a/tensorflow/compiler/xla/tests/reshape_test.cc +++ b/tensorflow/compiler/xla/tests/reshape_test.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal_util.h" diff --git a/tensorflow/compiler/xla/tests/reverse_test.cc b/tensorflow/compiler/xla/tests/reverse_test.cc index 23f0d26d93..41e49b4003 100644 --- a/tensorflow/compiler/xla/tests/reverse_test.cc +++ b/tensorflow/compiler/xla/tests/reverse_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/array4d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.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" diff --git a/tensorflow/compiler/xla/tests/scalar_computations_test.cc b/tensorflow/compiler/xla/tests/scalar_computations_test.cc index 5a3bcaf086..e42c71eb28 100644 --- a/tensorflow/compiler/xla/tests/scalar_computations_test.cc +++ b/tensorflow/compiler/xla/tests/scalar_computations_test.cc @@ -19,7 +19,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/literal_util.h" diff --git a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc index ceb795219a..e3d4f98dd7 100644 --- a/tensorflow/compiler/xla/tests/select_and_scatter_test.cc +++ b/tensorflow/compiler/xla/tests/select_and_scatter_test.cc @@ -22,7 +22,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" #include "tensorflow/compiler/xla/client/padding.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/layout_util.h" #include "tensorflow/compiler/xla/literal.h" diff --git a/tensorflow/compiler/xla/tests/select_test.cc b/tensorflow/compiler/xla/tests/select_test.cc index 59409ab26e..1c01402798 100644 --- a/tensorflow/compiler/xla/tests/select_test.cc +++ b/tensorflow/compiler/xla/tests/select_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.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" diff --git a/tensorflow/compiler/xla/tests/slice_test.cc b/tensorflow/compiler/xla/tests/slice_test.cc index a593faca00..b8ad6668f8 100644 --- a/tensorflow/compiler/xla/tests/slice_test.cc +++ b/tensorflow/compiler/xla/tests/slice_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/literal_test_util.h" diff --git a/tensorflow/compiler/xla/tests/test_utils_test.cc b/tensorflow/compiler/xla/tests/test_utils_test.cc index 8f424ae81f..a2f0338e25 100644 --- a/tensorflow/compiler/xla/tests/test_utils_test.cc +++ b/tensorflow/compiler/xla/tests/test_utils_test.cc @@ -15,7 +15,7 @@ limitations under the License. #include "tensorflow/compiler/xla/tests/test_utils.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/service/hlo_parser.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/tests/local_client_test_base.h" diff --git a/tensorflow/compiler/xla/tests/transpose_test.cc b/tensorflow/compiler/xla/tests/transpose_test.cc index 6ebb4324f8..fbe9d1b64a 100644 --- a/tensorflow/compiler/xla/tests/transpose_test.cc +++ b/tensorflow/compiler/xla/tests/transpose_test.cc @@ -17,7 +17,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/reference_util.h" #include "tensorflow/compiler/xla/tests/client_library_test_base.h" #include "tensorflow/compiler/xla/tests/hlo_test_base.h" diff --git a/tensorflow/compiler/xla/tests/tuple_test.cc b/tensorflow/compiler/xla/tests/tuple_test.cc index ad46eaa1c3..2fd70b72b5 100644 --- a/tensorflow/compiler/xla/tests/tuple_test.cc +++ b/tensorflow/compiler/xla/tests/tuple_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal_util.h" #include "tensorflow/compiler/xla/shape_util.h" diff --git a/tensorflow/compiler/xla/tests/unary_op_test.cc b/tensorflow/compiler/xla/tests/unary_op_test.cc index a90a6fb0a5..20ae68ab74 100644 --- a/tensorflow/compiler/xla/tests/unary_op_test.cc +++ b/tensorflow/compiler/xla/tests/unary_op_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.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" diff --git a/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc b/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc index ea3aba6df1..ef1b1445bb 100644 --- a/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc +++ b/tensorflow/compiler/xla/tests/vector_ops_reduce_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array3d.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.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" diff --git a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc index cacbe83b86..3848ec1684 100644 --- a/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc +++ b/tensorflow/compiler/xla/tests/vector_ops_simple_test.cc @@ -21,7 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/global_data.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/compiler/xla/statusor.h" diff --git a/tensorflow/compiler/xla/tests/while_test.cc b/tensorflow/compiler/xla/tests/while_test.cc index 0a39778002..c81c27891c 100644 --- a/tensorflow/compiler/xla/tests/while_test.cc +++ b/tensorflow/compiler/xla/tests/while_test.cc @@ -20,7 +20,7 @@ limitations under the License. #include "tensorflow/compiler/xla/client/client_library.h" #include "tensorflow/compiler/xla/client/lib/arithmetic.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/literal.h" #include "tensorflow/compiler/xla/service/platform_util.h" diff --git a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc index 10c51a1c84..0ee8e68c88 100644 --- a/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc +++ b/tensorflow/compiler/xla/tests/xla_hlo_profile_test.cc @@ -18,7 +18,7 @@ limitations under the License. #include "tensorflow/compiler/xla/array2d.h" #include "tensorflow/compiler/xla/client/local_client.h" -#include "tensorflow/compiler/xla/client/xla_client/xla_builder.h" +#include "tensorflow/compiler/xla/client/xla_builder.h" #include "tensorflow/compiler/xla/client/xla_computation.h" #include "tensorflow/compiler/xla/map_util.h" #include "tensorflow/compiler/xla/service/platform_util.h" -- GitLab From 33879661ed221167f3987bcc2fdc5298986d29fb Mon Sep 17 00:00:00 2001 From: Todd Wang Date: Wed, 25 Jul 2018 23:53:16 -0700 Subject: [PATCH 431/519] Ensure failed sub-streams are not re-used. Streams have a monotonic state machine; if a stream encounters any error, it will remain in an error state forever. Without this change, a previously failed sub-stream will be put back on sub_streams_, only to cause the next usage of the sub-stream to trivially fail. PiperOrigin-RevId: 206112024 --- tensorflow/stream_executor/stream.cc | 9 +- tensorflow/stream_executor/stream.h | 2 +- tensorflow/stream_executor/stream_test.cc | 139 ++++++++++++++++++++++ 3 files changed, 148 insertions(+), 2 deletions(-) create mode 100644 tensorflow/stream_executor/stream_test.cc diff --git a/tensorflow/stream_executor/stream.cc b/tensorflow/stream_executor/stream.cc index 2c495c99e1..6248aa2d01 100644 --- a/tensorflow/stream_executor/stream.cc +++ b/tensorflow/stream_executor/stream.cc @@ -1941,7 +1941,14 @@ void Stream::ReturnSubStream(Stream *sub_stream) { mutex_lock lock(mu_); for (auto &stream : sub_streams_) { if (stream.first.get() == sub_stream) { - stream.second = true; + // Streams have a monotonic state machine; if a stream + // encounters an error, it will remain in an error state + // forever. Only allow re-use of ok streams. + // + // TODO(toddw): Improve this mechanism, if necessary, to drop + // failed streams completely. + const bool ready_to_reuse = sub_stream->ok(); + stream.second = ready_to_reuse; return; } } diff --git a/tensorflow/stream_executor/stream.h b/tensorflow/stream_executor/stream.h index 63d64947c8..706442a666 100644 --- a/tensorflow/stream_executor/stream.h +++ b/tensorflow/stream_executor/stream.h @@ -125,7 +125,7 @@ class Stream { Stream *GetOrCreateSubStream() LOCKS_EXCLUDED(mu_); // Return the sub-stream back to the host stream so that it can be reused - // later. + // later. Sub-streams that are !ok() will not be reused. void ReturnSubStream(Stream *sub_stream) LOCKS_EXCLUDED(mu_); // Allocate temporary memories. The stream will deallocate them when blocked diff --git a/tensorflow/stream_executor/stream_test.cc b/tensorflow/stream_executor/stream_test.cc new file mode 100644 index 0000000000..47dd675834 --- /dev/null +++ b/tensorflow/stream_executor/stream_test.cc @@ -0,0 +1,139 @@ +/* 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/stream_executor/stream_executor.h" + +#include "tensorflow/core/platform/test.h" + +namespace stream_executor { +namespace { + +class StreamTest : public ::testing::Test { + protected: + std::unique_ptr NewStreamExecutor() { + Platform* platform = + MultiPlatformManager::PlatformWithName("Host").ConsumeValueOrDie(); + StreamExecutorConfig config(/*ordinal=*/0); + return platform->GetUncachedExecutor(config).ConsumeValueOrDie(); + } +}; + +TEST_F(StreamTest, NoInitNotOk) { + std::unique_ptr executor = NewStreamExecutor(); + Stream stream(executor.get()); + EXPECT_FALSE(stream.ok()); +} + +TEST_F(StreamTest, InitOk) { + std::unique_ptr executor = NewStreamExecutor(); + Stream stream(executor.get()); + stream.Init(); + EXPECT_TRUE(stream.ok()); +} + +TEST_F(StreamTest, OneSubStream) { + std::unique_ptr executor = NewStreamExecutor(); + Stream stream(executor.get()); + stream.Init(); + EXPECT_TRUE(stream.ok()); + + // Get and return a sub-stream. Sub-streams are always initialized. + Stream* sub_stream1 = stream.GetOrCreateSubStream(); + EXPECT_TRUE(sub_stream1->ok()); + stream.ReturnSubStream(sub_stream1); + + // Get and return another sub-stream. + Stream* sub_stream2 = stream.GetOrCreateSubStream(); + EXPECT_TRUE(sub_stream2->ok()); + stream.ReturnSubStream(sub_stream1); + + // The underlying sub-streams should be the same, since sub_stream1 + // was returned before we tried to get sub_stream2. + EXPECT_EQ(sub_stream1, sub_stream2); +} + +TEST_F(StreamTest, TwoSubStreams) { + std::unique_ptr executor = NewStreamExecutor(); + Stream stream(executor.get()); + stream.Init(); + EXPECT_TRUE(stream.ok()); + + // Get two sub-streams. + Stream* sub_stream1 = stream.GetOrCreateSubStream(); + EXPECT_TRUE(sub_stream1->ok()); + Stream* sub_stream2 = stream.GetOrCreateSubStream(); + EXPECT_TRUE(sub_stream2->ok()); + + // The underlying sub-streams should be different, since neither + // sub-stream has been returned. + EXPECT_NE(sub_stream1, sub_stream2); + + // Return sub_stream1 and get sub_stream3, which should be the same. + stream.ReturnSubStream(sub_stream1); + Stream* sub_stream3 = stream.GetOrCreateSubStream(); + EXPECT_TRUE(sub_stream3->ok()); + EXPECT_EQ(sub_stream1, sub_stream3); + EXPECT_NE(sub_stream2, sub_stream3); + + // Return sub_stream2 and get sub_stream4, which should be the same. + stream.ReturnSubStream(sub_stream2); + Stream* sub_stream4 = stream.GetOrCreateSubStream(); + EXPECT_TRUE(sub_stream4->ok()); + EXPECT_EQ(sub_stream2, sub_stream4); + EXPECT_NE(sub_stream3, sub_stream4); +} + +TEST_F(StreamTest, FailedSubStreamNotReused) { + std::unique_ptr executor = NewStreamExecutor(); + Stream stream(executor.get()); + stream.Init(); + EXPECT_TRUE(stream.ok()); + + // Get a sub-stream. + Stream* sub_stream1 = stream.GetOrCreateSubStream(); + EXPECT_TRUE(sub_stream1->ok()); + + // Force an error on the stream; here we call a method that requires + // DNN support, which we know the Host platform doesn't support. + sub_stream1->ThenDepthConcatenate({}, {}, nullptr); + EXPECT_FALSE(sub_stream1->ok()); + + // Return sub_stream1 and get sub_stream2. + stream.ReturnSubStream(sub_stream1); + Stream* sub_stream2 = stream.GetOrCreateSubStream(); + EXPECT_TRUE(sub_stream2->ok()); + + // The underlying streams should be different. They would have been + // the same, but since we forced an error on sub_stream1, it will + // not be re-used. Sadly we can't just check: + // EXPECT_NE(sub_stream1, sub_stream2); + // + // The above should hold logically, but it may fail if the new + // stream instance allocated for sub_stream2 happens to reside in + // the same memory address as sub_stream1. + // + // The check that sub_stream2->ok() serves as a good-enough check. + + // Return sub_stream2 and get sub_stream3. The previous error on + // sub_stream1 has no effect on these streams, and they are the + // same. + stream.ReturnSubStream(sub_stream2); + Stream* sub_stream3 = stream.GetOrCreateSubStream(); + EXPECT_TRUE(sub_stream3->ok()); + EXPECT_EQ(sub_stream2, sub_stream3); +} + +} // namespace +} // namespace stream_executor -- GitLab From b1dc68e816e2bf6b8acd3651077c890f2f2f3b7b Mon Sep 17 00:00:00 2001 From: Youlong Cheng Date: Thu, 26 Jul 2018 00:02:32 -0700 Subject: [PATCH 432/519] Support PREDICT mode for BROADCAST input pipeline. PiperOrigin-RevId: 206112531 --- .../contrib/tpu/python/tpu/tpu_estimator.py | 34 +++++++++++++++---- 1 file changed, 27 insertions(+), 7 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 7c7c97638e..6e7dae6fce 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -791,7 +791,15 @@ def generate_broadcast_enqueue_ops_fn(ctx, input_fn, inputs_structure_recorder, is_dataset = inputs.is_dataset if ctx.mode == model_fn_lib.ModeKeys.PREDICT: - raise TypeError('Mode PREDICT not yet supported in BROADCAST mode.') + if not is_dataset: + raise TypeError( + 'For mode PREDICT, `input_fn` must return `Dataset` instead of ' + '`features` and `labels`.') + + inputs = _InputsWithStoppingSignals( + dataset=inputs.dataset, + batch_size=ctx.batch_size_for_input_fn, + add_padding=True) if is_dataset: hooks.append(inputs.dataset_initializer_hook()) @@ -810,6 +818,7 @@ def generate_broadcast_enqueue_ops_fn(ctx, input_fn, inputs_structure_recorder, """Generates enqueue ops for all the hosts.""" broadcasted_inputs = [] flattened_inputs = None # Cache result from input_fn. + signals = None for host_id in xrange(num_hosts): with ops.device(ctx.tpu_host_placement_function(host_id=host_id)): for _ in xrange(ctx.num_of_replicas_per_host): @@ -819,11 +828,13 @@ def generate_broadcast_enqueue_ops_fn(ctx, input_fn, inputs_structure_recorder, # hosts). if flattened_inputs is None: features, labels = inputs.features_and_labels() # Calls get_next() + signals = inputs.signals() + inputs_structure_recorder.validate_and_record_structure( - features, labels) + features, labels, signals) flattened_inputs = ( inputs_structure_recorder.flatten_features_and_labels( - features, labels)) + features, labels, signals)) broadcasted_inputs.append(flattened_inputs) infeed_queue = tpu_feed.InfeedQueue( @@ -833,7 +844,14 @@ def generate_broadcast_enqueue_ops_fn(ctx, input_fn, inputs_structure_recorder, broadcasted_inputs, tpu_ordinal_function=tpu_ordinal_function_impl, placement_function=device_function_impl) - return enqueue_ops + + if signals is None: + return enqueue_ops + else: + return { + 'ops': enqueue_ops, + 'signals': signals, + } return enqueue_ops_fn, captured_infeed_queue, hooks, is_dataset @@ -1080,9 +1098,11 @@ class _InputPipeline(object): all_hooks.extend(hooks) if is_dataset: run_infeed_loop_on_coordinator = False - enqueue_ops.append( - _wrap_computation_in_while_loop( - device=host_device, op_fn=enqueue_ops_fn)) + wrap_fn = ( + _wrap_computation_in_while_loop + if self._ctx.mode != model_fn_lib.ModeKeys.PREDICT else + _wrap_computation_in_while_loop_with_stopping_signals) + enqueue_ops.append(wrap_fn(device=host_device, op_fn=enqueue_ops_fn)) else: enqueue_ops.append(enqueue_ops_fn()) infeed_queues.append(captured_infeed_queue.get()) -- GitLab From e5cc33df74ec4f761da26c87bb785edfa3fb8280 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 01:27:42 -0700 Subject: [PATCH 433/519] Convert device function stack into TraceableStack for use in error message interpolation. PiperOrigin-RevId: 206120307 --- .../copy_graph/python/util/copy_elements.py | 4 +- tensorflow/contrib/tpu/python/tpu/tpu.py | 4 +- tensorflow/python/data/ops/iterator_ops.py | 11 +- .../python/framework/error_interpolation.py | 53 +++++++ .../framework/error_interpolation_test.py | 105 +++++++++++--- tensorflow/python/framework/function.py | 26 +--- tensorflow/python/framework/ops.py | 137 +++++++++++++++--- tensorflow/python/framework/ops_test.py | 67 ++++++++- tensorflow/python/ops/cond_v2_impl.py | 10 +- tensorflow/python/util/function_utils.py | 31 ++++ 10 files changed, 372 insertions(+), 76 deletions(-) diff --git a/tensorflow/contrib/copy_graph/python/util/copy_elements.py b/tensorflow/contrib/copy_graph/python/util/copy_elements.py index 5931c8a279..6c9ab6aeb8 100644 --- a/tensorflow/contrib/copy_graph/python/util/copy_elements.py +++ b/tensorflow/contrib/copy_graph/python/util/copy_elements.py @@ -219,8 +219,10 @@ def copy_op_to_graph(org_instance, to_graph, variables, scope=''): op_def) #Use Graph's hidden methods to add the op to_graph._record_op_seen_by_control_dependencies(new_op) - for device_function in reversed(to_graph._device_function_stack): + # pylint: disable=protected-access + for device_function in to_graph._device_functions_outer_to_inner: new_op._set_device(device_function(new_op)) + # pylint: enable=protected-access return new_op diff --git a/tensorflow/contrib/tpu/python/tpu/tpu.py b/tensorflow/contrib/tpu/python/tpu/tpu.py index 06885bbc25..92c1eaba71 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu.py @@ -314,7 +314,9 @@ class TPUReplicateContext(control_flow_ops.XLAControlFlowContext): # Capture the device function stack at the time of first entry # since that is the stack that will be used outside_compilation. graph = ops.get_default_graph() - self._outer_device_function_stack = list(graph._device_function_stack) # pylint: disable=protected-access + # pylint: disable=protected-access + self._outer_device_function_stack = graph._device_function_stack.copy() + # pylint: enable=protected-access super(TPUReplicateContext, self).Enter() def HostComputeCore(self): diff --git a/tensorflow/python/data/ops/iterator_ops.py b/tensorflow/python/data/ops/iterator_ops.py index 3ef22cf981..494df178df 100644 --- a/tensorflow/python/data/ops/iterator_ops.py +++ b/tensorflow/python/data/ops/iterator_ops.py @@ -57,6 +57,13 @@ GET_NEXT_CALL_WARNING_MESSAGE = ( GLOBAL_ITERATORS = "iterators" +def _device_stack_is_empty(): + # pylint: disable=protected-access + device_stack = ops.get_default_graph()._device_functions_outer_to_inner + # pylint: enable=protected-access + return not bool(device_stack) + + @tf_export("data.Iterator") class Iterator(object): """Represents the state of iterating through a `Dataset`.""" @@ -174,7 +181,7 @@ class Iterator(object): if shared_name is None: shared_name = "" if compat.forward_compatible(2018, 8, 3): - if not ops.get_default_graph()._graph_device_function_stack: # pylint: disable=protected-access + if _device_stack_is_empty(): with ops.device("/cpu:0"): iterator_resource = gen_dataset_ops.iterator_v2( container="", @@ -263,7 +270,7 @@ class Iterator(object): nest.assert_same_structure(output_types, output_shapes) string_handle = ops.convert_to_tensor(string_handle, dtype=dtypes.string) if compat.forward_compatible(2018, 8, 3): - if not ops.get_default_graph()._graph_device_function_stack: # pylint: disable=protected-access + if _device_stack_is_empty(): with ops.device("/cpu:0"): iterator_resource = gen_dataset_ops.iterator_from_string_handle_v2( string_handle, diff --git a/tensorflow/python/framework/error_interpolation.py b/tensorflow/python/framework/error_interpolation.py index a79073b748..7719d03019 100644 --- a/tensorflow/python/framework/error_interpolation.py +++ b/tensorflow/python/framework/error_interpolation.py @@ -87,6 +87,53 @@ def _parse_message(message): return seps, tags +def _compute_device_summary_from_list(device_assignment_list, prefix=""): + """Return a summary of an op's device function stack. + + Args: + device_assignment_list: The op._device_assignments list. + prefix: An optional string prefix used before each line of the multi- + line string returned by this function. + + Returns: + A multi-line string similar to: + Device assignments active during op creation: + with tf.device(/cpu:0): + with tf.device(some_func): + The first line will have no padding to its left by default. Subsequent + lines will have two spaces of left-padding. Use the prefix argument + to increase indentation. + """ + if not device_assignment_list: + message = "No device assignments were active during op creation." + return prefix + message + + str_list = [] + str_list.append("%sDevice assignments active during op creation:" % prefix) + + for traceable_obj in device_assignment_list: + location_summary = "<{file}:{line}>".format(file=traceable_obj.filename, + line=traceable_obj.lineno) + subs = { + "prefix": prefix, + "indent": " ", + "dev_name": traceable_obj.obj, + "loc": location_summary, + } + str_list.append( + "{prefix}{indent}with tf.device({dev_name}): {loc}".format(**subs)) + + return "\n".join(str_list) + + +def _compute_device_assignment_summary_from_op(op, prefix=""): + if not op: + return "" + # pylint: disable=protected-access + return _compute_device_summary_from_list(op._device_assignments, prefix) + # pylint: enable=protected-access + + def _compute_colocation_summary_from_dict(colocation_dict, prefix=""): """Return a summary of an op's colocation stack. @@ -203,6 +250,7 @@ def _compute_field_dict(op): "file": default_value, "line": default_value, "colocations": default_value, + "devices": default_value, } frame = _get_defining_frame_from_op(op) if frame: @@ -211,6 +259,9 @@ def _compute_field_dict(op): colocation_summary = _compute_colocation_summary_from_op(op) if colocation_summary: field_dict["colocations"] = colocation_summary + device_summary = _compute_device_assignment_summary_from_op(op) + if device_summary: + field_dict["devices"] = device_summary return field_dict @@ -233,6 +284,8 @@ def interpolate(error_message, graph): node_name_to_substitution_dict = {} for name in [t.name for t in tags]: + if name in node_name_to_substitution_dict: + continue try: op = graph.get_operation_by_name(name) except KeyError: diff --git a/tensorflow/python/framework/error_interpolation_test.py b/tensorflow/python/framework/error_interpolation_test.py index 1e5cb73854..fbf182879b 100644 --- a/tensorflow/python/framework/error_interpolation_test.py +++ b/tensorflow/python/framework/error_interpolation_test.py @@ -57,13 +57,32 @@ def _modify_op_stack_with_filenames(op, num_user_frames, user_filename, op._traceback = stack -def assert_node_in_colocation_summary(test_obj, colocation_summary_string, - name, filename="", lineno=""): - lineno = str(lineno) - name_phrase = "colocate_with(%s)" % name - for term in [name_phrase, filename, lineno]: - test_obj.assertIn(term, colocation_summary_string) - test_obj.assertNotIn("loc:@", colocation_summary_string) +class ComputeDeviceSummaryFromOpTest(test.TestCase): + + def testCorrectFormatWithActiveDeviceAssignments(self): + assignments = [] + assignments.append( + traceable_stack.TraceableObject("/cpu:0", + filename="hope.py", + lineno=24)) + assignments.append( + traceable_stack.TraceableObject("/gpu:2", + filename="please.py", + lineno=42)) + + summary = error_interpolation._compute_device_summary_from_list( + assignments, prefix=" ") + + self.assertIn("tf.device(/cpu:0)", summary) + self.assertIn("", summary) + self.assertIn("tf.device(/gpu:2)", summary) + self.assertIn("", summary) + + def testCorrectFormatWhenNoColocationsWereActive(self): + device_assignment_list = [] + summary = error_interpolation._compute_device_summary_from_list( + device_assignment_list, prefix=" ") + self.assertIn("No device assignments", summary) class ComputeColocationSummaryFromOpTest(test.TestCase): @@ -81,15 +100,10 @@ class ComputeColocationSummaryFromOpTest(test.TestCase): } summary = error_interpolation._compute_colocation_summary_from_dict( colocation_dict, prefix=" ") - assert_node_in_colocation_summary(self, - summary, - name="test_node_1", - filename="test_1.py", - lineno=27) - assert_node_in_colocation_summary(self, summary, - name="test_node_2", - filename="test_2.py", - lineno=38) + self.assertIn("colocate_with(test_node_1)", summary) + self.assertIn("", summary) + self.assertIn("colocate_with(test_node_2)", summary) + self.assertIn("", summary) def testCorrectFormatWhenNoColocationsWereActive(self): colocation_dict = {} @@ -98,9 +112,10 @@ class ComputeColocationSummaryFromOpTest(test.TestCase): self.assertIn("No node-device colocations", summary) -class InterpolateTest(test.TestCase): +class InterpolateFilenamesAndLineNumbersTest(test.TestCase): def setUp(self): + ops.reset_default_graph() # Add nodes to the graph for retrieval by name later. constant_op.constant(1, name="One") constant_op.constant(2, name="Two") @@ -177,9 +192,57 @@ class InterpolateTest(test.TestCase): self.assertRegexpMatches(interpolated_string, expected_regex) +class InterpolateDeviceSummaryTest(test.TestCase): + + def _fancy_device_function(self, unused_op): + return "/cpu:*" + + def setUp(self): + ops.reset_default_graph() + self.zero = constant_op.constant([0.0], name="zero") + with ops.device("/cpu"): + self.one = constant_op.constant([1.0], name="one") + with ops.device("/cpu:0"): + self.two = constant_op.constant([2.0], name="two") + with ops.device(self._fancy_device_function): + self.three = constant_op.constant(3.0, name="three") + + self.graph = self.three.graph + + def testNodeZeroHasNoDeviceSummaryInfo(self): + message = "^^node:zero:${devices}^^" + result = error_interpolation.interpolate(message, self.graph) + self.assertIn("No device assignments were active", result) + + def testNodeOneHasExactlyOneInterpolatedDevice(self): + message = "^^node:one:${devices}^^" + result = error_interpolation.interpolate(message, self.graph) + num_devices = result.count("tf.device") + self.assertEqual(1, num_devices) + self.assertIn("tf.device(/cpu)", result) + + def testNodeTwoHasTwoInterpolatedDevice(self): + message = "^^node:two:${devices}^^" + result = error_interpolation.interpolate(message, self.graph) + num_devices = result.count("tf.device") + self.assertEqual(2, num_devices) + self.assertIn("tf.device(/cpu)", result) + self.assertIn("tf.device(/cpu:0)", result) + + def testNodeThreeHasFancyFunctionDisplayNameForInterpolatedDevice(self): + message = "^^node:three:${devices}^^" + result = error_interpolation.interpolate(message, self.graph) + num_devices = result.count("tf.device") + self.assertEqual(1, num_devices) + name_re = r"_fancy_device_function<.*error_interpolation_test.py, [0-9]+>" + expected_re = r"with tf.device\(.*%s\)" % name_re + self.assertRegexpMatches(result, expected_re) + + class InterpolateColocationSummaryTest(test.TestCase): def setUp(self): + ops.reset_default_graph() # Add nodes to the graph for retrieval by name later. node_one = constant_op.constant(1, name="One") node_two = constant_op.constant(2, name="Two") @@ -203,12 +266,12 @@ class InterpolateColocationSummaryTest(test.TestCase): def testNodeThreeHasColocationInterpolation(self): message = "^^node:Three_with_one:${colocations}^^" result = error_interpolation.interpolate(message, self.graph) - assert_node_in_colocation_summary(self, result, name="One") + self.assertIn("colocate_with(One)", result) def testNodeFourHasColocationInterpolationForNodeThreeOnly(self): message = "^^node:Four_with_three:${colocations}^^" result = error_interpolation.interpolate(message, self.graph) - assert_node_in_colocation_summary(self, result, name="Three_with_one") + self.assertIn("colocate_with(Three_with_one)", result) self.assertNotIn( "One", result, "Node One should not appear in Four_with_three's summary:\n%s" @@ -217,8 +280,8 @@ class InterpolateColocationSummaryTest(test.TestCase): def testNodeFiveHasColocationInterpolationForNodeOneAndTwo(self): message = "^^node:Five_with_one_with_two:${colocations}^^" result = error_interpolation.interpolate(message, self.graph) - assert_node_in_colocation_summary(self, result, name="One") - assert_node_in_colocation_summary(self, result, name="Two") + self.assertIn("colocate_with(One)", result) + self.assertIn("colocate_with(Two)", result) def testColocationInterpolationForNodeLackingColocation(self): message = "^^node:One:${colocations}^^" diff --git a/tensorflow/python/framework/function.py b/tensorflow/python/framework/function.py index 6525607fae..c76743d2c6 100644 --- a/tensorflow/python/framework/function.py +++ b/tensorflow/python/framework/function.py @@ -38,8 +38,8 @@ from tensorflow.python.ops import cond_v2_impl from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.util import compat +from tensorflow.python.util import function_utils from tensorflow.python.util import tf_contextlib -from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect # This is to avoid a circular dependency with cond_v2_impl. @@ -255,9 +255,12 @@ class _DefinedFunction(object): # Constructed only when C API is enabled, lazily self._c_func = None self._sub_functions = dict() # Constructed with _definition or _c_func - device_stack = ops.get_default_graph()._device_function_stack # pylint: disable=protected-access + # pylint: disable=protected-access + device_funcs = ops.get_default_graph()._device_functions_outer_to_inner + # pylint: enable=protected-access + # Get the innermost device if possbile. - self._caller_device = device_stack[-1] if device_stack else None + self._caller_device = device_funcs[-1] if device_funcs else None # Cached OpDef for this function. When C API is enabled, this is # the only part of FunctionDef that we cache in Python. When C API @@ -354,7 +357,7 @@ class _DefinedFunction(object): if self._func_name: base_func_name = self._func_name else: - base_func_name = _get_func_name(self._func) + base_func_name = function_utils.get_func_name(self._func) if self._grad_func: base_func_name += ("_%s" % self._grad_func.name) kwargs_attr = _parse_kwargs_as_attrs(base_func_name, **self._extra_kwargs) @@ -841,7 +844,7 @@ def func_graph_from_py_func(func, arg_names, arg_types, name=None, ValueError: if func returns None. """ if not name: - name = _get_func_name(func) + name = function_utils.get_func_name(func) func_graph = _FuncGraph(name, capture_by_value) with func_graph.as_default(), ops.device(device): @@ -1139,19 +1142,6 @@ def _parse_kwargs_as_attrs(func_name, **kwargs): return attrs -def _get_func_name(func): - _, func = tf_decorator.unwrap(func) - if callable(func): - if tf_inspect.isfunction(func): - return func.__name__ - elif tf_inspect.ismethod(func): - return "%s.%s" % (func.__self__.__name__, func.__name__) - else: # Probably a class instance with __call__ - return type(func) - else: - raise ValueError("Argument must be callable") - - def get_extra_vars(): """Returns the captured variables by the function. diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index 0fd028ebf0..197317cad9 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -50,14 +50,15 @@ from tensorflow.python.framework import registry from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import traceable_stack from tensorflow.python.framework import versions -from tensorflow.python.util import tf_stack from tensorflow.python.ops import control_flow_util from tensorflow.python.platform import app from tensorflow.python.platform import tf_logging as logging from tensorflow.python.util import compat from tensorflow.python.util import decorator_utils +from tensorflow.python.util import function_utils from tensorflow.python.util import lock_util from tensorflow.python.util import tf_contextlib +from tensorflow.python.util import tf_stack from tensorflow.python.util.deprecation import deprecated_args from tensorflow.python.util.tf_export import tf_export @@ -73,6 +74,27 @@ def tensor_id(tensor): return tensor._id # pylint: disable=protected-access +class _UserDeviceSpec(object): + """Store user-specified device and provide computation of merged device.""" + + def __init__(self, device_name_or_function): + self._device_name_or_function = device_name_or_function + + self.display_name = str(self._device_name_or_function) + if callable(self._device_name_or_function): + dev_func = self._device_name_or_function + func_name = function_utils.get_func_name(dev_func) + func_code = function_utils.get_func_code(dev_func) + self.display_name = "%s<%s, %d>" % (func_name, + func_code.co_filename, + func_code.co_firstlineno) + + self.function = self._device_name_or_function + if not (self._device_name_or_function is None or + callable(self._device_name_or_function)): + self.function = pydev.merge_device(self._device_name_or_function) + + class _NullContextmanager(object): def __enter__(self): @@ -1719,7 +1741,12 @@ class Operation(object): self._id_value = self._graph._next_id() self._original_op = original_op self._traceback = tf_stack.extract_stack() - # List of traceable_stack.TraceableObjects for colocation context managers. + + # List of _UserDevSpecs holding code location of device context manager + # invocations and the users original argument to them. + self._device_code_locations = None + # Dict mapping op name to file and line information for op colocation + # context managers. self._colocation_code_locations = None self._control_flow_context = self.graph._get_control_flow_context() # pylint: enable=protected-access @@ -1860,6 +1887,37 @@ class Operation(object): """ return c_api.TF_OperationDevice(self._c_op) + @property + def _device_assignments(self): + """Code locations for device context managers active at op creation. + + This property will return a list of traceable_stack.TraceableObject + instances where .obj is a string representing the assigned device + (or information about the function that would be applied to this op + to compute the desired device) and the filename and lineno members + record the location of the relevant device context manager. + + For example, suppose file_a contained these lines: + + file_a.py: + 15: with tf.device('/gpu:0'): + 16: node_b = tf.constant(4, name='NODE_B') + + Then a TraceableObject t_obj representing the device context manager + would have these member values: + + t_obj.obj -> '/gpu:0' + t_obj.filename = 'file_a.py' + t_obj.lineno = 15 + + and node_b.op._device_assignments would return the list [t_obj]. + + Returns: + [str: traceable_stack.TraceableObject, ...] as per this method's + description, above. + """ + return self._device_code_locations or [] + @property def _colocation_dict(self): """Code locations for colocation context managers active at op creation. @@ -1881,11 +1939,10 @@ class Operation(object): would have these member values: t_obj.obj -> None - t_obj.name = 'NODE_A' t_obj.filename = 'file_a.py' t_obj.lineno = 15 - and node_b.op._colocation_code_locations would return the dictionary + and node_b.op._colocation_dict would return the dictionary { 'NODE_A': t_obj } @@ -2735,7 +2792,7 @@ class Graph(object): # Functions that will be applied to choose a device if none is specified. # After switch_to_thread_local(), self._thread_local._device_function_stack # is used instead. - self._graph_device_function_stack = [] + self._graph_device_function_stack = traceable_stack.TraceableStack() # Default original_op applied to new ops. self._default_original_op = None # Current control flow context. It could be either CondContext or @@ -4047,7 +4104,7 @@ class Graph(object): # In the future, a caller may specify that device_functions win # over colocation, in which case we can add support. device_fn_tmp = self._device_function_stack - self._device_function_stack = [] + self._device_function_stack = traceable_stack.TraceableStack() if ignore_existing: current_stack = self._colocation_stack @@ -4071,6 +4128,13 @@ class Graph(object): if ignore_existing: self._colocation_stack = current_stack + def _add_device_to_stack(self, device_name_or_function, offset=0): + """Add device to stack manually, separate from a context manager.""" + total_offset = 1 + offset + spec = _UserDeviceSpec(device_name_or_function) + self._device_function_stack.push_obj(spec, offset=total_offset) + return spec + @tf_contextlib.contextmanager def device(self, device_name_or_function): # pylint: disable=line-too-long @@ -4128,31 +4192,26 @@ class Graph(object): Yields: A context manager that specifies the default device to use for newly created ops. - """ - # pylint: enable=line-too-long - if (device_name_or_function is not None and - not callable(device_name_or_function)): - device_function = pydev.merge_device(device_name_or_function) - else: - device_function = device_name_or_function - + self._add_device_to_stack(device_name_or_function, offset=2) try: - self._device_function_stack.append(device_function) yield finally: - self._device_function_stack.pop() + self._device_function_stack.pop_obj() def _apply_device_functions(self, op): """Applies the current device function stack to the given operation.""" - # Apply any device functions in reverse order, so that the most recently + # Apply any device functions in LIFO order, so that the most recently # pushed function has the first chance to apply a device to the op. # We apply here because the result can depend on the Operation's # signature, which is computed in the Operation constructor. - for device_function in reversed(self._device_function_stack): - if device_function is None: + # pylint: disable=protected-access + for device_spec in self._device_function_stack.peek_objs(): + if device_spec.function is None: break - op._set_device(device_function(op)) # pylint: disable=protected-access + op._set_device(device_spec.function(op)) + op._device_code_locations = self._snapshot_device_function_stack_metadata() + # pylint: enable=protected-access # pylint: disable=g-doc-return-or-yield @tf_contextlib.contextmanager @@ -4676,17 +4735,45 @@ class Graph(object): if self._stack_state_is_thread_local: # This may be called from a thread where device_function_stack doesn't yet # exist. + # pylint: disable=protected-access if not hasattr(self._thread_local, "_device_function_stack"): - self._thread_local._device_function_stack = ( - self._graph_device_function_stack[:]) + stack_copy_for_this_thread = self._graph_device_function_stack.copy() + self._thread_local._device_function_stack = stack_copy_for_this_thread return self._thread_local._device_function_stack + # pylint: enable=protected-access else: return self._graph_device_function_stack + @property + def _device_functions_outer_to_inner(self): + user_device_specs = self._device_function_stack.peek_objs() + device_functions = [spec.function for spec in user_device_specs] + device_functions_outer_to_inner = list(reversed(device_functions)) + return device_functions_outer_to_inner + + def _snapshot_device_function_stack_metadata(self): + """Return device function stack as a list of TraceableObjects. + + Returns: + [traceable_stack.TraceableObject, ...] where each TraceableObject's .obj + member is a displayable name for the user's argument to Graph.device, and + the filename and lineno members point to the code location where + Graph.device was called directly or indirectly by the user. + """ + traceable_objects = self._device_function_stack.peek_traceable_objs() + snapshot = [] + for obj in traceable_objects: + obj_copy = obj.copy_metadata() + obj_copy.obj = obj.obj.display_name + snapshot.append(obj_copy) + return snapshot + @_device_function_stack.setter def _device_function_stack(self, device_function_stack): if self._stack_state_is_thread_local: + # pylint: disable=protected-access self._thread_local._device_function_stack = device_function_stack + # pylint: enable=protected-access else: self._graph_device_function_stack = device_function_stack @@ -4696,12 +4783,12 @@ class Graph(object): if self._stack_state_is_thread_local: # This may be called from a thread where colocation_stack doesn't yet # exist. + # pylint: disable=protected-access if not hasattr(self._thread_local, "_colocation_stack"): stack_copy_for_this_thread = self._graph_colocation_stack.copy() - # pylint: disable=protected-access self._thread_local._colocation_stack = stack_copy_for_this_thread - # pylint: enable=protected-access return self._thread_local._colocation_stack + # pylint: enable=protected-access else: return self._graph_colocation_stack @@ -4713,7 +4800,9 @@ class Graph(object): @_colocation_stack.setter def _colocation_stack(self, colocation_stack): if self._stack_state_is_thread_local: + # pylint: disable=protected-access self._thread_local._colocation_stack = colocation_stack + # pylint: enable=protected-access else: self._graph_colocation_stack = colocation_stack diff --git a/tensorflow/python/framework/ops_test.py b/tensorflow/python/framework/ops_test.py index f848b69782..48328a7f58 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 os import threading import weakref @@ -2542,6 +2543,56 @@ class StatisticsTest(test_util.TensorFlowTestCase): self.assertEqual(3, flops_total.value) +class DeviceStackTest(test_util.TensorFlowTestCase): + + def testBasicDeviceAssignmentMetadata(self): + + def device_func(unused_op): + return "/cpu:*" + + const_zero = constant_op.constant([0.0], name="zero") + with ops.device("/cpu"): + const_one = constant_op.constant([1.0], name="one") + with ops.device("/cpu:0"): + const_two = constant_op.constant([2.0], name="two") + with ops.device(device_func): + const_three = constant_op.constant(3.0, name="three") + + self.assertEqual(0, len(const_zero.op._device_assignments)) + + one_list = const_one.op._device_assignments + self.assertEqual(1, len(one_list)) + self.assertEqual("/cpu", one_list[0].obj) + self.assertEqual("ops_test.py", os.path.basename(one_list[0].filename)) + + two_list = const_two.op._device_assignments + self.assertEqual(2, len(two_list)) + devices = [t.obj for t in two_list] + self.assertEqual(set(["/cpu", "/cpu:0"]), set(devices)) + + three_list = const_three.op._device_assignments + self.assertEqual(1, len(three_list)) + func_description = three_list[0].obj + expected_regex = r"device_func<.*ops_test.py, [0-9]+" + self.assertRegexpMatches(func_description, expected_regex) + + def testDeviceAssignmentMetadataForGraphDeviceAndTfDeviceFunctions(self): + + with ops.device("/cpu"): + const_one = constant_op.constant([1.0], name="one") + with ops.get_default_graph().device("/cpu"): + const_two = constant_op.constant([2.0], name="two") + + one_metadata = const_one.op._device_assignments[0] + two_metadata = const_two.op._device_assignments[0] + + # Verify both types of device assignment return the right stack info. + self.assertRegexpMatches("ops_test.py", + os.path.basename(one_metadata.filename)) + self.assertEqual(one_metadata.filename, two_metadata.filename) + self.assertEqual(one_metadata.lineno + 2, two_metadata.lineno) + + class ColocationGroupTest(test_util.TensorFlowTestCase): def testBasic(self): @@ -2554,13 +2605,17 @@ class ColocationGroupTest(test_util.TensorFlowTestCase): with self.assertRaises(ValueError): c.op.get_attr("_class") - # Roughly test that stack information is being saved correctly for the op. - locations_dict = b.op._colocation_dict - self.assertIn("a", locations_dict) - metadata = locations_dict["a"] + def testBasicColocationMetadata(self): + const_two = constant_op.constant([2.0], name="two") + with ops.colocate_with(const_two.op): + const_three = constant_op.constant(3.0, name="three") + locations_dict = const_three.op._colocation_dict + self.assertIn("two", locations_dict) + metadata = locations_dict["two"] self.assertIsNone(metadata.obj) - basename = metadata.filename.split("/")[-1] - self.assertEqual("ops_test.py", basename) + # Check that this test's filename is recorded as the file containing the + # colocation statement. + self.assertEqual("ops_test.py", os.path.basename(metadata.filename)) def testColocationDeviceInteraction(self): with ops.device("/cpu:0"): diff --git a/tensorflow/python/ops/cond_v2_impl.py b/tensorflow/python/ops/cond_v2_impl.py index 5cd0cb34de..44c5c050c0 100644 --- a/tensorflow/python/ops/cond_v2_impl.py +++ b/tensorflow/python/ops/cond_v2_impl.py @@ -58,12 +58,14 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): with ops.name_scope(name) as scope: # Identify if there is a caller device, & get the innermost if possible. - device_stack = ops.get_default_graph()._device_function_stack - caller_device = device_stack[-1] if device_stack else None + # pylint: disable=protected-access + device_funcs = ops.get_default_graph()._device_functions_outer_to_inner + caller_device = device_funcs[-1] if device_funcs else None caller_colocation_stack = ops.get_default_graph()._colocation_stack caller_container = ops.get_default_graph()._container caller_collection_ref = ops.get_default_graph()._collections + # pylint: enable=protected-access func_name_prefix = scope.replace("/", "_") @@ -106,7 +108,7 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): false_graph.outputs.extend(extra_false_outputs) # Create the If op. - tensors = gen_functional_ops._if( + tensors = gen_functional_ops._if( # pylint: disable=protected-access pred, cond_inputs, [t.dtype for t in true_graph.outputs], _create_new_tf_function(true_graph), _create_new_tf_function(false_graph), @@ -125,8 +127,10 @@ def cond_v2(pred, true_fn, false_fn, name="cond"): # TODO(b/110167197) this approach requires cond_v2 to have at least 1 output if_op = tensors[0].op if not control_flow_util.IsInXLAContext(if_op): + # pylint: disable=protected-access if_op._set_attr("_lower_using_switch_merge", attr_value_pb2.AttrValue(b=True)) + # pylint: enable=protected-access return tensors[:num_cond_outputs] diff --git a/tensorflow/python/util/function_utils.py b/tensorflow/python/util/function_utils.py index 7bbbde3cd2..61312feafd 100644 --- a/tensorflow/python/util/function_utils.py +++ b/tensorflow/python/util/function_utils.py @@ -20,6 +20,8 @@ from __future__ import print_function import functools +import six + from tensorflow.python.util import tf_decorator from tensorflow.python.util import tf_inspect @@ -55,3 +57,32 @@ def fn_args(fn): if _is_bounded_method(fn): args.remove('self') return tuple(args) + + +def get_func_name(func): + """Returns name of passed callable.""" + _, func = tf_decorator.unwrap(func) + if callable(func): + if tf_inspect.isfunction(func): + return func.__name__ + elif tf_inspect.ismethod(func): + return '%s.%s' % (six.get_method_self(func).__class__.__name__, + six.get_method_function(func).__name__) + else: # Probably a class instance with __call__ + return type(func) + else: + raise ValueError('Argument must be callable') + + +def get_func_code(func): + """Returns func_code of passed callable.""" + _, func = tf_decorator.unwrap(func) + if callable(func): + if tf_inspect.isfunction(func) or tf_inspect.ismethod(func): + return six.get_function_code(func) + elif hasattr(func, '__call__'): + return six.get_function_code(func.__call__) + else: + raise ValueError('Unhandled callable, type=%s' % type(func)) + else: + raise ValueError('Argument must be callable') -- GitLab From 939237af1479cf61a7c16ad4a1ead521da65a3a6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 02:15:48 -0700 Subject: [PATCH 434/519] Small changes to placer comments and remove one conditional from Find(). PiperOrigin-RevId: 206125313 --- tensorflow/core/common_runtime/placer.cc | 59 +++++++++--------------- 1 file changed, 23 insertions(+), 36 deletions(-) diff --git a/tensorflow/core/common_runtime/placer.cc b/tensorflow/core/common_runtime/placer.cc index 6781c87f6c..613470365d 100644 --- a/tensorflow/core/common_runtime/placer.cc +++ b/tensorflow/core/common_runtime/placer.cc @@ -41,10 +41,8 @@ namespace { const StringPiece kColocationAttrNameStringPiece(kColocationAttrName); const StringPiece kColocationGroupPrefixStringPiece(kColocationGroupPrefix); -// Returns a list of devices sorted by preferred type and then name -// from 'devices' whose type is in 'supported_device_types'. This -// function searches the device types in 'supported_device_types' and -// returns the subset of devices that match. +// Returns a list of devices having type in supported_device_types. The +// returned list is sorted by preferred type (higher numeric type is preferred). std::vector FilterSupportedDevices( const std::vector& devices, const DeviceTypeVector& supported_device_types) { @@ -81,12 +79,12 @@ std::vector FilterSupportedDevices( // DeviceSet device_set = ...; // ColocationGraph colocation_graph(graph, device_set); // -// // Add all the nodes of graph to colocation_graph. +// // Add all the nodes of the `graph` to the `colocation_graph`. // for (Node* node : graph.nodes()) { // TF_RETURN_IF_ERROR(colocation_graph.AddNode(*node)); // } // -// // Add one or more colocation constraint. +// // Add one or more colocation constraints. // Node node_1 = *graph.FindNodeId(...); // Node node_2 = *graph.FindNodeId(...); // TF_RETURN_IF_ERROR(colocation_graph.ColocateNodes(node_1, node_2)); @@ -96,9 +94,9 @@ std::vector FilterSupportedDevices( // TF_RETURN_IF_ERROR(colocation_graph.AssignDevice(node)); // } // -// The implementation uses the union-find algorithm to maintain the -// connected components efficiently and incrementally as edges -// (implied by ColocationGraph::ColocateNodes() invocations) are added. +// This implementation uses the Union-Find algorithm to efficiently maintain the +// connected components and incrementally adds edges via +// ColocationGraph::ColocateNodes() invocations. class ColocationGraph { public: ColocationGraph(Graph* graph, const DeviceSet* device_set, @@ -134,13 +132,9 @@ class ColocationGraph { std::unordered_map colocation_group_root; - for (Node* node : graph_->nodes()) { - if (!node->IsOp()) { - continue; - } - - // When adding the node, identify whether it is part of a - // colocation group. + for (Node* node : graph_->op_nodes()) { + // When adding the node, identify whether it is part of a colocation + // group. // This code is effectively the equivalent of GetNodeAttr() for a string // array, but it avoids all internal allocations (the allocation of the @@ -219,11 +213,10 @@ class ColocationGraph { Member& x_root_member = members_[x_root]; Member& y_root_member = members_[y_root]; - // Merge the sets by swinging the parent pointer of the smaller - // tree to point to the root of the larger tree. Together with - // path compression in ColocationGraph::FindRoot, this ensures - // that we do not experience pathological performance on graphs - // such as chains. + // Merge the sets by setting the parent pointer of the smaller tree's root + // node to point to the root of the larger tree. Together with path + // compression in ColocationGraph::FindRoot, this ensures that we do not + // experience pathological performance on graphs such as chains. int new_root, old_root; if (x_root_member.rank < y_root_member.rank) { // The tree rooted at x_root is shallower, so connect it to @@ -611,22 +604,16 @@ class ColocationGraph { // given id is connected. int FindRoot(int node_id) { Member& member = members_[node_id]; - - int parent = member.parent; - DCHECK_GE(parent, 0); - - if (parent != node_id) { - // NOTE: Compress paths from node_id to its root, so that future - // calls to FindRoot and ColocateNodes are more efficient. - int root = FindRoot(parent); - if (parent != root) { - parent = root; - member.parent = root; - } + DCHECK_GE(member.parent, 0); + if (member.parent == node_id) { + // member.parent is the root of this disjoint tree. Do nothing. + } else { + member.parent = FindRoot(member.parent); } - - DCHECK_GE(parent, 0); - return parent; + // Now it is guaranteed that member.parent is the root of this disjoint + // tree. + DCHECK_GE(member.parent, 0); + return member.parent; } // Ensures that the devices of 'dst's resource and reference match the device -- GitLab From 38bdd18f4babf01178f13509578d7fc85cf05f51 Mon Sep 17 00:00:00 2001 From: Lasse Espeholt Date: Thu, 26 Jul 2018 05:02:33 -0700 Subject: [PATCH 435/519] Internal change. PiperOrigin-RevId: 206139578 --- tensorflow/contrib/tpu/BUILD | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/contrib/tpu/BUILD b/tensorflow/contrib/tpu/BUILD index 643a7cc13a..5a7825f29a 100644 --- a/tensorflow/contrib/tpu/BUILD +++ b/tensorflow/contrib/tpu/BUILD @@ -15,6 +15,7 @@ package( default_visibility = [ "//cloud/vmm/testing/tests/tpu:__subpackages__", "//learning/brain:__subpackages__", + "//learning/deepmind:__subpackages__", "//tensorflow:__subpackages__", ], ) -- GitLab From 6d71d3fc659b317a38586f71ae94410ad3261f55 Mon Sep 17 00:00:00 2001 From: Adrian Kuegel Date: Thu, 26 Jul 2018 06:27:38 -0700 Subject: [PATCH 436/519] Support sorting of key/value pairs on GPU. This requires a slight modification in the emitted compare loop: now, we use another if to check if we need to swap instead of two selects. Speed is mostly the same, possibly even a little bit faster. PiperOrigin-RevId: 206148647 --- .../xla/service/gpu/ir_emitter_unnested.cc | 47 ++++++++++++++----- .../compiler/xla/service/llvm_ir/sort_util.cc | 40 ++++++++++------ .../compiler/xla/service/llvm_ir/sort_util.h | 2 + 3 files changed, 64 insertions(+), 25 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index db6a4e6f30..5445d7b3ab 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -75,6 +75,7 @@ limitations under the License. #include "tensorflow/core/lib/core/bits.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/optional.h" #include "tensorflow/core/platform/logging.h" namespace xla { @@ -2055,27 +2056,48 @@ Status IrEmitterUnnested::HandleSelect(HloInstruction* select) { Status IrEmitterUnnested::HandleSort(HloInstruction* sort) { std::vector> thunks; auto values = sort->operand_count() > 1 ? sort->operand(1) : nullptr; + ShapeIndex keys_shape_index({}); + ShapeIndex values_shape_index({}); if (values != nullptr) { - // TODO(b/26783907): Also sort the values by their corresponding key. - return Unimplemented("Key/Value Sort is not implemented on GPU"); + keys_shape_index = ShapeIndex({0}); + values_shape_index = ShapeIndex({1}); } + auto keys_destination = GetAllocationSlice(*sort, keys_shape_index); - // First copy the operand to the output, so that we can sort in-place. + // First copy the operand(s) to the output, so that we can sort in-place. // TODO(b/26783907): Share buffer of output and operand when it is possible. if (sort->operand(0)->IsConstant()) { thunks.push_back(MakeUnique( /*source_address=*/sort->operand(0)->literal().untyped_data(), - /*destination_buffer=*/GetAllocationSlice(*sort), - /*mem_size=*/ShapeUtil::ByteSizeOf(sort->shape()), sort)); + /*destination_buffer=*/keys_destination, + /*mem_size=*/ShapeUtil::ByteSizeOf(sort->operand(0)->shape()), + nullptr)); } else { thunks.push_back(MakeUnique( /*source_address=*/GetAllocationSlice(*sort->operand(0)), - /*destination_buffer=*/GetAllocationSlice(*sort), - /*mem_size=*/ShapeUtil::ByteSizeOf(sort->shape()), sort)); + /*destination_buffer=*/keys_destination, + /*mem_size=*/ShapeUtil::ByteSizeOf(sort->operand(0)->shape()), + nullptr)); + } + if (values != nullptr) { + if (values->IsConstant()) { + thunks.push_back(MakeUnique( + /*source_address=*/sort->operand(1)->literal().untyped_data(), + /*destination_buffer=*/GetAllocationSlice(*sort, values_shape_index), + /*mem_size=*/ShapeUtil::ByteSizeOf(sort->operand(1)->shape()), + nullptr)); + } else { + thunks.push_back(MakeUnique( + /*source_address=*/GetAllocationSlice(*sort->operand(1)), + /*destination_buffer=*/GetAllocationSlice(*sort, values_shape_index), + /*mem_size=*/ShapeUtil::ByteSizeOf(sort->operand(1)->shape()), + nullptr)); + } } int64 dimension_to_sort = sort->dimensions(0); - int64 dimension_to_sort_bound = sort->shape().dimensions(dimension_to_sort); + int64 dimension_to_sort_bound = + sort->operand(0)->shape().dimensions(dimension_to_sort); int64 num_stages = tensorflow::Log2Ceiling(dimension_to_sort_bound); auto index_type = b_.getInt64Ty(); @@ -2099,7 +2121,7 @@ Status IrEmitterUnnested::HandleSort(HloInstruction* sort) { thunks.push_back( BuildKernelThunk(sort, /*implements_whole_instruction=*/false)); LaunchDimensions launch_dimensions = CalculateLaunchDimensions( - sort->shape(), ir_emitter_context_->device_description()); + sort->operand(0)->shape(), ir_emitter_context_->device_description()); UpdateLaunchDimensions(launch_dimensions, thunks.back().get(), ir_emitter_context_->llvm_module()); @@ -2111,8 +2133,11 @@ Status IrEmitterUnnested::HandleSort(HloInstruction* sort) { } TF_RETURN_IF_ERROR(llvm_ir::EmitSortInPlace( - dimension_to_sort, GetIrArray(*sort, *sort), IrName(sort), xor_mask, - &b_, &launch_dimensions)); + dimension_to_sort, GetIrArray(*sort, *sort, keys_shape_index), + values != nullptr ? tensorflow::gtl::make_optional( + GetIrArray(*sort, *sort, values_shape_index)) + : tensorflow::gtl::nullopt, + IrName(sort), xor_mask, &b_, &launch_dimensions)); } } diff --git a/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc b/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc index 6f261c32f4..5187948e29 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/sort_util.cc @@ -30,6 +30,7 @@ limitations under the License. #include "tensorflow/compiler/xla/shape_util.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/lib/gtl/optional.h" #include "tensorflow/core/platform/types.h" namespace xla { @@ -38,19 +39,18 @@ namespace llvm_ir { namespace { // Adds the inner comparison loop where we compare elements pointed to by // 'keys_index' and 'compare_keys_index'. -void EmitCompareLoop(int64 dimension_to_sort, - const llvm_ir::IrArray::Index& keys_index, - const llvm_ir::IrArray::Index& compare_keys_index, - const llvm_ir::IrArray& keys_array, llvm::IRBuilder<>* b) { - // TODO(b/26783907): parallelize this loop. - +void EmitCompareLoop(int64 dimension_to_sort, const IrArray::Index& keys_index, + const IrArray::Index& compare_keys_index, + const IrArray& keys_array, + const tensorflow::gtl::optional& values_array, + llvm::IRBuilder<>* b) { // if (is_smaller_index && // compare_keys[dimension_to_sort] < dimension_to_sort_bound) llvm::Value* is_smaller_index = b->CreateICmpSLT( keys_index[dimension_to_sort], compare_keys_index[dimension_to_sort]); int64 dimension_to_sort_bound = keys_array.GetShape().dimensions(dimension_to_sort); - auto if_data = llvm_ir::EmitIfThenElse( + auto if_data = EmitIfThenElse( b->CreateAnd(is_smaller_index, b->CreateICmpSLT(compare_keys_index[dimension_to_sort], keys_index.GetConstantWithIndexType( @@ -63,19 +63,31 @@ void EmitCompareLoop(int64 dimension_to_sort, auto comparison = primitive_util::IsFloatingPointType(key_type) // TODO(b/26783907): Figure out how to handle NaNs. - ? b->CreateFCmp(llvm::FCmpInst::FCMP_ULT, key1, key2) + ? b->CreateFCmp(llvm::FCmpInst::FCMP_ULT, key2, key1) : b->CreateICmp(primitive_util::IsSignedIntegralType(key_type) ? llvm::ICmpInst::ICMP_SLT : llvm::ICmpInst::ICMP_ULT, - key1, key2); - auto min_key = b->CreateSelect(comparison, key1, key2); - auto max_key = b->CreateSelect(comparison, key2, key1); - keys_array.EmitWriteArrayElement(keys_index, min_key, b); - keys_array.EmitWriteArrayElement(compare_keys_index, max_key, b); + key2, key1); + // If key2 < key1 + auto if_smaller_data = + EmitIfThenElse(comparison, "is_smaller_than", b, /*emit_else=*/false); + SetToFirstInsertPoint(if_smaller_data.true_block, b); + // Swap key1 with key2. + keys_array.EmitWriteArrayElement(keys_index, key2, b); + keys_array.EmitWriteArrayElement(compare_keys_index, key1, b); + if (values_array.has_value()) { + // Also swap the values. + auto value1 = values_array.value().EmitReadArrayElement(keys_index, b); + auto value2 = + values_array.value().EmitReadArrayElement(compare_keys_index, b); + values_array.value().EmitWriteArrayElement(keys_index, value2, b); + values_array.value().EmitWriteArrayElement(compare_keys_index, value1, b); + } } } // namespace Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, + const tensorflow::gtl::optional& values_array, tensorflow::StringPiece name, llvm::Value* xor_mask, llvm::IRBuilder<>* b, const gpu::LaunchDimensions* launch_dimensions) { @@ -131,7 +143,7 @@ Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, compare_keys_index[dimension_to_sort] = b->CreateXor(compare_index[0], xor_mask); EmitCompareLoop(dimension_to_sort, keys_index, compare_keys_index, - keys_array, b); + keys_array, values_array, b); return Status::OK(); }; if (launch_dimensions != nullptr) { diff --git a/tensorflow/compiler/xla/service/llvm_ir/sort_util.h b/tensorflow/compiler/xla/service/llvm_ir/sort_util.h index e75f9b08fb..8458744c6b 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/sort_util.h +++ b/tensorflow/compiler/xla/service/llvm_ir/sort_util.h @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/llvm_ir/ir_array.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/core/stringpiece.h" +#include "tensorflow/core/lib/gtl/optional.h" #include "tensorflow/core/platform/types.h" namespace xla { @@ -30,6 +31,7 @@ namespace llvm_ir { // implements the inner loop of BitonicSort. If 'launch_dimensions' is nullptr, // the inner compare loop will not be parallelized. Status EmitSortInPlace(int64 dimension_to_sort, const IrArray& keys_array, + const tensorflow::gtl::optional& values_array, tensorflow::StringPiece name, llvm::Value* xor_mask, llvm::IRBuilder<>* b, const gpu::LaunchDimensions* launch_dimensions); -- GitLab From b0cdcbe37c219afda23cbb7fe7242486ce6066d4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 08:07:27 -0700 Subject: [PATCH 437/519] A TF Lite kernel to execute TensorFlow delegated ops via Eager. PiperOrigin-RevId: 206160001 --- tensorflow/contrib/lite/delegates/eager/BUILD | 37 +++ .../lite/delegates/eager/buffer_map.cc | 4 + .../lite/delegates/eager/delegate_data.cc | 3 +- .../contrib/lite/delegates/eager/kernel.cc | 265 ++++++++++++++++++ .../contrib/lite/delegates/eager/kernel.h | 34 +++ .../lite/delegates/eager/kernel_test.cc | 204 ++++++++++++++ 6 files changed, 546 insertions(+), 1 deletion(-) create mode 100644 tensorflow/contrib/lite/delegates/eager/kernel.cc create mode 100644 tensorflow/contrib/lite/delegates/eager/kernel.h create mode 100644 tensorflow/contrib/lite/delegates/eager/kernel_test.cc diff --git a/tensorflow/contrib/lite/delegates/eager/BUILD b/tensorflow/contrib/lite/delegates/eager/BUILD index 03a4b7bf1d..a28707382e 100644 --- a/tensorflow/contrib/lite/delegates/eager/BUILD +++ b/tensorflow/contrib/lite/delegates/eager/BUILD @@ -67,6 +67,43 @@ cc_test( ], ) +cc_library( + name = "kernel", + srcs = ["kernel.cc"], + hdrs = ["kernel.h"], + deps = [ + ":delegate_data", + ":util", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite:kernel_api", + "//tensorflow/contrib/lite/kernels:kernel_util", + "//tensorflow/core:protos_all_cc", + "//tensorflow/core/common_runtime/eager:context", + "//tensorflow/core/common_runtime/eager:execute", + "//tensorflow/core/common_runtime/eager:tensor_handle", + "@flatbuffers", + ], +) + +cc_test( + name = "kernel_test", + size = "small", + srcs = ["kernel_test.cc"], + tags = [ + "no_oss", + "tflite_not_portable", + ], + deps = [ + ":delegate_data", + ":kernel", + "//tensorflow/contrib/lite/kernels:test_util", + "//tensorflow/contrib/lite/testing:util", + "@com_google_absl//absl/memory", + "@com_google_googletest//:gtest", + "@flatbuffers", + ], +) + cc_library( name = "util", srcs = ["util.cc"], diff --git a/tensorflow/contrib/lite/delegates/eager/buffer_map.cc b/tensorflow/contrib/lite/delegates/eager/buffer_map.cc index 1d6453f498..e5a19c3997 100644 --- a/tensorflow/contrib/lite/delegates/eager/buffer_map.cc +++ b/tensorflow/contrib/lite/delegates/eager/buffer_map.cc @@ -91,6 +91,10 @@ void BufferMap::SetFromTfLite(int tensor_index, const TfLiteTensor* tensor) { for (int i = 0; i < num_dims; ++i) { shape.AddDim(tensor->dims->data[i]); } + // TODO(ahentz): we assume this is a new tensor and allocate a new buffer + // for it. This is not always the best approach. For example, this might + // be a reallocation after resizing tensors. In that case we would be + // preferable to somehow reuse the buffer. auto* buf = new TfLiteTensorBuffer(tensor); tensorflow::Tensor t = tensorflow::TensorCApi::MakeTensor( GetTensorFlowDataType(tensor->type), shape, buf); diff --git a/tensorflow/contrib/lite/delegates/eager/delegate_data.cc b/tensorflow/contrib/lite/delegates/eager/delegate_data.cc index 29687694bd..0fd5c976f8 100644 --- a/tensorflow/contrib/lite/delegates/eager/delegate_data.cc +++ b/tensorflow/contrib/lite/delegates/eager/delegate_data.cc @@ -23,7 +23,8 @@ tensorflow::Status DelegateData::Create(std::unique_ptr* data) { std::vector devices; TF_RETURN_IF_ERROR(tensorflow::DeviceFactory::AddDevices( - tensorflow::SessionOptions(), "/device:cpu:*", &devices)); + tensorflow::SessionOptions(), "/job:localhost/replica:0/task:0", + &devices)); std::unique_ptr device_mgr( new tensorflow::DeviceMgr(devices)); diff --git a/tensorflow/contrib/lite/delegates/eager/kernel.cc b/tensorflow/contrib/lite/delegates/eager/kernel.cc new file mode 100644 index 0000000000..7235867ca5 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/kernel.cc @@ -0,0 +1,265 @@ +/* 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/lite/delegates/eager/kernel.h" + +#include "third_party/flatbuffers/include/flatbuffers/flexbuffers.h" +#include "tensorflow/contrib/lite/builtin_ops.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/context_util.h" +#include "tensorflow/contrib/lite/delegates/eager/delegate_data.h" +#include "tensorflow/contrib/lite/delegates/eager/util.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/core/common_runtime/eager/context.h" +#include "tensorflow/core/common_runtime/eager/execute.h" +#include "tensorflow/core/common_runtime/eager/tensor_handle.h" +#include "tensorflow/core/framework/node_def.pb.h" + +// Note: this is part of TF Lite's Eager delegation code which is to be +// completed soon. + +// This is the TF Lite op that is created by the eager delegate to handle +// execution of a supported subgraph. The usual flow is that the delegate +// informs the interpreter of supported nodes in a graph, and each supported +// subgraph is replaced with one instance of this kernel. +// +// The kernel is initialized with TfLiteDelegateParams from which we retrieve +// the global EagerContext and BufferMap, as well as a list of inputs and +// outputs to the subgraph. Those are used to build the OpData, with a list of +// TensorFlow Ops that should be executed in order (which we call an OpNode). +// +// For each node included in the subgraph, we query the interpreter and +// retrieve the associated NodeDef, which is then used to configure the +// corresponding TensorFlow/Eager Op. + +namespace tflite { +namespace eager { +namespace kernel { +// Executes the TensorFlow op given by 'op_name', with the attributes specified +// in 'nodedef'. Inputs and outputs are given as indices into the 'buffer_map'. +tensorflow::Status ExecuteEagerOp(tensorflow::EagerContext* eager_context, + BufferMap* buffer_map, const string& op_name, + const tensorflow::NodeDef& nodedef, + const std::vector& inputs, + const std::vector& outputs) { + const tensorflow::AttrTypeMap* attr_types; + TF_RETURN_IF_ERROR( + tensorflow::AttrTypeMapForOp(op_name.c_str(), &attr_types)); + + tensorflow::EagerOperation op(eager_context, op_name.c_str(), attr_types); + for (const auto& attr : nodedef.attr()) { + op.MutableAttrs()->Set(attr.first, attr.second); + } + + for (int input_index : inputs) { + if (!buffer_map->HasTensor(input_index)) { + return tensorflow::errors::Internal("Invalid tensor index ", input_index); + } + auto* handle = new tensorflow::TensorHandle( + buffer_map->GetTensor(input_index), nullptr, nullptr, nullptr); + op.AddInput(handle); + handle->Unref(); + } + + int num_retvals = outputs.size(); + tensorflow::gtl::InlinedVector retvals( + num_retvals, nullptr); + + TF_RETURN_IF_ERROR(EagerExecute(&op, &retvals, &num_retvals)); + + if (outputs.size() != num_retvals) { + return tensorflow::errors::Internal( + "Unexpected number of outputs from EagerExecute"); + } + + for (int i = 0; i < num_retvals; ++i) { + const tensorflow::Tensor* tensor = nullptr; + TF_RETURN_IF_ERROR(retvals[i]->Tensor(&tensor)); + buffer_map->SetFromTensorFlow(outputs[i], *tensor); + retvals[i]->Unref(); + } + + return tensorflow::Status::OK(); +} + +// A single node within the larger 'op'. Note that this kernel executes many +// TensorFlow ops within a single TF Lite op. +struct OpNode { + // The name of the TensorFlow op to execute. + string name; + // The corresponding NodeDef, containing the attributes for the op. + tensorflow::NodeDef nodedef; + // List of inputs, as TF Lite tensor indices. + std::vector inputs; + // List of outputs, as TF Lite tensor indices. + std::vector outputs; +}; + +// The Larger 'op', which contains all the nodes in a supported subgraph. +struct OpData { + tensorflow::EagerContext* eager_context; + BufferMap* buffer_map; + std::vector nodes; + std::vector subgraph_inputs; + std::vector subgraph_outputs; +}; + +void* Init(TfLiteContext* context, const char* buffer, size_t length) { + auto* op_data = new OpData; + + const TfLiteDelegateParams* params = + reinterpret_cast(buffer); + CHECK(params); + CHECK(params->delegate); + CHECK(params->delegate->data_); + op_data->eager_context = + reinterpret_cast(params->delegate->data_) + ->GetEagerContext(); + op_data->buffer_map = + reinterpret_cast(params->delegate->data_)->GetBufferMap(); + + CHECK(params->output_tensors); + for (auto tensor_index : TfLiteIntArrayView(params->output_tensors)) { + op_data->subgraph_outputs.push_back(tensor_index); + } + + CHECK(params->input_tensors); + for (auto tensor_index : TfLiteIntArrayView(params->input_tensors)) { + op_data->subgraph_inputs.push_back(tensor_index); + } + + CHECK(params->nodes_to_replace); + for (auto node_index : TfLiteIntArrayView(params->nodes_to_replace)) { + TfLiteNode* node; + TfLiteRegistration* reg; + context->GetNodeAndRegistration(context, node_index, &node, ®); + + op_data->nodes.emplace_back(OpNode()); + OpNode& node_data = op_data->nodes.back(); + + node_data.name = ""; + if (node->custom_initial_data) { + // The flexbuffer contains a vector where the first elements is the + // op name and the second is a serialized NodeDef. + const flexbuffers::Vector& v = + flexbuffers::GetRoot( + reinterpret_cast(node->custom_initial_data), + node->custom_initial_data_size) + .AsVector(); + + node_data.name = v[0].AsString().str(); + if (!node_data.nodedef.ParseFromString(v[1].AsString().str())) { + // We will just leave the nodedef empty and error out in Eval(). + node_data.nodedef.Clear(); + } + } + + for (auto input_index : TfLiteIntArrayView(node->inputs)) { + node_data.inputs.push_back(input_index); + } + for (auto output_index : TfLiteIntArrayView(node->outputs)) { + node_data.outputs.push_back(output_index); + } + } + + return op_data; +} + +void Free(TfLiteContext* context, void* buffer) { + delete reinterpret_cast(buffer); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + const auto* op_data = reinterpret_cast(node->user_data); + TF_LITE_ENSURE_MSG( + context, op_data->eager_context != nullptr, + "Failed to initialize eager context. This often happens when a CPU " + "device has not been registered, presumably because some symbols from " + "tensorflow/core:core_cpu_impl were not linked into the binary."); + + // Whenever we find a constant tensor, insert it in the buffer map. + BufferMap* buffer_map = op_data->buffer_map; + for (auto tensor_index : op_data->subgraph_inputs) { + TfLiteTensor* tensor = &context->tensors[tensor_index]; + if (IsConstantTensor(tensor)) { + if (!buffer_map->HasTensor(tensor_index)) { + buffer_map->SetFromTfLite(tensor_index, tensor); + } + } + } + + // All output tensors are allocated by TensorFlow/Eager, so we + // mark them as kTfLiteDynamic. + for (auto tensor_index : op_data->subgraph_outputs) { + SetTensorToDynamic(&context->tensors[tensor_index]); + } + + return kTfLiteOk; +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + const auto* op_data = reinterpret_cast(node->user_data); + BufferMap* buffer_map = op_data->buffer_map; + tensorflow::EagerContext* eager_context = op_data->eager_context; + + // Insert a tensor in the buffer map for all inputs that are not constant. + // Constants were handled in Prepare() already. + for (auto tensor_index : op_data->subgraph_inputs) { + TfLiteTensor* tensor = &context->tensors[tensor_index]; + if (!IsConstantTensor(tensor)) { + buffer_map->SetFromTfLite(tensor_index, tensor); + } + } + + // Execute the TensorFlow Ops sequentially. + for (const auto& node_data : op_data->nodes) { + if (node_data.nodedef.op().empty()) { + context->ReportError(context, "Invalid NodeDef in Eager op '%s'", + node_data.name.c_str()); + return kTfLiteError; + } + auto status = + ExecuteEagerOp(eager_context, buffer_map, node_data.name, + node_data.nodedef, node_data.inputs, node_data.outputs); + TF_LITE_ENSURE_OK(context, ConvertStatus(context, status)); + } + + for (auto tensor_index : op_data->subgraph_outputs) { + if (!buffer_map->HasTensor(tensor_index)) { + context->ReportError(context, "Invalid tensor index %d", tensor_index); + return kTfLiteError; + } + + TfLiteTensor* tensor = &context->tensors[tensor_index]; + TF_LITE_ENSURE_OK( + context, + CopyShape(context, buffer_map->GetTensor(tensor_index), tensor)); + tensor->buffer_handle = tensor_index; + tensor->data_is_stale = true; + } + + return kTfLiteOk; +} + +} // namespace kernel + +TfLiteRegistration GetKernel() { + TfLiteRegistration registration{&kernel::Init, &kernel::Free, + &kernel::Prepare, &kernel::Eval, + nullptr, kTfLiteBuiltinDelegate}; + return registration; +} + +} // namespace eager +} // namespace tflite diff --git a/tensorflow/contrib/lite/delegates/eager/kernel.h b/tensorflow/contrib/lite/delegates/eager/kernel.h new file mode 100644 index 0000000000..100672c82d --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/kernel.h @@ -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. +==============================================================================*/ +#ifndef TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_KERNEL_H_ +#define TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_KERNEL_H_ + +#include "tensorflow/contrib/lite/context.h" + +namespace tflite { +namespace eager { + +// Return the registration object used to initialize and execute ops that will +// be delegated to TensorFlow's Eager runtime. This TF Lite op is created by +// the eager delegate to handle execution of a supported subgraph. The usual +// flow is that the delegate informs the interpreter of supported nodes in a +// graph, and each supported subgraph is replaced with one instance of this +// kernel. +TfLiteRegistration GetKernel(); + +} // namespace eager +} // namespace tflite + +#endif // TENSORFLOW_CONTRIB_LITE_DELEGATES_EAGER_KERNEL_H_ diff --git a/tensorflow/contrib/lite/delegates/eager/kernel_test.cc b/tensorflow/contrib/lite/delegates/eager/kernel_test.cc new file mode 100644 index 0000000000..f0e3d432f9 --- /dev/null +++ b/tensorflow/contrib/lite/delegates/eager/kernel_test.cc @@ -0,0 +1,204 @@ +/* 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/lite/delegates/eager/kernel.h" + +#include +#include +#include "absl/memory/memory.h" +#include "third_party/flatbuffers/include/flatbuffers/flexbuffers.h" +#include "tensorflow/contrib/lite/delegates/eager/delegate_data.h" +#include "tensorflow/contrib/lite/kernels/test_util.h" +#include "tensorflow/contrib/lite/testing/util.h" + +namespace tflite { +namespace eager { +namespace { + +using tensorflow::protobuf::TextFormat; +using ::testing::ElementsAre; + +// We will use these are custom_names, so they need to be static. +static const char kUnpack[] = "Unpack"; +static const char kAdd[] = "Add"; +static const char kMul[] = "Mul"; + +TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteDelegate* delegate, + const std::vector& supported_nodes) { + TfLiteIntArray* size_and_nodes = + ConvertVectorToTfLiteIntArray(supported_nodes); + context->ReplaceSubgraphsWithDelegateKernels(context, eager::GetKernel(), + size_and_nodes, delegate); + TfLiteIntArrayFree(size_and_nodes); + return kTfLiteOk; +} + +class KernelTest : public ::testing::Test { + public: + KernelTest() { + CHECK(DelegateData::Create(&delegate_data_).ok()); + interpreter_.reset(new Interpreter); + } + + void Invoke() { ASSERT_EQ(interpreter_->Invoke(), kTfLiteOk); } + + void SetValues(int tensor_index, const std::vector& values) { + float* v = interpreter_->typed_tensor(tensor_index); + for (float f : values) { + *v++ = f; + } + } + + std::vector GetValues(int tensor_index) { + TfLiteTensor* o = interpreter_->tensor(tensor_index); + return std::vector(o->data.f, o->data.f + o->bytes / sizeof(float)); + } + + void SetShape(int tensor_index, const std::vector& values) { + ASSERT_EQ(interpreter_->ResizeInputTensor(tensor_index, values), kTfLiteOk); + ASSERT_EQ(interpreter_->AllocateTensors(), kTfLiteOk); + } + + std::vector GetShape(int tensor_index) { + std::vector result; + auto* dims = interpreter_->tensor(tensor_index)->dims; + for (int i = 0; i < dims->size; ++i) { + result.push_back(dims->data[i]); + } + return result; + } + + template + void ConfigureDelegate(T prepare_function) { + delegate_.data_ = delegate_data_.get(); + delegate_.FreeBufferHandle = nullptr; + delegate_.Prepare = prepare_function; + delegate_.CopyFromBufferHandle = [](TfLiteDelegate* delegate, + TfLiteBufferHandle buffer_handle, + void* data, size_t size) { + auto* delegate_data = reinterpret_cast(delegate->data_); + tensorflow::StringPiece values = + delegate_data->GetBufferMap()->GetTensor(buffer_handle).tensor_data(); + memcpy(data, values.data(), values.size()); + return kTfLiteOk; + }; + CHECK(interpreter_->ModifyGraphWithDelegate( + &delegate_, /*allow_dynamic_tensors=*/true) == kTfLiteOk); + } + + void AddOp(const char* name, const std::vector& inputs, + const std::vector& outputs) { + auto attr = [](const string& key, const string& value) { + return " attr{ key: '" + key + "' value {" + value + "}}"; + }; + + if (name == string(kUnpack)) { + string attributes = attr("T", "type: DT_FLOAT") + attr("num", "i: 2") + + attr("axis", "i: 0"); + AddTfOp(name, attributes, inputs, outputs); + } else if (name == string(kAdd)) { + string attributes = attr("T", "type: DT_FLOAT"); + AddTfOp(name, attributes, inputs, outputs); + } else if (name == string(kMul)) { + string attributes = attr("T", "type: DT_FLOAT"); + AddTfOp(name, attributes, inputs, outputs); + } + } + + void AddTensors(int num_tensors, const std::vector& inputs, + const std::vector& outputs) { + interpreter_->AddTensors(num_tensors); + for (int i = 0; i < num_tensors; ++i) { + TfLiteQuantizationParams quant; + interpreter_->SetTensorParametersReadWrite(i, kTfLiteFloat32, /*name=*/"", + /*dims=*/{3}, quant); + } + + interpreter_->SetInputs(inputs); + interpreter_->SetOutputs(outputs); + } + + private: + void AddTfOp(const char* name, const string& nodedef_str, + const std::vector& inputs, + const std::vector& outputs) { + static TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr}; + reg.builtin_code = BuiltinOperator_CUSTOM; + reg.custom_name = name; + + tensorflow::NodeDef nodedef; + CHECK(TextFormat::ParseFromString(nodedef_str + " op: '" + name + "'", + &nodedef)); + string serialized_nodedef; + CHECK(nodedef.SerializeToString(&serialized_nodedef)); + flexbuffers::Builder fbb; + fbb.Vector([&]() { + fbb.String(nodedef.op()); + fbb.String(serialized_nodedef); + }); + fbb.Finish(); + + flexbuffers_.push_back(fbb.GetBuffer()); + auto& buffer = flexbuffers_.back(); + interpreter_->AddNodeWithParameters( + inputs, outputs, reinterpret_cast(buffer.data()), + buffer.size(), nullptr, ®); + } + + std::unique_ptr interpreter_; + std::unique_ptr delegate_data_; + TfLiteDelegate delegate_; + std::vector> flexbuffers_; +}; + +// TODO(ahentz): add a few more tests. In particular we need to be able to use +// TF Lite ops along with the Eager ops, and we should check that having two or +// more separate eager kernels (disjoint subgraphs) is OK. Also, we should be +// verifying failure modes too. +TEST_F(KernelTest, FullGraph) { + // Define the graph. + AddTensors(9, {0, 3}, {8}); + + AddOp(kUnpack, {0}, {1, 2}); + AddOp(kUnpack, {3}, {4, 5}); + AddOp(kAdd, {1, 4}, {6}); + AddOp(kAdd, {2, 5}, {7}); + AddOp(kMul, {6, 7}, {8}); + + // Apply Delegate. + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0, 1, 2, 3, 4}); + }); + + // Define inputs. + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + SetShape(3, {2, 2, 1}); + SetValues(3, {1.1f, 2.2f, 3.3f, 4.4f}); + + Invoke(); + + ASSERT_THAT(GetShape(8), ElementsAre(2, 1)); + ASSERT_THAT(GetValues(8), ElementsAre(14.52f, 38.72f)); +} + +} // namespace +} // namespace eager +} // namespace tflite + +int main(int argc, char** argv) { + ::tflite::LogToStderr(); + ::testing::InitGoogleTest(&argc, argv); + return RUN_ALL_TESTS(); +} -- GitLab From 47b73b8b255b6fdb439bc128d66d03f163c7920e Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Thu, 26 Jul 2018 08:27:01 -0700 Subject: [PATCH 438/519] Update error messages in tflite_diff. PiperOrigin-RevId: 206162326 --- tensorflow/contrib/lite/testing/generated_examples_zip_test.cc | 3 ++- tensorflow/contrib/lite/testing/tf_driver.cc | 2 +- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc index 770092e12c..106cbc1b8e 100644 --- a/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc +++ b/tensorflow/contrib/lite/testing/generated_examples_zip_test.cc @@ -226,7 +226,8 @@ TEST_P(OpsTest, RunZipTests) { string message = test_driver.GetErrorMessage(); if (bug_number.empty()) { if (FLAGS_use_nnapi && FLAGS_ignore_unsupported_nnapi && !result) { - EXPECT_EQ(message, string("Failed to invoke interpreter")) << message; + EXPECT_EQ(message, string("Failed to invoke NNAPI interpreter")) + << message; } else { EXPECT_TRUE(result) << message; } diff --git a/tensorflow/contrib/lite/testing/tf_driver.cc b/tensorflow/contrib/lite/testing/tf_driver.cc index d6a6ff8f56..ec435ca60d 100644 --- a/tensorflow/contrib/lite/testing/tf_driver.cc +++ b/tensorflow/contrib/lite/testing/tf_driver.cc @@ -179,7 +179,7 @@ void TfDriver::Invoke() { auto status = session_->Run({input_tensors_.begin(), input_tensors_.end()}, output_names_, {}, &output_tensors_); if (!status.ok()) { - Invalidate("Failed to invoke interpreter"); + Invalidate("Failed to run input data on graph"); } } -- GitLab From bb46cc3bf2f08fc67594828197f554c7d46db6db Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 08:38:13 -0700 Subject: [PATCH 439/519] remove as much as possible from the try-blocks of contextlib context managers. PiperOrigin-RevId: 206163905 --- tensorflow/python/framework/ops.py | 40 +++++++++++++++++------------- 1 file changed, 23 insertions(+), 17 deletions(-) diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index 197317cad9..6cef8964d3 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -3836,8 +3836,8 @@ class Graph(object): Nothing. """ old_original_op = self._default_original_op + self._default_original_op = op try: - self._default_original_op = op yield finally: self._default_original_op = old_original_op @@ -3954,15 +3954,15 @@ class Graph(object): # op name regex, which constrains the initial character. if not _VALID_OP_NAME_REGEX.match(name): raise ValueError("'%s' is not a valid scope name" % name) + old_stack = self._name_stack + if not name: # Both for name=None and name="" we re-set to empty scope. + new_stack = None + elif name[-1] == "/": + new_stack = _name_from_scope_name(name) + else: + new_stack = self.unique_name(name) + self._name_stack = new_stack try: - old_stack = self._name_stack - if not name: # Both for name=None and name="" we re-set to empty scope. - new_stack = None - elif name[-1] == "/": - new_stack = _name_from_scope_name(name) - else: - new_stack = self.unique_name(name) - self._name_stack = new_stack yield "" if new_stack is None else new_stack + "/" finally: self._name_stack = old_stack @@ -4043,8 +4043,8 @@ class Graph(object): ignore_existing=False): with self.colocate_with(op, ignore_existing): if gradient_uid is not None and self._control_flow_context is not None: + self._control_flow_context.EnterGradientColocation(op, gradient_uid) try: - self._control_flow_context.EnterGradientColocation(op, gradient_uid) yield finally: self._control_flow_context.ExitGradientColocation(op, gradient_uid) @@ -4086,7 +4086,6 @@ class Graph(object): Yields: A context manager that specifies the op with which to colocate newly created ops. - """ if op is None and not ignore_existing: raise ValueError("Trying to reset colocation (op is None) but " @@ -4260,8 +4259,8 @@ class Graph(object): yields the container name. """ original_container = self._container + self._container = container_name try: - self._container = container_name yield self._container finally: self._container = original_container @@ -4971,8 +4970,8 @@ class _DefaultStack(threading.local): @tf_contextlib.contextmanager def get_controller(self, default): """A context manager for manipulating a default stack.""" + self.stack.append(default) try: - self.stack.append(default) yield default finally: # stack may be empty if reset() was called @@ -5160,13 +5159,15 @@ class _DefaultGraphStack(_DefaultStack): # pylint: disable=protected-access @tf_contextlib.contextmanager def get_controller(self, default): + context.context().context_switches.push( + default.building_function, default.as_default) try: - context.context().context_switches.push( - default.building_function, default.as_default) with super(_DefaultGraphStack, self).get_controller( default) as g, context.graph_mode(): yield g finally: + # If an exception is raised here it may be hiding a related exception in + # the try-block (just above). context.context().context_switches.pop() @@ -5202,6 +5203,9 @@ def init_scope(): `init_scope` will simply install a fresh graph as the default one. (3) The gradient tape is paused while the scope is active. + + Raises: + RuntimeError: if graph state is incompatible with this initialization. """ # pylint: enable=g-doc-return-or-yield,line-too-long @@ -5214,10 +5218,10 @@ def init_scope(): # the name scope of the current context. default_graph = get_default_graph() scope = default_graph.get_name_scope() - if scope and scope[-1] != '/': + if scope and scope[-1] != "/": # Names that end with trailing slashes are treated by `name_scope` as # absolute. - scope = scope + '/' + scope = scope + "/" inner_device_stack = default_graph._device_function_stack # pylint: disable=protected-access outer_context = None @@ -5262,6 +5266,8 @@ def init_scope(): outer_graph._device_function_stack = inner_device_stack # pylint: disable=protected-access yield finally: + # If an exception is raised here it may be hiding a related exception in + # try-block (just above). if outer_graph is not None: outer_graph._device_function_stack = outer_device_stack # pylint: disable=protected-access -- GitLab From 8786b41d67241331ce0aa45c3df5d121039d5159 Mon Sep 17 00:00:00 2001 From: Bixia Zheng Date: Thu, 26 Jul 2018 08:47:00 -0700 Subject: [PATCH 440/519] [XLA:GPU] Enable TF RNG ops for the GPU backend. Remove flag tf_enable_prng_ops_gpu. Pass Tensorflow random number generation ops to the GPU backend. PiperOrigin-RevId: 206165176 --- tensorflow/compiler/tf2xla/BUILD | 4 -- .../backend_registration_flags.cc | 63 ------------------- .../legacy_flags/backend_registration_flags.h | 49 --------------- tensorflow/compiler/tf2xla/xla_gpu_backend.cc | 11 ---- 4 files changed, 127 deletions(-) delete mode 100644 tensorflow/compiler/tf2xla/legacy_flags/backend_registration_flags.cc delete mode 100644 tensorflow/compiler/tf2xla/legacy_flags/backend_registration_flags.h diff --git a/tensorflow/compiler/tf2xla/BUILD b/tensorflow/compiler/tf2xla/BUILD index a803f29160..338943201b 100644 --- a/tensorflow/compiler/tf2xla/BUILD +++ b/tensorflow/compiler/tf2xla/BUILD @@ -140,14 +140,12 @@ cc_library( "xla_op_registry.cc", "xla_resource.cc", "xla_cpu_backend.cc", - "legacy_flags/backend_registration_flags.cc", ] + if_cuda_is_configured([ "xla_gpu_backend.cc", ]), hdrs = [ "const_analysis.h", "graph_compiler.h", - "legacy_flags/backend_registration_flags.h", "xla_compilation_device.h", "xla_compiler.h", "xla_context.h", @@ -178,11 +176,9 @@ cc_library( "//tensorflow/compiler/xla/client/lib:arithmetic", "//tensorflow/compiler/xla/client/lib:constants", "//tensorflow/compiler/xla/client/lib:numeric", - "//tensorflow/compiler/xla/legacy_flags:parse_flags_from_env", "//tensorflow/core:core_cpu", "//tensorflow/core:core_cpu_internal", "//tensorflow/core:framework", - "//tensorflow/core:framework_internal", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "//tensorflow/core:protos_all_cc", diff --git a/tensorflow/compiler/tf2xla/legacy_flags/backend_registration_flags.cc b/tensorflow/compiler/tf2xla/legacy_flags/backend_registration_flags.cc deleted file mode 100644 index 661505021f..0000000000 --- a/tensorflow/compiler/tf2xla/legacy_flags/backend_registration_flags.cc +++ /dev/null @@ -1,63 +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. -==============================================================================*/ - -// Legacy flags for the XLA bridge's backend registration modules. - -#include // NOLINT -#include - -#include "tensorflow/compiler/tf2xla/legacy_flags/backend_registration_flags.h" -#include "tensorflow/compiler/xla/legacy_flags/parse_flags_from_env.h" -#include "tensorflow/core/platform/types.h" -#include "tensorflow/core/util/command_line_flags.h" - -namespace tensorflow { -namespace legacy_flags { - -// Pointers to the parsed value of the flags and flag descriptors, initialized -// via flags_init. -static BackendRegistrationFlags* flags; -static std::vector* flag_list; -static std::once_flag flags_init; - -// Allocate *flags. Called via call_once(&flags_init,...). -static void AllocateFlags() { - flags = new BackendRegistrationFlags; - flags->tf_enable_prng_ops_gpu = false; - flag_list = new std::vector({ - Flag("tf_enable_prng_ops_gpu", &flags->tf_enable_prng_ops_gpu, - "Whether to enable PRNG ops: [RandomStandardNormal | RandomUniform " - "| RandomUniformInt | TruncatedNormal] on GPU."), - }); - xla::legacy_flags::ParseFlagsFromEnv(*flag_list); -} - -// Append to *append_to flag definitions associated with the XLA bridge's -// backend registration modules. -void AppendBackendRegistrationFlags(std::vector* append_to) { - std::call_once(flags_init, &AllocateFlags); - append_to->insert(append_to->end(), flag_list->begin(), flag_list->end()); -} - -// Return a pointer to the BackendRegistrationFlags struct; -// repeated calls return the same pointer. -// This should be called only after Flags::Parse() has returned. -BackendRegistrationFlags* GetBackendRegistrationFlags() { - std::call_once(flags_init, &AllocateFlags); - return flags; -} - -} // namespace legacy_flags -} // namespace tensorflow diff --git a/tensorflow/compiler/tf2xla/legacy_flags/backend_registration_flags.h b/tensorflow/compiler/tf2xla/legacy_flags/backend_registration_flags.h deleted file mode 100644 index 861c923dd5..0000000000 --- a/tensorflow/compiler/tf2xla/legacy_flags/backend_registration_flags.h +++ /dev/null @@ -1,49 +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. -==============================================================================*/ - -#ifndef TENSORFLOW_COMPILER_TF2XLA_LEGACY_FLAGS_BACKEND_REGISTRATION_FLAGS_H_ -#define TENSORFLOW_COMPILER_TF2XLA_LEGACY_FLAGS_BACKEND_REGISTRATION_FLAGS_H_ - -// Legacy flags for the XLA bridge's backend registration modules. - -#include - -#include "tensorflow/core/platform/types.h" -#include "tensorflow/core/util/command_line_flags.h" - -namespace tensorflow { -namespace legacy_flags { - -// Append to *flag_list flag definitions associated with the XLA bridge's -// backend registration modules. -void AppendBackendRegistrationFlags(std::vector* append_to); - -// The values of flags associated with the XLA bridge's backend registration -// module. -typedef struct { - // Whether to enable RandomUniform op on GPU backend. - // TODO (b/32333178): Remove this flag or set its default to true. - bool tf_enable_prng_ops_gpu; -} BackendRegistrationFlags; - -// Return a pointer to the BackendRegistrationFlags struct; -// repeated calls return the same pointer. -// This should be called only after Flags::Parse() has returned. -BackendRegistrationFlags* GetBackendRegistrationFlags(); - -} // namespace legacy_flags -} // namespace tensorflow - -#endif // TENSORFLOW_COMPILER_TF2XLA_LEGACY_FLAGS_BACKEND_REGISTRATION_FLAGS_H_ diff --git a/tensorflow/compiler/tf2xla/xla_gpu_backend.cc b/tensorflow/compiler/tf2xla/xla_gpu_backend.cc index dc98d4fda6..24c812297a 100644 --- a/tensorflow/compiler/tf2xla/xla_gpu_backend.cc +++ b/tensorflow/compiler/tf2xla/xla_gpu_backend.cc @@ -13,7 +13,6 @@ See the License for the specific language governing permissions and limitations under the License. ==============================================================================*/ -#include "tensorflow/compiler/tf2xla/legacy_flags/backend_registration_flags.h" #include "tensorflow/compiler/tf2xla/tf2xla_util.h" #include "tensorflow/compiler/tf2xla/xla_op_registry.h" #include "tensorflow/core/framework/kernel_def.pb.h" @@ -21,16 +20,6 @@ limitations under the License. namespace tensorflow { bool GpuOpFilter(KernelDef* kdef) { - // TODO(b/31361304): The GPU backend does not parallelize PRNG ops, leading to - // slow code. - legacy_flags::BackendRegistrationFlags* flags = - legacy_flags::GetBackendRegistrationFlags(); - VLOG(2) << "flags->tf_enable_prng_ops_gpu: " << flags->tf_enable_prng_ops_gpu; - if (!flags->tf_enable_prng_ops_gpu && - (kdef->op() == "RandomStandardNormal" || kdef->op() == "RandomUniform" || - kdef->op() == "RandomUniformInt" || kdef->op() == "TruncatedNormal")) { - return false; - } // TODO(b/26783907): The GPU backend currently does not implement sort. if (kdef->op() == "XlaSort" || kdef->op() == "TopKV2") { return false; -- GitLab From deac85da170542596ba4d1a72ef5e63c0a398aba Mon Sep 17 00:00:00 2001 From: Russell Power Date: Thu, 26 Jul 2018 08:55:08 -0700 Subject: [PATCH 441/519] Automated rollback of commit b8a9d163d9cbb4b581c044d9c4b1b256c801a9c4 PiperOrigin-RevId: 206166233 --- tensorflow/core/BUILD | 8 + tensorflow/core/common_runtime/session_ref.cc | 170 ++++++++++++++++++ tensorflow/core/common_runtime/session_ref.h | 86 +++++++++ tensorflow/python/BUILD | 1 + tensorflow/python/client/session.py | 2 +- tensorflow/python/client/tf_session.i | 1 + tensorflow/python/client/tf_session_helper.cc | 14 ++ tensorflow/python/client/tf_session_helper.h | 3 + 8 files changed, 284 insertions(+), 1 deletion(-) create mode 100644 tensorflow/core/common_runtime/session_ref.cc create mode 100644 tensorflow/core/common_runtime/session_ref.h diff --git a/tensorflow/core/BUILD b/tensorflow/core/BUILD index 84555b60da..35a112e834 100644 --- a/tensorflow/core/BUILD +++ b/tensorflow/core/BUILD @@ -2924,6 +2924,14 @@ tf_cuda_library( ] + tf_additional_device_tracer_deps(), ) +cc_library( + name = "session_ref", + srcs = ["common_runtime/session_ref.cc"], + hdrs = ["common_runtime/session_ref.h"], + copts = tf_copts(), + deps = [":core_cpu_base"], +) + cc_library( name = "gpu_id", hdrs = [ diff --git a/tensorflow/core/common_runtime/session_ref.cc b/tensorflow/core/common_runtime/session_ref.cc new file mode 100644 index 0000000000..b931ef4229 --- /dev/null +++ b/tensorflow/core/common_runtime/session_ref.cc @@ -0,0 +1,170 @@ +/* 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/common_runtime/session_ref.h" + +#include + +namespace tensorflow { + +namespace { + +// Scope helper to track active calls and manage session lifetime. +struct RunCounter { + std::shared_ptr session; + uint64* value; + mutex* m; + condition_variable* cv; + + explicit RunCounter(std::shared_ptr s, uint64* v, mutex* m, + condition_variable* cv) + : session(std::move(s)), value(v), m(m), cv(cv) { + mutex_lock l(*m); + ++*value; + } + + ~RunCounter() { + mutex_lock l(*m); + if (--*value == 0) { + cv->notify_all(); + } + } +}; + +} // namespace + +Status SessionRef::CheckNotClosed() { + mutex_lock l(run_lock_); + if (session_ == nullptr) return errors::Cancelled("Session has been closed."); + return ::tensorflow::Status::OK(); +} + +Status SessionRef::Run(const RunOptions& run_options, + const std::vector >& inputs, + const std::vector& output_tensor_names, + const std::vector& target_node_names, + std::vector* outputs, + RunMetadata* run_metadata) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Run(run_options, inputs, output_tensor_names, + target_node_names, outputs, run_metadata); +} + +Status SessionRef::Create(const GraphDef& graph) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Create(graph); +} + +Status SessionRef::Create(const RunOptions& run_options, + const GraphDef& graph) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Create(run_options, graph); +} + +Status SessionRef::Extend(const RunOptions& run_options, + const GraphDef& graph) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Extend(run_options, graph); +} + +Status SessionRef::Extend(const GraphDef& graph) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Extend(graph); +} + +Status SessionRef::Close(const RunOptions& run_options) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + mutex_lock l(run_lock_); + Status status = session_->Close(run_options); + session_.reset(); + while (run_count_ > 0) { + run_finished_.wait(l); + } + return status; +} + +Status SessionRef::Close() { + TF_RETURN_IF_ERROR(CheckNotClosed()); + mutex_lock l(run_lock_); + Status status = session_->Close(); + session_.reset(); + while (run_count_ > 0) { + run_finished_.wait(l); + } + return status; +} + +Status SessionRef::Run(const std::vector >& inputs, + const std::vector& output_tensor_names, + const std::vector& target_node_names, + std::vector* outputs) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->Run(inputs, output_tensor_names, target_node_names, + outputs); +} + +Status SessionRef::ListDevices(std::vector* response) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->ListDevices(response); +} + +Status SessionRef::PRunSetup(const std::vector& input_names, + const std::vector& output_names, + const std::vector& target_nodes, + string* handle) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->PRunSetup(input_names, output_names, target_nodes, handle); +} + +Status SessionRef::PRun(const string& handle, + const std::vector >& inputs, + const std::vector& output_names, + std::vector* outputs) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->PRun(handle, inputs, output_names, outputs); +} + +Status SessionRef::MakeCallable(const CallableOptions& callable_options, + CallableHandle* out_handle) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->MakeCallable(callable_options, out_handle); +} + +Status SessionRef::RunCallable(CallableHandle handle, + const std::vector& feed_tensors, + std::vector* fetch_tensors, + RunMetadata* run_metadata) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->RunCallable(handle, feed_tensors, fetch_tensors, + run_metadata); +} + +Status SessionRef::ReleaseCallable(CallableHandle handle) { + TF_RETURN_IF_ERROR(CheckNotClosed()); + RunCounter rc(session_, &run_count_, &run_lock_, &run_finished_); + return rc.session->ReleaseCallable(handle); +} + +} // namespace tensorflow diff --git a/tensorflow/core/common_runtime/session_ref.h b/tensorflow/core/common_runtime/session_ref.h new file mode 100644 index 0000000000..6146933326 --- /dev/null +++ b/tensorflow/core/common_runtime/session_ref.h @@ -0,0 +1,86 @@ +/* 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_CORE_COMMON_RUNTIME_SESSION_REF_H_ +#define TENSORFLOW_CORE_COMMON_RUNTIME_SESSION_REF_H_ + +#include + +#include "tensorflow/core/platform/mutex.h" +#include "tensorflow/core/public/session.h" + +namespace tensorflow { + +// A `SessionRef` manages the lifetime of a wrapped `Session` pointer. +// +// SessionRef blocks the return of Close() until all pending operations have +// been completed or cancelled and underlying session has been freed. Any +// subsequent operations on the SessionRef object will return errors::Cancelled. +class SessionRef : public Session { + public: + SessionRef(Session* session) : session_(session) {} + virtual ~SessionRef() {} + + Status Create(const GraphDef& graph) override; + Status Extend(const GraphDef& graph) override; + Status Create(const RunOptions& run_options, const GraphDef& graph) override; + Status Extend(const RunOptions& run_options, const GraphDef& graph) override; + Status Run(const std::vector >& inputs, + const std::vector& output_tensor_names, + const std::vector& target_node_names, + std::vector* outputs) override; + + Status ListDevices(std::vector* response) override; + + Status Close() override; + Status Close(const RunOptions& run_options) override; + + Status Run(const RunOptions& run_options, + const std::vector >& inputs, + const std::vector& output_tensor_names, + const std::vector& target_node_names, + std::vector* outputs, RunMetadata* run_metadata) override; + + Status PRunSetup(const std::vector& input_names, + const std::vector& output_names, + const std::vector& target_nodes, + string* handle) override; + + Status PRun(const string& handle, + const std::vector >& inputs, + const std::vector& output_names, + std::vector* outputs) override; + + Status MakeCallable(const CallableOptions& callable_options, + CallableHandle* out_handle); + + Status RunCallable(CallableHandle handle, + const std::vector& feed_tensors, + std::vector* fetch_tensors, + RunMetadata* run_metadata); + + Status ReleaseCallable(CallableHandle handle); + + private: + mutex run_lock_; + condition_variable run_finished_; + uint64 run_count_ GUARDED_BY(run_lock_) = {0}; + std::shared_ptr session_; + + Status CheckNotClosed(); +}; + +} // namespace tensorflow + +#endif // TENSORFLOW_CORE_COMMON_RUNTIME_SESSION_REF_H_ diff --git a/tensorflow/python/BUILD b/tensorflow/python/BUILD index b5876c3457..d35731d3cd 100644 --- a/tensorflow/python/BUILD +++ b/tensorflow/python/BUILD @@ -3658,6 +3658,7 @@ tf_cuda_library( "//tensorflow/core:graph", "//tensorflow/core:lib", "//tensorflow/core:protos_all_cc", + "//tensorflow/core:session_ref", "//third_party/py/numpy:headers", "//third_party/python_runtime:headers", ], diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index 180bb74d00..861230e5a0 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -630,7 +630,7 @@ class BaseSession(SessionInterface): opts = tf_session.TF_NewSessionOptions(target=self._target, config=config) try: # pylint: disable=protected-access - self._session = tf_session.TF_NewSession(self._graph._c_graph, opts) + self._session = tf_session.TF_NewSessionRef(self._graph._c_graph, opts) # pylint: enable=protected-access finally: tf_session.TF_DeleteSessionOptions(opts) diff --git a/tensorflow/python/client/tf_session.i b/tensorflow/python/client/tf_session.i index 1cdd8e0b6a..39a2922ac0 100644 --- a/tensorflow/python/client/tf_session.i +++ b/tensorflow/python/client/tf_session.i @@ -777,6 +777,7 @@ def TF_Reset(target, containers=None, config=None): $1 = &types_local; } +%unignore TF_NewSessionRef; %unignore SetRequireShapeInferenceFns; %unignore TF_TryEvaluateConstant_wrapper; %noexception TF_TryEvaluateConstant_wrapper; diff --git a/tensorflow/python/client/tf_session_helper.cc b/tensorflow/python/client/tf_session_helper.cc index b6481e7e29..bcd4af2912 100644 --- a/tensorflow/python/client/tf_session_helper.cc +++ b/tensorflow/python/client/tf_session_helper.cc @@ -20,6 +20,7 @@ limitations under the License. #include "tensorflow/c/c_api.h" #include "tensorflow/c/c_api_internal.h" #include "tensorflow/c/tf_status_helper.h" +#include "tensorflow/core/common_runtime/session_ref.h" #include "tensorflow/core/framework/allocator.h" #include "tensorflow/core/framework/attr_value.pb.h" #include "tensorflow/core/framework/attr_value_util.h" @@ -42,6 +43,19 @@ static const char* kFeedDictErrorMsg = "feed_dict must be a dictionary mapping strings to NumPy arrays."; } // end namespace +TF_Session* TF_NewSessionRef(TF_Graph* graph, const TF_SessionOptions* opts, + TF_Status* status) { + TF_Session* tf_session = TF_NewSession(graph, opts, status); + if (tf_session == nullptr) { + return nullptr; + } + + Session* session = reinterpret_cast(tf_session->session); + SessionRef* session_ref = new SessionRef(session); + tf_session->session = session_ref; + return tf_session; +} + void TF_Run_wrapper_helper(TF_DeprecatedSession* session, const char* handle, const TF_Buffer* run_options, PyObject* feed_dict, const NameVector& output_names, diff --git a/tensorflow/python/client/tf_session_helper.h b/tensorflow/python/client/tf_session_helper.h index cfd27c2bee..dab7e71aac 100644 --- a/tensorflow/python/client/tf_session_helper.h +++ b/tensorflow/python/client/tf_session_helper.h @@ -40,6 +40,9 @@ typedef tensorflow::gtl::InlinedVector PyObjectVector; // A TF_TensorVector is a vector of borrowed pointers to TF_Tensors. typedef gtl::InlinedVector TF_TensorVector; +TF_Session* TF_NewSessionRef(TF_Graph* graph, const TF_SessionOptions* opts, + TF_Status* status); + // Run the graph associated with the session starting with the // supplied inputs[]. Regardless of success or failure, inputs[] are // stolen by the implementation (i.e. the implementation will -- GitLab From 86cffb1d9201f8072cef3eb13ef0dc524e0f4535 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 09:30:06 -0700 Subject: [PATCH 442/519] fixing bug for when num of trees is not set PiperOrigin-RevId: 206171508 --- tensorflow/contrib/boosted_trees/estimator_batch/model.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/model.py b/tensorflow/contrib/boosted_trees/estimator_batch/model.py index 2fbe72951a..dbfee16b9e 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/model.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/model.py @@ -126,14 +126,15 @@ def model_builder(features, create_estimator_spec_op = getattr(head, "create_estimator_spec", None) + training_hooks = [] if num_trees: if center_bias: num_trees += 1 + finalized_trees, attempted_trees = gbdt_model.get_number_of_trees_tensor() - training_hooks = [ + training_hooks.append( trainer_hooks.StopAfterNTrees(num_trees, attempted_trees, - finalized_trees) - ] + finalized_trees)) if output_type == ModelBuilderOutputType.MODEL_FN_OPS: if use_core_libs and callable(create_estimator_spec_op): -- GitLab From 8ae93f93c0d7df9ad9b143b5d7c888026759ab85 Mon Sep 17 00:00:00 2001 From: gracehoney <31743510+aaroey@users.noreply.github.com> Date: Thu, 26 Jul 2018 09:55:19 -0700 Subject: [PATCH 443/519] Fix formatting --- .../contrib/tensorrt/test/memory_alignment_test.py | 12 +++--------- 1 file changed, 3 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/tensorrt/test/memory_alignment_test.py b/tensorflow/contrib/tensorrt/test/memory_alignment_test.py index 227a0ee6ae..3dd95c6f62 100644 --- a/tensorflow/contrib/tensorrt/test/memory_alignment_test.py +++ b/tensorflow/contrib/tensorrt/test/memory_alignment_test.py @@ -39,18 +39,12 @@ class MemoryAlignmentTest(trt_test.TfTrtIntegrationTestBase): g = ops.Graph() with g.as_default(): inp = array_ops.placeholder( - dtype=dtype, - shape=[None] + input_dims[1:], - name=input_name) + dtype=dtype, shape=[None] + input_dims[1:], name=input_name) with g.device("/GPU:0"): e1 = constant_op.constant( - np.random.randn(1, 1, 3, 5), - name="kernel_1", - dtype=dtype) + np.random.randn(1, 1, 3, 5), name="kernel_1", dtype=dtype) e2 = constant_op.constant( - np.random.randn(1, 1, 5, 10), - name="kernel_2", - dtype=dtype) + np.random.randn(1, 1, 5, 10), name="kernel_2", dtype=dtype) conv = nn.conv2d( input=inp, filter=e1, -- GitLab From 883e8b6863511b46d7985e9ff8d1809ffe2a1bc0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 10:01:50 -0700 Subject: [PATCH 444/519] Make function_utils.get_func_code more tolerant of strange objects like functool.partial. PiperOrigin-RevId: 206175973 --- tensorflow/python/framework/ops.py | 10 ++- tensorflow/python/util/function_utils.py | 14 ++-- tensorflow/python/util/function_utils_test.py | 78 +++++++++++++++++++ 3 files changed, 94 insertions(+), 8 deletions(-) diff --git a/tensorflow/python/framework/ops.py b/tensorflow/python/framework/ops.py index 6cef8964d3..c25e29b0f4 100644 --- a/tensorflow/python/framework/ops.py +++ b/tensorflow/python/framework/ops.py @@ -85,9 +85,13 @@ class _UserDeviceSpec(object): dev_func = self._device_name_or_function func_name = function_utils.get_func_name(dev_func) func_code = function_utils.get_func_code(dev_func) - self.display_name = "%s<%s, %d>" % (func_name, - func_code.co_filename, - func_code.co_firstlineno) + if func_code: + fname = func_code.co_filename + lineno = func_code.co_firstlineno + else: + fname = "unknown" + lineno = -1 + self.display_name = "%s<%s, %d>" % (func_name, fname, lineno) self.function = self._device_name_or_function if not (self._device_name_or_function is None or diff --git a/tensorflow/python/util/function_utils.py b/tensorflow/python/util/function_utils.py index 61312feafd..4e9b07e20a 100644 --- a/tensorflow/python/util/function_utils.py +++ b/tensorflow/python/util/function_utils.py @@ -69,20 +69,24 @@ def get_func_name(func): return '%s.%s' % (six.get_method_self(func).__class__.__name__, six.get_method_function(func).__name__) else: # Probably a class instance with __call__ - return type(func) + return str(type(func)) else: raise ValueError('Argument must be callable') def get_func_code(func): - """Returns func_code of passed callable.""" + """Returns func_code of passed callable, or None if not available.""" _, func = tf_decorator.unwrap(func) if callable(func): if tf_inspect.isfunction(func) or tf_inspect.ismethod(func): return six.get_function_code(func) - elif hasattr(func, '__call__'): + # Since the object is not a function or method, but is a callable, we will + # try to access the __call__method as a function. This works with callable + # classes but fails with functool.partial objects despite their __call__ + # attribute. + try: return six.get_function_code(func.__call__) - else: - raise ValueError('Unhandled callable, type=%s' % type(func)) + except AttributeError: + return None else: raise ValueError('Argument must be callable') diff --git a/tensorflow/python/util/function_utils_test.py b/tensorflow/python/util/function_utils_test.py index e78cf6a5b0..1588328c26 100644 --- a/tensorflow/python/util/function_utils_test.py +++ b/tensorflow/python/util/function_utils_test.py @@ -24,6 +24,16 @@ from tensorflow.python.platform import test from tensorflow.python.util import function_utils +def silly_example_function(): + pass + + +class SillyCallableClass(object): + + def __call__(self): + pass + + class FnArgsTest(test.TestCase): def test_simple_function(self): @@ -124,5 +134,73 @@ class FnArgsTest(test.TestCase): self.assertEqual(3, double_wrapped_fn(3)) self.assertEqual(3, double_wrapped_fn(a=3)) + +class GetFuncNameTest(test.TestCase): + + def testWithSimpleFunction(self): + self.assertEqual( + 'silly_example_function', + function_utils.get_func_name(silly_example_function)) + + def testWithClassMethod(self): + self.assertEqual( + 'GetFuncNameTest.testWithClassMethod', + function_utils.get_func_name(self.testWithClassMethod)) + + def testWithCallableClass(self): + callable_instance = SillyCallableClass() + self.assertRegexpMatches( + function_utils.get_func_name(callable_instance), + '<.*SillyCallableClass.*>') + + def testWithFunctoolsPartial(self): + partial = functools.partial(silly_example_function) + self.assertRegexpMatches( + function_utils.get_func_name(partial), + '<.*functools.partial.*>') + + def testWithLambda(self): + anon_fn = lambda x: x + self.assertEqual('', function_utils.get_func_name(anon_fn)) + + def testRaisesWithNonCallableObject(self): + with self.assertRaises(ValueError): + function_utils.get_func_name(None) + + +class GetFuncCodeTest(test.TestCase): + + def testWithSimpleFunction(self): + code = function_utils.get_func_code(silly_example_function) + self.assertIsNotNone(code) + self.assertRegexpMatches(code.co_filename, 'function_utils_test.py') + + def testWithClassMethod(self): + code = function_utils.get_func_code(self.testWithClassMethod) + self.assertIsNotNone(code) + self.assertRegexpMatches(code.co_filename, 'function_utils_test.py') + + def testWithCallableClass(self): + callable_instance = SillyCallableClass() + code = function_utils.get_func_code(callable_instance) + self.assertIsNotNone(code) + self.assertRegexpMatches(code.co_filename, 'function_utils_test.py') + + def testWithLambda(self): + anon_fn = lambda x: x + code = function_utils.get_func_code(anon_fn) + self.assertIsNotNone(code) + self.assertRegexpMatches(code.co_filename, 'function_utils_test.py') + + def testWithFunctoolsPartial(self): + partial = functools.partial(silly_example_function) + code = function_utils.get_func_code(partial) + self.assertIsNone(code) + + def testRaisesWithNonCallableObject(self): + with self.assertRaises(ValueError): + function_utils.get_func_code(None) + + if __name__ == '__main__': test.main() -- GitLab From d6d169d511f83fc677288e67c65cf354d31341bb Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Thu, 26 Jul 2018 10:20:45 -0700 Subject: [PATCH 445/519] Allow setting server_def on tf.enable_eager_execution PiperOrigin-RevId: 206179345 --- tensorflow/contrib/eager/python/tfe.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tensorflow/contrib/eager/python/tfe.py b/tensorflow/contrib/eager/python/tfe.py index ca6430253b..2f0ab616e4 100644 --- a/tensorflow/contrib/eager/python/tfe.py +++ b/tensorflow/contrib/eager/python/tfe.py @@ -34,6 +34,7 @@ To use, at program startup, call `tfe.enable_eager_execution()`. @@run @@enable_eager_execution +@@enable_remote_eager_execution @@custom_gradient @@ -114,6 +115,7 @@ from tensorflow.python.eager.execution_callbacks import inf_nan_callback from tensorflow.python.eager.execution_callbacks import nan_callback from tensorflow.python.eager.execution_callbacks import seterr from tensorflow.python.framework.ops import enable_eager_execution +from tensorflow.python.framework.ops import enable_eager_execution_internal as enable_remote_eager_execution from tensorflow.python.framework.ops import eager_run as run from tensorflow.python.framework.test_util import run_in_graph_and_eager_modes as run_test_in_graph_and_eager_modes from tensorflow.python.framework.test_util import run_all_in_graph_and_eager_modes as run_all_tests_in_graph_and_eager_modes -- GitLab From 7871a8c13b8998cc1e06ce34fe54cad832a6f78e Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Thu, 26 Jul 2018 10:21:50 -0700 Subject: [PATCH 446/519] Don't merge co-located buffer sets containing constants This is analogous to cr/188117187, but for constants. PiperOrigin-RevId: 206179508 --- .../compiler/xla/service/buffer_assignment.cc | 18 ++++- .../xla/service/buffer_assignment_test.cc | 68 +++++++++++++++++++ 2 files changed, 84 insertions(+), 2 deletions(-) diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index b4c7cf0dd8..3b5bc49c59 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -1342,11 +1342,25 @@ BufferAssigner::MergeColocatedBufferSets( auto cannot_merge_buffer_sets = [&colocated_buffer_sets, &buffer_liveness, &buffer_size, &is_entry_parameter](int64 i, int64 j) { - // Do not merge if one of the sets includes live outs or entry parameters. + // Do not merge if one of the sets includes live outs, entry parameters or + // constants. + // + // Buffer liveness does not report the correct live range for entry + // parameter and live out buffers so we have to special case them here. On + // backends that support constant buffer allocations, constant buffers are + // assigned globals in readonly storage so we can't merge colocated buffer + // sets containing constants with colocated buffer sets containing writing + // instructions or other constants. + // + // Moreover (on the CPU/GPU backends) the entry parameter buffers belong to + // the caller of the executable so we can't write to entry parameters + // either, and the argument for not merging constants also applies to entry + // parameters. for (int64 key : {i, j}) { for (auto& buffer : colocated_buffer_sets[key]) { if (buffer_liveness.MaybeLiveOut(*buffer) || - is_entry_parameter(*buffer)) { + is_entry_parameter(*buffer) || + buffer->instruction()->opcode() == HloOpcode::kConstant) { return true; } } diff --git a/tensorflow/compiler/xla/service/buffer_assignment_test.cc b/tensorflow/compiler/xla/service/buffer_assignment_test.cc index dea855d39a..eccb146a0d 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment_test.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment_test.cc @@ -1923,6 +1923,74 @@ ENTRY %test_module { EXPECT_NE(slice_param, slice_while1); } +TEST_F(WhileBufferAssignmentTest, ColocatedBufferWithConstant) { + const Shape r0s32 = ShapeUtil::MakeShape(S32, {}); + + const char* module_str = R"( +HloModule test_module + +%cond.v0 { + %param = s32[] parameter(0) + ROOT %constant = pred[] constant(true) +} + +%cond.v1 { + %param.0 = s32[] parameter(0) + ROOT %constant.0 = pred[] constant(true) +} + +%body.v0 { + ROOT %param.1 = s32[] parameter(0) +} + +%body.v1 { + %param.2 = s32[] parameter(0) + ROOT add = s32[] add(%param.2, %param.2) +} + +ENTRY %test_module { + %constant.42 = s32[] constant(42) + %while.0 = s32[] while(%constant.42), condition=%cond.v0, body=%body.v0 + %mul = s32[] multiply(%while.0, %while.0) + %while.1 = s32[] while(%mul), condition=%cond.v1, body=%body.v1 + ROOT %bcast = s32[1024,1024]{1,0} broadcast(s32[] %while.1), dimensions={} +})"; + + TF_ASSERT_OK_AND_ASSIGN(std::unique_ptr module, + ParseHloString(module_str)); + + // Run CopyInsertion and check if the graph constructed above doesn't need + // any copies inserted for BufferAssignment to run. + int64 instruction_count = module->instruction_count(); + CopyInsertion copy_insertion; + ASSERT_IS_OK(copy_insertion.Run(module.get()).status()); + ASSERT_EQ(instruction_count, module->instruction_count()); + + // Get the instructions in the module. + const HloInstruction* bcast = module->entry_computation()->root_instruction(); + const HloInstruction* constant = + module->entry_computation()->GetInstructionWithName("constant.42"); + ASSERT_EQ(bcast->opcode(), HloOpcode::kBroadcast); + const HloInstruction* while1 = bcast->operand(0); + ASSERT_EQ(while1->opcode(), HloOpcode::kWhile); + const HloInstruction* while0 = while1->operand(0)->operand(0); + ASSERT_EQ(while0->opcode(), HloOpcode::kWhile); + + // Run buffer assignment. + auto assignment = RunBufferAssignment(module.get()); + TF_ASSERT_OK_AND_ASSIGN(auto slice_constant, + assignment->GetUniqueSlice(constant, {})); + TF_ASSERT_OK_AND_ASSIGN(auto slice_while0, + assignment->GetUniqueSlice(while0, {})); + TF_ASSERT_OK_AND_ASSIGN(auto slice_while1, + assignment->GetUniqueSlice(while1, {})); + + // The constant slice is part of the while0's colocation set (init value), but + // not merged into the while1's colocation set. + EXPECT_EQ(slice_constant, slice_while0); + EXPECT_NE(slice_constant, slice_while1); +} + // Tests that the colocated buffers for while instructions are properly assigned // during buffer assignment such that the result tuple elements are not assigned // to the same buffer. -- GitLab From 0a3155f7fbf56df5e81c7cbf35afd45173359635 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 10:45:19 -0700 Subject: [PATCH 447/519] Adding core estimator for a fusion model. PiperOrigin-RevId: 206183643 --- .../boosted_trees/estimator_batch/BUILD | 1 + .../dnn_tree_combined_estimator.py | 265 +++++++++++++----- .../dnn_tree_combined_estimator_test.py | 70 ++++- 3 files changed, 272 insertions(+), 64 deletions(-) diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/BUILD b/tensorflow/contrib/boosted_trees/estimator_batch/BUILD index ef0e80cd09..f4a375328e 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/BUILD +++ b/tensorflow/contrib/boosted_trees/estimator_batch/BUILD @@ -147,6 +147,7 @@ py_library( deps = [ ":distillation_loss", ":estimator_utils", + ":model", ":trainer_hooks", "//tensorflow/contrib/boosted_trees:gbdt_batch", "//tensorflow/contrib/boosted_trees:model_ops_py", diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py index 7eb429b636..dbfa69edcb 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator.py @@ -26,6 +26,7 @@ from __future__ import print_function import six from tensorflow.contrib import layers +from tensorflow.contrib.boosted_trees.estimator_batch import model from tensorflow.contrib.boosted_trees.estimator_batch import distillation_loss from tensorflow.contrib.boosted_trees.estimator_batch import estimator_utils from tensorflow.contrib.boosted_trees.estimator_batch import trainer_hooks @@ -34,6 +35,7 @@ from tensorflow.contrib.boosted_trees.python.training.functions import gbdt_batc from tensorflow.contrib.layers.python.layers import optimizers from tensorflow.contrib.learn.python.learn.estimators import estimator from tensorflow.contrib.learn.python.learn.estimators import head as head_lib +from tensorflow.python.estimator import estimator as core_estimator from tensorflow.contrib.learn.python.learn.estimators import model_fn from tensorflow.python.feature_column import feature_column as feature_column_lib from tensorflow.python.framework import ops @@ -62,27 +64,29 @@ def _add_hidden_layer_summary(value, tag): summary.histogram("%s_activation" % tag, value) -def _dnn_tree_combined_model_fn(features, - labels, - mode, - head, - dnn_hidden_units, - dnn_feature_columns, - tree_learner_config, - num_trees, - tree_examples_per_layer, - config=None, - dnn_optimizer="Adagrad", - dnn_activation_fn=nn.relu, - dnn_dropout=None, - dnn_input_layer_partitioner=None, - dnn_input_layer_to_tree=True, - dnn_steps_to_train=10000, - predict_with_tree_only=False, - tree_feature_columns=None, - tree_center_bias=False, - dnn_to_tree_distillation_param=None, - use_core_versions=False): +def _dnn_tree_combined_model_fn( + features, + labels, + mode, + head, + dnn_hidden_units, + dnn_feature_columns, + tree_learner_config, + num_trees, + tree_examples_per_layer, + config=None, + dnn_optimizer="Adagrad", + dnn_activation_fn=nn.relu, + dnn_dropout=None, + dnn_input_layer_partitioner=None, + dnn_input_layer_to_tree=True, + dnn_steps_to_train=10000, + predict_with_tree_only=False, + tree_feature_columns=None, + tree_center_bias=False, + dnn_to_tree_distillation_param=None, + use_core_versions=False, + output_type=model.ModelBuilderOutputType.MODEL_FN_OPS): """DNN and GBDT combined model_fn. Args: @@ -156,6 +160,10 @@ def _dnn_tree_combined_model_fn(features, partitioned_variables.min_max_variable_partitioner( max_partitions=config.num_ps_replicas, min_slice_size=64 << 20)) + if (output_type == model.ModelBuilderOutputType.ESTIMATOR_SPEC and + not use_core_versions): + raise ValueError("You must use core versions with Estimator Spec") + with variable_scope.variable_scope( dnn_parent_scope, values=tuple(six.itervalues(features)), @@ -235,7 +243,8 @@ def _dnn_tree_combined_model_fn(features, learner_config=tree_learner_config, feature_columns=tree_feature_columns, logits_dimension=head.logits_dimension, - features=tree_features) + features=tree_features, + use_core_columns=use_core_versions) with ops.name_scope("gbdt"): predictions_dict = gbdt_model.predict(mode) @@ -284,63 +293,96 @@ def _dnn_tree_combined_model_fn(features, del loss return control_flow_ops.no_op() - if use_core_versions: - model_fn_ops = head.create_estimator_spec( - features=features, - mode=mode, - labels=labels, - train_op_fn=_no_train_op_fn, - logits=tree_train_logits) - dnn_train_op = head.create_estimator_spec( - features=features, - mode=mode, - labels=labels, - train_op_fn=_dnn_train_op_fn, - logits=dnn_logits) - dnn_train_op = estimator_utils.estimator_spec_to_model_fn_ops( - dnn_train_op).train_op + if tree_center_bias: + num_trees += 1 + finalized_trees, attempted_trees = gbdt_model.get_number_of_trees_tensor() - tree_train_op = head.create_estimator_spec( - features=tree_features, - mode=mode, - labels=labels, - train_op_fn=_tree_train_op_fn, - logits=tree_train_logits) - tree_train_op = estimator_utils.estimator_spec_to_model_fn_ops( - tree_train_op).train_op + if output_type == model.ModelBuilderOutputType.MODEL_FN_OPS: + if use_core_versions: + model_fn_ops = head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_no_train_op_fn, + logits=tree_train_logits) + dnn_train_op = head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_dnn_train_op_fn, + logits=dnn_logits) + dnn_train_op = estimator_utils.estimator_spec_to_model_fn_ops( + dnn_train_op).train_op - model_fn_ops = estimator_utils.estimator_spec_to_model_fn_ops(model_fn_ops) - else: - model_fn_ops = head.create_model_fn_ops( + tree_train_op = head.create_estimator_spec( + features=tree_features, + mode=mode, + labels=labels, + train_op_fn=_tree_train_op_fn, + logits=tree_train_logits) + tree_train_op = estimator_utils.estimator_spec_to_model_fn_ops( + tree_train_op).train_op + + model_fn_ops = estimator_utils.estimator_spec_to_model_fn_ops( + model_fn_ops) + else: + model_fn_ops = head.create_model_fn_ops( + features=features, + mode=mode, + labels=labels, + train_op_fn=_no_train_op_fn, + logits=tree_train_logits) + dnn_train_op = head.create_model_fn_ops( + features=features, + mode=mode, + labels=labels, + train_op_fn=_dnn_train_op_fn, + logits=dnn_logits).train_op + tree_train_op = head.create_model_fn_ops( + features=tree_features, + mode=mode, + labels=labels, + train_op_fn=_tree_train_op_fn, + logits=tree_train_logits).train_op + + # Add the hooks + model_fn_ops.training_hooks.extend([ + trainer_hooks.SwitchTrainOp(dnn_train_op, dnn_steps_to_train, + tree_train_op), + trainer_hooks.StopAfterNTrees(num_trees, attempted_trees, + finalized_trees) + ]) + return model_fn_ops + + elif output_type == model.ModelBuilderOutputType.ESTIMATOR_SPEC: + fusion_spec = head.create_estimator_spec( features=features, mode=mode, labels=labels, train_op_fn=_no_train_op_fn, logits=tree_train_logits) - dnn_train_op = head.create_model_fn_ops( + dnn_spec = head.create_estimator_spec( features=features, mode=mode, labels=labels, train_op_fn=_dnn_train_op_fn, - logits=dnn_logits).train_op - tree_train_op = head.create_model_fn_ops( + logits=dnn_logits) + tree_spec = head.create_estimator_spec( features=tree_features, mode=mode, labels=labels, train_op_fn=_tree_train_op_fn, - logits=tree_train_logits).train_op - - if tree_center_bias: - num_trees += 1 - finalized_trees, attempted_trees = gbdt_model.get_number_of_trees_tensor() - - model_fn_ops.training_hooks.extend([ - trainer_hooks.SwitchTrainOp(dnn_train_op, dnn_steps_to_train, - tree_train_op), - trainer_hooks.StopAfterNTrees(num_trees, attempted_trees, finalized_trees) - ]) + logits=tree_train_logits) - return model_fn_ops + training_hooks = [ + trainer_hooks.SwitchTrainOp(dnn_spec.train_op, dnn_steps_to_train, + tree_spec.train_op), + trainer_hooks.StopAfterNTrees(num_trees, attempted_trees, + finalized_trees) + ] + fusion_spec = fusion_spec._replace(training_hooks=training_hooks + + list(fusion_spec.training_hooks)) + return fusion_spec class DNNBoostedTreeCombinedClassifier(estimator.Estimator): @@ -697,3 +739,100 @@ class DNNBoostedTreeCombinedEstimator(estimator.Estimator): model_dir=model_dir, config=config, feature_engineering_fn=feature_engineering_fn) + + +class CoreDNNBoostedTreeCombinedEstimator(core_estimator.Estimator): + """Initializes a core version of DNNBoostedTreeCombinedEstimator. + + Args: + dnn_hidden_units: List of hidden units per layer for DNN. + dnn_feature_columns: An iterable containing all the feature columns + used by the model's DNN. + tree_learner_config: A config for the tree learner. + num_trees: Number of trees to grow model to after training DNN. + tree_examples_per_layer: Number of examples to accumulate before + growing the tree a layer. This value has a big impact on model + quality and should be set equal to the number of examples in + training dataset if possible. It can also be a function that computes + the number of examples based on the depth of the layer that's + being built. + head: `Head` instance. + model_dir: Directory for model exports. + config: `RunConfig` of the estimator. + dnn_optimizer: string, `Optimizer` object, or callable that defines the + optimizer to use for training the DNN. If `None`, will use the Adagrad + optimizer with default learning rate. + dnn_activation_fn: Activation function applied to each layer of the DNN. + If `None`, will use `tf.nn.relu`. + dnn_dropout: When not `None`, the probability to drop out a given + unit in the DNN. + dnn_input_layer_partitioner: Partitioner for input layer of the DNN. + Defaults to `min_max_variable_partitioner` with `min_slice_size` + 64 << 20. + dnn_input_layer_to_tree: Whether to provide the DNN's input layer + as a feature to the tree. + dnn_steps_to_train: Number of steps to train dnn for before switching + to gbdt. + predict_with_tree_only: Whether to use only the tree model output as the + final prediction. + tree_feature_columns: An iterable containing all the feature columns + used by the model's boosted trees. If dnn_input_layer_to_tree is + set to True, these features are in addition to dnn_feature_columns. + tree_center_bias: Whether a separate tree should be created for + first fitting the bias. + dnn_to_tree_distillation_param: A Tuple of (float, loss_fn), where the + float defines the weight of the distillation loss, and the loss_fn, for + computing distillation loss, takes dnn_logits, tree_logits and weight + tensor. If the entire tuple is None, no distillation will be applied. If + only the loss_fn is None, we will take the sigmoid/softmax cross entropy + loss be default. When distillation is applied, `predict_with_tree_only` + will be set to True. + """ + + def __init__(self, + dnn_hidden_units, + dnn_feature_columns, + tree_learner_config, + num_trees, + tree_examples_per_layer, + head, + model_dir=None, + config=None, + dnn_optimizer="Adagrad", + dnn_activation_fn=nn.relu, + dnn_dropout=None, + dnn_input_layer_partitioner=None, + dnn_input_layer_to_tree=True, + dnn_steps_to_train=10000, + predict_with_tree_only=False, + tree_feature_columns=None, + tree_center_bias=False, + dnn_to_tree_distillation_param=None): + + def _model_fn(features, labels, mode, config): + return _dnn_tree_combined_model_fn( + features=features, + labels=labels, + mode=mode, + head=head, + dnn_hidden_units=dnn_hidden_units, + dnn_feature_columns=dnn_feature_columns, + tree_learner_config=tree_learner_config, + num_trees=num_trees, + tree_examples_per_layer=tree_examples_per_layer, + config=config, + dnn_optimizer=dnn_optimizer, + dnn_activation_fn=dnn_activation_fn, + dnn_dropout=dnn_dropout, + dnn_input_layer_partitioner=dnn_input_layer_partitioner, + dnn_input_layer_to_tree=dnn_input_layer_to_tree, + dnn_steps_to_train=dnn_steps_to_train, + predict_with_tree_only=predict_with_tree_only, + tree_feature_columns=tree_feature_columns, + tree_center_bias=tree_center_bias, + dnn_to_tree_distillation_param=dnn_to_tree_distillation_param, + output_type=model.ModelBuilderOutputType.ESTIMATOR_SPEC, + use_core_versions=True) + + super(CoreDNNBoostedTreeCombinedEstimator, self).__init__( + model_fn=_model_fn, model_dir=model_dir, config=config) diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py index 9b7acfa664..839eedd3a8 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/dnn_tree_combined_estimator_test.py @@ -28,10 +28,11 @@ from tensorflow.python.estimator.canned import head as head_lib from tensorflow.python.feature_column import feature_column_lib as core_feature_column 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.ops.losses import losses from tensorflow.python.platform import googletest - +from tensorflow.python.training import checkpoint_utils def _train_input_fn(): features = { @@ -156,5 +157,72 @@ class DNNBoostedTreeCombinedTest(test_util.TensorFlowTestCase): classifier.evaluate(input_fn=_eval_input_fn, steps=1) +class CoreDNNBoostedTreeCombinedTest(test_util.TensorFlowTestCase): + + def _assert_checkpoint(self, model_dir, global_step): + reader = checkpoint_utils.load_checkpoint(model_dir) + self.assertEqual(global_step, reader.get_tensor(ops.GraphKeys.GLOBAL_STEP)) + + def testTrainEvaluateInferDoesNotThrowErrorWithNoDnnInput(self): + head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + learner_config = learner_pb2.LearnerConfig() + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 3 + model_dir = tempfile.mkdtemp() + config = run_config.RunConfig() + + est = estimator.CoreDNNBoostedTreeCombinedEstimator( + head=head_fn, + dnn_hidden_units=[1], + dnn_feature_columns=[core_feature_column.numeric_column("x")], + tree_learner_config=learner_config, + num_trees=1, + tree_examples_per_layer=3, + model_dir=model_dir, + config=config, + dnn_steps_to_train=10, + dnn_input_layer_to_tree=False, + tree_feature_columns=[core_feature_column.numeric_column("x")]) + + # Train for a few steps. + est.train(input_fn=_train_input_fn, steps=1000) + # 10 steps for dnn, 3 for 1 tree of depth 3 + 1 after the tree finished + self._assert_checkpoint(est.model_dir, global_step=14) + res = est.evaluate(input_fn=_eval_input_fn, steps=1) + self.assertLess(0.5, res["auc"]) + est.predict(input_fn=_eval_input_fn) + + def testTrainEvaluateInferDoesNotThrowErrorWithDnnInput(self): + head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + learner_config = learner_pb2.LearnerConfig() + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 3 + model_dir = tempfile.mkdtemp() + config = run_config.RunConfig() + + est = estimator.CoreDNNBoostedTreeCombinedEstimator( + head=head_fn, + dnn_hidden_units=[1], + dnn_feature_columns=[core_feature_column.numeric_column("x")], + tree_learner_config=learner_config, + num_trees=1, + tree_examples_per_layer=3, + model_dir=model_dir, + config=config, + dnn_steps_to_train=10, + dnn_input_layer_to_tree=True, + tree_feature_columns=[]) + + # Train for a few steps. + est.train(input_fn=_train_input_fn, steps=1000) + res = est.evaluate(input_fn=_eval_input_fn, steps=1) + self.assertLess(0.5, res["auc"]) + est.predict(input_fn=_eval_input_fn) + + if __name__ == "__main__": googletest.main() -- GitLab From 6e658c0a5ca77677a954a34fb98f241c592c970d Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Thu, 26 Jul 2018 10:53:21 -0700 Subject: [PATCH 448/519] Add one_hot op support to TFLite PiperOrigin-RevId: 206185190 --- tensorflow/contrib/lite/build_def.bzl | 1 + tensorflow/contrib/lite/builtin_op_data.h | 4 + tensorflow/contrib/lite/builtin_ops.h | 1 + .../lite/g3doc/tf_ops_compatibility.md | 1 + tensorflow/contrib/lite/kernels/BUILD | 14 ++ tensorflow/contrib/lite/kernels/one_hot.cc | 199 ++++++++++++++++++ .../contrib/lite/kernels/one_hot_test.cc | 182 ++++++++++++++++ tensorflow/contrib/lite/kernels/register.cc | 2 + tensorflow/contrib/lite/model.cc | 8 + tensorflow/contrib/lite/nnapi_delegate.cc | 1 + tensorflow/contrib/lite/schema/schema.fbs | 6 + .../contrib/lite/schema/schema_generated.h | 141 ++++++++++++- .../contrib/lite/testing/generate_examples.py | 63 +++++- .../contrib/lite/toco/export_tensorflow.cc | 17 ++ .../propagate_array_data_types.cc | 12 ++ .../propagate_fixed_sizes.cc | 58 +++++ .../contrib/lite/toco/import_tensorflow.cc | 22 ++ tensorflow/contrib/lite/toco/model.h | 22 ++ .../contrib/lite/toco/tflite/operator.cc | 19 ++ .../contrib/lite/toco/tflite/operator_test.cc | 8 + tensorflow/contrib/lite/toco/tooling_util.cc | 1 + 21 files changed, 775 insertions(+), 7 deletions(-) create mode 100644 tensorflow/contrib/lite/kernels/one_hot.cc create mode 100644 tensorflow/contrib/lite/kernels/one_hot_test.cc diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index a8a49784c6..256334576c 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -248,6 +248,7 @@ def generated_test_models(): "mul", "neg", "not_equal", + "one_hot", "pack", "pad", "padv2", diff --git a/tensorflow/contrib/lite/builtin_op_data.h b/tensorflow/contrib/lite/builtin_op_data.h index fd16aa1063..70178b2faa 100644 --- a/tensorflow/contrib/lite/builtin_op_data.h +++ b/tensorflow/contrib/lite/builtin_op_data.h @@ -282,6 +282,10 @@ typedef struct { int axis; } TfLitePackParams; +typedef struct { + int axis; +} TfLiteOneHotParams; + #ifdef __cplusplus } // extern "C" #endif // __cplusplus diff --git a/tensorflow/contrib/lite/builtin_ops.h b/tensorflow/contrib/lite/builtin_ops.h index 1ae73b9738..0b6568fd2f 100644 --- a/tensorflow/contrib/lite/builtin_ops.h +++ b/tensorflow/contrib/lite/builtin_ops.h @@ -110,6 +110,7 @@ typedef enum { kTfLiteBuiltinReduceMax = 82, kTfLiteBuiltinPack = 83, kTfLiteBuiltinLogicalOr = 84, + kTfLiteBuiltinOneHot = 85, } TfLiteBuiltinOperator; #ifdef __cplusplus diff --git a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md index 0e8f4339fc..2ea7aeaa5d 100644 --- a/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md +++ b/tensorflow/contrib/lite/g3doc/tf_ops_compatibility.md @@ -62,6 +62,7 @@ counterparts: * [tf.nn.softmax](https://www.tensorflow.org/api_docs/python/tf/nn/softmax) - *as long as tensors are 2D and axis is the last dimension* * [tf.nn.top_k](https://www.tensorflow.org/api_docs/python/tf/nn/top_k) +* [tf.one_hot](https://www.tensorflow.org/api_docs/python/tf/one_hot) * [tf.pad](https://www.tensorflow.org/api_docs/python/tf/pad) - *as long as mode and constant_values are not used* * [tf.reduce_mean](https://www.tensorflow.org/api_docs/python/tf/reduce_mean) - diff --git a/tensorflow/contrib/lite/kernels/BUILD b/tensorflow/contrib/lite/kernels/BUILD index c224132cae..026ab4de03 100644 --- a/tensorflow/contrib/lite/kernels/BUILD +++ b/tensorflow/contrib/lite/kernels/BUILD @@ -176,6 +176,7 @@ cc_library( "mfcc.cc", "mul.cc", "neg.cc", + "one_hot.cc", "pack.cc", "pad.cc", "pooling.cc", @@ -1171,6 +1172,19 @@ tf_cc_test( ], ) +tf_cc_test( + name = "one_hot_test", + size = "small", + srcs = ["one_hot_test.cc"], + tags = ["tflite_not_portable_ios"], + deps = [ + ":builtin_ops", + "//tensorflow/contrib/lite:framework", + "//tensorflow/contrib/lite/kernels:test_util", + "@com_google_googletest//:gtest", + ], +) + filegroup( name = "all_files", srcs = glob( diff --git a/tensorflow/contrib/lite/kernels/one_hot.cc b/tensorflow/contrib/lite/kernels/one_hot.cc new file mode 100644 index 0000000000..9ff3dca932 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/one_hot.cc @@ -0,0 +1,199 @@ +/* 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/builtin_op_data.h" +#include "tensorflow/contrib/lite/context.h" +#include "tensorflow/contrib/lite/kernels/internal/tensor.h" +#include "tensorflow/contrib/lite/kernels/kernel_util.h" +#include "tensorflow/contrib/lite/kernels/op_macros.h" + +namespace tflite { +namespace ops { +namespace builtin { +namespace one_hot { + +constexpr int kIndicesTensor = 0; +constexpr int kDepthTensor = 1; +constexpr int kOnValueTensor = 2; +constexpr int kOffValueTensor = 3; +constexpr int kOutputTensor = 0; + +// Convenience utility for destructuring a node into the appropriate tensors and +// data for the op. Note that this destructuring is quite cheap, so we can avoid +// allocating op-specific, persistent data on the heap. +struct OneHotContext { + OneHotContext(TfLiteContext* context, TfLiteNode* node) { + indices = GetInput(context, node, kIndicesTensor); + depth = GetInput(context, node, kDepthTensor); + on_value = GetInput(context, node, kOnValueTensor); + off_value = GetInput(context, node, kOffValueTensor); + output = GetOutput(context, node, kOutputTensor); + + const auto* params = + reinterpret_cast(node->builtin_data); + const int indices_dims = indices->dims->size; + axis = (params->axis == -1) ? indices_dims : params->axis; + output_dims = indices_dims + 1; + dtype = on_value->type; + } + + const TfLiteTensor* indices; + const TfLiteTensor* depth; + const TfLiteTensor* on_value; + const TfLiteTensor* off_value; + TfLiteTensor* output; + int axis; + int output_dims; + TfLiteType dtype; +}; + +template +void OneHotComputeImpl(const OneHotContext& op_context) { + // prefix_dim_size == # of elements before the axis + // depth == # of elements per axis + // suffix_dim_size == # of elements after the axis + int prefix_dim_size = 1; + for (int i = 0; i < op_context.axis; ++i) { + prefix_dim_size *= op_context.indices->dims->data[i]; + } + const int suffix_dim_size = NumElements(op_context.indices) / prefix_dim_size; + const int depth = *op_context.depth->data.i32; + + const T on_value = *GetTensorData(op_context.on_value); + const T off_value = *GetTensorData(op_context.off_value); + + // View the indices as a matrix of size: + // prefix_dim_size x suffix_dim_size + // View the output as a matrix of size: + // prefix_dim_size x depth x suffix_dim_size + // Then the output is: + // output(i, j, k) == (indices(i, k) == j) ? on : off + T* output = GetTensorData(op_context.output); + const TI* indices = GetTensorData(op_context.indices); + for (int i = 0; i < prefix_dim_size; ++i) { + for (int j = 0; j < depth; ++j) { + for (int k = 0; k < suffix_dim_size; ++k, ++output) { + *output = static_cast(indices[i * suffix_dim_size + k]) == j + ? on_value + : off_value; + } + } + } +} + +template +void OneHotCompute(const OneHotContext& op_context) { + if (op_context.indices->type == kTfLiteInt64) { + OneHotComputeImpl(op_context); + } else { + OneHotComputeImpl(op_context); + } +} + +TfLiteStatus ResizeOutputTensor(TfLiteContext* context, + const OneHotContext& op_context) { + TF_LITE_ENSURE(context, *op_context.depth->data.i32 >= 0); + TfLiteIntArray* output_size = TfLiteIntArrayCreate(op_context.output_dims); + for (int i = 0; i < op_context.output_dims; ++i) { + if (i < op_context.axis) { + output_size->data[i] = op_context.indices->dims->data[i]; + } else if (i == op_context.axis) { + output_size->data[i] = *op_context.depth->data.i32; + } else { + output_size->data[i] = op_context.indices->dims->data[i - 1]; + } + } + return context->ResizeTensor(context, op_context.output, output_size); +} + +TfLiteStatus Prepare(TfLiteContext* context, TfLiteNode* node) { + TF_LITE_ENSURE_EQ(context, NumInputs(node), 4); + TF_LITE_ENSURE_EQ(context, NumOutputs(node), 1); + + OneHotContext op_context{context, node}; + switch (op_context.dtype) { + // TODO(b/111744875): Support uint8 and quantization. + case kTfLiteFloat32: + case kTfLiteInt16: + case kTfLiteInt32: + case kTfLiteInt64: + case kTfLiteBool: + op_context.output->type = op_context.dtype; + break; + default: + context->ReportError(context, "Unknown output data type: %d", + op_context.dtype); + return kTfLiteError; + } + + TF_LITE_ENSURE(context, op_context.indices->type == kTfLiteInt32 || + op_context.indices->type == kTfLiteInt64); + TF_LITE_ENSURE(context, op_context.axis >= 0 && + op_context.axis < op_context.output_dims); + TF_LITE_ENSURE_EQ(context, NumElements(op_context.depth), 1); + TF_LITE_ENSURE_EQ(context, NumElements(op_context.on_value), 1); + TF_LITE_ENSURE_EQ(context, NumElements(op_context.off_value), 1); + TF_LITE_ENSURE_EQ(context, op_context.on_value->type, op_context.dtype); + TF_LITE_ENSURE_EQ(context, op_context.off_value->type, op_context.dtype); + + if (!IsConstantTensor(op_context.depth)) { + SetTensorToDynamic(op_context.output); + return kTfLiteOk; + } + + return ResizeOutputTensor(context, op_context); +} + +TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { + OneHotContext op_context{context, node}; + + if (IsDynamicTensor(op_context.output)) { + ResizeOutputTensor(context, op_context); + } + + switch (op_context.output->type) { + case kTfLiteFloat32: + OneHotCompute(op_context); + break; + case kTfLiteInt32: + OneHotCompute(op_context); + break; + case kTfLiteInt64: + OneHotCompute(op_context); + break; + case kTfLiteBool: + OneHotCompute(op_context); + break; + default: + return kTfLiteError; + } + + return kTfLiteOk; +} + +} // namespace one_hot + +TfLiteRegistration* Register_ONE_HOT() { + static TfLiteRegistration r = { + nullptr, + nullptr, + one_hot::Prepare, + one_hot::Eval, + }; + return &r; +} + +} // namespace builtin +} // namespace ops +} // namespace tflite diff --git a/tensorflow/contrib/lite/kernels/one_hot_test.cc b/tensorflow/contrib/lite/kernels/one_hot_test.cc new file mode 100644 index 0000000000..6b604ec7a7 --- /dev/null +++ b/tensorflow/contrib/lite/kernels/one_hot_test.cc @@ -0,0 +1,182 @@ +/* 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 + +#include +#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; + +template +class OneHotOpModel : public SingleOpModel { + public: + OneHotOpModel(std::initializer_list input_shape, int depth_value, + TensorType dtype, int axis = -1, T on_value = 1, + T off_value = 0, TensorType indices_type = TensorType_INT32) { + indices_ = AddInput(indices_type); + int depth = AddInput(TensorType_INT32); + int on = AddInput(dtype); + int off = AddInput(dtype); + output_ = AddOutput(dtype); + SetBuiltinOp(BuiltinOperator_ONE_HOT, BuiltinOptions_OneHotOptions, + CreateOneHotOptions(builder_, axis).Union()); + BuildInterpreter({input_shape}); + + PopulateTensor(depth, {depth_value}); + PopulateTensor(on, {on_value}); + PopulateTensor(off, {off_value}); + } + + template + void SetIndices(std::initializer_list data) { + PopulateTensor(indices_, data); + } + + TfLiteStatus InvokeWithResult() { return interpreter_->Invoke(); } + + int32_t GetOutputSize() { return GetTensorSize(output_); } + std::vector GetOutput() { return ExtractVector(output_); } + std::vector GetOutputShape() { return GetTensorShape(output_); } + + private: + int indices_; + int output_; +}; + +TEST(OneHotOpTest, BasicFloat) { + const int depth = 3; + OneHotOpModel model({3}, depth, TensorType_FLOAT32); + model.SetIndices({0, 1, 2}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 3})); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({1.f, 0.f, 0.f, 0.f, 1.f, 0.f, 0.f, 0.f, 1.f})); +} + +TEST(OneHotOpTest, BasicInt) { + const int depth = 3; + OneHotOpModel model({3}, depth, TensorType_INT32); + model.SetIndices({0, 1, 2}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 3})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0, 0, 0, 1, 0, 0, 0, 1})); +} + +TEST(OneHotOpTest, BasicBool) { + const int depth = 3; + OneHotOpModel model({3}, depth, TensorType_BOOL); + model.SetIndices({0, 1, 2}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 3})); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({true, false, false, false, true, false, false, + false, true})); +} + +TEST(OneHotOpTest, SmallDepth) { + const int depth = 1; + OneHotOpModel model({3}, depth, TensorType_INT32); + model.SetIndices({0, 1, 2}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 1})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0, 0})); +} + +TEST(OneHotOpTest, BigDepth) { + const int depth = 4; + OneHotOpModel model({2}, depth, TensorType_INT32); + model.SetIndices({0, 1}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 4})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0, 0, 0, 0, 1, 0, 0})); +} + +TEST(OneHotOpTest, OnOffValues) { + const int depth = 3; + const int axis = -1; + const int on = 5; + const int off = 0; + OneHotOpModel model({4}, depth, TensorType_INT32, axis, on, off); + model.SetIndices({0, 2, -1, 1}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({4, 3})); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({5, 0, 0, 0, 0, 5, 0, 0, 0, 0, 5, 0})); +} + +TEST(OneHotOpTest, ZeroAxis) { + const int depth = 3; + const int axis = 0; + const int on = 5; + const int off = 0; + OneHotOpModel model({4}, depth, TensorType_INT32, axis, on, off); + model.SetIndices({0, 2, -1, 1}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 4})); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({5, 0, 0, 0, 0, 0, 0, 5, 0, 5, 0, 0})); +} + +TEST(OneHotOpTest, MultiDimensionalIndices) { + const int depth = 3; + const int axis = -1; + const float on = 2; + const float off = 0; + OneHotOpModel model({2, 2}, depth, TensorType_FLOAT32, axis, on, off); + model.SetIndices({0, 2, 1, -1}); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({2, 2, 3})); + EXPECT_THAT(model.GetOutput(), + ElementsAreArray({2, 0, 0, 0, 0, 2, 0, 2, 0, 0, 0, 0})); +} + +TEST(OneHotOpTest, Int64Indices) { + const int depth = 3; + const int axis = -1; + const int on = 1; + const int off = 0; + OneHotOpModel model({3}, depth, TensorType_INT32, axis, on, off, + TensorType_INT64); + std::initializer_list indices = {0, 1, 2}; + model.SetIndices(indices); + model.Invoke(); + + EXPECT_THAT(model.GetOutputShape(), ElementsAreArray({3, 3})); + EXPECT_THAT(model.GetOutput(), ElementsAreArray({1, 0, 0, 0, 1, 0, 0, 0, 1})); +} + +} // 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 0b70bed308..da69b85041 100644 --- a/tensorflow/contrib/lite/kernels/register.cc +++ b/tensorflow/contrib/lite/kernels/register.cc @@ -107,6 +107,7 @@ TfLiteRegistration* Register_SHAPE(); TfLiteRegistration* Register_POW(); TfLiteRegistration* Register_FAKE_QUANT(); TfLiteRegistration* Register_PACK(); +TfLiteRegistration* Register_ONE_HOT(); BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_RELU, Register_RELU()); @@ -197,6 +198,7 @@ BuiltinOpResolver::BuiltinOpResolver() { AddBuiltin(BuiltinOperator_POW, Register_POW()); AddBuiltin(BuiltinOperator_FAKE_QUANT, Register_FAKE_QUANT(), 1, 2); AddBuiltin(BuiltinOperator_PACK, Register_PACK()); + AddBuiltin(BuiltinOperator_ONE_HOT, Register_ONE_HOT()); // TODO(andrewharp, ahentz): Move these somewhere more appropriate so that // custom ops aren't always included by default. diff --git a/tensorflow/contrib/lite/model.cc b/tensorflow/contrib/lite/model.cc index c6869feb16..5814cddc5b 100644 --- a/tensorflow/contrib/lite/model.cc +++ b/tensorflow/contrib/lite/model.cc @@ -730,6 +730,14 @@ TfLiteStatus ParseOpData(const Operator* op, BuiltinOperator op_type, *builtin_data = static_cast(params); break; } + case BuiltinOperator_ONE_HOT: { + auto* params = MallocPOD(); + if (auto* schema_params = op->builtin_options_as_OneHotOptions()) { + params->axis = schema_params->axis(); + } + *builtin_data = static_cast(params); + break; + } // Below are the ops with no builtin_data strcture. case BuiltinOperator_BATCH_TO_SPACE_ND: diff --git a/tensorflow/contrib/lite/nnapi_delegate.cc b/tensorflow/contrib/lite/nnapi_delegate.cc index 551e8ed320..1c06b29deb 100644 --- a/tensorflow/contrib/lite/nnapi_delegate.cc +++ b/tensorflow/contrib/lite/nnapi_delegate.cc @@ -623,6 +623,7 @@ TfLiteStatus AddOpsAndParams( case tflite::BuiltinOperator_FAKE_QUANT: case tflite::BuiltinOperator_PACK: case tflite::BuiltinOperator_LOGICAL_OR: + case tflite::BuiltinOperator_ONE_HOT: logError("Op code %d is currently not delegated to NNAPI", builtin); return kTfLiteError; break; diff --git a/tensorflow/contrib/lite/schema/schema.fbs b/tensorflow/contrib/lite/schema/schema.fbs index a285bf9919..8ed98ddaf4 100644 --- a/tensorflow/contrib/lite/schema/schema.fbs +++ b/tensorflow/contrib/lite/schema/schema.fbs @@ -166,6 +166,7 @@ enum BuiltinOperator : byte { REDUCE_MAX = 82, PACK = 83, LOGICAL_OR = 84, + ONE_HOT = 85, } // Options for the builtin operators. @@ -230,6 +231,7 @@ union BuiltinOptions { FakeQuantOptions, PackOptions, LogicalOrOptions, + OneHotOptions, } enum Padding : byte { SAME, VALID } @@ -549,6 +551,10 @@ table PackOptions { table LogicalOrOptions { } +table OneHotOptions { + axis:int; +} + // 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 8c1d6d6a36..4402f89b85 100755 --- a/tensorflow/contrib/lite/schema/schema_generated.h +++ b/tensorflow/contrib/lite/schema/schema_generated.h @@ -211,6 +211,9 @@ struct PackOptionsT; struct LogicalOrOptions; struct LogicalOrOptionsT; +struct OneHotOptions; +struct OneHotOptionsT; + struct OperatorCode; struct OperatorCodeT; @@ -361,11 +364,12 @@ enum BuiltinOperator { BuiltinOperator_REDUCE_MAX = 82, BuiltinOperator_PACK = 83, BuiltinOperator_LOGICAL_OR = 84, + BuiltinOperator_ONE_HOT = 85, BuiltinOperator_MIN = BuiltinOperator_ADD, - BuiltinOperator_MAX = BuiltinOperator_LOGICAL_OR + BuiltinOperator_MAX = BuiltinOperator_ONE_HOT }; -inline BuiltinOperator (&EnumValuesBuiltinOperator())[84] { +inline BuiltinOperator (&EnumValuesBuiltinOperator())[85] { static BuiltinOperator values[] = { BuiltinOperator_ADD, BuiltinOperator_AVERAGE_POOL_2D, @@ -450,7 +454,8 @@ inline BuiltinOperator (&EnumValuesBuiltinOperator())[84] { BuiltinOperator_REDUCE_PROD, BuiltinOperator_REDUCE_MAX, BuiltinOperator_PACK, - BuiltinOperator_LOGICAL_OR + BuiltinOperator_LOGICAL_OR, + BuiltinOperator_ONE_HOT }; return values; } @@ -542,6 +547,7 @@ inline const char **EnumNamesBuiltinOperator() { "REDUCE_MAX", "PACK", "LOGICAL_OR", + "ONE_HOT", nullptr }; return names; @@ -614,11 +620,12 @@ enum BuiltinOptions { BuiltinOptions_FakeQuantOptions = 58, BuiltinOptions_PackOptions = 59, BuiltinOptions_LogicalOrOptions = 60, + BuiltinOptions_OneHotOptions = 61, BuiltinOptions_MIN = BuiltinOptions_NONE, - BuiltinOptions_MAX = BuiltinOptions_LogicalOrOptions + BuiltinOptions_MAX = BuiltinOptions_OneHotOptions }; -inline BuiltinOptions (&EnumValuesBuiltinOptions())[61] { +inline BuiltinOptions (&EnumValuesBuiltinOptions())[62] { static BuiltinOptions values[] = { BuiltinOptions_NONE, BuiltinOptions_Conv2DOptions, @@ -680,7 +687,8 @@ inline BuiltinOptions (&EnumValuesBuiltinOptions())[61] { BuiltinOptions_ArgMinOptions, BuiltinOptions_FakeQuantOptions, BuiltinOptions_PackOptions, - BuiltinOptions_LogicalOrOptions + BuiltinOptions_LogicalOrOptions, + BuiltinOptions_OneHotOptions }; return values; } @@ -748,6 +756,7 @@ inline const char **EnumNamesBuiltinOptions() { "FakeQuantOptions", "PackOptions", "LogicalOrOptions", + "OneHotOptions", nullptr }; return names; @@ -1002,6 +1011,10 @@ template<> struct BuiltinOptionsTraits { static const BuiltinOptions enum_value = BuiltinOptions_LogicalOrOptions; }; +template<> struct BuiltinOptionsTraits { + static const BuiltinOptions enum_value = BuiltinOptions_OneHotOptions; +}; + struct BuiltinOptionsUnion { BuiltinOptions type; void *value; @@ -1513,6 +1526,14 @@ struct BuiltinOptionsUnion { return type == BuiltinOptions_LogicalOrOptions ? reinterpret_cast(value) : nullptr; } + OneHotOptionsT *AsOneHotOptions() { + return type == BuiltinOptions_OneHotOptions ? + reinterpret_cast(value) : nullptr; + } + const OneHotOptionsT *AsOneHotOptions() const { + return type == BuiltinOptions_OneHotOptions ? + reinterpret_cast(value) : nullptr; + } }; bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *obj, BuiltinOptions type); @@ -5452,6 +5473,60 @@ inline flatbuffers::Offset CreateLogicalOrOptions( flatbuffers::Offset CreateLogicalOrOptions(flatbuffers::FlatBufferBuilder &_fbb, const LogicalOrOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +struct OneHotOptionsT : public flatbuffers::NativeTable { + typedef OneHotOptions TableType; + int32_t axis; + OneHotOptionsT() + : axis(0) { + } +}; + +struct OneHotOptions FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { + typedef OneHotOptionsT NativeTableType; + enum { + VT_AXIS = 4 + }; + int32_t axis() const { + return GetField(VT_AXIS, 0); + } + bool Verify(flatbuffers::Verifier &verifier) const { + return VerifyTableStart(verifier) && + VerifyField(verifier, VT_AXIS) && + verifier.EndTable(); + } + OneHotOptionsT *UnPack(const flatbuffers::resolver_function_t *_resolver = nullptr) const; + void UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver = nullptr) const; + static flatbuffers::Offset Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); +}; + +struct OneHotOptionsBuilder { + flatbuffers::FlatBufferBuilder &fbb_; + flatbuffers::uoffset_t start_; + void add_axis(int32_t axis) { + fbb_.AddElement(OneHotOptions::VT_AXIS, axis, 0); + } + explicit OneHotOptionsBuilder(flatbuffers::FlatBufferBuilder &_fbb) + : fbb_(_fbb) { + start_ = fbb_.StartTable(); + } + OneHotOptionsBuilder &operator=(const OneHotOptionsBuilder &); + flatbuffers::Offset Finish() { + const auto end = fbb_.EndTable(start_); + auto o = flatbuffers::Offset(end); + return o; + } +}; + +inline flatbuffers::Offset CreateOneHotOptions( + flatbuffers::FlatBufferBuilder &_fbb, + int32_t axis = 0) { + OneHotOptionsBuilder builder_(_fbb); + builder_.add_axis(axis); + return builder_.Finish(); +} + +flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher = nullptr); + struct OperatorCodeT : public flatbuffers::NativeTable { typedef OperatorCode TableType; BuiltinOperator builtin_code; @@ -5765,6 +5840,9 @@ struct Operator FLATBUFFERS_FINAL_CLASS : private flatbuffers::Table { const LogicalOrOptions *builtin_options_as_LogicalOrOptions() const { return builtin_options_type() == BuiltinOptions_LogicalOrOptions ? static_cast(builtin_options()) : nullptr; } + const OneHotOptions *builtin_options_as_OneHotOptions() const { + return builtin_options_type() == BuiltinOptions_OneHotOptions ? static_cast(builtin_options()) : nullptr; + } const flatbuffers::Vector *custom_options() const { return GetPointer *>(VT_CUSTOM_OPTIONS); } @@ -6036,6 +6114,10 @@ template<> inline const LogicalOrOptions *Operator::builtin_options_as inline const OneHotOptions *Operator::builtin_options_as() const { + return builtin_options_as_OneHotOptions(); +} + struct OperatorBuilder { flatbuffers::FlatBufferBuilder &fbb_; flatbuffers::uoffset_t start_; @@ -8151,6 +8233,32 @@ inline flatbuffers::Offset CreateLogicalOrOptions(flatbuffers: _fbb); } +inline OneHotOptionsT *OneHotOptions::UnPack(const flatbuffers::resolver_function_t *_resolver) const { + auto _o = new OneHotOptionsT(); + UnPackTo(_o, _resolver); + return _o; +} + +inline void OneHotOptions::UnPackTo(OneHotOptionsT *_o, const flatbuffers::resolver_function_t *_resolver) const { + (void)_o; + (void)_resolver; + { auto _e = axis(); _o->axis = _e; }; +} + +inline flatbuffers::Offset OneHotOptions::Pack(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT* _o, const flatbuffers::rehasher_function_t *_rehasher) { + return CreateOneHotOptions(_fbb, _o, _rehasher); +} + +inline flatbuffers::Offset CreateOneHotOptions(flatbuffers::FlatBufferBuilder &_fbb, const OneHotOptionsT *_o, const flatbuffers::rehasher_function_t *_rehasher) { + (void)_rehasher; + (void)_o; + struct _VectorArgs { flatbuffers::FlatBufferBuilder *__fbb; const OneHotOptionsT* __o; const flatbuffers::rehasher_function_t *__rehasher; } _va = { &_fbb, _o, _rehasher}; (void)_va; + auto _axis = _o->axis; + return tflite::CreateOneHotOptions( + _fbb, + _axis); +} + inline OperatorCodeT *OperatorCode::UnPack(const flatbuffers::resolver_function_t *_resolver) const { auto _o = new OperatorCodeT(); UnPackTo(_o, _resolver); @@ -8580,6 +8688,10 @@ inline bool VerifyBuiltinOptions(flatbuffers::Verifier &verifier, const void *ob auto ptr = reinterpret_cast(obj); return verifier.VerifyTable(ptr); } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(obj); + return verifier.VerifyTable(ptr); + } default: return false; } } @@ -8838,6 +8950,10 @@ inline void *BuiltinOptionsUnion::UnPack(const void *obj, BuiltinOptions type, c auto ptr = reinterpret_cast(obj); return ptr->UnPack(resolver); } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(obj); + return ptr->UnPack(resolver); + } default: return nullptr; } } @@ -9084,6 +9200,10 @@ inline flatbuffers::Offset BuiltinOptionsUnion::Pack(flatbuffers::FlatBuff auto ptr = reinterpret_cast(value); return CreateLogicalOrOptions(_fbb, ptr, _rehasher).Union(); } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(value); + return CreateOneHotOptions(_fbb, ptr, _rehasher).Union(); + } default: return 0; } } @@ -9330,6 +9450,10 @@ inline BuiltinOptionsUnion::BuiltinOptionsUnion(const BuiltinOptionsUnion &u) FL value = new LogicalOrOptionsT(*reinterpret_cast(u.value)); break; } + case BuiltinOptions_OneHotOptions: { + value = new OneHotOptionsT(*reinterpret_cast(u.value)); + break; + } default: break; } @@ -9637,6 +9761,11 @@ inline void BuiltinOptionsUnion::Reset() { delete ptr; break; } + case BuiltinOptions_OneHotOptions: { + auto ptr = reinterpret_cast(value); + delete ptr; + break; + } default: break; } value = nullptr; diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 41ece94237..3c7ad9d8b3 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -242,7 +242,9 @@ def create_tensor_data(dtype, shape, min_value=-100, max_value=100): value = (max_value-min_value)*np.random.random_sample(shape)+min_value elif dtype in (tf.int32, tf.uint8, tf.int64): value = np.random.randint(min_value, max_value+1, shape) - return value.astype(dtype) + + return np.dtype(dtype).type(value) if np.isscalar(value) else value.astype( + dtype) def create_scalar_data(dtype, min_value=-100, max_value=100): @@ -1665,6 +1667,65 @@ def make_shape_tests(zip_path): make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) +def make_one_hot_tests(zip_path): + """Make a set of tests to do one_hot.""" + + test_parameters = [{ + "indices_type": [tf.int32, tf.int64], + "indices_shape": [[3], [4, 4], [1, 5], [5, 1]], + "axis": [0, 1], + "dtype": [tf.int32, tf.int64, tf.float32], + "provide_optional_inputs": [True, False], + }] + + def build_graph(parameters): + indices = tf.placeholder( + dtype=parameters["indices_type"], + name="indices", + shape=parameters["indices_shape"]) + depth = tf.placeholder(dtype=tf.int32, name="depth", shape=()) + + if not parameters["provide_optional_inputs"]: + out = tf.one_hot(indices=indices, depth=depth) + return [indices, depth], [out] + + on_value = tf.placeholder( + dtype=parameters["dtype"], name="on_value", shape=()) + off_value = tf.placeholder( + dtype=parameters["dtype"], name="off_value", shape=()) + out = tf.one_hot( + indices=indices, + depth=depth, + on_value=on_value, + off_value=off_value, + axis=parameters["axis"], + dtype=parameters["dtype"]) + return [indices, depth, on_value, off_value], [out] + + def build_inputs(parameters, sess, inputs, outputs): + input_values = [ + create_tensor_data( + parameters["indices_type"], + shape=parameters["indices_shape"], + min_value=-1, + max_value=10), + create_tensor_data(tf.int32, shape=None, min_value=1, max_value=10), + ] + + if parameters["provide_optional_inputs"]: + input_values.append( + create_tensor_data( + parameters["dtype"], shape=None, min_value=1, max_value=10)) + input_values.append( + create_tensor_data( + parameters["dtype"], shape=None, min_value=-1, max_value=0)) + + return input_values, sess.run( + outputs, feed_dict=dict(zip(inputs, input_values))) + + make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) + + def make_resize_bilinear_tests(zip_path): """Make a set of tests to do resize_bilinear.""" diff --git a/tensorflow/contrib/lite/toco/export_tensorflow.cc b/tensorflow/contrib/lite/toco/export_tensorflow.cc index b79bb300f0..9983e59910 100644 --- a/tensorflow/contrib/lite/toco/export_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/export_tensorflow.cc @@ -1316,6 +1316,20 @@ void ConvertResizeBilinearOperator(const Model& model, (*resize_op->mutable_attr())["align_corners"].set_b(src_op.align_corners); } +void ConvertOneHotOperator(const Model& model, const OneHotOperator& src_op, + GraphDef* tensorflow_graph) { + tensorflow::NodeDef* onehot_op = tensorflow_graph->add_node(); + onehot_op->set_op("OneHot"); + onehot_op->set_name(src_op.outputs[0]); + CHECK_EQ(src_op.inputs.size(), 4); + for (const auto& input : src_op.inputs) { + *onehot_op->add_input() = input; + } + (*onehot_op->mutable_attr())["T"].set_type( + GetTensorFlowDataType(model, src_op.outputs[0])); + (*onehot_op->mutable_attr())["axis"].set_i(src_op.axis); +} + namespace { // TODO(aselle): Remove when available in absl absl::string_view FindLongestCommonPrefix(absl::string_view a, @@ -2158,6 +2172,9 @@ void ConvertOperator(const Model& model, const Operator& src_op, ConvertLogicalNotOperator(model, static_cast(src_op), tensorflow_graph); + } else if (src_op.type == OperatorType::kOneHot) { + ConvertOneHotOperator(model, static_cast(src_op), + tensorflow_graph); } else { LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(src_op.type); } 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 9c22497d5e..0f94006f34 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 @@ -201,6 +201,18 @@ bool PropagateArrayDataTypes::Run(Model* model, std::size_t op_index) { SetDataTypeForAllOutputs(model, op, data_type); break; } + case OperatorType::kOneHot: { + CHECK_EQ(op->inputs.size(), 4); + CHECK_EQ(op->outputs.size(), 1); + const ArrayDataType on_value_type = + model->GetArray(op->inputs[OneHotOperator::ON_VALUE_INPUT]).data_type; + const ArrayDataType off_value_type = + model->GetArray(op->inputs[OneHotOperator::OFF_VALUE_INPUT]) + .data_type; + CHECK(on_value_type == off_value_type); + model->GetArray(op->outputs[0]).data_type = on_value_type; + break; + } default: { // These operators produce outputs with the same type as their 1st input CHECK_GT(op->inputs.size(), 0); 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 a03b589bae..5aa0fddf57 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/propagate_fixed_sizes.cc @@ -1578,6 +1578,61 @@ void ProcessAnyOperator(Model* model, AnyOperator* op) { } } +void ProcessOneHotOperator(Model* model, OneHotOperator* op) { + CHECK_EQ(op->inputs.size(), 4); + CHECK_EQ(op->outputs.size(), 1); + auto& output_array = model->GetArray(op->outputs[0]); + if (output_array.has_shape()) { + // Shape already propagated + return; + } + + // Yield until indices dims have been resolved. + const auto& indices_array = + model->GetArray(op->inputs[OneHotOperator::INDICES_INPUT]); + if (!indices_array.has_shape()) { + return; + } + + // Yield until depth is constant and dims have been resolved. + if (!IsConstantParameterArray(*model, + op->inputs[OneHotOperator::DEPTH_INPUT])) { + return; + } + const auto& depth_array = + model->GetArray(op->inputs[OneHotOperator::DEPTH_INPUT]); + if (!depth_array.has_shape()) { + return; + } + + CHECK(depth_array.data_type == ArrayDataType::kInt32) + << "Depth array must be int32."; + CHECK_EQ(RequiredBufferSizeForShape(depth_array.shape()), 1) + << "Depth array must be scalar."; + + const int depth = depth_array.GetBuffer().data[0]; + CHECK_GE(depth, 0) << "Depth must be non-negative."; + + const int indices_dims = indices_array.shape().dimensions_count(); + const int output_dims = indices_dims + 1; + const int axis = op->axis == -1 ? indices_dims : op->axis; + CHECK_GE(axis, 0) << "Resolved axis must be non-negative."; + + auto* mutable_dims = output_array.mutable_shape()->mutable_dims(); + mutable_dims->resize(output_dims); + for (int i = 0; i < output_dims; ++i) { + int dim = 0; + if (i < axis) { + dim = indices_array.shape().dims(i); + } else if (i == axis) { + dim = depth; + } else { + dim = indices_array.shape().dims(i - 1); + } + (*mutable_dims)[i] = dim; + } +} + } // namespace bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { @@ -1825,6 +1880,9 @@ bool PropagateFixedSizes::Run(Model* model, std::size_t op_index) { case OperatorType::kAny: ProcessAnyOperator(model, static_cast(op)); break; + case OperatorType::kOneHot: + ProcessOneHotOperator(model, static_cast(op)); + break; default: // Unimplemented, another graph transformation should drop it. LOG(FATAL) << "Unhandled operator type " << OperatorTypeName(op->type); diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index f36f720857..f92f33497d 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -1833,6 +1833,27 @@ tensorflow::Status ConvertSparseToDenseOperator( return tensorflow::Status::OK(); } +tensorflow::Status ConvertOneHotOperator( + const NodeDef& node, const TensorFlowImportFlags& tf_import_flags, + Model* model) { + CHECK_EQ(node.op(), "OneHot"); + TF_QCHECK_OK(CheckInputsCount(node, tf_import_flags, 4)); + + const auto dtype = GetDataTypeAttr(node, "T"); + // TODO(b/111744875): Support DT_UINT8 and quantization. + CHECK(dtype == DT_INT32 || dtype == DT_INT64 || dtype == DT_FLOAT || + dtype == DT_BOOL); + + auto op = absl::make_unique(); + op->axis = HasAttr(node, "axis") ? GetIntAttr(node, "axis") : -1; + for (const string& input : node.input()) { + op->inputs.push_back(input); + } + op->outputs.push_back(node.name()); + model->operators.emplace_back(op.release()); + return tensorflow::Status::OK(); +} + } // namespace namespace internal { @@ -1909,6 +1930,7 @@ ConverterMapType GetTensorFlowNodeConverterMap() { {"NextIteration", ConvertOperatorSpecialCasedAsRNNBackEdge}, {"NoOp", ConvertNoOpOperator}, {"NotEqual", ConvertSimpleOperator}, + {"OneHot", ConvertOneHotOperator}, {"Pack", ConvertPackOperator}, {"Pad", ConvertSimpleOperator}, {"PadV2", ConvertSimpleOperator}, diff --git a/tensorflow/contrib/lite/toco/model.h b/tensorflow/contrib/lite/toco/model.h index 6459dccf64..a3827977fd 100644 --- a/tensorflow/contrib/lite/toco/model.h +++ b/tensorflow/contrib/lite/toco/model.h @@ -64,6 +64,7 @@ enum class OperatorType : uint8 { kMaxPool, kFakeQuant, kMul, + kOneHot, kRandomUniform, kRange, kRank, @@ -1768,6 +1769,27 @@ struct LogicalNotOperator : Operator { LogicalNotOperator() : Operator(OperatorType::kLogicalNot) {} }; +// OneHot operator: +// +// Inputs: +// Inputs[0]: required: indices. +// Inputs[1]: required: depth. +// Inputs[2]: required: on_value. +// Inputs[3]: required: off_value. +// +// TensorFlow equivalent: OneHot. +struct OneHotOperator : Operator { + enum Inputs { + INDICES_INPUT = 0, + DEPTH_INPUT = 1, + ON_VALUE_INPUT = 2, + OFF_VALUE_INPUT = 3, + }; + + OneHotOperator() : Operator(OperatorType::kOneHot) {} + int axis = -1; +}; + // 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/tflite/operator.cc b/tensorflow/contrib/lite/toco/tflite/operator.cc index 4b2ef756cc..769e350ea9 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator.cc @@ -1053,6 +1053,23 @@ class Shape int GetVersion(const Operator& op) const override { return 1; } }; +class OneHot : public BuiltinOperator { + public: + using BuiltinOperator::BuiltinOperator; + flatbuffers::Offset WriteOptions( + const TocoOperator& op, + flatbuffers::FlatBufferBuilder* builder) const override { + return ::tflite::CreateOneHotOptions(*builder, op.axis); + } + void ReadOptions(const TfLiteOptions& options, + TocoOperator* op) const override { + op->axis = options.axis(); + } + + int GetVersion(const Operator& op) const override { return 1; } +}; + class TensorFlowUnsupported : public BaseOperator { public: using BaseOperator::BaseOperator; @@ -1278,6 +1295,8 @@ std::vector> BuildOperatorList() { OperatorType::kFakeQuant)); ops.emplace_back( new Pack(::tflite::BuiltinOperator_PACK, OperatorType::kPack)); + ops.emplace_back( + new OneHot(::tflite::BuiltinOperator_ONE_HOT, OperatorType::kOneHot)); // Custom Operators. ops.emplace_back( diff --git a/tensorflow/contrib/lite/toco/tflite/operator_test.cc b/tensorflow/contrib/lite/toco/tflite/operator_test.cc index 44de6fbf64..7e1e32ae54 100644 --- a/tensorflow/contrib/lite/toco/tflite/operator_test.cc +++ b/tensorflow/contrib/lite/toco/tflite/operator_test.cc @@ -462,6 +462,14 @@ TEST_F(OperatorTest, BuiltinPack) { EXPECT_EQ(op.axis, output_toco_op->axis); } +TEST_F(OperatorTest, BuiltinOneHot) { + OneHotOperator op; + op.axis = 2; + auto output_toco_op = SerializeAndDeserialize( + GetOperator("ONE_HOT", OperatorType::kOneHot), op); + EXPECT_EQ(op.axis, output_toco_op->axis); +} + TEST_F(OperatorTest, TensorFlowUnsupported) { TensorFlowUnsupportedOperator op; op.tensorflow_op = "MyCustomUnsupportedOp"; diff --git a/tensorflow/contrib/lite/toco/tooling_util.cc b/tensorflow/contrib/lite/toco/tooling_util.cc index 98e416b76e..93c30dd0f8 100644 --- a/tensorflow/contrib/lite/toco/tooling_util.cc +++ b/tensorflow/contrib/lite/toco/tooling_util.cc @@ -356,6 +356,7 @@ const char* OperatorTypeName(OperatorType type) { HANDLE_OPERATORTYPENAME_CASE(ReduceMin) // Reduction Min HANDLE_OPERATORTYPENAME_CASE(Minimum) // Element-wise Minimum HANDLE_OPERATORTYPENAME_CASE(Neg) + HANDLE_OPERATORTYPENAME_CASE(OneHot) HANDLE_OPERATORTYPENAME_CASE(Pack) HANDLE_OPERATORTYPENAME_CASE(Pad) HANDLE_OPERATORTYPENAME_CASE(PadV2) -- GitLab From ecd8decac3d9f3c7cd772e1561b9c2d3f23aa830 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 11:05:28 -0700 Subject: [PATCH 449/519] Replace //tools/defaults:crosstool to @bazel_tools//tools/cpp:current_cc_toolchain //tools/defaults will be removed soon PiperOrigin-RevId: 206187625 --- tensorflow/tensorflow.bzl | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 340d3f393c..4da6493723 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -1456,7 +1456,7 @@ def tf_py_wrap_cc(name, srcs=srcs, swig_includes=swig_includes, deps=deps + extra_deps, - toolchain_deps=["//tools/defaults:crosstool"], + toolchain_deps=["@bazel_tools//tools/cpp:current_cc_toolchain"], module_name=module_name, py_module_name=name) vscriptname=name+"_versionscript" -- GitLab From de31d0185b8ccb1882112dda0ac763f1c869b7a4 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Thu, 26 Jul 2018 11:25:44 -0700 Subject: [PATCH 450/519] [SE] Try again to query the GPU driver for error descriptions This code hs been here since 2014, now the oldest supported version of CUDA is 8 so cuGetErrorName should always be available. Also the list of errors is (of course) out of sync with upstream CUDA. Also surface the description of the error to the user, if available. PiperOrigin-RevId: 206191424 --- .../stream_executor/cuda/cuda_driver.cc | 117 ++---------------- 1 file changed, 8 insertions(+), 109 deletions(-) diff --git a/tensorflow/stream_executor/cuda/cuda_driver.cc b/tensorflow/stream_executor/cuda/cuda_driver.cc index d508f6594a..dbece3adf9 100644 --- a/tensorflow/stream_executor/cuda/cuda_driver.cc +++ b/tensorflow/stream_executor/cuda/cuda_driver.cc @@ -102,117 +102,16 @@ class CreatedContexts { /* static */ int64 CreatedContexts::next_id_ = 1; // 0 means "no context" // Formats CUresult to output prettified values into a log stream. -// Error summaries taken from: -// http://docs.nvidia.com/cuda/cuda-driver-api/group__CUDA__TYPES.html#group__CUDA__TYPES_1gc6c391505e117393cc2558fff6bfc2e9 -// -// TODO(leary) switch to cuGetErrorName when updated cuda.h is available. string ToString(CUresult result) { -#define OSTREAM_CUDA_ERROR(__name) \ - case CUDA_ERROR_##__name: \ - return "CUDA_ERROR_" #__name; - -/////////////// -// NOTE: here we specify return code values outside of the enum explicitly -// because our in-tree cuda.h is from the CUDA 5.5 SDK, but CUDA 6.0+ driver -// libraries are deployed in the fleet these error codes are backwards -// compatible, but if we see a "new" one, we want to be able to identify it in -// the logs. -// -// Once we get a cuda.h that has cuGetErrorName (TODO is above) we can -// eliminate this function and just rely on the driver to provide us these -// strings. -// -// NOTE: "Must reboot all context" below is shorthand for, "must -// destroy/recreate the offending context and any allocation which come from -// it if you are to continue using CUDA." -#pragma GCC diagnostic push -#pragma GCC diagnostic ignored "-Wswitch" - switch (result) { - OSTREAM_CUDA_ERROR(INVALID_VALUE) - OSTREAM_CUDA_ERROR(OUT_OF_MEMORY) - OSTREAM_CUDA_ERROR(NOT_INITIALIZED) - OSTREAM_CUDA_ERROR(DEINITIALIZED) - OSTREAM_CUDA_ERROR(NO_DEVICE) - OSTREAM_CUDA_ERROR(INVALID_DEVICE) - OSTREAM_CUDA_ERROR(INVALID_IMAGE) - OSTREAM_CUDA_ERROR(INVALID_CONTEXT) - OSTREAM_CUDA_ERROR(INVALID_HANDLE) - OSTREAM_CUDA_ERROR(NOT_FOUND) - OSTREAM_CUDA_ERROR(NOT_READY) - OSTREAM_CUDA_ERROR(NO_BINARY_FOR_GPU) - - // Encountered an uncorrectable ECC error during execution. - OSTREAM_CUDA_ERROR(ECC_UNCORRECTABLE) - - // Load/store on an invalid address. Must reboot all context. - case 700: - return "CUDA_ERROR_ILLEGAL_ADDRESS"; - // Passed too many / wrong arguments, too many threads for register count. - case 701: - return "CUDA_ERROR_LAUNCH_OUT_OF_RESOURCES"; - // Kernel took too long to execute. - case 702: - return "CUDA_ERROR_LAUNCH_TIMEOUT"; - // Kernel launch uses an incompatible texturing mode. - case 703: - return "CUDA_ERROR_LAUNCH_INCOMPATIBLE_TEXTURING"; - // Trying to re-enable peer access that already has it enabled. - case 704: - return "CUDA_ERROR_PEER_ACCESS_ALREADY_ENABLED"; - // Trying to disable peer access that has not yet been enabled. - case 705: - return "CUDA_ERROR_PEER_ACCESS_NOT_ENABLED"; - // Primary context for the specified device has already been initialized. - case 708: - return "CUDA_ERROR_PRIMARY_CONTEXT_ACTIVE"; - // Context current to calling thread has been destroyed or is a primary - // context that has not yet been initialized. - case 709: - return "CUDA_ERROR_CONTEXT_IS_DESTROYED"; - // Device-side assert triggered during kernel execution. Must reboot all - // context. - case 710: - return "CUDA_ERROR_ASSERT"; - // Hardware resources to enable peer access have been exhausted. - case 711: - return "CUDA_ERROR_TOO_MANY_PEERS"; - // Memory range has already been registered. - case 712: - return "CUDA_ERROR_HOST_MEMORY_ALREADY_REGISTERED"; - // Pointer does not correspond to any currently registered memory region. - case 713: - return "CUDA_ERROR_HOST_MEMORY_NOT_REGISTERED"; - // Due to stack corruption or exceeding stack size limit. Must reboot all - // context. - case 714: - return "CUDA_ERROR_HARDWARE_STACK_ERROR"; - case 715: - return "CUDA_ERROR_ILLEGAL_INSTRUCTION"; - // Load/store on an unaligned memory address. Must reboot all context. - case 716: - return "CUDA_ERROR_MISALIGNED_ADDRESS"; - // Device instruction with specific address space given address not - // belonging to allowed address space. Must reboot all context. - case 717: - return "CUDA_ERROR_INVALID_ADDRESS_SPACE"; - // Device program counter wrapped its address space. Must reboot all - // context. - case 718: - return "CUDA_ERROR_INVALID_PC"; - // Exception on device while executing a kernel; e.g. deref invalid device - // pointer, accessing OOB shared memory. Must reboot all context. - case 719: - return "CUDA_ERROR_LAUNCH_FAILED"; - - OSTREAM_CUDA_ERROR(CONTEXT_ALREADY_IN_USE) - OSTREAM_CUDA_ERROR(PEER_ACCESS_UNSUPPORTED) - OSTREAM_CUDA_ERROR(NOT_PERMITTED) - OSTREAM_CUDA_ERROR(NOT_SUPPORTED) - OSTREAM_CUDA_ERROR(UNKNOWN) // Unknown internal error to CUDA. - default: - return port::StrCat("CUresult(", static_cast(result), ")"); + const char *error_name; + if (cuGetErrorName(result, &error_name)) { + return port::StrCat("UNKNOWN ERROR (", static_cast(result), ")"); + } + const char *error_string; + if (cuGetErrorString(result, &error_string)) { + return error_name; } -#pragma GCC diagnostic pop + return port::StrCat(error_name, ": ", error_string); } // Returns the current context and checks that it is in the set of CUDA contexts -- GitLab From a8218323db98a504fe359568c97d0c7e1b978c47 Mon Sep 17 00:00:00 2001 From: Zhenyu Tan Date: Thu, 26 Jul 2018 11:27:29 -0700 Subject: [PATCH 451/519] remove uncessary variable naming and comment PiperOrigin-RevId: 206191743 --- .../contrib/optimizer_v2/optimizer_v2_test.py | 40 +++++-------------- 1 file changed, 10 insertions(+), 30 deletions(-) diff --git a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py index ec033c4a01..a44bfd1bfd 100644 --- a/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py +++ b/tensorflow/contrib/optimizer_v2/optimizer_v2_test.py @@ -38,12 +38,8 @@ class OptimizerTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def testBasic(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): - # Note that we name the variables uniquely here since the variables don't - # seem to be getting deleted at the end of the loop. - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype, - name='a_%d' % i) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, - name='b_%d' % i) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) def loss(): return 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop # Note that for eager execution, minimize expects a function instead of a @@ -131,12 +127,8 @@ class OptimizerTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def testNoGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): - # Note that we name the variables uniquely here since the variables don't - # seem to be getting deleted at the end of the loop. - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype, - name='a%d' % i) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, - name='b%d' % i) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) # pylint: disable=cell-var-from-loop def loss(): return 5 * var0 @@ -149,12 +141,8 @@ class OptimizerTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_Minimize(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): - # Note that we name the variables uniquely here since the variables don't - # seem to be getting deleted at the end of the loop. - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype, - name='a_%d' % i) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, - name='b_%d' % i) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) def loss(): return constant_op.constant(5.0) sgd_op = gradient_descent.GradientDescentOptimizer(3.0) @@ -165,12 +153,8 @@ class OptimizerTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def testNoGradientsForAnyVariables_ApplyGradients(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): - # Note that we name the variables uniquely here since the variables don't - # seem to be getting deleted at the end of the loop. - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype, - name='a_%d' % i) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, - name='b_%d' % i) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) sgd_op = gradient_descent.GradientDescentOptimizer(3.0) with self.assertRaisesRegexp(ValueError, 'No gradients provided for any variable'): @@ -179,12 +163,8 @@ class OptimizerTest(test.TestCase): @test_util.run_in_graph_and_eager_modes def testGradientsAsVariables(self): for i, dtype in enumerate([dtypes.half, dtypes.float32, dtypes.float64]): - # Note that we name the variables uniquely here since the variables don't - # seem to be getting deleted at the end of the loop. - var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype, - name='a%d' % i) - var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype, - name='b%d' % i) + var0 = resource_variable_ops.ResourceVariable([1.0, 2.0], dtype=dtype) + var1 = resource_variable_ops.ResourceVariable([3.0, 4.0], dtype=dtype) def loss(): return 5 * var0 + 3 * var1 # pylint: disable=cell-var-from-loop sgd_op = gradient_descent.GradientDescentOptimizer(3.0) -- GitLab From e91c597b9aec76bb953567d84e6b92c3b2f5df8f Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Thu, 26 Jul 2018 11:29:10 -0700 Subject: [PATCH 452/519] Make model totally defunable. PiperOrigin-RevId: 206192038 --- .../eager/python/examples/revnet/blocks.py | 24 ++-- .../python/examples/revnet/blocks_test.py | 15 ++- .../eager/python/examples/revnet/main.py | 20 ++- .../python/examples/revnet/main_estimator.py | 11 +- .../examples/revnet/main_estimator_tpu.py | 21 +-- .../eager/python/examples/revnet/revnet.py | 126 +++++++----------- .../python/examples/revnet/revnet_test.py | 26 ++-- 7 files changed, 105 insertions(+), 138 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py index 8a530b0d71..2cb04ed258 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -91,12 +91,10 @@ class RevBlock(tf.keras.Model): h = block(h, training=training) return h - def backward_grads_and_vars(self, x, y, dy, training=True): + def backward_grads(self, x, y, dy, training=True): """Apply reversible block backward to outputs.""" grads_all = [] - vars_all = [] - for i in reversed(range(len(self.blocks))): block = self.blocks[i] if i == 0: @@ -104,19 +102,15 @@ class RevBlock(tf.keras.Model): with tf.GradientTape() as tape: tape.watch(x) y = block(x, training=training) - grads_combined = tape.gradient( y, [x] + block.trainable_variables, output_gradients=dy) dy = grads_combined[0] - grads_all += grads_combined[1:] - vars_all += block.trainable_variables + grads_all = grads_combined[1:] + grads_all else: - y, dy, grads, vars_ = block.backward_grads_and_vars( - y, dy, training=training) - grads_all += grads - vars_all += vars_ + y, dy, grads = block.backward_grads(y, dy, training=training) + grads_all = grads + grads_all - return dy, grads_all, vars_all + return dy, grads_all class _Residual(tf.keras.Model): @@ -195,7 +189,7 @@ class _Residual(tf.keras.Model): return tf.concat([y1, y2], axis=self.axis) - def backward_grads_and_vars(self, y, dy, training=True): + def backward_grads(self, y, dy, training=True): """Manually compute backward gradients given input and output grads.""" dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=self.axis) @@ -219,13 +213,11 @@ class _Residual(tf.keras.Model): del tape - grads = df + dg - vars_ = self.f.trainable_variables + self.g.trainable_variables - x = tf.concat([x1, x2], axis=self.axis) dx = tf.concat([dx1, dx2], axis=self.axis) + grads = df + dg - return x, dx, grads, vars_ + return x, dx, grads # Ideally, the following should be wrapped in `tf.keras.Sequential`, however diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py b/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py index d74785c8fe..3c6ea63e48 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks_test.py @@ -179,7 +179,7 @@ class RevBlockTest(tf.test.TestCase): degree = compute_degree(g1, g2) self.assertLessEqual(degree, atol) - def test_backward_grads_and_vars_channels_first(self): + def test_backward_grads_channels_first(self): """Test `backward` function with `channels_first` data format.""" if not tf.test.is_gpu_available(): self.skipTest("GPU not available") @@ -201,7 +201,8 @@ class RevBlockTest(tf.test.TestCase): tape.watch(x) y = block(x, training=True) # Compute grads from reconstruction - dx, dw, vars_ = block.backward_grads_and_vars(x, y, dy, training=True) + dx, dw = block.backward_grads(x, y, dy, training=True) + vars_ = block.trainable_variables # Compute true grads grads = tape.gradient(y, [x] + vars_, output_gradients=dy) dx_true, dw_true = grads[0], grads[1:] @@ -224,7 +225,8 @@ class RevBlockTest(tf.test.TestCase): tape.watch(x) y = block(x, training=True) # Compute grads from reconstruction - dx, dw, vars_ = block.backward_grads_and_vars(x, y, dy, training=True) + dx, dw = block.backward_grads(x, y, dy, training=True) + vars_ = block.trainable_variables # Compute true grads grads = tape.gradient(y, [x] + vars_, output_gradients=dy) dx_true, dw_true = grads[0], grads[1:] @@ -245,7 +247,7 @@ class _ResidualTest(tf.test.TestCase): _validate_block_call_channels_first(blocks._Residual, self) _validate_block_call_channels_last(blocks._Residual, self) - def test_backward_grads_and_vars_channels_first(self): + def test_backward_grads_channels_first(self): """Test `backward_grads` function with `channels_first` data format.""" if not tf.test.is_gpu_available(): self.skipTest("GPU not available") @@ -269,9 +271,8 @@ class _ResidualTest(tf.test.TestCase): y = residual(x_true, training=True) # Gradients computed due to reversibility - x, dx, dw, vars_ = residual.backward_grads_and_vars( - y, dy=dy, training=True) - + x, dx, dw = residual.backward_grads(y, dy=dy, training=True) + vars_ = residual.trainable_variables # True gradients computed by the tape grads = tape.gradient(y, [x_true] + vars_, output_gradients=dy) dx_true, dw_true = grads[0], grads[1:] diff --git a/tensorflow/contrib/eager/python/examples/revnet/main.py b/tensorflow/contrib/eager/python/examples/revnet/main.py index dcd4e1697f..b702e91f92 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/main.py +++ b/tensorflow/contrib/eager/python/examples/revnet/main.py @@ -29,6 +29,11 @@ from tensorflow.contrib.eager.python.examples.revnet import revnet tfe = tf.contrib.eager +def apply_gradients(optimizer, grads, vars_, global_step=None): + """Functional style apply_grads for `tfe.defun`.""" + optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) + + def main(_): """Eager execution workflow with RevNet trained on CIFAR-10.""" tf.enable_eager_execution() @@ -48,6 +53,11 @@ def main(_): if FLAGS.use_defun: model.call = tfe.defun(model.call) + model.compute_gradients = tfe.defun(model.compute_gradients) + model.get_moving_stats = tfe.defun(model.get_moving_stats) + model.restore_moving_stats = tfe.defun(model.restore_moving_stats) + global apply_gradients # pylint:disable=global-variable-undefined + apply_gradients = tfe.defun(apply_gradients) if FLAGS.train_dir: summary_writer = tf.contrib.summary.create_file_writer(FLAGS.train_dir) @@ -197,9 +207,13 @@ def get_datasets(data_dir, config): def train_one_iter(model, inputs, labels, optimizer, global_step=None): """Train for one iteration.""" - grads, vars_, logits, loss = model.compute_gradients( - inputs, labels, training=True) - optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) + logits, saved_hiddens = model(inputs, training=True) + values = model.get_moving_stats() + grads, loss = model.compute_gradients(saved_hiddens, labels) + # Restore moving averages when executing eagerly to avoid updating twice + model.restore_moving_stats(values) + apply_gradients( + optimizer, grads, model.trainable_variables, global_step=global_step) return logits, loss diff --git a/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py b/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py index 4868f1931f..df25b5066f 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py +++ b/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py @@ -53,10 +53,10 @@ def model_fn(features, labels, mode, params): global_step, config.lr_decay_steps, config.lr_list) optimizer = tf.train.MomentumOptimizer( learning_rate, momentum=config.momentum) - grads, vars_, logits, loss = model.compute_gradients( - inputs, labels, training=True) + logits, saved_hidden = model(inputs, training=True) + grads, loss = model.compute_gradients(saved_hidden, labels, training=True) train_op = optimizer.apply_gradients( - zip(grads, vars_), global_step=global_step) + zip(grads, model.trainable_variables), global_step=global_step) return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op) else: @@ -130,8 +130,7 @@ def get_input_fn(config, data_dir, split): return input_fn -def main(argv): - FLAGS = argv[0] # pylint:disable=invalid-name,redefined-outer-name +def main(_): tf.logging.set_verbosity(tf.logging.INFO) # RevNet specific configuration @@ -197,4 +196,4 @@ if __name__ == "__main__": help="[Optional] Architecture of network. " "Other options include `revnet-110` and `revnet-164`") FLAGS = flags.FLAGS - tf.app.run(main=main, argv=[FLAGS]) + tf.app.run() diff --git a/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py b/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py index d809bcd287..f0aad9b110 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py +++ b/tensorflow/contrib/eager/python/examples/revnet/main_estimator_tpu.py @@ -47,7 +47,6 @@ def model_fn(features, labels, mode, params): if isinstance(inputs, dict): inputs = features["image"] - FLAGS = params["FLAGS"] # pylint:disable=invalid-name,redefined-outer-name config = params["config"] model = revnet.RevNet(config=config) @@ -61,14 +60,10 @@ def model_fn(features, labels, mode, params): if FLAGS.use_tpu: optimizer = tf.contrib.tpu.CrossShardOptimizer(optimizer) - # Define gradients - grads, vars_, logits, loss = model.compute_gradients( - inputs, labels, training=True) + logits, saved_hidden = model(inputs, training=True) + grads, loss = model.compute_gradients(saved_hidden, labels, training=True) train_op = optimizer.apply_gradients( - zip(grads, vars_), global_step=global_step) - - names = [v.name for v in model.variables] - tf.logging.warn("{}".format(names)) + zip(grads, model.trainable_variables), global_step=global_step) return tf.contrib.tpu.TPUEstimatorSpec( mode=tf.estimator.ModeKeys.TRAIN, loss=loss, train_op=train_op) @@ -141,8 +136,7 @@ def get_input_fn(config, data_dir, split): return input_fn -def main(argv): - FLAGS = argv[0] # pylint:disable=invalid-name,redefined-outer-name +def main(_): tf.logging.set_verbosity(tf.logging.INFO) # RevNet specific configuration @@ -177,10 +171,7 @@ def main(argv): train_batch_size=config.tpu_batch_size, eval_batch_size=config.tpu_eval_batch_size, config=run_config, - params={ - "FLAGS": FLAGS, - "config": config, - }) + params={"config": config}) # Construct input functions train_input_fn = get_input_fn( @@ -325,4 +316,4 @@ if __name__ == "__main__": " possible (i.e. up to --train_steps, which evaluates the model only" " after finishing the entire training regime).")) FLAGS = flags.FLAGS - tf.app.run(main=main, argv=[FLAGS]) + tf.app.run() diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet.py b/tensorflow/contrib/eager/python/examples/revnet/revnet.py index b1cb312b74..1f2cb14972 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet.py @@ -24,7 +24,6 @@ from __future__ import absolute_import from __future__ import division from __future__ import print_function -import six import tensorflow as tf from tensorflow.contrib.eager.python.examples.revnet import blocks @@ -45,6 +44,7 @@ class RevNet(tf.keras.Model): self._init_block = blocks.InitBlock(config=self.config) self._final_block = blocks.FinalBlock(config=self.config) self._block_list = self._construct_intermediate_blocks() + self._moving_average_variables = [] def _construct_intermediate_blocks(self): # Precompute input shape after initial block @@ -128,126 +128,90 @@ class RevNet(tf.keras.Model): return tf.reduce_mean(cross_ent) - def compute_gradients(self, inputs, labels, training=True, l2_reg=True): + def compute_gradients(self, saved_hidden, labels, training=True, l2_reg=True): """Manually computes gradients. - When eager execution is enabled, this method also SILENTLY updates the - running averages of batch normalization when `training` is set to True. + This method silently updates the running averages of batch normalization. Args: - inputs: Image tensor, either NHWC or NCHW, conforming to `data_format` + saved_hidden: List of hidden states Tensors labels: One-hot labels for classification training: Use the mini-batch stats in batch norm if set to True l2_reg: Apply l2 regularization Returns: - A tuple with the first entry being a list of all gradients, the second - entry being a list of respective variables, the third being the logits, - and the forth being the loss + A tuple with the first entry being a list of all gradients and the second + being the loss """ - # Run forward pass to record hidden states - vars_and_vals = self.get_moving_stats() - _, saved_hidden = self(inputs, training=training) # pylint:disable=not-callable - if tf.executing_eagerly(): - # Restore moving averages when executing eagerly to avoid updating twice - self.restore_moving_stats(vars_and_vals) - else: - # Fetch batch norm updates in graph mode - updates = self.get_updates_for(inputs) - - grads_all = [] - vars_all = [] + def _defunable_pop(l): + """Functional style list pop that works with `tfe.defun`.""" + t, l = l[-1], l[:-1] + return t, l - # Manually backprop through last block + # Backprop through last block x = saved_hidden[-1] with tf.GradientTape() as tape: tape.watch(x) - # Running stats updated here logits = self._final_block(x, training=training) loss = self.compute_loss(logits, labels) - grads_combined = tape.gradient(loss, [x] + self._final_block.trainable_variables) - dy, grads_ = grads_combined[0], grads_combined[1:] - grads_all += grads_ - vars_all += self._final_block.trainable_variables + dy, final_grads = grads_combined[0], grads_combined[1:] - # Manually backprop through intermediate blocks + # Backprop through intermediate blocks + intermediate_grads = [] for block in reversed(self._block_list): - y = saved_hidden.pop() + y, saved_hidden = _defunable_pop(saved_hidden) x = saved_hidden[-1] - # Running stats updated here - dy, grads, vars_ = block.backward_grads_and_vars( - x, y, dy, training=training) - grads_all += grads - vars_all += vars_ - - # Manually backprop through first block - saved_hidden.pop() - x = saved_hidden.pop() - assert not saved_hidden # Cleared after backprop + dy, grads = block.backward_grads(x, y, dy, training=training) + intermediate_grads = grads + intermediate_grads + # Backprop through first block + _, saved_hidden = _defunable_pop(saved_hidden) + x, saved_hidden = _defunable_pop(saved_hidden) + assert not saved_hidden with tf.GradientTape() as tape: - # Running stats updated here y = self._init_block(x, training=training) - - grads_all += tape.gradient( + init_grads = tape.gradient( y, self._init_block.trainable_variables, output_gradients=dy) - vars_all += self._init_block.trainable_variables - # Apply weight decay + # Ordering match up with `model.trainable_variables` + grads_all = init_grads + final_grads + intermediate_grads if l2_reg: - grads_all = self._apply_weight_decay(grads_all, vars_all) - - if not tf.executing_eagerly(): - # Force updates to be executed before gradient computation in graph mode - # This does nothing when the function is wrapped in defun - with tf.control_dependencies(updates): - grads_all[0] = tf.identity(grads_all[0]) + grads_all = self._apply_weight_decay(grads_all) - return grads_all, vars_all, logits, loss + return grads_all, loss - def _apply_weight_decay(self, grads, vars_): + def _apply_weight_decay(self, grads): """Update gradients to reflect weight decay.""" - # Don't decay bias return [ g + self.config.weight_decay * v if v.name.endswith("kernel:0") else g - for g, v in zip(grads, vars_) + for g, v in zip(grads, self.trainable_variables) ] def get_moving_stats(self): - """Get moving averages of batch normalization. - - This is needed to avoid updating the running average twice in one iteration. - - Returns: - A dictionary mapping variables for batch normalization moving averages - to their current values. - """ - vars_and_vals = {} - - def _is_moving_var(v): - n = v.name - return n.endswith("moving_mean:0") or n.endswith("moving_variance:0") + """Get moving averages of batch normalization.""" + device = "/gpu:0" if tf.test.is_gpu_available() else "/cpu:0" + with tf.device(device): + return [v.read_value() for v in self.moving_average_variables] + def restore_moving_stats(self, values): + """Restore moving averages of batch normalization.""" device = "/gpu:0" if tf.test.is_gpu_available() else "/cpu:0" with tf.device(device): - for v in filter(_is_moving_var, self.variables): - vars_and_vals[v] = v.read_value() + for var_, val in zip(self.moving_average_variables, values): + var_.assign(val) - return vars_and_vals + @property + def moving_average_variables(self): + """Get all variables that are batch norm moving averages.""" - def restore_moving_stats(self, vars_and_vals): - """Restore moving averages of batch normalization. + def _is_moving_avg(v): + n = v.name + return n.endswith("moving_mean:0") or n.endswith("moving_variance:0") - This is needed to avoid updating the running average twice in one iteration. + if not self._moving_average_variables: + self._moving_average_variables = filter(_is_moving_avg, self.variables) - Args: - vars_and_vals: The dictionary mapping variables to their previous values. - """ - device = "/gpu:0" if tf.test.is_gpu_available() else "/cpu:0" - with tf.device(device): - for var_, val in six.iteritems(vars_and_vals): - # `assign` causes a copy to GPU (if variable is already on GPU) - var_.assign(val) + return self._moving_average_variables diff --git a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py index 26b0847523..84b2ddf0de 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py +++ b/tensorflow/contrib/eager/python/examples/revnet/revnet_test.py @@ -31,9 +31,11 @@ tfe = tf.contrib.eager def train_one_iter(model, inputs, labels, optimizer, global_step=None): """Train for one iteration.""" - grads, vars_, logits, loss = model.compute_gradients( - inputs, labels, training=True) - optimizer.apply_gradients(zip(grads, vars_), global_step=global_step) + logits, saved_hidden = model(inputs) + grads, loss = model.compute_gradients( + saved_hidden=saved_hidden, labels=labels) + optimizer.apply_gradients( + zip(grads, model.trainable_variables), global_step=global_step) return logits, loss @@ -96,9 +98,10 @@ class RevNetTest(tf.test.TestCase): def test_compute_gradients(self): """Test `compute_gradients` function.""" - self.model(self.x, training=False) # Initialize model - grads, vars_, logits, loss = self.model.compute_gradients( - inputs=self.x, labels=self.t, training=True, l2_reg=True) + _, saved_hidden = self.model(self.x) # Initialize model + grads, loss = self.model.compute_gradients( + saved_hidden=saved_hidden, labels=self.t) + vars_ = self.model.trainable_variables self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) self.assertEqual(len(grads), len(vars_)) @@ -107,7 +110,7 @@ class RevNetTest(tf.test.TestCase): # Compare against the true gradient computed by the tape with tf.GradientTape() as tape: - logits, _ = self.model(self.x, training=True) + logits, _ = self.model(self.x) loss_true = self.model.compute_loss(logits=logits, labels=self.t) grads_true = tape.gradient(loss_true, vars_) self.assertAllClose(loss, loss_true) @@ -122,7 +125,9 @@ class RevNetTest(tf.test.TestCase): def test_compute_gradients_defun(self): """Test `compute_gradients` function with defun.""" compute_gradients = tfe.defun(self.model.compute_gradients) - grads, vars_, _, _ = compute_gradients(self.x, self.t, training=True) + _, saved_hidden = self.model(self.x) + grads, _ = compute_gradients(saved_hidden=saved_hidden, labels=self.t) + vars_ = self.model.trainable_variables self.assertTrue(isinstance(grads, list)) self.assertTrue(isinstance(vars_, list)) self.assertEqual(len(grads), len(vars_)) @@ -146,10 +151,11 @@ class RevNetTest(tf.test.TestCase): dtype=tf.int32) global_step = tf.Variable(0., trainable=False) model = revnet.RevNet(config=config) - grads_all, vars_all, _, _ = model.compute_gradients(x, t, training=True) + _, saved_hidden = model(x) + grads, _ = model.compute_gradients(saved_hidden=saved_hidden, labels=t) optimizer = tf.train.AdamOptimizer(learning_rate=1e-3) train_op = optimizer.apply_gradients( - zip(grads_all, vars_all), global_step=global_step) + zip(grads, model.trainable_variables), global_step=global_step) with tf.Session() as sess: sess.run(tf.global_variables_initializer()) -- GitLab From c83525a1887ac3d7c03d4d25351e421cd90069a4 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Thu, 26 Jul 2018 11:32:54 -0700 Subject: [PATCH 453/519] Ergonomic fixes to tensorflow::dump_graph::* - Teach the routines to create the dump directories if they don't exist. I routinely forget to create the dump directory before launching the model. - Print out the file locations on LOG(INFO) and the error messages on LOG(WARNING). PiperOrigin-RevId: 206192822 --- tensorflow/compiler/tf2xla/dump_graph.cc | 53 ++++++++++++++---------- 1 file changed, 30 insertions(+), 23 deletions(-) diff --git a/tensorflow/compiler/tf2xla/dump_graph.cc b/tensorflow/compiler/tf2xla/dump_graph.cc index 03603ee9ba..24616c01c7 100644 --- a/tensorflow/compiler/tf2xla/dump_graph.cc +++ b/tensorflow/compiler/tf2xla/dump_graph.cc @@ -33,7 +33,7 @@ struct NameCounts { std::unordered_map counts; }; -string MakeUniquePath(string name) { +string MakeUniqueFilename(string name) { static NameCounts& instance = *new NameCounts; // Remove illegal characters from `name`. @@ -50,26 +50,41 @@ string MakeUniquePath(string name) { count = instance.counts[name]++; } - legacy_flags::DumpGraphFlags* flags = legacy_flags::GetDumpGraphFlags(); - string path = strings::StrCat(flags->tf_dump_graph_prefix, "/", name); + string filename = name; if (count > 0) { - strings::StrAppend(&path, "_", count); + strings::StrAppend(&filename, "_", count); } - strings::StrAppend(&path, ".pbtxt"); - return path; + strings::StrAppend(&filename, ".pbtxt"); + return filename; +} + +string WriteTextProtoToUniqueFile( + Env* env, const string& name, const char* proto_type, + const ::tensorflow::protobuf::Message& proto) { + const string& dirname = + legacy_flags::GetDumpGraphFlags()->tf_dump_graph_prefix; + Status status = env->RecursivelyCreateDir(dirname); + if (!status.ok()) { + LOG(WARNING) << "Failed to create " << dirname << " for dumping " + << proto_type << ": " << status; + return "(unavailable)"; + } + string filepath = strings::StrCat(dirname, "/", MakeUniqueFilename(name)); + status = WriteTextProto(Env::Default(), filepath, proto); + if (!status.ok()) { + LOG(WARNING) << "Failed to dump " << proto_type << " to file: " << filepath + << " : " << status; + return "(unavailable)"; + } + LOG(INFO) << "Dumped " << proto_type << " to " << filepath; + return filepath; } } // anonymous namespace string DumpGraphDefToFile(const string& name, GraphDef const& graph_def) { - string path = MakeUniquePath(name); - Status status = WriteTextProto(Env::Default(), path, graph_def); - if (!status.ok()) { - VLOG(1) << "Failed to dump GraphDef to file: " << path << " : " << status; - path.clear(); - path = "(unavailable)"; - } - return path; + return WriteTextProtoToUniqueFile(Env::Default(), name, "GraphDef", + graph_def); } string DumpGraphToFile(const string& name, Graph const& graph, @@ -83,15 +98,7 @@ string DumpGraphToFile(const string& name, Graph const& graph, } string DumpFunctionDefToFile(const string& name, FunctionDef const& fdef) { - string path = MakeUniquePath(name); - Status status = WriteTextProto(Env::Default(), path, fdef); - if (!status.ok()) { - VLOG(1) << "Failed to dump FunctionDef to file: " << path << " : " - << status; - path.clear(); - path = "(unavailable)"; - } - return path; + return WriteTextProtoToUniqueFile(Env::Default(), name, "FunctionDef", fdef); } } // namespace dump_graph -- GitLab From 91ff408ecd52dd167d966c9df222e840f1d43f8f Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 11:38:07 -0700 Subject: [PATCH 454/519] Boosted trees: Revealing pruning mode as one of the parameters for a gbdt estimator PiperOrigin-RevId: 206193733 --- .../python/estimator/boosted_trees.py | 30 ++++++-- .../python/estimator/boosted_trees_test.py | 68 +++++++++++++++++++ .../python/estimator/canned/boosted_trees.py | 45 +++++++++--- .../estimator/canned/boosted_trees_test.py | 3 +- tensorflow/python/ops/boosted_trees_ops.py | 11 +++ ....estimator.-boosted-trees-classifier.pbtxt | 2 +- ...w.estimator.-boosted-trees-regressor.pbtxt | 2 +- 7 files changed, 141 insertions(+), 20 deletions(-) diff --git a/tensorflow/contrib/estimator/python/estimator/boosted_trees.py b/tensorflow/contrib/estimator/python/estimator/boosted_trees.py index 43bfcffd79..7ed77bcce6 100644 --- a/tensorflow/contrib/estimator/python/estimator/boosted_trees.py +++ b/tensorflow/contrib/estimator/python/estimator/boosted_trees.py @@ -50,7 +50,8 @@ class _BoostedTreesEstimator(estimator.Estimator): tree_complexity=0., min_node_weight=0., config=None, - center_bias=False): + center_bias=False, + pruning_mode='none'): """Initializes a `BoostedTreesEstimator` instance. Args: @@ -89,13 +90,18 @@ class _BoostedTreesEstimator(estimator.Estimator): regression problems, the first node will return the mean of the labels. For binary classification problems, it will return a logit for a prior probability of label 1. + pruning_mode: one of 'none', 'pre', 'post' to indicate no pruning, pre- + pruning (do not split a node if not enough gain is observed) and post + pruning (build the tree up to a max depth and then prune branches with + negative gain). For pre and post pruning, you MUST provide + tree_complexity >0. """ # pylint:disable=protected-access # HParams for the model. tree_hparams = canned_boosted_trees._TreeHParams( n_trees, max_depth, learning_rate, l1_regularization, l2_regularization, - tree_complexity, min_node_weight, center_bias) + tree_complexity, min_node_weight, center_bias, pruning_mode) def _model_fn(features, labels, mode, config): return canned_boosted_trees._bt_model_fn( @@ -129,7 +135,8 @@ def boosted_trees_classifier_train_in_memory( min_node_weight=0., config=None, train_hooks=None, - center_bias=False): + center_bias=False, + pruning_mode='none'): """Trains a boosted tree classifier with in memory dataset. Example: @@ -208,6 +215,11 @@ def boosted_trees_classifier_train_in_memory( regression problems, the first node will return the mean of the labels. For binary classification problems, it will return a logit for a prior probability of label 1. + pruning_mode: one of 'none', 'pre', 'post' to indicate no pruning, pre- + pruning (do not split a node if not enough gain is observed) and post + pruning (build the tree up to a max depth and then prune branches with + negative gain). For pre and post pruning, you MUST provide + tree_complexity >0. Returns: a `BoostedTreesClassifier` instance created with the given arguments and @@ -228,7 +240,7 @@ def boosted_trees_classifier_train_in_memory( # HParams for the model. tree_hparams = canned_boosted_trees._TreeHParams( n_trees, max_depth, learning_rate, l1_regularization, l2_regularization, - tree_complexity, min_node_weight, center_bias) + tree_complexity, min_node_weight, center_bias, pruning_mode) def _model_fn(features, labels, mode, config): return canned_boosted_trees._bt_model_fn( @@ -269,7 +281,8 @@ def boosted_trees_regressor_train_in_memory( min_node_weight=0., config=None, train_hooks=None, - center_bias=False): + center_bias=False, + pruning_mode='none'): """Trains a boosted tree regressor with in memory dataset. Example: @@ -341,6 +354,11 @@ def boosted_trees_regressor_train_in_memory( regression problems, the first node will return the mean of the labels. For binary classification problems, it will return a logit for a prior probability of label 1. + pruning_mode: one of 'none', 'pre', 'post' to indicate no pruning, pre- + pruning (do not split a node if not enough gain is observed) and post + pruning (build the tree up to a max depth and then prune branches with + negative gain). For pre and post pruning, you MUST provide + tree_complexity >0. Returns: a `BoostedTreesClassifier` instance created with the given arguments and @@ -360,7 +378,7 @@ def boosted_trees_regressor_train_in_memory( # HParams for the model. tree_hparams = canned_boosted_trees._TreeHParams( n_trees, max_depth, learning_rate, l1_regularization, l2_regularization, - tree_complexity, min_node_weight, center_bias) + tree_complexity, min_node_weight, center_bias, pruning_mode) def _model_fn(features, labels, mode, config): return canned_boosted_trees._bt_model_fn( diff --git a/tensorflow/contrib/estimator/python/estimator/boosted_trees_test.py b/tensorflow/contrib/estimator/python/estimator/boosted_trees_test.py index 999c2aa5e2..b1581f3750 100644 --- a/tensorflow/contrib/estimator/python/estimator/boosted_trees_test.py +++ b/tensorflow/contrib/estimator/python/estimator/boosted_trees_test.py @@ -136,6 +136,49 @@ class BoostedTreesEstimatorTest(test_util.TensorFlowTestCase): eval_res = est.evaluate(input_fn=input_fn, steps=1) self.assertAllClose(eval_res['average_loss'], 0.614642) + def testTrainAndEvaluateEstimatorWithPrePruning(self): + input_fn = _make_train_input_fn(is_classification=False) + + est = boosted_trees._BoostedTreesEstimator( + feature_columns=self._feature_columns, + n_batches_per_layer=1, + n_trees=2, + head=self._head, + max_depth=5, + tree_complexity=0.001, + pruning_mode='pre') + + num_steps = 100 + # Train for a few steps, and validate final checkpoint. + est.train(input_fn, steps=num_steps) + # We stop actually after 2*depth*n_trees steps (via a hook) because we still + # could not grow 2 trees of depth 5 (due to pre-pruning). + self._assert_checkpoint( + est.model_dir, global_step=21, finalized_trees=0, attempted_layers=21) + eval_res = est.evaluate(input_fn=input_fn, steps=1) + self.assertAllClose(eval_res['average_loss'], 3.83943) + + def testTrainAndEvaluateEstimatorWithPostPruning(self): + input_fn = _make_train_input_fn(is_classification=False) + + est = boosted_trees._BoostedTreesEstimator( + feature_columns=self._feature_columns, + n_batches_per_layer=1, + n_trees=2, + head=self._head, + max_depth=5, + tree_complexity=0.001, + pruning_mode='post') + + # It will stop after 10 steps because of the max depth and num trees. + num_steps = 100 + # Train for a few steps, and validate final checkpoint. + est.train(input_fn, steps=num_steps) + self._assert_checkpoint( + est.model_dir, global_step=10, finalized_trees=2, attempted_layers=10) + eval_res = est.evaluate(input_fn=input_fn, steps=1) + self.assertAllClose(eval_res['average_loss'], 2.37652) + def testInferEstimator(self): train_input_fn = _make_train_input_fn(is_classification=False) predict_input_fn = numpy_io.numpy_input_fn( @@ -231,6 +274,31 @@ class BoostedTreesEstimatorTest(test_util.TensorFlowTestCase): self.assertAllClose([[0], [1], [1], [0], [0]], [pred['class_ids'] for pred in predictions]) + def testBinaryClassifierTrainInMemoryAndEvalAndInferWithPrePruning(self): + train_input_fn = _make_train_input_fn(is_classification=True) + predict_input_fn = numpy_io.numpy_input_fn( + x=FEATURES_DICT, y=None, batch_size=1, num_epochs=1, shuffle=False) + + est = boosted_trees.boosted_trees_classifier_train_in_memory( + train_input_fn=train_input_fn, + feature_columns=self._feature_columns, + n_trees=1, + max_depth=5, + pruning_mode='pre', + tree_complexity=0.01) + # We stop actually after 2*depth*n_trees steps (via a hook) because we still + # could not grow 1 trees of depth 5 (due to pre-pruning). + self._assert_checkpoint( + est.model_dir, global_step=11, finalized_trees=0, attempted_layers=11) + + # Check evaluate and predict. + eval_res = est.evaluate(input_fn=train_input_fn, steps=1) + self.assertAllClose(eval_res['accuracy'], 1.0) + # Validate predictions. + predictions = list(est.predict(input_fn=predict_input_fn)) + self.assertAllClose([[0], [1], [1], [0], [0]], + [pred['class_ids'] for pred in predictions]) + def testBinaryClassifierTrainInMemoryWithDataset(self): train_input_fn = _make_train_input_fn_dataset(is_classification=True) predict_input_fn = numpy_io.numpy_input_fn( diff --git a/tensorflow/python/estimator/canned/boosted_trees.py b/tensorflow/python/estimator/canned/boosted_trees.py index 3292e2724d..8b423f76de 100644 --- a/tensorflow/python/estimator/canned/boosted_trees.py +++ b/tensorflow/python/estimator/canned/boosted_trees.py @@ -46,7 +46,7 @@ from tensorflow.python.util.tf_export import estimator_export # TODO(nponomareva): Reveal pruning params here. _TreeHParams = collections.namedtuple('TreeHParams', [ 'n_trees', 'max_depth', 'learning_rate', 'l1', 'l2', 'tree_complexity', - 'min_node_weight', 'center_bias' + 'min_node_weight', 'center_bias', 'pruning_mode' ]) _HOLD_FOR_MULTI_CLASS_SUPPORT = object() @@ -410,9 +410,20 @@ class _EnsembleGrower(object): Args: tree_ensemble: A TreeEnsemble variable. tree_hparams: TODO. collections.namedtuple for hyper parameters. + Raises: + ValueError: when pruning mode is invalid or pruning is used and no tree + complexity is set. """ self._tree_ensemble = tree_ensemble self._tree_hparams = tree_hparams + # pylint: disable=protected-access + self._pruning_mode_parsed = boosted_trees_ops.PruningMode.from_str( + tree_hparams.pruning_mode) + + if (self._pruning_mode_parsed != boosted_trees_ops.PruningMode.NO_PRUNING + and tree_hparams.tree_complexity <= 0): + raise ValueError('For pruning, tree_complexity must be positive.') + # pylint: enable=protected-access @abc.abstractmethod def center_bias(self, center_bias_var, gradients, hessians): @@ -500,7 +511,7 @@ class _EnsembleGrower(object): right_node_contribs=right_node_contribs_list, learning_rate=self._tree_hparams.learning_rate, max_depth=self._tree_hparams.max_depth, - pruning_mode=boosted_trees_ops.PruningMode.NO_PRUNING) + pruning_mode=self._pruning_mode_parsed) return grow_op @@ -675,6 +686,7 @@ def _bt_model_fn( is_single_machine = (config.num_worker_replicas <= 1) sorted_feature_columns = sorted(feature_columns, key=lambda tc: tc.name) center_bias = tree_hparams.center_bias + if train_in_memory: assert n_batches_per_layer == 1, ( 'When train_in_memory is enabled, input_fn should return the entire ' @@ -925,7 +937,8 @@ class BoostedTreesClassifier(estimator.Estimator): tree_complexity=0., min_node_weight=0., config=None, - center_bias=False): + center_bias=False, + pruning_mode='none'): """Initializes a `BoostedTreesClassifier` instance. Example: @@ -999,7 +1012,11 @@ class BoostedTreesClassifier(estimator.Estimator): regression problems, the first node will return the mean of the labels. For binary classification problems, it will return a logit for a prior probability of label 1. - + pruning_mode: one of 'none', 'pre', 'post' to indicate no pruning, pre- + pruning (do not split a node if not enough gain is observed) and post + pruning (build the tree up to a max depth and then prune branches with + negative gain). For pre and post pruning, you MUST provide + tree_complexity >0. Raises: ValueError: when wrong arguments are given or unsupported functionalities @@ -1012,9 +1029,9 @@ class BoostedTreesClassifier(estimator.Estimator): n_classes, weight_column, label_vocabulary=label_vocabulary) # HParams for the model. - tree_hparams = _TreeHParams(n_trees, max_depth, learning_rate, - l1_regularization, l2_regularization, - tree_complexity, min_node_weight, center_bias) + tree_hparams = _TreeHParams( + n_trees, max_depth, learning_rate, l1_regularization, l2_regularization, + tree_complexity, min_node_weight, center_bias, pruning_mode) def _model_fn(features, labels, mode, config): return _bt_model_fn( # pylint: disable=protected-access @@ -1058,7 +1075,8 @@ class BoostedTreesRegressor(estimator.Estimator): tree_complexity=0., min_node_weight=0., config=None, - center_bias=False): + center_bias=False, + pruning_mode='none'): """Initializes a `BoostedTreesRegressor` instance. Example: @@ -1125,6 +1143,11 @@ class BoostedTreesRegressor(estimator.Estimator): regression problems, the first node will return the mean of the labels. For binary classification problems, it will return a logit for a prior probability of label 1. + pruning_mode: one of 'none', 'pre', 'post' to indicate no pruning, pre- + pruning (do not split a node if not enough gain is observed) and post + pruning (build the tree up to a max depth and then prune branches with + negative gain). For pre and post pruning, you MUST provide + tree_complexity >0. Raises: ValueError: when wrong arguments are given or unsupported functionalities @@ -1136,9 +1159,9 @@ class BoostedTreesRegressor(estimator.Estimator): head = _create_regression_head(label_dimension, weight_column) # HParams for the model. - tree_hparams = _TreeHParams(n_trees, max_depth, learning_rate, - l1_regularization, l2_regularization, - tree_complexity, min_node_weight, center_bias) + tree_hparams = _TreeHParams( + n_trees, max_depth, learning_rate, l1_regularization, l2_regularization, + tree_complexity, min_node_weight, center_bias, pruning_mode) def _model_fn(features, labels, mode, config): return _bt_model_fn( # pylint: disable=protected-access diff --git a/tensorflow/python/estimator/canned/boosted_trees_test.py b/tensorflow/python/estimator/canned/boosted_trees_test.py index f807641057..ec597e4686 100644 --- a/tensorflow/python/estimator/canned/boosted_trees_test.py +++ b/tensorflow/python/estimator/canned/boosted_trees_test.py @@ -1508,7 +1508,8 @@ class ModelFnTests(test_util.TensorFlowTestCase): l2=0.01, tree_complexity=0., min_node_weight=0., - center_bias=center_bias) + center_bias=center_bias, + pruning_mode='none') estimator_spec = boosted_trees._bt_model_fn( # pylint:disable=protected-access features=features, diff --git a/tensorflow/python/ops/boosted_trees_ops.py b/tensorflow/python/ops/boosted_trees_ops.py index 868a4f6b84..f7cbfe0312 100644 --- a/tensorflow/python/ops/boosted_trees_ops.py +++ b/tensorflow/python/ops/boosted_trees_ops.py @@ -37,8 +37,19 @@ from tensorflow.python.training import saver class PruningMode(object): + """Class for working with Pruning modes.""" NO_PRUNING, PRE_PRUNING, POST_PRUNING = range(0, 3) + _map = {'none': NO_PRUNING, 'pre': PRE_PRUNING, 'post': POST_PRUNING} + + @classmethod + def from_str(cls, mode): + if mode in cls._map: + return cls._map[mode] + else: + raise ValueError('pruning_mode mode must be one of: {}'.format(', '.join( + sorted(cls._map)))) + class _TreeEnsembleSavable(saver.BaseSaverBuilder.SaveableObject): """SaveableObject implementation for TreeEnsemble.""" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt index 9dbb5d16a4..c23b04b4ef 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-classifier.pbtxt @@ -21,7 +21,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\'], " + argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'n_classes\', \'weight_column\', \'label_vocabulary\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\', \'pruning_mode\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\', \'none\'], " } member_method { name: "eval_dir" diff --git a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt index 34a30c2874..6878d28fff 100644 --- a/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt +++ b/tensorflow/tools/api/golden/tensorflow.estimator.-boosted-trees-regressor.pbtxt @@ -21,7 +21,7 @@ tf_class { } member_method { name: "__init__" - argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'label_dimension\', \'weight_column\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\'], " + argspec: "args=[\'self\', \'feature_columns\', \'n_batches_per_layer\', \'model_dir\', \'label_dimension\', \'weight_column\', \'n_trees\', \'max_depth\', \'learning_rate\', \'l1_regularization\', \'l2_regularization\', \'tree_complexity\', \'min_node_weight\', \'config\', \'center_bias\', \'pruning_mode\'], varargs=None, keywords=None, defaults=[\'None\', \'\', \'None\', \'100\', \'6\', \'0.1\', \'0.0\', \'0.0\', \'0.0\', \'0.0\', \'None\', \'False\', \'none\'], " } member_method { name: "eval_dir" -- GitLab From 7e4d0d13e51faee4469808d577591f6cb73bbcc7 Mon Sep 17 00:00:00 2001 From: Zhenyu Tan Date: Thu, 26 Jul 2018 11:55:25 -0700 Subject: [PATCH 455/519] shard some estimator tests PiperOrigin-RevId: 206196742 --- tensorflow/python/estimator/BUILD | 3 +++ 1 file changed, 3 insertions(+) diff --git a/tensorflow/python/estimator/BUILD b/tensorflow/python/estimator/BUILD index fd46163050..817c8e6848 100644 --- a/tensorflow/python/estimator/BUILD +++ b/tensorflow/python/estimator/BUILD @@ -171,6 +171,7 @@ py_test( name = "baseline_test", size = "medium", srcs = ["canned/baseline_test.py"], + shard_count = 4, srcs_version = "PY2AND3", tags = [ "no_pip", @@ -207,6 +208,7 @@ py_test( name = "boosted_trees_test", size = "medium", srcs = ["canned/boosted_trees_test.py"], + shard_count = 2, srcs_version = "PY2AND3", tags = [ "optonly", @@ -676,6 +678,7 @@ py_test( name = "keras_test", size = "large", srcs = ["keras_test.py"], + shard_count = 4, srcs_version = "PY2AND3", tags = [ "no_windows", -- GitLab From b4cc9c34195e0fd557f56ceda29bd4830833ffbb Mon Sep 17 00:00:00 2001 From: Guangda Lai Date: Thu, 26 Jul 2018 11:57:51 -0700 Subject: [PATCH 456/519] Remove the gen_locally tag which is no more needed. PiperOrigin-RevId: 206197083 --- tensorflow/contrib/tensorrt/BUILD | 1 - tensorflow/tensorflow.bzl | 8 +------- 2 files changed, 1 insertion(+), 8 deletions(-) diff --git a/tensorflow/contrib/tensorrt/BUILD b/tensorflow/contrib/tensorrt/BUILD index 5889fd5aaf..033d5207f6 100644 --- a/tensorflow/contrib/tensorrt/BUILD +++ b/tensorflow/contrib/tensorrt/BUILD @@ -122,7 +122,6 @@ tf_cuda_library( tf_gen_op_wrapper_py( name = "trt_engine_op", - gen_locally = True, deps = [ ":trt_engine_op_op_lib", ":trt_logging", diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index 4da6493723..f8e55abe79 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -549,9 +549,6 @@ def tf_gen_op_wrappers_cc(name, # is invalid to specify both "hidden" and "op_whitelist". # cc_linkopts: Optional linkopts to be added to tf_cc_binary that contains the # specified ops. -# gen_locally: if True, the genrule to generate the Python library will be run -# without sandboxing. This would help when the genrule depends on symlinks -# which may not be supported in the sandbox. def tf_gen_op_wrapper_py(name, out=None, hidden=None, @@ -562,8 +559,7 @@ def tf_gen_op_wrapper_py(name, generated_target_name=None, op_whitelist=[], cc_linkopts=[], - api_def_srcs=[], - gen_locally=False): + api_def_srcs=[]): if (hidden or hidden_file) and op_whitelist: fail('Cannot pass specify both hidden and op_whitelist.') @@ -618,7 +614,6 @@ def tf_gen_op_wrapper_py(name, outs=[out], srcs=api_def_srcs + [hidden_file], tools=[tool_name] + tf_binary_additional_srcs(), - local = (1 if gen_locally else 0), cmd=("$(location " + tool_name + ") " + api_def_args_str + " @$(location " + hidden_file + ") " + ("1" if require_shape_functions else "0") + " > $@")) @@ -628,7 +623,6 @@ def tf_gen_op_wrapper_py(name, outs=[out], srcs=api_def_srcs, tools=[tool_name] + tf_binary_additional_srcs(), - local = (1 if gen_locally else 0), cmd=("$(location " + tool_name + ") " + api_def_args_str + " " + op_list_arg + " " + ("1" if require_shape_functions else "0") + " " + -- GitLab From 99deb5e0c561a926fc73c72d1488d20756f25861 Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Thu, 26 Jul 2018 12:25:44 -0700 Subject: [PATCH 457/519] Add example Unity plugin for the experimental TFLite C API. Instructions for using the Plugin are contained in the readme @ TensorFlowLitePlugin/README.md PiperOrigin-RevId: 206201614 --- tensorflow/contrib/lite/build_def.bzl | 3 + tensorflow/contrib/lite/experimental/c/BUILD | 8 +- .../contrib/lite/experimental/c/c_api.cc | 4 + .../unity/TensorFlowLitePlugin/.gitignore | 13 + .../Assets/TensorFlowLite.meta | 8 + .../Assets/TensorFlowLite/Examples.meta | 8 + .../TensorFlowLite/Examples/HelloTFLite.meta | 8 + .../Examples/HelloTFLite/Scenes.meta | 8 + .../HelloTFLite/Scenes/HelloTFLite.unity | 242 +++++++ .../HelloTFLite/Scenes/HelloTFLite.unity.meta | 7 + .../Examples/HelloTFLite/Scenes/add.bytes | Bin 0 -> 476 bytes .../HelloTFLite/Scenes/add.bytes.meta | 7 + .../Examples/HelloTFLite/Scripts.meta | 8 + .../HelloTFLite/Scripts/HelloTFLite.cs | 70 ++ .../HelloTFLite/Scripts/HelloTFLite.cs.meta | 11 + .../Assets/TensorFlowLite/SDK.meta | 8 + .../Assets/TensorFlowLite/SDK/Scripts.meta | 8 + .../TensorFlowLite/SDK/Scripts/Interpreter.cs | 145 ++++ 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tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/ProjectSettings/ProjectVersion.txt create mode 100644 tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/ProjectSettings/QualitySettings.asset create mode 100644 tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/ProjectSettings/TagManager.asset create mode 100644 tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/ProjectSettings/TimeManager.asset create mode 100644 tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/ProjectSettings/UnityConnectSettings.asset create mode 100644 tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md create mode 100644 tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/UnityPackageManager/manifest.json diff --git a/tensorflow/contrib/lite/build_def.bzl b/tensorflow/contrib/lite/build_def.bzl index 256334576c..2e91632459 100644 --- a/tensorflow/contrib/lite/build_def.bzl +++ b/tensorflow/contrib/lite/build_def.bzl @@ -27,6 +27,9 @@ def tflite_copts(): str(Label("//tensorflow:ios_x86_64")): [ "-msse4.1", ], + str(Label("//tensorflow:windows")): [ + "/DTF_COMPILE_LIBRARY", + ], "//conditions:default": [], }) + select({ str(Label("//tensorflow:with_default_optimizations")): [], diff --git a/tensorflow/contrib/lite/experimental/c/BUILD b/tensorflow/contrib/lite/experimental/c/BUILD index b09bb9ea10..50f8da66d0 100644 --- a/tensorflow/contrib/lite/experimental/c/BUILD +++ b/tensorflow/contrib/lite/experimental/c/BUILD @@ -5,6 +5,7 @@ licenses(["notice"]) # Apache 2.0 load( "//tensorflow/contrib/lite:build_def.bzl", "tflite_cc_shared_object", + "tflite_copts", "tflite_jni_binary", ) @@ -30,16 +31,11 @@ tflite_cc_shared_object( ], ) -tflite_jni_binary( - name = "libtensorflowlite_c_jni.so", - linkscript = ":version_script.lds", - deps = [":c_api"], -) - cc_library( name = "c_api", srcs = ["c_api.cc"], hdrs = ["c_api.h"], + copts = tflite_copts(), deps = [ "//tensorflow/contrib/lite:context", "//tensorflow/contrib/lite:framework", diff --git a/tensorflow/contrib/lite/experimental/c/c_api.cc b/tensorflow/contrib/lite/experimental/c/c_api.cc index add4c6813d..9d29e8b3e0 100644 --- a/tensorflow/contrib/lite/experimental/c/c_api.cc +++ b/tensorflow/contrib/lite/experimental/c/c_api.cc @@ -27,6 +27,8 @@ struct _TFL_Interpreter { std::unique_ptr impl; }; +// LINT.IfChange + TFL_Interpreter* TFL_NewInterpreter(const void* model_data, int32_t model_size) { auto model = tflite::FlatBufferModel::BuildFromBuffer( @@ -113,6 +115,8 @@ TFL_Status TFL_TensorCopyToBuffer(const TFL_Tensor* tensor, void* output_data, return kTfLiteOk; } +// LINT.ThenChange(//tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs) + #ifdef __cplusplus } // extern "C" #endif // __cplusplus diff --git 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+DefaultImporter: + externalObjects: {} + userData: + assetBundleName: + assetBundleVariant: diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples.meta b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples.meta new file mode 100644 index 0000000000..edcce00939 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples.meta @@ -0,0 +1,8 @@ +fileFormatVersion: 2 +guid: d948aead14abd4c88947c9886d16f774 +folderAsset: yes +DefaultImporter: + externalObjects: {} + userData: + assetBundleName: + assetBundleVariant: diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite.meta b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite.meta new file mode 100644 index 0000000000..36b35516f0 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b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite/Scripts/HelloTFLite.cs @@ -0,0 +1,70 @@ +/* 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. +==============================================================================*/ +using System; +using System.Collections; +using System.Collections.Generic; +using System.Linq; +using TensorFlowLite; +using UnityEngine; + +/// +/// Simple example demonstrating use of the experimental C# bindings for TensorFlowLite. +/// +public class HelloTFLite : MonoBehaviour { + + [Tooltip("Configurable TFLite model.")] + public TextAsset model; + + [Tooltip("Configurable TFLite input tensor data.")] + public float[] inputs; + + private Interpreter interpreter; + private float[] outputs; + + void Start () { + interpreter = new Interpreter(model.bytes); + Debug.LogFormat("InputCount: {0}, OutputCount: {1}", + interpreter.GetInputTensorCount(), + interpreter.GetOutputTensorCount()); + } + + void Update () { + if (inputs == null) { + return; + } + + if (outputs == null || outputs.Length != inputs.Length) { + interpreter.ResizeInputTensor(0, new int[]{inputs.Length}); + interpreter.AllocateTensors(); + outputs = new float[inputs.Length]; + } + + interpreter.SetInputTensorData(0, inputs); + interpreter.Invoke(); + interpreter.GetOutputTensorData(0, outputs); + + Debug.LogFormat("Input: {0}, Output: {1}", + ArrayToString(inputs), + ArrayToString(outputs)); + } + + void OnDestroy() { + interpreter.Dispose(); + } + + private static string ArrayToString(float[] values) { + return string.Join(",", values.Select(x => x.ToString()).ToArray()); + } +} diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite/Scripts/HelloTFLite.cs.meta b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite/Scripts/HelloTFLite.cs.meta new file mode 100644 index 0000000000..ba83f45084 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/Examples/HelloTFLite/Scripts/HelloTFLite.cs.meta @@ -0,0 +1,11 @@ +fileFormatVersion: 2 +guid: 899510441e0ca4be0879d3055e467878 +MonoImporter: + externalObjects: {} + serializedVersion: 2 + defaultReferences: [] + executionOrder: 0 + icon: {instanceID: 0} + userData: + assetBundleName: + assetBundleVariant: diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK.meta b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK.meta new file mode 100644 index 0000000000..bf5ce15c6a --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK.meta @@ -0,0 +1,8 @@ +fileFormatVersion: 2 +guid: 16dad1655bcdc48f7b325a2a634b9c69 +folderAsset: yes +DefaultImporter: + externalObjects: {} + userData: + assetBundleName: + assetBundleVariant: diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts.meta b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts.meta new file mode 100644 index 0000000000..22ed2c466b --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts.meta @@ -0,0 +1,8 @@ +fileFormatVersion: 2 +guid: d70863368f8904d509a9b73d3a555914 +folderAsset: yes +DefaultImporter: + externalObjects: {} + userData: + assetBundleName: + assetBundleVariant: diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs new file mode 100644 index 0000000000..ab966bae2e --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/Assets/TensorFlowLite/SDK/Scripts/Interpreter.cs @@ -0,0 +1,145 @@ +/* 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. +==============================================================================*/ +using System; +using System.Runtime.InteropServices; + +using TFL_Interpreter = System.IntPtr; +using TFL_Tensor = System.IntPtr; + +namespace TensorFlowLite +{ + /// + /// Simple C# bindings for the experimental TensorFlowLite C API. + /// + public class Interpreter : IDisposable + { + private const string TensorFlowLibrary = "tensorflowlite_c"; + + private TFL_Interpreter handle; + + public Interpreter(byte[] modelData) { + GCHandle modelDataHandle = GCHandle.Alloc(modelData, GCHandleType.Pinned); + IntPtr modelDataPtr = modelDataHandle.AddrOfPinnedObject(); + handle = TFL_NewInterpreter(modelDataPtr, modelData.Length); + if (handle == IntPtr.Zero) throw new Exception("Failed to create TensorFlowLite Interpreter"); + } + + ~Interpreter() { + Dispose(); + } + + public void Dispose() { + if (handle != IntPtr.Zero) TFL_DeleteInterpreter(handle); + handle = IntPtr.Zero; + } + + public void Invoke() { + ThrowIfError(TFL_InterpreterInvoke(handle)); + } + + public int GetInputTensorCount() { + return TFL_InterpreterGetInputTensorCount(handle); + } + + public void SetInputTensorData(int inputTensorIndex, Array inputTensorData) { + GCHandle tensorDataHandle = GCHandle.Alloc(inputTensorData, GCHandleType.Pinned); + IntPtr tensorDataPtr = tensorDataHandle.AddrOfPinnedObject(); + TFL_Tensor tensor = TFL_InterpreterGetInputTensor(handle, inputTensorIndex); + ThrowIfError(TFL_TensorCopyFromBuffer( + tensor, tensorDataPtr, Buffer.ByteLength(inputTensorData))); + } + + public void ResizeInputTensor(int inputTensorIndex, int[] inputTensorShape) { + ThrowIfError(TFL_InterpreterResizeInputTensor( + handle, inputTensorIndex, inputTensorShape, inputTensorShape.Length)); + } + + public void AllocateTensors() { + ThrowIfError(TFL_InterpreterAllocateTensors(handle)); + } + + public int GetOutputTensorCount() { + return TFL_InterpreterGetOutputTensorCount(handle); + } + + public void GetOutputTensorData(int outputTensorIndex, Array outputTensorData) { + GCHandle tensorDataHandle = GCHandle.Alloc(outputTensorData, GCHandleType.Pinned); + IntPtr tensorDataPtr = tensorDataHandle.AddrOfPinnedObject(); + TFL_Tensor tensor = TFL_InterpreterGetOutputTensor(handle, outputTensorIndex); + ThrowIfError(TFL_TensorCopyToBuffer( + tensor, tensorDataPtr, Buffer.ByteLength(outputTensorData))); + } + + private static void ThrowIfError(int resultCode) { + if (resultCode != 0) throw new Exception("TensorFlowLite operation failed."); + } + + #region Externs + + [DllImport (TensorFlowLibrary)] + private static extern unsafe TFL_Interpreter TFL_NewInterpreter( + IntPtr model_data, + int model_size); + + [DllImport (TensorFlowLibrary)] + private static extern unsafe void TFL_DeleteInterpreter(TFL_Interpreter interpreter); + + [DllImport (TensorFlowLibrary)] + private static extern unsafe int TFL_InterpreterGetInputTensorCount( + TFL_Interpreter interpreter); + + [DllImport (TensorFlowLibrary)] + private static extern unsafe TFL_Tensor TFL_InterpreterGetInputTensor( + TFL_Interpreter interpreter, + int input_index); + + [DllImport (TensorFlowLibrary)] + private static extern unsafe int TFL_InterpreterResizeInputTensor( + TFL_Interpreter interpreter, + int input_index, + int[] input_dims, + int input_dims_size); + + [DllImport (TensorFlowLibrary)] + private static extern unsafe int TFL_InterpreterAllocateTensors( + 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m_AndroidGameId: + m_GameIds: {} + m_GameId: + PerformanceReportingSettings: + m_Enabled: 0 diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md new file mode 100644 index 0000000000..0b3813fccb --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/README.md @@ -0,0 +1,24 @@ +# TF Lite Experimental Unity Plugin + +This directoryy contains an experimental sample Unity (2017) Plugin, based on +the experimental TF Lite C API. The sample demonstrates running inference within +Unity by way of a C# `Interpreter` wrapper. + +Note that the native TF Lite plugin(s) *must* be built before using the Unity +Plugin, and placed in Assets/TensorFlowLite/SDK/Plugins/. For the editor (note +that this has only been tested on Linux; the syntax may differ on Mac/Windows): + +```sh +bazel build -c opt --cxxopt=--std=c++11 \ + //tensorflow/contrib/lite/experimental/c:libtensorflowlite_c.so +``` + +and for Android: + +```sh +bazel build -c opt --cxxopt=--std=c++11 \ + --crosstool_top=//external:android/crosstool \ + --host_crosstool_top=@bazel_tools//tools/cpp:toolchain \ + --cpu=armeabi-v7a \ + //tensorflow/contrib/lite/experimental/c:libtensorflowlite_c.so +``` diff --git a/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/UnityPackageManager/manifest.json b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/UnityPackageManager/manifest.json new file mode 100644 index 0000000000..526aca6057 --- /dev/null +++ b/tensorflow/contrib/lite/experimental/examples/unity/TensorFlowLitePlugin/UnityPackageManager/manifest.json @@ -0,0 +1,4 @@ +{ + "dependencies": { + } +} -- GitLab From 20154feed54b667d5d9819aaa0d75626a6c02228 Mon Sep 17 00:00:00 2001 From: Jared Duke Date: Thu, 26 Jul 2018 12:35:26 -0700 Subject: [PATCH 458/519] More changes for TFLite portability PiperOrigin-RevId: 206202981 --- tensorflow/contrib/lite/context.h | 7 +++++++ .../internal/optimized/neon_tensor_utils.h | 8 ++++---- .../internal/optimized/tensor_utils_impl.h | 4 ++++ .../reference/portable_tensor_utils.cc | 11 ++++++----- .../internal/reference/portable_tensor_utils.h | 8 ++++++-- .../lite/kernels/internal/spectrogram.cc | 10 +++++----- .../lite/kernels/internal/tensor_utils.h | 8 ++++++-- tensorflow/contrib/lite/profiling/time.cc | 18 ++++++++++++++++++ tensorflow/contrib/lite/simple_memory_arena.cc | 1 + 9 files changed, 57 insertions(+), 18 deletions(-) diff --git a/tensorflow/contrib/lite/context.h b/tensorflow/contrib/lite/context.h index cbfce12d7e..5bc20106d3 100644 --- a/tensorflow/contrib/lite/context.h +++ b/tensorflow/contrib/lite/context.h @@ -29,6 +29,9 @@ limitations under the License. #ifndef TENSORFLOW_CONTRIB_LITE_CONTEXT_H_ #define TENSORFLOW_CONTRIB_LITE_CONTEXT_H_ +#if defined(_MSC_VER) +#include +#endif #include #include #include @@ -180,7 +183,11 @@ typedef union { uint8_t* uint8; bool* b; int16_t* i16; +#if defined(_MSC_VER) + _Fcomplex* c64; +#else _Complex float* c64; +#endif } TfLitePtrUnion; // Memory allocation strategies. kTfLiteMmapRo is for read-only memory-mapped diff --git a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h index 45c9f65b64..63c89d1eee 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/neon_tensor_utils.h @@ -115,10 +115,10 @@ void ClipVector(const float* vector, int v_size, float abs_limit, } void SymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min, float* max, - float* scaling_factor) { - NEON_OR_PORTABLE(SymmetricQuantizeFloats, values, size, quantized_values, min, - max, scaling_factor); + int8_t* quantized_values, float* min_value, + float* max_value, float* scaling_factor) { + NEON_OR_PORTABLE(SymmetricQuantizeFloats, values, size, quantized_values, + min_value, max_value, scaling_factor); } void VectorShiftLeft(float* vector, int v_size, float shift_value) { 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 db7926df9a..010b40b901 100644 --- a/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h +++ b/tensorflow/contrib/lite/kernels/internal/optimized/tensor_utils_impl.h @@ -19,6 +19,10 @@ limitations under the License. // structure. #include "tensorflow/contrib/lite/builtin_op_data.h" +#if defined(_MSC_VER) +#define __restrict__ __restrict +#endif + #ifndef USE_NEON #if defined(__ARM_NEON__) || defined(__ARM_NEON) #define USE_NEON diff --git a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc index 7ead449ca8..6bd88b5596 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc +++ b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.cc @@ -14,6 +14,7 @@ limitations under the License. ==============================================================================*/ #include #include +#include #include "tensorflow/contrib/lite/builtin_op_data.h" #include "tensorflow/contrib/lite/kernels/activation_functor.h" @@ -38,14 +39,14 @@ bool PortableIsZeroVector(const float* vector, int v_size) { void PortableSymmetricQuantizeFloats(const float* values, const int size, int8_t* quantized_values, - float* __restrict__ min, - float* __restrict__ max, + float* __restrict__ min_value, + float* __restrict__ max_value, float* __restrict__ scaling_factor) { auto minmax = std::minmax_element(values, values + size); - *min = *minmax.first; - *max = *minmax.second; + *min_value = *minmax.first; + *max_value = *minmax.second; const int kScale = 127; - const float range = std::max(std::abs(*min), std::abs(*max)); + const float range = std::max(std::abs(*min_value), std::abs(*max_value)); if (range == 0) { memset(quantized_values, 0, size * sizeof(int8_t)); *scaling_factor = 1; 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 d3a4fa8507..a375aaffa6 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/portable_tensor_utils.h @@ -19,6 +19,10 @@ limitations under the License. // structure. #include "tensorflow/contrib/lite/builtin_op_data.h" +#if defined(_MSC_VER) +#define __restrict__ __restrict +#endif + namespace tflite { namespace tensor_utils { @@ -28,8 +32,8 @@ float PortableClip(float f, float abs_limit); bool PortableIsZeroVector(const float* vector, int v_size); void PortableSymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min, - float* max, float* scaling_factor); + int8_t* quantized_values, float* min_value, + float* max_value, float* scaling_factor); // Multiply a matrix by a batch vector, and store results in a batch-size // vector. diff --git a/tensorflow/contrib/lite/kernels/internal/spectrogram.cc b/tensorflow/contrib/lite/kernels/internal/spectrogram.cc index 4eddf7bf0a..20abcb7258 100644 --- a/tensorflow/contrib/lite/kernels/internal/spectrogram.cc +++ b/tensorflow/contrib/lite/kernels/internal/spectrogram.cc @@ -43,13 +43,13 @@ bool Spectrogram::Initialize(int window_length, int step_length) { return Initialize(window, step_length); } -inline int Log2Floor(uint n) { +inline int Log2Floor(uint32_t n) { if (n == 0) return -1; int log = 0; - uint value = n; + uint32_t value = n; for (int i = 4; i >= 0; --i) { int shift = (1 << i); - uint x = value >> shift; + uint32_t x = value >> shift; if (x != 0) { value = x; log += shift; @@ -58,7 +58,7 @@ inline int Log2Floor(uint n) { return log; } -inline int Log2Ceiling(uint n) { +inline int Log2Ceiling(uint32_t n) { int floor = Log2Floor(n); if (n == (n & ~(n - 1))) // zero or a power of two return floor; @@ -66,7 +66,7 @@ inline int Log2Ceiling(uint n) { return floor + 1; } -inline uint NextPowerOfTwo(uint value) { +inline uint32_t NextPowerOfTwo(uint32_t value) { int exponent = Log2Ceiling(value); // DCHECK_LT(exponent, std::numeric_limits::digits); return 1 << exponent; diff --git a/tensorflow/contrib/lite/kernels/internal/tensor_utils.h b/tensorflow/contrib/lite/kernels/internal/tensor_utils.h index 82f4503127..1ff8cfe39c 100644 --- a/tensorflow/contrib/lite/kernels/internal/tensor_utils.h +++ b/tensorflow/contrib/lite/kernels/internal/tensor_utils.h @@ -17,6 +17,10 @@ limitations under the License. #include "tensorflow/contrib/lite/builtin_op_data.h" +#if defined(_MSC_VER) +#define __restrict__ __restrict +#endif + namespace tflite { namespace tensor_utils { @@ -31,8 +35,8 @@ bool IsZeroVector(const float* vector, int v_size); // It also outputs the range (min, max) of the floating point buffer, and the // scaling factor used to quantize the values. void SymmetricQuantizeFloats(const float* values, const int size, - int8_t* quantized_values, float* min, float* max, - float* scaling_factor); + int8_t* quantized_values, float* min_value, + float* max_value, float* scaling_factor); // Multiplies a matrix by a "batched" vector (i.e. a matrix with a batch // dimension composed by input vectors independent from each other). The result diff --git a/tensorflow/contrib/lite/profiling/time.cc b/tensorflow/contrib/lite/profiling/time.cc index 446660bb74..875ddb02bc 100644 --- a/tensorflow/contrib/lite/profiling/time.cc +++ b/tensorflow/contrib/lite/profiling/time.cc @@ -14,16 +14,34 @@ limitations under the License. ==============================================================================*/ #include "tensorflow/contrib/lite/profiling/time.h" +#if defined(_MSC_VER) +#include // NOLINT(build/c++11) +#else #include +#endif namespace tflite { namespace profiling { namespace time { + +#if defined(_MSC_VER) + +uint64_t NowMicros() { + return std::chrono::duration_cast( + std::chrono::system_clock::now().time_since_epoch()) + .count(); +} + +#else + uint64_t NowMicros() { struct timeval tv; gettimeofday(&tv, nullptr); return static_cast(tv.tv_sec) * 1000000 + tv.tv_usec; } + +#endif // defined(_MSC_VER) + } // namespace time } // namespace profiling } // namespace tflite diff --git a/tensorflow/contrib/lite/simple_memory_arena.cc b/tensorflow/contrib/lite/simple_memory_arena.cc index 24593d2a67..cd0f1f7c17 100644 --- a/tensorflow/contrib/lite/simple_memory_arena.cc +++ b/tensorflow/contrib/lite/simple_memory_arena.cc @@ -15,6 +15,7 @@ limitations under the License. #include "tensorflow/contrib/lite/simple_memory_arena.h" +#include #include #include #include -- GitLab From 4fd159cbd0492c21de08197ba4426a2e433ff402 Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Thu, 26 Jul 2018 12:43:17 -0700 Subject: [PATCH 459/519] [TF:XLA] Bump open source llvm revision to r338012 PiperOrigin-RevId: 206204159 --- tensorflow/workspace.bzl | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/tensorflow/workspace.bzl b/tensorflow/workspace.bzl index 314169fc19..45b1abeb10 100644 --- a/tensorflow/workspace.bzl +++ b/tensorflow/workspace.bzl @@ -485,11 +485,11 @@ def tf_workspace(path_prefix="", tf_repo_name=""): tf_http_archive( name = "llvm", urls = [ - "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/a9364fc18506373b10922802983f76229cc1f371.tar.gz", - "https://github.com/llvm-mirror/llvm/archive/a9364fc18506373b10922802983f76229cc1f371.tar.gz", + "https://mirror.bazel.build/github.com/llvm-mirror/llvm/archive/7b3bfc8151f3a6bcd9642c49c1f86f66cc43a428.tar.gz", + "https://github.com/llvm-mirror/llvm/archive/7b3bfc8151f3a6bcd9642c49c1f86f66cc43a428.tar.gz", ], - sha256 = "5d727fedfbb805a44a671db8f3fbaa09dbe5177a5c1cc0635fd61c324e6409f2", - strip_prefix = "llvm-a9364fc18506373b10922802983f76229cc1f371", + sha256 = "c6cbb21acd46e3e00faa8c379595ecffb99ef77622da17f29371db2bfad1d3d3", + strip_prefix = "llvm-7b3bfc8151f3a6bcd9642c49c1f86f66cc43a428", build_file = clean_dep("//third_party/llvm:llvm.autogenerated.BUILD"), ) -- GitLab From b5ee7b1bc75928825991957c189ded0b970a1081 Mon Sep 17 00:00:00 2001 From: Youlong Cheng Date: Thu, 26 Jul 2018 13:08:42 -0700 Subject: [PATCH 460/519] PUBLIC: Allow user passing training/evaluation/prediction_hooks from tf.estimator.EstimatorSpec. PiperOrigin-RevId: 206208119 --- .../contrib/tpu/python/tpu/tpu_estimator.py | 98 +++++++++++++------ tensorflow/python/estimator/model_fn.py | 58 ++++++----- 2 files changed, 101 insertions(+), 55 deletions(-) diff --git a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py index 6e7dae6fce..ee9ad525ee 100644 --- a/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py +++ b/tensorflow/contrib/tpu/python/tpu/tpu_estimator.py @@ -258,7 +258,10 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote eval_metrics=None, export_outputs=None, scaffold_fn=None, - host_call=None): + host_call=None, + training_hooks=None, + evaluation_hooks=None, + prediction_hooks=None): """Creates a validated `TPUEstimatorSpec` instance.""" host_calls = {} if eval_metrics is not None: @@ -266,6 +269,17 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote if host_call is not None: host_calls['host_call'] = host_call _OutfeedHostCall.validate(host_calls) + + training_hooks = list(training_hooks or []) + evaluation_hooks = list(evaluation_hooks or []) + prediction_hooks = list(prediction_hooks or []) + + for hook in training_hooks + evaluation_hooks + prediction_hooks: + if not isinstance(hook, session_run_hook.SessionRunHook): + raise TypeError( + 'All hooks must be SessionRunHook instances, given: {}'.format( + hook)) + return super(TPUEstimatorSpec, cls).__new__( cls, mode=mode, @@ -275,7 +289,10 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote eval_metrics=eval_metrics, export_outputs=export_outputs, scaffold_fn=scaffold_fn, - host_call=host_call) + host_call=host_call, + training_hooks=training_hooks, + evaluation_hooks=evaluation_hooks, + prediction_hooks=prediction_hooks) def as_estimator_spec(self): """Creates an equivalent `EstimatorSpec` used by CPU train/eval.""" @@ -291,6 +308,7 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote hooks = None if self.host_call is not None: hooks = [_OutfeedHostCallHook(host_call_ret['host_call'])] + hooks = list(hooks or []) scaffold = self.scaffold_fn() if self.scaffold_fn else None return model_fn_lib.EstimatorSpec( mode=self.mode, @@ -300,9 +318,9 @@ class TPUEstimatorSpec(model_fn_lib._TPUEstimatorSpec): # pylint: disable=prote eval_metric_ops=eval_metric_ops, export_outputs=self.export_outputs, scaffold=scaffold, - training_hooks=hooks, - evaluation_hooks=hooks, - prediction_hooks=hooks) + training_hooks=self.training_hooks + hooks, + evaluation_hooks=self.evaluation_hooks + hooks, + prediction_hooks=self.prediction_hooks + hooks) class _OpQueueContext(object): @@ -1220,6 +1238,7 @@ class _ModelFnWrapper(object): host_call = _OutfeedHostCall(self._ctx) captured_scaffold_fn = _CapturedObject() + captured_training_hooks = _CapturedObject() def train_step(loss): """Training step function for use inside a while loop.""" @@ -1236,6 +1255,8 @@ class _ModelFnWrapper(object): else: captured_scaffold_fn.capture(None) + captured_training_hooks.capture(estimator_spec.training_hooks) + # We must run train_op to update the variables prior to running the # outfeed. with ops.control_dependencies([train_op]): @@ -1247,7 +1268,8 @@ class _ModelFnWrapper(object): with ops.control_dependencies(host_call_outfeed_ops): return array_ops.identity(loss) - return train_step, host_call, captured_scaffold_fn + return (train_step, host_call, captured_scaffold_fn, + captured_training_hooks) def convert_to_single_tpu_eval_step(self, dequeue_fn): """Converts user provided model_fn` as a single eval step on TPU. @@ -1277,6 +1299,7 @@ class _ModelFnWrapper(object): """ host_calls = _OutfeedHostCall(self._ctx) captured_scaffold_fn = _CapturedObject() + captured_eval_hooks = _CapturedObject() def eval_step(total_loss): """Evaluation step function for use inside a while loop.""" @@ -1291,6 +1314,8 @@ class _ModelFnWrapper(object): loss = tpu_estimator_spec.loss captured_scaffold_fn.capture(tpu_estimator_spec.scaffold_fn) + captured_eval_hooks.capture(tpu_estimator_spec.evaluation_hooks) + to_record = {} if tpu_estimator_spec.eval_metrics: to_record['eval_metrics'] = tpu_estimator_spec.eval_metrics @@ -1303,7 +1328,7 @@ class _ModelFnWrapper(object): with ops.control_dependencies(host_calls.create_enqueue_op()): return math_ops.add(total_loss, loss) - return eval_step, host_calls, captured_scaffold_fn + return eval_step, host_calls, captured_scaffold_fn, captured_eval_hooks def convert_to_single_tpu_predict_step(self, dequeue_fn): """Converts user provided model_fn` as a single predict step on TPU. @@ -1318,6 +1343,7 @@ class _ModelFnWrapper(object): """ host_calls = _OutfeedHostCall(self._ctx) captured_scaffold_fn = _CapturedObject() + captured_predict_hooks = _CapturedObject() def predict_step(unused_scalar_stopping_signal): """Evaluation step function for use inside a while loop.""" @@ -1338,6 +1364,7 @@ class _ModelFnWrapper(object): self._verify_tpu_spec_predictions(tpu_estimator_spec.predictions) captured_scaffold_fn.capture(tpu_estimator_spec.scaffold_fn) + captured_predict_hooks.capture(tpu_estimator_spec.prediction_hooks) to_record = {} identity_fn = lambda **kwargs: kwargs to_record['predictions'] = [identity_fn, tpu_estimator_spec.predictions] @@ -1349,7 +1376,8 @@ class _ModelFnWrapper(object): with ops.control_dependencies(host_calls.create_enqueue_op()): return _StopSignals.as_scalar_stopping_signal(stopping_signals) - return predict_step, host_calls, captured_scaffold_fn + return (predict_step, host_calls, captured_scaffold_fn, + captured_predict_hooks) def _verify_tpu_spec_predictions(self, predictions): """Validates TPUEstimatorSpec.predictions dict.""" @@ -1471,11 +1499,9 @@ class _ModelFnWrapper(object): err_msg = '{} returned by EstimatorSpec is not supported in TPUEstimator.' if estimator_spec.training_chief_hooks: - raise ValueError(err_msg.format('training_chief_hooks')) - if estimator_spec.training_hooks: - raise ValueError(err_msg.format('training_hooks')) - if estimator_spec.evaluation_hooks: - raise ValueError(err_msg.format('evaluation_hooks')) + raise ValueError( + err_msg.format('training_chief_hooks') + 'If you want' + + ' to pass training hooks, please pass via training_hooks.') if estimator_spec.scaffold: logging.warning('EstimatorSpec.Scaffold is ignored by TPU train/eval. ' @@ -1957,10 +1983,9 @@ class TPUEstimator(estimator_lib.Estimator): """Constructs an `TPUEstimator` instance. Args: - model_fn: Model function as required by `Estimator`. For training, the - returned `EstimatorSpec` cannot have hooks as it is not supported in - `TPUEstimator`. Instead, the user can pass the training hooks as - an argument to `TPUEstimator.train()`. + model_fn: Model function as required by `Estimator` which returns + EstimatorSpec or TPUEstimatorSpec. `training_hooks`, 'evaluation_hooks', + and `prediction_hooks` must not capure any TPU Tensor inside the model_fn. model_dir: Directory to save model parameters, graph and etc. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model. If `None`, the model_dir in @@ -2429,7 +2454,7 @@ class TPUEstimator(estimator_lib.Estimator): graph.add_to_collection(_TPU_ENQUEUE_OPS, enqueue_op) if mode == model_fn_lib.ModeKeys.TRAIN: - loss, host_call, scaffold = ( + loss, host_call, scaffold, training_hooks = ( _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn)) host_ops = host_call.create_tpu_hostcall() if host_ops is None: @@ -2484,6 +2509,9 @@ class TPUEstimator(estimator_lib.Estimator): self._config.tpu_config.iterations_per_loop) hooks.append(examples_hook) + if training_hooks: + hooks.extend(training_hooks) + chief_hooks = [] if (self._config.save_checkpoints_secs or self._config.save_checkpoints_steps): @@ -2495,6 +2523,7 @@ class TPUEstimator(estimator_lib.Estimator): checkpoint_hook._set_steps_per_run( # pylint: disable=protected-access self._config.tpu_config.iterations_per_loop) chief_hooks.append(checkpoint_hook) + summary.scalar(model_fn_lib.LOSS_METRIC_KEY, loss) with ops.control_dependencies([loss]): update_ops = _sync_variables_ops() @@ -2514,7 +2543,7 @@ class TPUEstimator(estimator_lib.Estimator): scaffold=scaffold) if mode == model_fn_lib.ModeKeys.EVAL: - total_loss, host_calls, scaffold = _eval_on_tpu_system( + total_loss, host_calls, scaffold, eval_hooks = _eval_on_tpu_system( ctx, model_fn_wrapper, dequeue_fn) iterations_per_loop_var = _create_or_get_iterations_per_loop() mean_loss = math_ops.div(total_loss, @@ -2558,6 +2587,9 @@ class TPUEstimator(estimator_lib.Estimator): rendezvous=self._rendezvous[mode]), ] + input_hooks + if eval_hooks: + hooks.extend(eval_hooks) + return model_fn_lib.EstimatorSpec( mode, loss=mean_loss, @@ -2568,8 +2600,9 @@ class TPUEstimator(estimator_lib.Estimator): # Predict assert mode == model_fn_lib.ModeKeys.PREDICT - dummy_predict_op, host_calls, scaffold = _predict_on_tpu_system( - ctx, model_fn_wrapper, dequeue_fn) + (dummy_predict_op, host_calls, + scaffold, prediction_hooks) = _predict_on_tpu_system( + ctx, model_fn_wrapper, dequeue_fn) with ops.control_dependencies([dummy_predict_op]): internal_ops_to_run = _sync_variables_ops() with ops.control_dependencies(internal_ops_to_run): @@ -2625,6 +2658,9 @@ class TPUEstimator(estimator_lib.Estimator): ctx, enqueue_ops, host_ops, rendezvous=self._rendezvous[mode]), ] + input_hooks + if prediction_hooks: + hooks.extend(prediction_hooks) + return model_fn_lib.EstimatorSpec( mode, prediction_hooks=hooks, @@ -2708,8 +2744,8 @@ def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): """Executes `model_fn_wrapper` multiple times on all TPU shards.""" iterations_per_loop_var = _create_or_get_iterations_per_loop() - single_tpu_eval_step, host_calls, captured_scaffold_fn = ( - model_fn_wrapper.convert_to_single_tpu_eval_step(dequeue_fn)) + (single_tpu_eval_step, host_calls, captured_scaffold_fn, captured_eval_hooks + ) = model_fn_wrapper.convert_to_single_tpu_eval_step(dequeue_fn) def multi_tpu_eval_steps_on_single_shard(): return training_loop.repeat( @@ -2724,15 +2760,16 @@ def _eval_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): device_assignment=ctx.device_assignment) scaffold = _get_scaffold(captured_scaffold_fn) - return loss, host_calls, scaffold + return loss, host_calls, scaffold, captured_eval_hooks.get() def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): """Executes `model_fn_wrapper` multiple times on all TPU shards.""" iterations_per_loop_var = _create_or_get_iterations_per_loop() - single_tpu_train_step, host_call, captured_scaffold_fn = ( - model_fn_wrapper.convert_to_single_tpu_train_step(dequeue_fn)) + (single_tpu_train_step, host_call, captured_scaffold_fn, + captured_training_hooks) = ( + model_fn_wrapper.convert_to_single_tpu_train_step(dequeue_fn)) def multi_tpu_train_steps_on_single_shard(): return training_loop.repeat( @@ -2747,15 +2784,16 @@ def _train_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): device_assignment=ctx.device_assignment) scaffold = _get_scaffold(captured_scaffold_fn) - return loss, host_call, scaffold + return loss, host_call, scaffold, captured_training_hooks.get() def _predict_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): """Executes `model_fn_wrapper` multiple times on all TPU shards.""" num_cores = ctx.num_cores - single_tpu_predict_step, host_calls, captured_scaffold_fn = ( - model_fn_wrapper.convert_to_single_tpu_predict_step(dequeue_fn)) + (single_tpu_predict_step, host_calls, captured_scaffold_fn, + captured_predict_hooks + ) = model_fn_wrapper.convert_to_single_tpu_predict_step(dequeue_fn) def multi_tpu_predict_steps_on_single_shard(): @@ -2775,7 +2813,7 @@ def _predict_on_tpu_system(ctx, model_fn_wrapper, dequeue_fn): outputs_from_all_shards=False) scaffold = _get_scaffold(captured_scaffold_fn) - return dummy_predict_op, host_calls, scaffold + return dummy_predict_op, host_calls, scaffold, captured_predict_hooks.get() def _wrap_computation_in_while_loop(device, op_fn): diff --git a/tensorflow/python/estimator/model_fn.py b/tensorflow/python/estimator/model_fn.py index a9fd8f8e1a..9db9ccd01d 100644 --- a/tensorflow/python/estimator/model_fn.py +++ b/tensorflow/python/estimator/model_fn.py @@ -380,15 +380,12 @@ def _maybe_add_default_serving_output(export_outputs): return export_outputs -class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [ - 'mode', - 'predictions', - 'loss', - 'train_op', - 'eval_metrics', - 'export_outputs', - 'scaffold_fn', - 'host_call'])): +class _TPUEstimatorSpec( + collections.namedtuple('TPUEstimatorSpec', [ + 'mode', 'predictions', 'loss', 'train_op', 'eval_metrics', + 'export_outputs', 'scaffold_fn', 'host_call', 'training_hooks', + 'evaluation_hooks', 'prediction_hooks' + ])): """Ops and objects returned from a `model_fn` and passed to `TPUEstimator`. This is a simplified implementation of `tf.contrib.tpu.EstimatorSpec`. See @@ -404,17 +401,24 @@ class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [ eval_metrics=None, export_outputs=None, scaffold_fn=None, - host_call=None): + host_call=None, + training_hooks=None, + evaluation_hooks=None, + prediction_hooks=None): """Creates a `_TPUEstimatorSpec` instance.""" - return super(_TPUEstimatorSpec, cls).__new__(cls, - mode=mode, - predictions=predictions, - loss=loss, - train_op=train_op, - eval_metrics=eval_metrics, - export_outputs=export_outputs, - scaffold_fn=scaffold_fn, - host_call=host_call) + return super(_TPUEstimatorSpec, cls).__new__( + cls, + mode=mode, + predictions=predictions, + loss=loss, + train_op=train_op, + eval_metrics=eval_metrics, + export_outputs=export_outputs, + scaffold_fn=scaffold_fn, + host_call=host_call, + training_hooks=training_hooks, + evaluation_hooks=evaluation_hooks, + prediction_hooks=prediction_hooks) def as_estimator_spec(self): """Creates an equivalent `EstimatorSpec` used by CPU train/eval.""" @@ -423,12 +427,16 @@ class _TPUEstimatorSpec(collections.namedtuple('TPUEstimatorSpec', [ else: metric_fn, tensors = self.eval_metrics eval_metric_ops = metric_fn(**tensors) - return EstimatorSpec(mode=self.mode, - predictions=self.predictions, - loss=self.loss, - train_op=self.train_op, - eval_metric_ops=eval_metric_ops, - export_outputs=self.export_outputs) + return EstimatorSpec( + mode=self.mode, + predictions=self.predictions, + loss=self.loss, + train_op=self.train_op, + eval_metric_ops=eval_metric_ops, + export_outputs=self.export_outputs, + training_hooks=self.training_hooks, + evaluation_hooks=self.evaluation_hooks, + prediction_hooks=self.prediction_hooks) def _check_is_tensor_or_operation(x, name): -- GitLab From 53449eb635c4abce62c5f00d05fad1b1c8d1d9ab Mon Sep 17 00:00:00 2001 From: Priya Gupta Date: Thu, 26 Jul 2018 13:11:51 -0700 Subject: [PATCH 461/519] Restore tower local variables correctly in init_from_checkpoint. PiperOrigin-RevId: 206208637 --- .../python/checkpoint_utils_test.py | 8 +++++- .../contrib/distribute/python/values.py | 16 +++++++++--- .../python/training/checkpoint_utils.py | 25 +++++-------------- 3 files changed, 26 insertions(+), 23 deletions(-) diff --git a/tensorflow/contrib/distribute/python/checkpoint_utils_test.py b/tensorflow/contrib/distribute/python/checkpoint_utils_test.py index fe3df9cbb9..bcb977f640 100644 --- a/tensorflow/contrib/distribute/python/checkpoint_utils_test.py +++ b/tensorflow/contrib/distribute/python/checkpoint_utils_test.py @@ -49,17 +49,23 @@ class CheckpointUtilsWithDistributionStrategyTest( def testInitFromCheckpoint(self, distribution, in_tower_mode): checkpoint_dir = self.get_temp_dir() with self.test_session() as session: - v1_value, _, _, _ = checkpoint_utils_test._create_checkpoints( + v1_value, v2_value, _, _ = checkpoint_utils_test._create_checkpoints( session, checkpoint_dir) def init_and_verify(g): v1 = variable_scope.get_variable("new_var1", [1, 10]) + v2 = variable_scope.get_variable( + "new_var2", [10, 10], + synchronization=variable_scope.VariableSynchronization.ON_READ, + aggregation=variable_scope.VariableAggregation.MEAN) checkpoint_utils.init_from_checkpoint(checkpoint_dir, { "var1": "new_var1", + "var2": "new_var2" }) with self.test_session(graph=g) as session: session.run(variables.global_variables_initializer()) self.assertAllEqual(v1_value, self.evaluate(v1)) + self.assertAllEqual(v2_value, self.evaluate(v2)) with ops.Graph().as_default() as g, distribution.scope(): if in_tower_mode: diff --git a/tensorflow/contrib/distribute/python/values.py b/tensorflow/contrib/distribute/python/values.py index 47dcf679c2..4018b1e023 100644 --- a/tensorflow/contrib/distribute/python/values.py +++ b/tensorflow/contrib/distribute/python/values.py @@ -210,6 +210,11 @@ class DistributedVariable(DistributedDelegate): # without this it will use `__getattr__` which will delegate to a component # variable. self._keras_initialized = False + # Typically, a `DistributedVariable`'s initializer is composed of the + # initializers of the components variables. However, in some cases, such as + # when restoring from a checkpoint, we may set the _initializer_op + # property on the entire `DistributedVariable`. + self._initializer_op = None super(DistributedVariable, self).__init__(index) def is_initialized(self, name=None): @@ -239,9 +244,14 @@ class DistributedVariable(DistributedDelegate): @property def initializer(self): - # return grouped ops of all the var initializations of component values of - # the mirrored variable - return control_flow_ops.group([v.initializer for v in self._index.values()]) + if self._initializer_op: + init_op = self._initializer_op + else: + # return grouped ops of all the var initializations of component values of + # the mirrored variable + init_op = control_flow_ops.group( + [v.initializer for v in self._index.values()]) + return init_op @property def graph(self): diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index 883f4fd910..799729b017 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -311,29 +311,16 @@ def _set_checkpoint_initializer(variable, # TODO(priyag, allenl): Use `SaveableObject.restore` instead here. if resource_variable_ops.is_resource_variable(variable): init_op = variable.assign(restore_op, read_value=False) + # TODO(priyag): Remove this when using `SaveableObject.restore` instead. + if hasattr(init_op, "_index"): + init_op = distribute_lib.get_distribution_strategy().group(init_op) else: init_op = state_ops.assign(variable, restore_op) # pylint:disable=protected-access - # We need special handling for `DistributedVariable`s as they contain - # mutliple actual variables. `assign` on a `DistributedVariable` returns a - # combined `init_op` which contains initializers for all the contained - # variables. We then set each underlying variable's `_initializer_op` using - # the corresponding `init_op`. - # TODO(priyag): Use `isinstance` checks when `DistributedVariable` class - # moves out of contrib. - if any(base.__name__ == "DistributedVariable" - for base in variable.__class__.__bases__): - assert distribute_lib.get_cross_tower_context() - assert hasattr(variable, "_index") - for (d, v) in six.iteritems(variable._index): - v._initializer_op = init_op._index[d] - restore_op.set_shape(v.shape) - v._initial_value = restore_op - else: - variable._initializer_op = init_op - restore_op.set_shape(variable.shape) - variable._initial_value = restore_op + variable._initializer_op = init_op + restore_op.set_shape(variable.shape) + variable._initial_value = restore_op # pylint:enable=protected-access -- GitLab From 63563579653c1f0829d460eef5f05963111e08f0 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 13:16:04 -0700 Subject: [PATCH 462/519] Delete unused code in CXX11/src/NeuralNetworks PiperOrigin-RevId: 206209252 --- .../unsupported/Eigen/CXX11/NeuralNetworks | 35 - .../CXX11/src/NeuralNetworks/Activations.h | 116 --- .../CXX11/src/NeuralNetworks/Attention.h | 209 ----- .../BackwardCuboidConvolutions.h | 523 ------------ .../BackwardSpatialConvolutions.h | 351 -------- .../src/NeuralNetworks/CuboidConvolution.h | 179 ---- .../Eigen/CXX11/src/NeuralNetworks/Patch3d.h | 240 ------ .../Eigen/CXX11/src/NeuralNetworks/Pooling.h | 433 ---------- .../Eigen/CXX11/src/NeuralNetworks/SoftMax.h | 83 -- .../src/NeuralNetworks/SpatialConvolutions.h | 775 ------------------ .../NeuralNetworks/TensorConvolutionByFFT.h | 289 ------- 11 files changed, 3233 deletions(-) delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/NeuralNetworks delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Activations.h delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Attention.h delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Patch3d.h delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SoftMax.h delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SpatialConvolutions.h delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/TensorConvolutionByFFT.h diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/NeuralNetworks b/third_party/eigen3/unsupported/Eigen/CXX11/NeuralNetworks deleted file mode 100644 index 7741b68d8a..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/NeuralNetworks +++ /dev/null @@ -1,35 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CXX11_NEURAL_NETWORKS_MODULE -#define EIGEN_CXX11_NEURAL_NETWORKS_MODULE - -#include "unsupported/Eigen/CXX11/Tensor" - -/** \defgroup CXX11_NeuralNetworks_Module Neural Networks Module - * - * This module provides an efficient implementation of the common primitives - * used by neural networks. - * The primitives are built on top of the tensor library. - * - * \code - * #include - * \endcode - */ - -#include "unsupported/Eigen/CXX11/src/NeuralNetworks/Activations.h" -#include "unsupported/Eigen/CXX11/src/NeuralNetworks/Attention.h" -#include "unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h" -#include "unsupported/Eigen/CXX11/src/NeuralNetworks/SoftMax.h" -#include "unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h" -#include "unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h" -#include "unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h" -#include "unsupported/Eigen/CXX11/src/NeuralNetworks/SpatialConvolutions.h" - -#endif // EIGEN_CXX11_NEURAL_NETWORKS_MODULE diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Activations.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Activations.h deleted file mode 100644 index cbcce9e282..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Activations.h +++ /dev/null @@ -1,116 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Benoit Steiner -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -#ifndef EIGEN_CXX11_NEURAL_NETWORKS_ACTIVATIONS_H -#define EIGEN_CXX11_NEURAL_NETWORKS_ACTIVATIONS_H - -namespace Eigen { - -/** scalar_sigmoid_fast_derivative_op - * \ingroup CXX11_NeuralNetworks_Module - * \brief Template functor to compute the fast derivative of a sigmoid - * - * Input should be the backpropagated gradient. - * - * \sa class CwiseUnaryOp, Cwise::sigmoid_fast_derivative() - */ -template -struct scalar_sigmoid_fast_derivative_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_sigmoid_fast_derivative_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& y) const { - const T one = T(1); - return (one - y) * y; - } - - template - inline Packet packetOp(const Packet& y) const { - const Packet one = internal::pset1(1); - return internal::pmul(internal::psub(one, y), y); - } -}; - -namespace internal { -template -struct functor_traits > { - enum { - Cost = NumTraits::AddCost * 2 + NumTraits::MulCost, - PacketAccess = packet_traits::HasAdd && packet_traits::HasMul && - packet_traits::HasNegate - }; -}; -} // namespace internal - -/** scalar_tanh_fast_derivative_op - * \ingroup CXX11_NeuralNetworks_Module - * \brief Template functor to compute the fast derivative of a tanh - * - * Input should be the backpropagated gradient. - * - * \sa class CwiseUnaryOp, Cwise::tanh_fast_derivative() - */ -template -struct scalar_tanh_fast_derivative_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_tanh_fast_derivative_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T operator()(const T& y) const { - const T one = T(1); - return one - (y * y); - } - - template - inline Packet packetOp(const Packet& y) const { - const Packet one = internal::pset1(1); - return internal::psub(one, internal::pmul(y, y)); - } -}; - -namespace internal { -template -struct functor_traits > { - enum { - Cost = NumTraits::AddCost * 2 + NumTraits::MulCost * 1, - PacketAccess = packet_traits::HasAdd && packet_traits::HasMul && - packet_traits::HasNegate - }; -}; -} // namespace internal - -/** - * \ingroup CXX11_NeuralNetworks_Module - * \brief Template functor to clip the magnitude of the first scalar. - * - * \sa class CwiseBinaryOp, MatrixBase::Clip - */ -template -struct scalar_clip_op { - EIGEN_EMPTY_STRUCT_CTOR(scalar_clip_op) - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Scalar - operator()(const Scalar& a, const Scalar& b) const { - return numext::mini(numext::maxi(a, -b), b); - } - template - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Packet - packetOp(const Packet& a, const Packet& b) const { - return internal::pmin(internal::pmax(a, internal::pnegate(b)), b); - } -}; - -namespace internal { -template -struct functor_traits > { - enum { - Cost = NumTraits::AddCost * 3, - PacketAccess = packet_traits::HasMax && - packet_traits::HasMin && - packet_traits::HasNegate - }; -}; -} // namespace internal - -} // end namespace Eigen - -#endif // EIGEN_CXX11_NEURAL_NETWORKS_ACTIVATIONS_H diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Attention.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Attention.h deleted file mode 100644 index d4bc7a3515..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Attention.h +++ /dev/null @@ -1,209 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Benoit Steiner -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -#ifndef EIGEN_CXX11_NEURAL_NETWORKS_ATTENTION_H -#define EIGEN_CXX11_NEURAL_NETWORKS_ATTENTION_H - -namespace Eigen { - -/** ExtractGlimpses - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Extract glimpses from an input tensor. - * - * The input parameter is expected to be a col-major tensor with a rank of 4 (depth, x, y, and batch). - * The width and height parameters specify the extension of the returned glimpses. - * The offsets parameter specifies the x, y locations of the center of the glimpses relative to the center of the input image. The vector is expected to contain one IndexPair for each image in the batch dimension. - * The normalized boolean indicates if incoming coordinates are normalized so that 0.0 and 1.0 correspond to the minimum and maximum of each height and width dimension. - * The centered boolean indicates if incoming coordinates are centered relative to the image, in which case -1.0 and 1.0 correspond to minimum and maximum of each dimension while 0.0 corresponds to the center. - * - * The result can be assigned to a tensor of rank equal to that of the input. The result will be laid out in col-major order (depth, x, y, batch). - * The dimensions of the result will be equal to the dimensions of the input except for width and height which will be equal to the requested glimpse size. - */ -namespace { -template -struct GlimpseExtractionOp { - GlimpseExtractionOp(const Index width, const Index height, - const std::vector >& offsets, - const bool normalized, - const bool centered, - const bool uniform_noise) : - width_(width), height_(height), offsets_(offsets), - normalized_(normalized), centered_(centered), uniform_noise_(uniform_noise) { } - - template - DSizes dimensions(const Input& input) const { - typedef typename internal::traits::Index IndexType; - typedef TensorRef::Scalar, 4, - internal::traits::Layout, IndexType> > Ref; - Ref in(input); - - DSizes dims = in.dimensions(); - - dims[0] = in.dimension(0); - dims[1] = width_; - dims[2] = height_; - dims[3] = in.dimension(3); - return dims; - } - - template - EIGEN_DEVICE_FUNC - void eval(const Input& input, Output& output, const Device& device) const - { - typedef typename internal::traits::Index IndexType; - typedef TensorRef::Scalar, 4, - internal::traits::Layout, IndexType> > Ref; - Ref in(input); - - const Index num_channels = in.dimension(0); - const Index input_width = in.dimension(1); - const Index input_height = in.dimension(2); - const Index batch_size = in.dimension(3); - eigen_assert(input_width > 0); - eigen_assert(input_height > 0); - - for (Index i = 0; i < batch_size; ++i) { - float x = offsets_[i].first, y = offsets_[i].second; - - // Un-normalize coordinates back to pixel space if normalized. - if (normalized_) { - x *= input_width; - y *= input_height; - } - // Un-center if coordinates are centered on the image center. - if (centered_) { - x /= 2.0f; - y /= 2.0f; - x += input_width / 2.0f; - y += input_height / 2.0f; - } - // Remove half of the glimpse window. - x -= width_ / 2.0f; - y -= height_ / 2.0f; - - const Index offset_x = (Index) x; - const Index offset_y = (Index) y; - Index glimpse_width = width_; - Index glimpse_height = height_; - bool partial_overlap = false; - DSizes slice_offset(0, offset_x, offset_y); - DSizes slice_extent(num_channels, width_, height_); - DSizes base_offset(0, 0, 0); - - if (offset_x < 0) { - slice_offset[1] = 0; - glimpse_width = (std::max)(0, width_ + offset_x); - slice_extent[1] = glimpse_width; - base_offset[1] = width_ - glimpse_width; - partial_overlap = true; - } else if (offset_x + width_ >= input_width) { - glimpse_width = (std::max)(0, input_width - offset_x); - slice_extent[1] = glimpse_width; - partial_overlap = true; - } - if (offset_y < 0) { - slice_offset[2] = 0; - glimpse_height = (std::max)(0, height_ + offset_y); - slice_extent[2] = glimpse_height; - base_offset[2] = height_ - glimpse_height; - partial_overlap = true; - } else if (offset_y + height_ >= input_height) { - glimpse_height = (std::max)(0, input_height - offset_y); - slice_extent[2] = glimpse_height; - partial_overlap = true; - } - slice_extent[1] = std::min(input_width, slice_extent[1]); - slice_extent[2] = std::min(input_height, slice_extent[2]); - - if (partial_overlap) { - if (uniform_noise_) { - // Initialize the glimpse with uniform noise. - typedef typename internal::remove_const< - typename internal::traits::Scalar>::type Scalar; - TensorFixedSize > mini; - mini.device(device) = input.template chip<3>(i).minimum(); - TensorFixedSize > range; - range.device(device) = - (input.template chip<3>(i).maximum() - mini).template cast(); - - DSizes glimpse_size(num_channels, width_, height_); - TensorMap > tmp(NULL, glimpse_size); - output.template chip<3>(i).device(device) = - mini.reshape(Sizes<1,1,1>()).broadcast(glimpse_size) + - (tmp.random() * range.reshape(Sizes<1,1,1>()).broadcast(glimpse_size)).template cast(); - } else { - // Initialize the glimpse with white noise: compute the mean and sigma - // of each channel, and use them to shape the gaussian. - DSizes glimpse_size(width_, height_); - DSizes input_size(input_width, input_height); - typedef typename internal::remove_const< - typename internal::traits::Scalar>::type Scalar; - - for (int j = 0; j < num_channels; ++j) { - TensorFixedSize > mean; - mean.device(device) = input.template chip<3>(i).template chip<0>(j).template cast().mean(); - TensorFixedSize > sigma; - sigma.device(device) = - (input.template chip<3>(i).template chip<0>(j).template cast() - mean.reshape(Sizes<1,1>()).broadcast(input_size)).square().mean().sqrt(); - TensorFixedSize > mini; - mini.device(device) = input.template chip<3>(i).template chip<0>(j).minimum(); - TensorFixedSize > maxi; - maxi.device(device) = input.template chip<3>(i).template chip<0>(j).maximum(); - - TensorMap > tmp(NULL, glimpse_size); - output.template chip<3>(i).template chip<0>(j).device(device) = - (mean.reshape(Sizes<1,1>()).broadcast(glimpse_size) + - (tmp.random(internal::NormalRandomGenerator()) * sigma.reshape(Sizes<1,1>()).broadcast(glimpse_size)).template cast()).cwiseMin(maxi.reshape(Sizes<1,1>()).broadcast(glimpse_size)).cwiseMax(mini.reshape(Sizes<1,1>()).broadcast(glimpse_size)); - } - } - - // Copy the part of the glimpse that cover the input image if any. - if (glimpse_width == 0 || glimpse_height == 0) { - continue; - } - output.template chip<3>(i).slice(base_offset, slice_extent).device(device) = input.template chip<3>(i).slice(slice_offset, slice_extent); - } else { - output.template chip<3>(i).device(device) = input.template chip<3>(i).slice(slice_offset, slice_extent); - } - } - } - - private: - const Index width_; - const Index height_; - const std::vector > offsets_; - const bool normalized_; - const bool centered_; - const bool uniform_noise_; -}; -} - - -template -EIGEN_ALWAYS_INLINE -static const TensorCustomUnaryOp::Index>, const Input> -ExtractGlimpses(const Input& input, - const typename internal::traits::Index width, - const typename internal::traits::Index height, - const std::vector >& offsets, - const bool normalized = true, const bool centered = true, - const bool uniform_noise = true) -{ - EIGEN_STATIC_ASSERT(internal::traits::Layout == ColMajor, YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 4, YOU_MADE_A_PROGRAMMING_MISTAKE); - - typedef typename internal::traits::Index Index; - const GlimpseExtractionOp op(width, height, offsets, normalized, - centered, uniform_noise); - return input.customOp(op); -} - -} // end namespace Eigen - -#endif // EIGEN_CXX11_NEURAL_NETWORKS_ATTENTION_H diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h deleted file mode 100644 index 12ce23444c..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardCuboidConvolutions.h +++ /dev/null @@ -1,523 +0,0 @@ -#ifndef EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_CUBOID_CONVOLUTIONS_H -#define EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_CUBOID_CONVOLUTIONS_H - -#include "Patch3d.h" - -namespace Eigen { - -/** CuboidConvolutionBackwardInput - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Computes the backprop for the input of a 3D convolution. - * - * The output_backward parameter is expected to be a tensor with a rank of 4 or more (channels, depth, height, width, and optionally others) - * The kernel parameter is expected to be a 5D tensor (filters, channels, kernel_depth, kernel_height, kernel_width) - * output_backward and kernel have to be in the same layout. - * - * The dimensions of the result will be filters, depth, height, width (and others if applicable). - * - * It is possible to swap the order of the depth, width and height dimensions provided that the same order is used in the input, the kernel, and the output. - * - * All dimension orders above are given for col-major, and should be reversed for row-major. - */ - -template -EIGEN_ALWAYS_INLINE static const typename internal::conditional< - internal::traits::Layout == ColMajor, - TensorReshapingOp< - const DSizes::Index, - internal::traits::NumDimensions>, - const TensorContractionOp< - const array< IndexPair::Index>, 2>, - const TensorReshapingOp< - const DSizes< typename internal::traits::Index, 3>, - const TensorReverseOp, const Kernel> - >, - const TensorReshapingOp< - const DSizes< typename internal::traits::Index, 3>, - const TensorVolumePatchOp - > - > - >, - TensorReshapingOp< - const DSizes::Index, - internal::traits::NumDimensions>, - const TensorContractionOp< - const array< IndexPair::Index>, 2>, - const TensorReshapingOp< - const DSizes< typename internal::traits::Index, 3>, - const TensorVolumePatchOp - >, - const TensorReshapingOp< - const DSizes::Index, 3>, - const TensorReverseOp, const Kernel> - > - > - > ->::type -CuboidConvolutionBackwardInput( - const Kernel& kernel, const OutputBackward& output_backward, - typename internal::traits::Index inputPlanes, - typename internal::traits::Index inputRows, - typename internal::traits::Index inputCols, - const DenseIndex stridePlanes = 1, const DenseIndex strideRows = 1, - const DenseIndex strideCols = 1) { - typedef typename internal::traits::Index TensorIndex; - const TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > kern(kernel); - const TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > out(output_backward); - - EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE); - - static const bool isColMajor = (internal::traits::Layout == ColMajor); - - static const int NumDims = internal::traits::NumDimensions; - - // Number of filters to apply. This is the same as the output depth of the result - const TensorIndex kernelFilters = isColMajor ? kern.dimensions()[0] : kern.dimensions()[4]; - // Number of channels. This is the same as the input depth. - const TensorIndex kernelChannels = isColMajor ? kern.dimensions()[1] : kern.dimensions()[3]; - const TensorIndex kernelPlanes = isColMajor ? kern.dimensions()[2] : kern.dimensions()[2]; - const TensorIndex kernelRows = isColMajor ? kern.dimensions()[3] : kern.dimensions()[1]; - const TensorIndex kernelCols = isColMajor ? kern.dimensions()[4] : kern.dimensions()[0]; - - const TensorIndex outputPlanes = isColMajor ? out.dimensions()[1] : out.dimensions()[NumDims - 2]; - const TensorIndex outputRows = isColMajor ? out.dimensions()[2] : out.dimensions()[NumDims - 3]; - const TensorIndex outputCols = isColMajor ? out.dimensions()[3] : out.dimensions()[NumDims - 4]; - - TensorIndex forward_pad_z, forward_pad_y, forward_pad_x; - const TensorIndex size_z = ceil(inputPlanes / static_cast(stridePlanes)); - const TensorIndex size_y = ceil(inputRows / static_cast(strideRows)); - const TensorIndex size_x = ceil(inputCols / static_cast(strideCols)); - - // Infer padding type. - if (size_z == outputPlanes && size_y == outputRows && size_x == outputCols) { - // SAME padding. - const TensorIndex dz = size_z * stridePlanes + kernelPlanes - 1 - inputPlanes; - const TensorIndex dy = size_y * strideRows + kernelRows - 1 - inputRows; - const TensorIndex dx = size_x * strideCols + kernelCols - 1 - inputCols; - - forward_pad_z = dz - dz / 2; - forward_pad_y = dy - dy / 2; - forward_pad_x = dx - dx / 2; - } else { - // VALID padding. - forward_pad_z = 0; - forward_pad_y = 0; - forward_pad_x = 0; - } - const TensorIndex padding_ztop = kernelPlanes - 1 - forward_pad_z; - const TensorIndex padding_top = kernelRows - 1 - forward_pad_y; - const TensorIndex padding_left = kernelCols - 1 - forward_pad_x; - - const TensorIndex padding_zbottom = inputPlanes + kernelPlanes - 1 - (outputPlanes - 1) * stridePlanes - 1 - padding_ztop; - const TensorIndex padding_bottom = inputRows + kernelRows - 1 - (outputRows - 1) * strideRows - 1 - padding_top; - const TensorIndex padding_right = inputCols + kernelCols - 1 - (outputCols - 1) * strideCols - 1 - padding_left; - - eigen_assert(padding_ztop >= 0); - eigen_assert(padding_zbottom >= 0); - eigen_assert(padding_top >= 0); - eigen_assert(padding_left >= 0); - eigen_assert(padding_bottom >= 0); - eigen_assert(padding_right >= 0); - - // The kernel has dimensions filters X channels X patch_planes X patch_rows X patch_cols. - // We need to reverse the kernel along the spatial dimensions. - array kernel_reverse; - if (isColMajor) { - kernel_reverse[0] = false; - kernel_reverse[1] = false; - kernel_reverse[2] = true; - kernel_reverse[3] = true; - kernel_reverse[4] = true; - } else { - kernel_reverse[0] = true; - kernel_reverse[1] = true; - kernel_reverse[2] = true; - kernel_reverse[3] = false; - kernel_reverse[4] = false; - } - - DSizes kernel_dims; - if (isColMajor) { - kernel_dims[0] = kernelFilters; - kernel_dims[1] = kernelChannels; - kernel_dims[2] = kernelRows * kernelCols * kernelPlanes; - } else { - kernel_dims[0] = kernelRows * kernelCols * kernelPlanes; - kernel_dims[1] = kernelChannels; - kernel_dims[2] = kernelFilters; - } - - // The output_backward has dimensions out_depth X out_planes X out_rows X out_cols X OTHERS - // When we extract the image patches from output_backward, it will have dimensions: - // out_depth X (patch_planes * patch_rows * patch_cols) X (input_planes * input_rows * input_cols * OTHERS) - DSizes pre_contract_dims; - if (isColMajor) { - pre_contract_dims[0] = kernelFilters; - pre_contract_dims[1] = kernelRows * kernelCols * kernelPlanes; - pre_contract_dims[2] = inputRows * inputCols * inputPlanes; - for (int i = 4; i < NumDims; ++i) { - pre_contract_dims[2] *= out.dimension(i); - } - } else { - pre_contract_dims[2] = kernelFilters; - pre_contract_dims[1] = kernelRows * kernelCols * kernelPlanes; - pre_contract_dims[0] = inputRows * inputCols * inputPlanes; - for (int i = 0; i < NumDims - 4; ++i) { - pre_contract_dims[0] *= out.dimension(i); - } - } - - // We will contract along dimensions (0, 2) in kernel and (0, 1) in - // output_backward, if this is col-major, and - // dimensions (0, 2) in kernel and (1, 2) in output_backward, if this row-major. - array, 2> contract_dims; - if (isColMajor) { - // col-major: kernel.contract(output.patches) - contract_dims[0] = IndexPair(0, 0); - contract_dims[1] = IndexPair(2, 1); - } else { - // row-major: output.patches.contract(kernel) - contract_dims[0] = IndexPair(1, 0); - contract_dims[1] = IndexPair(2, 2); - } - - // Post contraction, the dimensions of the input_backprop is - // channels X input_planes X input_rows X input_cols X OTHERS - DSizes post_contract_dims; - if (isColMajor) { - post_contract_dims[0] = kernelChannels; - post_contract_dims[1] = inputPlanes; - post_contract_dims[2] = inputRows; - post_contract_dims[3] = inputCols; - for (int i = 4; i < NumDims; ++i) { - post_contract_dims[i] = out.dimension(i); - } - } else { - post_contract_dims[NumDims - 1] = kernelChannels; - post_contract_dims[NumDims - 2] = inputPlanes; - post_contract_dims[NumDims - 3] = inputRows; - post_contract_dims[NumDims - 4] = inputCols; - for (int i = 0; i < NumDims - 4; ++i) { - post_contract_dims[i] = out.dimension(i); - } - } - - DSizes strides; - for (int i = 0; i < NumDims; i++) { - strides[i] = 1; - } - if (isColMajor) { - strides[1] = stridePlanes; - strides[2] = strideRows; - strides[3] = strideCols; - } else { - strides[NumDims - 2] = stridePlanes; - strides[NumDims - 3] = strideRows; - strides[NumDims - 4] = strideCols; - } - - return choose( - Cond::Layout == ColMajor>(), - kernel.reverse(kernel_reverse) - .reshape(kernel_dims) - .contract( - output_backward.extract_volume_patches(kernelPlanes, kernelRows, kernelCols, - 1, 1, 1, stridePlanes, strideRows, strideCols, - padding_ztop, padding_zbottom, - padding_top, padding_bottom, - padding_left, padding_right) - .reshape(pre_contract_dims), - contract_dims) - .reshape(post_contract_dims), - output_backward.extract_volume_patches(kernelPlanes, kernelRows, kernelCols, - 1, 1, 1, stridePlanes, strideRows, strideCols, - padding_ztop, padding_zbottom, - padding_top, padding_bottom, - padding_left, padding_right) - .reshape(pre_contract_dims) - .contract(kernel.reverse(kernel_reverse).reshape(kernel_dims), - contract_dims) - .reshape(post_contract_dims)); -} - - -/** CuboidConvolutionBackwardKernel - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Computes the backprop for the filter of a 3D convolution. - * - * The output_backward parameter is expected to be a tensor with a rank of 4 or more (channels, depth, height, width, and optionally others) - * The kernel parameter is expected to be a 4D tensor (filters, channels, kernel_depth, kernel_height, kernel_width) - * output_backward and kernel have to be in the same layout. - * - * The dimensions of the result will be filters, depth, height, width (and others if applicable). - * - * It is possible to swap the order of the depth, width and height dimensions provided that the same order is used in the input, the kernel, and the output. - * - * All dimension orders above are given for col-major, and should be reversed for row-major. - */ -template -EIGEN_ALWAYS_INLINE static const typename internal::conditional< - internal::traits::Layout == ColMajor, - const TensorShufflingOp< - const array::Index, 5>, - const TensorReverseOp< - const array, - const TensorReshapingOp< - const DSizes::Index, 5>, - const TensorContractionOp< - const array< IndexPair::Index>, 2>, - const TensorReshapingOp< - const DSizes::Index, 3>, - const Input>, - const TensorReshapingOp< - const DSizes< typename internal::traits::Index, 4>, - const TensorVolumePatchOp - > - > - > - > - >, - const TensorShufflingOp< - const array::Index, 5>, - const TensorReverseOp< - const array, - const TensorReshapingOp< - const DSizes::Index, 5>, - const TensorContractionOp< - const array< IndexPair::Index>, 2>, - const TensorReshapingOp< - const DSizes< typename internal::traits::Index, 4>, - const TensorVolumePatchOp - >, - const TensorReshapingOp< - const DSizes::Index, 3>, - const Input - > - > - > - > - > ->::type -CuboidConvolutionBackwardKernel( - const Input& input, const OutputBackward& output_backward, - typename internal::traits::Index kernelPlanes, - typename internal::traits::Index kernelRows, - typename internal::traits::Index kernelCols, - const DenseIndex stridePlanes = 1, - const DenseIndex strideRows = 1, - const DenseIndex strideCols = 1) { - typedef typename internal::traits::Index TensorIndex; - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input); - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > out(output_backward); - - EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE); - - static const bool isColMajor = (internal::traits::Layout == ColMajor); - - static const int NumDims = internal::traits::NumDimensions; - EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == internal::traits::NumDimensions, YOU_MADE_A_PROGRAMMING_MISTAKE); - - const TensorIndex inputPlanes = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2); - const TensorIndex inputRows = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3); - const TensorIndex inputCols = isColMajor ? in.dimension(3) : in.dimension(NumDims - 4); - - const TensorIndex outputPlanes = isColMajor ? out.dimension(1) : out.dimension(NumDims - 2); - const TensorIndex outputRows = isColMajor ? out.dimension(2) : out.dimension(NumDims - 3); - const TensorIndex outputCols = isColMajor ? out.dimension(3) : out.dimension(NumDims - 4); - - const TensorIndex kernelFilters = isColMajor ? out.dimension(0) : out.dimension(NumDims - 1); - const TensorIndex kernelChannels = isColMajor ? in.dimension(0) : in.dimension(NumDims - 1); - - TensorIndex forward_pad_z, forward_pad_y, forward_pad_x; - const TensorIndex size_z = ceil(inputPlanes / static_cast(stridePlanes)); - const TensorIndex size_y = ceil(inputRows / static_cast(strideRows)); - const TensorIndex size_x = ceil(inputCols / static_cast(strideCols)); - - // Infer padding type. - if (size_z == outputPlanes && size_y == outputRows && size_x == outputCols) { - // SAME padding. - const TensorIndex dz = size_z * stridePlanes + kernelPlanes - 1 - inputPlanes; - const TensorIndex dy = size_y * strideRows + kernelRows - 1 - inputRows; - const TensorIndex dx = size_x * strideCols + kernelCols - 1 - inputCols; - - forward_pad_z = dz - dz / 2; - forward_pad_y = dy - dy / 2; - forward_pad_x = dx - dx / 2; - } else { - // VALID padding. - forward_pad_z = 0; - forward_pad_y = 0; - forward_pad_x = 0; - } - - const TensorIndex padding_ztop = kernelPlanes - 1 - forward_pad_z; - const TensorIndex padding_top = kernelRows - 1 - forward_pad_y; - const TensorIndex padding_left = kernelCols - 1 - forward_pad_x; - - const TensorIndex padding_zbottom = inputPlanes + kernelPlanes - 1 - (outputPlanes - 1) * stridePlanes - 1 - padding_ztop; - const TensorIndex padding_bottom = inputRows + kernelRows - 1 - (outputRows - 1) * strideRows - 1 - padding_top; - const TensorIndex padding_right = inputCols + kernelCols - 1 - (outputCols - 1) * strideCols - 1 - padding_left; - - eigen_assert(padding_ztop >= 0); - eigen_assert(padding_zbottom >= 0); - eigen_assert(padding_top >= 0); - eigen_assert(padding_left >= 0); - eigen_assert(padding_bottom >= 0); - eigen_assert(padding_right >= 0); - - // The output_backward has dimensions out_depth X out_plaens X out_rows X out_cols X OTHERS - // When we extract the image patches from output_backward (with input as the - // kernel), it will have dimensions - // (out_depth) X (input_planes * input_rows * input_cols) X (kernel_planes * kernel_rows * kernel_cols) X OTHERS - DSizes pre_contract_dims; - if (isColMajor) { - pre_contract_dims[0] = kernelFilters; - pre_contract_dims[1] = inputRows * inputCols * inputPlanes; - pre_contract_dims[2] = kernelRows * kernelCols * kernelPlanes; - pre_contract_dims[3] = 1; - for (int i = 4; i < NumDims; ++i) { - pre_contract_dims[3] *= out.dimension(i); - } - } else { - pre_contract_dims[3] = kernelFilters; - pre_contract_dims[2] = inputRows * inputCols * inputPlanes; - pre_contract_dims[1] = kernelRows * kernelCols * kernelPlanes; - pre_contract_dims[0] = 1; - for (int i = 0; i < NumDims - 4; ++i) { - pre_contract_dims[0] *= out.dimension(i); - } - } - - // The input has dimensions in_depth X (input_planes * input_rows * input_cols) X OTHERS - DSizes input_dims; - if (isColMajor) { - input_dims[0] = kernelChannels; - input_dims[1] = inputRows * inputCols * inputPlanes; - input_dims[2] = 1; - for (int i = 4; i < NumDims; ++i) { - input_dims[2] *= in.dimension(i); - } - eigen_assert(input_dims[2] == pre_contract_dims[3]); - } else { - input_dims[2] = kernelChannels; - input_dims[1] = inputRows * inputCols * inputPlanes; - input_dims[0] = 1; - for (int i = 0; i < NumDims - 4; ++i) { - input_dims[0] *= in.dimension(i); - } - eigen_assert(input_dims[0] == pre_contract_dims[0]); - } - - // We will contract along dimensions (1, 2) in in and (1, 3) in out, if - // this is col-major. - // For row-major, it's dimensions (0, 1) in in and (0, 2) in out. - array, 2> contract_dims; - if (isColMajor) { - // col-major: in.contract(output.patches) - contract_dims[0] = IndexPair(1, 1); - contract_dims[1] = IndexPair(2, 3); - } else { - // row-major: output.patches.contract(in) - contract_dims[0] = IndexPair(0, 0); - contract_dims[1] = IndexPair(2, 1); - } - - // After the contraction, the kernel will have dimension - // in_depth X out_depth X kernel_patches X kernel_rows X kernel_cols - // We will need to shuffle the first two dimensions and reverse the spatial dimensions. - // The end shape is: - // out_depth X in_shape X kernel_planes X kernel_rows X kernel_cols - - // This is the shape of the kernel *before* the shuffling. - DSizes kernel_dims; - if (isColMajor) { - kernel_dims[0] = kernelChannels; - kernel_dims[1] = kernelFilters; - kernel_dims[2] = kernelPlanes; - kernel_dims[3] = kernelRows; - kernel_dims[4] = kernelCols; - } else { - kernel_dims[0] = kernelCols; - kernel_dims[1] = kernelRows; - kernel_dims[2] = kernelPlanes; - kernel_dims[3] = kernelFilters; - kernel_dims[4] = kernelChannels; - } - - // Flip filters and channels. - array kernel_shuffle; - if (isColMajor) { - kernel_shuffle[0] = 1; - kernel_shuffle[1] = 0; - kernel_shuffle[2] = 2; - kernel_shuffle[3] = 3; - kernel_shuffle[4] = 4; - } else { - kernel_shuffle[0] = 0; - kernel_shuffle[1] = 1; - kernel_shuffle[2] = 2; - kernel_shuffle[3] = 4; - kernel_shuffle[4] = 3; - } - - // Reverse the spatial dimensions. - array kernel_reverse; - if (isColMajor) { - kernel_reverse[0] = false; - kernel_reverse[1] = false; - kernel_reverse[2] = true; - kernel_reverse[3] = true; - kernel_reverse[4] = true; - } else { - kernel_reverse[0] = true; - kernel_reverse[1] = true; - kernel_reverse[2] = true; - kernel_reverse[3] = false; - kernel_reverse[4] = false; - } - - DSizes strides; - for (int i = 0; i < NumDims; i++) { - strides[i] = 1; - } - if (isColMajor) { - strides[1] = stridePlanes; - strides[2] = strideRows; - strides[3] = strideCols; - } else { - strides[NumDims - 2] = stridePlanes; - strides[NumDims - 3] = strideRows; - strides[NumDims - 4] = strideCols; - } - return choose( - Cond::Layout == ColMajor>(), - input.reshape(input_dims) - .contract( - output_backward.extract_volume_patches( - inputPlanes, inputRows, inputCols, 1, - 1, 1, stridePlanes, strideRows, strideCols, - - padding_ztop, padding_zbottom, padding_top, - padding_bottom, padding_left, padding_right) - .reshape(pre_contract_dims), - contract_dims) - .reshape(kernel_dims) - .reverse(kernel_reverse) - .shuffle(kernel_shuffle), - output_backward.extract_volume_patches( - inputPlanes, inputRows, inputCols, 1, 1, 1, - stridePlanes, strideRows, strideCols, padding_ztop, - padding_zbottom, padding_top, padding_bottom, - padding_left, padding_right) - .reshape(pre_contract_dims) - .contract(input.reshape(input_dims), contract_dims) - .reshape(kernel_dims) - .reverse(kernel_reverse) - .shuffle(kernel_shuffle)); -} - -} // end namespace Eigen - -#endif // EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_CUBOID_CONVOLUTIONS_H diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h deleted file mode 100644 index 0f4ada246c..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/BackwardSpatialConvolutions.h +++ /dev/null @@ -1,351 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2015 Ke Yang -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_SPATIAL_CONVOLUTIONS_H -#define EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_SPATIAL_CONVOLUTIONS_H - -namespace Eigen { - -/** SpatialConvolutionBackwardInput - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Computes the backprop for the input of a 2D convolution. - * - * The output_backward parameter is expected to be a tensor with a rank of 3 or more (channels, height, width, and optionally others) - * The kernel parameter is expected to be a 4D tensor (filters, channels, kernel_height, kernel_width) - * The output_backward and the kernel must both be in col-major layout. The result will also be in col-major layout. - * - * If in_stride > 1, then applies convolution with holes (aka atrous convolution), sampling every in_stride input pixels. - * - * The result can be assigned to a tensor of rank equal to the rank of the output_backward. The dimensions of the result will be filters, height, width (and others if applicable). - * - * It is possible to swap the order of the width and height dimensions provided that the same order is used in the input, the kernel, and the output. - * - */ - -template -EIGEN_ALWAYS_INLINE -static const typename internal::conditional< - internal::traits::Layout == ColMajor, - TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorContractionOp::Index>, 2>, const TensorReshapingOp::Index, 3>, const TensorReverseOp, const Kernel> >, const TensorReshapingOp::Index, 3>, const TensorImagePatchOp > > >, - TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorContractionOp::Index>, 2>, const TensorReshapingOp::Index, 3>, const TensorImagePatchOp >, const TensorReshapingOp::Index, 3>, const TensorReverseOp, const Kernel> > > > >::type -SpatialConvolutionBackwardInput(const Kernel& kernel, const OutputBackward& output_backward, typename internal::traits::Index inputRows, typename internal::traits::Index inputCols, const DenseIndex stride = 1, const DenseIndex in_stride = 1) { - - typedef typename internal::traits::Index TensorIndex; - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > kern(kernel); - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > out(output_backward); - - EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE); - - static const bool isColMajor = (internal::traits::Layout == ColMajor); - - static const int NumDims = internal::traits::NumDimensions; - - // Number of filters to apply. This is the same as the output depth of the result - const TensorIndex kernelFilters = isColMajor ? kern.dimensions()[0] : kern.dimensions()[3]; - // Number of channels. This is the same as the input depth. - const TensorIndex kernelChannels = isColMajor ? kern.dimensions()[1] : kern.dimensions()[2]; - const TensorIndex kernelRows = isColMajor ? kern.dimensions()[2] : kern.dimensions()[1]; - const TensorIndex kernelCols = isColMajor ? kern.dimensions()[3] : kern.dimensions()[0]; - - // This is the effective kernel size, taking into account the (in_stride - 1) zero-values - // inserted between consecutive kernel elements in atrous convolution - const TensorIndex kernelRowsEff = kernelRows + (kernelRows - 1) * (in_stride - 1); - const TensorIndex kernelColsEff = kernelCols + (kernelCols - 1) * (in_stride - 1); - - const TensorIndex outputRows = isColMajor ? output_backward.dimension(1) : output_backward.dimension(NumDims - 2); - const TensorIndex outputCols = isColMajor ? output_backward.dimension(2) : output_backward.dimension(NumDims - 3); - - // Computing the forward padding - const TensorIndex forward_pad_top = ((outputRows - 1) * stride + kernelRowsEff - inputRows) / 2; - const TensorIndex forward_pad_left = ((outputCols - 1) * stride + kernelColsEff - inputCols) / 2; - - const TensorIndex padding_top = kernelRowsEff - 1 - forward_pad_top; - const TensorIndex padding_left = kernelColsEff - 1 - forward_pad_left; - const TensorIndex padding_bottom = inputRows + kernelRowsEff - 1 - (outputRows - 1) * stride - 1 - padding_top; - const TensorIndex padding_right = inputCols + kernelColsEff - 1 - (outputCols - 1) * stride - 1 - padding_left; - - eigen_assert(padding_top >= 0); - eigen_assert(padding_left >= 0); - eigen_assert(padding_bottom >= 0); - eigen_assert(padding_right >= 0); - - // The kernel has dimensions filters X channels X patch_rows X patch_cols - // We need to reverse the kernel along dimensions corresponding to rows and - // cols. - // TODO(yangke): we can make things slightly faster by collapsing the dimensions - // where we don't reverse. Try that once we have a faster compiler. - array kernel_reverse; - if (isColMajor) { - kernel_reverse[0] = false; - kernel_reverse[1] = false; - kernel_reverse[2] = true; - kernel_reverse[3] = true; - } else { - kernel_reverse[0] = true; - kernel_reverse[1] = true; - kernel_reverse[2] = false; - kernel_reverse[3] = false; - } - - DSizes kernel_dims; - if (isColMajor) { - kernel_dims[0] = kernelFilters; - kernel_dims[1] = kernelChannels; - kernel_dims[2] = kernelRows * kernelCols; - } else { - kernel_dims[0] = kernelRows * kernelCols; - kernel_dims[1] = kernelChannels; - kernel_dims[2] = kernelFilters; - } - - // The output_backward has dimensions out_depth X out_rows X out_cols X OTHERS - // When we extract the image patches from output_backward, it will have dimensions - // out_depth X (patch_rows * patch_cols) X (input_rows * input_cols * OTHERS) - DSizes pre_contract_dims; - if (isColMajor) { - pre_contract_dims[0] = kernelFilters; - pre_contract_dims[1] = kernelRows * kernelCols; - pre_contract_dims[2] = inputRows * inputCols; - for (int i = 3; i < NumDims; ++i) { - pre_contract_dims[2] *= out.dimension(i); - } - } else { - pre_contract_dims[2] = kernelFilters; - pre_contract_dims[1] = kernelRows * kernelCols; - pre_contract_dims[0] = inputRows * inputCols; - for (int i = 0; i < NumDims - 3; ++i) { - pre_contract_dims[0] *= out.dimension(i); - } - } - - // We will contract along dimensions (0, 2) in kernel and (0, 1) in - // output_backward, if this is col-major, and - // dimensions (0, 2) in kernel and (1, 2) in output_backward, if this row-major. - array, 2> contract_dims; - if (isColMajor) { - // col-major: kernel.contract(output.patches) - contract_dims[0] = IndexPair(0, 0); - contract_dims[1] = IndexPair(2, 1); - } else { - // row-major: output.patches.contract(kernel) - contract_dims[0] = IndexPair(1, 0); - contract_dims[1] = IndexPair(2, 2); - } - - // Post contraction, the dimensions of the input_backprop is - // channels X input_rows X input_cols X OTHERS - DSizes post_contract_dims; - if (isColMajor) { - post_contract_dims[0] = kernelChannels; - post_contract_dims[1] = inputRows; - post_contract_dims[2] = inputCols; - for (int i = 3; i < NumDims; ++i) { - post_contract_dims[i] = out.dimension(i); - } - } else { - post_contract_dims[NumDims - 1] = kernelChannels; - post_contract_dims[NumDims - 2] = inputRows; - post_contract_dims[NumDims - 3] = inputCols; - for (int i = 0; i < NumDims - 3; ++i) { - post_contract_dims[i] = out.dimension(i); - } - } - - return choose(Cond::Layout == ColMajor>(), - kernel.reverse(kernel_reverse).reshape(kernel_dims).contract(output_backward.extract_image_patches(kernelRows, kernelCols, 1, 1, in_stride, in_stride, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims), contract_dims).reshape(post_contract_dims), - output_backward.extract_image_patches(kernelRows, kernelCols, 1, 1, in_stride, in_stride, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims).contract(kernel.reverse(kernel_reverse).reshape(kernel_dims), contract_dims).reshape(post_contract_dims)); -} - - -/** SpatialConvolutionBackwardKernel - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Computes the backprop for the filter of a 2D convolution. - * - * The output_backward parameter is expected to be a tensor with a rank of 3 or more (channels, height, width, and optionally others) - * The kernel parameter is expected to be a 4D tensor (filters, channels, kernel_height, kernel_width) - * The output_backward and the kernel must both be in col-major layout. The result will also be in col-major layout. - * - * If in_stride > 1, then applies convolution with holes (aka atrous convolution), sampling every in_stride input pixels. - * - * The result can be assigned to a tensor of rank equal to the rank of the output_backward. The dimensions of the result will be filters, height, width (and others if applicable). - * - * It is possible to swap the order of the width and height dimensions provided that the same order is used in the input, the kernel, and the output. - * - */ -// TODO(gpapan): Resolve a bug in TensorContractionInputMapper at SpatialConvolutions.h that yangke circumvented by using .reshape().reshape(). -// This can significantly accelerate SpatialConvolutionBackwardKernel. - -template -EIGEN_ALWAYS_INLINE -static const typename internal::conditional< - internal::traits::Layout == ColMajor, - const TensorShufflingOp::Index, 4>, const TensorReverseOp, const TensorReshapingOp::Index, 4>, const TensorContractionOp::Index>, 2>, const TensorReshapingOp::Index, 3>, const Input>, const TensorReshapingOp::Index, 4>, const TensorReshapingOp::Index, 4>, const TensorImagePatchOp > > > > > >, - const TensorShufflingOp::Index, 4>, const TensorReverseOp, const TensorReshapingOp::Index, 4>, const TensorContractionOp::Index>, 2>, const TensorReshapingOp::Index, 4>, const TensorReshapingOp::Index, 4>, const TensorImagePatchOp > >, const TensorReshapingOp::Index, 3>, const Input> > > > > >::type -SpatialConvolutionBackwardKernel(const Input& input, const OutputBackward& output_backward, typename internal::traits::Index kernelRows, typename internal::traits::Index kernelCols, const DenseIndex stride = 1, const DenseIndex in_stride = 1) { - - typedef typename internal::traits::Index TensorIndex; - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input); - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > out(output_backward); - - EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE); - - // stride and in_stride cannot both be larger than 1 - eigen_assert(!(stride > 1 && in_stride > 1)); - - static const bool isColMajor = (internal::traits::Layout == ColMajor); - - static const int NumDims = internal::traits::NumDimensions; - EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == internal::traits::NumDimensions, YOU_MADE_A_PROGRAMMING_MISTAKE); - - const TensorIndex inputRows = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2); - const TensorIndex inputCols = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3); - - const TensorIndex outputRows = isColMajor ? output_backward.dimension(1) : output_backward.dimension(NumDims - 2); - const TensorIndex outputCols = isColMajor ? output_backward.dimension(2) : output_backward.dimension(NumDims - 3); - - // Number of filters to apply. This is the same as the output depth of the result - const TensorIndex kernelFilters = isColMajor ? out.dimensions()[0] : out.dimensions()[NumDims - 1]; - - // Number of channels. This is the same as the input depth. - const TensorIndex kernelChannels = isColMajor ? in.dimensions()[0] : in.dimensions()[NumDims - 1]; - - // This is the effective kernel size, taking into account the (in_stride - 1) zero-values - // inserted between consecutive kernel elements in atrous convolution - const TensorIndex kernelRowsEff = kernelRows + (kernelRows - 1) * (in_stride - 1); - const TensorIndex kernelColsEff = kernelCols + (kernelCols - 1) * (in_stride - 1); - - // Computing the forward padding - const TensorIndex forward_pad_top = ((outputRows - 1) * stride + kernelRowsEff - inputRows) / 2; - const TensorIndex forward_pad_left = ((outputCols - 1) * stride + kernelColsEff - inputCols) / 2; - - // TODO: factor out the padding computation. - const TensorIndex padding_top = kernelRowsEff - 1 - forward_pad_top; - const TensorIndex padding_left = kernelColsEff - 1 - forward_pad_left; - const TensorIndex padding_bottom = inputRows + kernelRowsEff - 1 - (outputRows - 1) * stride - 1 - padding_top; - const TensorIndex padding_right = inputCols + kernelColsEff - 1 - (outputCols - 1) * stride - 1 - padding_left; - - eigen_assert(padding_top >= 0); - eigen_assert(padding_left >= 0); - eigen_assert(padding_bottom >= 0); - eigen_assert(padding_right >= 0); - - // The output_backward has dimensions out_depth X out_rows X out_cols X OTHERS - // When we extract the image patches from output_backward (with input as the - // kernel), it will have dimensions - // (out_depth) X (input_rows * input_cols) X (kernel_rows * kernel_cols) X OTHERS - DSizes pre_contract_dims; - if (isColMajor) { - pre_contract_dims[0] = kernelFilters; - pre_contract_dims[1] = inputRows * inputCols; - pre_contract_dims[2] = kernelRows * kernelCols; - pre_contract_dims[3] = 1; - for (int i = 3; i < NumDims; ++i) { - pre_contract_dims[3] *= out.dimension(i); - } - } else { - pre_contract_dims[3] = kernelFilters; - pre_contract_dims[2] = inputRows * inputCols; - pre_contract_dims[1] = kernelRows * kernelCols; - pre_contract_dims[0] = 1; - for (int i = 0; i < NumDims - 3; ++i) { - pre_contract_dims[0] *= out.dimension(i); - } - } - - // The input has dimensions in_depth X (input_rows * input_cols) X OTHERS - DSizes input_dims; - if (isColMajor) { - input_dims[0] = kernelChannels; - input_dims[1] = inputRows * inputCols; - input_dims[2] = 1; - for (int i = 3; i < NumDims; ++i) { - input_dims[2] *= in.dimension(i); - } - eigen_assert(input_dims[2] == pre_contract_dims[3]); - } else { - input_dims[2] = kernelChannels; - input_dims[1] = inputRows * inputCols; - input_dims[0] = 1; - for (int i = 0; i < NumDims - 3; ++i) { - input_dims[0] *= in.dimension(i); - } - eigen_assert(input_dims[0] == pre_contract_dims[0]); - } - - // We will contract along dimensions (1, 2) in and (1, 3) in out, if - // this is col-major. - // For row-major, it's dimensions (0, 1) in and (0, 2) in out. - array, 2> contract_dims; - if (isColMajor) { - // col-major: in.contract(output.patches) - contract_dims[0] = IndexPair(1, 1); - contract_dims[1] = IndexPair(2, 3); - } else { - // row-major: output.patches.contract(in) - contract_dims[0] = IndexPair(0, 0); - contract_dims[1] = IndexPair(2, 1); - } - - // After the contraction, the kernel will have dimension - // in_depth X out_depth X kernel_rows X kernel_cols - // We will need to shuffle the first two dimensions and reverse the latter - // two dimensions. - // The end shape is - // out_depth X in_shape X kernel_rows X kernel_cols - - // This is the shape of the kernel *before* the shuffling. - DSizes kernel_dims; - if (isColMajor) { - kernel_dims[0] = kernelChannels; - kernel_dims[1] = kernelFilters; - kernel_dims[2] = kernelRows; - kernel_dims[3] = kernelCols; - } else { - kernel_dims[0] = kernelCols; - kernel_dims[1] = kernelRows; - kernel_dims[2] = kernelFilters; - kernel_dims[3] = kernelChannels; - } - - array kernel_shuffle; - if (isColMajor) { - kernel_shuffle[0] = 1; - kernel_shuffle[1] = 0; - kernel_shuffle[2] = 2; - kernel_shuffle[3] = 3; - } else { - kernel_shuffle[0] = 0; - kernel_shuffle[1] = 1; - kernel_shuffle[2] = 3; - kernel_shuffle[3] = 2; - } - - array kernel_reverse; - if (isColMajor) { - kernel_reverse[0] = false; - kernel_reverse[1] = false; - kernel_reverse[2] = true; - kernel_reverse[3] = true; - } else { - kernel_reverse[0] = true; - kernel_reverse[1] = true; - kernel_reverse[2] = false; - kernel_reverse[3] = false; - } - - return choose(Cond::Layout == ColMajor>(), - input.reshape(input_dims).contract(output_backward.extract_image_patches(inputRows, inputCols, in_stride, in_stride, 1, 1, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims).reshape(pre_contract_dims), contract_dims).reshape(kernel_dims).reverse(kernel_reverse).shuffle(kernel_shuffle), - output_backward.extract_image_patches(inputRows, inputCols, in_stride, in_stride, 1, 1, stride, stride, padding_top, padding_bottom, padding_left, padding_right, 0).reshape(pre_contract_dims).reshape(pre_contract_dims).contract(input.reshape(input_dims), contract_dims).reshape(kernel_dims).reverse(kernel_reverse).shuffle(kernel_shuffle)); -} - -} // end namespace Eigen - -#endif // EIGEN_CXX11_NEURAL_NETWORKS_BACKWARD_SPATIAL_CONVOLUTIONS_H diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h deleted file mode 100644 index dfb9dcedba..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/CuboidConvolution.h +++ /dev/null @@ -1,179 +0,0 @@ -#ifndef EIGEN_CXX11_SRC_NEURAL_NETWORKS_CUBOID_CONVOLUTION_H -#define EIGEN_CXX11_SRC_NEURAL_NETWORKS_CUBOID_CONVOLUTION_H - -#include "Patch3d.h" - -namespace Eigen { - -/** CuboidConvolution - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Applies a 3D convolution over a multichannel input voxel block. - * - * The input parameter is expected to be a tensor with a rank of 4 or more (channels, depth, height, width, and optionally others). - * The kernel parameter is expected to be a 5D tensor (filters, channels, kernel_depth, kernel_height, kernel_width). - * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be filters, depth, height, width (and others if applicable). - * - * The input and kernel have to be in the same layout, and both row-major and - * col-major are supported. The shapes given above are for col-major layout. - * For row-major, all dimensions should be reversed. - * - * It is possible to swap the order of the depth, width, and height dimensions provided that the same order is used in the input, the kernel, and the output. - */ -template -EIGEN_ALWAYS_INLINE -static const typename internal::conditional < - internal::traits::Layout == ColMajor, - TensorReshapingOp< - const DSizes::Index, - internal::traits::NumDimensions>, - const TensorContractionOp< - const array::Index>, 1>, - const TensorReshapingOp< - const DSizes::Index, 2>, - const Kernel>, - const TensorReshapingOp< - const DSizes::Index, 2>, - const TensorVolumePatchOp > > >, - TensorReshapingOp< - const DSizes::Index, - internal::traits::NumDimensions>, - const TensorContractionOp< - const array::Index>, 1>, - const TensorReshapingOp< - const DSizes::Index, 2>, - const TensorVolumePatchOp > , - const TensorReshapingOp< - const DSizes::Index, 2>, - const Kernel> > > >::type -CuboidConvolution(const Input& input, const Kernel& kernel, - const DenseIndex stridePlanes = 1, - const DenseIndex strideRows = 1, - const DenseIndex strideCols = 1, - const PaddingType padding_type = PADDING_SAME) { - typedef typename internal::traits::Index TensorIndex; - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input); - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > kern(kernel); - - EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE); - static const bool isColMajor = (internal::traits::Layout == ColMajor); - static const int NumDims = internal::traits::NumDimensions; - - // Number of filters to apply. This is the same as the output depth of the result. - const TensorIndex kernelFilters = isColMajor ? kern.dimensions()[0] : kern.dimensions()[4]; - const TensorIndex kernelChannels = isColMajor ? kern.dimensions()[1] : kern.dimensions()[3]; - - // Spatial size of the kernel. - const TensorIndex kernelDepth = isColMajor ? kern.dimensions()[2] : kern.dimensions()[2]; - const TensorIndex kernelRows = isColMajor ? kern.dimensions()[3] : kern.dimensions()[1]; - const TensorIndex kernelCols = isColMajor ? kern.dimensions()[4] : kern.dimensions()[0]; - - if (isColMajor) { - eigen_assert(kernelChannels == in.dimension(0)); - } else { - eigen_assert(kernelChannels == in.dimension(NumDims - 1)); - } - - const TensorIndex inputPlanes = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2); - const TensorIndex inputRows = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3); - const TensorIndex inputCols = isColMajor ? in.dimension(3) : in.dimension(NumDims - 4); - - const float stride_planes_f = static_cast(stridePlanes); - const float stride_rows_f = static_cast(strideRows); - const float stride_cols_f = static_cast(strideCols); - TensorIndex out_depth; - TensorIndex out_height; - TensorIndex out_width; - switch (padding_type) { - case PADDING_VALID: - out_depth = ceil((inputPlanes - kernelDepth + 1.f) / stride_planes_f); - out_height = ceil((inputRows - kernelRows + 1.f) / stride_rows_f); - out_width = ceil((inputCols - kernelCols + 1.f) / stride_cols_f); - break; - case PADDING_SAME: - out_depth = ceil(inputPlanes / stride_planes_f); - out_height = ceil(inputRows / stride_rows_f); - out_width = ceil(inputCols / stride_cols_f); - break; - default: - eigen_assert(false && "unexpected padding"); - } - - DSizes kernel_dims; - if (isColMajor) { - kernel_dims[0] = kernelFilters; - kernel_dims[1] = kernelChannels * kernelDepth * kernelRows * kernelCols; - } else { - kernel_dims[0] = kernelChannels * kernelDepth * kernelRows * kernelCols; - kernel_dims[1] = kernelFilters; - } - - // Molds the output of the patch extraction result into a 2D tensor: - // - the first dimension (dims[0]): the patch values to be multiplied with the kernels - // - the second dimension (dims[1]): everything else - DSizes pre_contract_dims; - if (isColMajor) { - pre_contract_dims[0] = kernelChannels * kernelDepth * kernelRows * kernelCols; - pre_contract_dims[1] = out_depth * out_height * out_width; - for (int i = 4; i < NumDims; ++i) { - pre_contract_dims[1] *= in.dimension(i); - } - } else { - pre_contract_dims[1] = kernelChannels * kernelDepth * kernelRows * kernelCols; - pre_contract_dims[0] = out_depth * out_height * out_width; - for (int i = 0; i < NumDims - 4; ++i) { - pre_contract_dims[0] *= in.dimension(i); - } - } - - array, 1> contract_dims; - contract_dims[0] = IndexPair(1, 0); - - // Molds the output of the contraction into the shape expected by the user - // (assuming ColMajor): - // - 1st dim: kernel filters - // - 2nd dim: output depth - // - 3nd dim: output height - // - 4rd dim: output width - // - 5th dim and beyond: everything else including batch size - DSizes post_contract_dims; - if (isColMajor) { - post_contract_dims[0] = kernelFilters; - post_contract_dims[1] = out_depth; - post_contract_dims[2] = out_height; - post_contract_dims[3] = out_width; - for (int i = 4; i < NumDims; ++i) { - post_contract_dims[i] = in.dimension(i); - } - } else { - post_contract_dims[NumDims - 1] = kernelFilters; - post_contract_dims[NumDims - 2] = out_depth; - post_contract_dims[NumDims - 3] = out_height; - post_contract_dims[NumDims - 4] = out_width; - for (int i = 0; i < NumDims - 4; ++i) { - post_contract_dims[i] = in.dimension(i); - } - } - - return choose( - Cond::Layout == ColMajor>(), - kernel.reshape(kernel_dims) - .contract(input.extract_volume_patches( - kernelDepth, kernelRows, kernelCols, stridePlanes, - strideRows, strideCols, padding_type) - .reshape(pre_contract_dims), - contract_dims) - .reshape(post_contract_dims), - input.extract_volume_patches(kernelDepth, kernelRows, kernelCols, - stridePlanes, strideRows, strideCols, - padding_type) - .reshape(pre_contract_dims) - .contract(kernel.reshape(kernel_dims), contract_dims) - .reshape(post_contract_dims)); -} - -} // end namespace Eigen - -#endif // EIGEN_CXX11_SRC_NEURAL_NETWORKS_CUBOID_CONVOLUTION_H diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Patch3d.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Patch3d.h deleted file mode 100644 index 2864f83299..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Patch3d.h +++ /dev/null @@ -1,240 +0,0 @@ -#ifndef EIGEN_CXX11_SRC_NEURAL_NETWORKS_PATCH3D_H -#define EIGEN_CXX11_SRC_NEURAL_NETWORKS_PATCH3D_H - -#if not defined(__CUDACC__) -#include -#endif - -namespace Eigen { -namespace internal { - -/** Extract3DPatches - * \ingroup CXX11_NeuralNetworksModule - * - * \brief Extracts 3D patches from a multichannel input volume. - * - * The input parameter is expected to be a tensor with a rank of 4 or more - * (channels, depth, height, width, optional others in col-major, and the - * reverse order in row-major). - - * The return value will be a tensor of 3 more dimension than the input tensor. - * In col-major, the first 4 dimensions of the result are: channels, patch_depth, - * patch_height, patch_width. The next dimensions will identify the patch - * position on the 3D grid of extracted patches: z, y, x. The remaining - * dimensions, if any, will be the same as the 'other' dimensions of the input - * tensor. - */ - -template -EIGEN_ALWAYS_INLINE static const TensorStridingOp< - const array::Index, - internal::traits::NumDimensions + 3>, - const TensorReshapingOp< - const DSizes::Index, - internal::traits::NumDimensions + 3>, - const TensorPatchOp< - const DSizes::Index, - internal::traits::NumDimensions>, - const TensorPaddingOp< - const array::Index>, - internal::traits::NumDimensions>, - const Input> > > > -Extract3DPatches( - const Input& input, const DenseIndex patchPlanes, - const DenseIndex patchRows, const DenseIndex patchCols, - const DenseIndex stridePlanes, const DenseIndex strideRows, - const DenseIndex strideCols, - const DenseIndex paddingZTop, const DenseIndex paddingZBottom, - const DenseIndex paddingTop, const DenseIndex paddingBottom, - const DenseIndex paddingLeft, const DenseIndex paddingRight, - const typename internal::traits::Scalar padding_value = 0) { - - typedef typename internal::traits::Index TensorIndex; - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input); - - EIGEN_STATIC_ASSERT(internal::traits::NumDimensions >= 4, YOU_MADE_A_PROGRAMMING_MISTAKE); - - static const bool isColMajor = (internal::traits::Layout == ColMajor); - static const int NumDims = internal::traits::NumDimensions; - static const int ExtDims = NumDims + 3; - - // Tensor size after patch extraction. We add three dimensions to unpack the - // linear patch index into a 3D grid over which stride() can work. - DSizes pre_stride_dims; - - if (isColMajor) { - pre_stride_dims[0] = in.dimension(0); - pre_stride_dims[1] = patchPlanes; - pre_stride_dims[2] = patchRows; - pre_stride_dims[3] = patchCols; - } else { - pre_stride_dims[ExtDims - 1] = in.dimension(NumDims - 1); - pre_stride_dims[ExtDims - 4] = patchCols; - pre_stride_dims[ExtDims - 3] = patchRows; - pre_stride_dims[ExtDims - 2] = patchPlanes; - } - - const TensorIndex inputPlanes = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2); - const TensorIndex inputRows = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3); - const TensorIndex inputCols = isColMajor ? in.dimension(3) : in.dimension(NumDims - 4); - - array, NumDims> paddings; - for (int i = 0; i < NumDims; ++i) { - paddings[i] = IndexPair(0, 0); - } - - paddings[isColMajor ? 1 : (NumDims - 2)] = IndexPair(paddingZTop, paddingZBottom); - paddings[isColMajor ? 2 : (NumDims - 3)] = IndexPair(paddingTop, paddingBottom); - paddings[isColMajor ? 3 : (NumDims - 4)] = IndexPair(paddingLeft, paddingRight); - - pre_stride_dims[isColMajor ? 4 : (ExtDims - 5)] = inputPlanes + paddingZBottom + paddingZTop - patchPlanes + 1; - pre_stride_dims[isColMajor ? 5 : (ExtDims - 6)] = inputRows + paddingTop + paddingBottom - patchRows + 1; - pre_stride_dims[isColMajor ? 6 : (ExtDims - 7)] = inputCols + paddingLeft + paddingRight - patchCols + 1; - - if (isColMajor) { - for (int i = 7; i < NumDims + 3; ++i) { - pre_stride_dims[i] = in.dimension(i - 3); - } - } else { - for (int i = 0; i < NumDims - 4; ++i) { - pre_stride_dims[i] = in.dimension(i); - } - } - - DSizes patch_dims; - if (isColMajor) { - patch_dims[0] = in.dimension(0); - patch_dims[1] = patchPlanes; - patch_dims[2] = patchRows; - patch_dims[3] = patchCols; - for (int i = 4; i < NumDims; ++i) { - patch_dims[i] = 1; - } - } else { - patch_dims[NumDims - 1] = in.dimension(NumDims - 1); - patch_dims[NumDims - 4] = patchCols; - patch_dims[NumDims - 3] = patchRows; - patch_dims[NumDims - 2] = patchPlanes; - for (int i = 0; i < NumDims - 4; i++) { - patch_dims[i] = 1; - } - } - - array strides; - if (isColMajor) { - // No striding within the patches. - for (int i = 0; i < 4; ++i) { - strides[i] = 1; - } - // Apply striding in the spatial patch grid dimensions only. - strides[4] = stridePlanes; - strides[5] = strideRows; - strides[6] = strideCols; - // No striding in the remaining dimensions (batches, ...). - for (int i = 7; i < NumDims + 3; i++) { - strides[i] = 1; - } - } else { - // No striding within the patches. - for (int i = 1; i <= 4; ++i) { - strides[ExtDims - i] = 1; - } - // Apply striding in the spatial patch grid dimensions only. - strides[ExtDims - 7] = strideCols; - strides[ExtDims - 6] = strideRows; - strides[ExtDims - 5] = stridePlanes; - // No striding in the remaining dimensions (batches, ...). - for (int i = 0; i < NumDims - 4; i++) { - strides[i] = 1; - } - } - - // TODO(mjanusz): Consider getting rid of pad(), and stride() and extend - // extract_patches to take additional parameters for padding/striding, - // similarly to extract_image_patches. - return input.pad(paddings, padding_value).extract_patches(patch_dims).reshape(pre_stride_dims).stride(strides); -} - - -template -EIGEN_ALWAYS_INLINE static const TensorStridingOp< - const array::Index, - internal::traits::NumDimensions + 3>, - const TensorReshapingOp< - const DSizes::Index, - internal::traits::NumDimensions + 3>, - const TensorPatchOp< - const DSizes::Index, - internal::traits::NumDimensions>, - const TensorPaddingOp< - const array::Index>, - internal::traits::NumDimensions>, - const Input> > > > -Extract3DPatches( - const Input& input, const DenseIndex patchPlanes, - const DenseIndex patchRows, const DenseIndex patchCols, - const DenseIndex stridePlanes, const DenseIndex strideRows, - const DenseIndex strideCols, const PaddingType padding_type, - const typename internal::traits::Scalar padding_value = 0) { - typedef typename internal::traits::Index TensorIndex; - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input); - - EIGEN_STATIC_ASSERT(internal::traits::NumDimensions >= 4, YOU_MADE_A_PROGRAMMING_MISTAKE); - - static const bool isColMajor = (internal::traits::Layout == ColMajor); - static const int NumDims = internal::traits::NumDimensions; - - const TensorIndex inputPlanes = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2); - const TensorIndex inputRows = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3); - const TensorIndex inputCols = isColMajor ? in.dimension(3) : in.dimension(NumDims - 4); - - switch (padding_type) { - case PADDING_VALID: - // No padding in any dimension. - return Extract3DPatches(input, patchPlanes, patchRows, patchCols, - stridePlanes, strideRows, strideCols, - 0, 0, 0, 0, 0, 0, padding_value); - case PADDING_SAME: { - // The side of the tensor before striding should be just the expected - // output times the stride. - const TensorIndex size_z = ceil(inputPlanes / static_cast(stridePlanes)) * stridePlanes; - const TensorIndex size_y = ceil(inputRows / static_cast(strideRows)) * strideRows; - const TensorIndex size_x = ceil(inputCols / static_cast(strideCols)) * strideCols; - - // The size of the patch space is going to be: padded_input_size - patch_size + 1. - // This has to match the expected size before striding (pre_stride_dims). - // The deltas below extend the input to the expected size. - const TensorIndex dz = size_z + patchPlanes - 1 - inputPlanes; - const TensorIndex dy = size_y + patchRows - 1 - inputRows; - const TensorIndex dx = size_x + patchCols - 1 - inputCols; - - return Extract3DPatches(input, patchPlanes, patchRows, patchCols, - stridePlanes, strideRows, strideCols, - dz - dz / 2, dz / 2, - dy - dy / 2, dy / 2, - dx - dx / 2, dx / 2, - padding_value); - } - default: - eigen_assert(false && "unexpected padding"); - // unreachable code to avoid missing return warning. - return Extract3DPatches(input, patchPlanes, patchRows, patchCols, - stridePlanes, strideRows, strideCols, - 0, 0, 0, 0, 0, 0, padding_value); - } -} - -// TODO(mjanusz): Switch this to a 'using' alias once CUDA supports C++11. -template -struct Extract3DPatchesType { - typedef const TensorStridingOp< const array::Index, internal::traits::NumDimensions + 3>, - const TensorReshapingOp< const DSizes::Index, internal::traits::NumDimensions + 3>, - const TensorPatchOp< const DSizes::Index, internal::traits::NumDimensions>, - const TensorPaddingOp< const array< IndexPair::Index>, internal::traits::NumDimensions>, - const Input> > > > type; -}; - -} // end namespace internal -} // end namespace Eigen - -#endif // EIGEN_CXX11_SRC_NEURAL_NETWORKS_PATCH3D_H diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h deleted file mode 100644 index 942b060ba7..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/Pooling.h +++ /dev/null @@ -1,433 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -#ifndef EIGEN_CXX11_NEURAL_NETWORKS_POOLING_H -#define EIGEN_CXX11_NEURAL_NETWORKS_POOLING_H - -#include "Patch3d.h" - -namespace Eigen { - -/** SpatialMaxPooling - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Applies a max-pooling over a multichannel input image. - * - * The input parameter is expected to be a with a rank of 4 (channels, height, width, others in col-major, and the reverse of that in row-major). - * - * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, height, width, and others (in col-major, and the reverse of that if the input was row-major). - * - * The order of the width and height dimensions can be swapped if needed. - * -*/ -#if !defined(EIGEN_HAS_INDEX_LIST) -template -EIGEN_ALWAYS_INLINE -static const TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorReductionOp::Scalar>::type>, const Eigen::array, const TensorImagePatchOp > > -#else -template -EIGEN_ALWAYS_INLINE -static const TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorReductionOp::Scalar>::type>, typename internal::conditional::Layout == ColMajor, const Eigen::IndexList, Eigen::type2index<2> >, const Eigen::IndexList, Eigen::type2index<3> > >::type, const TensorImagePatchOp > > -#endif -SpatialMaxPooling(const Input& input, DenseIndex patchRows, DenseIndex patchCols, - DenseIndex strideRows, DenseIndex strideCols, const PaddingType padding_type, - DenseIndex in_strideRows = 1, DenseIndex in_strideCols = 1) -{ - EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 4, YOU_MADE_A_PROGRAMMING_MISTAKE); - - typedef typename internal::traits::Index TensorIndex; - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input); - - const DenseIndex patchRowsEff = patchRows + (patchRows - 1) * (in_strideRows - 1); - const DenseIndex patchColsEff = patchCols + (patchCols - 1) * (in_strideCols - 1); - - static const bool isColMajor = (internal::traits::Layout == ColMajor); - static const int idxRows = isColMajor ? 1 : 2; - static const int idxCols = isColMajor ? 2 : 1; - - // Molds the output of the reduction into the shape expected by the user. - // (assuming col-major): - // - 1st dim: channels - // - 2nd dim: output height - // - 3rd dim: output width - // - 4th dim and beyond: everything else including batch size - Eigen::DSizes::NumDimensions> post_reduce_dims; - post_reduce_dims[0] = in.dimension(0); - if (padding_type == PADDING_VALID) { - post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRowsEff + 1.f) / static_cast(strideRows)); - post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchColsEff + 1.f) / static_cast(strideCols)); - } else { - post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast(strideRows)); - post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast(strideCols)); - } - post_reduce_dims[3] = in.dimension(3); - -#if !defined(EIGEN_HAS_INDEX_LIST) - // nvcc doesn't support cxx11 - Eigen::array reduction_dims; - if (isColMajor) { - reduction_dims[0] = 1; - reduction_dims[1] = 2; - } else { - reduction_dims[0] = 2; - reduction_dims[1] = 3; - } -#else - // Take advantage of cxx11 to give the compiler information it can use to - // optimize the code. - typename internal::conditional::Layout == ColMajor, const Eigen::IndexList, Eigen::type2index<2> >, const Eigen::IndexList, Eigen::type2index<3> > >::type reduction_dims; -#endif - - return input.extract_image_patches(patchRows, patchCols, strideRows, strideCols, in_strideRows, in_strideCols, padding_type, -Eigen::NumTraits::Scalar>::type>::highest()).maximum(reduction_dims).reshape(post_reduce_dims); -} - -/** CuboidMaxPooling - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Applies a max-pooling over a multichannel input volume. - * - * The input parameter is expected to be a tensor with a rank of 5 (channels, depth, height, width, others in col-major, and the reverse of that in row-major). - * - * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, depth, height, width, and others (in col-major, and the reverse of that if the input was row-major). - * - * The order of the depth, width and height dimensions can be swapped if needed. - * -*/ -#if !defined(EIGEN_HAS_INDEX_LIST) -template -EIGEN_ALWAYS_INLINE static const TensorReshapingOp< - const Eigen::DSizes::NumDimensions>, - const TensorReductionOp< - internal::MaxReducer, const Eigen::array, - const TensorReshapingOp< - const Eigen::DSizes, - const TensorVolumePatchOp > > > -#else -template -EIGEN_ALWAYS_INLINE static const TensorReshapingOp< - const Eigen::DSizes::NumDimensions>, - const TensorReductionOp< - internal::MaxReducer, - const Eigen::IndexList >, - const TensorReshapingOp< - const Eigen::DSizes, - const TensorVolumePatchOp > > > -#endif -CuboidMaxPooling(const Input& input, DenseIndex patchPlanes, - DenseIndex patchRows, DenseIndex patchCols, - DenseIndex stridePlanes, DenseIndex strideRows, - DenseIndex strideCols, const PaddingType padding_type) { - EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 5, YOU_MADE_A_PROGRAMMING_MISTAKE); - static const bool isColMajor = (internal::traits::Layout == ColMajor); - - typedef typename internal::traits::Index TensorIndex; - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input); - - static const int idxPlanes = isColMajor ? 1 : 3; - static const int idxRows = 2; - static const int idxCols = isColMajor ? 3 : 1; - - // Molds the output of the reduction into the shape expected by the used - // (assuming col-major): - // - 1st dim: channels - // - 2nd dim: output depth - // - 3rd dim: output height - // - 4th dim: output width - // - 5th dim and beyond: everything else including batch size - Eigen::DSizes::NumDimensions> post_reduce_dims; - post_reduce_dims[0] = in.dimension(0); - if (padding_type == PADDING_VALID) { - post_reduce_dims[idxPlanes] = numext::ceil((in.dimension(idxPlanes) - patchPlanes + 1.f) / static_cast(stridePlanes)); - post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRows + 1.f) / static_cast(strideRows)); - post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchCols + 1.f) / static_cast(strideCols)); - } else { - post_reduce_dims[idxPlanes] = numext::ceil(in.dimension(idxPlanes) / static_cast(stridePlanes)); - post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast(strideRows)); - post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast(strideCols)); - } - post_reduce_dims[4] = in.dimension(4); - - Eigen::DSizes pre_reduce_dims; - pre_reduce_dims[1] = patchRows * patchCols * patchPlanes; - if (isColMajor) { - pre_reduce_dims[0] = post_reduce_dims[0]; - pre_reduce_dims[2] = post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3] * post_reduce_dims[4]; - } else { - pre_reduce_dims[0] = post_reduce_dims[0] * post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3]; - pre_reduce_dims[2] = post_reduce_dims[4]; - } - -#if !defined(EIGEN_HAS_INDEX_LIST) - // nvcc doesn't support cxx11 - Eigen::array reduction_dims; - reduction_dims[0] = 1; -#else - // Take advantage of cxx11 to give the compiler information it can use to - // optimize the code. - Eigen::IndexList > reduction_dims; -#endif - return input.extract_volume_patches(patchPlanes, patchRows, patchCols, - stridePlanes, strideRows, strideCols, - padding_type, -Eigen::NumTraits::highest()) - .reshape(pre_reduce_dims) - .maximum(reduction_dims) - .reshape(post_reduce_dims); -} - - -/** SpatialAvgPooling - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Applies an average pooling over a multichannel input image. - * - * The input parameter is expected to be a tensor with a rank of 4 (channels, height, width, others in col-major, and the reverse of that in row-major). - * - * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, height, width, and others (in col-major, and the reverse of that if the input was row-major). - * - * The order of the width and height dimensions can be swapped if needed. - * -*/ -namespace internal { - -template struct AvgPoolMeanReducer -{ -#if (EIGEN_ARCH_i386 || EIGEN_ARCH_x86_64) && !defined(__CUDACC__) - // We only support packet access for floats. - static const bool PacketAccess = internal::is_same::value; -#else - static const bool PacketAccess = false; -#endif - static const bool IsStateful = true; - - EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE AvgPoolMeanReducer() : scalarCount_(0) { - typedef typename packet_traits::type Packet; - packetCount_ = pset1(0.0); - } - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reduce(const T t, T* accum) { - if (t != -Eigen::NumTraits::highest()) { - (*accum) = (*accum) + t; - scalarCount_++; - } - } - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T initialize() const { - return static_cast(0); - } - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalize(const T accum) const { - eigen_assert(scalarCount_ > 0); - return accum / scalarCount_; - } - -#if (EIGEN_ARCH_i386 || EIGEN_ARCH_x86_64) && !defined(__CUDACC__) -#ifdef EIGEN_VECTORIZE_AVX -#define pequal(a,b) _mm256_cmp_ps(a,b,_CMP_EQ_UQ) -#define psel(a,b,false_mask) _mm256_blendv_ps(a,b,false_mask) -#else -#define pequal(a,b) _mm_cmpeq_ps(a,b) -#define psel(a,b,false_mask) _mm_or_ps(_mm_andnot_ps(false_mask, a), _mm_and_ps(false_mask, b)) -#endif - - template - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void reducePacket(const Packet& p, Packet* accum) { - reducePacketWithType(static_cast(0), p, accum); - } - - template - void reducePacketWithType(T, const Packet& p, Packet* accum) { - Packet skip_mask = pequal(p, pset1(-Eigen::NumTraits::highest())); - (*accum) = padd(*accum, psel(p, pset1(0), skip_mask)); - packetCount_ = padd(packetCount_, psel(pset1(1), pset1(0), skip_mask)); - } - - template - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet initializePacket() const { - return pset1(0); - } - - template - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE Packet finalizePacket(const Packet& vaccum) const { - return pdiv(vaccum, packetCount_); - } - template - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE T finalizeBoth(const T saccum, const Packet& vaccum) const { - return (saccum + predux(vaccum)) / (scalarCount_ + predux(packetCount_)); - } -#endif - - protected: - typedef typename packet_traits::type Packet; - int scalarCount_; - Packet packetCount_; -}; - -} // namespace internal - -#if !defined(EIGEN_HAS_INDEX_LIST) -template -EIGEN_ALWAYS_INLINE -static const TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorReductionOp::Scalar>::type>, const Eigen::array, const TensorImagePatchOp > > -#else -template -EIGEN_ALWAYS_INLINE -static const TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorReductionOp::Scalar>::type>, typename internal::conditional::Layout == ColMajor, const Eigen::IndexList, Eigen::type2index<2> >, const Eigen::IndexList, Eigen::type2index<3> > >::type, const TensorImagePatchOp > > -#endif -SpatialAvgPooling(const Input& input, DenseIndex patchRows, DenseIndex patchCols, - DenseIndex strideRows, DenseIndex strideCols, const PaddingType padding_type, - DenseIndex in_strideRows = 1, DenseIndex in_strideCols = 1) -{ - EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 4, YOU_MADE_A_PROGRAMMING_MISTAKE); - - typedef typename internal::traits::Index TensorIndex; - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input); - - const DenseIndex patchRowsEff = patchRows + (patchRows - 1) * (in_strideRows - 1); - const DenseIndex patchColsEff = patchCols + (patchCols - 1) * (in_strideCols - 1); - - static const bool isColMajor = (internal::traits::Layout == ColMajor); - static const int idxRows = isColMajor ? 1 : 2; - static const int idxCols = isColMajor ? 2 : 1; - - // Molds the output of the reduction into the shape expected by the user. - // (assuming col-major): - // - 1st dim: channels - // - 2nd dim: output height - // - 3rd dim: output width - // - 4th dim and beyond: everything else including batch size - Eigen::DSizes::NumDimensions> post_reduce_dims; - post_reduce_dims[0] = in.dimension(0); - if (padding_type == PADDING_VALID) { - post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRowsEff + 1.f) / static_cast(strideRows)); - post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchColsEff + 1.f) / static_cast(strideCols)); - } else { - post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast(strideRows)); - post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast(strideCols)); - } - post_reduce_dims[3] = in.dimension(3); - - typedef typename internal::remove_const::Scalar>::type CoeffReturnType; - internal::AvgPoolMeanReducer mean_with_nan; - -#if !defined(EIGEN_HAS_INDEX_LIST) - // nvcc doesn't support cxx11 - Eigen::array reduction_dims; - if (isColMajor) { - reduction_dims[0] = 1; - reduction_dims[1] = 2; - } else { - reduction_dims[0] = 2; - reduction_dims[1] = 3; - } -#else - // Take advantage of cxx11 to give the compiler information it can use to - // optimize the code. - typename internal::conditional::Layout == ColMajor, const Eigen::IndexList, Eigen::type2index<2> >, const Eigen::IndexList, Eigen::type2index<3> > >::type reduction_dims; -#endif - return input.extract_image_patches(patchRows, patchCols, strideRows, strideCols, in_strideRows, in_strideCols, padding_type, -Eigen::NumTraits::Scalar>::type>::highest()).reduce(reduction_dims, mean_with_nan).reshape(post_reduce_dims); -} - - -/** CuboidAvgPooling - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Applies an average pooling over a multichannel input volume. - * - * The input parameter is expected to be a tensor with a rank of 5 (channels, depth, height, width, others, and the reverse of that in row-major). - * - * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be channels, depth, width, and others (in col-major, and the reverse of that if the input was row-major). - * - * The order of the depth, width and height dimensions can be swapped if needed. - * -*/ -#if !defined(EIGEN_HAS_INDEX_LIST) -template -EIGEN_ALWAYS_INLINE static const TensorReshapingOp< - const Eigen::DSizes::NumDimensions>, - const TensorReductionOp< - internal::AvgPoolMeanReducer, const Eigen::array, - const TensorReshapingOp< - const Eigen::DSizes, - const TensorVolumePatchOp > > > -#else -template -EIGEN_ALWAYS_INLINE static const TensorReshapingOp< - const Eigen::DSizes::NumDimensions>, - const TensorReductionOp< - internal::AvgPoolMeanReducer, - const Eigen::IndexList >, - const TensorReshapingOp< - const Eigen::DSizes, - const TensorVolumePatchOp > > > -#endif -CuboidAvgPooling(const Input& input, DenseIndex patchPlanes, - DenseIndex patchRows, DenseIndex patchCols, - DenseIndex stridePlanes, DenseIndex strideRows, - DenseIndex strideCols, const PaddingType padding_type) { - EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 5, YOU_MADE_A_PROGRAMMING_MISTAKE); - static const bool isColMajor = (internal::traits::Layout == ColMajor); - - typedef typename internal::traits::Index TensorIndex; - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input); - - static const int idxPlanes = isColMajor ? 1 : 3; - static const int idxRows = 2; - static const int idxCols = isColMajor ? 3 : 1; - // Molds the output of the reduction into the shape expected by the used - // (assuming col-major): - // - 1st dim: channels - // - 2nd dim: outupt depth - // - 3rd dim: output height - // - 4th dim: output width - // - 5th dim and beyond: everything else including batch size - Eigen::DSizes::NumDimensions> post_reduce_dims; - post_reduce_dims[0] = in.dimension(0); - if (padding_type == PADDING_VALID) { - post_reduce_dims[idxPlanes] = numext::ceil((in.dimension(idxPlanes) - patchPlanes + 1.f) / static_cast(stridePlanes)); - post_reduce_dims[idxRows] = numext::ceil((in.dimension(idxRows) - patchRows + 1.f) / static_cast(strideRows)); - post_reduce_dims[idxCols] = numext::ceil((in.dimension(idxCols) - patchCols + 1.f) / static_cast(strideCols)); - } else { - post_reduce_dims[idxPlanes] = numext::ceil(in.dimension(idxPlanes) / static_cast(stridePlanes)); - post_reduce_dims[idxRows] = numext::ceil(in.dimension(idxRows) / static_cast(strideRows)); - post_reduce_dims[idxCols] = numext::ceil(in.dimension(idxCols) / static_cast(strideCols)); - } - post_reduce_dims[4] = in.dimension(4); - - Eigen::DSizes pre_reduce_dims; - pre_reduce_dims[1] = patchRows * patchCols * patchPlanes; - if (isColMajor) { - pre_reduce_dims[0] = post_reduce_dims[0]; - pre_reduce_dims[2] = post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3] * post_reduce_dims[4]; - } else { - pre_reduce_dims[0] = post_reduce_dims[0] * post_reduce_dims[1] * post_reduce_dims[2] * post_reduce_dims[3]; - pre_reduce_dims[2] = post_reduce_dims[4]; - } - - typedef typename internal::remove_const::Scalar>::type CoeffReturnType; - internal::AvgPoolMeanReducer mean_with_nan; - -#if !defined(EIGEN_HAS_INDEX_LIST) - // nvcc doesn't support cxx11 - Eigen::array reduction_dims; - reduction_dims[0] = 1; -#else - // Take advantage of cxx11 to give the compiler information it can use to - // optimize the code. - Eigen::IndexList > reduction_dims; -#endif - return input.extract_volume_patches(patchPlanes, patchRows, patchCols, - stridePlanes, strideRows, strideCols, - padding_type, -Eigen::NumTraits::highest()) - .reshape(pre_reduce_dims) - .reduce(reduction_dims, mean_with_nan) - .reshape(post_reduce_dims); -} - -} // end namespace Eigen - -#endif // EIGEN_CXX11_NEURAL_NETWORKS_POOLING_H diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SoftMax.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SoftMax.h deleted file mode 100644 index f0e21ab9c2..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SoftMax.h +++ /dev/null @@ -1,83 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -#ifndef EIGEN_CXX11_NEURAL_NETWORKS_SOFTMAX_H -#define EIGEN_CXX11_NEURAL_NETWORKS_SOFTMAX_H - -namespace Eigen { - -/** SoftMax - * \ingroup CXX11_NeuralNetworks_Module - * - * \brief Applies a softmax - * - * The input parameter is expected to be a col-major tensor with a rank of 2 (depth and other). - * - * The result can be assigned to a tensor of rank and dimensions equal to that of the input. The result will be laid out in col-major order. - * -*/ - -namespace { -class SoftmaxOp { - public: - EIGEN_ALWAYS_INLINE SoftmaxOp(const float beta) : beta_(beta) { } - - template EIGEN_ALWAYS_INLINE - typename Input::Dimensions dimensions(const Input& input) const { - return input.dimensions(); - } - - template - void eval(const Input& input, Output& output, const Device& device) const - { -#if !defined(EIGEN_HAS_INDEX_LIST) - // nvcc doesn't support cxx11 - Eigen::array::Index, 1> depth_dim; - depth_dim[0] = 0; - Eigen::array::Index, 2> bcast; - bcast[0] = dimensions(input)[0]; - bcast[1] = 1; - DSizes::Index, 2> dims2d; - dims2d[0] = 1; - dims2d[1] = dimensions(input)[1]; -#else - // Take advantage of cxx11 to give the compiler information it can use to - // optimize the code. - Eigen::IndexList> depth_dim; - Eigen::IndexList> bcast; - bcast.set(0, dimensions(input)[0]); - Eigen::IndexList, typename internal::traits::Index> dims2d; - dims2d.set(1, dimensions(input)[1]); -#endif - - output.device(device) = ((input - input.maximum(depth_dim).eval().reshape(dims2d).broadcast(bcast)) * beta_).exp(); - output.device(device) = output / (output.sum(depth_dim).eval().reshape(dims2d).broadcast(bcast)); - } - - private: - const float beta_; -}; -} - - -template -EIGEN_ALWAYS_INLINE -static const TensorCustomUnaryOp -SoftMax(const Input& input, const float beta) -{ - EIGEN_STATIC_ASSERT(internal::traits::Layout == ColMajor, YOU_MADE_A_PROGRAMMING_MISTAKE); - EIGEN_STATIC_ASSERT(internal::traits::NumDimensions == 2, YOU_MADE_A_PROGRAMMING_MISTAKE); - - const SoftmaxOp op(beta); - return input.customOp(op); -} - - -} // end namespace Eigen - -#endif // EIGEN_CXX11_NEURAL_NETWORKS_SOFTMAX_H diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SpatialConvolutions.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SpatialConvolutions.h deleted file mode 100644 index 8e2ddca6b5..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/SpatialConvolutions.h +++ /dev/null @@ -1,775 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -#ifndef EIGEN_CXX11_NEURAL_NETWORKS_SPATIAL_CONVOLUTIONS_H -#define EIGEN_CXX11_NEURAL_NETWORKS_SPATIAL_CONVOLUTIONS_H - -namespace Eigen { - -namespace internal { - -// These optimizations require vector instructions -#ifdef EIGEN_VECTORIZE - -// TODO: Consolidate this part of the code with the image patch extraction code -// since they are both very similar. -template -class TensorContractionInputMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> -{ - public: - typedef TensorContractionInputMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> Self; - typedef TensorContractionSubMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> SubMapper; - typedef SubMapper VectorMapper; - typedef SubMapper LinearMapper; - typedef Scalar_ Scalar; - typedef typename packet_traits::type Packet; - - TensorContractionInputMapper(const TensorEvaluator >, Device>& tensor, - const nocontract_t&, const nocontract_t&, - const contract_t&, const contract_t&) - : m_impl(tensor.impl().impl()) - { - Index patch_rows; - Index patch_depth; - if (internal::traits::Layout == ColMajor) { - patch_depth = tensor.impl().dimensions()[0]; - patch_rows = tensor.impl().dimensions()[1]; - m_patch_cols = tensor.impl().dimensions()[2]; - m_num_patches = tensor.impl().dimensions()[3]; - } else { - static const int NumDims = tensor.impl().dimensions().size(); - patch_depth = tensor.impl().dimensions()[NumDims - 1]; - patch_rows = tensor.impl().dimensions()[NumDims - 2]; - m_patch_cols = tensor.impl().dimensions()[NumDims - 3]; - m_num_patches = tensor.impl().dimensions()[NumDims - 4]; - } - m_patch_row_inflate_strides = tensor.impl().rowInflateStride(); - m_patch_col_inflate_strides = tensor.impl().colInflateStride(); - - m_colStride = patch_rows; - - m_outputRows = tensor.impl().outputRows(); - m_row_strides = tensor.impl().userRowStride(); - m_col_strides = tensor.impl().userColStride(); - - m_in_row_strides = tensor.impl().userInRowStride(); - m_in_col_strides = tensor.impl().userInColStride(); - - if (internal::traits::Layout == ColMajor) { - m_inputRows = tensor.impl().impl().dimensions()[1]; - m_inputCols = tensor.impl().impl().dimensions()[2]; - } else { - static const int NumDims = tensor.impl().impl().dimensions().size(); - m_inputRows = tensor.impl().impl().dimensions()[NumDims - 2]; - m_inputCols = tensor.impl().impl().dimensions()[NumDims - 3]; - } - - m_rowInputStride = patch_depth; - m_colInputStride = patch_depth * m_inputRows; - m_patchInputStride = patch_depth * m_inputRows * m_inputCols; - - m_rowPaddingTop = tensor.impl().rowPaddingTop(); - m_colPaddingLeft = tensor.impl().colPaddingLeft(); - - m_fastInputRowStride = internal::TensorIntDivisor(m_patch_row_inflate_strides); - m_fastInputColStride = internal::TensorIntDivisor(m_patch_col_inflate_strides); - m_fastNumPatches = internal::TensorIntDivisor(m_num_patches); - m_fastColStride = internal::TensorIntDivisor(m_colStride); - m_fastOutputRows = internal::TensorIntDivisor(m_outputRows); - m_fastDimZero = internal::TensorIntDivisor(patch_depth); - } - - TensorContractionInputMapper(const TensorContractionInputMapper& base_mapper) : - m_impl(base_mapper.m_impl) { - m_patch_cols = base_mapper.m_patch_cols; - m_num_patches = base_mapper.m_num_patches; - m_patch_row_inflate_strides = base_mapper.m_patch_row_inflate_strides; - m_patch_col_inflate_strides = base_mapper.m_patch_col_inflate_strides; - - m_colStride = base_mapper.m_colStride; - - m_rowInputStride = base_mapper.m_rowInputStride; - m_colInputStride = base_mapper.m_colInputStride; - m_patchInputStride = base_mapper.m_patchInputStride; - - m_inputRows = base_mapper.m_inputRows; - m_inputCols = base_mapper.m_inputCols; - - m_outputRows = base_mapper.m_outputRows; - m_row_strides = base_mapper.m_row_strides; - m_col_strides = base_mapper.m_col_strides; - - m_in_row_strides = base_mapper.m_in_row_strides; - m_in_col_strides = base_mapper.m_in_col_strides; - - m_rowPaddingTop = base_mapper.m_rowPaddingTop; - m_colPaddingLeft = base_mapper.m_colPaddingLeft; - - m_fastInputRowStride = base_mapper.m_fastInputRowStride; - m_fastInputColStride = base_mapper.m_fastInputColStride; - m_fastNumPatches = base_mapper.m_fastNumPatches; - m_fastColStride = base_mapper.m_fastColStride; - m_fastOutputRows = base_mapper.m_fastOutputRows; - m_fastDimZero = base_mapper.m_fastDimZero; - } - - // If true, turns off some optimizations for loading packets since the image - // patches are "non-standard" such as there are non-trivial strides or - // inflations in the input. - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE bool nonStandardPatches() const { - return m_in_row_strides != 1 || m_in_col_strides != 1 || m_patch_row_inflate_strides != 1 || m_patch_col_inflate_strides != 1; - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE SubMapper getSubMapper(Index i, Index j) const { - return SubMapper(*this, i, j); - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE LinearMapper getLinearMapper(Index i, Index j) const { - return LinearMapper(*this, i, j); - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Scalar operator()(Index row) const { - Index rowIndex, colIndex, otherIndex; - computeBaseIndices(0, rowIndex, colIndex, otherIndex); - return loadCoeff(row, rowIndex, colIndex, otherIndex); - } - - // Load the coefficient at the patchIndex location instead of the usual m_rowIndex, - // m_colIndex, m_otherIndex. This is currently only used by the gpu code. EIGEN_DEVICE_FUNC - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar operator()(Index row, Index patchIndex) const { - Index rowIndex, colIndex, otherIndex; - computeBaseIndices(patchIndex, rowIndex, colIndex, otherIndex); - return loadCoeff(row, rowIndex, colIndex, otherIndex); - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Packet loadPacket(Index row) const { - Index rowIndex, colIndex, otherIndex; - computeBaseIndices(0, rowIndex, colIndex, otherIndex); - return loadPacket(row, rowIndex, colIndex, otherIndex); - } - - // Load the packet at the patchIndex location instead of the usual m_rowIndex, - // m_colIndex, m_otherIndex. This is currently only used by the gpu code. - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Packet loadPacket(Index row, Index patchIndex) const { - Index rowIndex, colIndex, otherIndex; - computeBaseIndices(patchIndex, rowIndex, colIndex, otherIndex); - return loadPacket(row, rowIndex, colIndex, otherIndex); - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE const TensorEvaluator& impl() const { return m_impl; } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Index patchDepth() const { return m_rowInputStride; } - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Index patchRows() const { return m_colStride; } - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Index patchCols() const { return m_patch_cols; } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Packet packetNoPadding(const Index depth, const Index baseIndex) const { - const Index inputIndex = depth + baseIndex; - return m_impl.template packet(inputIndex); - } - - private: - friend class TensorContractionSubMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment>; - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar loadCoeff(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const { - // Find the offset of the element wrt the location of the first element. - const Index patchOffset = patchId / m_fastDimZero; - - const Index colOffset = patchOffset / m_fastColStride; - const Index inputCol = colIndex + colOffset * m_in_col_strides; - const Index origInputCol = (m_patch_col_inflate_strides == 1) ? inputCol : ((inputCol >= 0) ? (inputCol / m_fastInputColStride) : 0); - const Index rowOffset = patchOffset - colOffset * m_colStride; - const Index inputRow = rowIndex + rowOffset * m_in_row_strides; - const Index origInputRow = (m_patch_row_inflate_strides == 1) ? inputRow : ((inputRow >= 0) ? (inputRow / m_fastInputRowStride) : 0); - if (origInputCol < 0 | origInputRow < 0 | origInputCol >= m_inputCols | origInputRow >= m_inputRows | - (inputCol != origInputCol * m_patch_col_inflate_strides) | (inputRow != origInputRow * m_patch_row_inflate_strides)) { - return Scalar(0); - } - const Index depth = patchId - patchOffset * patchDepth(); - const Index inputIndex = depth + origInputRow * m_rowInputStride + origInputCol * m_colInputStride + otherIndex; - return m_impl.coeff(inputIndex); - } - - EIGEN_DEVICE_FUNC - EIGEN_STRONG_INLINE Scalar loadCoeffStandard(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const { - eigen_assert(!nonStandardPatches()); - - // Find the offset of the element wrt the location of the first element. - const Index patchOffset = patchId / m_fastDimZero; - - const Index colOffset = patchOffset / m_fastColStride; - const Index inputCol = colIndex + colOffset; - const Index rowOffset = patchOffset - colOffset * m_colStride; - const Index inputRow = rowIndex + rowOffset; - if (inputCol < 0 || inputCol >= m_inputCols || inputRow < 0 || inputRow >= m_inputRows) { - return Scalar(0); - } - const Index depth = patchId - patchOffset * patchDepth(); - const Index inputIndex = depth + inputRow * m_rowInputStride + inputCol * m_colInputStride + otherIndex; - return m_impl.coeff(inputIndex); - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Packet loadPacket(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const { - const Index packetSize = internal::unpacket_traits::size; - EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE) - eigen_assert(patchId < patchDepth()*patchRows()*m_patch_cols); - - if (nonStandardPatches()) { - return packetWithPossibleZero(patchId, rowIndex, colIndex, otherIndex); - } - return loadPacketStandard(patchId, rowIndex, colIndex, otherIndex); - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Packet loadPacketStandard(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const { - const Index packetSize = internal::unpacket_traits::size; - EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE) - eigen_assert(patchId < patchDepth()*patchRows()*m_patch_cols); - - eigen_assert(!nonStandardPatches()); - - if ((patchDepth() % packetSize) == 0) { - return loadPacketFast(patchId, rowIndex, colIndex, otherIndex); - } - else { - const Index patchOffsets[2] = {patchId / m_fastDimZero, (patchId + packetSize - 1) / m_fastDimZero}; - - const Index colOffsets[2] = {patchOffsets[0] / m_fastColStride, patchOffsets[1] / m_fastColStride}; - - const Index inputCols[2] = {colIndex + colOffsets[0], colIndex + colOffsets[1]}; - if (inputCols[0] >= m_inputCols | inputCols[1] < 0) { - // all zeros - return internal::pset1(Scalar(0)); - } - - if (inputCols[0] == inputCols[1]) { - const Index rowOffsets[2] = {patchOffsets[0] - colOffsets[0]*m_colStride, patchOffsets[1] - colOffsets[1]*m_colStride}; - eigen_assert(rowOffsets[0] <= rowOffsets[1]); - const Index inputRows[2] = {rowIndex + rowOffsets[0], rowIndex + rowOffsets[1]}; - - if (inputRows[0] >= m_inputRows | inputRows[1] < 0) { - // all zeros - return internal::pset1(Scalar(0)); - } - - if (inputRows[0] >= 0 & inputRows[1] < m_inputRows) { - // no padding - const Index depth = patchId - patchOffsets[0] * patchDepth(); - const Index inputIndex = depth + inputRows[0] * m_rowInputStride + inputCols[0] * m_colInputStride + otherIndex; - return m_impl.template packet(inputIndex); - } - } - } - return packetWithPossibleZero(patchId, rowIndex, colIndex, otherIndex); - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Packet loadPacketFast(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const { - const Index packetSize = internal::unpacket_traits::size; - EIGEN_STATIC_ASSERT(packetSize > 1, YOU_MADE_A_PROGRAMMING_MISTAKE) - eigen_assert(patchId < patchDepth()*patchRows()*m_patch_cols); - - eigen_assert(!nonStandardPatches()); - eigen_assert((patchDepth() % packetSize) == 0); - // Find the offset of the element wrt the location of the first element. - const Index patchOffset = patchId / m_fastDimZero; - eigen_assert((patchId + packetSize - 1) / m_fastDimZero == patchOffset); - - const Index colOffset = patchOffset / m_fastColStride; - const Index inputCol = colIndex + colOffset; - const Index rowOffset = patchOffset - colOffset*m_colStride; - const Index inputRow = rowIndex + rowOffset; - if (inputCol < 0 | inputRow < 0 | inputCol >= m_inputCols | inputRow >= m_inputRows) { - // all zeros - return internal::pset1(Scalar(0)); - } - // no padding - const Index depth = patchId - patchOffset * patchDepth(); - const Index inputIndex = depth + inputRow * m_rowInputStride + inputCol * m_colInputStride + otherIndex; - return m_impl.template packet(inputIndex); - } - - EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet packetWithPossibleZero(Index patchId, Index rowIndex, Index colIndex, Index otherIndex) const - { - const int packetSize = internal::unpacket_traits::size; - EIGEN_ALIGN_MAX typename internal::remove_const::type values[packetSize]; - for (int i = 0; i < packetSize; ++i) { - values[i] = loadCoeff(patchId+i, rowIndex, colIndex, otherIndex); - } - Packet rslt = internal::pload(values); - return rslt; - } - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void computeBaseIndices(Index patchIndex, Index& rowIndex, Index& colIndex, Index& otherIndex) const { - const int NumInputDims = array_size::Dimensions>::value; - otherIndex = (NumInputDims == 3) ? 0 : patchIndex / m_fastNumPatches; - const Index patch2DIndex = (NumInputDims == 3) ? patchIndex : (patchIndex - otherIndex * m_num_patches); - otherIndex *= m_patchInputStride; - colIndex = patch2DIndex / m_fastOutputRows; - rowIndex = patch2DIndex - colIndex * m_outputRows; - colIndex = colIndex * m_col_strides - m_colPaddingLeft; - rowIndex = rowIndex * m_row_strides - m_rowPaddingTop; - } - - Index m_patch_cols; // number of colums in the patch - Index m_num_patches; // number of patches to extract. - Index m_patch_row_inflate_strides; // the strides for row inflation in the image patch - Index m_patch_col_inflate_strides; // the strides for col inflation in the image patch - // Fast representation of inflation strides. - internal::TensorIntDivisor m_fastInputRowStride; - internal::TensorIntDivisor m_fastInputColStride; - - Index m_otherStride; - Index m_colStride; - internal::TensorIntDivisor m_fastNumPatches; - internal::TensorIntDivisor m_fastColStride; - - Index m_rowInputStride; // row stride in the input tensor - Index m_colInputStride; // col stride in the input tensor - Index m_patchInputStride; // patch stride in the input tensor - - Index m_inputRows; // Number of rows in the input tensor - Index m_inputCols; // Number of cols in the input tensor - - Index m_outputRows; // Number of patch rows - - Index m_row_strides; // User specified row stride - Index m_col_strides; // User specified col stride - - Index m_in_row_strides; // User specified input row stride - Index m_in_col_strides; // User specified input col stride - - Index m_rowPaddingTop; // Row padding - Index m_colPaddingLeft; // Column padding - - internal::TensorIntDivisor m_fastOutputRows; - internal::TensorIntDivisor m_fastDimZero; - - const TensorEvaluator m_impl; -}; - - -template -class TensorContractionSubMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> -{ - public: - typedef Scalar_ Scalar; - typedef typename packet_traits::type Packet; - typedef typename packet_traits::half HalfPacket; - - typedef TensorContractionInputMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> ParentMapper; - typedef TensorContractionSubMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> Self; - typedef Self LinearMapper; - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionSubMapper(const ParentMapper& base_mapper, Index vert_offset, Index horiz_offset) - : m_base_mapper(base_mapper), m_depth_offset(vert_offset), m_col_offset(horiz_offset) { - m_base_mapper.computeBaseIndices(m_col_offset, m_rowIndex, m_colIndex, m_otherIndex); - } - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorContractionSubMapper(const Self& base_mapper, Index vert_offset, Index horiz_offset) - : m_base_mapper(base_mapper.m_base_mapper), m_depth_offset(vert_offset+base_mapper.m_depth_offset), m_col_offset(horiz_offset+base_mapper.m_col_offset) { - m_base_mapper.computeBaseIndices(m_col_offset, m_rowIndex, m_colIndex, m_otherIndex); - } - EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i) const { - return m_base_mapper.loadCoeff(i + m_depth_offset, m_rowIndex, m_colIndex, m_otherIndex); - } - EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar operator()(Index i, Index j) const { - return m_base_mapper(i + m_depth_offset, j + m_col_offset); - } - - EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacket(Index i) const { - return m_base_mapper.loadPacket(i + m_depth_offset, m_rowIndex, m_colIndex, m_otherIndex); - } - EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacket(Index i, Index j) const { - return m_base_mapper.template loadPacket(i + m_depth_offset, j + m_col_offset); - } - - EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Scalar loadCoeffStandard(Index i) const { - return m_base_mapper.loadCoeffStandard(i + m_depth_offset, m_rowIndex, m_colIndex, m_otherIndex); - } - - EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacketFast(Index i) const { - return m_base_mapper.loadPacketFast(i + m_depth_offset, m_rowIndex, m_colIndex, m_otherIndex); - } - EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE Packet loadPacketStandard(Index i) const { - return m_base_mapper.loadPacketStandard(i + m_depth_offset, m_rowIndex, m_colIndex, m_otherIndex); - } - template - EIGEN_DEVICE_FUNC bool aligned(Index) const { - return false; - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE bool nonStandardPatches() const { - return m_base_mapper.nonStandardPatches(); - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Index patchDepth() const { return m_base_mapper.m_rowInputStride; } - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Index patchRows() const { return m_base_mapper.m_colStride; } - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Index patchCols() const { return m_base_mapper.m_patch_cols; } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Packet packetNoPadding(const Index depth, const Index baseIndex) const { - const Index inputIndex = depth + baseIndex; - return m_base_mapper.m_impl.template packet(inputIndex); - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE bool padRow(const Index row) const { - const Index r = m_rowIndex + row; - return r < 0 | r >= m_base_mapper.m_inputRows; - } - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE bool padCol(const Index col) const { - const Index c = m_colIndex + col; - return c < 0 | c >= m_base_mapper.m_inputCols; - } - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Index baseIndex(const Index row, const Index col) const { - const Index r = m_rowIndex + row; - const Index c = m_colIndex + col; - return r * m_base_mapper.m_rowInputStride + c * m_base_mapper.m_colInputStride + m_otherIndex; - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Index rowOffset() const { - const Index patchOffset = m_depth_offset / m_base_mapper.m_fastDimZero; - const Index colOffset = patchOffset / m_base_mapper.m_fastColStride; - return patchOffset-colOffset*m_base_mapper.m_colStride; - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Index colOffset() const { - const Index patchOffset = m_depth_offset / m_base_mapper.m_fastDimZero; - const Index colOffset = patchOffset / m_base_mapper.m_fastColStride; - return colOffset; - } - - EIGEN_DEVICE_FUNC - EIGEN_ALWAYS_INLINE Index depthOffset() const { - const Index patchOffset = m_depth_offset % m_base_mapper.patchDepth(); - return patchOffset; - } - - EIGEN_DEVICE_FUNC EIGEN_ALWAYS_INLINE LinearMapper getLinearMapper(Index i, Index j) const { - return LinearMapper(m_base_mapper, i + m_depth_offset, j + m_col_offset); - } - - private: - const ParentMapper& m_base_mapper; // that was a reference before - Index m_depth_offset; // First row in the input matrix - Index m_col_offset; // First col in the input matrix - - Index m_rowIndex; // precomputed row index corresponding to the col offset - Index m_colIndex; // precomputed col index corresponding to the col offset - Index m_otherIndex; // precomputed other index corresponding to the col offset - -}; - - -template -struct gemm_pack_rhs >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment>, nr, ColMajor, false, false> { - - typedef TensorContractionSubMapper >, Device>, nocontract_t, contract_t, packet_size, inner_dim_contiguous, inner_dim_reordered, Alignment> SubMapper; - typedef SubMapper DataMapper; - - static inline Index ceil_div(Index a, Index b) { - return (a + b - 1) / b; - } - - EIGEN_DONT_INLINE void operator()(Scalar* block, const DataMapper& rhs, Index depth, Index cols, Index stride=0, Index offset=0) const { - eigen_assert(stride == 0); - eigen_assert(offset == 0); - - EIGEN_STATIC_ASSERT((nr == 4), YOU_MADE_A_PROGRAMMING_MISTAKE); - typedef typename DataMapper::LinearMapper LinearMapper; - typedef typename packet_traits::type Packet; - - const Index packet_cols4 = (cols/4) * 4; - const Index peeled_k = (depth/packet_size) * packet_size; - const bool non_standard_patches = rhs.nonStandardPatches(); - - for(Index j2=0; j2(ceil_div(peeled_k, patch_rows*patch_depth)+startCol, patch_cols); - - for (Index c = startCol; c < max_cols; ++c) { - eigen_assert(k < peeled_k); - const Index startRow = (c == startCol) ? rhs.rowOffset() : 0; - const Index max_rows = std::min(ceil_div(peeled_k-c*patch_rows*patch_depth, patch_depth)+startRow, patch_rows); - - const bool pad_col0 = dm0.padCol(c); - const bool pad_col1 = dm1.padCol(c); - const bool pad_col2 = dm2.padCol(c); - const bool pad_col3 = dm3.padCol(c); - for (Index r = startRow; r < max_rows; ++r) { - eigen_assert(k < peeled_k); - const bool pad0 = pad_col0 || dm0.padRow(r); - const bool pad1 = pad_col1 || dm1.padRow(r); - const bool pad2 = pad_col2 || dm2.padRow(r); - const bool pad3 = pad_col3 || dm3.padRow(r); - - const Index idx0 = dm0.baseIndex(r, c); - const Index idx1 = dm1.baseIndex(r, c); - const Index idx2 = dm2.baseIndex(r, c); - const Index idx3 = dm3.baseIndex(r, c); - - const Index startDepth = ((c == startCol) && (r == startRow)) ? rhs.depthOffset() : 0; - const Index max_depth = std::min(peeled_k-c*patch_rows*patch_depth-r*patch_depth+startDepth, patch_depth); - eigen_assert(max_depth % packet_size == 0); - for (Index d = startDepth; d < max_depth; d += packet_size) { - eigen_assert(k < peeled_k); - PacketBlock kernel; - kernel.packet[0] = pad0 ? pset1(0) : rhs.packetNoPadding(d, idx0); - kernel.packet[1] = pad1 ? pset1(0) : rhs.packetNoPadding(d, idx1); - kernel.packet[2] = pad2 ? pset1(0) : rhs.packetNoPadding(d, idx2); - kernel.packet[3] = pad3 ? pset1(0) : rhs.packetNoPadding(d, idx3); - ptranspose(kernel); - pstoreu(block+0*packet_size, kernel.packet[0]); - pstoreu(block+1*packet_size, kernel.packet[1]); - pstoreu(block+2*packet_size, kernel.packet[2]); - pstoreu(block+3*packet_size, kernel.packet[3]); - block+=4*packet_size; - k += packet_size; - } - } - } - - for(; k kernel; - kernel.packet[0] = dm0.loadPacketFast(k); - kernel.packet[1] = dm1.loadPacketFast(k); - kernel.packet[2] = dm2.loadPacketFast(k); - kernel.packet[3] = dm3.loadPacketFast(k); - ptranspose(kernel); - pstoreu(block+0*packet_size, kernel.packet[0]); - pstoreu(block+1*packet_size, kernel.packet[1]); - pstoreu(block+2*packet_size, kernel.packet[2]); - pstoreu(block+3*packet_size, kernel.packet[3]); - block+=4*packet_size; - } - } - else { - for(; k kernel; - kernel.packet[0] = dm0.loadPacketStandard(k); - kernel.packet[1] = dm1.loadPacketStandard(k); - kernel.packet[2] = dm2.loadPacketStandard(k); - kernel.packet[3] = dm3.loadPacketStandard(k); - ptranspose(kernel); - pstoreu(block+0*packet_size, kernel.packet[0]); - pstoreu(block+1*packet_size, kernel.packet[1]); - pstoreu(block+2*packet_size, kernel.packet[2]); - pstoreu(block+3*packet_size, kernel.packet[3]); - block+=4*packet_size; - } - } - } - if (!rhs.nonStandardPatches()) { - for(; k 1, then applies convolution with holes (aka atrous convolution), sampling every in_stride input pixels. - * - * The result can be assigned to a tensor of rank equal to the rank of the input. The dimensions of the result will be filters, height, width (and others if applicable). - * - * It is possible to swap the order of the width and height dimensions provided that the same order is used in the input, the kernel, and the output. - * - */ -template -EIGEN_ALWAYS_INLINE -static const typename internal::conditional< - internal::traits::Layout == ColMajor, - TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorContractionOp::Index>, 1>, const TensorReshapingOp::Index, 2>, const Kernel>, const TensorReshapingOp::Index, 2>, const TensorImagePatchOp > > >, - TensorReshapingOp::Index, internal::traits::NumDimensions>, const TensorContractionOp::Index>, 1>, const TensorReshapingOp::Index, 2>, const TensorImagePatchOp >, const TensorReshapingOp::Index, 2>, const Kernel> > > >::type -SpatialConvolution(const Input& input, const Kernel& kernel, const DenseIndex stride = 1, const PaddingType padding_type = PADDING_SAME, const DenseIndex in_stride = 1) { - - typedef typename internal::traits::Index TensorIndex; - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > in(input); - TensorRef::Scalar, internal::traits::NumDimensions, internal::traits::Layout, TensorIndex> > kern(kernel); - - EIGEN_STATIC_ASSERT(internal::traits::Layout == internal::traits::Layout, YOU_MADE_A_PROGRAMMING_MISTAKE); - static const bool isColMajor = (internal::traits::Layout == ColMajor); - - static const int NumDims = internal::traits::NumDimensions; - - // Number of filters to apply. This is the same as the output depth of the result - const TensorIndex kernelFilters = isColMajor ? kern.dimensions()[0] : kern.dimensions()[3]; - // Number of channels. This is the same as the input depth. - const TensorIndex kernelChannels = isColMajor ? kern.dimensions()[1] : kern.dimensions()[2]; - const TensorIndex kernelRows = isColMajor ? kern.dimensions()[2] : kern.dimensions()[1]; - const TensorIndex kernelCols = isColMajor ? kern.dimensions()[3] : kern.dimensions()[0]; - - const DenseIndex kernelRowsEff = kernelRows + (kernelRows - 1) * (in_stride - 1); - const DenseIndex kernelColsEff = kernelCols + (kernelCols - 1) * (in_stride - 1); - - array, 1> contract_dims; - contract_dims[0] = IndexPair(1, 0); - - const TensorIndex InputRows = isColMajor ? in.dimension(1) : in.dimension(NumDims - 2); - const TensorIndex InputCols = isColMajor ? in.dimension(2) : in.dimension(NumDims - 3); - - TensorIndex out_height; - TensorIndex out_width; - switch (padding_type) { - case PADDING_VALID: - out_height = numext::ceil((InputRows - kernelRowsEff + 1.f) / static_cast(stride)); - out_width = numext::ceil((InputCols - kernelColsEff + 1.f) / static_cast(stride)); - break; - case PADDING_SAME: - out_height = numext::ceil(InputRows / static_cast(stride)); - out_width = numext::ceil(InputCols / static_cast(stride)); - break; - default: - eigen_assert(false && "unexpected padding"); - } - - // Molds the output of the patch extraction code into a 2d tensor: - // - the first dimension (dims[0]): the patch values to be multiplied with the kernels - // - the second dimension (dims[1]): everything else - DSizes pre_contract_dims; - if (isColMajor) { - pre_contract_dims[0] = kernelChannels * kernelRows * kernelCols; - pre_contract_dims[1] = out_height * out_width; - for (int i = 3; i < NumDims; ++i) { - pre_contract_dims[1] *= in.dimension(i); - } - } else { - pre_contract_dims[1] = kernelChannels * kernelRows * kernelCols; - pre_contract_dims[0] = out_height * out_width; - for (int i = 0; i < NumDims - 3; ++i) { - pre_contract_dims[0] *= in.dimension(i); - } - } - - // Molds the output of the contraction into the shape expected by the used - // (assuming this is ColMajor): - // - 1st dim: kernel filters - // - 2nd dim: output height - // - 3rd dim: output width - // - 4th dim and beyond: everything else including batch size - DSizes post_contract_dims; - if (isColMajor) { - post_contract_dims[0] = kernelFilters; - post_contract_dims[1] = out_height; - post_contract_dims[2] = out_width; - for (int i = 3; i < NumDims; ++i) { - post_contract_dims[i] = in.dimension(i); - } - } else { - post_contract_dims[NumDims - 1] = kernelFilters; - post_contract_dims[NumDims - 2] = out_height; - post_contract_dims[NumDims - 3] = out_width; - for (int i = 0; i < NumDims - 3; ++i) { - post_contract_dims[i] = in.dimension(i); - } - } - - DSizes kernel_dims; - if (isColMajor) { - kernel_dims[0] = kernelFilters; - kernel_dims[1] = kernelChannels * kernelRows * kernelCols; - } else { - kernel_dims[0] = kernelChannels * kernelRows * kernelCols; - kernel_dims[1] = kernelFilters; - } - // TODO(yangke): choose() is defined in TensorContraction.h -- consider - // moving it to somewhere more "common". - return choose(Cond::Layout == ColMajor>(), - kernel.reshape(kernel_dims).contract(input.extract_image_patches(kernelRows, kernelCols, stride, stride, in_stride, in_stride, padding_type).reshape(pre_contract_dims), contract_dims).reshape(post_contract_dims), - input.extract_image_patches(kernelRows, kernelCols, stride, stride, in_stride, in_stride, padding_type).reshape(pre_contract_dims).contract(kernel.reshape(kernel_dims), contract_dims).reshape(post_contract_dims)); -} - -} // end namespace Eigen - -#endif // EIGEN_CXX11_NEURAL_NETWORKS_SPATIAL_CONVOLUTIONS_H diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/TensorConvolutionByFFT.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/TensorConvolutionByFFT.h deleted file mode 100644 index 0e72173536..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/NeuralNetworks/TensorConvolutionByFFT.h +++ /dev/null @@ -1,289 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2014 Benoit Steiner -// Copyright (C) 2015 Jianwei Cui -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTIONBYFFT_H -#define EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTIONBYFFT_H - -namespace Eigen { - -/** \class TensorConvolutionByFFT - * \ingroup CXX11_Tensor_Module - * - * \brief Tensor convolution class. - * - * - */ -namespace internal { - - -template -struct traits > -{ - // Type promotion to handle the case where the types of the lhs and the rhs are different. - typedef typename promote_storage_type::ret Scalar; - typedef typename packet_traits::type Packet; - typedef typename promote_storage_type::StorageKind, - typename traits::StorageKind>::ret StorageKind; - typedef typename promote_index_type::Index, - typename traits::Index>::type Index; - typedef typename InputXprType::Nested LhsNested; - typedef typename KernelXprType::Nested RhsNested; - typedef typename remove_reference::type _LhsNested; - typedef typename remove_reference::type _RhsNested; - static const int NumDimensions = traits::NumDimensions; - static const int Layout = traits::Layout; - - enum { - Flags = 0, - }; -}; - -template -struct eval, Eigen::Dense> -{ - typedef const TensorConvolutionByFFTOp& type; -}; - -template -struct nested, 1, typename eval >::type> -{ - typedef TensorConvolutionByFFTOp type; -}; - -} // end namespace internal - - - -template -class TensorConvolutionByFFTOp : public TensorBase > -{ - public: - typedef typename Eigen::internal::traits::Scalar Scalar; - typedef typename Eigen::internal::traits::Packet Packet; - typedef typename Eigen::NumTraits::Real RealScalar; - typedef typename internal::promote_storage_type::ret CoeffReturnType; - typedef typename internal::promote_storage_type::ret PacketReturnType; - typedef typename Eigen::internal::nested::type Nested; - typedef typename Eigen::internal::traits::StorageKind StorageKind; - typedef typename Eigen::internal::traits::Index Index; - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorConvolutionByFFTOp(const InputXprType& input, const KernelXprType& kernel, const Indices& dims) - : m_input_xpr(input), m_kernel_xpr(kernel), m_indices(dims) {} - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE - const Indices& indices() const { return m_indices; } - - /** \returns the nested expressions */ - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE - const typename internal::remove_all::type& - inputExpression() const { return m_input_xpr; } - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE - const typename internal::remove_all::type& - kernelExpression() const { return m_kernel_xpr; } - - protected: - typename InputXprType::Nested m_input_xpr; - typename KernelXprType::Nested m_kernel_xpr; - const Indices m_indices; -}; - - -template -struct TensorEvaluator, Device> -{ - typedef TensorConvolutionByFFTOp XprType; - - typedef typename XprType::Scalar Scalar; - typedef typename XprType::CoeffReturnType CoeffReturnType; - typedef typename XprType::PacketReturnType PacketReturnType; - - typedef typename Eigen::NumTraits::Real RealScalar; - - static const int NumDims = internal::array_size::Dimensions>::value; - static const int NumKernelDims = internal::array_size::value; - typedef typename XprType::Index Index; - typedef DSizes Dimensions; - - enum { - IsAligned = TensorEvaluator::IsAligned & - TensorEvaluator::IsAligned, - PacketAccess = false, - BlockAccess = false, - Layout = TensorEvaluator::Layout, - CoordAccess = false, // to be implemented - }; - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE TensorEvaluator(const XprType& op, const Device& device) - : m_inputImpl(op.inputExpression(), device), m_kernelImpl(op.kernelExpression(), device), m_kernelArg(op.kernelExpression()), m_kernel(NULL), m_local_kernel(false), m_device(device) - { - EIGEN_STATIC_ASSERT((static_cast(TensorEvaluator::Layout) == static_cast(TensorEvaluator::Layout)), YOU_MADE_A_PROGRAMMING_MISTAKE); - - const typename TensorEvaluator::Dimensions& input_dims = m_inputImpl.dimensions(); - const typename TensorEvaluator::Dimensions& kernel_dims = m_kernelImpl.dimensions(); - - if (static_cast(Layout) == static_cast(ColMajor)) { - m_inputStride[0] = 1; - for (int i = 1; i < NumDims; ++i) { - m_inputStride[i] = m_inputStride[i - 1] * input_dims[i - 1]; - } - } else { - m_inputStride[NumDims - 1] = 1; - for (int i = NumDims - 2; i >= 0; --i) { - m_inputStride[i] = m_inputStride[i + 1] * input_dims[i + 1]; - } - } - - m_dimensions = m_inputImpl.dimensions(); - if (static_cast(Layout) == static_cast(ColMajor)) { - for (int i = 0; i < NumKernelDims; ++i) { - const Index index = op.indices()[i]; - const Index input_dim = input_dims[index]; - const Index kernel_dim = kernel_dims[i]; - const Index result_dim = input_dim - kernel_dim + 1; - m_dimensions[index] = result_dim; - if (i > 0) { - m_kernelStride[i] = m_kernelStride[i - 1] * kernel_dims[i - 1]; - } else { - m_kernelStride[0] = 1; - } - m_indexStride[i] = m_inputStride[index]; - } - - m_outputStride[0] = 1; - for (int i = 1; i < NumDims; ++i) { - m_outputStride[i] = m_outputStride[i - 1] * m_dimensions[i - 1]; - } - } else { - for (int i = NumKernelDims - 1; i >= 0; --i) { - const Index index = op.indices()[i]; - const Index input_dim = input_dims[index]; - const Index kernel_dim = kernel_dims[i]; - const Index result_dim = input_dim - kernel_dim + 1; - m_dimensions[index] = result_dim; - if (i < NumKernelDims - 1) { - m_kernelStride[i] = m_kernelStride[i + 1] * kernel_dims[i + 1]; - } else { - m_kernelStride[NumKernelDims - 1] = 1; - } - m_indexStride[i] = m_inputStride[index]; - } - - m_outputStride[NumDims - 1] = 1; - for (int i = NumDims - 2; i >= 0; --i) { - m_outputStride[i] = m_outputStride[i + 1] * m_dimensions[i + 1]; - } - } - } - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE const Dimensions& dimensions() const { return m_dimensions; } - - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE bool evalSubExprsIfNeeded(Scalar* data) { - m_inputImpl.evalSubExprsIfNeeded(NULL); - m_kernelImpl.evalSubExprsIfNeeded(NULL); - - typedef typename internal::traits::Index TensorIndex; - - Tensor input(m_inputImpl.dimensions()); - for (int i = 0; i < m_inputImpl.dimensions().TotalSize(); ++i) { - input.data()[i] = m_inputImpl.coeff(i); - } - - Tensor kernel(m_kernelImpl.dimensions()); - for (int i = 0; i < m_kernelImpl.dimensions().TotalSize(); ++i) { - kernel.data()[i] = m_kernelImpl.coeff(i); - } - - array, NumDims> paddings; - for (int i = 0; i < NumDims; ++i) { - paddings[i] = std::make_pair(0, m_inputImpl.dimensions()[i] - m_kernelImpl.dimensions()[i]); - } - - Eigen::array reverse; - for (int i = 0; i < NumKernelDims; ++i) { - reverse[i] = true; - } - - Eigen::array fft; - for (int i = 0; i < NumDims; ++i) { - fft[i] = i; - } - - Eigen::DSizes slice_offsets; - for (int i = 0; i < NumDims; ++i) { - slice_offsets[i] = m_kernelImpl.dimensions()[i] - 1; - } - - Eigen::DSizes slice_extents; - for (int i = 0; i < NumDims; ++i) { - slice_extents[i] = m_inputImpl.dimensions()[i] - m_kernelImpl.dimensions()[i] + 1; - } - - Tensor kernel_variant = kernel.reverse(reverse).pad(paddings); - Tensor, NumDims, Layout, TensorIndex> kernel_fft = kernel_variant.template fft(fft); - //Tensor, NumDims, Layout|IndexType> kernel_fft = kernel.reverse(reverse).pad(paddings).template fft<2>(fft); - Tensor, NumDims, Layout, TensorIndex> input_fft = input.template fft(fft); - Tensor, NumDims, Layout, TensorIndex> prod = (input_fft * kernel_fft).template fft(fft); - Tensor, NumDims, Layout, TensorIndex> tensor_result = prod.slice(slice_offsets, slice_extents); - - for (int i = 0; i < tensor_result.size(); ++i) { - data[i] = std::real(tensor_result.data()[i]); - } - return false; - } - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE void cleanup() { - m_inputImpl.cleanup(); - if (m_local_kernel) { - m_device.deallocate((void*)m_kernel); - m_local_kernel = false; - } - m_kernel = NULL; - } - - void evalTo(typename XprType::Scalar* buffer) { - evalSubExprsIfNeeded(NULL); - for (int i = 0; i < dimensions().TotalSize(); ++i) { - buffer[i] += coeff(i); - } - cleanup(); - } - - EIGEN_DEVICE_FUNC EIGEN_STRONG_INLINE CoeffReturnType coeff(Index index) const - { - CoeffReturnType result = CoeffReturnType(0); - return result; - } - - EIGEN_DEVICE_FUNC Scalar* data() const { return NULL; } - - private: - array m_inputStride; - array m_outputStride; - - array m_indexStride; - array m_kernelStride; - TensorEvaluator m_inputImpl; - TensorEvaluator m_kernelImpl; - Dimensions m_dimensions; - - KernelArgType m_kernelArg; - const Scalar* m_kernel; - bool m_local_kernel; - const Device& m_device; -}; - -} // end namespace Eigen - -#endif // EIGEN_CXX11_TENSOR_TENSOR_CONVOLUTIONBYFFT_H -- GitLab From ca69ddc34b37258534d8327ec55a26b2add6a632 Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Thu, 26 Jul 2018 13:26:37 -0700 Subject: [PATCH 463/519] ResourceVariables shouldn't need twice the memory when initializing. This is safe because all ops which write to resource variables check whether there are other outstanding references to the buffer and copy if that's the case. So we can safely reuse the buffer of initializer tensors even in weird cases such as initializing from a constant (which should never be mutated) or using the same tensor to initialize multiple variables. PiperOrigin-RevId: 206211065 --- .../core/kernels/resource_variable_ops.cc | 68 +++++-------------- 1 file changed, 18 insertions(+), 50 deletions(-) diff --git a/tensorflow/core/kernels/resource_variable_ops.cc b/tensorflow/core/kernels/resource_variable_ops.cc index c5292e1ae1..cab9eb729d 100644 --- a/tensorflow/core/kernels/resource_variable_ops.cc +++ b/tensorflow/core/kernels/resource_variable_ops.cc @@ -213,64 +213,32 @@ class AssignVariableOp : public OpKernel { "Variable and value dtypes don't match; respectively, ", dtype_, " and ", context->input(1).dtype())); Var* variable = nullptr; - OP_REQUIRES_OK( - context, - LookupOrCreateResource( - context, HandleFromInput(context, 0), &variable, - [this, context](Var** ptr) { - *ptr = new Var(dtype_); - PersistentTensor unused; - Tensor* tmp; - AllocatorAttributes attr; - if (!relax_constraints_) { - attr.set_gpu_compatible(true); - attr.set_nic_compatible(true); - } - TF_RETURN_IF_ERROR(context->allocate_persistent( - dtype_, context->input(1).shape(), &unused, &tmp, attr)); - *(*ptr)->tensor() = *tmp; - return Status::OK(); - })); + const Tensor& value = context->input(1); + // Note: every resource-variable-manipulating op assumes copy-on-write + // semantics, and creates a copy of the variable's Tensor if its refcount is + // bigger than 1 when we try to modify it. This means we never need to copy + // the original tensor for AssignVariableOp; even if there are other live + // users of it we know none can modify it so this is always safe (even in + // esoteric cases where the same tensor is used to initialize multiple + // variables or the tensor is a constant this is safe, as future writes will + // trigger copies). + OP_REQUIRES_OK(context, LookupOrCreateResource( + context, HandleFromInput(context, 0), &variable, + [this, &value](Var** ptr) { + *ptr = new Var(dtype_); + *(*ptr)->tensor() = value; + (*ptr)->is_initialized = true; + return Status::OK(); + })); core::ScopedUnref s(variable); - OP_REQUIRES(context, variable->tensor()->dtype() == dtype_, errors::InvalidArgument( "Trying to assign variable with wrong dtype. Expected ", DataTypeString(variable->tensor()->dtype()), " got ", DataTypeString(dtype_))); - - const Tensor& value = context->input(1); - AllocatorAttributes attr; - if (!relax_constraints_) { - attr.set_gpu_compatible(true); - attr.set_nic_compatible(true); - } - - // Copying is unnecessary if we are the last user of the value - // tensor, we can just adopt the input tensor's buffer instead. - std::unique_ptr input_alias = context->forward_input( - 1, OpKernelContext::Params::kNoReservation /*output_index*/, dtype_, - value.shape(), DEVICE_MEMORY, attr); mutex_lock ml(*variable->mu()); variable->is_initialized = true; - if (input_alias) { - *variable->tensor() = *input_alias; - return; - } - - // Need to copy, but maybe we can re-use variable's buffer? - if (!variable->tensor()->RefCountIsOne() || - !variable->tensor()->shape().IsSameSize(value.shape())) { - // Copy to new buffer - PersistentTensor unused; - Tensor* tmp; - OP_REQUIRES_OK(context, context->allocate_persistent( - dtype_, value.shape(), &unused, &tmp, attr)); - *variable->tensor() = *tmp; - } - functor::DenseUpdate copy_functor; - copy_functor(context->eigen_device(), variable->tensor()->flat(), - value.flat()); + *variable->tensor() = value; } private: -- GitLab From 58841d73a4877398fe678d38c56de1ab9c30be68 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 13:28:00 -0700 Subject: [PATCH 464/519] Full set of unittests for the Eager delegate. PiperOrigin-RevId: 206211243 --- .../contrib/lite/delegates/eager/kernel.cc | 48 +++-- .../lite/delegates/eager/kernel_test.cc | 193 +++++++++++++++--- 2 files changed, 206 insertions(+), 35 deletions(-) diff --git a/tensorflow/contrib/lite/delegates/eager/kernel.cc b/tensorflow/contrib/lite/delegates/eager/kernel.cc index 7235867ca5..1727981807 100644 --- a/tensorflow/contrib/lite/delegates/eager/kernel.cc +++ b/tensorflow/contrib/lite/delegates/eager/kernel.cc @@ -46,6 +46,28 @@ limitations under the License. namespace tflite { namespace eager { namespace kernel { + +// Controls the lifetime of tensor handles in a vector. +class VectorOfHandles { + public: + explicit VectorOfHandles(int num_elements) : vector_(num_elements, nullptr) {} + + ~VectorOfHandles() { + for (auto* handle : vector_) { + if (handle) handle->Unref(); + } + } + + tensorflow::gtl::InlinedVector* GetVector() { + return &vector_; + } + + tensorflow::TensorHandle* GetHandle(int index) { return vector_[index]; } + + private: + tensorflow::gtl::InlinedVector vector_; +}; + // Executes the TensorFlow op given by 'op_name', with the attributes specified // in 'nodedef'. Inputs and outputs are given as indices into the 'buffer_map'. tensorflow::Status ExecuteEagerOp(tensorflow::EagerContext* eager_context, @@ -54,8 +76,9 @@ tensorflow::Status ExecuteEagerOp(tensorflow::EagerContext* eager_context, const std::vector& inputs, const std::vector& outputs) { const tensorflow::AttrTypeMap* attr_types; - TF_RETURN_IF_ERROR( - tensorflow::AttrTypeMapForOp(op_name.c_str(), &attr_types)); + TF_RETURN_WITH_CONTEXT_IF_ERROR( + tensorflow::AttrTypeMapForOp(op_name.c_str(), &attr_types), + " (while processing attributes of '", op_name, "')"); tensorflow::EagerOperation op(eager_context, op_name.c_str(), attr_types); for (const auto& attr : nodedef.attr()) { @@ -64,7 +87,8 @@ tensorflow::Status ExecuteEagerOp(tensorflow::EagerContext* eager_context, for (int input_index : inputs) { if (!buffer_map->HasTensor(input_index)) { - return tensorflow::errors::Internal("Invalid tensor index ", input_index); + return tensorflow::errors::Internal( + "Cannot read from invalid tensor index ", input_index); } auto* handle = new tensorflow::TensorHandle( buffer_map->GetTensor(input_index), nullptr, nullptr, nullptr); @@ -73,21 +97,20 @@ tensorflow::Status ExecuteEagerOp(tensorflow::EagerContext* eager_context, } int num_retvals = outputs.size(); - tensorflow::gtl::InlinedVector retvals( - num_retvals, nullptr); - - TF_RETURN_IF_ERROR(EagerExecute(&op, &retvals, &num_retvals)); + VectorOfHandles retvals(num_retvals); + TF_RETURN_WITH_CONTEXT_IF_ERROR( + EagerExecute(&op, retvals.GetVector(), &num_retvals), + " (while executing '", op_name, "' via Eager)"); - if (outputs.size() != num_retvals) { + if (num_retvals != outputs.size()) { return tensorflow::errors::Internal( "Unexpected number of outputs from EagerExecute"); } for (int i = 0; i < num_retvals; ++i) { const tensorflow::Tensor* tensor = nullptr; - TF_RETURN_IF_ERROR(retvals[i]->Tensor(&tensor)); + TF_RETURN_IF_ERROR(retvals.GetHandle(i)->Tensor(&tensor)); buffer_map->SetFromTensorFlow(outputs[i], *tensor); - retvals[i]->Unref(); } return tensorflow::Status::OK(); @@ -145,7 +168,7 @@ void* Init(TfLiteContext* context, const char* buffer, size_t length) { TfLiteRegistration* reg; context->GetNodeAndRegistration(context, node_index, &node, ®); - op_data->nodes.emplace_back(OpNode()); + op_data->nodes.push_back(OpNode()); OpNode& node_data = op_data->nodes.back(); node_data.name = ""; @@ -237,7 +260,8 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { for (auto tensor_index : op_data->subgraph_outputs) { if (!buffer_map->HasTensor(tensor_index)) { - context->ReportError(context, "Invalid tensor index %d", tensor_index); + context->ReportError(context, "Cannot write to invalid tensor index %d", + tensor_index); return kTfLiteError; } diff --git a/tensorflow/contrib/lite/delegates/eager/kernel_test.cc b/tensorflow/contrib/lite/delegates/eager/kernel_test.cc index f0e3d432f9..7d9dddef93 100644 --- a/tensorflow/contrib/lite/delegates/eager/kernel_test.cc +++ b/tensorflow/contrib/lite/delegates/eager/kernel_test.cc @@ -27,9 +27,11 @@ namespace eager { namespace { using tensorflow::protobuf::TextFormat; +using ::testing::ContainsRegex; using ::testing::ElementsAre; // We will use these are custom_names, so they need to be static. +static const char kIdentity[] = "Identity"; static const char kUnpack[] = "Unpack"; static const char kAdd[] = "Add"; static const char kMul[] = "Mul"; @@ -38,8 +40,8 @@ TfLiteStatus GenericPrepare(TfLiteContext* context, TfLiteDelegate* delegate, const std::vector& supported_nodes) { TfLiteIntArray* size_and_nodes = ConvertVectorToTfLiteIntArray(supported_nodes); - context->ReplaceSubgraphsWithDelegateKernels(context, eager::GetKernel(), - size_and_nodes, delegate); + TF_LITE_ENSURE_STATUS(context->ReplaceSubgraphsWithDelegateKernels( + context, eager::GetKernel(), size_and_nodes, delegate)); TfLiteIntArrayFree(size_and_nodes); return kTfLiteOk; } @@ -48,10 +50,10 @@ class KernelTest : public ::testing::Test { public: KernelTest() { CHECK(DelegateData::Create(&delegate_data_).ok()); - interpreter_.reset(new Interpreter); + interpreter_.reset(new Interpreter(&error_reporter_)); } - void Invoke() { ASSERT_EQ(interpreter_->Invoke(), kTfLiteOk); } + bool Invoke() { return interpreter_->Invoke() == kTfLiteOk; } void SetValues(int tensor_index, const std::vector& values) { float* v = interpreter_->typed_tensor(tensor_index); @@ -103,17 +105,18 @@ class KernelTest : public ::testing::Test { return " attr{ key: '" + key + "' value {" + value + "}}"; }; + string attributes; if (name == string(kUnpack)) { - string attributes = attr("T", "type: DT_FLOAT") + attr("num", "i: 2") + - attr("axis", "i: 0"); - AddTfOp(name, attributes, inputs, outputs); + attributes = attr("T", "type: DT_FLOAT") + attr("num", "i: 2") + + attr("axis", "i: 0"); + } else if (name == string(kIdentity)) { + attributes = attr("T", "type: DT_FLOAT"); } else if (name == string(kAdd)) { - string attributes = attr("T", "type: DT_FLOAT"); - AddTfOp(name, attributes, inputs, outputs); + attributes = attr("T", "type: DT_FLOAT"); } else if (name == string(kMul)) { - string attributes = attr("T", "type: DT_FLOAT"); - AddTfOp(name, attributes, inputs, outputs); + attributes = attr("T", "type: DT_FLOAT"); } + AddTfOp(name, attributes, inputs, outputs); } void AddTensors(int num_tensors, const std::vector& inputs, @@ -121,12 +124,41 @@ class KernelTest : public ::testing::Test { interpreter_->AddTensors(num_tensors); for (int i = 0; i < num_tensors; ++i) { TfLiteQuantizationParams quant; - interpreter_->SetTensorParametersReadWrite(i, kTfLiteFloat32, /*name=*/"", - /*dims=*/{3}, quant); + CHECK_EQ(interpreter_->SetTensorParametersReadWrite(i, kTfLiteFloat32, + /*name=*/"", + /*dims=*/{3}, quant), + kTfLiteOk); } - interpreter_->SetInputs(inputs); - interpreter_->SetOutputs(outputs); + CHECK_EQ(interpreter_->SetInputs(inputs), kTfLiteOk); + CHECK_EQ(interpreter_->SetOutputs(outputs), kTfLiteOk); + } + + const TestErrorReporter& error_reporter() const { return error_reporter_; } + + void AddTfLiteOp(const char* name, const std::vector& inputs, + const std::vector& outputs) { + CHECK_EQ(string(name), kMul); // can only add MUL + static TfLiteRegistration reg = {nullptr, nullptr, nullptr, nullptr}; + reg.builtin_code = BuiltinOperator_MUL; + reg.prepare = [](TfLiteContext* context, TfLiteNode* node) { + auto* i0 = &context->tensors[node->inputs->data[0]]; + auto* o = &context->tensors[node->outputs->data[0]]; + return context->ResizeTensor(context, o, TfLiteIntArrayCopy(i0->dims)); + }; + reg.invoke = [](TfLiteContext* context, TfLiteNode* node) { + auto* i0 = &context->tensors[node->inputs->data[0]]; + auto* i1 = &context->tensors[node->inputs->data[1]]; + auto* o = &context->tensors[node->outputs->data[0]]; + for (int i = 0; i < o->bytes / sizeof(float); ++i) { + o->data.f[i] = i0->data.f[i] * i1->data.f[i]; + } + return kTfLiteOk; + }; + + CHECK_EQ(interpreter_->AddNodeWithParameters(inputs, outputs, nullptr, 0, + nullptr, ®), + kTfLiteOk); } private: @@ -151,21 +183,19 @@ class KernelTest : public ::testing::Test { flexbuffers_.push_back(fbb.GetBuffer()); auto& buffer = flexbuffers_.back(); - interpreter_->AddNodeWithParameters( - inputs, outputs, reinterpret_cast(buffer.data()), - buffer.size(), nullptr, ®); + CHECK_EQ(interpreter_->AddNodeWithParameters( + inputs, outputs, reinterpret_cast(buffer.data()), + buffer.size(), nullptr, ®), + kTfLiteOk); } std::unique_ptr interpreter_; std::unique_ptr delegate_data_; TfLiteDelegate delegate_; std::vector> flexbuffers_; + TestErrorReporter error_reporter_; }; -// TODO(ahentz): add a few more tests. In particular we need to be able to use -// TF Lite ops along with the Eager ops, and we should check that having two or -// more separate eager kernels (disjoint subgraphs) is OK. Also, we should be -// verifying failure modes too. TEST_F(KernelTest, FullGraph) { // Define the graph. AddTensors(9, {0, 3}, {8}); @@ -187,12 +217,129 @@ TEST_F(KernelTest, FullGraph) { SetShape(3, {2, 2, 1}); SetValues(3, {1.1f, 2.2f, 3.3f, 4.4f}); - Invoke(); + ASSERT_TRUE(Invoke()); ASSERT_THAT(GetShape(8), ElementsAre(2, 1)); ASSERT_THAT(GetValues(8), ElementsAre(14.52f, 38.72f)); } +TEST_F(KernelTest, BadTensorFlowOp) { + AddTensors(2, {0}, {1}); + AddOp("NonExistentOp", {0}, {1}); + + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0}); + }); + + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + + ASSERT_FALSE(Invoke()); + ASSERT_THAT(error_reporter().error_messages(), + ContainsRegex("while processing attributes of 'NonExistentOp'")); +} + +TEST_F(KernelTest, BadNumberOfOutputs) { + AddTensors(3, {0}, {1, 2}); + AddOp(kIdentity, {0}, {1, 2}); + + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0}); + }); + + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + + ASSERT_FALSE(Invoke()); + ASSERT_THAT(error_reporter().error_messages(), + ContainsRegex("Unexpected number of outputs")); +} + +TEST_F(KernelTest, IncompatibleNodeDef) { + AddTensors(2, {0}, {1}); + + // Cast is a TF op, but we don't add the proper nodedef to it in AddOp. + AddOp("Cast", {0}, {1}); + + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0}); + }); + + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + + ASSERT_FALSE(Invoke()); + ASSERT_THAT(error_reporter().error_messages(), + ContainsRegex("while executing 'Cast' via Eager")); +} + +TEST_F(KernelTest, WrongSetOfNodes) { + AddTensors(4, {0}, {3}); + AddOp(kUnpack, {0}, {1, 2}); + AddTfLiteOp(kMul, {1, 2}, {3}); + + // Specify that kMul (#1) is supported when it actually isn't. + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0, 1}); + }); + + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + + ASSERT_FALSE(Invoke()); + ASSERT_THAT(error_reporter().error_messages(), + ContainsRegex("Invalid NodeDef in Eager op")); +} + +TEST_F(KernelTest, MixedGraph) { + AddTensors(9, {0, 3}, {8}); + + AddOp(kUnpack, {0}, {1, 2}); + AddOp(kUnpack, {3}, {4, 5}); + AddOp(kAdd, {1, 4}, {6}); + AddOp(kAdd, {2, 5}, {7}); + AddTfLiteOp(kMul, {6, 7}, {8}); + + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0, 1, 2, 3}); + }); + + SetShape(0, {2, 2, 1}); + SetValues(0, {1.1f, 2.2f, 3.3f, 4.4f}); + SetShape(3, {2, 2, 1}); + SetValues(3, {1.1f, 2.2f, 3.3f, 4.4f}); + + ASSERT_TRUE(Invoke()); + + ASSERT_THAT(GetShape(8), ElementsAre(2, 1)); + ASSERT_THAT(GetValues(8), ElementsAre(14.52f, 38.72f)); +} + +TEST_F(KernelTest, SplitGraph) { + AddTensors(10, {0}, {9}); + + AddOp(kUnpack, {0}, {1, 2}); + AddOp(kAdd, {1, 2}, {3}); + AddOp(kUnpack, {3}, {4, 5}); + + AddTfLiteOp(kMul, {4, 5}, {6}); + + AddOp(kUnpack, {6}, {7, 8}); + AddOp(kAdd, {7, 8}, {9}); + + ConfigureDelegate([](TfLiteContext* context, TfLiteDelegate* delegate) { + return GenericPrepare(context, delegate, {0, 1, 2, 4, 5}); + }); + + SetShape(0, {2, 2, 2, 1}); + SetValues(0, {3.0f, 1.0f, 0.5f, -1.0f, 0.0f, 1.0f, 1.5f, 3.0f}); + + ASSERT_TRUE(Invoke()); + + ASSERT_THAT(GetShape(9), ElementsAre(1)); + ASSERT_THAT(GetValues(9), ElementsAre(10.0f)); +} + } // namespace } // namespace eager } // namespace tflite -- GitLab From 6f4e330eb8dc18a44fa496467c4a5caa19a64e14 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Thu, 26 Jul 2018 13:41:53 -0700 Subject: [PATCH 465/519] [XLA:GPU] Don't fuse random things into fusions that become a cublas call Only transpose and broadcast are valid. I think this used to work because we didn't emit cublas calls for fused dots until recently. PiperOrigin-RevId: 206213730 --- tensorflow/compiler/xla/service/gpu/instruction_fusion.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc index af6259ae83..0f2c83aeb2 100644 --- a/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc +++ b/tensorflow/compiler/xla/service/gpu/instruction_fusion.cc @@ -202,6 +202,7 @@ bool GpuInstructionFusion::ShouldFuse(HloInstruction* consumer, IsIEEEFloatingPointScalarConstant(producer->operand(0)) && fused_parameter_users[0]->opcode() == HloOpcode::kMultiply; } + return false; } // Other output fusions are not currently supported on GPUs. -- GitLab From 1a306c949565bfc2649ccc91780bfee44e010f68 Mon Sep 17 00:00:00 2001 From: Yuanzhong Xu Date: Thu, 26 Jul 2018 14:00:14 -0700 Subject: [PATCH 466/519] [XLA] Fix BF16 propagation type adjustment AllUsersConsumeBF16() incorrectly used ValueTypeAfterChange() for the current value being checked, but it should be the original type. Also fusion computation should be adjusted as soon as the fusion root is adjusted. There was also redundant work for while computations. Now removed. PiperOrigin-RevId: 206216822 --- .../xla/service/bfloat16_propagation.cc | 14 +++- .../xla/service/bfloat16_propagation_test.cc | 69 ++++++++++++++++++- 2 files changed, 77 insertions(+), 6 deletions(-) diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation.cc b/tensorflow/compiler/xla/service/bfloat16_propagation.cc index b21c83a07f..2fb401c428 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation.cc @@ -215,7 +215,12 @@ bool BFloat16Propagation::AllUsersConsumeBF16(const HloInstruction& hlo, if (ContainsKey(values_that_must_be_kept_as_f32_, value)) { return false; } - if (ValueTypeAfterChange(value) == BF16) { + // We use the original type for the value because we are going to examine + // the uses of it, instead of the value itself. If ValueTypeAfterChange() + // were used, it would cause problems when there are aliasing buffers, i.e., + // ResolveInconsistencyOfAliasingBuffers() would fail to revert the + // tentative change to BF16 even if the uses require F32. + if (value->shape().element_type() == BF16) { continue; } for (const HloUse& use : value->uses()) { @@ -566,6 +571,9 @@ bool BFloat16Propagation::ResolveInconsistencyOfAliasingBuffersHelper( } visited_computations->insert(visited_in_while.begin(), visited_in_while.end()); + } else if (hlo->opcode() == HloOpcode::kFusion) { + ResolveInconsistencyOfAliasingBuffersHelper( + hlo->fused_instructions_computation(), visited_computations); } } // Now adjust parameters of called computations. @@ -769,8 +777,7 @@ StatusOr BFloat16Propagation::Run(HloModule* module) { // 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. + if (ContainsKey(computations_visited_in_backward_pass_, *comp_it)) { continue; } auto insts = (*comp_it)->MakeInstructionPostOrder(); @@ -778,6 +785,7 @@ StatusOr BFloat16Propagation::Run(HloModule* module) { DetermineInstructionPrecision(*inst_it, /*skip_parameters=*/true); } + computations_visited_in_backward_pass_.insert(*comp_it); } // It's possible that an instruction does not define a buffer, but the diff --git a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc index aeafb25ad7..69b654d30e 100644 --- a/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc +++ b/tensorflow/compiler/xla/service/bfloat16_propagation_test.cc @@ -508,6 +508,63 @@ TEST_F(BFloat16PropagationTest, PropagateThroughSimpleWhile) { EXPECT_FALSE(OutputsBF16(dot)); } +// Tests that if the while condition prevents using BF16, no changes should be +// made to the while body and thus the fusion node inside it. +TEST_F(BFloat16PropagationTest, + ConditionPreventsPropagationForFusionInsideWhile) { + 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* add = builder.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kAdd, param0, param1)); + + auto builder_cond = HloComputation::Builder("cond"); + auto cond_param = builder_cond.AddInstruction( + HloInstruction::CreateParameter(0, shape, "cond_param")); + builder_cond.AddInstruction(HloInstruction::CreateBinary( + ShapeUtil::MakeShape(PRED, {}), HloOpcode::kGt, + builder_cond.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond_param, {0, 0}, {1, 1}, {1, 1})), + builder_cond.AddInstruction(HloInstruction::CreateSlice( + ShapeUtil::MakeShape(F32, {}), cond_param, {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, shape, "body_param")); + auto body_transpose = builder_body.AddInstruction( + HloInstruction::CreateTranspose(shape, body_param, {0, 1})); + + auto builder_f = HloComputation::Builder("fusion"); + HloInstruction* a_f = + builder_f.AddInstruction(HloInstruction::CreateParameter(0, shape, "a")); + builder_f.AddInstruction(HloInstruction::CreateTranspose(shape, a_f, {0, 1})); + auto comp_f = module->AddEmbeddedComputation(builder_f.Build()); + auto body_fusion = builder_body.AddInstruction(HloInstruction::CreateFusion( + shape, HloInstruction::FusionKind::kCustom, {body_transpose}, comp_f)); + auto body = module->AddEmbeddedComputation(builder_body.Build()); + + auto while_hlo = builder.AddInstruction( + HloInstruction::CreateWhile(shape, cond, body, add)); + + auto dot = builder.AddInstruction(HloInstruction::CreateBinary( + shape, HloOpcode::kDot, while_hlo, while_hlo)); + auto computation = module->AddEntryComputation(builder.Build()); + + EXPECT_FALSE(PropagatePrecision(module.get())); + EXPECT_EQ(computation->root_instruction(), dot); + EXPECT_FALSE(OutputsBF16(add)); + EXPECT_FALSE(OutputsBF16(body_fusion)); + EXPECT_FALSE(OutputsBF16(body_param)); + EXPECT_FALSE(OutputsBF16(body_transpose)); + EXPECT_FALSE(OutputsBF16(a_f)); +} + // Tests that BF16 is propagated properly through while computations with // tuple-shaped input/output. TEST_F(BFloat16PropagationTest, PropagateThroughTupleWhile) { @@ -553,10 +610,14 @@ TEST_F(BFloat16PropagationTest, PropagateThroughTupleWhile) { HloInstruction::CreateGetTupleElement(shape, body_param, 0)); auto body_rhs = builder_body.AddInstruction( HloInstruction::CreateGetTupleElement(shape, body_param, 1)); - auto body_dot = builder_body.AddInstruction( + auto body_dot1 = builder_body.AddInstruction( HloInstruction::CreateBinary(shape, HloOpcode::kDot, body_lhs, body_rhs)); + auto body_dot2 = builder_body.AddInstruction( + HloInstruction::CreateBinary(shape, HloOpcode::kDot, body_rhs, body_lhs)); + auto body_transpose = builder_body.AddInstruction( + HloInstruction::CreateTranspose(shape, body_dot2, {0, 1})); builder_body.AddInstruction( - HloInstruction::CreateTuple({body_dot, body_rhs})); + HloInstruction::CreateTuple({body_dot1, body_transpose})); auto body = module->AddEmbeddedComputation(builder_body.Build()); auto while_hlo = builder.AddInstruction( @@ -575,9 +636,11 @@ TEST_F(BFloat16PropagationTest, PropagateThroughTupleWhile) { EXPECT_EQ(computation->root_instruction(), dot); EXPECT_TRUE(OutputsBF16(lhs)); EXPECT_FALSE(OutputsBF16(rhs)); - EXPECT_TRUE(OutputsBF16(body_dot)); + EXPECT_TRUE(OutputsBF16(body_dot1)); EXPECT_TRUE(OutputsBF16(body_lhs)); EXPECT_FALSE(OutputsBF16(body_rhs)); + EXPECT_FALSE(OutputsBF16(body_dot2)); + EXPECT_FALSE(OutputsBF16(body_transpose)); EXPECT_TRUE(OutputsBF16(cond_lhs)); EXPECT_FALSE(OutputsBF16(cond_rhs)); EXPECT_TRUE(OutputsBF16(add0)); -- GitLab From 6aa9b864d9c77d9cfdb8a4f0dd93fe20d23b686c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 14:08:57 -0700 Subject: [PATCH 467/519] This change adds an attribute to the _HostCast op to have the same interface as the Cast op. PiperOrigin-RevId: 206218592 --- tensorflow/core/ops/math_ops.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/core/ops/math_ops.cc b/tensorflow/core/ops/math_ops.cc index 77697756c4..1667c398f4 100644 --- a/tensorflow/core/ops/math_ops.cc +++ b/tensorflow/core/ops/math_ops.cc @@ -122,6 +122,7 @@ REGISTER_OP("_HostCast") .Output("y: DstT") .Attr("SrcT: type") .Attr("DstT: type") + .Attr("Truncate: bool = false") .SetShapeFn(shape_inference::UnchangedShape) .Doc(R"doc( Cast x of type SrcT to y of DstT. -- GitLab From 46332e1cd5fc5ca3edd5587d7e5bf1ad9c3baa92 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 14:42:33 -0700 Subject: [PATCH 468/519] Test tf.constant directly (still doesn't work with scalars) PiperOrigin-RevId: 206224062 --- .../contrib/lite/testing/generate_examples.py | 25 +++++++++++-------- 1 file changed, 14 insertions(+), 11 deletions(-) diff --git a/tensorflow/contrib/lite/testing/generate_examples.py b/tensorflow/contrib/lite/testing/generate_examples.py index 3c7ad9d8b3..4234d0b811 100644 --- a/tensorflow/contrib/lite/testing/generate_examples.py +++ b/tensorflow/contrib/lite/testing/generate_examples.py @@ -104,6 +104,8 @@ KNOWN_BUGS = { r"div.*int32": "72051395", # No support for SplitV r"split.*num_or_size_splits=\[2,2\]": "73377559", + # Scalar constants don't work. + r"constant.*shape=\[\]": "109811500", } @@ -481,7 +483,7 @@ def make_zip_of_tests(zip_path, else report_lib.FAILED) report["toco_log"] = toco_log - if FLAGS.save_graphdefs: + if True or FLAGS.save_graphdefs: archive.writestr(label + ".pbtxt", text_format.MessageToString(graph_def), zipfile.ZIP_DEFLATED) @@ -736,21 +738,22 @@ def make_constant_tests(zip_path): test_parameters = [{ "dtype": [tf.float32, tf.int32], - "input_shape": [[1], [2], [1, 1, 1, 1], [2, 2, 2, 2]], + "input_shape": [[], [1], [2], [1, 1, 1, 1], [2, 2, 2, 2]], }] def build_graph(parameters): - # Since Toco & Tflite can't have a single constant op in the entire graph, - # 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 - return [input1], [out] + dummy_input = tf.placeholder( + dtype=parameters["dtype"], + name="input1", + shape=parameters["input_shape"]) + out = tf.constant( + create_tensor_data(parameters["dtype"], parameters["input_shape"])) + return [dummy_input], [out] def build_inputs(parameters, sess, inputs, outputs): - input1 = np.zeros(parameters["input_shape"], - dtype=_TF_TYPE_INFO[parameters["dtype"]][0]) - return [input1], sess.run(outputs, feed_dict={inputs[0]: input1}) + dummy_input = np.zeros( + parameters["input_shape"], dtype=_TF_TYPE_INFO[parameters["dtype"]][0]) + return [dummy_input], sess.run(outputs, feed_dict={inputs[0]: dummy_input}) make_zip_of_tests(zip_path, test_parameters, build_graph, build_inputs) -- GitLab From d4cb01f242dc3ff0f7b0aae7284def46281755f2 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 15:49:48 -0700 Subject: [PATCH 469/519] Keras Tensorboard Callback - enable metrics logging in Eager mode PiperOrigin-RevId: 206235264 --- tensorflow/python/keras/callbacks.py | 73 ++++++++++++++++------- tensorflow/python/keras/callbacks_test.py | 59 +++++++++++++++--- 2 files changed, 104 insertions(+), 28 deletions(-) diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py index d1b9dc27bd..070d41147d 100644 --- a/tensorflow/python/keras/callbacks.py +++ b/tensorflow/python/keras/callbacks.py @@ -31,12 +31,14 @@ import time import numpy as np import six +from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.keras import backend as K from tensorflow.python.keras.engine.training_utils import standardize_input_data from tensorflow.python.keras.utils.generic_utils import Progbar from tensorflow.python.ops import array_ops from tensorflow.python.ops import state_ops +from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary import summary as tf_summary @@ -716,6 +718,15 @@ class TensorBoard(Callback): `embeddings_layer_names`. Numpy array (if the model has a single input) or list of Numpy arrays (if the model has multiple inputs). Learn [more about embeddings](https://www.tensorflow.org/programmers_guide/embedding) + + Raises: + ValueError: If histogram_freq is set and no validation data is provided. + + @compatbility(eager) + Using `Tensorboard` callback will work while eager execution is enabled, + however outputting histogram summaries of weights and gradients is not + supported, and thus `histogram_freq` will be ignored. + @end_compatibility """ # pylint: enable=line-too-long @@ -734,6 +745,11 @@ class TensorBoard(Callback): super(TensorBoard, self).__init__() self.log_dir = log_dir self.histogram_freq = histogram_freq + if self.histogram_freq and context.executing_eagerly(): + logging.warning( + UserWarning('Weight and gradient histograms not supported for eager' + 'execution, setting `histogram_freq` to `0`.')) + self.histogram_freq = 0 self.merged = None self.write_graph = write_graph self.write_grads = write_grads @@ -741,18 +757,22 @@ class TensorBoard(Callback): self.batch_size = batch_size self._current_batch = 0 self._total_batches_seen = 0 - # abstracted writer class to be able to stub for testing - self._writer_class = tf_summary.FileWriter self.embeddings_freq = embeddings_freq self.embeddings_layer_names = embeddings_layer_names self.embeddings_metadata = embeddings_metadata self.embeddings_data = embeddings_data - def set_model(self, model): - """Sets Keras model and creates summary ops.""" + def _init_writer(self): + """Sets file writer.""" + if context.executing_eagerly(): + self.writer = summary_ops_v2.create_file_writer(self.log_dir) + elif self.write_graph: + self.writer = tf_summary.FileWriter(self.log_dir, K.get_session().graph) + else: + self.writer = tf_summary.FileWriter(self.log_dir) - self.model = model - self.sess = K.get_session() + def _make_histogram_ops(self, model): + """Defines histogram ops when histogram_freq > 0.""" # only make histogram summary op if it hasn't already been made if self.histogram_freq and self.merged is None: for layer in self.model.layers: @@ -793,8 +813,10 @@ class TensorBoard(Callback): def is_indexed_slices(grad): return type(grad).__name__ == 'IndexedSlices' - grads = [grad.values if is_indexed_slices(grad) else grad - for grad in grads] + grads = [ + grad.values if is_indexed_slices(grad) else grad + for grad in grads + ] tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads) if hasattr(layer, 'output'): @@ -803,12 +825,16 @@ class TensorBoard(Callback): tf_summary.histogram('{}_out_{}'.format(layer.name, i), output) else: tf_summary.histogram('{}_out'.format(layer.name), layer.output) - self.merged = tf_summary.merge_all() - if self.write_graph: - self.writer = self._writer_class(self.log_dir, self.sess.graph) - else: - self.writer = self._writer_class(self.log_dir) + def set_model(self, model): + """Sets Keras model and creates summary ops.""" + + self.model = model + self._init_writer() + # histogram summaries only enabled in graph mode + if not context.executing_eagerly(): + self._make_histogram_ops(model) + self.merged = tf_summary.merge_all() # If both embedding_freq and embeddings_data are available, we will # visualize embeddings. @@ -894,17 +920,24 @@ class TensorBoard(Callback): """ logs = logs or {} - for name, value in logs.items(): - summary = tf_summary.Summary() - summary_value = summary.value.add() - summary_value.simple_value = value.item() - summary_value.tag = name - self.writer.add_summary(summary, step) + if context.executing_eagerly(): + # use v2 summary ops + with self.writer.as_default(), summary_ops_v2.always_record_summaries(): + for name, value in logs.items(): + summary_ops_v2.scalar(name, value.item(), step=step) + else: + # use FileWriter from v1 summary + for name, value in logs.items(): + summary = tf_summary.Summary() + summary_value = summary.value.add() + summary_value.simple_value = value.item() + summary_value.tag = name + self.writer.add_summary(summary, step) self.writer.flush() def on_train_begin(self, logs=None): """Checks if histogram summaries can be run.""" - + # will never be set when in eager if self.histogram_freq: if 'validation_steps' in self.params: self._validation_batches = self.params['validation_steps'] diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py index 7d830078ce..6d61e9bcee 100644 --- a/tensorflow/python/keras/callbacks_test.py +++ b/tensorflow/python/keras/callbacks_test.py @@ -29,10 +29,12 @@ import numpy as np from tensorflow.core.framework import summary_pb2 from tensorflow.python import keras +from tensorflow.python.framework import test_util from tensorflow.python.keras import testing_utils from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary.writer import writer_cache +from tensorflow.python.training import adam try: import h5py # pylint:disable=g-import-not-at-top @@ -917,6 +919,9 @@ class KerasCallbacksTest(test.TestCase): def close(self): pass + def _init_writer(obj): + obj.writer = FileWriterStub(obj.log_dir) + np.random.seed(1337) tmpdir = self.get_temp_dir() self.addCleanup(shutil.rmtree, tmpdir) @@ -940,13 +945,13 @@ class KerasCallbacksTest(test.TestCase): loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) + keras.callbacks.TensorBoard._init_writer = _init_writer tsb = keras.callbacks.TensorBoard( log_dir=tmpdir, histogram_freq=1, write_images=True, write_grads=True, batch_size=5) - tsb._writer_class = FileWriterStub cbks = [tsb] # fit with validation data @@ -1118,11 +1123,11 @@ class KerasCallbacksTest(test.TestCase): def close(self): pass - logdir = 'fake_dir' + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) - # log every batch - tb_cbk = keras.callbacks.TensorBoard(logdir) - tb_cbk.writer = FileWriterStub(logdir) + tb_cbk = keras.callbacks.TensorBoard(temp_dir) + tb_cbk.writer = FileWriterStub(temp_dir) for batch in range(5): tb_cbk.on_batch_end(batch, {'acc': np.float32(batch)}) @@ -1150,10 +1155,11 @@ class KerasCallbacksTest(test.TestCase): def close(self): pass - logdir = 'fake_dir' + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) - tb_cbk = keras.callbacks.TensorBoard(logdir) - tb_cbk.writer = FileWriterStub(logdir) + tb_cbk = keras.callbacks.TensorBoard(temp_dir) + tb_cbk.writer = FileWriterStub(temp_dir) tb_cbk.on_batch_end(0, {'acc': np.float32(5.0)}) tb_cbk.on_epoch_end(0, {'acc': np.float32(10.0)}) @@ -1164,6 +1170,43 @@ class KerasCallbacksTest(test.TestCase): self.assertEqual(epoch_step, 0) self.assertEqual(epoch_summary.value[0].simple_value, 10.0) + @test_util.run_in_graph_and_eager_modes + def test_Tensorboard_eager(self): + with self.test_session(): + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + (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) + y_test = keras.utils.to_categorical(y_test) + y_train = keras.utils.to_categorical(y_train) + + model = keras.models.Sequential() + model.add( + keras.layers.Dense( + NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu')) + model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax')) + model.compile( + loss='binary_crossentropy', + optimizer=adam.AdamOptimizer(0.01), + metrics=['accuracy']) + + cbks = [keras.callbacks.TensorBoard(log_dir=temp_dir)] + + model.fit( + x_train, + y_train, + batch_size=BATCH_SIZE, + validation_data=(x_test, y_test), + callbacks=cbks, + epochs=2, + verbose=0) + + self.assertTrue(os.path.exists(temp_dir)) + def test_RemoteMonitorWithJsonPayload(self): if requests is None: self.skipTest('`requests` required to run this test') -- GitLab From 62df725269a89a0a5d877eae18d0c83155f2ea9d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 15:52:35 -0700 Subject: [PATCH 470/519] Increase the input dimension size from 4 to 6 to address the RetinaNet model PiperOrigin-RevId: 206235660 --- tensorflow/contrib/lite/toco/import_tensorflow.cc | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/tensorflow/contrib/lite/toco/import_tensorflow.cc b/tensorflow/contrib/lite/toco/import_tensorflow.cc index f92f33497d..7e593029e5 100644 --- a/tensorflow/contrib/lite/toco/import_tensorflow.cc +++ b/tensorflow/contrib/lite/toco/import_tensorflow.cc @@ -215,7 +215,7 @@ tensorflow::Status ImportFloatArray(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_FLOAT); const auto& input_shape = input_tensor.tensor_shape(); - CHECK_LE(input_shape.dim_size(), 4); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); @@ -253,7 +253,7 @@ tensorflow::Status ImportQuint8Array(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_QUINT8); const auto& input_shape = input_tensor.tensor_shape(); - CHECK_LE(input_shape.dim_size(), 4); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); @@ -290,7 +290,7 @@ tensorflow::Status ImportInt32Array(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_INT32); const auto& input_shape = input_tensor.tensor_shape(); - CHECK_LE(input_shape.dim_size(), 4); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); @@ -326,7 +326,7 @@ tensorflow::Status ImportInt64Array(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_INT64); const auto& input_shape = input_tensor.tensor_shape(); - CHECK_LE(input_shape.dim_size(), 4); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); @@ -363,7 +363,7 @@ tensorflow::Status 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); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); @@ -409,7 +409,7 @@ tensorflow::Status ImportStringArray(const TensorProto& input_tensor, Array* output_array) { CHECK_EQ(input_tensor.dtype(), DT_STRING); const auto& input_shape = input_tensor.tensor_shape(); - CHECK_LE(input_shape.dim_size(), 4); + CHECK_LE(input_shape.dim_size(), 6); int input_flat_size; auto status = ImportShape(input_shape.dim(), &input_flat_size, output_array->mutable_shape()); -- GitLab From 4e0c5685af914684a77177002e4265a721c3e0ff Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Thu, 26 Jul 2018 15:56:19 -0700 Subject: [PATCH 471/519] Don't make remote copy call when both send/recv devices are the same. PiperOrigin-RevId: 206236233 --- tensorflow/core/common_runtime/eager/execute.cc | 14 +++++++++++++- 1 file changed, 13 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/common_runtime/eager/execute.cc b/tensorflow/core/common_runtime/eager/execute.cc index 0c0fbc729c..f97fa4fadc 100644 --- a/tensorflow/core/common_runtime/eager/execute.cc +++ b/tensorflow/core/common_runtime/eager/execute.cc @@ -448,6 +448,14 @@ bool IsLocal(EagerContext* ctx, tensorflow::Device* d) { return ctx->local_device_mgr()->LookupDevice(d->name(), &tmp).ok(); } +bool OnSameTask(EagerContext* ctx, Device* first, Device* second) { + if (first == nullptr) first = ctx->HostCPU(); + if (second == nullptr) second = ctx->HostCPU(); + return first->parsed_name().job == second->parsed_name().job && + first->parsed_name().replica == second->parsed_name().replica && + first->parsed_name().task == second->parsed_name().task; +} + Status EagerLocalExecute(EagerOperation* op, gtl::InlinedVector* retvals, int* num_retvals) { @@ -689,7 +697,11 @@ Status EagerRemoteExecute(EagerOperation* op, TensorHandle** retvals, for (int i = 0; i < op->Inputs().size(); i++) { tensorflow::Device* input_device; TF_RETURN_IF_ERROR(op->Inputs()[i]->Device(&input_device)); - if (op->Device() != input_device) { + if (op->Device() != input_device && + // If the expected and actual devices are on the same task, don't + // explicitly copy, and instead depend on the copy to happen locally + // when the op is executed on the device. + !OnSameTask(ctx, op->Device(), input_device)) { // TODO(b/110044833): It's possible the same tensor gets copied to the // remote device repeatedly. TF_RETURN_IF_ERROR(MaybeCopyInputToExpectedDevice( -- GitLab From 0c55fddc407a6e868a86b9ad111c437d9c5fb22c Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Thu, 26 Jul 2018 15:58:57 -0700 Subject: [PATCH 472/519] [tf.data] Switch `batch_and_drop_remainder` to use the fused op. This change also switches `padded_batch_and_drop_remainder` to use the corresponding fused op. PiperOrigin-RevId: 206236616 --- tensorflow/contrib/data/python/ops/batching.py | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/tensorflow/contrib/data/python/ops/batching.py b/tensorflow/contrib/data/python/ops/batching.py index a4914f4cde..42fc20ec01 100644 --- a/tensorflow/contrib/data/python/ops/batching.py +++ b/tensorflow/contrib/data/python/ops/batching.py @@ -515,10 +515,7 @@ def batch_and_drop_remainder(batch_size): def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" - # TODO(jsimsa): Switch to using `batch(..., drop_remainder=True)` any time - # after 6/30/2018. - batched = dataset.batch(batch_size) - return _filter_irregular_batches(batch_size)(batched) + return dataset.batch(batch_size, drop_remainder=True) return _apply_fn @@ -553,11 +550,9 @@ def padded_batch_and_drop_remainder(batch_size, def _apply_fn(dataset): """Function from `Dataset` to `Dataset` that applies the transformation.""" - # TODO(jsimsa): Switch to using `padded_batch(..., drop_remainder=True)` - # any time after 6/30/2018. - batched = dataset.padded_batch( - batch_size, padded_shapes=padded_shapes, padding_values=padding_values) - return _filter_irregular_batches(batch_size)(batched) + return dataset.padded_batch( + batch_size, padded_shapes=padded_shapes, padding_values=padding_values, + drop_remainder=True) return _apply_fn -- GitLab From 00dd73bea970c9d90cd8c9c653835963b77d29c3 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 16:07:03 -0700 Subject: [PATCH 473/519] [TF:XLA] Remove unused parameter. PiperOrigin-RevId: 206237934 --- tensorflow/compiler/xla/service/BUILD | 1 - tensorflow/compiler/xla/service/buffer_assignment.cc | 7 +++---- tensorflow/compiler/xla/service/buffer_assignment.h | 4 +--- 3 files changed, 4 insertions(+), 8 deletions(-) diff --git a/tensorflow/compiler/xla/service/BUILD b/tensorflow/compiler/xla/service/BUILD index cfab7622c3..528b7fdfd3 100644 --- a/tensorflow/compiler/xla/service/BUILD +++ b/tensorflow/compiler/xla/service/BUILD @@ -947,7 +947,6 @@ cc_library( "//tensorflow/compiler/xla:statusor", "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:util", - "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", ], diff --git a/tensorflow/compiler/xla/service/buffer_assignment.cc b/tensorflow/compiler/xla/service/buffer_assignment.cc index 3b5bc49c59..e4d2e73b99 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.cc +++ b/tensorflow/compiler/xla/service/buffer_assignment.cc @@ -817,8 +817,7 @@ bool BufferAssigner::MaybeAssignBuffer(BufferAllocation* allocation, } Status BufferAssigner::AssignBuffersForComputation( - const HloComputation* computation, const DebugOptions& debug_options, - bool is_thread_local, + const HloComputation* computation, bool is_thread_local, const FlatSet& colocated_buffers, const FlatSet& colocated_allocations, FlatMap>* @@ -1678,7 +1677,7 @@ StatusOr> BufferAssigner::CreateAssignment( buffers_to_assign_sequentially; for (auto* computation : global_computations) { TF_RETURN_IF_ERROR(AssignBuffersForComputation( - computation, module->config().debug_options(), + computation, /*is_thread_local=*/false, colocated_buffers, colocated_allocations, &buffers_to_assign_sequentially, assignment.get())); } @@ -1699,7 +1698,7 @@ StatusOr> BufferAssigner::CreateAssignment( continue; } TF_RETURN_IF_ERROR(AssignBuffersForComputation( - computation, module->config().debug_options(), + computation, /*is_thread_local=*/true, colocated_buffers, colocated_allocations, /*buffers_to_assign_sequentially=*/nullptr, assignment.get())); } diff --git a/tensorflow/compiler/xla/service/buffer_assignment.h b/tensorflow/compiler/xla/service/buffer_assignment.h index 4fcf1fc73d..94495290c1 100644 --- a/tensorflow/compiler/xla/service/buffer_assignment.h +++ b/tensorflow/compiler/xla/service/buffer_assignment.h @@ -32,7 +32,6 @@ limitations under the License. #include "tensorflow/compiler/xla/service/tuple_points_to_analysis.h" #include "tensorflow/compiler/xla/statusor.h" #include "tensorflow/compiler/xla/types.h" -#include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/lib/gtl/flatmap.h" #include "tensorflow/core/lib/gtl/flatset.h" @@ -543,8 +542,7 @@ class BufferAssigner { // true, then all assigned buffers have the is_thread_local flag set to // true. Status AssignBuffersForComputation( - const HloComputation* computation, const DebugOptions& debug_options, - bool is_thread_local, + const HloComputation* computation, bool is_thread_local, const tensorflow::gtl::FlatSet& colocated_buffers, const tensorflow::gtl::FlatSet& colocated_allocations, -- GitLab From fad8d28c8afb5bbedabb91110b07fc130a9ca36e Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Thu, 26 Jul 2018 16:13:53 -0700 Subject: [PATCH 474/519] Avoid using cuda_fp16.h for fp16 definition. PiperOrigin-RevId: 206238991 --- tensorflow/contrib/cmake/CMakeLists.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/tensorflow/contrib/cmake/CMakeLists.txt b/tensorflow/contrib/cmake/CMakeLists.txt index 6c93487e0d..f6c928e2be 100644 --- a/tensorflow/contrib/cmake/CMakeLists.txt +++ b/tensorflow/contrib/cmake/CMakeLists.txt @@ -471,7 +471,6 @@ if (tensorflow_ENABLE_GPU) ${CUDA_TOOLKIT_TARGET_DIR}/include/cuComplex.h ${CUDA_TOOLKIT_TARGET_DIR}/include/cublas_v2.h ${CUDA_TOOLKIT_TARGET_DIR}/include/cusolverDn.h - ${CUDA_TOOLKIT_TARGET_DIR}/include/cuda_fp16.h ${CUDA_TOOLKIT_TARGET_DIR}/include/device_functions.h ${CUDA_TOOLKIT_TARGET_DIR}/include/cufft.h ${CUDA_TOOLKIT_TARGET_DIR}/include/curand.h -- GitLab From 111745bdf9338926626d3aeec6736c75f55c608a Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 16:20:46 -0700 Subject: [PATCH 475/519] [TF:XLA] Align the two implementations of CanShareOperandBufferWithUser. Eventually (when TuplePointsToAnalysis is removed), there will be only one implementation left. Also, use early return instead of else-if to make the code less indented. PiperOrigin-RevId: 206240067 --- .../xla/service/hlo_dataflow_analysis.cc | 39 ++++++++++--------- 1 file changed, 21 insertions(+), 18 deletions(-) diff --git a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc index de1a32d8bd..1abfcb7703 100644 --- a/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc +++ b/tensorflow/compiler/xla/service/hlo_dataflow_analysis.cc @@ -1017,19 +1017,17 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( } if (user->opcode() == HloOpcode::kFusion) { + if (fusion_can_share_buffer_ != nullptr) { + return fusion_can_share_buffer_(user, operand); + } // Get the parameter associated with 'operand'; HloInstruction* fusion_param = user->fused_parameter(user->operand_index(operand)); const HloValue& value = GetValueDefinedAt(fusion_param, operand_index); - if (value.uses().size() != 1) { - if (MultiDynamicSliceUseShareSameIndices(value.uses())) { - return true; - } - return false; + if (MultiDynamicSliceUseShareSameIndices(value.uses())) { + return true; } - const HloUse& use = value.uses()[0]; - if (user->fusion_kind() == HloInstruction::FusionKind::kLoop || user->fusion_kind() == HloInstruction::FusionKind::kInput) { if (user->fused_expression_root()->opcode() == @@ -1039,13 +1037,17 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( // Returns true iff there is exactly one use of 'operand' at shape index // 'operand_index', and this singleton use is the fused root at operand // index 0. - return use.instruction == user->fused_expression_root() && - use.operand_number == 0; - } else { - return AreTransitiveUsesElementwiseOrTuple(fusion_param); + if (value.uses().size() == 1) { + const HloUse& use = value.uses()[0]; + return use.instruction == user->fused_expression_root() && + use.operand_number == 0; + } + return false; } - } else if (user->fusion_kind() == HloInstruction::FusionKind::kOutput && - user->fused_expression_root()->opcode() == HloOpcode::kAdd) { + return AreTransitiveUsesElementwiseOrTuple(fusion_param); + } + if (user->fusion_kind() == HloInstruction::FusionKind::kOutput && + user->fused_expression_root()->opcode() == HloOpcode::kAdd) { // Output fusion with kAdd fused root. // Check if one operand of kAdd fused root is kDot or kConvolution. @@ -1066,11 +1068,12 @@ bool HloDataflowAnalysis::CanShareOperandBufferWithUser( // Returns true iff there is exactly one use of 'operand' at shape index // 'operand_index', and this singleton use is the fused root (at operand // index 'other_add_operand_index'). - return use.instruction == user->fused_expression_root() && - use.operand_number == other_add_operand_index; - } else if (fusion_can_share_buffer_ != nullptr && - fusion_can_share_buffer_(user, operand)) { - return true; + if (value.uses().size() == 1) { + const HloUse& use = value.uses()[0]; + return use.instruction == user->fused_expression_root() && + use.operand_number == other_add_operand_index; + } + return false; } } -- GitLab From 403845d3e26291d6013c623b9130f4404c969ca6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 16:27:07 -0700 Subject: [PATCH 476/519] Remove tensorflow/opensource_only/Core as it is not used by tensorflow. PiperOrigin-RevId: 206240947 --- .../eigen3/unsupported/Eigen/CXX11/Core | 46 ------------------- 1 file changed, 46 deletions(-) delete mode 100644 third_party/eigen3/unsupported/Eigen/CXX11/Core diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/Core b/third_party/eigen3/unsupported/Eigen/CXX11/Core deleted file mode 100644 index 1b3690716c..0000000000 --- a/third_party/eigen3/unsupported/Eigen/CXX11/Core +++ /dev/null @@ -1,46 +0,0 @@ -// This file is part of Eigen, a lightweight C++ template library -// for linear algebra. -// -// Copyright (C) 2013 Christian Seiler -// Copyright (C) 2014 Benoit Steiner -// -// This Source Code Form is subject to the terms of the Mozilla -// Public License v. 2.0. If a copy of the MPL was not distributed -// with this file, You can obtain one at http://mozilla.org/MPL/2.0/. - -#ifndef EIGEN_CXX11_CORE_MODULE -#define EIGEN_CXX11_CORE_MODULE - -#include - -#include - -/** \defgroup CXX11_Core_Module C++11 Core Module - * - * This module provides common core features for all modules that - * explicitly depend on C++11. Currently, this is only the Tensor - * module. Note that at this stage, you should not need to include - * this module directly. - * - * It also provides a limited fallback for compilers that don't support - * CXX11 yet, such as nvcc. - * - * \code - * #include - * \endcode - */ - -// Only a subset of cxx11 is allowed at Google, so we default to emulate the -// cxx11 functionality that we need. -#include "src/Core/util/FixedSizeVector.h" -#if 1 -#include -#include "src/Core/util/EmulateCXX11Meta.h" -#else -#include "src/Core/util/CXX11Workarounds.h" -#include "src/Core/util/CXX11Meta.h" -#endif -#include - -#endif // EIGEN_CXX11_CORE_MODULE - -- GitLab From e336ee65a5c887e9a2f0b4c82c333bca405707a5 Mon Sep 17 00:00:00 2001 From: Pavithra Vijay Date: Thu, 26 Jul 2018 16:38:21 -0700 Subject: [PATCH 477/519] Fix: When sample_weight_mode is a list/dict set default sample_weight values so that we do not require sample_weight to be set during training/eval PiperOrigin-RevId: 206242625 --- tensorflow/python/keras/engine/training.py | 57 +++++-------------- .../python/keras/engine/training_test.py | 48 ++++++++++++++++ .../python/keras/engine/training_utils.py | 24 ++++++++ 3 files changed, 87 insertions(+), 42 deletions(-) diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 4df739254b..39d207cc6b 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -24,7 +24,6 @@ import numpy as np from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context -from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -374,21 +373,14 @@ class Model(Network): 'sample_weight_mode dictionary: "' + name + '". ' 'Only expected the following keys: ' + str(self.output_names)) for i, name in enumerate(self.output_names): - if i in skip_target_weighing_indices: - weight = None - sample_weight_modes.append(None) - else: - if name not in sample_weight_mode: - raise ValueError( - 'Output "' + name + '" missing from sample_weight_modes ' - 'dictionary') - if sample_weight_mode.get(name) == 'temporal': - weight = K.placeholder(ndim=2, name=name + '_sample_weights') - sample_weight_modes.append('temporal') - else: - weight = K.placeholder(ndim=1, name=name + 'sample_weights') - sample_weight_modes.append(None) + if (i not in skip_target_weighing_indices and + name not in sample_weight_mode): + raise ValueError('Output "' + name + + '" missing from sample_weight_modes dictionary') + weight, mode = training_utils.get_output_sample_weight_and_mode( + skip_target_weighing_indices, sample_weight_mode.get(name), name, i) sample_weights.append(weight) + sample_weight_modes.append(mode) elif isinstance(sample_weight_mode, list): if len(sample_weight_mode) != len(self.outputs): raise ValueError('When passing a list as sample_weight_mode, ' @@ -396,36 +388,17 @@ class Model(Network): 'The model has ' + str(len(self.outputs)) + ' outputs, but you passed ' 'sample_weight_mode=' + str(sample_weight_mode)) - for i in range(len(self.output_names)): - if i in skip_target_weighing_indices: - weight = None - sample_weight_modes.append(None) - else: - mode = sample_weight_mode[i] - name = self.output_names[i] - if mode == 'temporal': - weight = K.placeholder(ndim=2, name=name + '_sample_weights') - sample_weight_modes.append('temporal') - else: - weight = K.placeholder(ndim=1, name=name + '_sample_weights') - sample_weight_modes.append(None) + for i, name in enumerate(self.output_names): + weight, mode = training_utils.get_output_sample_weight_and_mode( + skip_target_weighing_indices, sample_weight_mode[i], name, i) sample_weights.append(weight) + sample_weight_modes.append(mode) else: for i, name in enumerate(self.output_names): - if i in skip_target_weighing_indices: - sample_weight_modes.append(None) - sample_weights.append(None) - else: - if sample_weight_mode == 'temporal': - sample_weights.append(array_ops.placeholder_with_default( - constant_op.constant([[1.]], dtype=K.floatx()), - shape=[None, None], name=name + '_sample_weights')) - sample_weight_modes.append('temporal') - else: - sample_weights.append(array_ops.placeholder_with_default( - constant_op.constant([1.], dtype=K.floatx()), - shape=[None], name=name + '_sample_weights')) - sample_weight_modes.append(None) + weight, mode = training_utils.get_output_sample_weight_and_mode( + skip_target_weighing_indices, sample_weight_mode, name, i) + sample_weights.append(weight) + sample_weight_modes.append(mode) self.sample_weight_modes = sample_weight_modes self._feed_sample_weight_modes = [] for i in range(len(self.outputs)): diff --git a/tensorflow/python/keras/engine/training_test.py b/tensorflow/python/keras/engine/training_test.py index 301a6ca866..129441d159 100644 --- a/tensorflow/python/keras/engine/training_test.py +++ b/tensorflow/python/keras/engine/training_test.py @@ -731,6 +731,54 @@ class LossWeightingTest(test.TestCase): model.fit(x_np, [y_np, y_np], epochs=1, sample_weight={'1': bad_w_np}) + def test_default_sample_weight(self): + """Verifies that fit works without having to set sample_weight.""" + + num_classes = 5 + input_dim = 5 + timesteps = 3 + with self.test_session(): + model = keras.models.Sequential() + model.add( + keras.layers.TimeDistributed( + keras.layers.Dense(num_classes), + input_shape=(timesteps, input_dim))) + + x = np.random.random((10, timesteps, input_dim)) + y = np.random.random((10, timesteps, num_classes)) + + # sample_weight_mode is a list and mode value is None + model.compile(loss='mse', optimizer='rmsprop', sample_weight_mode=[None]) + model.fit(x, y, epochs=1, batch_size=10) + + # sample_weight_mode is a list and mode value is `temporal` + model.compile( + loss='mse', optimizer='rmsprop', sample_weight_mode=['temporal']) + model.fit(x, y, epochs=1, batch_size=10) + + # sample_weight_mode is a dict and mode value is None + model.compile( + loss='mse', + optimizer='rmsprop', + sample_weight_mode={'time_distributed': None}) + model.fit(x, y, epochs=1, batch_size=10) + + # sample_weight_mode is a dict and mode value is `temporal` + model.compile( + loss='mse', + optimizer='rmsprop', + sample_weight_mode={'time_distributed': 'temporal'}) + model.fit(x, y, epochs=1, batch_size=10) + + # sample_weight_mode is a not a list/dict and mode value is None + model.compile(loss='mse', optimizer='rmsprop', sample_weight_mode=None) + model.fit(x, y, epochs=1, batch_size=10) + + # sample_weight_mode is a not a list/dict and mode value is `temporal` + model.compile( + loss='mse', optimizer='rmsprop', sample_weight_mode='temporal') + model.fit(x, y, epochs=1, batch_size=10) + class LossMaskingTest(test.TestCase): diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py index dbbc87daf9..21495fd0bd 100644 --- a/tensorflow/python/keras/engine/training_utils.py +++ b/tensorflow/python/keras/engine/training_utils.py @@ -26,10 +26,12 @@ import numpy as np from tensorflow.python.data.ops import dataset_ops from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context +from tensorflow.python.framework import constant_op from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend as K from tensorflow.python.keras import losses from tensorflow.python.keras import metrics as metrics_module +from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops @@ -856,3 +858,25 @@ def cast_if_floating_dtype(x): for val in x ] return math_ops.cast(x, dtype=K.floatx()) if x.dtype.is_floating else x + + +def get_output_sample_weight_and_mode(skip_target_weighing_indices, + sample_weight_mode, output_name, + output_index): + """Returns the sample weight and weight mode for a single output.""" + if output_index in skip_target_weighing_indices: + return None, None + + if sample_weight_mode == 'temporal': + default_value = [[1.]] + shape = [None, None] + mode = 'temporal' + else: + default_value = [1.] + shape = [None] + mode = None + weight = array_ops.placeholder_with_default( + constant_op.constant(default_value, dtype=K.floatx()), + shape=shape, + name=output_name + '_sample_weights') + return weight, mode -- GitLab From 4009f82f71f0421e4ed1f50d38e9105074062d1e Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Thu, 26 Jul 2018 16:43:58 -0700 Subject: [PATCH 478/519] Implement constant buffer allocation for XLA:GPU This CL teaches XLA:GPU to use "normal" buffer assignment for constant instructions. Constant instructions are mapped to a BufferAllocation, like all other instructions, except the storage for this buffer is allocated statically as a global in the generated PTX. This CL does not change how we access the constants -- in IrEmitterUnnested::BuildKernelThunk (used for top level computations) and in HloToIrBindings::EmitBasePointersForHlos (used for nested computations) we bind the kConstant instructions to the llvm::GlobalVariable backing them. So users of constant instructions still access the globals corresponding to the constants directly. However, we no longer emit the constant literals inline. Instead we emit a constant with a zero initializer and then memcpy in the contents of the literal when we load the CUBIN/PTX. This works around compile time issues in LLVM and ptxas caused by large constants. We also populate `BufferAllocations` with the device pointers for the constant globals. This is at least needed for TupleThunk today because TupleThunk wants the addresses for the sub-buffers on the host. I'm not sure if there are other places in XLA:GPU that rely on there being an entry in BufferAllocations for every BufferAllocation. PiperOrigin-RevId: 206243319 --- tensorflow/compiler/xla/service/gpu/BUILD | 2 + .../xla/service/gpu/buffer_allocations.cc | 62 +++++++++- .../xla/service/gpu/buffer_allocations.h | 15 +++ .../compiler/xla/service/gpu/gpu_constants.cc | 2 + .../compiler/xla/service/gpu/gpu_constants.h | 3 + .../xla/service/gpu/gpu_copy_insertion.cc | 79 ++---------- .../xla/service/gpu/gpu_executable.cc | 55 ++++++++- .../compiler/xla/service/gpu/gpu_executable.h | 21 +++- .../xla/service/gpu/hlo_to_ir_bindings.cc | 23 +++- .../compiler/xla/service/gpu/ir_emitter.cc | 13 -- .../xla/service/gpu/ir_emitter_unnested.cc | 116 ++++++++++++++---- .../xla/service/gpu/ir_emitter_unnested.h | 3 + .../xla/service/gpu/nvptx_compiler.cc | 15 ++- .../compiler/xla/service/gpu/while_thunk.cc | 3 + 14 files changed, 291 insertions(+), 121 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 4c21811698..625d1448e7 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -114,6 +114,7 @@ cc_library( srcs = ["hlo_to_ir_bindings.cc"], hdrs = ["hlo_to_ir_bindings.h"], deps = [ + ":buffer_allocations", ":ir_emission_utils", "//tensorflow/compiler/xla:util", "//tensorflow/compiler/xla/service:buffer_assignment", @@ -142,6 +143,7 @@ cc_library( ], deps = [ ":backend_configs", + ":buffer_allocations", ":cudnn_convolution_runner", ":elemental_ir_emitter", ":gpu_constants", diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc index b095d4cd73..20d4285766 100644 --- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc +++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc @@ -44,12 +44,22 @@ StatusOr> BufferAllocations::Builder::Build( num_buffers, device_ordinal, memory_allocator, buffer_assignment)); for (BufferAllocation::Index i = 0; i < num_buffers; ++i) { + const BufferAllocation& allocation = buffer_assignment->GetAllocation(i); + const int64 expected_alignment = [&] { + if (allocation.is_entry_computation_parameter()) { + return kEntryParameterAlignBytes; + } else if (allocation.is_constant()) { + return kConstantBufferAlignBytes; + } else { + return kXlaAllocatedBufferAlignBytes; + } + }(); + // If buffer #i's address is already registered (e.g. external arguments or // result buffers), use that registered buffer. if (registered_buffers_.count(i)) { se::DeviceMemoryBase address = FindOrDie(registered_buffers_, i); - if (reinterpret_cast(address.opaque()) % - kEntryParameterAlignBytes != + if (reinterpret_cast(address.opaque()) % expected_alignment != 0) { return InternalError( "Address of registered buffer %lld must be a multiple of %llx, but " @@ -62,7 +72,6 @@ StatusOr> BufferAllocations::Builder::Build( // Allocate each allocation that might escape, or is the temp buffer. bool seen_temp_buffer = false; - const BufferAllocation& allocation = buffer_assignment->GetAllocation(i); if (allocation.maybe_live_out() || allocation.IsPreallocatedTempBuffer()) { const int64 buffer_size = allocation.size(); se::DeviceMemoryBase buffer_address; @@ -70,8 +79,7 @@ StatusOr> BufferAllocations::Builder::Build( OwningDeviceMemory buffer; TF_ASSIGN_OR_RETURN( buffer, memory_allocator->Allocate(device_ordinal, buffer_size)); - if (reinterpret_cast(buffer.opaque()) % - kXlaAllocatedBufferAlignBytes != + if (reinterpret_cast(buffer.opaque()) % expected_alignment != 0) { return InternalError( "Address returned by memory_allocator->Allocate must be a " @@ -165,5 +173,49 @@ void BufferAllocations::SetBuffer(BufferAllocation::Index buffer_index, buffers_[buffer_index] = buffer; } +static const HloInstruction& InstrForConstantBufferAllocation( + const BufferAllocation& allocation) { + CHECK(allocation.is_constant()); + HloInstruction* const_instr = nullptr; + for (const auto& buffer_offset_pair : allocation.assigned_buffers()) { + const LogicalBuffer* buffer = buffer_offset_pair.first; + // BufferAssignment may have assigned non-constant instructions to this + // allocation too so we can't CHECK this condition. E.g. for + // + // while(init = constant, body = identity, cond = ...) + // + // the LogicalBuffer for the kWhile instruction will have the same + // BufferAllocation as the LogicalBuffer for the (init) constant. + if (buffer->instruction()->opcode() == HloOpcode::kConstant) { + CHECK_EQ(const_instr, nullptr) + << const_instr->ToString() << " " << buffer->ToString(); + const_instr = buffer->instruction(); + } + } + CHECK_NE(const_instr, nullptr); + return *const_instr; +} + +string ConstantBufferAllocationToGlobalName( + const BufferAllocation& allocation) { + string instr_name = InstrForConstantBufferAllocation(allocation).name(); + for (char& c : instr_name) { + if (c == '.') { + c = '_'; + } + } + return tensorflow::strings::StrCat("buffer_for_", instr_name); +} + +const Literal& LiteralForConstantAllocation( + const BufferAllocation& allocation) { + return InstrForConstantBufferAllocation(allocation).literal(); +} + +bool ShouldEmitLiteralInLlvmIr(const Literal& literal) { + // LLVM can sometimes do interesting optimizations using scalar constants. + return ShapeUtil::IsScalar(literal.shape()); +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.h b/tensorflow/compiler/xla/service/gpu/buffer_allocations.h index 6366235025..f21861ed81 100644 --- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.h +++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.h @@ -107,6 +107,21 @@ class BufferAllocations { bool torn_down_ = false; }; +// In XLA:GPU we map constant buffer allocations to globals in the generated +// LLVM IR. This function gives us the name of the global variable a constant +// buffer is mapped to. +string ConstantBufferAllocationToGlobalName(const BufferAllocation& allocation); + +// Return the Literal corresponding to `allocation`, which must be a constant +// allocation. +const Literal& LiteralForConstantAllocation(const BufferAllocation& allocation); + +// LLVM and PTXAS don't deal well with large constants, so we only emit very +// small constants directly in LLVM IR. Larger constants are emitted with zero +// initializers in LLVM IR and are later overwritten when the PTX/CUBIN is +// loaded. +bool ShouldEmitLiteralInLlvmIr(const Literal& literal); + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/gpu_constants.cc b/tensorflow/compiler/xla/service/gpu/gpu_constants.cc index e6ddea6d25..7f0b030fec 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_constants.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_constants.cc @@ -30,5 +30,7 @@ const int64 kEntryParameterAlignBytes = 16; const int64 kXlaAllocatedBufferAlignBytes = tensorflow::Allocator::kAllocatorAlignment; +const int64 kConstantBufferAlignBytes = kXlaAllocatedBufferAlignBytes; + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/gpu_constants.h b/tensorflow/compiler/xla/service/gpu/gpu_constants.h index 925e6927b6..6f5f1fa09c 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_constants.h +++ b/tensorflow/compiler/xla/service/gpu/gpu_constants.h @@ -28,6 +28,9 @@ extern const int64 kEntryParameterAlignBytes; // out (result) buffers. extern const int64 kXlaAllocatedBufferAlignBytes; +// Minimum alignment for constant buffers. +extern const int64 kConstantBufferAlignBytes; + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc index fbc1303085..75f414e47f 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_copy_insertion.cc @@ -48,80 +48,17 @@ StatusOr GpuCopyInsertion::Run(HloModule* module) { TF_ASSIGN_OR_RETURN(bool changed, generic_copy_insertion.Run(module)); - TF_ASSIGN_OR_RETURN(std::unique_ptr dataflow, - HloDataflowAnalysis::Run(*module)); - - // Make sure all operands of a library call are in memory instead of constants - // in IR. Also, init values of while and conditional nodes cannot be - // constants. Insert copies for any constants found at the operands of these - // nodes. - tensorflow::gtl::FlatSet inserted_copies; + // Check the assumption that the epsilon and feature_index constants of the + // CUDNN batchnorm op are not shared with other ops where we would replace + // them with a copy. These custom op calls are generated with the + // CudnnBatchNormRewriter, so this would only happen if HloCSE merges them. for (HloComputation* computation : module->computations()) { for (HloInstruction* hlo : computation->instructions()) { - // Inserts a copy of hlo->operand(n) if it's a constant. - auto copy_operand_if_constant = [&](int64 n) -> Status { - HloInstruction* operand = hlo->mutable_operand(n); - // Skip the operands that have already been replaced with a copy in a - // previous iteration (which is possible when a constant is used as an - // operand in multiple places). - if (ContainsKey(inserted_copies, operand)) { - return Status::OK(); - } - for (auto& pair : dataflow->GetInstructionValueSet(operand)) { - const HloValueSet& value_set = pair.second; - for (const HloValue* value : value_set.values()) { - if (value->defining_instruction()->IsConstant() && - !ContainsKey(hlo_to_copy_map_, value->defining_instruction())) { - HloInstruction* constant = value->defining_instruction(); - TF_ASSIGN_OR_RETURN(HloInstruction * copy, - FindOrInsertCopy(constant)); - TF_RETURN_IF_ERROR(constant->ReplaceAllUsesWith(copy)); - inserted_copies.insert(copy); - changed = true; - } - } - } - return Status::OK(); - }; - - if (IsCustomCallToDnnBatchNorm(*hlo)) { - // The epsilon and feature_index operands to a CUDNN batchnorm op don't - // need to be materialized in memory -- in fact, they must be constants. - // These are the last two operands of all three batchnorm ops. - for (int64 i = 0; i < hlo->operand_count() - 2; ++i) { - TF_RETURN_IF_ERROR(copy_operand_if_constant(i)); - } - } else if (ImplementedAsLibraryCall(*hlo) || - hlo->opcode() == HloOpcode::kCrossReplicaSum || - hlo->opcode() == HloOpcode::kWhile || - hlo->opcode() == HloOpcode::kConditional) { - // For all other library calls, cross-replica-sum, while and conditional - // ops materialize all the operands into memory. (Cross-replica-sum - // gets its constant args materialized even if it's not implemented as a - // libcall to simplify the implementation. It's slower, but we can - // constant fold away constant args *anyway*, so we just need to make it - // work.) - for (int64 i = 0; i < hlo->operand_count(); ++i) { - TF_RETURN_IF_ERROR(copy_operand_if_constant(i)); - } + if (!IsCustomCallToDnnBatchNorm(*hlo)) { + continue; } - } - } - - if (changed) { - // Check the assumption that the epsilon and feature_index constants of the - // CUDNN batchnorm op are not shared with other ops where we would replace - // them with a copy. These custom op calls are generated with the - // CudnnBatchNormRewriter, so this would only happen if HloCSE merges them. - for (HloComputation* computation : module->computations()) { - for (HloInstruction* hlo : computation->instructions()) { - if (!IsCustomCallToDnnBatchNorm(*hlo)) { - continue; - } - for (int64 i = hlo->operand_count() - 2; i < hlo->operand_count(); - ++i) { - CHECK_EQ(hlo->operand(i)->opcode(), HloOpcode::kConstant); - } + for (int64 i = hlo->operand_count() - 2; i < hlo->operand_count(); ++i) { + CHECK_EQ(hlo->operand(i)->opcode(), HloOpcode::kConstant); } } } diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index 9767836cd6..52c8595ffb 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -181,6 +181,51 @@ Status GpuExecutable::ExecuteThunks( return Status::OK(); } +StatusOr +GpuExecutable::ResolveConstantGlobals(se::StreamExecutor* executor) { + tensorflow::mutex_lock lock(module_handle_mutex_); + auto it = module_globals_.find(executor); + if (it != module_globals_.end()) { + return &it->second; + } + + se::MultiModuleLoaderSpec module_spec; + module_spec.AddCudaCubinInMemory(cubin()); + module_spec.AddCudaPtxInMemory(ptx().c_str()); + + tensorflow::gtl::FlatMap globals; + se::ModuleHandle module_handle; + executor->LoadModule(module_spec, &module_handle); + + for (BufferAllocation::Index i = 0; i < assignment_->Allocations().size(); + ++i) { + const BufferAllocation& allocation = assignment_->GetAllocation(i); + if (allocation.is_constant()) { + TF_ASSIGN_OR_RETURN( + se::DeviceMemoryBase global, + executor->GetUntypedSymbol( + ConstantBufferAllocationToGlobalName(allocation), module_handle)); + VLOG(3) << "Resolved global " + << ConstantBufferAllocationToGlobalName(allocation) << " to " + << global.opaque(); + InsertOrDie(&globals, i, global); + + const Literal& literal = LiteralForConstantAllocation(allocation); + CHECK(ShapeUtil::IsArray(literal.shape())); + if (!ShouldEmitLiteralInLlvmIr(literal)) { + VLOG(3) << "H2D memcpy for constant with shape " + << ShapeUtil::HumanString(literal.shape()); + TF_RETURN_IF_ERROR(executor->SynchronousMemcpyH2D( + literal.untyped_data(), allocation.size(), &global)); + } + } + } + + module_handles_.emplace(executor, + se::ScopedModuleHandle(executor, module_handle)); + return &module_globals_.emplace(executor, std::move(globals)).first->second; +} + StatusOr GpuExecutable::ExecuteOnStream( const ServiceExecutableRunOptions* run_options, tensorflow::gtl::ArraySlice arguments, @@ -192,6 +237,10 @@ StatusOr GpuExecutable::ExecuteOnStream( } BufferAllocations::Builder buffer_allocations_builder; + se::StreamExecutor* executor = run_options->stream()->parent(); + + TF_ASSIGN_OR_RETURN(auto* const globals, ResolveConstantGlobals(executor)); + for (BufferAllocation::Index i = 0; i < assignment_->Allocations().size(); ++i) { const BufferAllocation& allocation = assignment_->GetAllocation(i); @@ -213,8 +262,12 @@ StatusOr GpuExecutable::ExecuteOnStream( buffer_allocations_builder.RegisterBuffer(i, buffer); } + + if (allocation.is_constant()) { + buffer_allocations_builder.RegisterBuffer(i, FindOrDie(*globals, i)); + } } - se::StreamExecutor* executor = run_options->stream()->parent(); + TF_ASSIGN_OR_RETURN( auto buffer_allocations, buffer_allocations_builder.Build( diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.h b/tensorflow/compiler/xla/service/gpu/gpu_executable.h index 80ec38c3ac..c7ce6d0acb 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.h +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.h @@ -34,6 +34,8 @@ limitations under the License. #include "tensorflow/compiler/xla/types.h" #include "tensorflow/core/lib/core/stringpiece.h" #include "tensorflow/core/lib/gtl/array_slice.h" +#include "tensorflow/core/lib/gtl/flatmap.h" +#include "tensorflow/core/lib/gtl/optional.h" #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -66,7 +68,7 @@ class GpuExecutable : public Executable { } // Returns the compiled PTX for the computation. - tensorflow::StringPiece ptx() const { return ptx_; } + const string& ptx() const { return ptx_; } // Returns the cubin (compiled PTX) stored in this GpuExecutable. May be // empty, in which case compilation is left up to the GPU driver. @@ -98,6 +100,15 @@ class GpuExecutable : public Executable { // computation. Uses points-to analysis from buffer assignment. const PointsToSet& GetRootPointsToSet() const; + using BufferAllocToDeviceMemoryMap = + tensorflow::gtl::FlatMap; + + // Loads the PTX or CUBIN for this executable into `executor` and resolves the + // globals corresponding to constant buffers. Returns a map mapping buffer + // allocation indices to GPU pointers. + StatusOr ResolveConstantGlobals( + stream_executor::StreamExecutor* executor); + // The LLVM IR, in string format, of the unoptimized module generated for this // GpuExecutable. We save a string instead of an llvm::Module* because leaving // llvm::Module* in a singleton can cause the heap checker to emit false @@ -126,6 +137,14 @@ class GpuExecutable : public Executable { // memory for every output/temp buffers. const std::unique_ptr assignment_; + // Cache of module handles and constant buffer allocation maps used by + // `ResolveConstantGlobals`. + tensorflow::mutex module_handle_mutex_; + std::map + module_handles_ GUARDED_BY(module_handle_mutex_); + std::map + module_globals_ GUARDED_BY(module_handle_mutex_); + TF_DISALLOW_COPY_AND_ASSIGN(GpuExecutable); }; diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc index 1b6315ec03..c02a95d193 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc @@ -18,6 +18,7 @@ limitations under the License. #include "llvm/IR/BasicBlock.h" #include "llvm/IR/Function.h" #include "llvm/IR/Instructions.h" +#include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" @@ -110,6 +111,11 @@ void HloToIrBindings::EmitBasePointersForHlos( llvm_ir::ShapeToIrType(non_io_hlo->shape(), module_); BindHloToIrValue(*non_io_hlo, b_->CreateAlloca(pointee_type), index); + } else if (slice.allocation()->is_constant()) { + llvm::Value* global_for_constant = + module_->getGlobalVariable(llvm_ir::AsStringRef( + ConstantBufferAllocationToGlobalName(*slice.allocation()))); + BindHloToIrValue(*non_io_hlo, global_for_constant); } else { const int64 offset = slice.offset(); CHECK_NE(nullptr, temp_buffer_base_); @@ -135,6 +141,14 @@ llvm::Value* HloToIrBindings::EmitGetTupleElement(const HloInstruction* gte, EmitGetTupleElement(gte->operand(0), base_ptr), b_, module_); } +// Returns true if `value` has a name that should not be changed. +static bool HasMeaningfulName(llvm::Value* value) { + if (auto* global = llvm::dyn_cast(value)) { + return global->getLinkage() != llvm::GlobalValue::PrivateLinkage; + } + return false; +} + llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo, ShapeIndexView shape_index, llvm::Value* ir_value) { @@ -149,8 +163,13 @@ llvm::Value* HloToIrBindings::GetTypedIrValue(const HloInstruction& hlo, } else { typed_ir_value = b_->CreateBitCast(ir_value, pointee_type->getPointerTo()); } - ir_value->setName(llvm_ir::AsStringRef(llvm_ir::IrName(&hlo, "raw"))); - typed_ir_value->setName(llvm_ir::AsStringRef(llvm_ir::IrName(&hlo, "typed"))); + if (!HasMeaningfulName(ir_value)) { + ir_value->setName(llvm_ir::AsStringRef(llvm_ir::IrName(&hlo, "raw"))); + } + if (!HasMeaningfulName(typed_ir_value)) { + typed_ir_value->setName( + llvm_ir::AsStringRef(llvm_ir::IrName(&hlo, "typed"))); + } return typed_ir_value; } diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc index 973848c336..1295e83c0c 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter.cc @@ -81,19 +81,6 @@ Status IrEmitter::DefaultAction(HloInstruction* hlo) { } Status IrEmitter::HandleConstant(HloInstruction* constant) { - const Literal& literal = constant->literal(); - llvm::Constant* initializer = - llvm_ir::ConvertLiteralToIrConstant(literal, module_); - llvm::GlobalVariable* global_for_const = new llvm::GlobalVariable( - *module_, initializer->getType(), - /*isConstant=*/true, llvm::GlobalValue::PrivateLinkage, initializer, - /*Name=*/""); - VLOG(2) << "HandleConstant: " << constant->ToString() << std::endl - << " emitted_value: " << llvm_ir::DumpToString(*global_for_const) - << std::endl - << " its type: " - << llvm_ir::DumpToString(*global_for_const->getType()); - bindings_.BindHloToIrValue(*constant, global_for_const); return Status::OK(); } diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index 5445d7b3ab..fb9540b7ef 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -33,6 +33,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/buffer_assignment.h" #include "tensorflow/compiler/xla/service/dfs_hlo_visitor.h" #include "tensorflow/compiler/xla/service/gpu/backend_configs.pb.h" +#include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" #include "tensorflow/compiler/xla/service/gpu/conditional_thunk.h" #include "tensorflow/compiler/xla/service/gpu/convolution_thunk.h" #include "tensorflow/compiler/xla/service/gpu/copy_thunk.h" @@ -231,11 +232,20 @@ llvm::Function* IrEmitterUnnested::BuildKernelPrototype( ++arg_it; kernel->addDereferenceableAttr(arg_no + 1, alloc->size()); + + const int64 alignment = [&] { + if (alloc->is_entry_computation_parameter()) { + return kEntryParameterAlignBytes; + } else if (alloc->is_constant()) { + return kConstantBufferAlignBytes; + } else { + return kXlaAllocatedBufferAlignBytes; + } + }(); + kernel->addParamAttr( - arg_no, llvm::Attribute::get(context, llvm::Attribute::Alignment, - alloc->is_entry_computation_parameter() - ? kEntryParameterAlignBytes - : kXlaAllocatedBufferAlignBytes)); + arg_no, + llvm::Attribute::get(context, llvm::Attribute::Alignment, alignment)); if (alloc->IsPreallocatedTempBuffer()) { fn_arg->setName("temp_buf"); @@ -1763,6 +1773,8 @@ Status IrEmitterUnnested::HandleTuple(HloInstruction* tuple) { .GetUniqueTopLevelSlice(tuple_element) .ok(); }); + // TODO(b/111689850): This logic isn't quite correct. + // // Tuples (especially tuples that are the final result of a computation) can // be so huge that if we were to emit a kernel that took each tuple element as // a parameter, we would exceed the max allowable number of parameters to a @@ -1770,9 +1782,9 @@ Status IrEmitterUnnested::HandleTuple(HloInstruction* tuple) { // buffer, we collect their buffer addresses in a host array, and then copy // that array to the tuple's buffer. // - // Some tuple elements (e.g. const or bitcast of const) might not have a - // buffer -- their contents are stored in code. In that case, we fall back to - // emitting kernels which have access to their buffer addresses in code. + // Some tuple elements might not have an unambiguous buffer (like the result + // of a select-tuple). In that case, we fall back to emitting kernels which + // have access to their buffer addresses in code. if (all_tuple_elements_have_buffer) { std::vector tuple_element_buffers; for (const HloInstruction* tuple_element : tuple->operands()) { @@ -2299,11 +2311,6 @@ GetHloBufferSlices(const HloInstruction* hlo, // Adds entries for all subshapes of instr to `slices`. auto add_slices_for = [&](const HloInstruction* instr) { - // GPU constants don't have buffers; don't bother looking for one. - if (instr->IsConstant()) { - return; - } - ShapeUtil::ForEachSubshape( instr->shape(), [&](const Shape& /*shape*/, const ShapeIndex& index) { if (slices.count({instr, index})) { @@ -2365,21 +2372,25 @@ std::unique_ptr IrEmitterUnnested::BuildKernelThunk( // We'll pass a pointer to each of the elements of `buffers` to our kernel, in // this order. - std::vector buffers(buffers_needed.begin(), - buffers_needed.end()); - std::sort(buffers.begin(), buffers.end(), + std::vector non_constant_buffers; + c_copy_if(buffers_needed, std::back_inserter(non_constant_buffers), + [](const BufferAllocation* allocation) { + return !allocation->is_constant(); + }); + + std::sort(non_constant_buffers.begin(), non_constant_buffers.end(), [](const BufferAllocation* a, const BufferAllocation* b) { return a->index() < b->index(); }); - llvm::Function* kernel = BuildKernelPrototype(*inst, buffers); + llvm::Function* kernel = BuildKernelPrototype(*inst, non_constant_buffers); // Build a map from a BufferAllocation to the corresponding argument in our // kernel. std::unordered_map kernel_args; { auto arg_it = kernel->arg_begin(); - auto buffers_it = buffers.begin(); + auto buffers_it = non_constant_buffers.begin(); for (; arg_it != kernel->arg_end(); ++arg_it, ++buffers_it) { kernel_args[*buffers_it] = arg_it; } @@ -2397,8 +2408,16 @@ std::unique_ptr IrEmitterUnnested::BuildKernelThunk( << " is found in slice " << slice.ToString() << " at GTE index " << gte_index.ToString(); - llvm::Value* loc = b_.CreateInBoundsGEP(kernel_args.at(slice.allocation()), - {b_.getInt64(slice.offset())}); + llvm::Value* loc; + if (slice.allocation()->is_constant()) { + loc = ir_emitter_context_->llvm_module()->getGlobalVariable( + llvm_ir::AsStringRef( + ConstantBufferAllocationToGlobalName(*slice.allocation()))); + CHECK_NE(loc, nullptr); + } else { + loc = b_.CreateInBoundsGEP(kernel_args.at(slice.allocation()), + {b_.getInt64(slice.offset())}); + } // If gte_index is nonempty, we have to dereference `loc` to get to the // value we're ultimately interested in. @@ -2421,9 +2440,9 @@ std::unique_ptr IrEmitterUnnested::BuildKernelThunk( llvm::ConstantPointerNull::get(b_.getInt8PtrTy())); } - return MakeUnique(buffers, llvm_ir::AsString(kernel->getName()), - implements_whole_instruction ? inst : nullptr, - unroll_factor); + return MakeUnique( + non_constant_buffers, llvm_ir::AsString(kernel->getName()), + implements_whole_instruction ? inst : nullptr, unroll_factor); } std::unique_ptr IrEmitterUnnested::BuildHostToDeviceCopyThunk( @@ -2660,7 +2679,17 @@ StatusOr> IrEmitterUnnested::BuildInitializerThunk( // If the init_value was fused into this reduce we have to generate it first. if (fused && init_value_operand->opcode() != HloOpcode::kParameter) { CHECK_EQ(HloOpcode::kConstant, init_value_operand->opcode()); - TF_RETURN_IF_ERROR(HandleConstant(const_cast(init_value))); + + const Literal& literal = init_value_operand->literal(); + llvm::Constant* initializer = + llvm_ir::ConvertLiteralToIrConstant(literal, module_); + + llvm::GlobalVariable* global_for_const = new llvm::GlobalVariable( + *module_, initializer->getType(), + /*isConstant=*/true, llvm::GlobalValue::PrivateLinkage, initializer, + /*Name=*/""); + global_for_const->setAlignment(kConstantBufferAlignBytes); + bindings_.BindHloToIrValue(*init_value_operand, global_for_const); } TF_RETURN_IF_ERROR(ParallelLoopEmitter( [=](const IrArray::Index& index) { @@ -3392,5 +3421,46 @@ bool IrEmitterUnnested::CheckAndEmitHloWithTile021(HloInstruction* hlo) { return true; } +Status IrEmitterUnnested::EmitConstantGlobals() { + for (const BufferAllocation& allocation : + ir_emitter_context_->buffer_assignment().Allocations()) { + if (!allocation.is_constant()) { + continue; + } + + const Literal& literal = LiteralForConstantAllocation(allocation); + const bool should_emit_initializer = ShouldEmitLiteralInLlvmIr(literal); + llvm::ArrayType* global_type = + llvm::ArrayType::get(b_.getInt8Ty(), allocation.size()); + llvm::Constant* initializer = + should_emit_initializer + ? llvm_ir::ConvertLiteralToIrConstant(literal, module_) + : llvm::ConstantAggregateZero::get(global_type); + if (should_emit_initializer) { + VLOG(3) << "Emitted initializer for constant with shape " + << ShapeUtil::HumanString(literal.shape()); + } + + // These globals will be looked up by name by GpuExecutable so we need to + // give them an external linkage. Not all of their uses are visible in the + // LLVM IR (e.g. TupleThunk) so we can't give then a linkage that merely + // preserves their names (like available_externally), we also need to ensure + // that they stick around even if they're "unused". + // + // We may have to be more more clever here in the future if we notice that + // we're keeping around too many globals because of their linkage. + llvm::GlobalVariable* global_for_const = new llvm::GlobalVariable( + global_type, /*isConstant=*/should_emit_initializer, + llvm::GlobalValue::ExternalLinkage, + /*Initializer=*/initializer, + llvm_ir::AsStringRef(ConstantBufferAllocationToGlobalName(allocation))); + global_for_const->setAlignment(kConstantBufferAlignBytes); + ir_emitter_context_->llvm_module()->getGlobalList().push_back( + global_for_const); + } + + return Status::OK(); +} + } // namespace gpu } // namespace xla diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h index 616d8a2206..5254419907 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.h @@ -92,6 +92,9 @@ class IrEmitterUnnested : public IrEmitter { const HloInstruction& hlo, const llvm_ir::ElementGenerator& body_emitter, KernelThunk* thunk); + // Emits LLVM global variables corresponding to constant instructions. + Status EmitConstantGlobals(); + private: // Builds the appropriate thunk for the instruction hlo and returns the owning // pointer to it. The caller needs to make sure `inst` outlives the lifetime diff --git a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc index 2eefadebcd..6d8996dac1 100644 --- a/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc +++ b/tensorflow/compiler/xla/service/gpu/nvptx_compiler.cc @@ -540,11 +540,13 @@ StatusOr> NVPTXCompiler::RunBackend( // temporary buffers are required to run the computation. TF_ASSIGN_OR_RETURN( std::unique_ptr buffer_assignment, - BufferAssigner::Run(module.get(), hlo_schedule->ConsumeHloOrdering(), - BufferSizeBytesFunction(), - /*color_alignment=*/[](LogicalBuffer::Color) { - return kXlaAllocatedBufferAlignBytes; - })); + BufferAssigner::Run( + module.get(), hlo_schedule->ConsumeHloOrdering(), + BufferSizeBytesFunction(), + /*color_alignment=*/ + [](LogicalBuffer::Color) { return kXlaAllocatedBufferAlignBytes; }, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true)); // BufferAssignment::Stats::ToString() and BufferAssignment::ToString() // include headers, so no need for us to print them ourselves. XLA_VLOG_LINES(1, buffer_assignment->GetStats().ToString()); @@ -565,6 +567,9 @@ StatusOr> NVPTXCompiler::RunBackend( HloComputation* entry_computation = module->entry_computation(); IrEmitterUnnested ir_emitter(module->config(), entry_computation, &ir_emitter_context); + + TF_RETURN_IF_ERROR(ir_emitter.EmitConstantGlobals()); + { XLA_SCOPED_LOGGING_TIMER("NVPTXCompiler::RunBackend - IR emission"); TF_RETURN_IF_ERROR(entry_computation->Accept(&ir_emitter)); diff --git a/tensorflow/compiler/xla/service/gpu/while_thunk.cc b/tensorflow/compiler/xla/service/gpu/while_thunk.cc index 1315a4183a..d81d87e7dc 100644 --- a/tensorflow/compiler/xla/service/gpu/while_thunk.cc +++ b/tensorflow/compiler/xla/service/gpu/while_thunk.cc @@ -57,6 +57,7 @@ Status WhileThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, while (true) { // Invoke thunk sequence for while 'condition' computation. profiler->StartHloComputation(); + VLOG(3) << "Executing condition computation"; TF_RETURN_IF_ERROR(condition_thunk_sequence_->ExecuteOnStream( buffer_allocations, stream, profiler)); profiler->FinishHloComputation(hlo_instruction()->while_condition()); @@ -64,6 +65,7 @@ Status WhileThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, // Copy the result of condition computation and break the loop if 'false'. bool condition_result; stream->ThenMemcpy(&condition_result, condition_result_data, sizeof(bool)); + VLOG(3) << "condition_result = " << condition_result; Status block_status = stream->BlockHostUntilDone(); if (!block_status.ok()) { return InternalError( @@ -78,6 +80,7 @@ Status WhileThunk::ExecuteOnStream(const BufferAllocations& buffer_allocations, // We measure the time of one execution of the while body computation. The // while body may be executed more than once, the last measurement "wins". profiler->StartHloComputation(); + VLOG(3) << "Executing body computation"; // Invoke thunk sequence for while 'body' computation, and pass on // 'profiler' to measure the timing of the thunks in 'body_thunk_sequence_'. TF_RETURN_IF_ERROR(body_thunk_sequence_->ExecuteOnStream(buffer_allocations, -- GitLab From eea66eec3f69b261835d1ff23d7ba30aaefee032 Mon Sep 17 00:00:00 2001 From: Priya Gupta Date: Thu, 26 Jul 2018 17:03:22 -0700 Subject: [PATCH 479/519] Use `SaveableObject.restore` in init_from_checkpoint instead of `assign`. PiperOrigin-RevId: 206245967 --- tensorflow/python/training/checkpoint_utils.py | 17 ++++++++--------- .../python/training/checkpoint_utils_test.py | 4 +++- 2 files changed, 11 insertions(+), 10 deletions(-) diff --git a/tensorflow/python/training/checkpoint_utils.py b/tensorflow/python/training/checkpoint_utils.py index 799729b017..a052081630 100644 --- a/tensorflow/python/training/checkpoint_utils.py +++ b/tensorflow/python/training/checkpoint_utils.py @@ -24,7 +24,6 @@ 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 from tensorflow.python.platform import gfile @@ -308,14 +307,14 @@ def _set_checkpoint_initializer(variable, restore_op = io_ops.restore_v2( ckpt_file, [tensor_name], [slice_spec], [base_type], name=name)[0] - # TODO(priyag, allenl): Use `SaveableObject.restore` instead here. - if resource_variable_ops.is_resource_variable(variable): - init_op = variable.assign(restore_op, read_value=False) - # TODO(priyag): Remove this when using `SaveableObject.restore` instead. - if hasattr(init_op, "_index"): - init_op = distribute_lib.get_distribution_strategy().group(init_op) - else: - init_op = state_ops.assign(variable, restore_op) + names_to_saveables = saver.BaseSaverBuilder.OpListToDict([variable]) + saveable_objects = [] + for name, op in names_to_saveables.items(): + for s in saver.BaseSaverBuilder.SaveableObjectsForOp(op, name): + saveable_objects.append(s) + + assert len(saveable_objects) == 1 # Should be only one variable. + init_op = saveable_objects[0].restore([restore_op], restored_shapes=None) # pylint:disable=protected-access variable._initializer_op = init_op diff --git a/tensorflow/python/training/checkpoint_utils_test.py b/tensorflow/python/training/checkpoint_utils_test.py index 4e08a1c859..1c1f126ce9 100644 --- a/tensorflow/python/training/checkpoint_utils_test.py +++ b/tensorflow/python/training/checkpoint_utils_test.py @@ -386,7 +386,9 @@ class CheckpointsTest(test.TestCase): 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") + ) and + op.type != "AssignVariableOp" and + op.type != "Identity") ] self.assertEqual(ops_in_init_from_checkpoint_scope, []) -- GitLab From f83263d3eca91f52b5b1005af2c09f16a7b70b20 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Thu, 26 Jul 2018 17:36:22 -0700 Subject: [PATCH 480/519] Update backward pass to save memory in eager mode. PiperOrigin-RevId: 206249965 --- .../eager/python/examples/revnet/blocks.py | 21 +++++++++---------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py index 2cb04ed258..63e86803ef 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -192,26 +192,25 @@ class _Residual(tf.keras.Model): def backward_grads(self, y, dy, training=True): """Manually compute backward gradients given input and output grads.""" dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=self.axis) + y1, y2 = tf.split(y, num_or_size_splits=2, axis=self.axis) - with tf.GradientTape(persistent=True) as tape: - tape.watch(y) - y1, y2 = tf.split(y, num_or_size_splits=2, axis=self.axis) + with tf.GradientTape() as gtape: + gtape.watch(y1) gy1 = self.g(y1, training=training) - x2 = y2 - gy1 - fx2 = self.f(x2, training=training) - x1 = y1 - fx2 - - grads_combined = tape.gradient( + grads_combined = gtape.gradient( gy1, [y1] + self.g.trainable_variables, output_gradients=dy2) dg = grads_combined[1:] dx1 = dy1 + grads_combined[0] + x2 = y2 - gy1 - grads_combined = tape.gradient( + with tf.GradientTape() as ftape: + ftape.watch(x2) + fx2 = self.f(x2, training=training) + grads_combined = ftape.gradient( fx2, [x2] + self.f.trainable_variables, output_gradients=dx1) dx2 = dy2 + grads_combined[0] df = grads_combined[1:] - - del tape + x1 = y1 - fx2 x = tf.concat([x1, x2], axis=self.axis) dx = tf.concat([dx1, dx2], axis=self.axis) -- GitLab From c7fc5b013ba9379e2211acc0b08bdd6774dd468d Mon Sep 17 00:00:00 2001 From: Dustin Tran Date: Thu, 26 Jul 2018 17:36:27 -0700 Subject: [PATCH 481/519] Add low and high as properties to quantized distribution. PiperOrigin-RevId: 206249977 --- .../python/ops/quantized_distribution.py | 20 ++++++++++++++----- 1 file changed, 15 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py index ef3bdfa75f..18a0f754e6 100644 --- a/tensorflow/contrib/distributions/python/ops/quantized_distribution.py +++ b/tensorflow/contrib/distributions/python/ops/quantized_distribution.py @@ -326,6 +326,21 @@ class QuantizedDistribution(distributions.Distribution): graph_parents=graph_parents, name=name) + @property + def distribution(self): + """Base distribution, p(x).""" + return self._dist + + @property + def low(self): + """Lowest value that quantization returns.""" + return self._low + + @property + def high(self): + """Highest value that quantization returns.""" + return self._high + def _batch_shape_tensor(self): return self.distribution.batch_shape_tensor() @@ -569,8 +584,3 @@ class QuantizedDistribution(distributions.Distribution): dependencies = [distribution_util.assert_integer_form( value, message="value has non-integer components.")] return control_flow_ops.with_dependencies(dependencies, value) - - @property - def distribution(self): - """Base distribution, p(x).""" - return self._dist -- GitLab From 21d94be838f7a7917f072e5ec0bbe6e3593177b9 Mon Sep 17 00:00:00 2001 From: Allen Lavoie Date: Thu, 26 Jul 2018 17:53:31 -0700 Subject: [PATCH 482/519] Simulate eager variable resoration in tf.keras.Model.load_weights when graph building Previously, the first Model build after load_weights (e.g. a predict()) would trigger restore ops, and any variables added later (e.g. slot variables from an added optimizer) would not be restored when graph building. This change makes behavior consistent between eager execution and graph building by running new restore ops as they come in. PiperOrigin-RevId: 206251879 --- tensorflow/python/keras/engine/base_layer.py | 9 ---- tensorflow/python/keras/engine/network.py | 23 ++--------- tensorflow/python/keras/engine/saving_test.py | 41 +++++++++++++------ tensorflow/python/keras/engine/training.py | 3 -- .../python/training/checkpointable/base.py | 2 +- .../python/training/checkpointable/util.py | 37 ++++++++++++++++- 6 files changed, 68 insertions(+), 47 deletions(-) diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py index b41f6ee03b..7af71f17a9 100644 --- a/tensorflow/python/keras/engine/base_layer.py +++ b/tensorflow/python/keras/engine/base_layer.py @@ -786,17 +786,8 @@ class Layer(checkpointable.CheckpointableBase): if hasattr(self, '_initial_weights') and self._initial_weights is not None: self.set_weights(self._initial_weights) del self._initial_weights - self._post_build_cleanup() return outputs - def _post_build_cleanup(self): - """Hooks to run after all sub-Layers are built.""" - # Note that in addition to Layer.__call__, this method is called by Model - # after building a graph network (which skips __call__). It should be called - # when possible if self.built may have switched from False to True, and is - # idempotent. - pass # No-op for Layers which don't override this method. - def apply(self, inputs, *args, **kwargs): """Apply the layer on a input. diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index 752e9963ca..3e7e3e3d21 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -20,7 +20,6 @@ from __future__ import division from __future__ import print_function import copy -import functools import json import os import weakref @@ -144,10 +143,6 @@ class Network(base_layer.Layer): self._checkpointable_saver = checkpointable_utils.CheckpointableSaver( weakref.ref(self)) - # A zero-argument function which should be called and set back to None as - # soon as the network is built (only applicable to subclassed Models). Runs - # restore operations when graph building. - self._in_progress_restore_finalizer = None @checkpointable.no_automatic_dependency_tracking def _init_graph_network(self, inputs, outputs, name=None): @@ -1423,13 +1418,9 @@ class Network(base_layer.Layer): 'load_weights).') if not context.executing_eagerly(): session = backend.get_session() - finalizer = functools.partial(status.run_restore_ops, session=session) - if self.built: - finalizer() - else: - # Hold on to this status object until the network is built (for - # subclassed Models). Then we'll run restore ops if necessary. - self._in_progress_restore_finalizer = finalizer + # Restore existing variables (if any) immediately, and set up a + # streaming restore for any variables created in the future. + checkpointable_utils.streaming_restore(status=status, session=session) return status if h5py is None: raise ImportError( @@ -1447,14 +1438,6 @@ class Network(base_layer.Layer): else: saving.load_weights_from_hdf5_group(f, self.layers) - def _post_build_cleanup(self): - super(Network, self)._post_build_cleanup() - if self._in_progress_restore_finalizer is not None: - # Runs queued restore operations left over from load_weights when graph - # building. - self._in_progress_restore_finalizer() - self._in_progress_restore_finalizer = None - def _updated_config(self): """Util shared between different serialization methods. diff --git a/tensorflow/python/keras/engine/saving_test.py b/tensorflow/python/keras/engine/saving_test.py index 030328f2a6..e029e614e0 100644 --- a/tensorflow/python/keras/engine/saving_test.py +++ b/tensorflow/python/keras/engine/saving_test.py @@ -722,18 +722,23 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): self.assertEqual(len(graph.get_operations()), op_count) def _weight_loading_test_template(self, make_model_fn): - with self.test_session() as session: + with self.test_session(): model = make_model_fn() + model.compile( + loss='mse', + optimizer=training_module.RMSPropOptimizer(0.1), + metrics=['acc']) temp_dir = self.get_temp_dir() prefix = os.path.join(temp_dir, 'ckpt') + train_x = np.random.random((3, 2)) + train_y = np.random.random((3,)) + x = constant_op.constant(train_x, dtype=dtypes.float32) - x = constant_op.constant(np.random.random((3, 2)), dtype=dtypes.float32) - executing_eagerly = context.executing_eagerly() - ref_y_tensor = model(x) - if not executing_eagerly: - session.run([v.initializer for v in model.variables]) - ref_y = self.evaluate(ref_y_tensor) + model.train_on_batch(train_x, train_y) model.save_weights(prefix, save_format='tf') + ref_y_before_train = model.predict(train_x) + model.train_on_batch(train_x, train_y) + ref_y_after_train = model.predict(train_x) for v in model.variables: self.evaluate( v.assign(random_ops.random_normal(shape=array_ops.shape(v)))) @@ -741,16 +746,27 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): self.addCleanup(shutil.rmtree, temp_dir) model.load_weights(prefix) - y = self.evaluate(model(x)) - self.assertAllClose(ref_y, y) + self.assertAllClose(ref_y_before_train, self.evaluate(model(x))) # Test restore-on-create if this is a subclassed Model (graph Networks # will have already created their variables). load_model = make_model_fn() load_model.load_weights(prefix) - restore_on_create_y_tensor = load_model(x) - restore_on_create_y = self.evaluate(restore_on_create_y_tensor) - self.assertAllClose(ref_y, restore_on_create_y) + self.assertAllClose( + ref_y_before_train, + self.evaluate(load_model(x))) + load_model = make_model_fn() + load_model.load_weights(prefix) + # We need to run some of the restore ops for predict(), but not all + # variables have been created yet (optimizer slot variables). Tests + # incremental restore. + load_model.predict(train_x) + load_model.compile( + loss='mse', + optimizer=training_module.RMSPropOptimizer(0.1), + metrics=['acc']) + load_model.train_on_batch(train_x, train_y) + self.assertAllClose(ref_y_after_train, self.evaluate(load_model(x))) @test_util.run_in_graph_and_eager_modes def test_weight_loading_graph_model(self): @@ -858,5 +874,6 @@ class TestWeightSavingAndLoadingTFFormat(test.TestCase): SubclassedModel, SubclassedModelRestore, _restore_init_fn) + if __name__ == '__main__': test.main() diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 39d207cc6b..315d88d418 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -671,7 +671,6 @@ class Model(Network): updates=updates, name='train_function', **self._function_kwargs) - self._post_build_cleanup() def _make_test_function(self): if not hasattr(self, 'test_function'): @@ -689,7 +688,6 @@ class Model(Network): updates=self.state_updates + self.metrics_updates, name='test_function', **self._function_kwargs) - self._post_build_cleanup() def _make_predict_function(self): if not hasattr(self, 'predict_function'): @@ -708,7 +706,6 @@ class Model(Network): updates=self.state_updates, name='predict_function', **kwargs) - self._post_build_cleanup() def _get_iterator_get_next_tensors(self, iterator): get_next_op = self._iterator_get_next.get(iterator, None) diff --git a/tensorflow/python/training/checkpointable/base.py b/tensorflow/python/training/checkpointable/base.py index f0703c8af4..66837ee52f 100644 --- a/tensorflow/python/training/checkpointable/base.py +++ b/tensorflow/python/training/checkpointable/base.py @@ -144,7 +144,7 @@ class _CheckpointPosition(object): # process deferred restorations for it and its dependencies. restore_ops = checkpointable._restore_from_checkpoint_position(self) # pylint: disable=protected-access if restore_ops: - self._checkpoint.restore_ops.extend(restore_ops) + self._checkpoint.new_restore_ops(restore_ops) def bind_object(self, checkpointable): """Set a checkpoint<->object correspondence and process slot variables. diff --git a/tensorflow/python/training/checkpointable/util.py b/tensorflow/python/training/checkpointable/util.py index 5d26a817d4..664b2348c0 100644 --- a/tensorflow/python/training/checkpointable/util.py +++ b/tensorflow/python/training/checkpointable/util.py @@ -101,6 +101,7 @@ class _CheckpointRestoreCoordinator(object): # this checkpoint. self.restore_ops = [] self.restore_ops_by_name = {} + self.new_restore_ops_callback = None # 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 @@ -121,6 +122,11 @@ class _CheckpointRestoreCoordinator(object): slot_variable_id=slot_reference.slot_variable_node_id, slot_name=slot_reference.slot_name)) + def new_restore_ops(self, new_ops): + self.restore_ops.extend(new_ops) + if self.new_restore_ops_callback: + self.new_restore_ops_callback(new_ops) # pylint: disable=not-callable + class _NameBasedRestoreCoordinator(object): """Keeps the status of a name-based checkpoint restore.""" @@ -821,6 +827,31 @@ class _LoadStatus(object): pass +def streaming_restore(status, session=None): + """When graph building, runs restore ops as soon as they come in. + + Args: + status: A _LoadStatus objects from an object-based saver's + restore(). Streaming restore from name-based checkpoints is not currently + supported. + session: A session to run new restore ops in. + """ + if context.executing_eagerly(): + # Streaming restore is the default/only behavior when executing eagerly. + return + if session is None: + session = ops.get_default_session() + if isinstance(status, NameBasedSaverStatus): + raise NotImplementedError( + "Streaming restore not supported from name-based checkpoints. File a " + "feature request if this limitation bothers you.") + status.run_restore_ops(session=session) + # pylint: disable=protected-access + status._checkpoint.new_restore_ops_callback = ( + lambda ops: session.run(ops, feed_dict=status._feed_dict)) + # pylint: enable=protected-access + + class CheckpointLoadStatus(_LoadStatus): """Checks the status of checkpoint loading and manages restore ops. @@ -992,11 +1023,13 @@ _DEPRECATED_RESTORE_INSTRUCTIONS = ( "one this message is coming from) and use that checkpoint in the future.") -@deprecation.deprecated( - date=None, instructions=_DEPRECATED_RESTORE_INSTRUCTIONS) class NameBasedSaverStatus(_LoadStatus): """Status for loading a name-based training checkpoint.""" + # Ideally this deprecation decorator would be on the class, but that + # interferes with isinstance checks. + @deprecation.deprecated( + date=None, instructions=_DEPRECATED_RESTORE_INSTRUCTIONS) def __init__(self, checkpoint, root_checkpointable): self._checkpoint = checkpoint self._root_checkpointable = root_checkpointable -- GitLab From 65c2baa72da17a3c20d230863c4fe975e88444d6 Mon Sep 17 00:00:00 2001 From: Guangda Lai Date: Thu, 26 Jul 2018 18:01:13 -0700 Subject: [PATCH 483/519] Add build option to exclude TensorRT from cuda build. PiperOrigin-RevId: 206252639 --- tensorflow/BUILD | 8 -------- 1 file changed, 8 deletions(-) diff --git a/tensorflow/BUILD b/tensorflow/BUILD index 388ca3f293..c9d67021e1 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -408,14 +408,6 @@ config_setting( visibility = ["//visibility:public"], ) -# TODO(laigd): consider removing this option and make TensorRT enabled -# automatically when CUDA is enabled. -config_setting( - name = "with_tensorrt_support", - values = {"define": "with_tensorrt_support=true"}, - visibility = ["//visibility:public"], -) - package_group( name = "internal", packages = [ -- GitLab From 9beaf7038c4f8ca5b6a5168c47efbb3fc669b64b Mon Sep 17 00:00:00 2001 From: Francois Chollet Date: Thu, 26 Jul 2018 19:27:30 -0700 Subject: [PATCH 484/519] Layers/Networks now have a default implementation for static shape inference in eager execution (when possible). Also move `build` implementation for subclassed networks from Model to Network (where it belongs) and slightly refactor it to minimize code duplication. PiperOrigin-RevId: 206260286 --- tensorflow/python/keras/engine/base_layer.py | 55 +++++++++++ tensorflow/python/keras/engine/network.py | 83 ++++++++++++++++ .../python/keras/engine/topology_test.py | 99 ++++++++++++++++++- tensorflow/python/keras/engine/training.py | 92 ----------------- .../python/keras/model_subclassing_test.py | 3 - 5 files changed, 233 insertions(+), 99 deletions(-) diff --git a/tensorflow/python/keras/engine/base_layer.py b/tensorflow/python/keras/engine/base_layer.py index 7af71f17a9..c7ab6750cb 100644 --- a/tensorflow/python/keras/engine/base_layer.py +++ b/tensorflow/python/keras/engine/base_layer.py @@ -26,6 +26,7 @@ import numpy as np from six.moves import zip # pylint: disable=redefined-builtin from tensorflow.python.eager import context +from tensorflow.python.eager import function as eager_function from tensorflow.python.framework import dtypes from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -174,6 +175,12 @@ class Layer(checkpointable.CheckpointableBase): self.supports_masking = False + call_argspec = tf_inspect.getargspec(self.call) + if 'training' in call_argspec.args: + self._expects_training_arg = True + else: + self._expects_training_arg = 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 @@ -961,6 +968,39 @@ class Layer(checkpointable.CheckpointableBase): Returns: An input shape tuple. """ + if context.executing_eagerly(): + # In this case we build the model first in order to do shape inference. + # This is acceptable because the framework only calls + # `compute_output_shape` on shape values that the layer would later be + # built for. It would however cause issues in case a user attempts to + # use `compute_output_shape` manually (these users will have to + # implement `compute_output_shape` themselves). + self.build(input_shape) + + with context.graph_mode(): + graph = eager_function.CapturingGraph({}) + with graph.as_default(): + if isinstance(input_shape, list): + inputs = [generate_placeholders_from_shape(shape) + for shape in input_shape] + else: + inputs = generate_placeholders_from_shape(input_shape) + + try: + if self._expects_training_arg: + outputs = self(inputs, training=False) + else: + outputs = self(inputs) + except TypeError: + raise NotImplementedError('We could not automatically infer ' + 'the static shape of the layer\'s output.' + ' Please implement the ' + '`compute_output_shape` method on your ' + 'layer (%s).' % self.__class__.__name__) + if isinstance(outputs, list): + return [output.shape for output in outputs] + else: + return outputs.shape raise NotImplementedError def compute_mask(self, inputs, mask=None): # pylint: disable=unused-argument @@ -1916,3 +1956,18 @@ def make_variable(name, synchronization=synchronization, aggregation=aggregation) return v + + +def generate_dummy_data_from_shape(shape): + if isinstance(shape, tensor_shape.TensorShape): + shape = shape.as_list() + + # Replace Nones in input shape with dummy `1` value + shape = [x.value if isinstance(x, tensor_shape.Dimension) else x + for x in shape] + shape = [1 if x is None else x for x in shape] + return array_ops.ones(shape, dtype=backend.floatx()) + + +def generate_placeholders_from_shape(shape): + return array_ops.placeholder(shape=shape, dtype=backend.floatx()) diff --git a/tensorflow/python/keras/engine/network.py b/tensorflow/python/keras/engine/network.py index 3e7e3e3d21..d9b031d080 100644 --- a/tensorflow/python/keras/engine/network.py +++ b/tensorflow/python/keras/engine/network.py @@ -29,6 +29,7 @@ from six.moves import zip # pylint: disable=redefined-builtin from tensorflow.python import pywrap_tensorflow from tensorflow.python.eager import context +from tensorflow.python.framework import errors from tensorflow.python.framework import errors_impl from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_shape @@ -734,6 +735,86 @@ class Network(base_layer.Layer): return specs[0] return specs + def build(self, input_shape): + """Builds the model based on input shapes received. + + This is to be used for subclassed models, which do not know at instantiation + time what their inputs look like. + + Args: + input_shape: Single tuple, TensorShape, or list of shapes, where shapes + are tuples, integers, or TensorShapes. + + Raises: + ValueError: + 1. In case of invalid user-provided data (not of type tuple, + list, or TensorShape). + 2. If the model requires call arguments that are agnostic + to the input shapes (positional or kwarg in call signature). + 3. If not all layers were properly built. + 4. If float type inputs are not supported within the layers. + + In each of these cases, the user should build their model by calling it + on real tensor data. + """ + if self._is_graph_network: + self.built = True + return + + # If subclass network + if input_shape is None: + raise ValueError('Input shape must be defined when calling build on a ' + 'model subclass network.') + valid_types = (tuple, list, tensor_shape.TensorShape) + if not isinstance(input_shape, valid_types): + raise ValueError('Specified input shape is not one of the valid types. ' + 'Please specify a batch input shape of type tuple or ' + 'list of input shapes. User provided ' + 'input type: {}'.format(type(input_shape))) + + if input_shape and not self.inputs: + if isinstance(input_shape, list): + # List of input shapes + x = [base_layer.generate_dummy_data_from_shape(shape) + for shape in input_shape] + else: + x = base_layer.generate_dummy_data_from_shape(input_shape) + + kwargs = {} + num_call_args = len(tf_inspect.getargspec(self.call).args) + if self._expects_training_arg and num_call_args == 3: + # Has call signature of call(self, input, training) + kwargs['training'] = False + elif num_call_args > 2: + # Has invalid call signature of call(self, input, *args, **kwargs) + raise ValueError('Currently, you cannot build your model if it has ' + 'positional or keyword arguments that are not ' + 'inputs to the model, but are required for its ' + '`call` method. Instead, in order to instantiate ' + 'and build your model, `call` your model on real ' + 'tensor data with all expected call arguments.') + + try: + self.call(x, **kwargs) + except (errors.InvalidArgumentError, TypeError): + raise ValueError('You cannot build your model by calling `build` ' + 'if your layers do not support float type inputs. ' + 'Instead, in order to instantiate and build your ' + 'model, `call` your model on real tensor data (of ' + 'the correct dtype).') + + if self._layers: + self._track_layers(self._layers) + if self.layers: + for layer in self.layers: + if not layer.built: + raise ValueError('Layer: {} was not built in your model. Calling ' + '`build` manually on a subclassed model is only ' + 'allowed for models with a static topology. ' + 'In this case, you can build your model by ' + 'calling it on real tensor data.'.format(layer)) + self.built = True + def call(self, inputs, training=None, mask=None): """Calls the model on new inputs. @@ -774,6 +855,8 @@ class Network(base_layer.Layer): def compute_output_shape(self, input_shape): if not self._is_graph_network: + if context.executing_eagerly(): + return super(Network, self).compute_output_shape(input_shape) raise NotImplementedError if isinstance(input_shape, list): diff --git a/tensorflow/python/keras/engine/topology_test.py b/tensorflow/python/keras/engine/topology_test.py index 3eb69bd7f3..34f74db6ef 100644 --- a/tensorflow/python/keras/engine/topology_test.py +++ b/tensorflow/python/keras/engine/topology_test.py @@ -110,7 +110,6 @@ class TopologyConstructionTest(test.TestCase): layer = keras.layers.BatchNormalization() _ = layer.apply(x1) - print('BN updates', layer._updates) self.assertEqual(len(layer.updates), 2) self.assertEqual(len(layer.get_updates_for(x1)), 2) self.assertEqual(len(layer.get_updates_for(None)), 0) @@ -960,9 +959,6 @@ class DeferredModeTest(test.TestCase): 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) @@ -978,6 +974,101 @@ class DeferredModeTest(test.TestCase): self.assertEqual(outputs[1].shape.as_list(), [10, 2]) +class DefaultShapeInferenceBehaviorTest(test.TestCase): + + def _testShapeInference(self, model, input_shape, expected_output_shape): + input_value = np.random.random(input_shape) + output_value = model.predict(input_value) + self.assertEqual(output_value.shape, expected_output_shape) + + @test_util.run_in_graph_and_eager_modes() + def testSingleInputCase(self): + + class LayerWithOneInput(keras.layers.Layer): + + def build(self, input_shape): + self.w = array_ops.ones(shape=(3, 4)) + + def call(self, inputs): + return keras.backend.dot(inputs, self.w) + + inputs = input_layer_lib.Input(shape=(3,)) + layer = LayerWithOneInput() + + if context.executing_eagerly(): + self.assertEqual( + layer.compute_output_shape((None, 3)).as_list(), [None, 4]) + # As a side-effect, compute_output_shape builds the layer. + self.assertTrue(layer.built) + # We can still query the layer's compute_output_shape with compatible + # input shapes. + self.assertEqual( + layer.compute_output_shape((6, 3)).as_list(), [6, 4]) + + outputs = layer(inputs) + model = keras.Model(inputs, outputs) + self._testShapeInference(model, (2, 3), (2, 4)) + + @test_util.run_in_graph_and_eager_modes() + def testMultiInputOutputCase(self): + + class MultiInputOutputLayer(keras.layers.Layer): + + def build(self, input_shape): + self.w = array_ops.ones(shape=(3, 4)) + + def call(self, inputs): + a = keras.backend.dot(inputs[0], self.w) + b = a + inputs[1] + return [a, b] + + input_a = input_layer_lib.Input(shape=(3,)) + input_b = input_layer_lib.Input(shape=(4,)) + output_a, output_b = MultiInputOutputLayer()([input_a, input_b]) + model = keras.Model([input_a, input_b], [output_a, output_b]) + output_a_val, output_b_val = model.predict( + [np.random.random((2, 3)), np.random.random((2, 4))]) + self.assertEqual(output_a_val.shape, (2, 4)) + self.assertEqual(output_b_val.shape, (2, 4)) + + @test_util.run_in_graph_and_eager_modes() + def testTrainingArgument(self): + + class LayerWithTrainingArg(keras.layers.Layer): + + def build(self, input_shape): + self.w = array_ops.ones(shape=(3, 4)) + + def call(self, inputs, training): + return keras.backend.dot(inputs, self.w) + + inputs = input_layer_lib.Input(shape=(3,)) + outputs = LayerWithTrainingArg()(inputs, training=False) + model = keras.Model(inputs, outputs) + self._testShapeInference(model, (2, 3), (2, 4)) + + @test_util.run_in_graph_and_eager_modes() + def testUnsupportedSignature(self): + + class LayerWithAdditionalArg(keras.layers.Layer): + + def build(self, input_shape): + self.w = array_ops.ones(shape=(3, 4)) + + def call(self, inputs, some_arg): + return keras.backend.dot(inputs, self.w) + some_arg + + inputs = input_layer_lib.Input(shape=(3,)) + if context.executing_eagerly(): + with self.assertRaises(NotImplementedError): + outputs = LayerWithAdditionalArg()(inputs, some_arg=0) + else: + # Works with graph mode because the graph of ops is built together with + # the graph of layers. + outputs = LayerWithAdditionalArg()(inputs, some_arg=0) + _ = keras.Model(inputs, outputs) + + class GraphUtilsTest(test.TestCase): def testGetReachableFromInputs(self): diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index 315d88d418..bfcb0e1d43 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -26,7 +26,6 @@ from tensorflow.python.data.ops import iterator_ops from tensorflow.python.eager import context from tensorflow.python.framework import errors from tensorflow.python.framework import ops -from tensorflow.python.framework import tensor_shape from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend as K from tensorflow.python.keras import losses @@ -39,11 +38,9 @@ from tensorflow.python.keras.engine import training_generator from tensorflow.python.keras.engine import training_utils from tensorflow.python.keras.engine.network import Network from tensorflow.python.keras.utils.generic_utils import slice_arrays -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.training.checkpointable import base as checkpointable -from tensorflow.python.util import tf_inspect from tensorflow.python.util.tf_export import tf_export @@ -535,95 +532,6 @@ class Model(Network): trainable_weights = self.trainable_weights self._collected_trainable_weights = trainable_weights - def build(self, input_shape): - """Build the model based on input shapes received. - - This is to be used for subclassed models, which do not know at instantiation - time what their inputs look like. - - Args: - input_shape: Single tuple, TensorShape, or list of shapes, where shapes - are tuples, integers, or TensorShapes. - - Raises: - ValueError: - 1. In case of invalid user-provided data (not of type tuple, - list, or TensorShape). - 2. If the model requires call arguments that are agnostic - to the input shapes (positional or kwarg in call signature). - 3. If not all layers were properly built. - 4. If float type inputs are not supported within the layers. - - In each of these cases, the user should build their model by calling it - on real tensor data. - """ - if self._is_graph_network: - self.built = True - return - - # If subclass network - if input_shape is None: - raise ValueError('Input shape must be defined when calling build on a ' - 'model subclass network.') - valid_types = (tuple, list, tensor_shape.TensorShape) - if not isinstance(input_shape, valid_types): - raise ValueError('Specified input shape is not one of the valid types. ' - 'Please specify a batch input shape of type tuple or ' - 'list of input shapes. User provided ' - 'input type: {}'.format(type(input_shape))) - - def _generate_dummy_data_from_shape(shape): - if isinstance(shape, tensor_shape.TensorShape): - shape = shape.as_list() - - # Replace Nones in input shape with dummy `1` value - shape = [x.value if isinstance(x, tensor_shape.Dimension) else x - for x in shape] - shape = [1 if x is None else x for x in shape] - return array_ops.ones(shape, dtype=K.floatx()) - - if input_shape and not self.inputs: - if isinstance(input_shape, list): - # List of input shapes - x = [_generate_dummy_data_from_shape(shape) for shape in input_shape] - else: - x = _generate_dummy_data_from_shape(input_shape) - - kwargs = {} - num_call_args = len(tf_inspect.getargspec(self.call).args) - if self._expects_training_arg and num_call_args == 3: - # Has call signature of call(self, input, training) - kwargs['training'] = False - elif num_call_args > 2: - # Has invalid call signature of call(self, input, *args, **kwargs) - raise ValueError('Currently, you cannot build your model if it has ' - 'positional or keyword arguments that are not ' - 'inputs to the model, but are required for its ' - '`call` method. Instead, in order to instantiate ' - 'and build your model, `call` your model on real ' - 'tensor data with all expected call arguments.') - - try: - self.call(x, **kwargs) - except (errors.InvalidArgumentError, TypeError): - raise ValueError('You cannot build your model by calling `build` ' - 'if your layers do not support float type inputs. ' - 'Instead, in order to instantiate and build your ' - 'model, `call` your model on real tensor data (of ' - 'the correct dtype).') - - if self._layers: - self._track_layers(self._layers) - if self.layers: - for layer in self.layers: - if not layer.built: - raise ValueError('Layer: {} was not built in your model. Calling ' - '`build` manually on a subclassed model is only ' - 'allowed for models with a static topology. ' - 'In this case, you can build your model by ' - 'calling it on real tensor data.'.format(layer)) - self.built = True - def _check_trainable_weights_consistency(self): """Check trainable weights count consistency. diff --git a/tensorflow/python/keras/model_subclassing_test.py b/tensorflow/python/keras/model_subclassing_test.py index 5fbc191e78..3a153573f8 100644 --- a/tensorflow/python/keras/model_subclassing_test.py +++ b/tensorflow/python/keras/model_subclassing_test.py @@ -180,9 +180,6 @@ def get_nested_model_3(input_dim, num_classes): x = self.dense2(x) return self.bn(x) - def compute_output_shape(self, input_shape): - return tensor_shape.TensorShape((input_shape[0], 5)) - test_model = Inner() x = test_model(x) outputs = keras.layers.Dense(num_classes)(x) -- GitLab From 82293a9cb0606b4dfbe45fc4e2f4aa8778ff7e9a Mon Sep 17 00:00:00 2001 From: Suharsh Sivakumar Date: Thu, 26 Jul 2018 20:41:37 -0700 Subject: [PATCH 485/519] SpaceToBatchND should pad with zero_point when inference_type is uint8 PiperOrigin-RevId: 206265356 --- .../internal/reference/reference_ops.h | 16 +- .../contrib/lite/kernels/space_to_batch_nd.cc | 22 +-- .../lite/kernels/space_to_batch_nd_test.cc | 142 +++++++++++++++--- 3 files changed, 149 insertions(+), 31 deletions(-) diff --git a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h index 31a54c2b62..ce16394082 100644 --- a/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h +++ b/tensorflow/contrib/lite/kernels/internal/reference/reference_ops.h @@ -3284,7 +3284,8 @@ inline void SpaceToBatchND(const T* input_data, const Dims<4>& input_dims, const Dims<4>& block_shape_dims, const int32* paddings_data, const Dims<4>& paddings_dims, T* output_data, - const Dims<4>& output_dims) { + const Dims<4>& output_dims, + const int32_t pad_value) { const int output_batch_size = ArraySize(output_dims, 3); const int output_height = ArraySize(output_dims, 2); const int output_width = ArraySize(output_dims, 1); @@ -3309,7 +3310,7 @@ inline void SpaceToBatchND(const T* input_data, const Dims<4>& input_dims, padding_top + input_height || out_w * block_shape_width + shift_w < padding_left || out_w * block_shape_width + shift_w >= padding_left + input_width) { - memset(out, 0, depth * sizeof(T)); + memset(out, pad_value, depth * sizeof(T)); } else { const T* in = input_data + @@ -3324,6 +3325,17 @@ inline void SpaceToBatchND(const T* input_data, const Dims<4>& input_dims, } } +template +inline void SpaceToBatchND(const T* input_data, const Dims<4>& input_dims, + const int32* block_shape_data, + const Dims<4>& block_shape_dims, + const int32* paddings_data, + const Dims<4>& paddings_dims, T* output_data, + const Dims<4>& output_dims) { + SpaceToBatchND(input_data, input_dims, block_shape_data, block_shape_dims, + paddings_data, paddings_dims, output_data, output_dims, 0); +} + template inline void BatchToSpaceND(const T* input_data, const Dims<4>& input_dims, const int32* block_shape_data, diff --git a/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc b/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc index c9269599e5..03079f1c3b 100644 --- a/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc +++ b/tensorflow/contrib/lite/kernels/space_to_batch_nd.cc @@ -113,7 +113,7 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { TF_LITE_ENSURE_OK(context, ResizeOutputTensor(context, &op_context)); } -#define TF_LITE_SPACE_TO_BATCH_ND(type, scalar) \ +#define TF_LITE_SPACE_TO_BATCH_ND(type, scalar, pad_value) \ type::SpaceToBatchND(GetTensorData(op_context.input), \ GetTensorDims(op_context.input), \ GetTensorData(op_context.block_shape), \ @@ -121,34 +121,36 @@ TfLiteStatus Eval(TfLiteContext* context, TfLiteNode* node) { GetTensorData(op_context.paddings), \ GetTensorDims(op_context.paddings), \ GetTensorData(op_context.output), \ - GetTensorDims(op_context.output)) + GetTensorDims(op_context.output), pad_value) switch (op_context.input->type) { // Already know in/out types are same. case kTfLiteFloat32: if (kernel_type == kReference) { - TF_LITE_SPACE_TO_BATCH_ND(reference_ops, float); + TF_LITE_SPACE_TO_BATCH_ND(reference_ops, float, 0); } else { - TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, float); + TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, float, 0); } break; case kTfLiteUInt8: if (kernel_type == kReference) { - TF_LITE_SPACE_TO_BATCH_ND(reference_ops, uint8_t); + TF_LITE_SPACE_TO_BATCH_ND(reference_ops, uint8_t, + op_context.output->params.zero_point); } else { - TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, uint8_t); + TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, uint8_t, + op_context.output->params.zero_point); } break; case kTfLiteInt32: if (kernel_type == kReference) { - TF_LITE_SPACE_TO_BATCH_ND(reference_ops, int32_t); + TF_LITE_SPACE_TO_BATCH_ND(reference_ops, int32_t, 0); } else { - TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, int32_t); + TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, int32_t, 0); } break; case kTfLiteInt64: if (kernel_type == kReference) { - TF_LITE_SPACE_TO_BATCH_ND(reference_ops, int64_t); + TF_LITE_SPACE_TO_BATCH_ND(reference_ops, int64_t, 0); } else { - TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, int64_t); + TF_LITE_SPACE_TO_BATCH_ND(optimized_ops, int64_t, 0); } break; default: diff --git a/tensorflow/contrib/lite/kernels/space_to_batch_nd_test.cc b/tensorflow/contrib/lite/kernels/space_to_batch_nd_test.cc index 92a4a037d5..5756573629 100644 --- a/tensorflow/contrib/lite/kernels/space_to_batch_nd_test.cc +++ b/tensorflow/contrib/lite/kernels/space_to_batch_nd_test.cc @@ -23,6 +23,7 @@ namespace tflite { namespace { using ::testing::ElementsAreArray; +using ::testing::Matcher; class SpaceToBatchNDOpModel : public SingleOpModel { public: @@ -30,6 +31,10 @@ class SpaceToBatchNDOpModel : public SingleOpModel { PopulateTensor(input_, data); } + void SetQuantizedInput(std::initializer_list data) { + QuantizeAndPopulate(input_, data); + } + void SetBlockShape(std::initializer_list data) { PopulateTensor(block_shape_, data); } @@ -41,6 +46,11 @@ class SpaceToBatchNDOpModel : public SingleOpModel { std::vector GetOutput() { return ExtractVector(output_); } std::vector GetOutputShape() { return GetTensorShape(output_); } + std::vector GetDequantizedOutput() { + return Dequantize(ExtractVector(output_), + GetScale(output_), GetZeroPoint(output_)); + } + protected: int input_; int block_shape_; @@ -56,18 +66,19 @@ class SpaceToBatchNDOpModel : public SingleOpModel { // m.Invoke(); class SpaceToBatchNDOpConstModel : public SpaceToBatchNDOpModel { public: - SpaceToBatchNDOpConstModel(std::initializer_list input_shape, + SpaceToBatchNDOpConstModel(const TensorData& input, std::initializer_list block_shape, - std::initializer_list paddings) { - input_ = AddInput(TensorType_FLOAT32); + std::initializer_list paddings, + const TensorData& output) { + input_ = AddInput(input); block_shape_ = AddConstInput(TensorType_INT32, block_shape, {2}); paddings_ = AddConstInput(TensorType_INT32, paddings, {2, 2}); - output_ = AddOutput(TensorType_FLOAT32); + output_ = AddOutput(output); SetBuiltinOp(BuiltinOperator_SPACE_TO_BATCH_ND, BuiltinOptions_SpaceToBatchNDOptions, CreateSpaceToBatchNDOptions(builder_).Union()); - BuildInterpreter({input_shape}); + BuildInterpreter({input.shape}); } }; @@ -81,26 +92,30 @@ class SpaceToBatchNDOpConstModel : public SpaceToBatchNDOpModel { // m.Invoke(); class SpaceToBatchNDOpDynamicModel : public SpaceToBatchNDOpModel { public: - SpaceToBatchNDOpDynamicModel(std::initializer_list input_shape) { - input_ = AddInput(TensorType_FLOAT32); + SpaceToBatchNDOpDynamicModel(const TensorData& input, + const TensorData& output) { + input_ = AddInput(input); block_shape_ = AddInput(TensorType_INT32); paddings_ = AddInput(TensorType_INT32); - output_ = AddOutput(TensorType_FLOAT32); + output_ = AddOutput(output); SetBuiltinOp(BuiltinOperator_SPACE_TO_BATCH_ND, BuiltinOptions_SpaceToBatchNDOptions, CreateSpaceToBatchNDOptions(builder_).Union()); - BuildInterpreter({input_shape, {2}, {2, 2}}); + BuildInterpreter({input.shape, {2}, {2, 2}}); } }; TEST(SpaceToBatchNDOpTest, InvalidShapeTest) { - EXPECT_DEATH(SpaceToBatchNDOpConstModel({1, 3, 3, 1}, {2, 2}, {0, 0, 0, 0}), - "Cannot allocate tensors"); + EXPECT_DEATH( + SpaceToBatchNDOpConstModel({TensorType_FLOAT32, {1, 3, 3, 1}}, {2, 2}, + {0, 0, 0, 0}, {TensorType_FLOAT32}), + "Cannot allocate tensors"); } TEST(SpaceToBatchNDOpTest, SimpleConstTest) { - SpaceToBatchNDOpConstModel m({1, 4, 4, 1}, {2, 2}, {0, 0, 0, 0}); + SpaceToBatchNDOpConstModel m({TensorType_FLOAT32, {1, 4, 4, 1}}, {2, 2}, + {0, 0, 0, 0}, {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({4, 2, 2, 1})); @@ -109,7 +124,8 @@ TEST(SpaceToBatchNDOpTest, SimpleConstTest) { } TEST(SpaceToBatchNDOpTest, SimpleDynamicTest) { - SpaceToBatchNDOpDynamicModel m({1, 4, 4, 1}); + SpaceToBatchNDOpDynamicModel m({TensorType_FLOAT32, {1, 4, 4, 1}}, + {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); m.SetBlockShape({2, 2}); m.SetPaddings({0, 0, 0, 0}); @@ -120,7 +136,8 @@ TEST(SpaceToBatchNDOpTest, SimpleDynamicTest) { } TEST(SpaceToBatchNDOpTest, MultipleInputBatchesConstTest) { - SpaceToBatchNDOpConstModel m({2, 2, 4, 1}, {2, 2}, {0, 0, 0, 0}); + SpaceToBatchNDOpConstModel m({TensorType_FLOAT32, {2, 2, 4, 1}}, {2, 2}, + {0, 0, 0, 0}, {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({8, 1, 2, 1})); @@ -129,7 +146,8 @@ TEST(SpaceToBatchNDOpTest, MultipleInputBatchesConstTest) { } TEST(SpaceToBatchNDOpTest, MultipleInputBatchesDynamicTest) { - SpaceToBatchNDOpDynamicModel m({2, 2, 4, 1}); + SpaceToBatchNDOpDynamicModel m({TensorType_FLOAT32, {2, 2, 4, 1}}, + {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16}); m.SetBlockShape({2, 2}); m.SetPaddings({0, 0, 0, 0}); @@ -140,7 +158,8 @@ TEST(SpaceToBatchNDOpTest, MultipleInputBatchesDynamicTest) { } TEST(SpaceToBatchNDOpTest, SimplePaddingConstTest) { - SpaceToBatchNDOpConstModel m({1, 5, 2, 1}, {3, 2}, {1, 0, 2, 0}); + SpaceToBatchNDOpConstModel m({TensorType_FLOAT32, {1, 5, 2, 1}}, {3, 2}, + {1, 0, 2, 0}, {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 2, 1})); @@ -151,7 +170,8 @@ TEST(SpaceToBatchNDOpTest, SimplePaddingConstTest) { } TEST(SpaceToBatchNDOpTest, SimplePaddingDynamicTest) { - SpaceToBatchNDOpDynamicModel m({1, 5, 2, 1}); + SpaceToBatchNDOpDynamicModel m({TensorType_FLOAT32, {1, 5, 2, 1}}, + {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8, 9, 10}); m.SetBlockShape({3, 2}); m.SetPaddings({1, 0, 2, 0}); @@ -164,7 +184,8 @@ TEST(SpaceToBatchNDOpTest, SimplePaddingDynamicTest) { } TEST(SpaceToBatchNDOpTest, ComplexPaddingConstTest) { - SpaceToBatchNDOpConstModel m({1, 4, 2, 1}, {3, 2}, {1, 1, 2, 4}); + SpaceToBatchNDOpConstModel m({TensorType_FLOAT32, {1, 4, 2, 1}}, {3, 2}, + {1, 1, 2, 4}, {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8}); m.Invoke(); EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 4, 1})); @@ -176,7 +197,8 @@ TEST(SpaceToBatchNDOpTest, ComplexPaddingConstTest) { } TEST(SpaceToBatchNDOpTest, ComplexPaddingDynamicTest) { - SpaceToBatchNDOpDynamicModel m({1, 4, 2, 1}); + SpaceToBatchNDOpDynamicModel m({TensorType_FLOAT32, {1, 4, 2, 1}}, + {TensorType_FLOAT32}); m.SetInput({1, 2, 3, 4, 5, 6, 7, 8}); m.SetBlockShape({3, 2}); m.SetPaddings({1, 1, 2, 4}); @@ -189,6 +211,88 @@ TEST(SpaceToBatchNDOpTest, ComplexPaddingDynamicTest) { })); } +class QuantizedSpaceToBatchNDOpTest : public ::testing::Test { + protected: + std::vector> DequantizedArrayNear( + const std::vector& values, const float min, const float max) { + const float quantization_tolerance = (max - min) / 255.0; + return ArrayFloatNear(values, quantization_tolerance); + } +}; + +TEST_F(QuantizedSpaceToBatchNDOpTest, ZeroNotInQuantizationRange) { + // The test_util and actual quantization code currently ensure that the range + // must include zero, but if that ever changes, this test will catch it. + EXPECT_DEATH(SpaceToBatchNDOpConstModel m( + {TensorType_UINT8, {1, 2, 2, 1}, 1.0, 2.0}, {4, 2}, + {0, 0, 1, 1, 1, 1, 0, 0}, {TensorType_UINT8, {}, 1.0, 2.0}), + ".*Check failed: f_min <= 0.*"); +} + +TEST_F(QuantizedSpaceToBatchNDOpTest, SimplePaddingConstTest) { + SpaceToBatchNDOpConstModel m({TensorType_UINT8, {1, 5, 2, 1}, -1.0, 1.0}, + {3, 2}, {1, 0, 2, 0}, + {TensorType_UINT8, {}, -1.0, 1.0}); + m.SetQuantizedInput({-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8, -0.9, 0.1}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 2, 1})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(DequantizedArrayNear( + {0, 0, 0, -0.5, 0, 0, 0, 0.6, 0, -0.1, 0, -0.7, + 0, 0.2, 0, 0.8, 0, -0.3, 0, -0.9, 0, 0.4, 0, 0.1}, + -1.0, 1.0))); +} + +TEST_F(QuantizedSpaceToBatchNDOpTest, SimplePaddingDynamicTest) { + SpaceToBatchNDOpDynamicModel m({TensorType_UINT8, {1, 5, 2, 1}, -1.0, 1.0}, + {TensorType_UINT8, {}, -1.0, 1.0}); + m.SetQuantizedInput({-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8, -0.9, 0.1}); + m.SetBlockShape({3, 2}); + m.SetPaddings({1, 0, 2, 0}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 2, 1})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(DequantizedArrayNear( + {0, 0, 0, -0.5, 0, 0, 0, 0.6, 0, -0.1, 0, -0.7, + 0, 0.2, 0, 0.8, 0, -0.3, 0, -0.9, 0, 0.4, 0, 0.1}, + -1.0, 1.0))); +} + +TEST_F(QuantizedSpaceToBatchNDOpTest, ComplexPaddingConstTest) { + SpaceToBatchNDOpConstModel m({TensorType_UINT8, {1, 4, 2, 1}, -1.0, 1.0}, + {3, 2}, {1, 1, 2, 4}, + {TensorType_UINT8, {}, -1.0, 1.0}); + m.SetQuantizedInput({-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 4, 1})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(DequantizedArrayNear( + { + 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, + 0, -0.1, 0, 0, 0, -0.7, 0, 0, 0, 0.2, 0, 0, 0, 0.8, 0, 0, + 0, -0.3, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, + }, + -1.0, 1.0))); +} + +TEST_F(QuantizedSpaceToBatchNDOpTest, ComplexPaddingDynamicTest) { + SpaceToBatchNDOpDynamicModel m({TensorType_UINT8, {1, 4, 2, 1}, -1.0, 1.0}, + {TensorType_UINT8, {}, -1.0, 1.0}); + m.SetQuantizedInput({-0.1, 0.2, -0.3, 0.4, -0.5, 0.6, -0.7, 0.8}); + m.SetBlockShape({3, 2}); + m.SetPaddings({1, 1, 2, 4}); + m.Invoke(); + EXPECT_THAT(m.GetOutputShape(), ElementsAreArray({6, 2, 4, 1})); + EXPECT_THAT(m.GetDequantizedOutput(), + ElementsAreArray(DequantizedArrayNear( + { + 0, 0, 0, 0, 0, -0.5, 0, 0, 0, 0, 0, 0, 0, 0.6, 0, 0, + 0, -0.1, 0, 0, 0, -0.7, 0, 0, 0, 0.2, 0, 0, 0, 0.8, 0, 0, + 0, -0.3, 0, 0, 0, 0, 0, 0, 0, 0.4, 0, 0, 0, 0, 0, 0, + }, + -1.0, 1.0))); +} + } // namespace } // namespace tflite -- GitLab From 19defc68c99483049e14ec18718cb037f1ca050b Mon Sep 17 00:00:00 2001 From: Christopher Olston Date: Thu, 26 Jul 2018 21:04:48 -0700 Subject: [PATCH 486/519] Have the SavedModel loader use Session's Make/Run/ReleaseCallable() API instead of Run(), to avoid leaving behind non-GC'ed state after model initialization. PiperOrigin-RevId: 206266841 --- tensorflow/cc/saved_model/loader.cc | 56 ++++++++++++++++++++++++++--- 1 file changed, 52 insertions(+), 4 deletions(-) diff --git a/tensorflow/cc/saved_model/loader.cc b/tensorflow/cc/saved_model/loader.cc index d47b025743..98be66a6ad 100644 --- a/tensorflow/cc/saved_model/loader.cc +++ b/tensorflow/cc/saved_model/loader.cc @@ -74,6 +74,54 @@ void AddAssetsTensorsToInputs(const StringPiece export_dir, } } +// Like Session::Run(), but uses the Make/Run/ReleaseCallable() API to avoid +// leaving behind non-GC'ed state. +// +// Detailed motivation behind this approach, from ashankar@: +// +// Each call to Session::Run() that identifies a new subgraph (based on feeds +// and fetches) creates some datastructures that live as long as the session +// (the partitioned graph, associated executors etc.). +// +// A pathological case of this would be if say the initialization op +// (main_op/legacy_init_op) involves the use of a large constant. Then we +// allocate memory for that large constant that will just stick around till the +// session dies. With this Callable mechanism, that memory will be released +// right after ReleaseCallable returns. +// +// However, the resource manager state remains. +Status RunOnce(const RunOptions& run_options, + const std::vector>& inputs, + const std::vector& output_tensor_names, + const std::vector& target_node_names, + std::vector* outputs, RunMetadata* run_metadata, + Session* session) { + CallableOptions callable_options; + std::vector feed_tensors; + *callable_options.mutable_run_options() = run_options; + for (const auto& input : inputs) { + const string& name = input.first; + const Tensor& tensor = input.second; + callable_options.add_feed(name); + feed_tensors.push_back(tensor); + } + for (const string& output_tensor_name : output_tensor_names) { + callable_options.add_fetch(output_tensor_name); + } + for (const string& target_node_name : target_node_names) { + callable_options.add_target(target_node_name); + } + + Session::CallableHandle callable_handle; + TF_RETURN_IF_ERROR(session->MakeCallable(callable_options, &callable_handle)); + const Status run_status = session->RunCallable(callable_handle, feed_tensors, + outputs, run_metadata); + // Be sure to call ReleaseCallable() regardless of the outcome of + // RunCallable(). + session->ReleaseCallable(callable_handle).IgnoreError(); + return run_status; +} + bool HasMainOp(const MetaGraphDef& meta_graph_def) { const auto& collection_def_map = meta_graph_def.collection_def(); if (collection_def_map.find(kSavedModelMainOpKey) != @@ -100,8 +148,8 @@ Status RunMainOp(const RunOptions& run_options, const string& export_dir, AddAssetsTensorsToInputs(export_dir, asset_file_defs, &inputs); RunMetadata run_metadata; const StringPiece main_op_name = main_op_it->second.node_list().value(0); - return session->Run(run_options, inputs, {}, {main_op_name.ToString()}, - nullptr /* outputs */, &run_metadata); + return RunOnce(run_options, inputs, {}, {main_op_name.ToString()}, + nullptr /* outputs */, &run_metadata, session); } return Status::OK(); } @@ -138,8 +186,8 @@ Status RunRestore(const RunOptions& run_options, const string& export_dir, AddAssetsTensorsToInputs(export_dir, asset_file_defs, &inputs); RunMetadata run_metadata; - return session->Run(run_options, inputs, {}, {restore_op_name.ToString()}, - nullptr /* outputs */, &run_metadata); + return RunOnce(run_options, inputs, {}, {restore_op_name.ToString()}, + nullptr /* outputs */, &run_metadata, session); } Status GetAssetFileDefs(const MetaGraphDef& meta_graph_def, -- GitLab From 21fbc78e69b3e79f883161601f1ccc62be42a06e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Thu, 26 Jul 2018 23:07:53 -0700 Subject: [PATCH 487/519] Set the correct context when calling cudnnCreate. When running with multiple devices, using the wrong context will lead to a check-fail when trying to set a stream that has been created with a different context. This resolves a check-fail on resnet50 with 8 GPUs. PiperOrigin-RevId: 206274741 --- tensorflow/stream_executor/cuda/cuda_dnn.cc | 1 + 1 file changed, 1 insertion(+) diff --git a/tensorflow/stream_executor/cuda/cuda_dnn.cc b/tensorflow/stream_executor/cuda/cuda_dnn.cc index 766a0dafb5..1c3940e92c 100644 --- a/tensorflow/stream_executor/cuda/cuda_dnn.cc +++ b/tensorflow/stream_executor/cuda/cuda_dnn.cc @@ -322,6 +322,7 @@ port::Status GetLoadedCudnnVersion(CudnnVersion* version) { CudnnSupport::CudnnSupport(CUDAExecutor* parent) : parent_(parent) {} port::Status CudnnSupport::Init() { + ScopedActivateExecutorContext context(parent_); cudnnHandle_t cudnn_handle = nullptr; auto status = cudnnCreate(&cudnn_handle); if (status == CUDNN_STATUS_SUCCESS) { -- GitLab From 4af0e25bfe0791f0ec3e9262c8d5051415bf026e Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 00:43:16 -0700 Subject: [PATCH 488/519] Automated rollback of commit d4cb01f242dc3ff0f7b0aae7284def46281755f2 PiperOrigin-RevId: 206281287 --- tensorflow/python/keras/callbacks.py | 73 +++++++---------------- tensorflow/python/keras/callbacks_test.py | 59 +++--------------- 2 files changed, 28 insertions(+), 104 deletions(-) diff --git a/tensorflow/python/keras/callbacks.py b/tensorflow/python/keras/callbacks.py index 070d41147d..d1b9dc27bd 100644 --- a/tensorflow/python/keras/callbacks.py +++ b/tensorflow/python/keras/callbacks.py @@ -31,14 +31,12 @@ import time import numpy as np import six -from tensorflow.python.eager import context from tensorflow.python.framework import dtypes from tensorflow.python.keras import backend as K from tensorflow.python.keras.engine.training_utils import standardize_input_data from tensorflow.python.keras.utils.generic_utils import Progbar from tensorflow.python.ops import array_ops from tensorflow.python.ops import state_ops -from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.ops import variables from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary import summary as tf_summary @@ -718,15 +716,6 @@ class TensorBoard(Callback): `embeddings_layer_names`. Numpy array (if the model has a single input) or list of Numpy arrays (if the model has multiple inputs). Learn [more about embeddings](https://www.tensorflow.org/programmers_guide/embedding) - - Raises: - ValueError: If histogram_freq is set and no validation data is provided. - - @compatbility(eager) - Using `Tensorboard` callback will work while eager execution is enabled, - however outputting histogram summaries of weights and gradients is not - supported, and thus `histogram_freq` will be ignored. - @end_compatibility """ # pylint: enable=line-too-long @@ -745,11 +734,6 @@ class TensorBoard(Callback): super(TensorBoard, self).__init__() self.log_dir = log_dir self.histogram_freq = histogram_freq - if self.histogram_freq and context.executing_eagerly(): - logging.warning( - UserWarning('Weight and gradient histograms not supported for eager' - 'execution, setting `histogram_freq` to `0`.')) - self.histogram_freq = 0 self.merged = None self.write_graph = write_graph self.write_grads = write_grads @@ -757,22 +741,18 @@ class TensorBoard(Callback): self.batch_size = batch_size self._current_batch = 0 self._total_batches_seen = 0 + # abstracted writer class to be able to stub for testing + self._writer_class = tf_summary.FileWriter self.embeddings_freq = embeddings_freq self.embeddings_layer_names = embeddings_layer_names self.embeddings_metadata = embeddings_metadata self.embeddings_data = embeddings_data - def _init_writer(self): - """Sets file writer.""" - if context.executing_eagerly(): - self.writer = summary_ops_v2.create_file_writer(self.log_dir) - elif self.write_graph: - self.writer = tf_summary.FileWriter(self.log_dir, K.get_session().graph) - else: - self.writer = tf_summary.FileWriter(self.log_dir) + def set_model(self, model): + """Sets Keras model and creates summary ops.""" - def _make_histogram_ops(self, model): - """Defines histogram ops when histogram_freq > 0.""" + self.model = model + self.sess = K.get_session() # only make histogram summary op if it hasn't already been made if self.histogram_freq and self.merged is None: for layer in self.model.layers: @@ -813,10 +793,8 @@ class TensorBoard(Callback): def is_indexed_slices(grad): return type(grad).__name__ == 'IndexedSlices' - grads = [ - grad.values if is_indexed_slices(grad) else grad - for grad in grads - ] + grads = [grad.values if is_indexed_slices(grad) else grad + for grad in grads] tf_summary.histogram('{}_grad'.format(mapped_weight_name), grads) if hasattr(layer, 'output'): @@ -825,16 +803,12 @@ class TensorBoard(Callback): tf_summary.histogram('{}_out_{}'.format(layer.name, i), output) else: tf_summary.histogram('{}_out'.format(layer.name), layer.output) + self.merged = tf_summary.merge_all() - def set_model(self, model): - """Sets Keras model and creates summary ops.""" - - self.model = model - self._init_writer() - # histogram summaries only enabled in graph mode - if not context.executing_eagerly(): - self._make_histogram_ops(model) - self.merged = tf_summary.merge_all() + if self.write_graph: + self.writer = self._writer_class(self.log_dir, self.sess.graph) + else: + self.writer = self._writer_class(self.log_dir) # If both embedding_freq and embeddings_data are available, we will # visualize embeddings. @@ -920,24 +894,17 @@ class TensorBoard(Callback): """ logs = logs or {} - if context.executing_eagerly(): - # use v2 summary ops - with self.writer.as_default(), summary_ops_v2.always_record_summaries(): - for name, value in logs.items(): - summary_ops_v2.scalar(name, value.item(), step=step) - else: - # use FileWriter from v1 summary - for name, value in logs.items(): - summary = tf_summary.Summary() - summary_value = summary.value.add() - summary_value.simple_value = value.item() - summary_value.tag = name - self.writer.add_summary(summary, step) + for name, value in logs.items(): + summary = tf_summary.Summary() + summary_value = summary.value.add() + summary_value.simple_value = value.item() + summary_value.tag = name + self.writer.add_summary(summary, step) self.writer.flush() def on_train_begin(self, logs=None): """Checks if histogram summaries can be run.""" - # will never be set when in eager + if self.histogram_freq: if 'validation_steps' in self.params: self._validation_batches = self.params['validation_steps'] diff --git a/tensorflow/python/keras/callbacks_test.py b/tensorflow/python/keras/callbacks_test.py index 6d61e9bcee..7d830078ce 100644 --- a/tensorflow/python/keras/callbacks_test.py +++ b/tensorflow/python/keras/callbacks_test.py @@ -29,12 +29,10 @@ import numpy as np from tensorflow.core.framework import summary_pb2 from tensorflow.python import keras -from tensorflow.python.framework import test_util from tensorflow.python.keras import testing_utils from tensorflow.python.platform import test from tensorflow.python.platform import tf_logging as logging from tensorflow.python.summary.writer import writer_cache -from tensorflow.python.training import adam try: import h5py # pylint:disable=g-import-not-at-top @@ -919,9 +917,6 @@ class KerasCallbacksTest(test.TestCase): def close(self): pass - def _init_writer(obj): - obj.writer = FileWriterStub(obj.log_dir) - np.random.seed(1337) tmpdir = self.get_temp_dir() self.addCleanup(shutil.rmtree, tmpdir) @@ -945,13 +940,13 @@ class KerasCallbacksTest(test.TestCase): loss='categorical_crossentropy', optimizer='sgd', metrics=['accuracy']) - keras.callbacks.TensorBoard._init_writer = _init_writer tsb = keras.callbacks.TensorBoard( log_dir=tmpdir, histogram_freq=1, write_images=True, write_grads=True, batch_size=5) + tsb._writer_class = FileWriterStub cbks = [tsb] # fit with validation data @@ -1123,11 +1118,11 @@ class KerasCallbacksTest(test.TestCase): def close(self): pass - temp_dir = self.get_temp_dir() - self.addCleanup(shutil.rmtree, temp_dir) + logdir = 'fake_dir' - tb_cbk = keras.callbacks.TensorBoard(temp_dir) - tb_cbk.writer = FileWriterStub(temp_dir) + # log every batch + tb_cbk = keras.callbacks.TensorBoard(logdir) + tb_cbk.writer = FileWriterStub(logdir) for batch in range(5): tb_cbk.on_batch_end(batch, {'acc': np.float32(batch)}) @@ -1155,11 +1150,10 @@ class KerasCallbacksTest(test.TestCase): def close(self): pass - temp_dir = self.get_temp_dir() - self.addCleanup(shutil.rmtree, temp_dir) + logdir = 'fake_dir' - tb_cbk = keras.callbacks.TensorBoard(temp_dir) - tb_cbk.writer = FileWriterStub(temp_dir) + tb_cbk = keras.callbacks.TensorBoard(logdir) + tb_cbk.writer = FileWriterStub(logdir) tb_cbk.on_batch_end(0, {'acc': np.float32(5.0)}) tb_cbk.on_epoch_end(0, {'acc': np.float32(10.0)}) @@ -1170,43 +1164,6 @@ class KerasCallbacksTest(test.TestCase): self.assertEqual(epoch_step, 0) self.assertEqual(epoch_summary.value[0].simple_value, 10.0) - @test_util.run_in_graph_and_eager_modes - def test_Tensorboard_eager(self): - with self.test_session(): - temp_dir = self.get_temp_dir() - self.addCleanup(shutil.rmtree, temp_dir) - - (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) - y_test = keras.utils.to_categorical(y_test) - y_train = keras.utils.to_categorical(y_train) - - model = keras.models.Sequential() - model.add( - keras.layers.Dense( - NUM_HIDDEN, input_dim=INPUT_DIM, activation='relu')) - model.add(keras.layers.Dense(NUM_CLASSES, activation='softmax')) - model.compile( - loss='binary_crossentropy', - optimizer=adam.AdamOptimizer(0.01), - metrics=['accuracy']) - - cbks = [keras.callbacks.TensorBoard(log_dir=temp_dir)] - - model.fit( - x_train, - y_train, - batch_size=BATCH_SIZE, - validation_data=(x_test, y_test), - callbacks=cbks, - epochs=2, - verbose=0) - - self.assertTrue(os.path.exists(temp_dir)) - def test_RemoteMonitorWithJsonPayload(self): if requests is None: self.skipTest('`requests` required to run this test') -- GitLab From dc437d53a395438070739c3d509fa0c21b3bffbb Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Fri, 27 Jul 2018 02:12:17 -0700 Subject: [PATCH 489/519] Add parameter server distribution. PiperOrigin-RevId: 206289143 --- tensorflow/contrib/distribute/python/BUILD | 46 ++ .../distribute/python/cross_tower_ops.py | 39 +- .../distribute/python/mirrored_strategy.py | 297 ++++++------ .../python/mirrored_strategy_multigpu_test.py | 4 +- .../python/multi_worker_test_base.py | 38 +- .../python/parameter_server_strategy.py | 353 ++++++++++++++ .../python/parameter_server_strategy_test.py | 455 ++++++++++++++++++ 7 files changed, 1071 insertions(+), 161 deletions(-) create mode 100644 tensorflow/contrib/distribute/python/parameter_server_strategy.py create mode 100644 tensorflow/contrib/distribute/python/parameter_server_strategy_test.py diff --git a/tensorflow/contrib/distribute/python/BUILD b/tensorflow/contrib/distribute/python/BUILD index f5d7e24ae2..cbe741de5a 100644 --- a/tensorflow/contrib/distribute/python/BUILD +++ b/tensorflow/contrib/distribute/python/BUILD @@ -100,6 +100,23 @@ py_library( ], ) +py_library( + name = "parameter_server_strategy", + srcs = ["parameter_server_strategy.py"], + visibility = ["//tensorflow:internal"], + deps = [ + ":cross_tower_ops", + ":mirrored_strategy", + ":values", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:framework_ops", + "//tensorflow/python:resource_variable_ops", + "//tensorflow/python:training", + "//tensorflow/python:util", + ], +) + py_library( name = "one_device_strategy", srcs = ["one_device_strategy.py"], @@ -207,6 +224,35 @@ py_test( ], ) +py_test( + name = "parameter_server_strategy_test", + srcs = ["parameter_server_strategy_test.py"], + srcs_version = "PY2AND3", + tags = [ + "no_pip", + ], + deps = [ + ":combinations", + ":multi_worker_test_base", + ":parameter_server_strategy", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:array_ops", + "//tensorflow/python:client_testlib", + "//tensorflow/python:constant_op", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:framework_ops", + "//tensorflow/python:gradients", + "//tensorflow/python:layers", + "//tensorflow/python:session", + "//tensorflow/python:training", + "//tensorflow/python:variable_scope", + "//tensorflow/python:variables", + "//tensorflow/python/eager:context", + "//tensorflow/python/estimator:run_config", + "@absl_py//absl/testing:parameterized", + ], +) + cuda_py_test( name = "mirrored_strategy_multigpu_test", srcs = ["mirrored_strategy_multigpu_test.py"], diff --git a/tensorflow/contrib/distribute/python/cross_tower_ops.py b/tensorflow/contrib/distribute/python/cross_tower_ops.py index b0baf0dad1..b6037d2133 100644 --- a/tensorflow/contrib/distribute/python/cross_tower_ops.py +++ b/tensorflow/contrib/distribute/python/cross_tower_ops.py @@ -28,18 +28,37 @@ from tensorflow.python.eager import context from tensorflow.python.framework import ops from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops +from tensorflow.python.ops import resource_variable_ops from tensorflow.python.ops import variable_scope as vs from tensorflow.python.platform import tf_logging as logging from tensorflow.python.training import device_util +def check_destinations(destinations): + """Checks whether `destinations` is not None and not empty. + + Args: + destinations: a DistributedValues, Variable, string or a list of strings. + + Returns: + Boolean indicating whether `destinations` is not None and not empty. + """ + # Calling bool() on a ResourceVariable is not allowed. + if isinstance(destinations, resource_variable_ops.ResourceVariable): + return bool(destinations.device) + return bool(destinations) + + def validate_destinations(destinations): - if not isinstance(destinations, - (value_lib.DistributedValues, six.string_types, list)): + if not isinstance( + destinations, + (value_lib.DistributedValues, resource_variable_ops.ResourceVariable, + six.string_types, list)): raise ValueError("destinations must be one of a `DistributedValues` object," - " a device string, a list of device strings or None") + " a tf.Variable object, a device string, a list of device " + "strings or None") - if not destinations: + if not check_destinations(destinations): raise ValueError("destinations can not be empty") @@ -59,6 +78,8 @@ def _validate_value_destination_pairs(value_destination_pairs): def get_devices_from(destinations): if isinstance(destinations, value_lib.DistributedValues): return list(destinations.devices) + elif isinstance(destinations, resource_variable_ops.ResourceVariable): + return [destinations.device] elif isinstance(destinations, six.string_types): return [device_util.resolve(destinations)] else: @@ -225,7 +246,10 @@ class ReductionToOneDeviceCrossTowerOps(CrossTowerOps): super(ReductionToOneDeviceCrossTowerOps, self).__init__() def _reduce(self, aggregation, per_device_value, destinations): - devices = get_devices_from(destinations or per_device_value) + if check_destinations(destinations): + devices = get_devices_from(destinations) + else: + devices = get_devices_from(per_device_value) reduce_to_device = self.reduce_to_device or devices[0] reduced = _simple_reduce(per_device_value, reduce_to_device, self.accumulation_fn, aggregation) @@ -508,7 +532,10 @@ class AllReduceCrossTowerOps(CrossTowerOps): logging.WARN, "Efficient allreduce is not supported for IndexedSlices.", 10) - devices = get_devices_from(destinations or per_device_value) + if check_destinations(destinations): + devices = get_devices_from(destinations) + else: + devices = get_devices_from(per_device_value) reduce_to_device = devices[0] reduced = _simple_reduce(per_device_value, reduce_to_device, math_ops.add_n, aggregation) diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy.py b/tensorflow/contrib/distribute/python/mirrored_strategy.py index dcbc6b0878..eb2d102012 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy.py @@ -20,7 +20,6 @@ from __future__ import print_function import contextlib import threading -import six from tensorflow.contrib.distribute.python import cross_tower_ops as cross_tower_ops_lib from tensorflow.contrib.distribute.python import shared_variable_creator @@ -60,6 +59,156 @@ class _RequestedStop(Exception): pass +# Make _call_for_each_tower and _reduce_non_distributed_value not members of +# MirroredStrategy so that they are generally not allowed to use anything +# specific to MirroredStrategy and thus can be shared with other distribution +# strategies. + + +# TODO(yuefengz): maybe create a common class for those who need to call this +# _call_for_each_tower. +def _call_for_each_tower(distribution, fn, *args, **kwargs): + """Run `fn` in separate threads, once per tower/worker device. + + Args: + distribution: the DistributionStrategy object. + fn: function to run (will be run once per device, each in its own thread). + *args: positional arguments for `fn` + **kwargs: keyword arguments for `fn`. + `"run_concurrently"`: Boolean indicating whether executions of `fn` + can be run concurrently (under eager execution only), defaults to + `True`. + + Returns: + Merged return value of `fn` across all towers. + + Raises: + RuntimeError: If fn() calls get_tower_context().merge_call() a different + number of times from the available devices. + """ + run_concurrently = kwargs.pop("run_concurrently", True) + if not context.executing_eagerly(): + # Lots of TF library code isn't thread-safe in graph mode, and + # there is little to be gained by turning on multithreading when + # constructing a graph. + run_concurrently = False + # Needed for per-thread device, etc. contexts in graph mode. + ops.get_default_graph().switch_to_thread_local() + elif run_concurrently is None: + run_concurrently = True + + coord = coordinator.Coordinator(clean_stop_exception_types=(_RequestedStop,)) + + shared_variable_store = {} + + # TODO(isaprykin): Create these threads once instead of during every run() + # call. + threads = [] + for index, d in enumerate(distribution.worker_devices): + variable_creator_fn = shared_variable_creator.make_fn( + shared_variable_store, index) + t = MirroredStrategy._MirroredTowerThread( # pylint: disable=protected-access + distribution, coord, d, variable_creator_fn, fn, + *values.select_device(d, args), **values.select_device(d, kwargs)) + threads.append(t) + + for t in threads: + t.start() + + # When `fn` starts `should_run` event is set on _MirroredTowerThread + # (`MTT`) threads. The execution waits until + # `MTT.has_paused` is set, which indicates that either `fn` is + # complete or a `get_tower_context().merge_call()` is called. If `fn` is + # complete, then `MTT.done` is set to True. Otherwise, arguments + # of `get_tower_context().merge_call` from all paused threads are grouped + # and the `merge_fn` is performed. Results of the + # `get_tower_context().merge_call` are then set to `MTT.merge_result`. + # Each such `get_tower_context().merge_call` call returns the + # `MTT.merge_result` for that thread when `MTT.should_run` event + # is reset again. Execution of `fn` resumes. + + try: + with coord.stop_on_exception(): + all_done = False + while not all_done and not coord.should_stop(): + done = [] + if run_concurrently: + for t in threads: + t.should_run.set() + for t in threads: + t.has_paused.wait() + t.has_paused.clear() + if coord.should_stop(): + return None + done.append(t.done) + else: + for t in threads: + t.should_run.set() + t.has_paused.wait() + t.has_paused.clear() + if coord.should_stop(): + return None + done.append(t.done) + if coord.should_stop(): + return None + all_done = all(done) + if not all_done: + if any(done): + raise RuntimeError("Some towers made a different number of " + "tower_context().merge_call() calls.") + # get_tower_context().merge_call() case + merge_args = values.regroup({t.device: t.merge_args for t in threads}) + merge_kwargs = values.regroup( + {t.device: t.merge_kwargs for t in threads}) + # We capture the name_scope of the MTT when we call merge_fn + # to ensure that if we have opened a name scope in the MTT, + # it will be respected when executing the merge function. We only + # capture the name_scope from the first MTT and assume it is + # the same for all other MTTs. + mtt_captured_name_scope = threads[0].captured_name_scope + with ops.name_scope(mtt_captured_name_scope): + merge_result = threads[0].merge_fn(distribution, *merge_args, + **merge_kwargs) + for t in threads: + t.merge_result = values.select_device(t.device, merge_result) + finally: + for t in threads: + t.should_run.set() + coord.join(threads) + + return values.regroup({t.device: t.main_result for t in threads}) + + +def _reduce_non_distributed_value(distribution, aggregation, value, + destinations): + """Reduce a non-DistributedValue `value` to `destinations`.""" + if isinstance(value, values.DistributedValues): + raise ValueError("You are passing a `DistributedValue` to " + "`_reduce_non_distributed_value`, which is not allowed.") + + if value == 0: + return 0 + if aggregation == variable_scope.VariableAggregation.MEAN: + return distribution.broadcast(value, destinations) + + cross_tower_ops_lib.validate_destinations(destinations) + if (len(distribution.worker_devices) != 1 or + not cross_tower_ops_lib.check_destinations(destinations)): + raise ValueError("A non-DistributedValues value cannot be reduced with the " + "given aggregation.") + # TODO(anjalisridhar): Moves these methods to a device utility file? + devices = cross_tower_ops_lib.get_devices_from(destinations) + if len(devices) == 1: + with ops.device(devices[0]): + return array_ops.identity(value) + else: + value_updates = {} + for d in devices: + with ops.device(d): + value_updates[d] = array_ops.identity(value) + return values.Mirrored(value_updates) + + class MirroredStrategy(distribute_lib.DistributionStrategy): """Mirrors vars to distribute across multiple devices on a single machine. @@ -198,116 +347,7 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): self._devices) def _call_for_each_tower(self, fn, *args, **kwargs): - """Run `fn` in separate threads, once per tower/worker device. - - Args: - fn: function to run (will be run once per device, each in its own thread). - *args: positional arguments for `fn` - **kwargs: keyword arguments for `fn`. - `"run_concurrently"`: Boolean indicating whether executions of `fn` - can be run concurrently (under eager execution only), defaults to - `True`. - - Returns: - Merged return value of `fn` across all towers. - - Raises: - RuntimeError: If fn() calls get_tower_context().merge_call() a different - number of times for when called for different devices. - """ - run_concurrently = kwargs.pop("run_concurrently", True) - if not context.executing_eagerly(): - # Lots of TF library code isn't thread-safe in graph mode, and - # there is little to be gained by turning on multithreading when - # constructing a graph. - run_concurrently = False - # Needed for per-thread device, etc. contexts in graph mode. - ops.get_default_graph().switch_to_thread_local() - elif run_concurrently is None: - run_concurrently = True - - coord = coordinator.Coordinator( - clean_stop_exception_types=(_RequestedStop,)) - - shared_variable_store = {} - - # TODO(isaprykin): Create these threads once instead of during every run() - # call. - threads = [] - for index, d in enumerate(self._devices): - variable_creator_fn = shared_variable_creator.make_fn( - shared_variable_store, index) - t = MirroredStrategy._MirroredTowerThread( - self, coord, d, variable_creator_fn, fn, - *values.select_device(d, args), **values.select_device(d, kwargs)) - threads.append(t) - - for t in threads: - t.start() - - # When `fn` starts `should_run` event is set on _MirroredTowerThread - # (`MTT`) threads. The execution waits until - # `MTT.has_paused` is set, which indicates that either `fn` is - # complete or a `get_tower_context().merge_call()` is called. If `fn` is - # complete, then `MTT.done` is set to True. Otherwise, arguments - # of `get_tower_context().merge_call` from all paused threads are grouped - # and the `merge_fn` is performed. Results of the - # `get_tower_context().merge_call` are then set to `MTT.merge_result`. - # Each such `get_tower_context().merge_call` call returns the - # `MTT.merge_result` for that thread when `MTT.should_run` event - # is reset again. Execution of `fn` resumes. - - try: - with coord.stop_on_exception(): - all_done = False - while not all_done and not coord.should_stop(): - done = [] - if run_concurrently: - for t in threads: - t.should_run.set() - for t in threads: - t.has_paused.wait() - t.has_paused.clear() - if coord.should_stop(): - return None - done.append(t.done) - else: - for t in threads: - t.should_run.set() - t.has_paused.wait() - t.has_paused.clear() - if coord.should_stop(): - return None - done.append(t.done) - if coord.should_stop(): - return None - all_done = all(done) - if not all_done: - if any(done): - raise RuntimeError("Some towers made a different number of " - "tower_context().merge_call() calls.") - # get_tower_context().merge_call() case - merge_args = values.regroup( - {t.device: t.merge_args for t in threads}) - merge_kwargs = values.regroup( - {t.device: t.merge_kwargs for t in threads}) - # We capture the name_scope of the MTT when we call merge_fn - # to ensure that if we have opened a name scope in the MTT, - # it will be respected when executing the merge function. We only - # capture the name_scope from the first MTT and assume it is - # the same for all other MTTs. - mtt_captured_name_scope = threads[0].captured_name_scope - with ops.name_scope(mtt_captured_name_scope): - merge_result = threads[0].merge_fn( - self, *merge_args, **merge_kwargs) - for t in threads: - t.merge_result = values.select_device(t.device, merge_result) - finally: - for t in threads: - t.should_run.set() - coord.join(threads) - - return values.regroup({t.device: t.main_result for t in threads}) + return _call_for_each_tower(self, fn, *args, **kwargs) def map(self, map_over, fn, *args, **kwargs): # TODO(josh11b): In eager mode, use one thread per device. @@ -337,29 +377,9 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): def _reduce(self, aggregation, value, destinations): assert not isinstance(value, values.Mirrored) - if not isinstance(value, values.PerDevice): - if value == 0: - return 0 - if aggregation == variable_scope.VariableAggregation.MEAN: - return self._broadcast(value, destinations) - - cross_tower_ops_lib.validate_destinations(destinations) - if len(self._devices) == 1: - if destinations: - # TODO(anjalisridhar): Moves these methods to a device utility file? - devices = cross_tower_ops_lib.get_devices_from(destinations) - if len(devices) == 1: - with ops.device(devices[0]): - return array_ops.identity(value) - else: - value_updates = {} - for d in devices: - with ops.device(d): - value_updates[d] = array_ops.identity(value) - return values.Mirrored(value_updates) - raise ValueError("A non PerDevice value cannot be reduced with the given " - "aggregation.") - + if not isinstance(value, values.DistributedValues): + return _reduce_non_distributed_value(self, aggregation, value, + destinations) return self._get_cross_tower_ops().reduce( aggregation, value, destinations=destinations) @@ -433,15 +453,8 @@ class MirroredStrategy(distribute_lib.DistributionStrategy): def _get_devices_from(self, colocate_with=None): if colocate_with is None: return self._devices - elif isinstance(colocate_with, values.DistributedValues): - # pylint: disable=protected-access - return list(colocate_with._index.keys()) - elif isinstance(colocate_with, six.string_types): - return [device_util.resolve(colocate_with)] - elif isinstance(colocate_with, list): - return [device_util.resolve(d) for d in colocate_with] else: - return colocate_with + return cross_tower_ops_lib.get_devices_from(colocate_with) class _MirroredTowerThread(threading.Thread): """A thread that runs() a function on a device.""" diff --git a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py index 9807ce4351..aab7119901 100644 --- a/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py +++ b/tensorflow/contrib/distribute/python/mirrored_strategy_multigpu_test.py @@ -792,8 +792,8 @@ class MirroredVariableUpdateTest(test.TestCase): return mirrored_var.assign(5.0) with self.assertRaisesRegexp( - ValueError, "A non PerDevice value cannot be reduced with the given " - "aggregation."): + ValueError, "A non-DistributedValues value cannot be reduced with " + "the given aggregation."): self.evaluate(dist.unwrap(dist.call_for_each_tower(model_fn))) @test_util.run_in_graph_and_eager_modes(config=config) diff --git a/tensorflow/contrib/distribute/python/multi_worker_test_base.py b/tensorflow/contrib/distribute/python/multi_worker_test_base.py index f659be5f42..fa479918bd 100644 --- a/tensorflow/contrib/distribute/python/multi_worker_test_base.py +++ b/tensorflow/contrib/distribute/python/multi_worker_test_base.py @@ -28,23 +28,39 @@ from tensorflow.python.eager import test from tensorflow.python.framework import test_util +def create_in_process_cluster(num_workers, num_ps): + """Create an in-process cluster that consists of only standard server.""" + # Leave some memory for cuda runtime. + gpu_mem_frac = 0.7 / num_workers + worker_config = config_pb2.ConfigProto() + worker_config.gpu_options.per_process_gpu_memory_fraction = gpu_mem_frac + + ps_config = config_pb2.ConfigProto() + ps_config.device_count['GPU'] = 0 + + # Create in-process servers. Once an in-process tensorflow server is created, + # there is no way to terminate it. So we create one cluster per test process. + # We could've started the server in another process, we could then kill that + # process to terminate the server. The reasons why we don't want multiple + # processes are + # 1) it is more difficult to manage these processes + # 2) there is something global in CUDA such that if we initialize CUDA in the + # parent process, the child process cannot initialize it again and thus cannot + # use GPUs (https://stackoverflow.com/questions/22950047). + return test_util.create_local_cluster( + num_workers, + num_ps=num_ps, + worker_config=worker_config, + ps_config=ps_config) + + class MultiWorkerTestBase(test.TestCase): """Base class for testing multi node strategy and dataset.""" @classmethod def setUpClass(cls): """Create a local cluster with 2 workers.""" - num_workers = 2 - # Leave some memory for cuda runtime. - gpu_mem_frac = 0.7 / num_workers - default_config = config_pb2.ConfigProto() - default_config.gpu_options.per_process_gpu_memory_fraction = gpu_mem_frac - - # The local cluster takes some portion of the local GPUs and there is no way - # for the cluster to terminate unless using multiple processes. Therefore, - # we have to only create only one cluster throughout a test process. - workers, _ = test_util.create_local_cluster( - num_workers, num_ps=0, worker_config=default_config) + workers, _ = create_in_process_cluster(num_workers=2, num_ps=0) cls._master_target = workers[0].target @contextlib.contextmanager diff --git a/tensorflow/contrib/distribute/python/parameter_server_strategy.py b/tensorflow/contrib/distribute/python/parameter_server_strategy.py new file mode 100644 index 0000000000..d53582279e --- /dev/null +++ b/tensorflow/contrib/distribute/python/parameter_server_strategy.py @@ -0,0 +1,353 @@ +# 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. +# ============================================================================== +"""Classes implementing a multi-worker ps DistributionStrategy.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import json +import os + +from tensorflow.contrib.distribute.python import cross_tower_ops as cross_tower_ops_lib +from tensorflow.contrib.distribute.python import mirrored_strategy +from tensorflow.contrib.distribute.python import values +from tensorflow.core.protobuf import cluster_pb2 +from tensorflow.python.framework import device as tf_device +from tensorflow.python.framework import ops +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import resource_variable_ops +from tensorflow.python.training import device_setter +from tensorflow.python.training import device_util +from tensorflow.python.training import distribute as distribute_lib +from tensorflow.python.training import server_lib +from tensorflow.python.util import nest + +_LOCAL_CPU = "/device:CPU:0" +_LOCAL_GPU_0 = "/device:GPU:0" + + +def _normalize_cluster_spec(cluster_spec): + if isinstance(cluster_spec, (dict, cluster_pb2.ClusterDef)): + return server_lib.ClusterSpec(cluster_spec) + elif not isinstance(cluster_spec, server_lib.ClusterSpec): + raise ValueError( + "`cluster_spec' should be dict or a `tf.train.ClusterSpec` or a " + "`tf.train.ClusterDef` object") + + +# TODO(yuefengz): maybe cache variables on local CPU. +# TODO(yuefengz): we may want to set session options to disallow communication +# between workers. +class ParameterServerStrategy(distribute_lib.DistributionStrategy): + """A parameter server DistributionStrategy. + + This strategy class works for both local training and between-graph replicated + training for multiple workers. If `cluster_spec` is specified, either passed + in to __init__() method or parsed from the + ["TF_CONFIG" environment + variable](https://www.tensorflow.org/api_docs/python/tf/estimator/RunConfig), + variables and updates to those variables are assigned to parameter servers and + other operations are assigned to workers. If `cluster_spec` is not set, it + becomes local training where variables are assigned to local CPU or the only + GPU. When each worker has more than one GPU, operations will be replicated on + these GPUs. In both cases, operations are replicated but variables are not and + these workers share a common view for which paramater server a variable is + assigned to. + + This class assumes between-graph replication will be used and works on a graph + for a particular worker. + + It is expected to call `call_for_each_tower(fn, *args, **kwargs)` for any + operations which potentially can be replicated across towers (i.e. multiple + GPUs) even if there is only CPU or one GPU. When defining the `fn`, extra + caution needs to be taken: + + 1) Always use @{tf.get_variable} instead of @{tf.Variable} which is not able + to refer to the same variable on different towers. + + 2) It is generally not recommended to open a device scope under the strategy's + scope. A device scope (i.e. calling @{tf.device}) will be merged with or + override the device for operations but will not change the device for + variables. + + 3) It is also not recommended to open a colocation scope (i.e. calling + @{tf.colocate_with}) under the strategy's scope. For colocating variables, + use `distribution.colocate_vars_with` instead. Colocation of ops will possibly + create conflicts of device assignement. + """ + + def __init__(self, + num_gpus_per_worker=0, + cluster_spec=None, + task_type=None, + task_id=None): + """Initiailizes this strategy. + + Args: + num_gpus_per_worker: number of local GPUs or GPUs per worker. + cluster_spec: a dict, ClusterDef or ClusterSpec object specifying the + cluster configurations. + task_type: the current task type. + task_id: the current task id. + """ + super(ParameterServerStrategy, self).__init__() + self._num_gpus_per_worker = num_gpus_per_worker + if cluster_spec: + cluster_spec = _normalize_cluster_spec(cluster_spec) + self._cluster_spec = cluster_spec + + # We typically don't need to do all-reduce in this strategy. + self._cross_tower_ops = ( + cross_tower_ops_lib.ReductionToOneDeviceCrossTowerOps( + reduce_to_device=_LOCAL_CPU)) + + self._initialize_devices(num_gpus_per_worker, cluster_spec, task_type, + task_id) + + def _initialize_devices(self, num_gpus_per_worker, cluster_spec, task_type, + task_id): + """Initialize internal devices. + + It creates variable devices and compute devices. Variables and operations + will be assigned to them respectively. We have one compute device per tower. + The variable device is a device function or device string. The default + variable device assigns variables to parameter servers in a round-robin + fashion. + + Args: + num_gpus_per_worker: number of local GPUs or GPUs per worker. + cluster_spec: a dict, ClusterDef or ClusterSpec object specifying the + cluster configurations. + task_type: the current task type. + task_id: the current task id. + + Raises: + ValueError: if the cluster_spec doesn't have ps jobs. + """ + self._task_type = task_type or "worker" + self._task_id = task_id or 0 + self._worker_device = "/job:%s/task:%d" % (self._task_type, self._task_id) + + # TODO(yuefengz): maybe clearer to split it into two classes, one for + # the distribuetd case and one for the local case, once we have the factory + # class/method. + + # Define compute devices which is a list of device strings and one for each + # tower. When there are GPUs, replicate operations on these GPUs. Otherwise, + # place operations on CPU. + if cluster_spec is None: + # Local mode. + if num_gpus_per_worker > 0: + self._compute_devices = list( + map("/device:GPU:{}".format, range(num_gpus_per_worker))) + else: + self._compute_devices = [_LOCAL_CPU] + else: + # Distributed mode. + if num_gpus_per_worker > 0: + self._compute_devices = [ + "%s/device:GPU:%d" % (self._worker_device, i) + for i in range(num_gpus_per_worker) + ] + else: + self._compute_devices = [self._worker_device] + + self._compute_devices = list( + map(device_util.resolve, self._compute_devices)) + self._canonical_compute_device_set = set(self._compute_devices) + + # Define variable device which is a device string in the local case and a + # device function in the distributed case. It is used to open a device scope + # where varibles are defined. + # The `_parameter_devices` is needed for the `parameter_devices` property + # and is a list of all variable devices. + if cluster_spec is None: + # Local mode. If there is only one GPU, put everything on that GPU. + # Otherwise, place variables on CPU. + if num_gpus_per_worker == 1: + assert len(list(self._compute_devices)) == 1 + self._variable_device = _LOCAL_GPU_0 + self._parameter_devices = [_LOCAL_GPU_0] + else: + self._variable_device = _LOCAL_CPU + self._parameter_devices = [_LOCAL_CPU] + else: + # Distributed mode. Place variables on ps jobs in a round-robin fashion. + # Note that devices returned from `replica_device_setter` are not + # canonical and therefore we don't canonicalize all variable devices to + # make them consistent. + # TODO(yuefengz): support passing a strategy object to control variable + # assignment. + # TODO(yuefengz): merge the logic of replica_device_setter into this + # class. + num_ps_replicas = len(cluster_spec.as_dict().get("ps", [])) + if num_ps_replicas == 0: + raise ValueError("The cluster spec needs to have `ps` jobs.") + self._variable_device = device_setter.replica_device_setter( + ps_tasks=num_ps_replicas, + worker_device=self._worker_device, + merge_devices=True, + cluster=cluster_spec) + + # Parameter devices are all tasks of the "ps" job. + self._parameter_devices = map("/job:ps/task:{}".format, + range(num_ps_replicas)) + + # Define the default device in cross-tower mode. In the distributed case, we + # set the default device to the corresponding worker to prevent these ops + # from being placed on other workers. + if cluster_spec is None: + self._default_device = None + else: + self._default_device = self._worker_device + + def distribute_dataset(self, dataset_fn): + """Distributes the dataset to each local GPU.""" + return values.PerDeviceDataset( + self._call_dataset_fn(dataset_fn), self._compute_devices, True) + + def _broadcast(self, tensor, destinations): + if not cross_tower_ops_lib.check_destinations(destinations): + destinations = self._compute_devices + return self._cross_tower_ops.broadcast(tensor, destinations) + + # TODO(yuefengz): not all ops in device_setter.STANDARD_PS_OPS will go through + # this creator, such as "MutableHashTable". + def _create_variable(self, next_creator, *args, **kwargs): + if "colocate_with" in kwargs: + with ops.device(None): + with ops.colocate_with(kwargs["colocate_with"]): + return next_creator(*args, **kwargs) + + with ops.colocate_with(None, ignore_existing=True): + with ops.device(self._variable_device): + return next_creator(*args, **kwargs) + + def _call_for_each_tower(self, fn, *args, **kwargs): + # pylint: disable=protected-access + return mirrored_strategy._call_for_each_tower(self, fn, *args, **kwargs) + + def _verify_destinations_not_different_worker(self, destinations): + if destinations is None: + return + for d in cross_tower_ops_lib.get_devices_from(destinations): + d_spec = tf_device.DeviceSpec.from_string(d) + if d_spec.job == self._task_type and d_spec.task != self._task_id: + raise ValueError( + "Cannot reduce to another worker: %r, current worker is %r" % + (d, self._worker_device)) + + def _reduce(self, aggregation, value, destinations): + self._verify_destinations_not_different_worker(destinations) + if not isinstance(value, values.DistributedValues): + # pylint: disable=protected-access + return mirrored_strategy._reduce_non_distributed_value( + self, aggregation, value, destinations) + + return self._cross_tower_ops.reduce( + aggregation, value, destinations=destinations) + + def _batch_reduce(self, aggregation, value_destination_pairs): + for _, destinations in value_destination_pairs: + self._verify_destinations_not_different_worker(destinations) + return self._cross_tower_ops.batch_reduce(aggregation, + value_destination_pairs) + + def _select_single_value(self, structured): + """Select any single values in `structured`.""" + + def _select_fn(x): # pylint: disable=g-missing-docstring + if isinstance(x, values.Mirrored): + if len(x.devices) == 1: + return list(x._index.values())[0] # pylint: disable=protected-access + else: + raise ValueError( + "You cannot update variable with a Mirrored object with multiple " + "components %r when using ParameterServerStrategy. You must " + "specify a single value or a Mirrored with a single value." % x) + elif isinstance(x, values.PerDevice): + raise ValueError( + "You cannot update variable with a PerDevice object %r when using " + "ParameterServerStrategy. You must specify a single value or a " + "Mirrored with a single value" % x) + else: + return x + + return nest.map_structure(_select_fn, structured) + + def _update(self, var, fn, *args, **kwargs): + if not isinstance(var, resource_variable_ops.ResourceVariable): + raise ValueError( + "You can not update `var` %r. It must be a Variable." % var) + with ops.colocate_with(var), distribute_lib.UpdateContext(var.device): + return fn(var, *self._select_single_value(args), + **self._select_single_value(kwargs)) + + # TODO(yuefengz): does it need to call _select_single_value? + def _update_non_slot(self, colocate_with, fn, *args, **kwargs): + with ops.device( + colocate_with.device), distribute_lib.UpdateContext(colocate_with): + return fn(*args, **kwargs) + + def _unwrap(self, val): + if isinstance(val, values.DistributedValues): + # Return in a deterministic order. + if set(val.devices) == self._canonical_compute_device_set: + return [val.get(device=d) for d in self._compute_devices] + return [val.get(device=d) for d in sorted(val.devices)] + return [val] + + def read_var(self, var): + # No need to distinguish between normal variables and tower-local variables. + return array_ops.identity(var) + + def configure(self, session_config=None): + del session_config + + # Use TF_CONFIG to get the cluster spec and the current job. + tf_config = json.loads(os.environ.get("TF_CONFIG", "{}")) + cluster_spec = _normalize_cluster_spec(tf_config.get("cluster", {})) + + task_env = tf_config.get("task", {}) + if task_env: + task_type = task_env.get("type", "worker") + task_id = int(task_env.get("index", "0")) + else: + task_type = "worker" + task_id = None + + # Set the devices if cluster_spec is defined in TF_CONFIG but not passed in + # the constructor. + if not self._cluster_spec and cluster_spec: + self._cluster_spec = cluster_spec + self._initialize_devices(self._num_gpus_per_worker, cluster_spec, + task_type, task_id) + + @property + def num_towers(self): + return len(self._compute_devices) + + @property + def worker_devices(self): + # Make a copy to prevent users from accidentally mutating our copy. + return list(self._compute_devices) + + @property + def parameter_devices(self): + return list(self._parameter_devices) + + def non_slot_devices(self, var_list): + return min(var_list, key=lambda x: x.name) diff --git a/tensorflow/contrib/distribute/python/parameter_server_strategy_test.py b/tensorflow/contrib/distribute/python/parameter_server_strategy_test.py new file mode 100644 index 0000000000..ad538b9e8e --- /dev/null +++ b/tensorflow/contrib/distribute/python/parameter_server_strategy_test.py @@ -0,0 +1,455 @@ +# 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 ParameterServerStrategy.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import contextlib +import json +import threading +from absl.testing import parameterized + +from tensorflow.contrib.distribute.python import combinations +from tensorflow.contrib.distribute.python import multi_worker_test_base +from tensorflow.contrib.distribute.python import parameter_server_strategy +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session +from tensorflow.python.eager import context +from tensorflow.python.estimator import run_config +from tensorflow.python.framework import constant_op +from tensorflow.python.framework import ops +from tensorflow.python.layers import core +from tensorflow.python.ops import array_ops +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import gradients +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables +from tensorflow.python.platform import test +from tensorflow.python.training import device_util +from tensorflow.python.training import distribute as distribute_lib + + +class ParameterServerStrategyTest(test.TestCase, parameterized.TestCase): + + @classmethod + def setUpClass(cls): + cls._workers, cls._ps = multi_worker_test_base.create_in_process_cluster( + num_workers=3, num_ps=2) + + def setUp(self): + self._result = 0 + self._lock = threading.Lock() + self._init_condition = threading.Condition() + self._init_reached = 0 + self._finish_condition = threading.Condition() + self._finish_reached = 0 + + def _get_ps_distribution_strategy(self, task_type, task_index, num_gpus=0): + tf_config = { + 'cluster': { + run_config.TaskType.WORKER: [ + 'fake_worker_0', 'fake_worker_1', 'fake_worker_2' + ], + run_config.TaskType.PS: ['fake_ps_0', 'fake_ps_1'] + }, + 'task': { + 'type': task_type, + 'index': task_index + } + } + distribution = parameter_server_strategy.ParameterServerStrategy( + num_gpus_per_worker=num_gpus) + with self._lock: + # Accessing environment variables should be protected by locks because + # environment variables are shared by all threads. + with test.mock.patch.dict('os.environ', + {'TF_CONFIG': json.dumps(tf_config)}): + distribution.configure() + return distribution + + @contextlib.contextmanager + def _test_session(self, target): + config = config_pb2.ConfigProto(allow_soft_placement=True) + config.graph_options.optimizer_options.opt_level = -1 + with session.Session(graph=None, config=config, target=target) as sess: + yield sess + + def _test_device_assignment_distributed(self, d, num_gpus=0): + with ops.Graph().as_default(), \ + self._test_session(target=self._workers[0].target) as sess, \ + d.scope(): + + # Define a variable outside the call_for_each_tower scope. This is not + # recommended. + n = variable_scope.get_variable('n', initializer=10.0) + self.assertEqual(n.device, '/job:ps/task:0') + + def model_fn(): + if num_gpus == 0: + last_part_device = 'device:CPU:0' + else: + last_part_device = ( + 'device:GPU:%d' % distribute_lib.get_tower_context().tower_id) + + a = constant_op.constant(1.0) + b = constant_op.constant(2.0) + c = a + b + self.assertEqual(a.device, + '/job:worker/replica:0/task:1/%s' % last_part_device) + self.assertEqual(b.device, + '/job:worker/replica:0/task:1/%s' % last_part_device) + self.assertEqual(c.device, + '/job:worker/replica:0/task:1/%s' % last_part_device) + + # The device scope is ignored for variables but not for normal ops. + with ops.device('/job:worker/task:0'): + x = variable_scope.get_variable('x', initializer=10.0) + x_add = x.assign_add(c) + e = a + c + # The variable x is on the task 1 since the device_function has been + # called once before the model_fn. + self.assertEqual(x.device, '/job:ps/task:1') + self.assertEqual(x_add.device, x.device) + self.assertEqual(e.device, + '/job:worker/replica:0/task:0/%s' % last_part_device) + + # The colocate_vars_with can override the distribution's device. + with d.colocate_vars_with(x): + y = variable_scope.get_variable('y', initializer=20.0) + y_add = y.assign_add(x_add) + self.assertEqual(y.device, '/job:ps/task:1') + self.assertEqual(y_add.device, y.device) + self.assertEqual(y.device, x.device) + + z = variable_scope.get_variable('z', initializer=10.0) + self.assertEqual(z.device, '/job:ps/task:0') + self.assertNotEqual(z.device, x.device) + + with ops.control_dependencies([y_add]): + z_add = z.assign_add(y) + with ops.control_dependencies([z_add]): + f = z + c + self.assertEqual(f.device, + '/job:worker/replica:0/task:1/%s' % last_part_device) + + # The device scope would merge with the default worker device. + with ops.device('/CPU:1'): + g = e + 1.0 + self.assertEqual(g.device, '/job:worker/replica:0/task:1/device:CPU:1') + + # Ths ops.colocate_with will be ignored when defining a variale but not + # for a normal tensor. + with ops.colocate_with(x): + u = variable_scope.get_variable('u', initializer=30.0) + v = variable_scope.get_variable('v', initializer=30.0) + h = f + 1.0 + self.assertIn('/job:ps/', u.device) + self.assertIn('/job:ps/', v.device) + # u and v are on different parameter servers. + self.assertTrue(u.device != x.device or v.device != x.device) + self.assertTrue(u.device == x.device or v.device == x.device) + # Here h is not on one worker. Note h.device is canonical while x.device + # is not but. + self.assertIn('/job:ps/', h.device) + return y_add, z_add, f + + y, z, f = d.call_for_each_tower(model_fn) + self.assertNotEqual(y, None) + self.assertNotEqual(z, None) + self.assertNotEqual(f, None) + + if context.num_gpus() >= 1 and num_gpus <= 1: + variables.global_variables_initializer().run() + y_val, z_val, f_val = sess.run([y, z, f]) + self.assertEqual(y_val, 33.0) + self.assertEqual(z_val, 43.0) + self.assertEqual(f_val, 46.0) + + @combinations.generate( + combinations.combine(mode=['graph'], num_gpus=[0, 1, 2])) + def testDeviceAssignmentDistributed(self, num_gpus): + d = self._get_ps_distribution_strategy('worker', 1, num_gpus=num_gpus) + self._test_device_assignment_distributed(d, num_gpus=num_gpus) + + def _test_device_assignment_local(self, + d, + compute_device='CPU', + variable_device='CPU', + num_gpus=0): + with ops.Graph().as_default(), \ + self._test_session(target=self._workers[0].target) as sess, \ + d.scope(): + + def model_fn(): + if 'CPU' in compute_device: + tower_compute_device = '/device:CPU:0' + else: + tower_compute_device = ( + '/device:GPU:%d' % distribute_lib.get_tower_context().tower_id) + tower_compute_device = device_util.canonicalize(tower_compute_device) + + if 'CPU' in variable_device: + tower_variable_device = '/device:CPU:0' + else: + tower_variable_device = ( + '/device:GPU:%d' % distribute_lib.get_tower_context().tower_id) + tower_variable_device = device_util.canonicalize(tower_variable_device) + + a = constant_op.constant(1.0) + b = constant_op.constant(2.0) + c = a + b + self.assertEqual(a.device, tower_compute_device) + self.assertEqual(b.device, tower_compute_device) + self.assertEqual(c.device, tower_compute_device) + + # The device scope is ignored for variables but not for normal ops. + with ops.device('/device:GPU:2'): + x = variable_scope.get_variable('x', initializer=10.0) + x_add = x.assign_add(c) + e = a + c + self.assertEqual( + device_util.canonicalize(x.device), tower_variable_device) + self.assertEqual(x_add.device, x.device) + self.assertEqual(e.device, device_util.canonicalize('/device:GPU:2')) + + # The colocate_vars_with can override the distribution's device. + with d.colocate_vars_with(x): + y = variable_scope.get_variable('y', initializer=20.0) + y_add = y.assign_add(x_add) + self.assertEqual( + device_util.canonicalize(y.device), tower_variable_device) + self.assertEqual(y_add.device, y.device) + self.assertEqual(y.device, x.device) + + z = variable_scope.get_variable('z', initializer=10.0) + self.assertEqual( + device_util.canonicalize(z.device), tower_variable_device) + + with ops.control_dependencies([y_add]): + z_add = z.assign_add(y) + with ops.control_dependencies([z_add]): + f = z + c + self.assertEqual(f.device, tower_compute_device) + + # The device scope would merge with the default worker device. + with ops.device('/CPU:1'): + g = e + 1.0 + self.assertEqual(g.device, device_util.canonicalize('/device:CPU:1')) + + # Ths ops.colocate_with will be ignored when defining a variale but not + # for a normal tensor. + with ops.colocate_with(x): + u = variable_scope.get_variable('u', initializer=30.0) + h = f + 1.0 + self.assertEqual( + device_util.canonicalize(u.device), tower_variable_device) + self.assertEqual(device_util.canonicalize(x.device), h.device) + return y_add, z_add, f + + y, z, f = d.call_for_each_tower(model_fn) + self.assertNotEqual(y, None) + self.assertNotEqual(z, None) + self.assertNotEqual(f, None) + + if context.num_gpus() >= 1 and num_gpus <= 1: + variables.global_variables_initializer().run() + y_val, z_val, f_val = sess.run([y, z, f]) + self.assertEqual(y_val, 33.0) + self.assertEqual(z_val, 43.0) + self.assertEqual(f_val, 46.0) + + def testDeviceAssignmentLocal(self): + distribution = parameter_server_strategy.ParameterServerStrategy( + num_gpus_per_worker=0) + self._test_device_assignment_local( + distribution, compute_device='CPU', variable_device='CPU', num_gpus=0) + + distribution = parameter_server_strategy.ParameterServerStrategy( + num_gpus_per_worker=1) + self._test_device_assignment_local( + distribution, compute_device='GPU', variable_device='GPU', num_gpus=1) + + distribution = parameter_server_strategy.ParameterServerStrategy( + num_gpus_per_worker=2) + self._test_device_assignment_local( + distribution, compute_device='GPU', variable_device='CPU', num_gpus=2) + + def _test_simple_increment(self, d, task_type, task_index, master_target): + if hasattr(d, '_cluster_spec') and d._cluster_spec: + num_workers = len(d._cluster_spec.as_dict().get('worker', + ['dummy_worker'])) + else: + num_workers = 1 + with ops.Graph().as_default(), \ + self._test_session(target=master_target) as sess, \ + d.scope(): + + def model_fn(): + x = variable_scope.get_variable('x', initializer=10.0) + y = variable_scope.get_variable('y', initializer=20.0) + + x_add = x.assign_add(1.0, use_locking=True) + y_add = y.assign_add(1.0, use_locking=True) + + train_op = control_flow_ops.group([x_add, y_add]) + return x, y, train_op + + x, y, train_op = d.call_for_each_tower(model_fn) + train_op = d.group(d.unwrap(train_op)) + + if context.num_gpus() < d._num_gpus_per_worker: + return True + + if task_index == 0: + variables.global_variables_initializer().run() + + # Workers waiting for chief worker's initializing variables. + self._init_condition.acquire() + self._init_reached += 1 + while self._init_reached != num_workers: + self._init_condition.wait() + self._init_condition.notify_all() + self._init_condition.release() + + sess.run(train_op) + + # Wait for other workers to finish training. + self._finish_condition.acquire() + self._finish_reached += 1 + while self._finish_reached != num_workers: + self._finish_condition.wait() + self._finish_condition.notify_all() + self._finish_condition.release() + + x_val, y_val = sess.run([x, y]) + self.assertEqual(x_val, 10.0 + 1.0 * num_workers * d.num_towers) + self.assertEqual(y_val, 20.0 + 1.0 * num_workers * d.num_towers) + return (x_val == 10.0 + 1.0 * num_workers * d.num_towers and + y_val == 20.0 + 1.0 * num_workers * d.num_towers) + + def _test_minimize_loss_graph(self, d, task_type, task_index, master_target): + with ops.Graph().as_default(), \ + self._test_session(target=master_target) as sess, \ + d.scope(): + l = core.Dense(1, use_bias=False) + + def loss_fn(x): + y = array_ops.reshape(l(x), []) - constant_op.constant(1.) + return y * y + + # TODO(yuefengz, apassos): eager.backprop.implicit_grad is not safe for + # multiple graphs (b/111216820). + def grad_fn(x): + loss = loss_fn(x) + var_list = ( + variables.trainable_variables() + ops.get_collection( + ops.GraphKeys.TRAINABLE_RESOURCE_VARIABLES)) + grads = gradients.gradients(loss, var_list) + ret = list(zip(grads, var_list)) + return ret + + def update(v, g): + return v.assign_sub(0.05 * g, use_locking=True) + + one = d.broadcast(constant_op.constant([[1.]])) + + def step(): + """Perform one optimization step.""" + # Run forward & backward to get gradients, variables list. + g_v = d.call_for_each_tower(grad_fn, one) + # Update the variables using the gradients and the update() function. + before_list = [] + after_list = [] + for g, v in g_v: + fetched = d.read_var(v) + before_list.append(fetched) + with ops.control_dependencies([fetched]): + # TODO(yuefengz): support non-Mirrored variable as destinations. + g = d.reduce( + variable_scope.VariableAggregation.SUM, g, destinations=v) + with ops.control_dependencies(d.unwrap(d.update(v, update, g))): + after_list.append(d.read_var(v)) + return before_list, after_list + + before_out, after_out = step() + + if context.num_gpus() < d._num_gpus_per_worker: + return True + + if task_index == 0: + variables.global_variables_initializer().run() + + # Workers waiting for chief worker's initializing variables. + self._init_condition.acquire() + self._init_reached += 1 + while self._init_reached != 3: + self._init_condition.wait() + self._init_condition.notify_all() + self._init_condition.release() + + for i in range(10): + b, a = sess.run((before_out, after_out)) + if i == 0: + before, = b + after, = a + + error_before = abs(before - 1) + error_after = abs(after - 1) + # Error should go down + self.assertLess(error_after, error_before) + return error_after < error_before + + def _run_client(self, index, model_fn, num_gpus): + task_type = run_config.TaskType.WORKER + result = model_fn( + self._get_ps_distribution_strategy(task_type, index, num_gpus=num_gpus), + task_type, index, self._workers[index].target) + if result: + with self._lock: + self._result += 1 + + def _run_multiple_clients(self, num_clients, model_fn, num_gpus=0): + threads = [] + for i in range(num_clients): + t = threading.Thread( + target=self._run_client, args=(i, model_fn, num_gpus)) + t.start() + threads.append(t) + for t in threads: + t.join() + + def testSimpleBetweenGraph(self): + self._run_multiple_clients(3, self._test_simple_increment) + self.assertEqual(self._result, 3) + + @combinations.generate( + combinations.combine(mode=['graph'], num_gpus=[0, 1, 2])) + def testLocalSimpleIncrement(self, num_gpus): + d = parameter_server_strategy.ParameterServerStrategy( + num_gpus_per_worker=num_gpus) + self._test_simple_increment(d, 'dummy_worker', 0, '') + + @combinations.generate( + combinations.combine(mode=['graph'], num_gpus=[0, 1, 2])) + def testMinimizeLossGraph(self, num_gpus): + self._run_multiple_clients( + 3, self._test_minimize_loss_graph, num_gpus=num_gpus) + self.assertEqual(self._result, 3) + + +if __name__ == '__main__': + test.main() -- GitLab From ed2b2dbd1dbbb5c0e41f0bc6bb77219f0bfa2c96 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 08:06:24 -0700 Subject: [PATCH 490/519] Adding core estimator for ranking. PiperOrigin-RevId: 206318440 --- .../estimator_batch/estimator.py | 129 +++++++++++++++++- .../estimator_batch/estimator_test.py | 30 +++- .../boosted_trees/estimator_batch/model.py | 73 +++++++--- 3 files changed, 210 insertions(+), 22 deletions(-) diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py b/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py index 38fa8c3834..2df879f924 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/estimator.py @@ -26,6 +26,12 @@ from tensorflow.python.estimator import estimator as core_estimator from tensorflow.python.ops import math_ops +# ================== Old estimator interface=================================== +# The estimators below were designed for old feature columns and old estimator +# interface. They can be used with new feature columns and losses by setting +# use_core_libs = True. + + class GradientBoostedDecisionTreeClassifier(estimator.Estimator): """An estimator using gradient boosted decision trees.""" @@ -356,9 +362,16 @@ class GradientBoostedDecisionTreeRanker(estimator.Estimator): config=config, feature_engineering_fn=feature_engineering_fn) +# ================== New Estimator interface=================================== +# The estimators below use new core Estimator interface and must be used with +# new feature columns and heads. + class CoreGradientBoostedDecisionTreeEstimator(core_estimator.Estimator): - """An estimator using gradient boosted decision trees.""" + """An estimator using gradient boosted decision trees. + + Useful for training with user specified `Head`. + """ def __init__(self, learner_config, @@ -374,6 +387,36 @@ class CoreGradientBoostedDecisionTreeEstimator(core_estimator.Estimator): logits_modifier_function=None, center_bias=True, output_leaf_index=False): + """Initializes a core version of GradientBoostedDecisionTreeEstimator. + + Args: + learner_config: A config for the learner. + examples_per_layer: Number of examples to accumulate before growing a + layer. It can also be a function that computes the number of examples + based on the depth of the layer that's being built. + head: `Head` instance. + num_trees: An int, number of trees to build. + feature_columns: A list of feature columns. + weight_column_name: Name of the column for weights, or None if not + weighted. + model_dir: Directory for model exports, etc. + config: `RunConfig` object to configure the runtime settings. + label_keys: Optional list of strings with size `[n_classes]` defining the + label vocabulary. Only supported for `n_classes` > 2. + feature_engineering_fn: Feature engineering function. Takes features and + labels which are the output of `input_fn` and returns features and + labels which will be fed into the model. + logits_modifier_function: A modifier function for the logits. + center_bias: Whether a separate tree should be created for first fitting + the bias. + output_leaf_index: whether to output leaf indices along with predictions + during inference. The leaf node indexes are available in predictions + dict by the key 'leaf_index'. For example, + result_dict = classifier.predict(...) + for example_prediction_result in result_dict: + # access leaf index list by example_prediction_result["leaf_index"] + # which contains one leaf index per tree + """ def _model_fn(features, labels, mode, config): return model.model_builder( @@ -397,3 +440,87 @@ class CoreGradientBoostedDecisionTreeEstimator(core_estimator.Estimator): super(CoreGradientBoostedDecisionTreeEstimator, self).__init__( model_fn=_model_fn, model_dir=model_dir, config=config) + + +class CoreGradientBoostedDecisionTreeRanker(core_estimator.Estimator): + """A ranking estimator using gradient boosted decision trees.""" + + def __init__( + self, + learner_config, + examples_per_layer, + head, + ranking_model_pair_keys, + num_trees=None, + feature_columns=None, + weight_column_name=None, + model_dir=None, + config=None, + label_keys=None, + logits_modifier_function=None, + center_bias=False, + output_leaf_index=False, + ): + """Initializes a GradientBoostedDecisionTreeRanker instance. + + This is an estimator that can be trained off the pairwise data and can be + used for inference on non-paired data. This is essentially LambdaMart. + Args: + learner_config: A config for the learner. + examples_per_layer: Number of examples to accumulate before growing a + layer. It can also be a function that computes the number of examples + based on the depth of the layer that's being built. + head: `Head` instance. + ranking_model_pair_keys: Keys to distinguish between features + for left and right part of the training pairs for ranking. For example, + for an Example with features "a.f1" and "b.f1", the keys would be + ("a", "b"). + num_trees: An int, number of trees to build. + feature_columns: A list of feature columns. + weight_column_name: Name of the column for weights, or None if not + weighted. + model_dir: Directory for model exports, etc. + config: `RunConfig` object to configure the runtime settings. + label_keys: Optional list of strings with size `[n_classes]` defining the + label vocabulary. Only supported for `n_classes` > 2. + logits_modifier_function: A modifier function for the logits. + center_bias: Whether a separate tree should be created for first fitting + the bias. + output_leaf_index: whether to output leaf indices along with predictions + during inference. The leaf node indexes are available in predictions + dict by the key 'leaf_index'. It is a Tensor of rank 2 and its shape is + [batch_size, num_trees]. + For example, + result_iter = classifier.predict(...) + for result_dict in result_iter: + # access leaf index list by result_dict["leaf_index"] + # which contains one leaf index per tree + + Raises: + ValueError: If learner_config is not valid. + """ + + def _model_fn(features, labels, mode, config): + return model.ranking_model_builder( + features=features, + labels=labels, + mode=mode, + config=config, + params={ + 'head': head, + 'n_classes': 2, + 'feature_columns': feature_columns, + 'learner_config': learner_config, + 'num_trees': num_trees, + 'weight_column_name': weight_column_name, + 'examples_per_layer': examples_per_layer, + 'center_bias': center_bias, + 'logits_modifier_function': logits_modifier_function, + 'use_core_libs': True, + 'output_leaf_index': output_leaf_index, + 'ranking_model_pair_keys': ranking_model_pair_keys, + }, + output_type=model.ModelBuilderOutputType.ESTIMATOR_SPEC) + + super(CoreGradientBoostedDecisionTreeRanker, self).__init__( + model_fn=_model_fn, model_dir=model_dir, config=config) diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py b/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py index f787d3cdb8..9e9febbbef 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/estimator_test.py @@ -203,7 +203,7 @@ class BoostedTreeEstimatorTest(test_util.TensorFlowTestCase): model.predict(input_fn=_infer_ranking_train_input_fn) -class CoreGradientBoostedDecisionTreeEstimator(test_util.TensorFlowTestCase): +class CoreGradientBoostedDecisionTreeEstimators(test_util.TensorFlowTestCase): def testTrainEvaluateInferDoesNotThrowError(self): head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( @@ -229,6 +229,34 @@ class CoreGradientBoostedDecisionTreeEstimator(test_util.TensorFlowTestCase): est.evaluate(input_fn=_eval_input_fn, steps=1) est.predict(input_fn=_eval_input_fn) + def testRankingDontThrowExceptionForForEstimator(self): + learner_config = learner_pb2.LearnerConfig() + learner_config.num_classes = 2 + learner_config.constraints.max_tree_depth = 1 + model_dir = tempfile.mkdtemp() + config = run_config.RunConfig() + + head_fn = head_lib._binary_logistic_head_with_sigmoid_cross_entropy_loss( + loss_reduction=losses.Reduction.SUM_OVER_NONZERO_WEIGHTS) + + est = estimator.CoreGradientBoostedDecisionTreeRanker( + head=head_fn, + learner_config=learner_config, + num_trees=1, + examples_per_layer=3, + model_dir=model_dir, + config=config, + feature_columns=[ + core_feature_column.numeric_column("f1"), + core_feature_column.numeric_column("f2") + ], + ranking_model_pair_keys=("a", "b")) + + # Train for a few steps. + est.train(input_fn=_ranking_train_input_fn, steps=1000) + est.evaluate(input_fn=_ranking_train_input_fn, steps=1) + est.predict(input_fn=_infer_ranking_train_input_fn) + if __name__ == "__main__": googletest.main() diff --git a/tensorflow/contrib/boosted_trees/estimator_batch/model.py b/tensorflow/contrib/boosted_trees/estimator_batch/model.py index dbfee16b9e..161cc42cb0 100644 --- a/tensorflow/contrib/boosted_trees/estimator_batch/model.py +++ b/tensorflow/contrib/boosted_trees/estimator_batch/model.py @@ -59,6 +59,8 @@ def model_builder(features, * center_bias: Whether a separate tree should be created for first fitting the bias. config: `RunConfig` of the estimator. + output_type: Whether to return ModelFnOps (old interface) or EstimatorSpec + (new interface). Returns: A `ModelFnOps` object. @@ -176,7 +178,12 @@ def model_builder(features, return model_fn_ops -def ranking_model_builder(features, labels, mode, params, config): +def ranking_model_builder(features, + labels, + mode, + params, + config, + output_type=ModelBuilderOutputType.MODEL_FN_OPS): """Multi-machine batch gradient descent tree model for ranking. Args: @@ -200,6 +207,9 @@ def ranking_model_builder(features, labels, mode, params, config): for an Example with features "a.f1" and "b.f1", the keys would be ("a", "b"). config: `RunConfig` of the estimator. + output_type: Whether to return ModelFnOps (old interface) or EstimatorSpec + (new interface). + Returns: A `ModelFnOps` object. @@ -327,31 +337,54 @@ def ranking_model_builder(features, labels, mode, params, config): return update_op create_estimator_spec_op = getattr(head, "create_estimator_spec", None) - if use_core_libs and callable(create_estimator_spec_op): - model_fn_ops = head.create_estimator_spec( - features=features, - mode=mode, - labels=labels, - train_op_fn=_train_op_fn, - logits=logits) - model_fn_ops = estimator_utils.estimator_spec_to_model_fn_ops(model_fn_ops) - else: - model_fn_ops = head.create_model_fn_ops( - features=features, - mode=mode, - labels=labels, - train_op_fn=_train_op_fn, - logits=logits) - if output_leaf_index and gbdt_batch.LEAF_INDEX in predictions_dict: - model_fn_ops.predictions[gbdt_batch.LEAF_INDEX] = predictions_dict[ - gbdt_batch.LEAF_INDEX] + training_hooks = [] if num_trees: if center_bias: num_trees += 1 + finalized_trees, attempted_trees = ( gbdt_model_main.get_number_of_trees_tensor()) - model_fn_ops.training_hooks.append( + training_hooks.append( trainer_hooks.StopAfterNTrees(num_trees, attempted_trees, finalized_trees)) + + if output_type == ModelBuilderOutputType.MODEL_FN_OPS: + if use_core_libs and callable(create_estimator_spec_op): + model_fn_ops = head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + model_fn_ops = estimator_utils.estimator_spec_to_model_fn_ops( + model_fn_ops) + else: + model_fn_ops = head.create_model_fn_ops( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + + if output_leaf_index and gbdt_batch.LEAF_INDEX in predictions_dict: + model_fn_ops.predictions[gbdt_batch.LEAF_INDEX] = predictions_dict[ + gbdt_batch.LEAF_INDEX] + + model_fn_ops.training_hooks.extend(training_hooks) + return model_fn_ops + + elif output_type == ModelBuilderOutputType.ESTIMATOR_SPEC: + assert callable(create_estimator_spec_op) + estimator_spec = head.create_estimator_spec( + features=features, + mode=mode, + labels=labels, + train_op_fn=_train_op_fn, + logits=logits) + + estimator_spec = estimator_spec._replace( + training_hooks=training_hooks + list(estimator_spec.training_hooks)) + return estimator_spec + return model_fn_ops -- GitLab From 375c023d307a74d32f222486bb2fdd3ba96c92da Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 08:23:54 -0700 Subject: [PATCH 491/519] Fix typo in FileWriter docs. PiperOrigin-RevId: 206320196 --- tensorflow/python/summary/writer/writer.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tensorflow/python/summary/writer/writer.py b/tensorflow/python/summary/writer/writer.py index aca084fc91..60e96ee947 100644 --- a/tensorflow/python/summary/writer/writer.py +++ b/tensorflow/python/summary/writer/writer.py @@ -325,7 +325,7 @@ class FileWriter(SummaryToEventTransformer): ``` The `session` argument to the constructor makes the returned `FileWriter` a - a compatibility layer over new graph-based summaries (`tf.contrib.summary`). + compatibility layer over new graph-based summaries (`tf.contrib.summary`). Crucially, this means the underlying writer resource and events file will be shared with any other `FileWriter` using the same `session` and `logdir`, and with any `tf.contrib.summary.SummaryWriter` in this session using the -- GitLab From 194e724cf9581ae7c3688b3942a66321d9fb514d Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 09:12:58 -0700 Subject: [PATCH 492/519] Add support for len calls on tensor lists. PiperOrigin-RevId: 206325816 --- tensorflow/contrib/autograph/utils/builtins.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/tensorflow/contrib/autograph/utils/builtins.py b/tensorflow/contrib/autograph/utils/builtins.py index 71079cfdc0..ccbe5fc954 100644 --- a/tensorflow/contrib/autograph/utils/builtins.py +++ b/tensorflow/contrib/autograph/utils/builtins.py @@ -27,6 +27,7 @@ from tensorflow.contrib.autograph.utils import type_check from tensorflow.python.framework import dtypes from tensorflow.python.framework import tensor_util from tensorflow.python.ops import array_ops +from tensorflow.python.ops import list_ops from tensorflow.python.ops import logging_ops from tensorflow.python.ops import math_ops @@ -50,7 +51,9 @@ def dynamic_builtin(f, *args, **kwargs): def dynamic_len(list_or_tensor): """Implementation of len using dynamic dispatch.""" - if tensor_util.is_tensor(list_or_tensor): + if _is_tensor_list(list_or_tensor): + return list_ops.tensor_list_length(list_or_tensor) + elif tensor_util.is_tensor(list_or_tensor): shape = list_or_tensor.shape if not shape.ndims: raise ValueError( @@ -59,6 +62,11 @@ def dynamic_len(list_or_tensor): return len(list_or_tensor) +def _is_tensor_list(list_or_tensor): + return (tensor_util.is_tensor(list_or_tensor) + and list_or_tensor.dtype == dtypes.variant) + + def dynamic_int(num_or_tensor, **kwargs): """Implementation of int() using dynamic dispatch.""" if tensor_util.is_tensor(num_or_tensor): -- GitLab From e6138e96def4b6432ad930cfb34b3806c5f9fb8b Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Fri, 27 Jul 2018 09:31:48 -0700 Subject: [PATCH 493/519] Properly call PrepareToUpdateVariable in resource strided slice assign. PiperOrigin-RevId: 206327963 --- tensorflow/core/kernels/BUILD | 1 + tensorflow/core/kernels/strided_slice_op.cc | 3 +++ 2 files changed, 4 insertions(+) diff --git a/tensorflow/core/kernels/BUILD b/tensorflow/core/kernels/BUILD index 2cb54bd973..65a7f8ccf3 100644 --- a/tensorflow/core/kernels/BUILD +++ b/tensorflow/core/kernels/BUILD @@ -124,6 +124,7 @@ tf_kernel_library( ":bounds_check", ":dense_update_functor", ":ops_util", + ":training_op_helpers", ":variable_ops", "//tensorflow/core:framework", "//tensorflow/core:lib", diff --git a/tensorflow/core/kernels/strided_slice_op.cc b/tensorflow/core/kernels/strided_slice_op.cc index 1e3e92a68a..c08ff47583 100644 --- a/tensorflow/core/kernels/strided_slice_op.cc +++ b/tensorflow/core/kernels/strided_slice_op.cc @@ -32,6 +32,7 @@ limitations under the License. #include "tensorflow/core/framework/tensor.h" #include "tensorflow/core/kernels/bounds_check.h" #include "tensorflow/core/kernels/ops_util.h" +#include "tensorflow/core/kernels/training_op_helpers.h" #include "tensorflow/core/kernels/variable_ops.h" #include "tensorflow/core/lib/core/status.h" #include "tensorflow/core/lib/gtl/array_slice.h" @@ -304,6 +305,8 @@ class StridedSliceAssignOp : public OpKernel { Var* v; OP_REQUIRES_OK(context, LookupResource(context, HandleFromInput(context, 0), &v)); + OP_REQUIRES_OK(context, + PrepareToUpdateVariable(context, v->tensor())); old_lhs = *v->tensor(); OP_REQUIRES(context, old_lhs.dtype() == DataTypeToEnum::value, errors::InvalidArgument( -- GitLab From 96c5b537c331011bccafd87d3f90c870c777f349 Mon Sep 17 00:00:00 2001 From: Russell Power Date: Fri, 27 Jul 2018 09:47:23 -0700 Subject: [PATCH 494/519] Improve restore performance for large checkpoints. When loading large tensors, the cost of creating a new BundleReader is small relative to the load time for the Tensor. When reading from network storage, using a threadpool for large tensor loads allows us to push expensive operations (alloc, fetch, checksum) to separate cores. PiperOrigin-RevId: 206330021 --- .../core/kernels/save_restore_tensor.cc | 150 ++++++++++++++---- tensorflow/core/platform/env_test.cc | 2 +- tensorflow/python/training/saver_test.py | 31 ++++ 3 files changed, 150 insertions(+), 33 deletions(-) diff --git a/tensorflow/core/kernels/save_restore_tensor.cc b/tensorflow/core/kernels/save_restore_tensor.cc index 990bd2bff9..7930ce4615 100644 --- a/tensorflow/core/kernels/save_restore_tensor.cc +++ b/tensorflow/core/kernels/save_restore_tensor.cc @@ -23,6 +23,7 @@ limitations under the License. #include "tensorflow/core/framework/register_types.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/kernels/bounds_check.h" +#include "tensorflow/core/lib/core/threadpool.h" #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/lib/strings/stringprintf.h" @@ -226,43 +227,53 @@ void RestoreTensor(OpKernelContext* context, #undef READER_COPY } -Status RestoreTensorsV2(OpKernelContext* context, const Tensor& prefix, - const Tensor& tensor_names, - const Tensor& shape_and_slices, - gtl::ArraySlice dtypes) { - const string& prefix_string = prefix.scalar()(); +namespace { - const auto& tensor_names_flat = tensor_names.flat(); - const auto& shape_and_slices_flat = shape_and_slices.flat(); +// Tensors larger than this threshold will be restored from a thread-pool. +const int64 kLargeShapeThreshold = 16 << 20; // 16M - // Sort lookup keys to improve locality when reading multiple tensors. - std::vector sorted_name_idx(tensor_names_flat.size()); - std::iota(sorted_name_idx.begin(), sorted_name_idx.end(), 0); - std::sort(sorted_name_idx.begin(), sorted_name_idx.end(), - [&tensor_names_flat](size_t a, size_t b) { - return tensor_names_flat(a) < tensor_names_flat(b); - }); +// A restore operation for a single tensor. Small tensors may be restored +// directly from the op thread to improve read locality. Large tensors can be +// restored from a thread pool: this requires creating a separate BundleReader +// for each restore. +struct RestoreOp { + RestoreOp& operator=(const RestoreOp&) = delete; - BundleReader reader(Env::Default(), prefix_string); - TF_RETURN_IF_ERROR(reader.status()); + bool should_run_in_pool(BundleReader* reader) const { + TensorShape restored_full_shape; - // TODO(zongheng): potential optimization: one Seek() in first lookup. - // TODO(zongheng): consider measuring speed and issuing concurrent lookups - // within a fixed memory budget. - TensorShape restored_full_shape; - Tensor* restored_tensor = nullptr; - for (auto i : sorted_name_idx) { - const string& tensor_name = tensor_names_flat(i); - const string& shape_and_slice = shape_and_slices_flat(i); + // Ignore status here; we'll catch the error later. + if (!reader->LookupTensorShape(tensor_name, &restored_full_shape).ok()) { + return false; + } + return restored_full_shape.num_elements() > kLargeShapeThreshold; + } + + // Run this restore operation using a new BundleReader. + void run_with_new_reader() { + BundleReader reader(Env::Default(), reader_prefix); + if (!reader.status().ok()) { + status = reader.status(); + return; + } + + status = run(&reader); + } + + Status run(BundleReader* reader) { + TensorShape restored_full_shape; TF_RETURN_IF_ERROR( - reader.LookupTensorShape(tensor_name, &restored_full_shape)); + reader->LookupTensorShape(tensor_name, &restored_full_shape)); + VLOG(1) << "Restoring tensor " << idx << " : " << tensor_name << " : " + << restored_full_shape.num_elements(); + Tensor* restored_tensor; if (shape_and_slice.empty()) { // Lookup the full tensor. TF_RETURN_IF_ERROR( - context->allocate_output(i, restored_full_shape, &restored_tensor)); - TF_RETURN_IF_ERROR(reader.Lookup(tensor_name, restored_tensor)); + context->allocate_output(idx, restored_full_shape, &restored_tensor)); + TF_RETURN_IF_ERROR(reader->Lookup(tensor_name, restored_tensor)); } else { // Lookup the slice. TensorShape parsed_full_shape; @@ -272,6 +283,7 @@ Status RestoreTensorsV2(OpKernelContext* context, const Tensor& prefix, TF_RETURN_IF_ERROR( checkpoint::ParseShapeAndSlice(shape_and_slice, &parsed_full_shape, &parsed_slice, &parsed_slice_shape)); + if (!restored_full_shape.IsSameSize(parsed_full_shape)) { return errors::InvalidArgument( "tensor_name = ", tensor_name, "; shape in shape_and_slice spec ", @@ -279,19 +291,93 @@ Status RestoreTensorsV2(OpKernelContext* context, const Tensor& prefix, " does not match the shape stored in checkpoint: ", restored_full_shape.DebugString()); } - TF_RETURN_IF_ERROR( - context->allocate_output(i, parsed_slice_shape, &restored_tensor)); + context->allocate_output(idx, parsed_slice_shape, &restored_tensor)); TF_RETURN_IF_ERROR( - reader.LookupSlice(tensor_name, parsed_slice, restored_tensor)); + reader->LookupSlice(tensor_name, parsed_slice, restored_tensor)); + } + return Status::OK(); + } + + OpKernelContext* context; + size_t idx; + string tensor_name; + string shape_and_slice; + string reader_prefix; + + ::tensorflow::Status status; +}; + +} // namespace + +Status RestoreTensorsV2(OpKernelContext* context, const Tensor& prefix, + const Tensor& tensor_names, + const Tensor& shape_and_slices, + gtl::ArraySlice dtypes) { + const string& prefix_string = prefix.scalar()(); + + const auto& tensor_names_flat = tensor_names.flat(); + const auto& shape_and_slices_flat = shape_and_slices.flat(); + + // Sort lookup keys to improve locality when reading multiple tensors. + std::vector sorted_name_idx(tensor_names_flat.size()); + std::iota(sorted_name_idx.begin(), sorted_name_idx.end(), 0); + std::sort(sorted_name_idx.begin(), sorted_name_idx.end(), + [&tensor_names_flat](size_t a, size_t b) { + return tensor_names_flat(a) < tensor_names_flat(b); + }); + + std::vector > pool_restore_ops; + std::vector > direct_restore_ops; + + BundleReader default_reader(Env::Default(), prefix_string); + TF_RETURN_IF_ERROR(default_reader.status()); + + for (auto i : sorted_name_idx) { + const string& tensor_name = tensor_names_flat(i); + const string& shape_and_slice = shape_and_slices_flat(i); + auto op = + new RestoreOp{context, i, tensor_name, shape_and_slice, prefix_string}; + if (op->should_run_in_pool(&default_reader)) { + pool_restore_ops.emplace_back(op); + } else { + direct_restore_ops.emplace_back(op); } - if (dtypes[i] != restored_tensor->dtype()) { + } + + { + // Schedule any threaded operations first, skipping thread pool creation if + // we don't have any expensive operations. + std::unique_ptr reader_pool; + if (!pool_restore_ops.empty()) { + reader_pool.reset( + new thread::ThreadPool(Env::Default(), "restore_tensors", 8)); + for (auto& op : pool_restore_ops) { + reader_pool->Schedule([&op]() { op->run_with_new_reader(); }); + } + } + + // Read small tensors from the op thread + for (auto& op : direct_restore_ops) { + TF_RETURN_IF_ERROR(op->run(&default_reader)); + } + } + + // Check status of pool ops; this must come after the pool shuts down. + for (auto& op : pool_restore_ops) { + TF_RETURN_IF_ERROR(op->status); + } + + for (auto i : sorted_name_idx) { + const string& tensor_name = tensor_names_flat(i); + if (dtypes[i] != context->mutable_output(i)->dtype()) { return errors::InvalidArgument( "tensor_name = ", tensor_name, "; expected dtype ", DataTypeString(dtypes[i]), " does not equal restored dtype ", - DataTypeString(restored_tensor->dtype())); + DataTypeString(context->mutable_output(i)->dtype())); } } + return Status::OK(); } diff --git a/tensorflow/core/platform/env_test.cc b/tensorflow/core/platform/env_test.cc index c461a40086..305a9a682f 100644 --- a/tensorflow/core/platform/env_test.cc +++ b/tensorflow/core/platform/env_test.cc @@ -86,7 +86,7 @@ TEST_F(DefaultEnvTest, IncompleteReadOutOfRange) { TEST_F(DefaultEnvTest, ReadFileToString) { for (const int length : {0, 1, 1212, 2553, 4928, 8196, 9000, (1 << 20) - 1, - 1 << 20, (1 << 20) + 1}) { + 1 << 20, (1 << 20) + 1, (256 << 20) + 100}) { const string filename = strings::StrCat(BaseDir(), "/bar/..//file", length); // Write a file with the given length diff --git a/tensorflow/python/training/saver_test.py b/tensorflow/python/training/saver_test.py index ecce8ae6bd..204e81dda0 100644 --- a/tensorflow/python/training/saver_test.py +++ b/tensorflow/python/training/saver_test.py @@ -786,6 +786,37 @@ class SaverTest(test.TestCase): self.assertEqual(20.0, v1.eval()) save.save(sess, save_path) + # Test restoring large tensors (triggers a thread pool) + def testRestoreLargeTensors(self): + save_dir = self.get_temp_dir() + def _model(): + small_v = [variable_scope.get_variable( + "small%d" % i, shape=[10, 2], use_resource=True) for i in range(5)] + large_v = [variable_scope.get_variable( + "large%d" % i, shape=[32000, 1000], use_resource=True) + for i in range(3)] + return small_v + large_v + + save_graph = ops_lib.Graph() + with save_graph.as_default(), self.test_session(graph=save_graph) as sess: + orig_vars = _model() + sess.run(variables.global_variables_initializer()) + save = saver_module.Saver(max_to_keep=1) + variables.global_variables_initializer().run() + save.save(sess, save_dir) + orig_vals = sess.run(orig_vars) + + restore_graph = ops_lib.Graph() + with restore_graph.as_default(), self.test_session( + graph=restore_graph) as sess: + restored_vars = _model() + save = saver_module.Saver(max_to_keep=1) + save.restore(sess, save_dir) + restored_vals = sess.run(restored_vars) + + for orig, restored in zip(orig_vals, restored_vals): + self.assertAllEqual(orig, restored) + class SaveRestoreShardedTest(test.TestCase): -- GitLab From 7a7690a6e079bdb39d157f287e3739f51d8bfe93 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 10:04:17 -0700 Subject: [PATCH 495/519] Quick FusedBatchNorm performance regression fix: When not compiled with "--config=opt", or when compiling with "--config=opt --distinct_host_configuration=false" (to skip host-specific optimizations), the following code incurs casting overhead even when T == U, y.reshape(rest_by_depth).device(d) = x_shifted.template cast(); The fix: explicitly avoid calling cast() if T == U. PiperOrigin-RevId: 206332285 --- .../core/kernels/fused_batch_norm_op.cc | 24 ++++++++++++++++++- 1 file changed, 23 insertions(+), 1 deletion(-) diff --git a/tensorflow/core/kernels/fused_batch_norm_op.cc b/tensorflow/core/kernels/fused_batch_norm_op.cc index f99dd643f7..d89f1592bd 100644 --- a/tensorflow/core/kernels/fused_batch_norm_op.cc +++ b/tensorflow/core/kernels/fused_batch_norm_op.cc @@ -45,6 +45,24 @@ struct FusedBatchNorm; template struct FusedBatchNormGrad; +template +struct CastIfNecessary { + static inline void process( + Y& y, X& x_shifted, const Eigen::DSizes& rest_by_depth, + const CPUDevice& d) { + y.reshape(rest_by_depth).device(d) = x_shifted.template cast(); + } +}; + +template +struct CastIfNecessary { + static inline void process( + Y& y, X& x_shifted, const Eigen::DSizes& rest_by_depth, + const CPUDevice& d) { + y.reshape(rest_by_depth).device(d) = x_shifted; + } +}; + template struct FusedBatchNorm { void operator()(OpKernelContext* context, const Tensor& x_input, @@ -125,7 +143,11 @@ struct FusedBatchNorm { auto x_shifted = x_scaled + offset.reshape(one_by_depth).broadcast(bcast_spec); - y.reshape(rest_by_depth).device(d) = x_shifted.template cast(); + // Explicitly checks the types of T and U and only casts x_shifted when + // T != U. (Not doing so caused a 35-50% performance slowdown for + // some compiler flags.) + CastIfNecessary::value, decltype(y), decltype(x_shifted), + T>::process(y, x_shifted, rest_by_depth, d); } }; -- GitLab From a9911cb06b931be7207ac2938dfffe9db3313e3c Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 10:26:19 -0700 Subject: [PATCH 496/519] Bug fixes and 16 bit matmul added in CXX11/src/FixedPoint. PiperOrigin-RevId: 206335619 --- .../CXX11/src/FixedPoint/FixedPointTypes.h | 6 +- .../CXX11/src/FixedPoint/MatMatProduct.h | 86 +++- .../CXX11/src/FixedPoint/MatMatProductAVX2.h | 482 +++++++++++++++++- .../CXX11/src/FixedPoint/MatMatProductNEON.h | 9 +- .../CXX11/src/FixedPoint/MatVecProduct.h | 39 +- .../CXX11/src/FixedPoint/PacketMathAVX2.h | 8 +- .../CXX11/src/FixedPoint/PacketMathAVX512.h | 6 +- .../CXX11/src/FixedPoint/TypeCastingAVX2.h | 16 +- .../CXX11/src/FixedPoint/TypeCastingAVX512.h | 53 +- 9 files changed, 659 insertions(+), 46 deletions(-) diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h index 6b625abc3e..5ab3664918 100644 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h +++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/FixedPointTypes.h @@ -7,8 +7,8 @@ // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -#ifndef EIGEN_CXX11_FIXED_POINT_TYPES_H -#define EIGEN_CXX11_FIXED_POINT_TYPES_H +#ifndef CXX11_SRC_FIXEDPOINT_FIXEDPOINTTYPES_H_ +#define CXX11_SRC_FIXEDPOINT_FIXEDPOINTTYPES_H_ #include #include @@ -339,4 +339,4 @@ EIGEN_STRONG_INLINE std::ostream& operator<<(std::ostream& os, QInt32 a) { } // namespace Eigen -#endif // EIGEN_CXX11_FIXED_POINT_TYPES_H +#endif // CXX11_SRC_FIXEDPOINT_FIXEDPOINTTYPES_H_ diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProduct.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProduct.h index 4d0dca07df..e6f4080ae1 100644 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProduct.h +++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProduct.h @@ -7,9 +7,8 @@ // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -#ifndef EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_H -#define EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_H - +#ifndef CXX11_SRC_FIXEDPOINT_MATMATPRODUCT_H_ +#define CXX11_SRC_FIXEDPOINT_MATMATPRODUCT_H_ namespace Eigen { namespace internal { @@ -24,6 +23,14 @@ template<> struct scalar_product_traits typedef QInt32 ReturnType; }; +// Accumulate the product of 2 QInt16 inputs on 32 bits to prevent +// overflows +template <> +struct scalar_product_traits { + enum { Defined = 1 }; + typedef QInt32 ReturnType; +}; + // Accumulate the product of QInt8 inputs with QUint8 inputs on 32 bits // to prevent overflows template<> struct scalar_product_traits @@ -247,9 +254,76 @@ void gebp_kernel +class gebp_traits { + public: + typedef QInt16 LhsScalar; + typedef QInt16 RhsScalar; + typedef QInt32 ResScalar; + + enum { + // register block size along the M and N directions + // One for the current implementation + nr = 1, + mr = 1, + // Progress made at each iteration of the product loop + // also 1 for the current implementation + LhsProgress = 1, + RhsProgress = 1 + }; +}; + +// The signed 16bit Mat-Mat product itself. +template +struct gebp_kernel { + EIGEN_DONT_INLINE + void operator()(const DataMapper& res, const QInt16* blockA, + const QInt16* blockB, Index rows, Index depth, Index cols, + QInt32 alpha, Index strideA = -1, Index strideB = -1, + Index offsetA = 0, Index offsetB = 0); +}; + +template +EIGEN_DONT_INLINE void gebp_kernel:: +operator()(const DataMapper& res, const QInt16* blockA, const QInt16* blockB, + Index rows, Index depth, Index cols, QInt32 alpha, Index strideA, + Index strideB, Index offsetA, Index offsetB) { + EIGEN_STATIC_ASSERT(!ConjugateLhs, YOU_MADE_A_PROGRAMMING_MISTAKE); + EIGEN_STATIC_ASSERT(!ConjugateRhs, YOU_MADE_A_PROGRAMMING_MISTAKE); + eigen_assert(alpha.value == 1); + eigen_assert(strideA == -1); + eigen_assert(strideB == -1); + eigen_assert(offsetA == 0); + eigen_assert(offsetB == 0); + + eigen_assert(rows > 0); + eigen_assert(cols > 0); + eigen_assert(depth > 0); + eigen_assert(blockA); + eigen_assert(blockB); + + for (Index j = 0; j < cols; ++j) { + Index startB = j * depth; + for (Index i = 0; i < rows; ++i) { + Index startA = i * depth; + + for (Index k = 0; k < depth; ++k) { + res(i, j) += blockA[startA + k] * blockB[startB + k]; + } + } + } +} +#endif + +} // namespace internal +} // namespace Eigen -#endif // EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_H +#endif // CXX11_SRC_FIXEDPOINT_MATMATPRODUCT_H_ diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h index 6b4b0edcfb..66532fb600 100644 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h +++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/MatMatProductAVX2.h @@ -3,17 +3,493 @@ // // Copyright (C) 2015 Benoit Steiner // Copyright (C) 2015 Matthew Sarett +// Copyright (C) 2016 Nishant Patil // // This Source Code Form is subject to the terms of the Mozilla // Public License v. 2.0. If a copy of the MPL was not distributed // with this file, You can obtain one at http://mozilla.org/MPL/2.0/. -#ifndef EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_AVX2_H -#define EIGEN_CXX11_FIXED_POINT_MAT_MAT_PRODUCT_AVX2_H +#ifndef CXX11_SRC_FIXEDPOINT_MATMATPRODUCTAVX2_H_ +#define CXX11_SRC_FIXEDPOINT_MATMATPRODUCTAVX2_H_ namespace Eigen { namespace internal { +// AVX2 optimized implementation of Mat-Mat product. +// LHS is encoded using signed 16-bit integers. +// RHS is encoded using signed 16-bit integers. +#ifdef EIGEN_USE_OPTIMIZED_INT16_INT16_MAT_MAT_PRODUCT + +// Define quantized traits +template +class gebp_traits { + public: + typedef QInt16 LhsScalar; + typedef QInt16 RhsScalar; + typedef QInt32 ResScalar; + + enum { + // Define register blocking scheme. + nr = 16, + mr = 16, + kr = 4, + // Ignore progress tracking per loop iteration. + LhsProgress = -1, + RhsProgress = -1 + }; +}; + +// Specialized blocking for quantized implementations. +// Used by TensorContractionThreadPool, inputs must have dimensions that are +// multiples of 32. +template +class TensorContractionBlocking { + public: + TensorContractionBlocking(Index k, Index m, Index n, Index num_threads = 1) + : kc_(((k + 15) / 16) * 16), + mc_(((m + 15) / 16) * 16), + nc_(((n + 15) / 16) * 16) { + eigen_assert(mc_ % 16 == 0); + eigen_assert(kc_ % 16 == 0); + if (!k || !m || !n) { + return; + } + + if (ShardingType == ShardByCol) { + eigen_assert(nc_ % 16 == 0); + nc_ = (((nc_ / num_threads) + 15) / 16) * 16; + } else { + eigen_assert(nc_ % 16 == 0); + mc_ = (((mc_ / num_threads) + 15) / 16) * 16; + } + } + + EIGEN_ALWAYS_INLINE Index kc() const { return kc_; } + EIGEN_ALWAYS_INLINE Index mc() const { return mc_; } + EIGEN_ALWAYS_INLINE Index nc() const { return nc_; } + + private: + Index kc_; + Index mc_; + Index nc_; +}; + +// Specialized blocking for quantized implementations. +// Used by TensorContraction and GeneralMatrixMatrix, inputs are padded to +// multiples of 32. +template +class gemm_blocking_space + : public level3_blocking { + DenseIndex m_sizeA; + DenseIndex m_sizeB; + + public: + gemm_blocking_space(DenseIndex rows, DenseIndex cols, DenseIndex depth, + DenseIndex /*num_threads*/, bool /*l3_blocking*/) { + this->m_mc = ((rows + 15) / 16) * 16; + this->m_nc = ((cols + 15) / 16) * 16; + this->m_kc = ((depth + 15) / 16) * 16; + m_sizeA = this->m_mc * this->m_kc; + m_sizeB = this->m_kc * this->m_nc; + } + void allocateA() { + if (this->m_blockA == 0) this->m_blockA = aligned_new(m_sizeA); + } + void allocateB() { + if (this->m_blockB == 0) this->m_blockB = aligned_new(m_sizeB); + } + void allocateAll() { + allocateA(); + allocateB(); + } + ~gemm_blocking_space() { + aligned_delete(this->m_blockA, m_sizeA); + aligned_delete(this->m_blockB, m_sizeB); + } +}; + +// Below are the fully optimized versions that are correct only for sizes that +// are multiple of 16. It is about a 10% performance benefit to keep these +// implementations separate. + +// Arrange a block of the left input matrix in contiguous memory. +// +// Given column major input (A0 beside A1 in memory): +// A0 B0 C0 D0 E0 F0 G0 H0 ... +// A1 B1 C1 D1 E1 F1 G1 H1 ... +// A2 B2 C2 D2 E2 F2 G2 H2 ... +// A3 B3 C3 D3 E3 F3 G3 H3 ... +// A4 B4 C4 D4 E4 F4 G4 H4 ... +// A5 B5 C5 D5 E5 F5 G5 H5 ... +// A6 B6 C6 D6 E6 F6 G6 H6 ... +// A7 B7 C7 D7 E7 F7 G7 H7 ... +// A8 ... +// ... +// +// Packing with m = 8 yields row major output (A0 beside B0 in memory): +// A0 B0 +// A1 B1 +// A2 B2 +// A3 B3 +// A4 B4 +// A5 B5 +// A6 B6 +// A7 B7 +// ... +// +// The purpose is to collect m rows of size k. Two elements of the same +// row are arranged contiguously because madd performs an adjacent addition +// in the kernel. + +template +struct gemm_pack_lhs { + EIGEN_DONT_INLINE void operator()(QInt16* blockA, const DataMapper& lhs, + Index depth, Index rows, Index stride = 0, + Index offset = 0); +}; + +template +EIGEN_DONT_INLINE void gemm_pack_lhs:: +operator()(QInt16* blockA, const DataMapper& lhs, Index depth, Index rows, + Index stride, Index offset) { + eigen_assert(stride == 0); + eigen_assert(offset == 0); + + // Use alternate function for weird sizes + if (rows % 16 != 0 || depth % 16 != 0) { + assert(false && + "only depths and rows that are a multiple of 16 are currently " + "supported"); + // gemm_pack_lhs_any lhs_pack; + // return lhs_pack(blockA, lhs, depth, rows, stride, offset); + } + + // Get vector pointer + __m256i* blockA_256 = reinterpret_cast<__m256i*>(blockA); + + // Pack rows in sets of 16 + for (Index m = 0; m < rows; m += 16) { + // Pack depth in sets of 4 + for (Index k = 0; k < depth; k += 4) { + // Load vectors + __m256i L_A = lhs.loadPacket(m, k); + __m256i L_B = lhs.loadPacket(m, k + 1); + __m256i L_C = lhs.loadPacket(m, k + 2); + __m256i L_D = lhs.loadPacket(m, k + 3); + + // Rearrange the inputs as required by the kernel + __m256i L_AB0_AB7 = _mm256_unpacklo_epi16(L_A, L_B); + __m256i L_AB8_AB15 = _mm256_unpackhi_epi16(L_A, L_B); + __m256i L_CD0_CD7 = _mm256_unpacklo_epi16(L_C, L_D); + __m256i L_CD8_CD15 = _mm256_unpackhi_epi16(L_C, L_D); + + __m256i L_AD0 = _mm256_permute2x128_si256(L_AB0_AB7, L_AB8_AB15, 0x20); + _mm256_store_si256(blockA_256++, L_AD0); + __m256i L_AD8 = _mm256_permute2x128_si256(L_CD0_CD7, L_CD8_CD15, 0x20); + _mm256_store_si256(blockA_256++, L_AD8); + __m256i L_AD16 = _mm256_permute2x128_si256(L_AB0_AB7, L_AB8_AB15, 0x31); + _mm256_store_si256(blockA_256++, L_AD16); + __m256i L_AD24 = _mm256_permute2x128_si256(L_CD0_CD7, L_CD8_CD15, 0x31); + _mm256_store_si256(blockA_256++, L_AD24); + } + } +} + +// Arrange a block of the right input matrix in contiguous memory. +// +// Given column major input (A0 beside A1 in memory): +// A0 B0 C0 D0 E0 F0 G0 H0 ... +// A1 B1 C1 D1 E1 F1 G1 H1 ... +// A2 B2 C2 D2 E2 F2 G2 H2 ... +// A3 B3 C3 D3 E3 F3 G3 H3 ... +// A4 B4 C4 D4 E4 F4 G4 H4 ... +// A5 B5 C5 D5 E5 F5 G5 H5 ... +// A6 B6 C6 D6 E6 F6 G6 H6 ... +// A7 B7 C7 D7 E7 F7 G7 H7 ... +// A8 ... +// ... +// Packing yields row major output (A0 beside A1 in memory): +// A0 A1 A2 A3 A4 A5 A6 A7 +// B0 B1 B2 B3 B4 B5 B6 B7 +// ... +// +// At least two elements of the same col are arranged contiguously because +// maddubs and madd both perform an adjacent addition in the kernel. We can +// save work by leaving 4 adjacent elements because kr = 4. +// The purpose is to collect n cols of size k. Two elements of the same +// col are arranged contiguously because madd performs an adjacent addition +// in the kernel. +template +struct gemm_pack_rhs { + EIGEN_DONT_INLINE void operator()(QInt16* blockB, const DataMapper& rhs, + Index depth, Index cols, Index stride = 0, + Index offset = 0); +}; + +template +EIGEN_DONT_INLINE void +gemm_pack_rhs:: +operator()(QInt16* blockB, const DataMapper& rhs, Index depth, Index cols, + Index stride, Index offset) { + eigen_assert(stride == 0); + eigen_assert(offset == 0); + + // Use alternate function for weird sizes + if (cols % 16 != 0 || depth % 16 != 0) { + assert(false && + "only depths and cols that are a multiple of 16 are currently " + "supported"); + // gemm_pack_rhs_any rhs_pack; + // return rhs_pack(blockB, rhs, depth, cols, stride, offset); + } + + // Get vector pointer + __m256i* blockB_256 = reinterpret_cast<__m256i*>(blockB); + + // Perform a step of the packing for 4 columns + __m256i R_AB_L, R_AB_H, R_CD_L, R_CD_H, R_AD_0, R_AD_4, R_AD_8, R_AD_12; +#define PACK_STEP \ + R_AB_L = _mm256_unpacklo_epi64(R_A, R_B); \ + R_CD_L = _mm256_unpacklo_epi64(R_C, R_D); \ + R_AB_H = _mm256_unpackhi_epi64(R_A, R_B); \ + R_CD_H = _mm256_unpackhi_epi64(R_C, R_D); \ + R_AD_0 = _mm256_permute2x128_si256(R_AB_L, R_CD_L, 0x20); \ + R_AD_8 = _mm256_permute2x128_si256(R_AB_L, R_CD_L, 0x31); \ + R_AD_4 = _mm256_permute2x128_si256(R_AB_H, R_CD_H, 0x20); \ + R_AD_12 = _mm256_permute2x128_si256(R_AB_H, R_CD_H, 0x31); \ + _mm256_store_si256(blockB_256, R_AD_0); \ + _mm256_store_si256(blockB_256 + 4, R_AD_4); \ + _mm256_store_si256(blockB_256 + 8, R_AD_8); \ + _mm256_store_si256(blockB_256 + 12, R_AD_12); \ + blockB_256++; + + // Pack cols in sets of 16 + for (Index n = 0; n < cols; n += 16) { + // Pack depth in sets of 16 + for (Index k = 0; k < depth; k += 16) { + __m256i R_A = rhs.loadPacket(k, n); + __m256i R_B = rhs.loadPacket(k, n + 1); + __m256i R_C = rhs.loadPacket(k, n + 2); + __m256i R_D = rhs.loadPacket(k, n + 3); + PACK_STEP; + + R_A = rhs.loadPacket(k, n + 4); + R_B = rhs.loadPacket(k, n + 5); + R_C = rhs.loadPacket(k, n + 6); + R_D = rhs.loadPacket(k, n + 7); + PACK_STEP; + + R_A = rhs.loadPacket(k, n + 8); + R_B = rhs.loadPacket(k, n + 9); + R_C = rhs.loadPacket(k, n + 10); + R_D = rhs.loadPacket(k, n + 11); + PACK_STEP; + + R_A = rhs.loadPacket(k, n + 12); + R_B = rhs.loadPacket(k, n + 13); + R_C = rhs.loadPacket(k, n + 14); + R_D = rhs.loadPacket(k, n + 15); + PACK_STEP; + + blockB_256 += 12; + } + } +#undef PACK_STEP +} + +// Perform the actual multiplication on packed inputs +template +struct gebp_kernel { + typedef typename DataMapper::LinearMapper LinearMapper; + + EIGEN_DONT_INLINE + void operator()(const DataMapper& res, const QInt16* blockA, + const QInt16* blockB, Index rows, Index depth, Index cols, + QInt32 alpha, Index strideA = -1, Index strideB = -1, + Index offsetA = 0, Index offsetB = 0); +}; + +template +EIGEN_DONT_INLINE void gebp_kernel:: +operator()(const DataMapper& res, const QInt16* blockA, const QInt16* blockB, + Index rows, Index depth, Index cols, QInt32 alpha, Index strideA, + Index strideB, Index offsetA, Index offsetB) { + EIGEN_STATIC_ASSERT(!ConjugateLhs, YOU_MADE_A_PROGRAMMING_MISTAKE); + EIGEN_STATIC_ASSERT(!ConjugateRhs, YOU_MADE_A_PROGRAMMING_MISTAKE); + eigen_assert(alpha.value == 1); + eigen_assert(strideA == -1); + eigen_assert(strideB == -1); + eigen_assert(offsetA == 0); + eigen_assert(offsetB == 0); + eigen_assert(rows > 0); + eigen_assert(cols > 0); + eigen_assert(depth > 0); + eigen_assert(blockA); + eigen_assert(blockB); + + // Use alternate function for weird sizes + if (rows % 16 != 0 || cols % 16 != 0 || depth % 16 != 0) { + assert(false && + "only depths, cols and rows that are a multiple of 16 are currently " + "supported"); + // gebp_kernel_any gebp; + // return gebp(res, blockA, blockB, rows, depth, cols, alpha, strideA, + // strideB, offsetA, offsetB); + } + + // Create result block + QInt32* blockO = aligned_new(16 * 16); + memset(blockO, 0, 16 * 16 * sizeof(QInt32)); + + // Get vectorized pointers + __m256i* blockO_256 = reinterpret_cast<__m256i*>(blockO); + const __m256i* blockA_256 = reinterpret_cast(blockA); + const __m256i* blockB_256 = reinterpret_cast(blockB); + + // Loop over blocks of 16 columns + for (Index n = 0; n < cols; n += 16) { + // Reset index into blockA + Index indexL = 0; + // Loop over blocks of 16 rows + for (Index m = 0; m < rows; m += 16) { + // Reset index into blockB + Index indexR = n / 16 * depth; + // Loop over blocks of 4 on depth + for (Index k = 0; k < depth; k += 4) { + // Load inputs + __m256i L_AD0 = blockA_256[indexL++]; + __m256i L_AD8 = blockA_256[indexL++]; + __m256i L_EH0 = blockA_256[indexL++]; + __m256i L_EH8 = blockA_256[indexL++]; + + __m256i R_AH0 = blockB_256[indexR++]; + __m256i R_AH4 = blockB_256[indexR++]; + __m256i R_AH8 = blockB_256[indexR++]; + __m256i R_AH12 = blockB_256[indexR++]; + + // Declare variables used in COMPUTE_STEP + __m256i P_32_A, P_32_B, P_32; + +#define COMPUTE_STEP(R_INPUT_A, R_INPUT_B, OFFSET) \ + P_32_A = _mm256_madd_epi16(R_INPUT_A, L_AD0); \ + P_32_B = _mm256_madd_epi16(R_INPUT_B, L_AD8); \ + P_32 = _mm256_add_epi32(P_32_A, P_32_B); \ + _mm256_store_si256( \ + blockO_256 + 2 * OFFSET, \ + _mm256_add_epi32(_mm256_load_si256(blockO_256 + 2 * OFFSET), P_32)); \ + \ + P_32_A = _mm256_madd_epi16(R_INPUT_A, L_EH0); \ + P_32_B = _mm256_madd_epi16(R_INPUT_B, L_EH8); \ + P_32 = _mm256_add_epi32(P_32_A, P_32_B); \ + _mm256_store_si256( \ + blockO_256 + 2 * OFFSET + 1, \ + _mm256_add_epi32(_mm256_load_si256(blockO_256 + 2 * OFFSET + 1), P_32)); + + // Permute and shuffle to copy a single value across the entire vector + // Then compute the multiplication + // Replicate lower 128-bits of R_AH0 across both lanes + __m256i R_AH0_ = _mm256_permute2x128_si256(R_AH0, R_AH0, 0x00); + // Copy first two elements of R_AH0 across entire vector + __m256i R_AD0 = _mm256_shuffle_epi32(R_AH0_, 0x00); + // Copy second two elements of R_AH0 across entire vector + __m256i R_EH0 = _mm256_shuffle_epi32(R_AH0_, 0x55); + + COMPUTE_STEP(R_AD0, R_EH0, 0); + __m256i R_AD1 = _mm256_shuffle_epi32(R_AH0_, 0xAA); + __m256i R_EH1 = _mm256_shuffle_epi32(R_AH0_, 0xFF); + COMPUTE_STEP(R_AD1, R_EH1, 1); + + // Replicate upper 128-bits of R_AH0 across both lanes + R_AH0_ = _mm256_permute2x128_si256(R_AH0, R_AH0, 0x11); + __m256i R_AD2 = _mm256_shuffle_epi32(R_AH0_, 0x00); + __m256i R_EH2 = _mm256_shuffle_epi32(R_AH0_, 0x55); + COMPUTE_STEP(R_AD2, R_EH2, 2); + __m256i R_AD3 = _mm256_shuffle_epi32(R_AH0_, 0xAA); + __m256i R_EH3 = _mm256_shuffle_epi32(R_AH0_, 0xFF); + COMPUTE_STEP(R_AD3, R_EH3, 3); + + R_AH0_ = _mm256_permute2x128_si256(R_AH4, R_AH4, 0x00); + R_AD0 = _mm256_shuffle_epi32(R_AH0_, 0x00); + R_EH0 = _mm256_shuffle_epi32(R_AH0_, 0x55); + COMPUTE_STEP(R_AD0, R_EH0, 4); + R_AD1 = _mm256_shuffle_epi32(R_AH0_, 0xAA); + R_EH1 = _mm256_shuffle_epi32(R_AH0_, 0xFF); + COMPUTE_STEP(R_AD1, R_EH1, 5); + R_AH0_ = _mm256_permute2x128_si256(R_AH4, R_AH4, 0x11); + R_AD2 = _mm256_shuffle_epi32(R_AH0_, 0x00); + R_EH2 = _mm256_shuffle_epi32(R_AH0_, 0x55); + COMPUTE_STEP(R_AD2, R_EH2, 6); + R_AD3 = _mm256_shuffle_epi32(R_AH0_, 0xAA); + R_EH3 = _mm256_shuffle_epi32(R_AH0_, 0xFF); + COMPUTE_STEP(R_AD3, R_EH3, 7); + + R_AH0_ = _mm256_permute2x128_si256(R_AH8, R_AH8, 0x00); + R_AD0 = _mm256_shuffle_epi32(R_AH0_, 0x00); + R_EH0 = _mm256_shuffle_epi32(R_AH0_, 0x55); + COMPUTE_STEP(R_AD0, R_EH0, 8); + R_AD1 = _mm256_shuffle_epi32(R_AH0_, 0xAA); + R_EH1 = _mm256_shuffle_epi32(R_AH0_, 0xFF); + COMPUTE_STEP(R_AD1, R_EH1, 9); + R_AH0_ = _mm256_permute2x128_si256(R_AH8, R_AH8, 0x11); + R_AD2 = _mm256_shuffle_epi32(R_AH0_, 0x00); + R_EH2 = _mm256_shuffle_epi32(R_AH0_, 0x55); + COMPUTE_STEP(R_AD2, R_EH2, 10); + R_AD3 = _mm256_shuffle_epi32(R_AH0_, 0xAA); + R_EH3 = _mm256_shuffle_epi32(R_AH0_, 0xFF); + COMPUTE_STEP(R_AD3, R_EH3, 11); + + R_AH0_ = _mm256_permute2x128_si256(R_AH12, R_AH12, 0x00); + R_AD0 = _mm256_shuffle_epi32(R_AH0_, 0x00); + R_EH0 = _mm256_shuffle_epi32(R_AH0_, 0x55); + COMPUTE_STEP(R_AD0, R_EH0, 12); + R_AD1 = _mm256_shuffle_epi32(R_AH0_, 0xAA); + R_EH1 = _mm256_shuffle_epi32(R_AH0_, 0xFF); + COMPUTE_STEP(R_AD1, R_EH1, 13); + R_AH0_ = _mm256_permute2x128_si256(R_AH12, R_AH12, 0x11); + R_AD2 = _mm256_shuffle_epi32(R_AH0_, 0x00); + R_EH2 = _mm256_shuffle_epi32(R_AH0_, 0x55); + COMPUTE_STEP(R_AD2, R_EH2, 14); + R_AD3 = _mm256_shuffle_epi32(R_AH0_, 0xAA); + R_EH3 = _mm256_shuffle_epi32(R_AH0_, 0xFF); + COMPUTE_STEP(R_AD3, R_EH3, 15); + +#undef COMPUTE_STEP + } + + // Transfer the results to the result matrix + Index i = 0; + for (Index j = n; j < n + 16; j++) { + LinearMapper r0 = res.getLinearMapper(m, j); + LinearMapper r1 = res.getLinearMapper(m + 8, j); + + r0.storePacket(0, _mm256_add_epi32(blockO_256[i++], r0.loadPacket(0))); + r1.storePacket(0, _mm256_add_epi32(blockO_256[i++], r1.loadPacket(0))); + } + + // Zero the result block so it can be reused + memset(blockO, 0, 16 * 16 * sizeof(QInt32)); + } + } + aligned_delete(blockO, 16 * 16); +} + +#endif + // AVX2 optimized implementation of Mat-Mat product. // LHS is encoded using signed 8-bit integers. // RHS is encoded using unsigned 8-bit integers. @@ -1751,4 +2227,4 @@ void gebp_kernel +struct general_matrix_vector_product { + EIGEN_DONT_INLINE static void run(Index rows, Index cols, + const LhsMapper& lhs, const RhsMapper& rhs, + QInt32* res, Index resIncr, QInt16 alpha); +}; + +template +EIGEN_DONT_INLINE void general_matrix_vector_product< + Index, QInt16, LhsMapper, ColMajor, ConjugateLhs, QInt16, RhsMapper, + ConjugateRhs, Version>::run(Index rows, Index cols, const LhsMapper& lhs, + const RhsMapper& rhs, QInt32* res, + Index resIncr, QInt16 alpha) { + eigen_assert(alpha.value == 1); + eigen_assert(resIncr == 1); + eigen_assert(rows > 0); + eigen_assert(cols > 0); + + for (Index i = 0; i < rows; ++i) { + for (Index j = 0; j < cols; ++j) { + res[i] += lhs(i, j) * rhs(j, 0); + } + } +} // Mat-Vec product // The lhs is encoded using 8bit signed integers, the rhs using 8bit unsigned integers @@ -118,6 +147,4 @@ EIGEN_DONT_INLINE void general_matrix_vector_product @@ -29,7 +28,6 @@ inline int _mm256_extract_epi8_N1(const __m256i X) return _mm_extract_epi8(_mm256_extractf128_si256((X), 1 >> 4), 1 % 16); } - namespace Eigen { namespace internal { @@ -502,4 +500,4 @@ struct functor_traits> { } // end namespace internal } // end namespace Eigen -#endif // EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_PACKETMATHAVX2_H_ +#endif // CXX11_SRC_FIXEDPOINT_PACKETMATHAVX2_H_ diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/PacketMathAVX512.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/PacketMathAVX512.h index 8f9906dbf9..2092ce1d4c 100644 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/PacketMathAVX512.h +++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/PacketMathAVX512.h @@ -1,5 +1,5 @@ -#ifndef EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_ -#define EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_ +#ifndef CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_ +#define CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_ #include "PacketMathAVX2.h" @@ -542,4 +542,4 @@ EIGEN_STRONG_INLINE QInt8 predux_max(const Packet64q8i& a) { } // end namespace internal } // end namespace Eigen -#endif // EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_ +#endif // CXX11_SRC_FIXEDPOINT_PACKETMATHAVX512_H_ diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX2.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX2.h index 7b4ecc752f..9561d6a338 100644 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX2.h +++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX2.h @@ -1,5 +1,5 @@ -#ifndef EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_ -#define EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_ +#ifndef CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_ +#define CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_ namespace Eigen { namespace internal { @@ -52,8 +52,16 @@ template <> EIGEN_STRONG_INLINE Packet32q8u pcast(const Packet8q32i& a, const Packet8q32i& b, const Packet8q32i& c, const Packet8q32i& d) { + // _mm256_packus_epi32 trims negative numbers to 0 but we can't allow numbers + // that are too large because _mm256_packus_epi16 expects signed input + // (example of problem input: 0x11111111, which saturates to 0xffff = -1, + // which saturates to 0). + const __m256i a_clip = _mm256_min_epi32(a, _mm256_set1_epi32(255)); + const __m256i b_clip = _mm256_min_epi32(b, _mm256_set1_epi32(255)); + const __m256i c_clip = _mm256_min_epi32(c, _mm256_set1_epi32(255)); + const __m256i d_clip = _mm256_min_epi32(d, _mm256_set1_epi32(255)); const __m256i converted = _mm256_packus_epi16( - _mm256_packs_epi32(a.val, b.val), _mm256_packs_epi32(c.val, d.val)); + _mm256_packus_epi32(a_clip, b_clip), _mm256_packus_epi32(c_clip, d_clip)); // Since packus does not cross 128 bit lane boundaries, // we have to permute to properly order the final result. const __m256i permute_mask = _mm256_set_epi32(7, 3, 6, 2, 5, 1, 4, 0); @@ -63,4 +71,4 @@ pcast(const Packet8q32i& a, const Packet8q32i& b, } // end namespace internal } // end namespace Eigen -#endif // EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_ +#endif // CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX2_H_ diff --git a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX512.h b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX512.h index 26735743d4..a09eac6707 100644 --- a/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX512.h +++ b/third_party/eigen3/unsupported/Eigen/CXX11/src/FixedPoint/TypeCastingAVX512.h @@ -1,5 +1,5 @@ -#ifndef EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_ -#define EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_ +#ifndef CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_ +#define CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_ namespace Eigen { namespace internal { @@ -132,8 +132,15 @@ pcast(const Packet16q32i& a, const Packet16q32i& b, const Packet16q32i& c, const Packet16q32i& d) { - __m512i converted = _mm512_packs_epi16(_mm512_packs_epi32(a.val, b.val), - _mm512_packs_epi32(c.val, d.val)); + __m128i a_part = _mm512_cvtsepi32_epi8(a); + __m128i b_part = _mm512_cvtsepi32_epi8(b); + __m128i c_part = _mm512_cvtsepi32_epi8(c); + __m128i d_part = _mm512_cvtsepi32_epi8(d); + __m256i ab = + _mm256_inserti128_si256(_mm256_castsi128_si256(a_part), b_part, 1); + __m256i cd = + _mm256_inserti128_si256(_mm256_castsi128_si256(c_part), d_part, 1); + __m512i converted = _mm512_inserti64x4(_mm512_castsi256_si512(ab), cd, 1); return converted; } @@ -141,7 +148,10 @@ template <> EIGEN_STRONG_INLINE Packet32q16i pcast(const Packet16q32i& a, const Packet16q32i& b) { - __m512i converted = _mm512_packs_epi32(a.val, b.val); + __m256i a_part = _mm512_cvtsepi32_epi16(a); + __m256i b_part = _mm512_cvtsepi32_epi16(b); + __m512i converted = + _mm512_inserti64x4(_mm512_castsi256_si512(a_part), b_part, 1); return converted; } @@ -154,22 +164,45 @@ template <> EIGEN_STRONG_INLINE Packet64q8u pcast(const Packet16q32i& a, const Packet16q32i& b, const Packet16q32i& c, const Packet16q32i& d) { - const __m512i converted = _mm512_packus_epi16( - _mm512_packus_epi32(a.val, b.val), _mm512_packus_epi32(c.val, d.val)); + // Brute-force saturation since there isn't a pack operation for unsigned + // numbers that keeps the elements in order. + __m128i a_part = _mm512_cvtepi32_epi8(_mm512_max_epi32( + _mm512_min_epi32(a, _mm512_set1_epi32(255)), _mm512_setzero_si512())); + __m128i b_part = _mm512_cvtepi32_epi8(_mm512_max_epi32( + _mm512_min_epi32(b, _mm512_set1_epi32(255)), _mm512_setzero_si512())); + __m128i c_part = _mm512_cvtepi32_epi8(_mm512_max_epi32( + _mm512_min_epi32(c, _mm512_set1_epi32(255)), _mm512_setzero_si512())); + __m128i d_part = _mm512_cvtepi32_epi8(_mm512_max_epi32( + _mm512_min_epi32(d, _mm512_set1_epi32(255)), _mm512_setzero_si512())); + __m256i ab = + _mm256_inserti128_si256(_mm256_castsi128_si256(a_part), b_part, 1); + __m256i cd = + _mm256_inserti128_si256(_mm256_castsi128_si256(c_part), d_part, 1); + __m512i converted = _mm512_inserti64x4(_mm512_castsi256_si512(ab), cd, 1); return converted; } +#if 0 +// The type Packet32q16u does not exist for AVX-512 yet template <> struct type_casting_traits { enum { VectorizedCast = 1, SrcCoeffRatio = 2, TgtCoeffRatio = 1 }; }; -#if 0 template <> EIGEN_STRONG_INLINE Packet32q16u pcast(const Packet16q32i& a, const Packet16q32i& b) { - const __m512i converted = _mm512_packus_epi32(a.val, b.val); + // Brute-force saturation since there isn't a pack operation for unsigned + // numbers that keeps the elements in order. + __m256i a_part = + _mm512_cvtepi32_epi16(_mm512_max_epi32( + _mm512_min_epi32(a, _mm512_set1_epi32(65535)), _mm512_setzero_si512())); + __m256i b_part = _mm512_cvtepi32_epi16( + _mm512_max_epi32(_mm512_min_epi32(b, _mm512_set1_epi32(65535)), + _mm512_setzero_si512())); + __m512i converted = + _mm512_inserti64x4(_mm512_castsi256_si512(a_part), b_part, 1); return converted; } #endif @@ -177,4 +210,4 @@ pcast(const Packet16q32i& a, } // end namespace internal } // end namespace Eigen -#endif // EIGEN3_UNSUPPORTED_EIGEN_CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_ +#endif // CXX11_SRC_FIXEDPOINT_TYPECASTINGAVX512_H_ -- GitLab From c72748b673a1b76b1b7b12376a772d04edb16154 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 10:46:17 -0700 Subject: [PATCH 497/519] Always use either kAllNCHW or kAllNHWC for GPU convolution layout assignment. PiperOrigin-RevId: 206338966 --- .../xla/service/gpu/gpu_layout_assignment.cc | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc index 8786bb6262..1e51113452 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc @@ -72,14 +72,20 @@ HeuristicLayoutAssignment(const HloInstruction* instr, VLOG(2) << "Using heuristic to figure out layouts for " << instr->ToString(); // Empirically we've found with Volta and cudnn 7 that backward-input convs - // with stride are significantly faster with input in NHWC and filter/output - // in NCHW. + // with stride are significantly faster with NCHW layouts. + // + // We could have used a mixed layout combination, e.g. (NHWC, NCHW, NCHW), + // which on paper gives good performance. However, there are two observations: + // * a mixed layout combination is more cuDNN-bug prone, based on empirical + // envidence. + // * we've also observed that for mixed layouts, cuDNN transposes data back + // and forth from a different layout combination. If we end up with + // transposes anyway, we prefer to have them in XLA, as they can be fused. + // TODO(timshen): Figure out the exact condition. This may be achieved by + // auto-tuning layouts offline. if (instr->custom_call_target() == kCudnnConvBackwardInputCallTarget && window_util::HasStride(instr->window())) { - return std::make_tuple(DataLayout::kBatchYXDepth, // NHWC - FilterLayout::kOutputInputYX, // NCHW - DataLayout::kBatchDepthYX // NCHW - ); + return kAllNCHW; } // For other Volta f16 convolutions, use NHWC. -- GitLab From e888f2cb5ed6f60526a23063bfb30b27f9233980 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Fri, 27 Jul 2018 10:47:49 -0700 Subject: [PATCH 498/519] [XLA] Parse window and dim_labels that appear on a custom call. XLA:GPU uses a custom-call with window/dim_labels to represent a call to cudnn. PiperOrigin-RevId: 206339219 --- tensorflow/compiler/xla/service/hlo_parser.cc | 11 +++++++++++ tensorflow/compiler/xla/service/hlo_parser_test.cc | 11 +++++++++++ 2 files changed, 22 insertions(+) diff --git a/tensorflow/compiler/xla/service/hlo_parser.cc b/tensorflow/compiler/xla/service/hlo_parser.cc index e8eaf54949..d71d3c8170 100644 --- a/tensorflow/compiler/xla/service/hlo_parser.cc +++ b/tensorflow/compiler/xla/service/hlo_parser.cc @@ -1132,13 +1132,24 @@ bool HloParser::ParseInstruction(HloComputation::Builder* builder, } case HloOpcode::kCustomCall: { optional custom_call_target; + optional window; + optional dnums; attrs["custom_call_target"] = {/*required=*/true, AttrTy::kString, &custom_call_target}; + attrs["window"] = {/*required=*/false, AttrTy::kWindow, &window}; + attrs["dim_labels"] = {/*required=*/false, + AttrTy::kConvolutionDimensionNumbers, &dnums}; if (!ParseOperands(&operands) || !ParseAttributes(attrs)) { return false; } instruction = builder->AddInstruction(HloInstruction::CreateCustomCall( shape, operands, *custom_call_target)); + if (window.has_value()) { + instruction->set_window(*window); + } + if (dnums.has_value()) { + instruction->set_convolution_dimension_numbers(*dnums); + } break; } case HloOpcode::kHostCompute: { diff --git a/tensorflow/compiler/xla/service/hlo_parser_test.cc b/tensorflow/compiler/xla/service/hlo_parser_test.cc index 1f0572c576..1c08c51220 100644 --- a/tensorflow/compiler/xla/service/hlo_parser_test.cc +++ b/tensorflow/compiler/xla/service/hlo_parser_test.cc @@ -1015,6 +1015,17 @@ ENTRY Iota { ROOT iota = f32[100]{0} iota() } +)" +}, +// custom-call with window and dim_labels +{ +"CustomCallWithWindowAndDimLabels", +R"(HloModule CustomCallWithWindowAndDimLabels + +ENTRY Computation { + ROOT r = f32[100]{0} custom-call(), window={size=2x2}, dim_labels=b01f_01io->b01f, custom_call_target="target" +} + )" } }); -- GitLab From 85a265031bbd61f5924cdbfdc316df6979883581 Mon Sep 17 00:00:00 2001 From: Benjamin Kramer Date: Fri, 27 Jul 2018 11:04:02 -0700 Subject: [PATCH 499/519] [XLA:GPU] Only add the cubin if it is available It's only non-empty if we were able to run ptxas. If the PTX is going to be JIT'ed by the driver it won't be around. Loading an empty cubin will result in a fatal error. PiperOrigin-RevId: 206341931 --- tensorflow/compiler/xla/service/gpu/gpu_executable.cc | 4 +++- tensorflow/stream_executor/module_spec.h | 1 + 2 files changed, 4 insertions(+), 1 deletion(-) diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index 52c8595ffb..0179b43240 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -190,7 +190,9 @@ GpuExecutable::ResolveConstantGlobals(se::StreamExecutor* executor) { } se::MultiModuleLoaderSpec module_spec; - module_spec.AddCudaCubinInMemory(cubin()); + if (!cubin().empty()) { + module_spec.AddCudaCubinInMemory(cubin()); + } module_spec.AddCudaPtxInMemory(ptx().c_str()); tensorflow::gtl::FlatMap globals; diff --git a/tensorflow/stream_executor/module_spec.h b/tensorflow/stream_executor/module_spec.h index 212ae7ba9c..75bdfed2d7 100644 --- a/tensorflow/stream_executor/module_spec.h +++ b/tensorflow/stream_executor/module_spec.h @@ -43,6 +43,7 @@ class MultiModuleLoaderSpec { } void AddCudaCubinInMemory(port::ArraySlice cubin_bytes) { + CHECK(!cubin_bytes.empty()); has_cuda_cubin_in_memory_ = true; cuda_cubin_in_memory_ = cubin_bytes; } -- GitLab From ae7eb76ced31bc3806e22fe087874fa88273b324 Mon Sep 17 00:00:00 2001 From: Rui Zhao Date: Fri, 27 Jul 2018 11:36:36 -0700 Subject: [PATCH 500/519] Support nested inputs for bidirectional_dynamic_rnn. PiperOrigin-RevId: 206347779 --- tensorflow/python/ops/rnn.py | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/tensorflow/python/ops/rnn.py b/tensorflow/python/ops/rnn.py index 7096e0dd84..7b6ab20975 100644 --- a/tensorflow/python/ops/rnn.py +++ b/tensorflow/python/ops/rnn.py @@ -432,9 +432,15 @@ def bidirectional_dynamic_rnn(cell_fw, cell_bw, inputs, sequence_length=None, return array_ops.reverse(input_, axis=[seq_axis]) with vs.variable_scope("bw") as bw_scope: - inputs_reverse = _reverse( - inputs, seq_lengths=sequence_length, - seq_axis=time_axis, batch_axis=batch_axis) + + def _map_reverse(inp): + return _reverse( + inp, + seq_lengths=sequence_length, + seq_axis=time_axis, + batch_axis=batch_axis) + + inputs_reverse = nest.map_structure(_map_reverse, inputs) tmp, output_state_bw = dynamic_rnn( cell=cell_bw, inputs=inputs_reverse, sequence_length=sequence_length, initial_state=initial_state_bw, dtype=dtype, -- GitLab From f6cc77189d84328425f83325be5ff428835bb680 Mon Sep 17 00:00:00 2001 From: Justin Lebar Date: Fri, 27 Jul 2018 12:05:12 -0700 Subject: [PATCH 501/519] [XLA] Use se:: rather than stream_executor:: in a few places. No functional change. PiperOrigin-RevId: 206352602 --- tensorflow/compiler/xla/service/gpu/BUILD | 1 + .../xla/service/gpu/gpu_layout_assignment.cc | 6 +++--- .../compiler/xla/service/gpu/stream_executor_util.cc | 8 ++++---- .../compiler/xla/service/gpu/stream_executor_util.h | 12 ++++++------ 4 files changed, 14 insertions(+), 13 deletions(-) diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 625d1448e7..885365105f 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -812,6 +812,7 @@ cc_library( deps = [ "//tensorflow/compiler/xla:shape_util", "//tensorflow/compiler/xla:statusor", + "//tensorflow/compiler/xla:types", "//tensorflow/compiler/xla:xla_data_proto", "//tensorflow/core:stream_executor_no_cuda", ], diff --git a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc index 1e51113452..cc67804d56 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_layout_assignment.cc @@ -31,8 +31,8 @@ limitations under the License. namespace xla { namespace gpu { -using stream_executor::dnn::DataLayout; -using stream_executor::dnn::FilterLayout; +using se::dnn::DataLayout; +using se::dnn::FilterLayout; static bool IsVoltaOrLater(const se::StreamExecutor& stream_executor) { int major, minor; @@ -44,7 +44,7 @@ static bool IsVoltaOrLater(const se::StreamExecutor& stream_executor) { // Returns (input, filter, output) layouts. static std::tuple HeuristicLayoutAssignment(const HloInstruction* instr, - stream_executor::StreamExecutor* stream_executor) { + se::StreamExecutor* stream_executor) { // DataLayout and FilterLayout uses weird enum names. Translations: // N <=> Batch or Output // C <=> Depth or Input diff --git a/tensorflow/compiler/xla/service/gpu/stream_executor_util.cc b/tensorflow/compiler/xla/service/gpu/stream_executor_util.cc index a50ddf6ac6..f313a9f172 100644 --- a/tensorflow/compiler/xla/service/gpu/stream_executor_util.cc +++ b/tensorflow/compiler/xla/service/gpu/stream_executor_util.cc @@ -20,10 +20,10 @@ limitations under the License. namespace xla { namespace gpu { -using stream_executor::dnn::DataLayout; -using stream_executor::dnn::DataLayoutString; -using stream_executor::dnn::FilterLayout; -using stream_executor::dnn::FilterLayoutString; +using se::dnn::DataLayout; +using se::dnn::DataLayoutString; +using se::dnn::FilterLayout; +using se::dnn::FilterLayoutString; StatusOr> StreamExecutorConvLayoutsToXlaLayouts(const ConvolutionDimensionNumbers& dnums, diff --git a/tensorflow/compiler/xla/service/gpu/stream_executor_util.h b/tensorflow/compiler/xla/service/gpu/stream_executor_util.h index 39a6a38d00..5c5e7f2a56 100644 --- a/tensorflow/compiler/xla/service/gpu/stream_executor_util.h +++ b/tensorflow/compiler/xla/service/gpu/stream_executor_util.h @@ -17,6 +17,7 @@ limitations under the License. #define TENSORFLOW_COMPILER_XLA_SERVICE_GPU_STREAM_EXECUTOR_UTIL_H_ #include "tensorflow/compiler/xla/statusor.h" +#include "tensorflow/compiler/xla/types.h" #include "tensorflow/compiler/xla/xla_data.pb.h" #include "tensorflow/core/platform/stream_executor_no_cuda.h" @@ -29,14 +30,13 @@ namespace gpu { // layouts. StatusOr> StreamExecutorConvLayoutsToXlaLayouts(const ConvolutionDimensionNumbers& dnums, - stream_executor::dnn::DataLayout input, - stream_executor::dnn::FilterLayout filter, - stream_executor::dnn::DataLayout output); + se::dnn::DataLayout input, + se::dnn::FilterLayout filter, + se::dnn::DataLayout output); // Returns (input, filter, output) StreamExecutor layouts given the XLA layouts. -StatusOr> +StatusOr< + std::tuple> XlaConvLayoutsToStreamExecutorLayouts(const ConvolutionDimensionNumbers& dnums, const Layout& input, const Layout& filter, const Layout& output); -- GitLab From 78d225ef8a6a32423febc67803fabdff05b378c0 Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Fri, 27 Jul 2018 12:05:43 -0700 Subject: [PATCH 502/519] Update backward pass to save memory in graph mode. PiperOrigin-RevId: 206352708 --- .../eager/python/examples/revnet/blocks.py | 61 +++++++++++++++---- 1 file changed, 50 insertions(+), 11 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/revnet/blocks.py b/tensorflow/contrib/eager/python/examples/revnet/blocks.py index 63e86803ef..89712f2c45 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/blocks.py +++ b/tensorflow/contrib/eager/python/examples/revnet/blocks.py @@ -99,16 +99,11 @@ class RevBlock(tf.keras.Model): block = self.blocks[i] if i == 0: # First block usually contains downsampling that can't be reversed - with tf.GradientTape() as tape: - tape.watch(x) - y = block(x, training=training) - grads_combined = tape.gradient( - y, [x] + block.trainable_variables, output_gradients=dy) - dy = grads_combined[0] - grads_all = grads_combined[1:] + grads_all + dy, grads = block.backward_grads_with_downsample( + x, y, dy, training=True) else: y, dy, grads = block.backward_grads(y, dy, training=training) - grads_all = grads + grads_all + grads_all = grads + grads_all return dy, grads_all @@ -201,16 +196,21 @@ class _Residual(tf.keras.Model): gy1, [y1] + self.g.trainable_variables, output_gradients=dy2) dg = grads_combined[1:] dx1 = dy1 + grads_combined[0] - x2 = y2 - gy1 + # This doesn't affect eager execution, but improves memory efficiency with + # graphs + with tf.control_dependencies(dg + [dx1]): + x2 = y2 - gy1 with tf.GradientTape() as ftape: ftape.watch(x2) fx2 = self.f(x2, training=training) grads_combined = ftape.gradient( fx2, [x2] + self.f.trainable_variables, output_gradients=dx1) - dx2 = dy2 + grads_combined[0] df = grads_combined[1:] - x1 = y1 - fx2 + dx2 = dy2 + grads_combined[0] + # Same behavior as above + with tf.control_dependencies(df + [dx2]): + x1 = y1 - fx2 x = tf.concat([x1, x2], axis=self.axis) dx = tf.concat([dx1, dx2], axis=self.axis) @@ -218,6 +218,45 @@ class _Residual(tf.keras.Model): return x, dx, grads + def backward_grads_with_downsample(self, x, y, dy, training=True): + """Manually compute backward gradients given input and output grads.""" + # Splitting this from `backward_grads` for better readability + x1, x2 = tf.split(x, num_or_size_splits=2, axis=self.axis) + y1, _ = tf.split(y, num_or_size_splits=2, axis=self.axis) + dy1, dy2 = tf.split(dy, num_or_size_splits=2, axis=self.axis) + + with tf.GradientTape() as gtape: + gtape.watch(y1) + gy1 = self.g(y1, training=training) + grads_combined = gtape.gradient( + gy1, [y1] + self.g.trainable_variables, output_gradients=dy2) + dg = grads_combined[1:] + dz1 = dy1 + grads_combined[0] + + # dx1 need one more step to backprop through downsample + with tf.GradientTape() as x1tape: + x1tape.watch(x1) + z1 = ops.downsample(x1, self.filters // 2, self.strides, axis=self.axis) + dx1 = x1tape.gradient(z1, x1, output_gradients=dz1) + + with tf.GradientTape() as ftape: + ftape.watch(x2) + fx2 = self.f(x2, training=training) + grads_combined = ftape.gradient( + fx2, [x2] + self.f.trainable_variables, output_gradients=dz1) + dx2, df = grads_combined[0], grads_combined[1:] + + # dx2 need one more step to backprop through downsample + with tf.GradientTape() as x2tape: + x2tape.watch(x2) + z2 = ops.downsample(x2, self.filters // 2, self.strides, axis=self.axis) + dx2 += x2tape.gradient(z2, x2, output_gradients=dy2) + + dx = tf.concat([dx1, dx2], axis=self.axis) + grads = df + dg + + return dx, grads + # Ideally, the following should be wrapped in `tf.keras.Sequential`, however # there are subtle issues with its placeholder insertion policy and batch norm -- GitLab From ab9f0a628f61fcb19b6b09cb51bf05ff8c702a80 Mon Sep 17 00:00:00 2001 From: Nupur Garg Date: Fri, 27 Jul 2018 12:15:09 -0700 Subject: [PATCH 503/519] Update functionality of --allow_nudging_weights_to_use_fast_gemm_kernel. PiperOrigin-RevId: 206354203 --- ...ensure_uint8_weights_safe_for_fast_int8_kernels.cc | 11 ++++++++++- .../graph_transformations/graph_transformations.h | 4 ++++ tensorflow/contrib/lite/toco/toco_tooling.cc | 7 +++++-- 3 files changed, 19 insertions(+), 3 deletions(-) diff --git a/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc b/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc index 75642bbc37..c13fc0de75 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc +++ b/tensorflow/contrib/lite/toco/graph_transformations/ensure_uint8_weights_safe_for_fast_int8_kernels.cc @@ -181,7 +181,7 @@ bool EnsureUint8WeightsSafeForFastInt8Kernels::Run(Model* model, // future without worrying. static constexpr int kMinDistanceBetweenBadValues = 16; if (distance < kMinDistanceBetweenBadValues) { - if (allow_nudging_weights()) { + if (allow_nudging_weights() || has_default_ranges_flag()) { buffer_data[i] = 1; changed = true; continue; @@ -200,6 +200,15 @@ bool EnsureUint8WeightsSafeForFastInt8Kernels::Run(Model* model, } if (changed) { + if (has_default_ranges_flag()) { + std::cerr + << "Since the specified values of --default_ranges_min and " + "--default_ranges_max result in values incompatible with TFLite's " + "fast int8 kernels, " + "--allow_nudging_weights_to_use_fast_gemm_kernel " + "has been enabled. This may affect the accuracy of the model." + << std::endl; + } AddMessageF("Tweaked weights values for %s", LogName(op)); } diff --git a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h index b7634e28c6..8d9a4c4700 100644 --- a/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h +++ b/tensorflow/contrib/lite/toco/graph_transformations/graph_transformations.h @@ -262,8 +262,12 @@ class EnsureUint8WeightsSafeForFastInt8Kernels : public GraphTransformation { bool allow_nudging_weights() const { return allow_nudging_weights_; } void set_allow_nudging_weights(bool val) { allow_nudging_weights_ = val; } + bool has_default_ranges_flag() const { return has_default_ranges_flag_; } + void set_has_default_ranges_flag(bool val) { has_default_ranges_flag_ = val; } + private: bool allow_nudging_weights_ = false; + bool has_default_ranges_flag_ = false; }; #undef DECLARE_GRAPH_TRANSFORMATION diff --git a/tensorflow/contrib/lite/toco/toco_tooling.cc b/tensorflow/contrib/lite/toco/toco_tooling.cc index aa7f6996eb..fcd3cbab07 100644 --- a/tensorflow/contrib/lite/toco/toco_tooling.cc +++ b/tensorflow/contrib/lite/toco/toco_tooling.cc @@ -309,8 +309,9 @@ void Transform(const TocoFlags& toco_flags, Model* model) { // HardcodeMinMax to move changes through the graph as we make changes. auto propagate_default_min_max = absl::make_unique(); - if (toco_flags.has_default_ranges_min() && - toco_flags.has_default_ranges_max()) { + bool has_default_ranges_flag = (toco_flags.has_default_ranges_min() && + toco_flags.has_default_ranges_max()); + if (has_default_ranges_flag) { propagate_default_min_max->DefineTypeRange( ArrayDataType::kUint8, toco_flags.default_ranges_min(), toco_flags.default_ranges_max()); @@ -335,6 +336,8 @@ void Transform(const TocoFlags& toco_flags, Model* model) { new EnsureUint8WeightsSafeForFastInt8Kernels; ensure_safe_for_int8_kernels->set_allow_nudging_weights( toco_flags.allow_nudging_weights_to_use_fast_gemm_kernel()); + ensure_safe_for_int8_kernels->set_has_default_ranges_flag( + has_default_ranges_flag); RunGraphTransformations(model, "quantization graph transformations", { new RemoveTrivialQuantizedActivationFunc, -- GitLab From 6a19ae36acb6ac60f46b046efc3cc0672a7dca42 Mon Sep 17 00:00:00 2001 From: Katherine Wu Date: Fri, 27 Jul 2018 13:07:31 -0700 Subject: [PATCH 504/519] Fix SavedModelEstimator docstring formatting. PiperOrigin-RevId: 206361654 --- .../python/estimator/saved_model_estimator.py | 14 +++++++++----- 1 file changed, 9 insertions(+), 5 deletions(-) diff --git a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py index f3d0f6b047..b0082f7e55 100644 --- a/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py +++ b/tensorflow/contrib/estimator/python/estimator/saved_model_estimator.py @@ -46,6 +46,7 @@ class SavedModelEstimator(estimator_lib.Estimator): Example with `tf.estimator.DNNClassifier`: **Step 1: Create and train DNNClassifier.** + ```python feature1 = tf.feature_column.embedding_column( tf.feature_column.categorical_column_with_vocabulary_list( @@ -66,13 +67,14 @@ class SavedModelEstimator(estimator_lib.Estimator): **Step 2: Export classifier.** First, build functions that specify the expected inputs. + ```python # During train and evaluation, both the features and labels should be defined. supervised_input_receiver_fn = ( tf.contrib.estimator.build_raw_supervised_input_receiver_fn( - {'feature1': tf.placeholder(dtype=tf.string, shape=[None]), - 'feature2': tf.placeholder(dtype=tf.float32, shape=[None])}, - tf.placeholder(dtype=tf.float32, shape=[None]))) + {'feature1': tf.placeholder(dtype=tf.string, shape=[None]), + 'feature2': tf.placeholder(dtype=tf.float32, shape=[None])}, + tf.placeholder(dtype=tf.float32, shape=[None]))) # During predict mode, expect to receive a `tf.Example` proto, so a parsing # function is used. @@ -83,6 +85,7 @@ class SavedModelEstimator(estimator_lib.Estimator): Next, export the model as a SavedModel. A timestamped directory will be created (for example `/tmp/export_all/1234567890`). + ```python # Option 1: Save all modes (train, eval, predict) export_dir = tf.contrib.estimator.export_all_saved_models( @@ -93,10 +96,11 @@ class SavedModelEstimator(estimator_lib.Estimator): # Option 2: Only export predict mode export_dir = classifier.export_savedmodel( - '/tmp/export_predict', serving_input_receiver_fn) + '/tmp/export_predict', serving_input_receiver_fn) ``` **Step 3: Create a SavedModelEstimator from the exported SavedModel.** + ```python est = tf.contrib.estimator.SavedModelEstimator(export_dir) @@ -108,7 +112,7 @@ class SavedModelEstimator(estimator_lib.Estimator): est.train(input_fn=input_fn, steps=20) def predict_input_fn(): - example = example_pb2.Example() + example = tf.train.Example() example.features.feature['feature1'].bytes_list.value.extend(['yellow']) example.features.feature['feature2'].float_list.value.extend([1.]) return {'inputs':tf.constant([example.SerializeToString()])} -- GitLab From 90fe37ab8d056c56a5127e9b7ae237c04a7907ec Mon Sep 17 00:00:00 2001 From: Alexandre Passos Date: Fri, 27 Jul 2018 13:15:07 -0700 Subject: [PATCH 505/519] Always lock resource variables in training ops. PiperOrigin-RevId: 206362555 --- tensorflow/core/kernels/training_op_helpers.cc | 9 ++++++++- tensorflow/core/kernels/training_op_helpers.h | 14 ++------------ 2 files changed, 10 insertions(+), 13 deletions(-) diff --git a/tensorflow/core/kernels/training_op_helpers.cc b/tensorflow/core/kernels/training_op_helpers.cc index f288e124ee..d3c4f62071 100644 --- a/tensorflow/core/kernels/training_op_helpers.cc +++ b/tensorflow/core/kernels/training_op_helpers.cc @@ -39,8 +39,15 @@ mutex* GetTrainingVariableMutex(OpKernelContext* ctx, int input) { // GetInputTensor which will signal a failure. std::vector MaybeLockVariableInputMutexesInOrder( OpKernelContext* ctx, bool do_lock, const std::vector& input_ids) { + bool any_resource = false; + for (auto i : input_ids) { + if (ctx->input_dtype(i) == DT_RESOURCE) { + any_resource = true; + break; + } + } std::vector locks; - if (!do_lock) { + if (!do_lock && !any_resource) { return locks; } std::vector mutexes; diff --git a/tensorflow/core/kernels/training_op_helpers.h b/tensorflow/core/kernels/training_op_helpers.h index 7e56e15450..765335d3a0 100644 --- a/tensorflow/core/kernels/training_op_helpers.h +++ b/tensorflow/core/kernels/training_op_helpers.h @@ -80,18 +80,8 @@ Status GetInputTensorFromVariable(OpKernelContext* ctx, int input, Var* var; TF_RETURN_IF_ERROR(LookupResource(ctx, HandleFromInput(ctx, input), &var)); core::ScopedUnref unref_var(var); - if (lock_held) { - TF_RETURN_IF_ERROR( - PrepareToUpdateVariable(ctx, var->tensor())); - *out = *var->tensor(); - } else { - mutex_lock ml(*var->mu()); - if (!sparse) { - TF_RETURN_IF_ERROR( - PrepareToUpdateVariable(ctx, var->tensor())); - } - *out = *var->tensor(); - } + TF_RETURN_IF_ERROR(PrepareToUpdateVariable(ctx, var->tensor())); + *out = *var->tensor(); return Status::OK(); } *out = ctx->mutable_input(input, lock_held); -- GitLab From 388d0d860110a19a9d133fe4de85f8f6fa060cde Mon Sep 17 00:00:00 2001 From: Sanjoy Das Date: Fri, 27 Jul 2018 13:24:46 -0700 Subject: [PATCH 506/519] Use constant buffer allocations for XLA:CPU This is simpler than the corresponding change to XLA:GPU because on XLA:CPU all instructions are codegened so we can always embed a pointer to the constant global variable directly in the generated LLVM IR. PiperOrigin-RevId: 206363887 --- tensorflow/compiler/xla/service/cpu/BUILD | 1 + .../compiler/xla/service/cpu/cpu_compiler.cc | 16 ++++- .../xla/service/cpu/cpu_executable.cc | 5 ++ .../compiler/xla/service/cpu/ir_emitter.cc | 44 +++++++++----- .../compiler/xla/service/cpu/ir_emitter.h | 6 ++ tensorflow/compiler/xla/service/gpu/BUILD | 4 +- .../xla/service/gpu/buffer_allocations.cc | 39 ------------ .../xla/service/gpu/buffer_allocations.h | 9 --- .../xla/service/gpu/gpu_executable.cc | 11 ++-- .../xla/service/gpu/hlo_to_ir_bindings.cc | 4 +- .../xla/service/gpu/ir_emitter_unnested.cc | 10 ++-- tensorflow/compiler/xla/service/llvm_ir/BUILD | 9 +++ .../service/llvm_ir/alias_analysis_test.cc | 2 +- .../service/llvm_ir/buffer_assignment_util.cc | 59 +++++++++++++++++++ .../service/llvm_ir/buffer_assignment_util.h | 34 +++++++++++ .../xla/tests/local_client_aot_test_helper.cc | 3 +- 16 files changed, 179 insertions(+), 77 deletions(-) create mode 100644 tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.cc create mode 100644 tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h diff --git a/tensorflow/compiler/xla/service/cpu/BUILD b/tensorflow/compiler/xla/service/cpu/BUILD index 6c997a068d..504b61d134 100644 --- a/tensorflow/compiler/xla/service/cpu/BUILD +++ b/tensorflow/compiler/xla/service/cpu/BUILD @@ -252,6 +252,7 @@ cc_library( "//tensorflow/compiler/xla/service:hlo_module_config", "//tensorflow/compiler/xla/service:name_uniquer", "//tensorflow/compiler/xla/service/llvm_ir:alias_analysis", + "//tensorflow/compiler/xla/service/llvm_ir:buffer_assignment_util", "//tensorflow/compiler/xla/service/llvm_ir:dynamic_update_slice_util", "//tensorflow/compiler/xla/service/llvm_ir:fused_ir_emitter", "//tensorflow/compiler/xla/service/llvm_ir:ir_array", diff --git a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc index 29fa29d33a..b49ea89896 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_compiler.cc @@ -562,7 +562,9 @@ StatusOr> CpuCompiler::RunBackend( BufferAssigner::Run( module.get(), xla::MakeUnique(module.get(), module_sequence), - BufferSizeBytesFunction(), memory_alignment)); + BufferSizeBytesFunction(), memory_alignment, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true)); // BufferAssignment::ToString() includes a header, so no need for us to // print one ourselves. XLA_VLOG_LINES(2, assignment->ToString()); @@ -584,6 +586,8 @@ StatusOr> CpuCompiler::RunBackend( std::move(computation_to_profile_idx), &target_machine_features); + TF_RETURN_IF_ERROR(ir_emitter.EmitConstantGlobals()); + for (auto embedded_computation : entry_computation->MakeEmbeddedComputationsList()) { if (embedded_computation->IsFusionComputation()) { @@ -747,7 +751,9 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, BufferAssigner::Run( module, xla::MakeUnique(module, module_sequence), - BufferSizeBytesFunction(), memory_alignment)); + BufferSizeBytesFunction(), memory_alignment, + /*allow_input_output_aliasing=*/false, + /*allocate_buffers_for_constants=*/true)); // BufferAssignment::ToString() includes a header, so no need for us to // print one ourselves. XLA_VLOG_LINES(2, assignment->ToString()); @@ -776,6 +782,9 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, std::move(instruction_to_profile_idx), std::move(computation_to_profile_idx), &target_machine_features); + + TF_RETURN_IF_ERROR(ir_emitter.EmitConstantGlobals()); + HloComputation* computation = module->entry_computation(); for (auto embedded_computation : computation->MakeEmbeddedComputationsList()) { @@ -832,7 +841,8 @@ CpuCompiler::CompileAheadOfTime(std::vector> modules, BufferSizes buffer_sizes; for (const BufferAllocation& allocation : assignment->Allocations()) { // Callers don't need to allocate temporary buffers for parameters. - if (allocation.is_entry_computation_parameter()) { + if (allocation.is_entry_computation_parameter() || + allocation.is_constant()) { buffer_sizes.push_back(-1); continue; } diff --git a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc index 1093559892..81e17a5cd4 100644 --- a/tensorflow/compiler/xla/service/cpu/cpu_executable.cc +++ b/tensorflow/compiler/xla/service/cpu/cpu_executable.cc @@ -88,6 +88,11 @@ Status CpuExecutable::AllocateBuffers( continue; } + if (allocation.is_constant()) { + VLOG(3) << "allocation #" << i << " is a constant"; + continue; + } + if (allocation.is_thread_local()) { VLOG(3) << "buffer #" << i << " is thread-local"; continue; diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc index 9d9d3e04a9..a6d8551841 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.cc +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.cc @@ -51,6 +51,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/hlo_casting_utils.h" #include "tensorflow/compiler/xla/service/hlo_instructions.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_loop.h" @@ -175,25 +176,36 @@ llvm::Constant* IrEmitter::EmitGlobalForLiteral(const Literal& literal) { result_global, IrShapeType(literal.shape())->getPointerTo()); } -Status IrEmitter::HandleConstant(HloInstruction* constant) { - VLOG(2) << "HandleConstant: " << constant->ToString(); - const Literal& literal = constant->literal(); - llvm::Constant* global_for_const; +Status IrEmitter::EmitConstantGlobals() { + for (const BufferAllocation& allocation : assignment_.Allocations()) { + if (!allocation.is_constant()) { + continue; + } - auto it = emitted_literals_.find(&literal); - if (it != emitted_literals_.end()) { - global_for_const = it->second; - } else { - global_for_const = EmitGlobalForLiteral(literal); - emitted_literals_[&literal] = global_for_const; + const Literal& literal = llvm_ir::LiteralForConstantAllocation(allocation); + llvm::Constant* global_for_const; + auto it = emitted_literals_.find(&literal); + if (it != emitted_literals_.end()) { + global_for_const = it->second; + } else { + global_for_const = EmitGlobalForLiteral(literal); + InsertOrDie(&emitted_literals_, &literal, global_for_const); + } + + InsertOrDie(&constant_buffer_to_global_, allocation.index(), + global_for_const); } - emitted_value_[constant] = global_for_const; - VLOG(2) << " emitted value: " << llvm_ir::DumpToString(*global_for_const); - VLOG(2) << " its type: " - << llvm_ir::DumpToString(*global_for_const->getType()); + return Status::OK(); } +Status IrEmitter::HandleConstant(HloInstruction* constant) { + VLOG(2) << "HandleConstant: " << constant->ToString(); + // IrEmitter::EmitConstantGlobals has already taken care of emitting the body + // of the constant. + return EmitTargetAddressForOp(constant); +} + Status IrEmitter::HandleCopy(HloInstruction* copy) { if (ShapeUtil::IsTuple(copy->shape())) { // kCopy shallow copies a tuple so just memcpy the top-level buffer. @@ -2712,6 +2724,10 @@ llvm::Value* IrEmitter::EmitTempBufferPointer( return b_.CreateBitCast(tempbuf_address, element_type->getPointerTo()); } + if (allocation.is_constant()) { + return FindOrDie(constant_buffer_to_global_, allocation.index()); + } + llvm::Value* tempbuf_address_ptr = llvm_ir::EmitBufferIndexingGEP( GetTempBuffersArgument(), slice.index(), &b_); llvm::LoadInst* tempbuf_address_base = b_.CreateLoad(tempbuf_address_ptr); diff --git a/tensorflow/compiler/xla/service/cpu/ir_emitter.h b/tensorflow/compiler/xla/service/cpu/ir_emitter.h index cf7fa05b20..03bbb2afb5 100644 --- a/tensorflow/compiler/xla/service/cpu/ir_emitter.h +++ b/tensorflow/compiler/xla/service/cpu/ir_emitter.h @@ -105,6 +105,9 @@ class IrEmitter : public DfsHloVisitorWithDefault { PrimitiveType return_type, HloComputation* computation, const std::vector& arguments, tensorflow::StringPiece name); + // Emit an LLVM global variable for every constant buffer allocation. + Status EmitConstantGlobals(); + protected: // // The following methods implement the DfsHloVisitor interface. @@ -560,6 +563,9 @@ class IrEmitter : public DfsHloVisitorWithDefault { LiteralPtrHashFunctor, LiteralPtrEqualityFunctor> emitted_literals_; + tensorflow::gtl::FlatMap + constant_buffer_to_global_; + TF_DISALLOW_COPY_AND_ASSIGN(IrEmitter); }; diff --git a/tensorflow/compiler/xla/service/gpu/BUILD b/tensorflow/compiler/xla/service/gpu/BUILD index 885365105f..a73a341fdb 100644 --- a/tensorflow/compiler/xla/service/gpu/BUILD +++ b/tensorflow/compiler/xla/service/gpu/BUILD @@ -120,6 +120,7 @@ cc_library( "//tensorflow/compiler/xla/service:buffer_assignment", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service/llvm_ir:alias_analysis", + "//tensorflow/compiler/xla/service/llvm_ir:buffer_assignment_util", "//tensorflow/compiler/xla/service/llvm_ir:ir_array", "//tensorflow/compiler/xla/service/llvm_ir:llvm_util", "//tensorflow/compiler/xla/service/llvm_ir:tuple_ops", @@ -165,6 +166,7 @@ cc_library( "//tensorflow/compiler/xla/service:elemental_ir_emitter", "//tensorflow/compiler/xla/service:hlo", "//tensorflow/compiler/xla/service:name_uniquer", + "//tensorflow/compiler/xla/service/llvm_ir:buffer_assignment_util", "//tensorflow/compiler/xla/service/llvm_ir:dynamic_update_slice_util", "//tensorflow/compiler/xla/service/llvm_ir:fused_ir_emitter", "//tensorflow/compiler/xla/service/llvm_ir:ir_array", @@ -323,9 +325,9 @@ cc_library( "//tensorflow/compiler/xla/service:hlo_execution_profile", "//tensorflow/compiler/xla/service:logical_buffer", "//tensorflow/compiler/xla/service:shaped_buffer", - "//tensorflow/compiler/xla/service:stream_pool", "//tensorflow/compiler/xla/service:transfer_manager", "//tensorflow/compiler/xla/service:tuple_points_to_analysis", + "//tensorflow/compiler/xla/service/llvm_ir:buffer_assignment_util", "//tensorflow/core:lib", "//tensorflow/core:lib_internal", "//tensorflow/core:stream_executor_no_cuda", diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc index 20d4285766..537295292b 100644 --- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc +++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.cc @@ -173,45 +173,6 @@ void BufferAllocations::SetBuffer(BufferAllocation::Index buffer_index, buffers_[buffer_index] = buffer; } -static const HloInstruction& InstrForConstantBufferAllocation( - const BufferAllocation& allocation) { - CHECK(allocation.is_constant()); - HloInstruction* const_instr = nullptr; - for (const auto& buffer_offset_pair : allocation.assigned_buffers()) { - const LogicalBuffer* buffer = buffer_offset_pair.first; - // BufferAssignment may have assigned non-constant instructions to this - // allocation too so we can't CHECK this condition. E.g. for - // - // while(init = constant, body = identity, cond = ...) - // - // the LogicalBuffer for the kWhile instruction will have the same - // BufferAllocation as the LogicalBuffer for the (init) constant. - if (buffer->instruction()->opcode() == HloOpcode::kConstant) { - CHECK_EQ(const_instr, nullptr) - << const_instr->ToString() << " " << buffer->ToString(); - const_instr = buffer->instruction(); - } - } - CHECK_NE(const_instr, nullptr); - return *const_instr; -} - -string ConstantBufferAllocationToGlobalName( - const BufferAllocation& allocation) { - string instr_name = InstrForConstantBufferAllocation(allocation).name(); - for (char& c : instr_name) { - if (c == '.') { - c = '_'; - } - } - return tensorflow::strings::StrCat("buffer_for_", instr_name); -} - -const Literal& LiteralForConstantAllocation( - const BufferAllocation& allocation) { - return InstrForConstantBufferAllocation(allocation).literal(); -} - bool ShouldEmitLiteralInLlvmIr(const Literal& literal) { // LLVM can sometimes do interesting optimizations using scalar constants. return ShapeUtil::IsScalar(literal.shape()); diff --git a/tensorflow/compiler/xla/service/gpu/buffer_allocations.h b/tensorflow/compiler/xla/service/gpu/buffer_allocations.h index f21861ed81..f13eab0dd7 100644 --- a/tensorflow/compiler/xla/service/gpu/buffer_allocations.h +++ b/tensorflow/compiler/xla/service/gpu/buffer_allocations.h @@ -107,15 +107,6 @@ class BufferAllocations { bool torn_down_ = false; }; -// In XLA:GPU we map constant buffer allocations to globals in the generated -// LLVM IR. This function gives us the name of the global variable a constant -// buffer is mapped to. -string ConstantBufferAllocationToGlobalName(const BufferAllocation& allocation); - -// Return the Literal corresponding to `allocation`, which must be a constant -// allocation. -const Literal& LiteralForConstantAllocation(const BufferAllocation& allocation); - // LLVM and PTXAS don't deal well with large constants, so we only emit very // small constants directly in LLVM IR. Larger constants are emitted with zero // initializers in LLVM IR and are later overwritten when the PTX/CUBIN is diff --git a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc index 0179b43240..bb71c79fd7 100644 --- a/tensorflow/compiler/xla/service/gpu/gpu_executable.cc +++ b/tensorflow/compiler/xla/service/gpu/gpu_executable.cc @@ -24,6 +24,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" #include "tensorflow/compiler/xla/service/gpu/hlo_execution_profiler.h" #include "tensorflow/compiler/xla/service/hlo_instruction.h" +#include "tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h" #include "tensorflow/compiler/xla/service/logical_buffer.h" #include "tensorflow/compiler/xla/service/shaped_buffer.h" #include "tensorflow/compiler/xla/service/transfer_manager.h" @@ -206,13 +207,15 @@ GpuExecutable::ResolveConstantGlobals(se::StreamExecutor* executor) { TF_ASSIGN_OR_RETURN( se::DeviceMemoryBase global, executor->GetUntypedSymbol( - ConstantBufferAllocationToGlobalName(allocation), module_handle)); + llvm_ir::ConstantBufferAllocationToGlobalName(allocation), + module_handle)); VLOG(3) << "Resolved global " - << ConstantBufferAllocationToGlobalName(allocation) << " to " - << global.opaque(); + << llvm_ir::ConstantBufferAllocationToGlobalName(allocation) + << " to " << global.opaque(); InsertOrDie(&globals, i, global); - const Literal& literal = LiteralForConstantAllocation(allocation); + const Literal& literal = + llvm_ir::LiteralForConstantAllocation(allocation); CHECK(ShapeUtil::IsArray(literal.shape())); if (!ShouldEmitLiteralInLlvmIr(literal)) { VLOG(3) << "H2D memcpy for constant with shape " diff --git a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc index c02a95d193..8c11cd0541 100644 --- a/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc +++ b/tensorflow/compiler/xla/service/gpu/hlo_to_ir_bindings.cc @@ -21,6 +21,7 @@ limitations under the License. #include "tensorflow/compiler/xla/service/gpu/buffer_allocations.h" #include "tensorflow/compiler/xla/service/gpu/ir_emission_utils.h" #include "tensorflow/compiler/xla/service/hlo_opcode.h" +#include "tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/llvm_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/tuple_ops.h" #include "tensorflow/core/lib/strings/str_util.h" @@ -114,7 +115,8 @@ void HloToIrBindings::EmitBasePointersForHlos( } else if (slice.allocation()->is_constant()) { llvm::Value* global_for_constant = module_->getGlobalVariable(llvm_ir::AsStringRef( - ConstantBufferAllocationToGlobalName(*slice.allocation()))); + llvm_ir::ConstantBufferAllocationToGlobalName( + *slice.allocation()))); BindHloToIrValue(*non_io_hlo, global_for_constant); } else { const int64 offset = slice.offset(); diff --git a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc index fb9540b7ef..3a5394dac6 100644 --- a/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc +++ b/tensorflow/compiler/xla/service/gpu/ir_emitter_unnested.cc @@ -60,6 +60,7 @@ limitations under the License. #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/service/llvm_ir/buffer_assignment_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/dynamic_update_slice_util.h" #include "tensorflow/compiler/xla/service/llvm_ir/fused_ir_emitter.h" #include "tensorflow/compiler/xla/service/llvm_ir/kernel_support_library.h" @@ -2411,8 +2412,8 @@ std::unique_ptr IrEmitterUnnested::BuildKernelThunk( llvm::Value* loc; if (slice.allocation()->is_constant()) { loc = ir_emitter_context_->llvm_module()->getGlobalVariable( - llvm_ir::AsStringRef( - ConstantBufferAllocationToGlobalName(*slice.allocation()))); + llvm_ir::AsStringRef(llvm_ir::ConstantBufferAllocationToGlobalName( + *slice.allocation()))); CHECK_NE(loc, nullptr); } else { loc = b_.CreateInBoundsGEP(kernel_args.at(slice.allocation()), @@ -3428,7 +3429,7 @@ Status IrEmitterUnnested::EmitConstantGlobals() { continue; } - const Literal& literal = LiteralForConstantAllocation(allocation); + const Literal& literal = llvm_ir::LiteralForConstantAllocation(allocation); const bool should_emit_initializer = ShouldEmitLiteralInLlvmIr(literal); llvm::ArrayType* global_type = llvm::ArrayType::get(b_.getInt8Ty(), allocation.size()); @@ -3453,7 +3454,8 @@ Status IrEmitterUnnested::EmitConstantGlobals() { global_type, /*isConstant=*/should_emit_initializer, llvm::GlobalValue::ExternalLinkage, /*Initializer=*/initializer, - llvm_ir::AsStringRef(ConstantBufferAllocationToGlobalName(allocation))); + llvm_ir::AsStringRef( + llvm_ir::ConstantBufferAllocationToGlobalName(allocation))); global_for_const->setAlignment(kConstantBufferAlignBytes); ir_emitter_context_->llvm_module()->getGlobalList().push_back( global_for_const); diff --git a/tensorflow/compiler/xla/service/llvm_ir/BUILD b/tensorflow/compiler/xla/service/llvm_ir/BUILD index 309a186e58..cdd3daf73b 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/BUILD +++ b/tensorflow/compiler/xla/service/llvm_ir/BUILD @@ -224,6 +224,15 @@ cc_library( ], ) +cc_library( + name = "buffer_assignment_util", + srcs = ["buffer_assignment_util.cc"], + hdrs = ["buffer_assignment_util.h"], + deps = [ + "//tensorflow/compiler/xla/service:buffer_assignment", + ], +) + cc_library( name = "math_ops", srcs = ["math_ops.cc"], diff --git a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis_test.cc b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis_test.cc index 2552ff4a6a..941d940684 100644 --- a/tensorflow/compiler/xla/service/llvm_ir/alias_analysis_test.cc +++ b/tensorflow/compiler/xla/service/llvm_ir/alias_analysis_test.cc @@ -58,7 +58,7 @@ ENTRY while3 { CompileAndVerifyIr(hlo_string, R"( ; CHECK-LABEL: @body(i8* align 4 dereferenceable(4) %retval ; CHECK: %[[add_result:.*]] = fadd fast float %[[fadd_lhs:.*]], %[[fadd_rhs:.*]] -; CHECK: store float %[[add_result]], float* %[[store_dest:.*]], !alias.scope ![[alias_scope_md_for_store:.*]] +; CHECK: store float %[[add_result]], float* %[[store_dest:.*]], !alias.scope ![[alias_scope_md_for_store:[0-9]+]] ; ; CHECK-LABEL: @condition(i8* align 1 dereferenceable(1) %fusion, i8* noalias %run_options, i8** noalias %params ; CHECK: %[[cond_state_buf_ptr:.*]] = getelementptr inbounds i8*, i8** %params, i64 0 diff --git a/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.cc b/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.cc new file mode 100644 index 0000000000..4eb5d9fb47 --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.cc @@ -0,0 +1,59 @@ +/* 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/llvm_ir/buffer_assignment_util.h" + +namespace xla { +namespace llvm_ir { +static const HloInstruction& InstrForConstantBufferAllocation( + const BufferAllocation& allocation) { + CHECK(allocation.is_constant()); + HloInstruction* const_instr = nullptr; + for (const auto& buffer_offset_pair : allocation.assigned_buffers()) { + const LogicalBuffer* buffer = buffer_offset_pair.first; + // BufferAssignment may have assigned non-constant instructions to this + // allocation too so we can't CHECK this condition. E.g. for + // + // while(init = constant, body = identity, cond = ...) + // + // the LogicalBuffer for the kWhile instruction will have the same + // BufferAllocation as the LogicalBuffer for the (init) constant. + if (buffer->instruction()->opcode() == HloOpcode::kConstant) { + CHECK_EQ(const_instr, nullptr) + << const_instr->ToString() << " " << buffer->ToString(); + const_instr = buffer->instruction(); + } + } + CHECK_NE(const_instr, nullptr); + return *const_instr; +} + +string ConstantBufferAllocationToGlobalName( + const BufferAllocation& allocation) { + string instr_name = InstrForConstantBufferAllocation(allocation).name(); + for (char& c : instr_name) { + if (c == '.') { + c = '_'; + } + } + return tensorflow::strings::StrCat("buffer_for_", instr_name); +} + +const Literal& LiteralForConstantAllocation( + const BufferAllocation& allocation) { + return InstrForConstantBufferAllocation(allocation).literal(); +} +} // namespace llvm_ir +} // namespace xla diff --git a/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h b/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h new file mode 100644 index 0000000000..bfb6eecb87 --- /dev/null +++ b/tensorflow/compiler/xla/service/llvm_ir/buffer_assignment_util.h @@ -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. +==============================================================================*/ + +#ifndef TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_BUFFER_ASSIGNMENT_UTIL_H_ +#define TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_BUFFER_ASSIGNMENT_UTIL_H_ + +#include "tensorflow/compiler/xla/service/buffer_assignment.h" + +namespace xla { +namespace llvm_ir { +// In XLA:GPU we map constant buffer allocations to globals in the generated +// LLVM IR. This function gives us the name of the global variable a constant +// buffer is mapped to. Not used on XLA:CPU. +string ConstantBufferAllocationToGlobalName(const BufferAllocation& allocation); + +// Returns the Literal corresponding to `allocation`, which must be a constant +// allocation. +const Literal& LiteralForConstantAllocation(const BufferAllocation& allocation); +} // namespace llvm_ir +} // namespace xla + +#endif // TENSORFLOW_COMPILER_XLA_SERVICE_LLVM_IR_BUFFER_ASSIGNMENT_UTIL_H_ diff --git a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc index 9e21c53569..74494e60e8 100644 --- a/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc +++ b/tensorflow/compiler/xla/tests/local_client_aot_test_helper.cc @@ -92,9 +92,10 @@ int main(int argc, char** argv) { // It's lame to hard-code the buffer assignments, but we need // local_client_aot_test.cc to be able to easily invoke the function. CHECK_EQ(result->result_buffer_index(), 1); - CHECK_EQ(result->buffer_sizes().size(), 2); + CHECK_EQ(result->buffer_sizes().size(), 3); CHECK_EQ(result->buffer_sizes()[0], -1); // param buffer CHECK_EQ(result->buffer_sizes()[1], sizeof(float)); // result buffer + CHECK_EQ(result->buffer_sizes()[2], -1); // const buffer if (triple.isOSBinFormatELF()) { // Check the ELF magic. CHECK_EQ(result->object_file_data()[0], 0x7F); -- GitLab From 81d927bfc7e1e4c7b28aabdb0c64a12cec2833fe Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 14:03:25 -0700 Subject: [PATCH 507/519] Adding NodeDef names to error messages for better debuggability. The format used is as follows: {{node }} PiperOrigin-RevId: 206370355 --- .../tf2xla/functionalize_control_flow.cc | 37 ++++++----- .../tf2xla/functionalize_control_flow_test.cc | 5 +- tensorflow/compiler/tf2xla/xla_compiler.cc | 13 ++-- .../compiler/tf2xla/xla_compiler_test.cc | 13 ++-- .../notebooks/dev_summit_2018_demo.ipynb | 2 +- .../core/common_runtime/function_test.cc | 5 +- tensorflow/core/framework/node_def_util.cc | 13 +++- tensorflow/core/framework/node_def_util.h | 6 ++ .../core/framework/node_def_util_test.cc | 52 ++++++++++----- .../core/framework/op_compatibility_test.cc | 65 +++++++++++-------- tensorflow/core/graph/control_flow.cc | 37 ++++++----- tensorflow/core/graph/control_flow_test.cc | 17 +++++ tensorflow/core/lib/core/errors.h | 20 ++++++ tensorflow/core/util/equal_graph_def_test.cc | 6 +- tensorflow/docs_src/guide/using_gpu.md | 2 +- tensorflow/python/client/session.py | 10 ++- tensorflow/python/framework/test_util_test.py | 2 +- 17 files changed, 204 insertions(+), 101 deletions(-) diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc index 6cc95149a1..0904778f97 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow.cc @@ -177,8 +177,8 @@ Status CheckNoCycleContains(const Node* node, const int num_nodes) { visited[current_node->id()] = true; for (const Edge* out : current_node->out_edges()) { if (out->dst() == node) { - return errors::Internal("Detect a cycle: Node \"", node->name(), "\"(", - node->def().op(), ") feeds into itself."); + return errors::Internal("Detected a cycle: ", FormatNodeForError(*node), + "(", node->def().op(), ") feeds into itself."); } else if (!visited[out->dst()->id()]) { ready.push_back(out->dst()); } @@ -324,7 +324,7 @@ Status AddMissingFunctionDef(const FunctionDef& fdef, if (library->Find(node.op())) { continue; } - // The function refered by 'SymbolicGradient' node is specified in its + // The function referred by 'SymbolicGradient' node is specified in its // attribute 'f'. if (node.op() == FunctionLibraryDefinition::kGradientOp) { const AttrValue* attr = @@ -437,22 +437,24 @@ Status FunctionalizeLoop(const FunctionLibraryDefinition* lookup_library, continue; } if (enter_merge != nullptr) { - return errors::Internal( - "Enter node for loop-varying argument ", arg.enter->name(), - " has multiple successors: ", enter_merge->dst()->name(), " and ", - e->dst()->name()); + return errors::Internal("Enter node for loop-varying argument ", + FormatNodeForError(*arg.enter), + " has multiple successors: ", + FormatNodeForError(*enter_merge->dst()), + " and ", FormatNodeForError(*e->dst())); } enter_merge = e; } if (enter_merge == nullptr) { return errors::Internal("Enter node for loop-varying argument ", - arg.enter->name(), " has zero successors"); + FormatNodeForError(*arg.enter), + " has zero successors"); } arg.merge = enter_merge->dst(); if (!IsMerge(arg.merge)) { return errors::InvalidArgument( "Successor of Enter node for loop-varying argument ", - arg.merge->name(), + FormatNodeForError(*arg.merge), " is not a Merge node; got: ", arg.merge->type_string()); } @@ -462,7 +464,7 @@ Status FunctionalizeLoop(const FunctionLibraryDefinition* lookup_library, return errors::InvalidArgument( "Unexpected number of inputs to Merge node for loop-varying " "argument ", - arg.merge->name(), "; expected 2, got ", + FormatNodeForError(*arg.merge), "; expected 2, got ", arg.merge->input_types().size()); } TF_RETURN_IF_ERROR(arg.merge->input_node(1 - enter_merge->dst_input(), @@ -470,7 +472,7 @@ Status FunctionalizeLoop(const FunctionLibraryDefinition* lookup_library, if (!IsNextIteration(arg.next_iteration)) { return errors::InvalidArgument( "Expected NextIteration node as input to Merge node; got node ", - arg.next_iteration->name(), " with kind ", + FormatNodeForError(*arg.next_iteration), " with kind ", arg.next_iteration->type_string()); } @@ -481,14 +483,14 @@ Status FunctionalizeLoop(const FunctionLibraryDefinition* lookup_library, switches.find(edge->dst()) != switches.end()) { if (arg.switch_node != nullptr) { return errors::InvalidArgument("Duplicate Switch successors to ", - arg.merge->name()); + FormatNodeForError(*arg.merge)); } arg.switch_node = edge->dst(); } } if (arg.switch_node == nullptr) { return errors::InvalidArgument("Missing Switch successor to ", - arg.merge->name()); + FormatNodeForError(*arg.merge)); } // Update the device on the Identity outputs of the switch to match their @@ -516,14 +518,15 @@ Status FunctionalizeLoop(const FunctionLibraryDefinition* lookup_library, possible_exit.pop_front(); if (IsExit(edge->dst())) { if (arg.exit != nullptr) { - return errors::InvalidArgument("Duplicate Exit successors to ", - arg.switch_node->name()); + return errors::InvalidArgument( + "Duplicate Exit successors to ", + FormatNodeForError(*arg.switch_node)); } arg.exit = edge->dst(); } else { if (!IsIdentity(edge->dst())) { return errors::Unimplemented("General graph between switch (", - arg.switch_node->name(), + FormatNodeForError(*arg.switch_node), ") and exit node of frame ", frame->name, " not supported yet."); } @@ -1470,7 +1473,7 @@ Status FunctionalizeControlFlow(const FunctionLibraryDefinition* lookup_library, if (!unreachable_nodes.empty()) { return errors::InvalidArgument( "The following nodes are unreachable from the source in the graph: ", - tensorflow::str_util::Join(unreachable_nodes, ", ")); + errors::FormatNodeNamesForError(unreachable_nodes)); } // Builds Frames, indexed by name. diff --git a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc index aae2f8ee5a..ccf249b35d 100644 --- a/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc +++ b/tensorflow/compiler/tf2xla/functionalize_control_flow_test.cc @@ -1064,7 +1064,10 @@ TEST(FunctionalizeControlFlow, Cycle) { // less -> XlaIf <--> identity. Status status = FunctionalizeControlFlow(graph.get(), &library); EXPECT_FALSE(status.ok()); - EXPECT_TRUE(str_util::StrContains(status.error_message(), "Detect a cycle")) + EXPECT_TRUE(str_util::StrContains(status.error_message(), "Detected a cycle")) + << status.error_message(); + EXPECT_TRUE( + str_util::StrContains(status.error_message(), "{{node cond/Less_5_If}}")) << status.error_message(); } diff --git a/tensorflow/compiler/tf2xla/xla_compiler.cc b/tensorflow/compiler/tf2xla/xla_compiler.cc index 59dcd0870b..226c89bcf1 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler.cc @@ -35,6 +35,7 @@ limitations under the License. #include "tensorflow/core/common_runtime/function.h" #include "tensorflow/core/common_runtime/graph_optimizer.h" #include "tensorflow/core/framework/attr_value_util.h" +#include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/graph/algorithm.h" #include "tensorflow/core/graph/graph_constructor.h" #include "tensorflow/core/graph/node_builder.h" @@ -689,12 +690,12 @@ Status ValidateFunctionDef(const FunctionDef* fdef, Status ValidateGraph(const Graph* graph, const FunctionLibraryDefinition& flib_def, const DeviceType& device_type, const string& name) { - auto maybe_error = [&](const string& op, const Status& s) -> Status { + auto maybe_error = [&](const Node* node, const Status& s) -> Status { if (!s.ok()) { return errors::InvalidArgument(strings::StrCat( "Detected unsupported operations when trying to compile graph ", name, - " on ", device_type.type_string(), ": ", op, " (", s.error_message(), - ")")); + " on ", device_type.type_string(), ": ", node->def().op(), " (", + s.error_message(), ")", FormatNodeForError(*node))); } return Status::OK(); }; @@ -707,15 +708,15 @@ Status ValidateGraph(const Graph* graph, Status s; if (fdef) { s = ValidateFunctionDef(fdef, flib_def); - TF_RETURN_IF_ERROR(maybe_error(node->def().op(), s)); + TF_RETURN_IF_ERROR(maybe_error(node, s)); continue; } const OpDef* op_def; s = OpRegistry::Global()->LookUpOpDef(node->def().op(), &op_def); - TF_RETURN_IF_ERROR(maybe_error(node->def().op(), s)); + TF_RETURN_IF_ERROR(maybe_error(node, s)); TF_RETURN_IF_ERROR(ValidateNodeDef(node->def(), *op_def)); s = FindKernelDef(device_type, node->def(), nullptr, nullptr); - TF_RETURN_IF_ERROR(maybe_error(node->def().op(), s)); + TF_RETURN_IF_ERROR(maybe_error(node, s)); } return Status::OK(); } diff --git a/tensorflow/compiler/tf2xla/xla_compiler_test.cc b/tensorflow/compiler/tf2xla/xla_compiler_test.cc index 2fb93be01d..be00ed8813 100644 --- a/tensorflow/compiler/tf2xla/xla_compiler_test.cc +++ b/tensorflow/compiler/tf2xla/xla_compiler_test.cc @@ -312,7 +312,7 @@ TEST_F(XlaCompilerTest, HasSaneErrorOnNonCompileTimeConstantInputToReshape) { str_util::StrContains(status.error_message(), "depends on a parameter")) << status.error_message(); EXPECT_TRUE( - str_util::StrContains(status.error_message(), "[[Node: C = Reshape")) + str_util::StrContains(status.error_message(), "[[{{node C}} = Reshape")) << status.error_message(); } @@ -1077,6 +1077,8 @@ TEST_F(XlaCompilerTest, FunctionWithInvalidOp) { ASSERT_FALSE(status.ok()); EXPECT_TRUE(str_util::StrContains(status.error_message(), "InvalidOp")) << status.error_message(); + EXPECT_TRUE(str_util::StrContains(status.error_message(), "{{node fill_fn}}")) + << status.error_message(); } // Tests a graph which has a node with invalid data type. @@ -1101,6 +1103,8 @@ TEST_F(XlaCompilerTest, NodeWithInvalidDataType) { EXPECT_TRUE(str_util::StrContains(status.error_message(), "is not in the list of allowed values")) << status.error_message(); + EXPECT_TRUE(str_util::StrContains(status.error_message(), "{{node Shape}}")) + << status.error_message(); } TEST_F(XlaCompilerTest, SingleOpWithoutInputs) { @@ -1122,9 +1126,10 @@ TEST_F(XlaCompilerTest, SingleOpWithoutInputs) { status = compiler.CompileGraph(XlaCompiler::CompileOptions(), "NoOp", std::move(graph_copy), args, &result); ASSERT_FALSE(status.ok()); - EXPECT_TRUE(str_util::StrContains(status.error_message(), - "The following nodes are unreachable " - "from the source in the graph: NoOp")) + EXPECT_TRUE( + str_util::StrContains(status.error_message(), + "The following nodes are unreachable " + "from the source in the graph: {{node NoOp}}")) << status.error_message(); } diff --git a/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb b/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb index a3109fa5db..7e9cc54d4c 100644 --- a/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb +++ b/tensorflow/contrib/autograph/examples/notebooks/dev_summit_2018_demo.ipynb @@ -392,7 +392,7 @@ "output_type": "stream", "text": [ "Got error message: assertion failed: [Do not pass zero!]\n", - "\t [[Node: f/Assert/Assert = Assert[T=[DT_STRING], summarize=3, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](f/NotEqual, f/Assert/Assert/data_0)]]\n" + "\t [[{{node f/Assert/Assert}} = Assert[T=[DT_STRING], summarize=3, _device=\"/job:localhost/replica:0/task:0/device:CPU:0\"](f/NotEqual, f/Assert/Assert/data_0)]]\n" ] } ], diff --git a/tensorflow/core/common_runtime/function_test.cc b/tensorflow/core/common_runtime/function_test.cc index 1e837e9a7e..120f480198 100644 --- a/tensorflow/core/common_runtime/function_test.cc +++ b/tensorflow/core/common_runtime/function_test.cc @@ -1019,8 +1019,9 @@ TEST_F(FunctionLibraryRuntimeTest, Error_BadControlFlow) { DCHECK_EQ(x.dtype(), DT_INT32); Tensor y; HasError(InstantiateAndRun(flr0_, "InvalidControlFlow", {}, {x}, {&y}), - "The node 'add' has inputs from different frames. The input 'enter' " - "is in frame 'while'. The input 'i' is in frame ''."); + "{{node add}} has inputs from different frames. The input" + " {{node enter}} is in frame 'while'. The input {{node i}} is in" + " frame ''."); } TEST_F(FunctionLibraryRuntimeTest, Gradient_XTimesTwo) { diff --git a/tensorflow/core/framework/node_def_util.cc b/tensorflow/core/framework/node_def_util.cc index e8ea904ebd..0bd79366eb 100644 --- a/tensorflow/core/framework/node_def_util.cc +++ b/tensorflow/core/framework/node_def_util.cc @@ -86,7 +86,8 @@ string AttrSlice::SummarizeNode() const { string SummarizeNode(const Node& node) { return SummarizeNodeDef(node.def()); } string SummarizeNodeDef(const NodeDef& node_def) { - string ret = strings::StrCat(node_def.name(), " = ", node_def.op(), "["); + string ret = strings::StrCat(FormatNodeDefForError(node_def), " = ", + node_def.op(), "["); strings::StrAppend(&ret, SummarizeAttrsHelper(node_def, node_def.device())); strings::StrAppend(&ret, "]("); @@ -101,6 +102,14 @@ string SummarizeNodeDef(const NodeDef& node_def) { return ret; } +string FormatNodeForError(const Node& node) { + return FormatNodeDefForError(node.def()); +} + +string FormatNodeDefForError(const NodeDef& node_def) { + return errors::FormatNodeNameForError(node_def.name()); +} + const AttrValue* AttrSlice::Find(StringPiece attr_name) const { // Currently, the collection used for NodeDef::attr() (google::protobuf::Map) // requires that the keys used for lookups have type 'const string&'. Because @@ -634,7 +643,7 @@ Status ValidateExternalNodeDefSyntax(const NodeDef& node_def) { Status AttachDef(const Status& status, const NodeDef& node_def) { Status ret = status; errors::AppendToMessage( - &ret, strings::StrCat(" [[Node: ", SummarizeNodeDef(node_def), "]]")); + &ret, strings::StrCat(" [[", SummarizeNodeDef(node_def), "]]")); return ret; } diff --git a/tensorflow/core/framework/node_def_util.h b/tensorflow/core/framework/node_def_util.h index 64c8b386e8..c012b7c3d3 100644 --- a/tensorflow/core/framework/node_def_util.h +++ b/tensorflow/core/framework/node_def_util.h @@ -50,6 +50,12 @@ extern const char* const kColocationGroupPrefix; string SummarizeNode(const Node& node); string SummarizeNodeDef(const NodeDef& node_def); +// Produces a formatted string pattern from the node which can uniquely identify +// this node upstream to produce an informative error message. The pattern +// followed is: {{node }} +string FormatNodeForError(const Node& node); +string FormatNodeDefForError(const NodeDef& node_def); + typedef protobuf::Map AttrValueMap; // Adds an attr with name and value to *node_def. diff --git a/tensorflow/core/framework/node_def_util_test.cc b/tensorflow/core/framework/node_def_util_test.cc index 35b7b2272b..74cc594863 100644 --- a/tensorflow/core/framework/node_def_util_test.cc +++ b/tensorflow/core/framework/node_def_util_test.cc @@ -20,6 +20,8 @@ limitations under the License. #include "tensorflow/core/framework/node_def_builder.h" #include "tensorflow/core/framework/op_def_builder.h" #include "tensorflow/core/framework/op_def_util.h" +#include "tensorflow/core/graph/graph.h" +#include "tensorflow/core/graph/node_builder.h" #include "tensorflow/core/lib/core/errors.h" #include "tensorflow/core/lib/core/status_test_util.h" #include "tensorflow/core/lib/strings/str_util.h" @@ -79,7 +81,7 @@ TEST(NodeDefUtilTest, In) { )proto"); ExpectSuccess(node_def, op); - EXPECT_EQ("n = In[T=DT_FLOAT](a)", SummarizeNodeDef(node_def)); + EXPECT_EQ("{{node n}} = In[T=DT_FLOAT](a)", SummarizeNodeDef(node_def)); // Mismatching Op names. NodeDef bad = node_def; @@ -144,7 +146,7 @@ TEST(NodeDefUtilTest, Out) { )proto"); ExpectSuccess(node_def, op); - EXPECT_EQ("n = Out[T=DT_INT32]()", SummarizeNodeDef(node_def)); + EXPECT_EQ("{{node n}} = Out[T=DT_INT32]()", SummarizeNodeDef(node_def)); // Non-number type. NodeDef bad = node_def; @@ -164,7 +166,7 @@ TEST(NodeDefUtilTest, Enum) { )proto"); ExpectSuccess(node_def, op); - EXPECT_EQ("n = Enum[e=\"apple\"]()", SummarizeNodeDef(node_def)); + EXPECT_EQ("{{node n}} = Enum[e=\"apple\"]()", SummarizeNodeDef(node_def)); NodeDef good = node_def; good.clear_attr(); @@ -191,7 +193,8 @@ TEST(NodeDefUtilTest, SameIn) { )proto"); ExpectSuccess(node_def, op); - EXPECT_EQ("n = SameIn[N=2, T=DT_DOUBLE](a, b)", SummarizeNodeDef(node_def)); + EXPECT_EQ("{{node n}} = SameIn[N=2, T=DT_DOUBLE](a, b)", + SummarizeNodeDef(node_def)); // Illegal type NodeDef bad = ToNodeDef(R"proto( @@ -220,7 +223,7 @@ TEST(NodeDefUtilTest, AnyIn) { )proto"); ExpectSuccess(node_def, op); - EXPECT_EQ("n = AnyIn[T=[DT_INT32, DT_STRING]](a, b)", + EXPECT_EQ("{{node n}} = AnyIn[T=[DT_INT32, DT_STRING]](a, b)", SummarizeNodeDef(node_def)); const NodeDef bad = ToNodeDef(R"proto( @@ -243,13 +246,14 @@ TEST(NodeDefUtilTest, Device) { const NodeDef node_def1 = ToNodeDef(NodeDefBuilder("d", &op_def1).Device("/cpu:17")); ExpectSuccess(node_def1, op_def1); - EXPECT_EQ("d = None[_device=\"/cpu:17\"]()", SummarizeNodeDef(node_def1)); + EXPECT_EQ("{{node d}} = None[_device=\"/cpu:17\"]()", + SummarizeNodeDef(node_def1)); const OpDef op_def2 = ToOpDef(OpDefBuilder("WithAttr").Attr("v: int")); const NodeDef node_def2 = ToNodeDef(NodeDefBuilder("d", &op_def2).Attr("v", 7).Device("/cpu:5")); ExpectSuccess(node_def2, op_def2); - EXPECT_EQ("d = WithAttr[v=7, _device=\"/cpu:5\"]()", + EXPECT_EQ("{{node d}} = WithAttr[v=7, _device=\"/cpu:5\"]()", SummarizeNodeDef(node_def2)); } @@ -284,7 +288,7 @@ TEST(NodeDefUtilTest, ValidSyntax) { )proto"); ExpectValidSyntax(node_def_explicit_inputs); - EXPECT_EQ("n = AnyIn[T=[DT_INT32, DT_STRING]](a:0, b:123)", + EXPECT_EQ("{{node n}} = AnyIn[T=[DT_INT32, DT_STRING]](a:0, b:123)", SummarizeNodeDef(node_def_explicit_inputs)); const NodeDef node_def_partial_shape = ToNodeDef(R"proto( @@ -379,7 +383,7 @@ TEST(NameRangesForNodeTest, Simple) { EXPECT_EQ(NameRangeMap({{"a", {0, 1}}, {"b", {1, 2}}}), inputs); EXPECT_EQ(NameRangeMap({{"c", {0, 1}}, {"d", {1, 2}}}), outputs); - EXPECT_EQ("simple = Simple[](a, b)", SummarizeNodeDef(node_def)); + EXPECT_EQ("{{node simple}} = Simple[](a, b)", SummarizeNodeDef(node_def)); OpDef bad_op_def = op_def; bad_op_def.mutable_input_arg(0)->clear_type(); @@ -399,7 +403,7 @@ TEST(NameRangesForNodeTest, Polymorphic) { TF_EXPECT_OK(NameRangesForNode(node_def1, op_def, &inputs, &outputs)); EXPECT_EQ(NameRangeMap({{"a", {0, 1}}, {"b", {1, 2}}}), inputs); EXPECT_EQ(NameRangeMap({{"c", {0, 1}}}), outputs); - EXPECT_EQ("poly = Polymorphic[T=DT_INT32](a, b)", + EXPECT_EQ("{{node poly}} = Polymorphic[T=DT_INT32](a, b)", SummarizeNodeDef(node_def1)); const NodeDef node_def2 = ToNodeDef(NodeDefBuilder("poly", &op_def) @@ -408,7 +412,8 @@ TEST(NameRangesForNodeTest, Polymorphic) { TF_EXPECT_OK(NameRangesForNode(node_def2, op_def, &inputs, &outputs)); EXPECT_EQ(NameRangeMap({{"a", {0, 1}}, {"b", {1, 2}}}), inputs); EXPECT_EQ(NameRangeMap({{"c", {0, 1}}}), outputs); - EXPECT_EQ("poly = Polymorphic[T=DT_BOOL](a, b)", SummarizeNodeDef(node_def2)); + EXPECT_EQ("{{node poly}} = Polymorphic[T=DT_BOOL](a, b)", + SummarizeNodeDef(node_def2)); } TEST(NameRangesForNodeTest, NRepeats) { @@ -431,7 +436,8 @@ TEST(NameRangesForNodeTest, NRepeats) { EXPECT_EQ(NameRangeMap({{"c", {0, 1}}, {"d", {1, 5}}, {"e", {5, 8}}}), outputs); EXPECT_EQ( - "nr = NRepeats[M=3, N=4, T=DT_FLOAT](a, a:1, a:2, a:3, b, b:1, b:2, b:3)", + "{{node nr}} = NRepeats[M=3, N=4, T=DT_FLOAT](a, a:1, a:2, a:3, b, b:1, " + "b:2, b:3)", SummarizeNodeDef(node_def1)); const NodeDef node_def2 = ToNodeDef(NodeDefBuilder("nr", &op_def) @@ -442,7 +448,7 @@ TEST(NameRangesForNodeTest, NRepeats) { EXPECT_EQ(NameRangeMap({{"a", {0, 2}}, {"b", {2, 4}}}), inputs); EXPECT_EQ(NameRangeMap({{"c", {0, 1}}, {"d", {1, 3}}, {"e", {3, 10}}}), outputs); - EXPECT_EQ("nr = NRepeats[M=7, N=2, T=DT_DOUBLE](a, a:1, b, b:1)", + EXPECT_EQ("{{node nr}} = NRepeats[M=7, N=2, T=DT_DOUBLE](a, a:1, b, b:1)", SummarizeNodeDef(node_def2)); NodeDef bad_node_def = node_def2; @@ -471,7 +477,7 @@ TEST(NameRangesForNodeTest, TypeList) { EXPECT_EQ(NameRangeMap({{"c", {0, 4}}, {"d", {4, 7}}, {"e", {7, 9}}}), outputs); EXPECT_EQ( - "tl = TypeList[T1=[DT_BOOL, DT_FLOAT]," + "{{node tl}} = TypeList[T1=[DT_BOOL, DT_FLOAT]," " T2=[DT_FLOAT, DT_FLOAT, DT_FLOAT, DT_FLOAT]," " T3=[DT_INT32, DT_DOUBLE, DT_STRING]](a, a:1, b, b:1, b:2, b:3)", SummarizeNodeDef(node_def1)); @@ -485,7 +491,8 @@ TEST(NameRangesForNodeTest, TypeList) { EXPECT_EQ(NameRangeMap({{"c", {0, 1}}, {"d", {1, 3}}, {"e", {3, 10}}}), outputs); EXPECT_EQ( - "tl = TypeList[T1=[DT_INT32, DT_INT32, DT_INT32, DT_INT32, DT_INT32," + "{{node tl}} = TypeList[T1=[DT_INT32, DT_INT32, DT_INT32, DT_INT32, " + "DT_INT32," " DT_INT32, DT_INT32], T2=[DT_DOUBLE], T3=[DT_DOUBLE, DT_STRING]]" "(a, a:1, a:2, a:3, a:4, a:5, a:6, b)", SummarizeNodeDef(node_def2)); @@ -509,5 +516,20 @@ TEST(AddPrefixAndSuffixToNode, Enter) { EXPECT_EQ("prefix/test_frame/suffix", frame_name); } +TEST(FormatNodeForErrorTest, Node) { + Graph g(OpRegistry::Global()); + Node* node; + TF_CHECK_OK(NodeBuilder("enter", "NoOp").Finalize(&g, &node)); + EXPECT_EQ("{{node enter}}", FormatNodeForError(*node)); +} + +TEST(FormatNodeForErrorTest, NodeDef) { + NodeDef node_def; + node_def.set_name("enter"); + node_def.set_op("Enter"); + AddNodeAttr("frame_name", "test_frame", &node_def); + EXPECT_EQ("{{node enter}}", FormatNodeDefForError(node_def)); +} + } // namespace } // namespace tensorflow diff --git a/tensorflow/core/framework/op_compatibility_test.cc b/tensorflow/core/framework/op_compatibility_test.cc index c782480f1f..140f201085 100644 --- a/tensorflow/core/framework/op_compatibility_test.cc +++ b/tensorflow/core/framework/op_compatibility_test.cc @@ -209,8 +209,8 @@ TEST_F(OpCompatibilityTest, Same) { .Finalize(node_def())); ExpectSuccess(*RegisteredOpDef()); EXPECT_EQ( - "same = Same[N=3, T=DT_FLOAT, TList=[DT_BOOL, DT_BOOL]](a, b, c, c:1, " - "c:2, d, d:1, d:2, e, e:1)", + "{{node same}} = Same[N=3, T=DT_FLOAT, TList=[DT_BOOL, DT_BOOL]](a, b, " + "c, c:1, c:2, d, d:1, d:2, e, e:1)", Result()); } @@ -224,7 +224,7 @@ TEST_F(OpCompatibilityTest, AddAttr) { OpDefBuilder("AddAttr").Output("ndef: string").Finalize(&old_op)); TF_ASSERT_OK(NodeDefBuilder("add_attr", &old_op.op_def).Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("add_attr = AddAttr[a=42]()", Result()); + EXPECT_EQ("{{node add_attr}} = AddAttr[a=42]()", Result()); } // Should be able to make an attr restriction less strict. @@ -241,7 +241,7 @@ TEST_F(OpCompatibilityTest, LessStrict) { .Attr("a", "B") .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("less_strict = LessStrict[a=\"B\"]()", Result()); + EXPECT_EQ("{{node less_strict}} = LessStrict[a=\"B\"]()", Result()); } // Should be able to remove an attr restriction. @@ -259,7 +259,8 @@ TEST_F(OpCompatibilityTest, RemoveRestriction) { .Attr("a", DT_INT32) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("remove_restriction = RemoveRestriction[a=DT_INT32]()", Result()); + EXPECT_EQ("{{node remove_restriction}} = RemoveRestriction[a=DT_INT32]()", + Result()); } // Should be able to change the order of attrs. @@ -278,7 +279,7 @@ TEST_F(OpCompatibilityTest, AttrOrder) { .Attr("a", 7) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("attr_order = AttrOrder[a=7, b=true]()", Result()); + EXPECT_EQ("{{node attr_order}} = AttrOrder[a=7, b=true]()", Result()); } // Should be able to make an input/output polymorphic. @@ -299,7 +300,8 @@ TEST_F(OpCompatibilityTest, TypePolymorphic) { .Input(FakeInput()) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("type_polymorphic = TypePolymorphic[T=DT_INT32](a)", Result()); + EXPECT_EQ("{{node type_polymorphic}} = TypePolymorphic[T=DT_INT32](a)", + Result()); } // Should be able to make a single input/output into a list. @@ -320,7 +322,7 @@ TEST_F(OpCompatibilityTest, MakeList) { .Input(FakeInput()) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("make_list = MakeList[N=1](a)", Result()); + EXPECT_EQ("{{node make_list}} = MakeList[N=1](a)", Result()); } // Should be able to make a single input/output into a polymorphic list. @@ -343,7 +345,8 @@ TEST_F(OpCompatibilityTest, MakePolyList) { .Input(FakeInput()) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("make_poly_list = MakePolyList[N=1, T=DT_INT32](a)", Result()); + EXPECT_EQ("{{node make_poly_list}} = MakePolyList[N=1, T=DT_INT32](a)", + Result()); } // Should be able to make a single input/output into an arbitrary list. @@ -364,7 +367,7 @@ TEST_F(OpCompatibilityTest, MakeAnyList) { .Input(FakeInput()) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("make_any_list = MakeAnyList[T=[DT_INT32]](a)", Result()); + EXPECT_EQ("{{node make_any_list}} = MakeAnyList[T=[DT_INT32]](a)", Result()); } // Should be able to make a single polymorphic input/output into a list of @@ -387,7 +390,8 @@ TEST_F(OpCompatibilityTest, PolyIntoList) { .Input(FakeInput(DT_INT32)) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("poly_into_list = PolyIntoList[N=1, T=DT_INT32](a)", Result()); + EXPECT_EQ("{{node poly_into_list}} = PolyIntoList[N=1, T=DT_INT32](a)", + Result()); } // Should be able to make a multiple inputs/outputs into a list with @@ -413,7 +417,7 @@ TEST_F(OpCompatibilityTest, MakeMultipleSameList) { .Input(FakeInput()) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("make_list = MakeMultipleSameList[N=2](a, b)", Result()); + EXPECT_EQ("{{node make_list}} = MakeMultipleSameList[N=2](a, b)", Result()); } // Changing from int32, float -> T @@ -437,8 +441,9 @@ TEST_F(OpCompatibilityTest, MakeMultipleAnyList) { .Input(FakeInput()) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("make_list = MakeMultipleAnyList[T=[DT_INT32, DT_FLOAT]](a, b)", - Result()); + EXPECT_EQ( + "{{node make_list}} = MakeMultipleAnyList[T=[DT_INT32, DT_FLOAT]](a, b)", + Result()); } // Should be able to change the name of an input/output. @@ -455,7 +460,7 @@ TEST_F(OpCompatibilityTest, ChangeName) { .Input(FakeInput()) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("change_name = ChangeName[](a)", Result()); + EXPECT_EQ("{{node change_name}} = ChangeName[](a)", Result()); } // Should be able to add an input/output of type @@ -473,7 +478,7 @@ TEST_F(OpCompatibilityTest, AddNInts) { TF_ASSERT_OK( NodeDefBuilder("add_n_ints", &old_op.op_def).Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("add_n_ints = AddNInts[N=0]()", Result()); + EXPECT_EQ("{{node add_n_ints}} = AddNInts[N=0]()", Result()); } // Should be able to add an input/output of type N * T @@ -492,7 +497,7 @@ TEST_F(OpCompatibilityTest, AddNSame) { TF_ASSERT_OK( NodeDefBuilder("add_n_same", &old_op.op_def).Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("add_n_same = AddNSame[N=0, T=DT_BOOL]()", Result()); + EXPECT_EQ("{{node add_n_same}} = AddNSame[N=0, T=DT_BOOL]()", Result()); } // Should be able to add an input/output of type N * T @@ -517,8 +522,10 @@ TEST_F(OpCompatibilityTest, AddNSameAsExisting) { .Input(FakeInput(DT_STRING)) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("add_n_same_as_existing = AddNSameAsExisting[N=0, T=DT_STRING](a)", - Result()); + EXPECT_EQ( + "{{node add_n_same_as_existing}} = AddNSameAsExisting[N=0, " + "T=DT_STRING](a)", + Result()); } // Should be able to add an input/output of type T @@ -536,7 +543,7 @@ TEST_F(OpCompatibilityTest, AddAnyList) { TF_ASSERT_OK( NodeDefBuilder("add_any_list", &old_op.op_def).Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("add_any_list = AddAnyList[T=[]]()", Result()); + EXPECT_EQ("{{node add_any_list}} = AddAnyList[T=[]]()", Result()); } // Should be able to allow shorter lists. @@ -557,8 +564,10 @@ TEST_F(OpCompatibilityTest, ShorterAnyList) { .Input(FakeInput(2, DT_BOOL)) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("shorter_any_list = ShorterAnyList[T=[DT_BOOL, DT_BOOL]](a, a:1)", - Result()); + EXPECT_EQ( + "{{node shorter_any_list}} = ShorterAnyList[T=[DT_BOOL, DT_BOOL]](a, " + "a:1)", + Result()); } REGISTER_OP("ShorterSameList") @@ -578,7 +587,8 @@ TEST_F(OpCompatibilityTest, ShorterSameList) { .Input(FakeInput(2)) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("shorter_same_list = ShorterSameList[N=2](a, a:1)", Result()); + EXPECT_EQ("{{node shorter_same_list}} = ShorterSameList[N=2](a, a:1)", + Result()); } // Can remove a restriction to an attr @@ -597,7 +607,7 @@ TEST_F(OpCompatibilityTest, AttrRemoveRestriction) { .Attr("t", DT_INT32) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("remove_restriction = AttrRemoveRestriction[t=DT_INT32]()", + EXPECT_EQ("{{node remove_restriction}} = AttrRemoveRestriction[t=DT_INT32]()", Result()); } @@ -619,7 +629,8 @@ TEST_F(OpCompatibilityTest, AttrLessRestrictive) { .Attr("t", DT_INT32) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("less_restrictive = AttrLessRestrictive[t=DT_INT32]()", Result()); + EXPECT_EQ("{{node less_restrictive}} = AttrLessRestrictive[t=DT_INT32]()", + Result()); } // Can remove a minimum from an attr. @@ -637,7 +648,7 @@ TEST_F(OpCompatibilityTest, AttrRemoveMin) { .Attr("n", 4) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("remove_min = AttrRemoveMin[n=4]()", Result()); + EXPECT_EQ("{{node remove_min}} = AttrRemoveMin[n=4]()", Result()); } // Can lower the minimum on an attr. @@ -655,7 +666,7 @@ TEST_F(OpCompatibilityTest, AttrLowerMin) { .Attr("n", 4) .Finalize(node_def())); ExpectSuccess(old_op.op_def); - EXPECT_EQ("lower_min = AttrLowerMin[n=4]()", Result()); + EXPECT_EQ("{{node lower_min}} = AttrLowerMin[n=4]()", Result()); } // Can make a ref input into a non-ref input. diff --git a/tensorflow/core/graph/control_flow.cc b/tensorflow/core/graph/control_flow.cc index 1778e48ef6..8e1e56d29b 100644 --- a/tensorflow/core/graph/control_flow.cc +++ b/tensorflow/core/graph/control_flow.cc @@ -18,6 +18,7 @@ limitations under the License. #include #include +#include "tensorflow/core/framework/node_def_util.h" #include "tensorflow/core/framework/types.h" #include "tensorflow/core/graph/node_builder.h" #include "tensorflow/core/lib/core/errors.h" @@ -54,10 +55,11 @@ Status ValidateControlFlowInfo(const Graph* graph, frame.parent = parent; frame.name = cf.frame_name; } else if (frame.parent != parent) { - return errors::InvalidArgument( + return errors::Internal( "Invalid loop structure: Mismatched parent frames for \"", cf.frame_name, "\": \"", parent->name, "\" vs \"", frame.parent->name, - "\". This is an internal bug, please file a bug report with " + "\". The node giving this error: ", FormatNodeForError(*node), + "This is an internal bug, please file a bug report with " "instructions on how to reproduce the error."); } if (IsLoopCond(node)) { @@ -69,9 +71,9 @@ Status ValidateControlFlowInfo(const Graph* graph, !str_util::StrContains(node->name(), "LoopCounter")) { return errors::InvalidArgument( "Invalid loop structure: Loop \"", cf.frame_name, - "\" has more than one LoopCond node: \"", node->name(), "\" and \"", - frame.loop_cond->name(), - "\". This is an internal bug, please file a bug report with " + "\" has more than one LoopCond node: ", FormatNodeForError(*node), + " and ", FormatNodeForError(*frame.loop_cond), + ". This is an internal bug, please file a bug report with " "instructions on how to reproduce the error."); } frame.loop_cond = node; @@ -135,12 +137,11 @@ Status BuildControlFlowInfo(const Graph* g, std::vector* info, const string& parent_frame = (*info)[out_parent->id()].frame_name; if (parent_frame != frame_name) { return errors::InvalidArgument( - "The node '", out->name(), - "' has inputs from different " - "frames. The input '", - curr_node->name(), "' is in frame '", frame_name, - "'. The input '", parent_nodes[out->id()]->name(), - "' is in frame '", parent_frame, "'."); + FormatNodeForError(*out), + " has inputs from different frames. The input ", + FormatNodeForError(*curr_node), " is in frame '", frame_name, + "'. The input ", FormatNodeForError(*parent_nodes[out->id()]), + " is in frame '", parent_frame, "'."); } } else { out_info->frame = out; @@ -148,7 +149,8 @@ Status BuildControlFlowInfo(const Graph* g, std::vector* info, TF_RETURN_IF_ERROR( GetNodeAttr(out->attrs(), "frame_name", &out_info->frame_name)); if (out_info->frame_name.empty()) { - return errors::InvalidArgument("The Enter node ", out->name(), + return errors::InvalidArgument("The Enter ", + FormatNodeForError(*out), " must have a frame name."); } } @@ -156,12 +158,11 @@ Status BuildControlFlowInfo(const Graph* g, std::vector* info, if (is_visited) { if (out_info->frame_name != frame_name) { return errors::InvalidArgument( - "The node '", out->name(), - "' has inputs from different " - "frames. The input '", - curr_node->name(), "' is in frame '", frame_name, - "'. The input '", parent_nodes[out->id()]->name(), - "' is in frame '", out_info->frame_name, "'."); + FormatNodeForError(*out), + " has inputs from different frames. The input ", + FormatNodeForError(*curr_node), " is in frame '", frame_name, + "'. The input ", FormatNodeForError(*parent_nodes[out->id()]), + " is in frame '", out_info->frame_name, "'."); } } else { out_info->frame = frame; diff --git a/tensorflow/core/graph/control_flow_test.cc b/tensorflow/core/graph/control_flow_test.cc index eb7937400f..803c757c3f 100644 --- a/tensorflow/core/graph/control_flow_test.cc +++ b/tensorflow/core/graph/control_flow_test.cc @@ -63,6 +63,15 @@ TEST(ValidateControlFlowTest, InputsFromDifferentFrames) { EXPECT_TRUE(str_util::StrContains(status.error_message(), "has inputs from different frames")) << status.error_message(); + EXPECT_TRUE(str_util::StrContains(status.error_message(), + "{{node outer/body/inner/Merge}}")) + << status.error_message(); + EXPECT_TRUE(str_util::StrContains(status.error_message(), + "{{node outer/body/inner/Enter}}")) + << status.error_message(); + EXPECT_TRUE( + str_util::StrContains(status.error_message(), "{{node outer/Switch}}")) + << status.error_message(); } TEST(ValidateControlFlowTest, MismatchedParentFrames) { @@ -102,6 +111,8 @@ TEST(ValidateControlFlowTest, MismatchedParentFrames) { EXPECT_TRUE( str_util::StrContains(status.error_message(), "Mismatched parent frames")) << status.error_message(); + EXPECT_TRUE(str_util::StrContains(status.error_message(), "{{node Enter2}}")) + << status.error_message(); } TEST(ValidateControlFlowTest, TwoLoopCond) { @@ -125,6 +136,12 @@ TEST(ValidateControlFlowTest, TwoLoopCond) { EXPECT_TRUE(str_util::StrContains(status.error_message(), "more than one LoopCond node")) << status.error_message(); + EXPECT_TRUE( + str_util::StrContains(status.error_message(), "{{node sub/LoopCond}}")) + << status.error_message(); + EXPECT_TRUE( + str_util::StrContains(status.error_message(), "{{node LoopCond}}")) + << status.error_message(); } } // namespace diff --git a/tensorflow/core/lib/core/errors.h b/tensorflow/core/lib/core/errors.h index 51c09032df..a631d9815a 100644 --- a/tensorflow/core/lib/core/errors.h +++ b/tensorflow/core/lib/core/errors.h @@ -19,6 +19,7 @@ limitations under the License. #include #include "tensorflow/core/lib/core/status.h" +#include "tensorflow/core/lib/strings/str_util.h" #include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/macros.h" @@ -118,6 +119,25 @@ DECLARE_ERROR(Unauthenticated, UNAUTHENTICATED) #undef DECLARE_ERROR +// Produces a formatted string pattern from the name which can uniquely identify +// this node upstream to produce an informative error message. The pattern +// followed is: {{node }} +// Note: The pattern below determines the regex _NODEDEF_NAME_RE in the file +// tensorflow/python/client/session.py +// LINT.IfChange +inline string FormatNodeNameForError(const string& name) { + return strings::StrCat("{{node ", name, "}}"); +} +// LINT.ThenChange(//tensorflow/python/client/session.py) +template +string FormatNodeNamesForError(const T& names) { + ::tensorflow::str_util::Formatter f( + [](string* output, const string& s) { + ::tensorflow::strings::StrAppend(output, FormatNodeNameForError(s)); + }); + return ::tensorflow::str_util::Join(names, ", ", f); +} + // The CanonicalCode() for non-errors. using ::tensorflow::error::OK; diff --git a/tensorflow/core/util/equal_graph_def_test.cc b/tensorflow/core/util/equal_graph_def_test.cc index c54540332e..77ca8eaec3 100644 --- a/tensorflow/core/util/equal_graph_def_test.cc +++ b/tensorflow/core/util/equal_graph_def_test.cc @@ -85,7 +85,7 @@ TEST_F(EqualGraphDefTest, NoMatch) { Input(e_.opts().WithName("A")); Input(a_.opts().WithName("B")); EXPECT_FALSE(Match()); - EXPECT_EQ("Did not find expected node 'A = Input[]()'", diff_); + EXPECT_EQ("Did not find expected node '{{node A}} = Input[]()'", diff_); } TEST_F(EqualGraphDefTest, MissingNode) { @@ -93,7 +93,7 @@ TEST_F(EqualGraphDefTest, MissingNode) { Input(e_.opts().WithName("B")); Input(a_.opts().WithName("A")); EXPECT_FALSE(Match()); - EXPECT_EQ("Did not find expected node 'B = Input[]()'", diff_); + EXPECT_EQ("Did not find expected node '{{node B}} = Input[]()'", diff_); } TEST_F(EqualGraphDefTest, ExtraNode) { @@ -101,7 +101,7 @@ TEST_F(EqualGraphDefTest, ExtraNode) { Input(a_.opts().WithName("A")); Input(a_.opts().WithName("B")); EXPECT_FALSE(Match()); - EXPECT_EQ("Found unexpected node 'B = Input[]()'", diff_); + EXPECT_EQ("Found unexpected node '{{node B}} = Input[]()'", diff_); } TEST_F(EqualGraphDefTest, NodeOrder) { diff --git a/tensorflow/docs_src/guide/using_gpu.md b/tensorflow/docs_src/guide/using_gpu.md index c429ca4750..c0218fd12e 100644 --- a/tensorflow/docs_src/guide/using_gpu.md +++ b/tensorflow/docs_src/guide/using_gpu.md @@ -143,7 +143,7 @@ If the device you have specified does not exist, you will get ``` InvalidArgumentError: Invalid argument: Cannot assign a device to node 'b': Could not satisfy explicit device specification '/device:GPU:2' - [[Node: b = Const[dtype=DT_FLOAT, value=Tensor, _device="/device:GPU:2"]()]] ``` diff --git a/tensorflow/python/client/session.py b/tensorflow/python/client/session.py index 861230e5a0..f8e20e1b89 100644 --- a/tensorflow/python/client/session.py +++ b/tensorflow/python/client/session.py @@ -1235,8 +1235,12 @@ class BaseSession(SessionInterface): return _fetch_handler_run - # Captures the name of a node in an error status. - _NODEDEF_NAME_RE = re.compile(r'\[\[Node: ([^ ]*?) =') + # Captures the name of a node in an error status. The regex below matches + # both the old and the new formats: + # Old format: [[Node: = ...]] + # New format: [[{{node }} = ...]] + _NODEDEF_NAME_RE = re.compile( + r'\[\[(Node: )?(\{\{node )?([^\} ]*)(\}\})?\s*=') def _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata): @@ -1291,7 +1295,7 @@ class BaseSession(SessionInterface): node_def = None op = None if m is not None: - node_name = m.group(1) + node_name = m.group(3) try: op = self._graph.get_operation_by_name(node_name) node_def = op.node_def diff --git a/tensorflow/python/framework/test_util_test.py b/tensorflow/python/framework/test_util_test.py index 122c14c847..f983cbef04 100644 --- a/tensorflow/python/framework/test_util_test.py +++ b/tensorflow/python/framework/test_util_test.py @@ -73,7 +73,7 @@ class TestUtilTest(test_util.TensorFlowTestCase): test_util.assert_equal_graph_def(def_57, def_75) # Compare two unequal graphs with self.assertRaisesRegexp(AssertionError, - r"^Found unexpected node 'seven"): + r"^Found unexpected node '{{node seven}}"): test_util.assert_equal_graph_def(def_57, def_empty) def testIsGoogleCudaEnabled(self): -- GitLab From b53f5aa1f6f1abea6457e9828f2041c95b3b1720 Mon Sep 17 00:00:00 2001 From: Derek Murray Date: Fri, 27 Jul 2018 14:21:42 -0700 Subject: [PATCH 508/519] Prevent deadlocks when OP_REQUIRES is used in AsyncOpKernel::ComputeAsync(). If OP_REQUIRES or OP_REQUIRES_OK are used in an override of AsyncOpKernel::ComputeAsync() and the condition does not hold, the process will deadlock because the `done` callback is never called. This change raises a fatal error with an actionable error message in this case. PiperOrigin-RevId: 206373312 --- tensorflow/core/framework/op_kernel.cc | 6 ++++ tensorflow/core/framework/op_kernel.h | 43 ++++++++++++++++++-------- 2 files changed, 36 insertions(+), 13 deletions(-) diff --git a/tensorflow/core/framework/op_kernel.cc b/tensorflow/core/framework/op_kernel.cc index 507aa9e447..b53bd8d53d 100644 --- a/tensorflow/core/framework/op_kernel.cc +++ b/tensorflow/core/framework/op_kernel.cc @@ -1288,4 +1288,10 @@ void OpKernelContext::CtxFailureWithWarning(const char* file, int line, SetStatus(s); } +void CheckNotInComputeAsync(OpKernelContext* ctx, + const char* correct_macro_name) { + CHECK_EQ(nullptr, ctx->op_kernel().AsAsync()) + << "Use " << correct_macro_name << " in AsyncOpKernel implementations."; +} + } // namespace tensorflow diff --git a/tensorflow/core/framework/op_kernel.h b/tensorflow/core/framework/op_kernel.h index 1fc5e9908e..2b7cc867da 100644 --- a/tensorflow/core/framework/op_kernel.h +++ b/tensorflow/core/framework/op_kernel.h @@ -113,6 +113,7 @@ class OpKernel { // Returns nullptr iff this op kernel is synchronous. virtual AsyncOpKernel* AsAsync() { return nullptr; } + virtual const AsyncOpKernel* AsAsync() const { return nullptr; } // Returns true iff this op kernel is considered "expensive". The // runtime may use this flag to optimize graph execution for example @@ -197,6 +198,7 @@ class AsyncOpKernel : public OpKernel { virtual void ComputeAsync(OpKernelContext* context, DoneCallback done) = 0; AsyncOpKernel* AsAsync() final { return this; } + const AsyncOpKernel* AsAsync() const final { return this; } void Compute(OpKernelContext* context) final; @@ -1542,21 +1544,36 @@ inline void OpOutputList::set_ref(int i, mutex* mu, Tensor* tensor_for_ref) { // ... // } -#define OP_REQUIRES(CTX, EXP, STATUS) \ - do { \ - if (!TF_PREDICT_TRUE(EXP)) { \ - (CTX)->CtxFailure(__FILE__, __LINE__, (STATUS)); \ - return; \ - } \ +// Generate a fatal error if OP_REQUIRES or OP_REQUIRES_OK are used in +// AsyncOpKernel implementations. If these macros are used and the condition +// does not hold, the `done` callback will never be called and the system will +// deadlock, so a crash failure is preferable. Since the OP_REQUIRES[_OK] macros +// are legal to use in AsyncOpKernel constructors, we use overload resolution +// to distinguish between OpKernelConstruction* and OpKernelContext* context +// types. +class XlaOpKernelContext; +inline void CheckNotInComputeAsync(XlaOpKernelContext*, const char*) {} +inline void CheckNotInComputeAsync(OpKernelConstruction*, const char*) {} +void CheckNotInComputeAsync(OpKernelContext* ctx, + const char* correct_macro_name); + +#define OP_REQUIRES(CTX, EXP, STATUS) \ + do { \ + if (!TF_PREDICT_TRUE(EXP)) { \ + CheckNotInComputeAsync((CTX), "OP_REQUIRES_ASYNC"); \ + (CTX)->CtxFailure(__FILE__, __LINE__, (STATUS)); \ + return; \ + } \ } while (0) -#define OP_REQUIRES_OK(CTX, ...) \ - do { \ - ::tensorflow::Status _s(__VA_ARGS__); \ - if (!TF_PREDICT_TRUE(_s.ok())) { \ - (CTX)->CtxFailureWithWarning(__FILE__, __LINE__, _s); \ - return; \ - } \ +#define OP_REQUIRES_OK(CTX, ...) \ + do { \ + ::tensorflow::Status _s(__VA_ARGS__); \ + if (!TF_PREDICT_TRUE(_s.ok())) { \ + CheckNotInComputeAsync((CTX), "OP_REQUIRES_OK_ASYNC"); \ + (CTX)->CtxFailureWithWarning(__FILE__, __LINE__, _s); \ + return; \ + } \ } while (0) #define OP_REQUIRES_ASYNC(CTX, EXP, STATUS, CALLBACK) \ -- GitLab From 470b43af3153942e4ef838610aa07e93f904fd5f Mon Sep 17 00:00:00 2001 From: Akshay Modi Date: Fri, 27 Jul 2018 14:28:34 -0700 Subject: [PATCH 509/519] Make TFE_DeleteContext not take a status, and allow TFE_DeleteTensorHandle to take a nullptr. None of the other TFE_Delete* functions take a status, so this makes things a little more consistent. PiperOrigin-RevId: 206374382 --- tensorflow/c/eager/c_api.cc | 4 +- tensorflow/c/eager/c_api.h | 3 +- tensorflow/c/eager/c_api_test.cc | 54 +++++++++++------------ tensorflow/python/eager/pywrap_tfe_src.cc | 4 +- 4 files changed, 29 insertions(+), 36 deletions(-) diff --git a/tensorflow/c/eager/c_api.cc b/tensorflow/c/eager/c_api.cc index 6c510536d6..7321b4b791 100644 --- a/tensorflow/c/eager/c_api.cc +++ b/tensorflow/c/eager/c_api.cc @@ -288,7 +288,7 @@ TFE_Context* TFE_NewContext(const TFE_ContextOptions* opts, TF_Status* status) { opts->async, std::move(device_mgr), r); } -void TFE_DeleteContext(TFE_Context* ctx, TF_Status* status) { delete ctx; } +void TFE_DeleteContext(TFE_Context* ctx) { delete ctx; } TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx, TF_Status* status) { TF_DeviceList* list = new TF_DeviceList; @@ -336,7 +336,7 @@ TFE_TensorHandle* TFE_NewTensorHandle(TF_Tensor* t, TF_Status* status) { } void TFE_DeleteTensorHandle(TFE_TensorHandle* h) { - DCHECK(h); + if (h == nullptr) return; if (h->handle) { h->handle->Unref(); } diff --git a/tensorflow/c/eager/c_api.h b/tensorflow/c/eager/c_api.h index fdbd5374b2..ea019a5711 100644 --- a/tensorflow/c/eager/c_api.h +++ b/tensorflow/c/eager/c_api.h @@ -102,8 +102,7 @@ typedef struct TFE_Context TFE_Context; TF_CAPI_EXPORT extern TFE_Context* TFE_NewContext( const TFE_ContextOptions* opts, TF_Status* status); -TF_CAPI_EXPORT extern void TFE_DeleteContext(TFE_Context* ctx, - TF_Status* status); +TF_CAPI_EXPORT extern void TFE_DeleteContext(TFE_Context* ctx); TF_CAPI_EXPORT extern TF_DeviceList* TFE_ContextListDevices(TFE_Context* ctx, TF_Status* status); diff --git a/tensorflow/c/eager/c_api_test.cc b/tensorflow/c/eager/c_api_test.cc index 3504a8b5e7..0bdea70fe6 100644 --- a/tensorflow/c/eager/c_api_test.cc +++ b/tensorflow/c/eager/c_api_test.cc @@ -49,7 +49,7 @@ void BM_InitOp(int iters) { } tensorflow::testing::StopTiming(); TFE_DeleteTensorHandle(m); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -80,7 +80,7 @@ void BM_Execute(int iters, int async) { tensorflow::testing::StopTiming(); TFE_DeleteOp(matmul); TFE_DeleteTensorHandle(m); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -95,7 +95,7 @@ TEST(CAPI, Context) { TF_DeviceList* devices = TFE_ContextListDevices(ctx, status); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); const int num_devices = TF_DeviceListCount(devices); @@ -195,7 +195,7 @@ void TestRemoteExecute(bool async) { TFE_DeleteOp(matmul); TFE_ContextAsyncWait(ctx, status); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); @@ -281,7 +281,7 @@ void TestRemoteExecuteSilentCopies(bool async) { TFE_DeleteOp(matmul); TFE_ContextAsyncWait(ctx, status); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); @@ -380,8 +380,7 @@ void TensorHandleCopyBetweenDevices(bool async) { TF_DeleteDeviceList(devices); TF_DeleteTensor(t); TFE_DeleteTensorHandle(hcpu); - TFE_DeleteContext(ctx, status.get()); - EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); + TFE_DeleteContext(ctx); } TEST(CAPI, TensorHandleCopyBetweenDevices) { @@ -418,7 +417,7 @@ void TensorHandleCopyBetweenDevicesError(bool async) { TFE_DeleteTensorHandle(hcopy); TFE_DeleteTensorHandle(hcpu); if (hdevice != nullptr) TFE_DeleteTensorHandle(hdevice); - TFE_DeleteContext(ctx, status.get()); + TFE_DeleteContext(ctx); } TEST(CAPI, TensorHandleCopyBetweenDevicesError) { @@ -451,7 +450,7 @@ void TensorHandleCopyBetweenTwoGPUDevices(bool async) { TF_DeleteDeviceList(devices); TF_DeleteTensor(t); TFE_DeleteTensorHandle(hcpu); - TFE_DeleteContext(ctx, status.get()); + TFE_DeleteContext(ctx); return; } const string gpu_1_name(TF_DeviceListName(devices, 1, status.get())); @@ -484,8 +483,7 @@ void TensorHandleCopyBetweenTwoGPUDevices(bool async) { TF_DeleteDeviceList(devices); TF_DeleteTensor(t); TFE_DeleteTensorHandle(hcpu); - TFE_DeleteContext(ctx, status.get()); - EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); + TFE_DeleteContext(ctx); } TEST(CAPI, TensorHandleCopyBetweenTwoGPUDevices) { @@ -533,8 +531,7 @@ void TensorHandleSilentCopy(bool async) { TFE_DeleteTensorHandle(hcpu); TFE_ContextAsyncWait(ctx, status.get()); EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); - TFE_DeleteContext(ctx, status.get()); - EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); + TFE_DeleteContext(ctx); } TEST(CAPI, TensorHandleSilentCopy) { TensorHandleSilentCopy(false); } @@ -580,8 +577,7 @@ void TensorHandleSilentCopyLocal(bool async) { TFE_DeleteTensorHandle(hcpu); TFE_ContextAsyncWait(ctx, status.get()); EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); - TFE_DeleteContext(ctx, status.get()); - EXPECT_EQ(TF_OK, TF_GetCode(status.get())) << TF_Message(status.get()); + TFE_DeleteContext(ctx); } TEST(CAPI, TensorHandleSilentCopyLocal) { TensorHandleSilentCopyLocal(false); } TEST(CAPI, TensorHandleSilentCopyLocalAsync) { @@ -614,7 +610,7 @@ void SetAndGetOpDevices(bool async) { TFE_DeleteOp(matmul); TFE_DeleteTensorHandle(m); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -640,7 +636,7 @@ void Execute_MatMul_CPU(bool async) { TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TFE_DeleteTensorHandle(retvals[0]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); float product[4] = {0}; EXPECT_EQ(sizeof(product), TF_TensorByteSize(t)); @@ -712,7 +708,7 @@ void Execute_MatMul_CPU_Runtime_Error(bool async) { TFE_DeleteTensorHandle(m1); TFE_DeleteTensorHandle(m2); TFE_DeleteTensorHandle(retvals[0]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); TF_DeleteStatus(status); } TEST(CAPI, Execute_MatMul_CPU_Runtime_Error) { @@ -743,7 +739,7 @@ void Execute_MatMul_CPU_Type_Error(bool async) { if (retvals[0] != nullptr) { TFE_DeleteTensorHandle(retvals[0]); } - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); TF_DeleteStatus(status); } @@ -781,7 +777,7 @@ TEST(CAPI, Execute_Min_CPU) { TF_DeleteTensor(t); EXPECT_EQ(1, output[0]); EXPECT_EQ(3, output[1]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -823,7 +819,7 @@ void Execute_MatMul_XLA_CPU(bool async) { EXPECT_EQ(10, product[1]); EXPECT_EQ(15, product[2]); EXPECT_EQ(22, product[3]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); TF_DeleteStatus(status); } TEST(CAPI, Execute_MatMul_XLA_CPU) { Execute_MatMul_XLA_CPU(false); } @@ -862,7 +858,7 @@ void Execute_Min_XLA_CPU(bool async) { TF_DeleteTensor(t); EXPECT_EQ(1, output[0]); EXPECT_EQ(3, output[1]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); TF_DeleteStatus(status); } TEST(CAPI, Execute_Min_XLA_CPU) { Execute_Min_XLA_CPU(false); } @@ -898,7 +894,7 @@ void ExecuteWithTracing(bool async) { TF_Tensor* t = TFE_TensorHandleResolve(retvals[0], status); TFE_DeleteTensorHandle(retvals[0]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); float product[4] = {0}; EXPECT_EQ(sizeof(product), TF_TensorByteSize(t)); @@ -974,7 +970,7 @@ TEST(CAPI, Function_ident_CPU) { TF_DeleteTensor(r); TFE_DeleteTensorHandle(result[0]); } - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); TF_DeleteStatus(status); } @@ -1044,7 +1040,7 @@ TEST(CAPI, Function_ident_XLA_CPU) { TF_DeleteTensor(r); TFE_DeleteTensorHandle(result[0]); } - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); ASSERT_TRUE(TF_GetCode(status) == TF_OK) << TF_Message(status); TF_DeleteStatus(status); } @@ -1120,7 +1116,7 @@ void FunctionDefAndExecute(bool async) { EXPECT_EQ(10, product[1]); EXPECT_EQ(15, product[2]); EXPECT_EQ(22, product[3]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -1161,7 +1157,7 @@ void BM_ExecuteFunction(int iters, int async) { tensorflow::testing::StopTiming(); TFE_DeleteTensorHandle(m); TFE_DeleteTensorHandle(retval[0]); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); EXPECT_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -1249,7 +1245,7 @@ TEST(CAPI, Variables) { TFE_DeleteTensorHandle(var_handle); TFE_DeleteTensorHandle(value_handle); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } @@ -1288,7 +1284,7 @@ void BM_ReadVariable(int iters) { TFE_DeleteOp(op); TFE_DeleteTensorHandle(var_handle); - TFE_DeleteContext(ctx, status); + TFE_DeleteContext(ctx); CHECK_EQ(TF_OK, TF_GetCode(status)) << TF_Message(status); TF_DeleteStatus(status); } diff --git a/tensorflow/python/eager/pywrap_tfe_src.cc b/tensorflow/python/eager/pywrap_tfe_src.cc index 4d28e98961..0eabea321c 100644 --- a/tensorflow/python/eager/pywrap_tfe_src.cc +++ b/tensorflow/python/eager/pywrap_tfe_src.cc @@ -845,11 +845,9 @@ int64_t get_uid() { PyObject* TFE_Py_UID() { return PyLong_FromLongLong(get_uid()); } void TFE_DeleteContextCapsule(PyObject* context) { - TF_Status* status = TF_NewStatus(); TFE_Context* ctx = reinterpret_cast(PyCapsule_GetPointer(context, nullptr)); - TFE_DeleteContext(ctx, status); - TF_DeleteStatus(status); + TFE_DeleteContext(ctx); } static tensorflow::int64 MakeInt(PyObject* integer) { -- GitLab From 2beb3a9d8b9df294e7635cc23d195a76fd78de79 Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Fri, 27 Jul 2018 14:58:10 -0700 Subject: [PATCH 510/519] Add distribute_coordinator: a unified and split client for distributed traning. PiperOrigin-RevId: 206378953 --- tensorflow/python/distribute/BUILD | 40 ++ .../distribute/distribute_coordinator.py | 361 ++++++++++++++++++ .../distribute/distribute_coordinator_test.py | 291 ++++++++++++++ 3 files changed, 692 insertions(+) create mode 100644 tensorflow/python/distribute/BUILD create mode 100644 tensorflow/python/distribute/distribute_coordinator.py create mode 100644 tensorflow/python/distribute/distribute_coordinator_test.py diff --git a/tensorflow/python/distribute/BUILD b/tensorflow/python/distribute/BUILD new file mode 100644 index 0000000000..a29043d8b8 --- /dev/null +++ b/tensorflow/python/distribute/BUILD @@ -0,0 +1,40 @@ +licenses(["notice"]) # Apache 2.0 + +package( + default_visibility = [ + "//tensorflow:internal", + ], +) + +py_library( + name = "distribute_coordinator", + srcs = [ + "distribute_coordinator.py", + ], + srcs_version = "PY2AND3", + deps = [ + "//tensorflow/core:protos_all_py", + "//tensorflow/python:training", + ], +) + +py_test( + name = "distribute_coordinator_test", + size = "small", + srcs = ["distribute_coordinator_test.py"], + srcs_version = "PY2AND3", + deps = [ + ":distribute_coordinator", + "//tensorflow/core:protos_all_py", + "//tensorflow/python:client_testlib", + "//tensorflow/python:control_flow_ops", + "//tensorflow/python:distributed_framework_test_lib", + "//tensorflow/python:framework_ops", + "//tensorflow/python:framework_test_lib", + "//tensorflow/python:math_ops", + "//tensorflow/python:session", + "//tensorflow/python:training", + "//tensorflow/python:variable_scope", + "//tensorflow/python:variables", + ], +) diff --git a/tensorflow/python/distribute/distribute_coordinator.py b/tensorflow/python/distribute/distribute_coordinator.py new file mode 100644 index 0000000000..04c50dbafc --- /dev/null +++ b/tensorflow/python/distribute/distribute_coordinator.py @@ -0,0 +1,361 @@ +# 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. +# ============================================================================== +"""A unified and split coordinator for distributed TensorFlow.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import copy +import json +import os +import threading + +from tensorflow.core.protobuf import cluster_pb2 +from tensorflow.python.training import server_lib + + +class _TaskType(object): + PS = "ps" + WORKER = "worker" + CHIEF = "chief" + EVALUATOR = "evaluator" + + +_coordinator_context = threading.local() + + +def get_current_coordinator_context(): + """Returns the current coordinator context.""" + try: + return _coordinator_context.current + except AttributeError: + return None + + +class _Barrier(object): + """A reusable barrier class for worker synchronization.""" + + def __init__(self, num_participants): + """Initializes the barrier object. + + Args: + num_participants: an integer which is the expected number of calls of + `wait` pass to through this barrier. + """ + self._num_participants = num_participants + self._counter = 0 + self._flag = False + self._local_sense = threading.local() + self._lock = threading.Lock() + self._condition = threading.Condition() + + def wait(self): + """Waits until all other callers reach the same wait call.""" + if not hasattr(self._local_sense, "value"): + self._local_sense.value = False + self._local_sense.value = not self._flag + with self._lock: + self._counter += 1 + if self._counter == self._num_participants: + self._counter = 0 + self._flag = self._local_sense.value + with self._condition: + while self._flag != self._local_sense.value: + self._condition.wait() + self._condition.notify_all() + + +def _get_num_workers(cluster_spec): + """Gets number of workers including chief.""" + if not cluster_spec: + return 0 + return len(cluster_spec.as_dict().get(_TaskType.WORKER, [])) + len( + cluster_spec.as_dict().get(_TaskType.CHIEF, [])) + + +class _CoordinatorContext(object): + """The coordinator context class. + + This context object provides configuration information for each task. One + context manager with a coordinator context object will be created per + invocation to the `worker_fn` where `get_current_coordinator_context` can be + called to access the coordinator context object. + """ + + def __init__(self, + cluster_spec, + task_type, + task_id, + between_graph=False, + rpc_layer="grpc", + worker_barrier=None): + """Initialize the coordinator context object. + + Args: + cluster_spec: a ClusterSpec object. It can be empty or None in the local + training case. + task_type: a string indicating the role of the corresponding task, such as + "worker" or "ps". It can be None if it is local training or + `between_graph` is False. + task_id: an integer indicating id of the corresponding task. It can be + None if it is local training or `between_graph` is False. + between_graph: whether it is between-graph replication or not. + rpc_layer: optional string specifying the RPC protocol for communication + with worker masters. If None or empty, hosts in the `cluster_spec` will + be used directly. + worker_barrier: optional, the barrier object for worker synchronization. + + Raises: + ValueError: if task_type or task_id is Node or empty and it is distributed + between-graph replicated training. + """ + if cluster_spec and between_graph: + if not task_type or task_id is None: + raise ValueError("`task_type` and `task_id` must be set in the " + "distributed between-graph replicated training.") + if task_type not in cluster_spec.jobs: + raise ValueError("`task_type` %r not found in the `cluster_spec` %r" % + (task_type, cluster_spec)) + self._cluster_spec = cluster_spec + self._task_type = task_type + self._task_id = task_id + self._worker_barrier = worker_barrier + self._rpc_layer = rpc_layer + self._master_target = self._get_master_target() + self._num_workers = _get_num_workers(cluster_spec) + self._is_chief_node = self._is_chief() + + def __enter__(self): + old_context = get_current_coordinator_context() + if old_context: + raise ValueError( + "You cannot run distribute coordinator in a `worker_fn`.") + _coordinator_context.current = self + + def __exit__(self, unused_exception_type, unused_exception_value, + unused_traceback): + _coordinator_context.current = None + + def _get_master_target(self): + """Return the master target for a task.""" + # If cluster_spec is None or empty, we use local master. + if not self._cluster_spec: + return "local" + + # If task_type is None, then it is in-graph replicated training. In this + # case we use the chief or first worker's master target. + if not self._task_type: + if _TaskType.CHIEF in self._cluster_spec.jobs: + assert not self.between_graph + task_type = _TaskType.CHIEF + task_id = 0 + else: + assert _TaskType.WORKER in self._cluster_spec.jobs + task_type = _TaskType.WORKER + task_id = 0 + else: + task_type = self._task_type + task_id = self._task_id + + prefix = "" + if self._rpc_layer: + prefix = self._rpc_layer + "://" + return prefix + self._cluster_spec.job_tasks(task_type)[task_id or 0] + + def _is_chief(self): + """Return whether the task is the chief worker.""" + if (not self._cluster_spec or self._task_type in [_TaskType.CHIEF, None]): + return True + + # If not local and chief not in the cluster_spec, use the first worker as + # chief. + if (_TaskType.CHIEF not in self._cluster_spec.jobs and + self._task_type == _TaskType.WORKER and self._task_id == 0): + return True + return False + + def wait_for_other_workers(self): + """Waits for other workers to reach the same call to this method. + + Raises: + ValueError: if `worker_barrier` is not passed to the __init__ method. + """ + if not self._worker_barrier: + raise ValueError( + "`worker_barrier is not set in the coordinator context.`") + self._worker_barrier.wait() + + @property + def distributed_mode(self): + """Whether it is distributed training or not.""" + return bool(self._cluster_spec) + + @property + def cluster_spec(self): + """Returns a copy of the cluster_spec object.""" + return copy.deepcopy(self._cluster_spec) + + @property + def task_type(self): + """Returns the role of the corresponing task.""" + return self._task_type + + @property + def task_id(self): + """Returns the id or index of the corresponing task.""" + return self._task_id + + @property + def master_target(self): + """Returns the session master for the corresponding task to connect to.""" + return self._master_target + + @property + def is_chief(self): + """Returns whether the task is a chief node.""" + return self._is_chief_node + + @property + def num_workers(self): + """Returns number of workers in the cluster, including chief.""" + return self._num_workers + + +def _run(worker_fn, cluster_spec, task_type, task_id, between_graph, rpc_layer, + worker_barrier): + with _CoordinatorContext(cluster_spec, task_type, task_id, between_graph, + rpc_layer, worker_barrier): + worker_fn() + + +def run_distribute_coordinator(worker_fn, + cluster_spec=None, + between_graph=False, + rpc_layer=None): + """Run the coordinator for distributed TensorFlow. + + This function runs a unified and split coordinator for distributed TensorFlow. + Given a `cluster_spec` specifying server addresses and their roles in a + cluster, this coordinator will figure out how to set them up, give the + underlying function the right targets for master sessions and coordinate their + training. + + In addition to be the distribute coordinator, this is also the source of + configurations for each job in the distributed training. As there are multiple + ways to configure a distributed TensorFlow cluster, its context object + provides these configurations so that users or higher-level APIs don't have to + figure out the configuration for each job by themselves. + + In the between-graph replicated training, this coordinator will create + multiple threads and each calls the `worker_fn` which is supposed to create + its own graph and connect to one worker master given by its coordinator + context. In the in-graph replicated training, it has only one thread calling + this `worker_fn`. + + The `worker_fn` defines the training logic and is called under a its own + coordinator context which can be accessed to via + `get_current_coordinator_context`. A coordinator context provides access to + configurations for each task, e.g. the task_type, task_id, master target and + so on. Since `worker_fn` will be called in a thread and possibly multiple + times, caller should be careful when it accesses global data. For example, it + is unsafe to define flags in a `worker_fn` or to define different environment + variables for different `worker_fn`s. + + The `worker_fn` for the between-graph replication is defined as if there are + only one worker corresponding to the `worker_fn` and possibly ps jobs. It + assigns variables to parameter servers and all other operations to that + worker. In the in-graph replication case, the `worker_fn` has to define + operations for all worker jobs. Using a distribution strategy can simplify the + `worker_fn` by not having to worry about the replication and device assignment + of variables and operations. + + This method is intended to be invoked by high-level APIs so that users don't + have to explictly call it to run this coordinator. For those who don't use + high-level APIs, to change a program to use this coordinator, wrap everything + in a the program after global data definitions such as commandline flag + definition into the `worker_fn` and get task-specific configurations from + the coordinator context. + + The `cluster_spec` can be either passed by the argument or parsed from the + "TF_CONFIG" envrionment variable. Example of a TF_CONFIG: + ``` + cluster = {'chief': ['host0:2222'], + 'ps': ['host1:2222', 'host2:2222'], + 'worker': ['host3:2222', 'host4:2222', 'host5:2222']} + os.environ['TF_CONFIG'] = json.dumps({'cluster': cluster}) + ``` + + If `cluster_spec` is not given in any format, it becomes local training and + this coordinator will connect to a local session. + + For evaluation, if "evaluator" exist in the cluster_spec, a separate thread + will be created with its `task_type` set to "evaluator". If "evaluator" is not + set in the cluster_spec, it entirely depends on the `worker_fn` for how to do + evaluation. + + Args: + worker_fn: the function to be called and given the access to a coordinator + context object. + cluster_spec: a dict, ClusterDef or ClusterSpec specifying servers and roles + in a cluster. If not set or empty, fall back to local training. + between_graph: a boolean. It is only useful when `cluster_spec` is set and + not empty. If true, it will use between-graph replicated training; + otherwise it will use in-graph replicated training. + rpc_layer: optional string, the protocol for RPC, e.g. "grpc". + + Raises: + ValueError: if `cluster_spec` is supplied but not a dict or a ClusterDef or + a ClusterSpec. + """ + if not cluster_spec: + tf_config = json.loads(os.environ.get("TF_CONFIG", "{}")) + cluster_spec = tf_config.get("cluster", {}) + + if cluster_spec: + if isinstance(cluster_spec, (dict, cluster_pb2.ClusterDef)): + cluster_spec = server_lib.ClusterSpec(cluster_spec) + elif not isinstance(cluster_spec, server_lib.ClusterSpec): + raise ValueError( + "`cluster_spec' should be dict or a `tf.train.ClusterSpec` or a " + "`tf.train.ClusterDef` object") + # TODO(yuefengz): validate cluster_spec. + + threads = [] + if cluster_spec and _TaskType.EVALUATOR in cluster_spec.jobs: + t = threading.Thread( + target=_run, + args=(worker_fn, cluster_spec, _TaskType.EVALUATOR, 0, between_graph, + rpc_layer, None)) + t.start() + threads.append(t) + + if cluster_spec and between_graph: + worker_barrier = _Barrier(_get_num_workers(cluster_spec)) + for task_type in [_TaskType.CHIEF, _TaskType.WORKER]: + for task_id in range(len(cluster_spec.as_dict().get(task_type, []))): + t = threading.Thread( + target=_run, + args=(worker_fn, cluster_spec, task_type, task_id, between_graph, + rpc_layer, worker_barrier)) + t.start() + threads.append(t) + else: + # Local or in-graph replicated training. + _run(worker_fn, cluster_spec, None, None, between_graph, rpc_layer, None) + + # TODO(yuefengz): wrapper threads into thread coordinator? + for t in threads: + t.join() diff --git a/tensorflow/python/distribute/distribute_coordinator_test.py b/tensorflow/python/distribute/distribute_coordinator_test.py new file mode 100644 index 0000000000..82fd823352 --- /dev/null +++ b/tensorflow/python/distribute/distribute_coordinator_test.py @@ -0,0 +1,291 @@ +# 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 distribute coordinator.""" + +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import contextlib +import copy +import threading +import six + +from tensorflow.core.protobuf import config_pb2 +from tensorflow.python.client import session +from tensorflow.python.distribute import distribute_coordinator +from tensorflow.python.framework import ops +from tensorflow.python.framework import test_util +from tensorflow.python.ops import control_flow_ops +from tensorflow.python.ops import math_ops +from tensorflow.python.ops import variable_scope +from tensorflow.python.ops import variables +from tensorflow.python.platform import test + +CHIEF = distribute_coordinator._TaskType.CHIEF +WORKER = distribute_coordinator._TaskType.WORKER +PS = distribute_coordinator._TaskType.PS +EVALUATOR = distribute_coordinator._TaskType.EVALUATOR + +NUM_WORKERS = 3 +NUM_PS = 2 + + +def _bytes_to_str(maybe_bytes): + if isinstance(maybe_bytes, six.string_types): + return maybe_bytes + else: + return str(maybe_bytes, "utf-8") + + +class DistributeCoordinatorTest(test.TestCase): + + @classmethod + def setUpClass(cls): + # We have to create a global in-process cluster because once an in-process + # tensorflow server is created, there is no way to terminate it. Please see + # multi_worker_test_base.py for more details. + cls._workers, cls._ps = test_util.create_local_cluster( + NUM_WORKERS, num_ps=NUM_PS) + cls._cluster_spec = { + WORKER: [_bytes_to_str(w.target) for w in cls._workers], + PS: [_bytes_to_str(ps.target) for ps in cls._ps] + } + + def setUp(self): + self._result_correct = 0 + self._lock = threading.Lock() + self._task_context = {} + + @contextlib.contextmanager + def _test_session(self, target): + config = config_pb2.ConfigProto(allow_soft_placement=True) + config.graph_options.optimizer_options.opt_level = -1 + with session.Session(graph=None, config=config, target=target) as sess: + yield sess + + def _in_graph_worker_fn(self): + context = distribute_coordinator.get_current_coordinator_context() + self.assertTrue(context is not None) + with self._test_session(target=context.master_target) as sess: + xs = [] + expected = 0.0 + for i in range(context.num_workers): + with ops.device("/job:worker/task:%d" % i): + x = variable_scope.get_variable("x_%d" % i, initializer=10.0) + x_add = x.assign_add(float(i)) + xs.append(x_add) + expected += i + 10.0 + + with ops.device("/job:worker/task:0"): + result = math_ops.add_n(xs) + + variables.global_variables_initializer().run() + result_value = sess.run(result) + self.assertEqual(result_value, expected) + if result_value == expected: + self._result_correct += 1 + + def testInGraph(self): + """Test it runs in-graph replicated training correctly.""" + distribute_coordinator.run_distribute_coordinator( + self._in_graph_worker_fn, + cluster_spec=self._cluster_spec, + between_graph=False) + self.assertEqual(self._result_correct, 1) + + def _between_graph_worker_fn(self): + context = distribute_coordinator.get_current_coordinator_context() + self.assertTrue(context is not None) + with self._test_session(target=context.master_target) as sess: + with ops.device("/job:ps/task:0"): + # TODO(yuefengz): investigate why not using resource variable will make + # the test flaky. + x = variable_scope.get_variable( + "x", initializer=10.0, use_resource=True) + with ops.device("/job:ps/task:1"): + y = variable_scope.get_variable( + "y", initializer=20.0, use_resource=True) + + x_add = x.assign_add(2.0) + y_sub = y.assign_sub(2.0) + train_op = control_flow_ops.group([x_add, y_sub]) + + if context.is_chief: + variables.global_variables_initializer().run() + + # Synchronize workers after initializaton. + context.wait_for_other_workers() + + sess.run(train_op) + + # Synchronize workers after one step to make sure they all have finished + # training. + context.wait_for_other_workers() + + x_val, y_val = sess.run([x, y]) + + self.assertEqual(x_val, 16.0) + self.assertEqual(y_val, 14.0) + if x_val == 16.0 and y_val == 14.0: + with self._lock: + self._result_correct += 1 + + def testBetweenGraph(self): + """Test it runs between-graph replicated training correctly.""" + distribute_coordinator.run_distribute_coordinator( + self._between_graph_worker_fn, + cluster_spec=self._cluster_spec, + between_graph=True) + + # Each finished worker will increment self._result_correct. + self.assertEqual(self._result_correct, NUM_WORKERS) + + def _dump_task_context(self): + """Dumps the propoerties of each coordinator context. + + It dumps the context properties to a dict mapping from task_type to a list + of tuples of master_target, num_workers, is_chief and distribute_mode, where + the list is indexed by the task_id. + """ + context = distribute_coordinator.get_current_coordinator_context() + self.assertTrue(context is not None) + task_type = str(context.task_type) + task_id = context.task_id or 0 + with self._lock: + if task_type not in self._task_context: + self._task_context[task_type] = [] + while len(self._task_context[task_type]) <= task_id: + self._task_context[task_type].append(None) + self._task_context[task_type][task_id] = (context.master_target, + context.num_workers, + context.is_chief, + context.distributed_mode) + + def testBetweenGraphContext(self): + # Dumps the task contexts to the self._task_context dict. + distribute_coordinator.run_distribute_coordinator( + self._dump_task_context, + cluster_spec=self._cluster_spec, + between_graph=True) + + # There is only one type of task and there three such tasks. + self.assertEqual(len(self._task_context), 1) + self.assertTrue(WORKER in self._task_context) + self.assertEqual(len(self._task_context[WORKER]), NUM_WORKERS) + + # Check whether each task has the right master_target, num_workers, is_chief + # and distributed_mode. + self.assertEqual( + self._task_context[WORKER][0], + (_bytes_to_str(self._workers[0].target), NUM_WORKERS, True, True)) + self.assertEqual( + self._task_context[WORKER][1], + (_bytes_to_str(self._workers[1].target), NUM_WORKERS, False, True)) + self.assertEqual( + self._task_context[WORKER][2], + (_bytes_to_str(self._workers[2].target), NUM_WORKERS, False, True)) + + def testInGraphContext(self): + # Dumps the task contexts to the self._task_context dict. + distribute_coordinator.run_distribute_coordinator( + self._dump_task_context, + cluster_spec=self._cluster_spec, + between_graph=False) + + # There is only a "None" task in the dumped task context. + self.assertEqual(len(self._task_context), 1) + self.assertTrue("None" in self._task_context) + self.assertEqual(len(self._task_context["None"]), 1) + + # Check whether each task has the right master_target, num_workers, is_chief + # and distributed_mode. + self.assertEqual( + self._task_context["None"][0], + (_bytes_to_str(self._workers[0].target), NUM_WORKERS, True, True)) + + def testLocalContext(self): + # Dumps the task contexts to the self._task_context dict. + distribute_coordinator.run_distribute_coordinator( + self._dump_task_context, cluster_spec=None, between_graph=True) + + # There is only a "None" task. + self.assertEqual(len(self._task_context), 1) + self.assertTrue("None" in self._task_context) + self.assertEqual(len(self._task_context["None"]), 1) + + # Check whether each task has the right master_target, num_workers, is_chief + # and distributed_mode. + self.assertEqual(self._task_context["None"][0], ("local", 0, True, False)) + + def testBetweenGraphContextWithChief(self): + # Adds a chief node, so there are NUM_WORKERS + 1 workers in total. + cluster_spec = copy.deepcopy(self._cluster_spec) + cluster_spec[CHIEF] = ["fake_chief"] + + # Dumps the task contexts to the self._task_context dict. + distribute_coordinator.run_distribute_coordinator( + self._dump_task_context, + cluster_spec=cluster_spec, + between_graph=True, + rpc_layer="grpc") + + # There are one CHIEF and three workers. + self.assertEqual(len(self._task_context), 2) + self.assertTrue(CHIEF in self._task_context) + self.assertTrue(WORKER in self._task_context) + self.assertEqual(len(self._task_context[CHIEF]), 1) + self.assertEqual(len(self._task_context[WORKER]), NUM_WORKERS) + + # Check whether each task has the right master_target, num_workers, is_chief + # and distributed_mode. + self.assertEqual(self._task_context[CHIEF][0], + ("grpc://fake_chief", 4, True, True)) + self.assertEqual(self._task_context[WORKER][0], + ("grpc://" + _bytes_to_str(self._workers[0].target), + NUM_WORKERS + 1, False, True)) + self.assertEqual(self._task_context[WORKER][1], + ("grpc://" + _bytes_to_str(self._workers[1].target), + NUM_WORKERS + 1, False, True)) + self.assertEqual(self._task_context[WORKER][2], + ("grpc://" + _bytes_to_str(self._workers[2].target), + NUM_WORKERS + 1, False, True)) + + def testInGraphContextWithEval(self): + # Adds a EVALUATOR job. + cluster_spec = copy.deepcopy(self._cluster_spec) + cluster_spec[EVALUATOR] = ["fake_evaluator"] + + # Dumps the task contexts to the self._task_context dict. + distribute_coordinator.run_distribute_coordinator( + self._dump_task_context, cluster_spec=cluster_spec, between_graph=False) + + # There are one "None" task and one EVALUATOR task. + self.assertEqual(len(self._task_context), 2) + self.assertTrue("None" in self._task_context) + self.assertTrue(EVALUATOR in self._task_context) + self.assertEqual(len(self._task_context["None"]), 1) + self.assertEqual(len(self._task_context[EVALUATOR]), 1) + + # Check whether each task has the right master_target, num_workers, is_chief + # and distributed_mode. + self.assertEqual(self._task_context["None"][0], + (_bytes_to_str(self._workers[0].target), 3, True, True)) + self.assertEqual(self._task_context[EVALUATOR][0], + ("fake_evaluator", 3, False, True)) + + +if __name__ == "__main__": + test.main() -- GitLab From 13a9453aa3c9deccb0bccd7805fb1c34004a5525 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 16:09:26 -0700 Subject: [PATCH 511/519] Remove references to std::string in MKL-related code. tensorflow::string is sometimes ::string and sometimes std::string, which makes code that uses both subtly dangerous. For example, FactoryKeyCreator::AddAsKey() has an overload for tensorflow::string but had many callsites passing a std::string, causing incorrect behavior on the google platform. PiperOrigin-RevId: 206389641 --- .../core/common_runtime/mkl_cpu_allocator.h | 1 - tensorflow/core/graph/mkl_graph_util.h | 1 - tensorflow/core/graph/mkl_layout_pass.cc | 1 - tensorflow/core/graph/mkl_layout_pass_test.cc | 1 - .../core/graph/mkl_tfconversion_pass.cc | 1 - .../core/graph/mkl_tfconversion_pass_test.cc | 1 - tensorflow/core/kernels/mkl_concat_op.cc | 8 +++---- .../core/kernels/mkl_conv_grad_filter_ops.cc | 9 ++++---- .../core/kernels/mkl_conv_grad_input_ops.cc | 9 ++++---- tensorflow/core/kernels/mkl_conv_ops.cc | 17 +++++++------- tensorflow/core/kernels/mkl_conv_ops.h | 1 - .../core/kernels/mkl_pooling_ops_common.h | 1 - tensorflow/core/util/mkl_util.h | 22 +++++++++---------- 13 files changed, 29 insertions(+), 44 deletions(-) diff --git a/tensorflow/core/common_runtime/mkl_cpu_allocator.h b/tensorflow/core/common_runtime/mkl_cpu_allocator.h index 29f702699f..94e10dbfa2 100644 --- a/tensorflow/core/common_runtime/mkl_cpu_allocator.h +++ b/tensorflow/core/common_runtime/mkl_cpu_allocator.h @@ -22,7 +22,6 @@ limitations under the License. #ifdef INTEL_MKL #include -#include #include "tensorflow/core/common_runtime/bfc_allocator.h" #include "tensorflow/core/common_runtime/visitable_allocator.h" #include "tensorflow/core/lib/strings/numbers.h" diff --git a/tensorflow/core/graph/mkl_graph_util.h b/tensorflow/core/graph/mkl_graph_util.h index 5f51d6083b..333bf761b0 100644 --- a/tensorflow/core/graph/mkl_graph_util.h +++ b/tensorflow/core/graph/mkl_graph_util.h @@ -17,7 +17,6 @@ limitations under the License. #define TENSORFLOW_CORE_GRAPH_MKL_GRAPH_UTIL_H_ #ifdef INTEL_MKL -#include #include "tensorflow/core/framework/op_kernel.h" namespace tensorflow { diff --git a/tensorflow/core/graph/mkl_layout_pass.cc b/tensorflow/core/graph/mkl_layout_pass.cc index b9667998d6..3e769b5303 100644 --- a/tensorflow/core/graph/mkl_layout_pass.cc +++ b/tensorflow/core/graph/mkl_layout_pass.cc @@ -22,7 +22,6 @@ limitations under the License. #include #include #include -#include #include #include #include diff --git a/tensorflow/core/graph/mkl_layout_pass_test.cc b/tensorflow/core/graph/mkl_layout_pass_test.cc index fc474c0dc8..f2bffa2113 100644 --- a/tensorflow/core/graph/mkl_layout_pass_test.cc +++ b/tensorflow/core/graph/mkl_layout_pass_test.cc @@ -19,7 +19,6 @@ limitations under the License. #include "tensorflow/core/graph/mkl_graph_util.h" #include -#include #include #include "tensorflow/core/framework/op.h" diff --git a/tensorflow/core/graph/mkl_tfconversion_pass.cc b/tensorflow/core/graph/mkl_tfconversion_pass.cc index e9ced4d2b6..aa39af637f 100644 --- a/tensorflow/core/graph/mkl_tfconversion_pass.cc +++ b/tensorflow/core/graph/mkl_tfconversion_pass.cc @@ -18,7 +18,6 @@ limitations under the License. #include #include #include -#include #include #include diff --git a/tensorflow/core/graph/mkl_tfconversion_pass_test.cc b/tensorflow/core/graph/mkl_tfconversion_pass_test.cc index bbdbe78bbd..ebcb6de551 100644 --- a/tensorflow/core/graph/mkl_tfconversion_pass_test.cc +++ b/tensorflow/core/graph/mkl_tfconversion_pass_test.cc @@ -19,7 +19,6 @@ limitations under the License. #include "tensorflow/core/graph/mkl_graph_util.h" #include -#include #include #include "tensorflow/core/framework/op.h" diff --git a/tensorflow/core/kernels/mkl_concat_op.cc b/tensorflow/core/kernels/mkl_concat_op.cc index 6f490cdc23..d8efb1be3e 100644 --- a/tensorflow/core/kernels/mkl_concat_op.cc +++ b/tensorflow/core/kernels/mkl_concat_op.cc @@ -308,11 +308,9 @@ class MklConcatOp : public OpKernel { } if (invoke_eigen) { - string msg = std::string("Invoking Eigen version of Concat. Reason:") + - (!is_concat_dim_channel - ? std::string("Concat dimension is not channel") - : std::string("Not all tensors are in Mkl layout")); - VLOG(1) << "_MklConcatOp: " << msg; + VLOG(1) << "_MklConcatOp: Invoking Eigen version of Concat. Reason:" + << (!is_concat_dim_channel ? "Concat dimension is not channel" + : "Not all tensors are in Mkl layout"); CallEigenVersion(context, input_tensors, input_shapes); return; } diff --git a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc index a370037d97..b73a119a88 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_filter_ops.cc @@ -328,9 +328,8 @@ class MklConv2DBwdFilterPrimitiveFactory : public MklPrimitiveFactory { return instance_; } - static std::string CreateKey( - const MklConvBwdFilterParams& convBwdFilterDims) { - std::string prefix = "conv2d_bwd_filter"; + static string CreateKey(const MklConvBwdFilterParams& convBwdFilterDims) { + string prefix = "conv2d_bwd_filter"; FactoryKeyCreator key_creator; key_creator.AddAsKey(prefix); key_creator.AddAsKey(convBwdFilterDims.src_dims); @@ -346,13 +345,13 @@ class MklConv2DBwdFilterPrimitiveFactory : public MklPrimitiveFactory { MklPrimitive* GetConv2dBwdFilter( const MklConvBwdFilterParams& convBwdFilterDims) { - std::string key = CreateKey(convBwdFilterDims); + string key = CreateKey(convBwdFilterDims); return this->GetOp(key); } void SetConv2dBwdFilter( const MklConvBwdFilterParams& convBwdFilterDims, MklPrimitive* op) { - std::string key = CreateKey(convBwdFilterDims); + string key = CreateKey(convBwdFilterDims); this->SetOp(key, op); } }; diff --git a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc index b0f7faaa1a..39498f1a80 100644 --- a/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_grad_input_ops.cc @@ -265,9 +265,8 @@ class MklConv2DBwdInputPrimitiveFactory : public MklPrimitiveFactory { return instance_; } - static std::string CreateKey( - const MklConvBwdInputParams& convBwdInputDims) { - std::string prefix = "conv2d_bwd_input"; + static string CreateKey(const MklConvBwdInputParams& convBwdInputDims) { + string prefix = "conv2d_bwd_input"; FactoryKeyCreator key_creator; key_creator.AddAsKey(prefix); key_creator.AddAsKey(convBwdInputDims.diff_src_dims); @@ -282,13 +281,13 @@ class MklConv2DBwdInputPrimitiveFactory : public MklPrimitiveFactory { MklPrimitive* GetConv2dBwdInput( const MklConvBwdInputParams& convBwdInputDims) { - std::string key = CreateKey(convBwdInputDims); + string key = CreateKey(convBwdInputDims); return this->GetOp(key); } void SetConv2dBwdInput( const MklConvBwdInputParams& convBwdInputDims, MklPrimitive *op) { - std::string key = CreateKey(convBwdInputDims); + string key = CreateKey(convBwdInputDims); this->SetOp(key, op); } }; diff --git a/tensorflow/core/kernels/mkl_conv_ops.cc b/tensorflow/core/kernels/mkl_conv_ops.cc index b568973220..62396eeb8b 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.cc +++ b/tensorflow/core/kernels/mkl_conv_ops.cc @@ -18,7 +18,6 @@ limitations under the License. #include #include -#include #include #include @@ -35,6 +34,7 @@ limitations under the License. #include "tensorflow/core/lib/gtl/array_slice.h" #include "tensorflow/core/lib/strings/numbers.h" #include "tensorflow/core/lib/strings/str_util.h" +#include "tensorflow/core/lib/strings/strcat.h" #include "tensorflow/core/platform/logging.h" #include "tensorflow/core/platform/macros.h" #include "tensorflow/core/util/padding.h" @@ -298,8 +298,8 @@ class MklConv2DFwdPrimitiveFactory : public MklPrimitiveFactory { return instance_; } - static std::string CreateKey(const MklConvFwdParams& convFwdDims) { - std::string prefix = "conv2d_fwd_"; + static string CreateKey(const MklConvFwdParams& convFwdDims) { + string prefix = "conv2d_fwd_"; FactoryKeyCreator key_creator; key_creator.AddAsKey(prefix); key_creator.AddAsKey(convFwdDims.src_dims); @@ -314,12 +314,12 @@ class MklConv2DFwdPrimitiveFactory : public MklPrimitiveFactory { } MklPrimitive* GetConv2DFwd(const MklConvFwdParams& convFwdDims) { - std::string key = CreateKey(convFwdDims); + string key = CreateKey(convFwdDims); return this->GetOp(key); } void SetConv2DFwd(const MklConvFwdParams& convFwdDims, MklPrimitive* op) { - std::string key = CreateKey(convFwdDims); + string key = CreateKey(convFwdDims); this->SetOp(key, op); } }; @@ -930,10 +930,9 @@ class MklConv2DOp : public OpKernel { conv2d_fwd->Execute(src_data, filter_data, dst_data); } } catch (mkldnn::error &e) { - string error_msg = "Status: " + std::to_string(e.status) + - ", message: " + std::string(e.message) + - ", in file " + std::string(__FILE__) + ":" + - std::to_string(__LINE__); + string error_msg = tensorflow::strings::StrCat( + "Status: ", e.status, ", message: ", string(e.message), ", in file ", + __FILE__, ":", __LINE__); OP_REQUIRES_OK(context, errors::Aborted("Operation received an exception:", error_msg)); } diff --git a/tensorflow/core/kernels/mkl_conv_ops.h b/tensorflow/core/kernels/mkl_conv_ops.h index 5e1a5001dc..3f154ff33b 100644 --- a/tensorflow/core/kernels/mkl_conv_ops.h +++ b/tensorflow/core/kernels/mkl_conv_ops.h @@ -17,7 +17,6 @@ limitations under the License. #define TENSORFLOW_CORE_KERNELS_MKL_CONV_OPS_H_ #include -#include #include #include diff --git a/tensorflow/core/kernels/mkl_pooling_ops_common.h b/tensorflow/core/kernels/mkl_pooling_ops_common.h index c0dfed7d7d..cb1eecb36a 100644 --- a/tensorflow/core/kernels/mkl_pooling_ops_common.h +++ b/tensorflow/core/kernels/mkl_pooling_ops_common.h @@ -17,7 +17,6 @@ limitations under the License. #define TENSORFLOW_CORE_KERNELS_MKL_POOLING_OPS_COMMON_H_ #ifdef INTEL_MKL -#include #include #include "tensorflow/core/util/mkl_util.h" #include "tensorflow/core/util/padding.h" diff --git a/tensorflow/core/util/mkl_util.h b/tensorflow/core/util/mkl_util.h index bb447e0393..566a42dbd5 100644 --- a/tensorflow/core/util/mkl_util.h +++ b/tensorflow/core/util/mkl_util.h @@ -17,7 +17,6 @@ limitations under the License. #define TENSORFLOW_CORE_UTIL_MKL_UTIL_H_ #ifdef INTEL_MKL -#include #include #include #include @@ -1896,16 +1895,17 @@ class MklPrimitiveFactory { MklPrimitiveFactory() {} ~MklPrimitiveFactory() {} - MklPrimitive* GetOp(const std::string& key) { + MklPrimitive* GetOp(const string& key) { auto stream_iter = MklPrimitiveFactory::GetHashMap().find(key); if (stream_iter == MklPrimitiveFactory::GetHashMap().end()) { return nullptr; } else { + CHECK(stream_iter->second != nullptr) << "nullptr present in map"; return stream_iter->second; } } - void SetOp(const std::string& key, MklPrimitive* op) { + void SetOp(const string& key, MklPrimitive* op) { auto stream_iter = MklPrimitiveFactory::GetHashMap().find(key); CHECK(stream_iter == MklPrimitiveFactory::GetHashMap().end()); @@ -1914,8 +1914,8 @@ class MklPrimitiveFactory { } private: - static inline std::unordered_map& GetHashMap() { - static thread_local std::unordered_map map_; + static inline std::unordered_map& GetHashMap() { + static thread_local std::unordered_map map_; return map_; } }; @@ -1943,9 +1943,7 @@ class FactoryKeyCreator { Append(StringPiece(buffer, sizeof(T))); } - std::string GetKey() { - return key_; - } + string GetKey() { return key_; } private: string key_; @@ -2020,8 +2018,8 @@ class MklReorderPrimitiveFactory : public MklPrimitiveFactory { MklReorderPrimitiveFactory() {}; ~MklReorderPrimitiveFactory() {}; - static std::string CreateKey(const memory* from, const memory* to) { - std::string prefix = "reorder"; + static string CreateKey(const memory* from, const memory* to) { + string prefix = "reorder"; FactoryKeyCreator key_creator; auto const &from_desc = from->get_primitive_desc().desc().data; auto const &to_desc = to->get_primitive_desc().desc().data; @@ -2038,12 +2036,12 @@ class MklReorderPrimitiveFactory : public MklPrimitiveFactory { } MklPrimitive* GetReorder(const memory* from, const memory* to) { - std::string key = CreateKey(from, to); + string key = CreateKey(from, to); return this->GetOp(key); } void SetReorder(const memory* from, const memory* to, MklPrimitive* op) { - std::string key = CreateKey(from, to); + string key = CreateKey(from, to); this->SetOp(key, op); } }; -- GitLab From 0cda579958a24c1947a086b4c9b3a1c374c3d1c6 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 17:17:07 -0700 Subject: [PATCH 512/519] Remove bytes prefix "b'" from tpu_cluster_resolver error messages PiperOrigin-RevId: 206397604 --- .../python/training/tpu_cluster_resolver.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) 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 8f521ffee4..f9dc3effd0 100644 --- a/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py +++ b/tensorflow/contrib/cluster_resolver/python/training/tpu_cluster_resolver.py @@ -259,11 +259,11 @@ class TPUClusterResolver(ClusterResolver): if 'state' in response and response['state'] != 'READY': raise RuntimeError('TPU "%s" is not yet ready; state: "%s"' % - (self._tpu, response['state'])) + (compat.as_text(self._tpu), response['state'])) if 'health' in response and response['health'] != 'HEALTHY': - raise RuntimeError('TPU "%s" is unhealthy: "%s"' % (self._tpu, - response['health'])) + raise RuntimeError('TPU "%s" is unhealthy: "%s"' % + (compat.as_text(self._tpu), response['health'])) if 'networkEndpoints' in response: worker_list = [ -- GitLab From 444fae503e93152caf9286686c5ca4cfcaf27b54 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 17:39:53 -0700 Subject: [PATCH 513/519] Introduce protobuf fields to support monitoring feature on cloud. PiperOrigin-RevId: 206399834 --- .../contrib/tpu/profiler/tpu_profiler.proto | 26 +++++++++++++++++++ 1 file changed, 26 insertions(+) diff --git a/tensorflow/contrib/tpu/profiler/tpu_profiler.proto b/tensorflow/contrib/tpu/profiler/tpu_profiler.proto index f0fca63db0..da4a95e045 100644 --- a/tensorflow/contrib/tpu/profiler/tpu_profiler.proto +++ b/tensorflow/contrib/tpu/profiler/tpu_profiler.proto @@ -11,6 +11,9 @@ service TPUProfiler { // Starts a profiling session, blocks until it completes, and returns data. rpc Profile(ProfileRequest) returns (ProfileResponse) { } + // Collects profiling data and returns user-friendly metrics. + rpc Monitor(MonitorRequest) returns (MonitorResponse) { + } } message ProfileOptions { @@ -104,3 +107,26 @@ message ProfileResponse { // next-field: 8 } + +message MonitorRequest { + // Duration for which to profile between each update. + uint64 duration_ms = 1; + + // Indicates the level at which we want to monitor. Currently, two levels are + // supported: + // Level 1: An ultra lightweight mode that captures only some utilization + // metrics. + // Level 2: More verbose than level 1. Collects utilization metrics, device + // information, step time information, etc. Do not use this option if the TPU + // host is being very heavily used. + int32 monitoring_level = 2; + + // next-field: 3 +} + +message MonitorResponse { + // Properly formatted string data that can be directly returned back to user. + string data = 1; + + // next-field: 2 +} -- GitLab From e56f30652248d83ffce0de048d0c59244b4aa9c4 Mon Sep 17 00:00:00 2001 From: Daryl Ng Date: Fri, 27 Jul 2018 17:49:39 -0700 Subject: [PATCH 514/519] Changing syntax of optimization_parameters.proto from proto2 to proto3. Since the lower and upper fields of ClippingLimits used non-zero defaults, they had to be converted to use the google.protobuf.FloatValue wrappers. PiperOrigin-RevId: 206400787 --- .../tpu/proto/optimization_parameters.proto | 114 +++++++++--------- 1 file changed, 58 insertions(+), 56 deletions(-) diff --git a/tensorflow/contrib/tpu/proto/optimization_parameters.proto b/tensorflow/contrib/tpu/proto/optimization_parameters.proto index 9150606f5e..2cc17d6d92 100644 --- a/tensorflow/contrib/tpu/proto/optimization_parameters.proto +++ b/tensorflow/contrib/tpu/proto/optimization_parameters.proto @@ -1,10 +1,12 @@ -syntax = "proto2"; +syntax = "proto3"; package tensorflow.tpu; +import "google/protobuf/wrappers.proto"; + message ClippingLimits { - optional float lower = 1 [default = -inf]; - optional float upper = 2 [default = inf]; + google.protobuf.FloatValue lower = 1; // -inf if not set + google.protobuf.FloatValue upper = 2; // +inf if not set } // Get the learning rate from a source that can change @@ -21,18 +23,18 @@ message LearningRate { } message AdagradParameters { - optional float initial_accumulator = 1 [default = 0.]; + float initial_accumulator = 1; } message StochasticGradientDescentParameters { } message FtrlParameters { - optional float l1 = 1 [default = 0.]; - optional float l2 = 2 [default = 0.]; - optional float lr_power = 3 [default = 0.]; - optional float initial_accum = 4 [default = 0.]; - optional float initial_linear = 5 [default = 0.]; + float l1 = 1; + float l2 = 2; + float lr_power = 3; + float initial_accum = 4; + float initial_linear = 5; } // The Adam optimizer does not implement hyper-parameter update; use the dynamic @@ -41,84 +43,84 @@ message FtrlParameters { // Here, t is the current timestep. // https://github.com/tensorflow/tensorflow/blob/ab51450c817674c8ff08a7ae4f8ac50cdc4bed8b/tensorflow/python/training/adam.py#L54 message AdamParameters { - optional float beta1 = 3 [default = 0.]; - optional float beta2 = 4 [default = 0.]; - optional float epsilon = 5 [default = 0.]; - optional float initial_m = 6 [default = 0.]; - optional float initial_v = 7 [default = 0.]; + float beta1 = 3; + float beta2 = 4; + float epsilon = 5; + float initial_m = 6; + float initial_v = 7; } message MomentumParameters { - optional float momentum = 1 [default = 0.]; - optional bool use_nesterov = 2 [default = false]; - optional float initial_accum = 3 [default = 0.]; + float momentum = 1; + bool use_nesterov = 2; + float initial_accum = 3; } message RmsPropParameters { - optional float rho = 1 [default = 0.]; - optional float momentum = 2 [default = 0.]; - optional float epsilon = 3 [default = 0.]; - optional float initial_ms = 4 [default = 0.]; - optional float initial_mom = 5 [default = 0.]; + float rho = 1; + float momentum = 2; + float epsilon = 3; + float initial_ms = 4; + float initial_mom = 5; } message CenteredRmsPropParameters { - optional float rho = 1 [default = 0.]; - optional float momentum = 2 [default = 0.]; - optional float epsilon = 3 [default = 0.]; - optional float initial_ms = 4 [default = 0.]; - optional float initial_mom = 5 [default = 0.]; - optional float initial_mg = 6 [default = 0.]; + float rho = 1; + float momentum = 2; + float epsilon = 3; + float initial_ms = 4; + float initial_mom = 5; + float initial_mg = 6; } message MdlAdagradLightParameters { - optional float l2 = 1; - optional float lr_power = 2; - optional float min_servable_mdl_benefit = 3; - optional float mdl_mix_in_margin = 4; - optional float mdl_benefit_rampup_coeff = 5; - optional float mdl_min_weight = 6; - optional float benefit_revisit_scale = 7; - optional float max_event_benefit = 8; - optional float max_total_benefit = 9; - optional float mdl_hard_limit = 10; - optional bool hard_limit_min_benefit = 11; - optional bool mdl_regularize = 12; - optional float initial_accumulator = 13; - optional float initial_weight = 14; - optional float initial_benefit = 15; + float l2 = 1; + float lr_power = 2; + float min_servable_mdl_benefit = 3; + float mdl_mix_in_margin = 4; + float mdl_benefit_rampup_coeff = 5; + float mdl_min_weight = 6; + float benefit_revisit_scale = 7; + float max_event_benefit = 8; + float max_total_benefit = 9; + float mdl_hard_limit = 10; + bool hard_limit_min_benefit = 11; + bool mdl_regularize = 12; + float initial_accumulator = 13; + float initial_weight = 14; + float initial_benefit = 15; } message AdadeltaParameters { - optional float rho = 1; - optional float epsilon = 2; - optional float initial_accumulator = 3 [default = 0.]; - optional float initial_update = 4 [default = 0.]; + float rho = 1; + float epsilon = 2; + float initial_accumulator = 3; + float initial_update = 4; } message ProximalAdagradParameters { - optional float l1 = 1; - optional float l2 = 2; - optional float initial_accumulator = 3; + float l1 = 1; + float l2 = 2; + float initial_accumulator = 3; } message OptimizationParameters { // Learning rate used for updating the embedding layer parameters. - optional LearningRate learning_rate = 13; + LearningRate learning_rate = 13; reserved 1; // Old learning rate tag. // Limits to which to clip the weight values after the backward pass; not // present means no limits are applied. - optional ClippingLimits clipping_limits = 2; + ClippingLimits clipping_limits = 2; // Limits to which to clip the backward pass gradient before using it for // updates; not present means no limits are applied. - optional ClippingLimits gradient_clipping_limits = 7; + ClippingLimits gradient_clipping_limits = 7; // Whether to use gradient accumulation (do two passes over the input // gradients: one to accumulate them into a temporary array and another to // apply them using the actual optimization algorithm). - optional bool use_gradient_accumulation = 15 [default = false]; + bool use_gradient_accumulation = 15; // Optimization algorithm parameters; which field is selected determines which // algorithm to use. @@ -140,7 +142,7 @@ message OptimizationParameters { // value vector and any extra accumulators, etc.). message StateVariableSpecification { // Parameter name for the state variable. - optional string name = 1; + string name = 1; // A normal state variable that should be saved and restored in checkpoints // and used as an input or output to non-debug TensorFlow ops. @@ -151,7 +153,7 @@ message StateVariableSpecification { // from users (used for intermediate gradients being accumulated, for // example). message FillWithConstant { - optional double initial_value = 1; + double initial_value = 1; } // Usage type of this state variable. -- GitLab From 3aa0bd836e295674a5d071861c98f5f3c2c5e2cf Mon Sep 17 00:00:00 2001 From: Yuefeng Zhou Date: Fri, 27 Jul 2018 17:55:09 -0700 Subject: [PATCH 515/519] Fix build failure. PiperOrigin-RevId: 206401196 --- tensorflow/python/distribute/BUILD | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/tensorflow/python/distribute/BUILD b/tensorflow/python/distribute/BUILD index a29043d8b8..2bd0b4320a 100644 --- a/tensorflow/python/distribute/BUILD +++ b/tensorflow/python/distribute/BUILD @@ -1,11 +1,13 @@ -licenses(["notice"]) # Apache 2.0 - package( - default_visibility = [ - "//tensorflow:internal", - ], + default_visibility = ["//tensorflow:internal"], ) +licenses(["notice"]) # Apache 2.0 + +exports_files(["LICENSE"]) + +load("//tensorflow:tensorflow.bzl", "py_test") + py_library( name = "distribute_coordinator", srcs = [ @@ -23,6 +25,7 @@ py_test( size = "small", srcs = ["distribute_coordinator_test.py"], srcs_version = "PY2AND3", + tags = ["no_pip"], deps = [ ":distribute_coordinator", "//tensorflow/core:protos_all_py", -- GitLab From 919e59cc49ada3d529e080ee8eebaaec7f621844 Mon Sep 17 00:00:00 2001 From: Pavithra Vijay Date: Fri, 27 Jul 2018 19:27:01 -0700 Subject: [PATCH 516/519] De-dup and clean get metric name and function code between graph and eager mode Also allow usage of string 'crossentropy'/'ce' for cross entropy metrics in eager mode PiperOrigin-RevId: 206407222 --- tensorflow/python/keras/engine/training.py | 48 +++++-------------- .../python/keras/engine/training_eager.py | 38 ++------------- .../python/keras/engine/training_utils.py | 39 +++++++++++++-- 3 files changed, 52 insertions(+), 73 deletions(-) diff --git a/tensorflow/python/keras/engine/training.py b/tensorflow/python/keras/engine/training.py index bfcb0e1d43..0fe14e99e0 100644 --- a/tensorflow/python/keras/engine/training.py +++ b/tensorflow/python/keras/engine/training.py @@ -29,7 +29,6 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend as K from tensorflow.python.keras import losses -from tensorflow.python.keras import metrics as metrics_module from tensorflow.python.keras import optimizers from tensorflow.python.keras.engine import base_layer from tensorflow.python.keras.engine import training_arrays @@ -458,43 +457,21 @@ class Model(Network): weights = sample_weights[i] output_metrics = nested_metrics[i] output_weighted_metrics = nested_weighted_metrics[i] + output_shape = self.outputs[i].get_shape().as_list() + loss_fn = self.loss_functions[i] - def handle_metrics(metrics, weights=None): + def handle_metrics(metrics, output_shape, loss_fn, weights=None): + """Invokes metric functions for the output.""" for metric in metrics: - if metric in ('accuracy', 'acc', 'crossentropy', 'ce'): - # custom handling of accuracy/crossentropy - # (because of class mode duality) - output_shape = self.outputs[i].get_shape().as_list() - if (output_shape[-1] == 1 or - self.loss_functions[i] == losses.binary_crossentropy): - # case: binary accuracy/crossentropy - if metric in ('accuracy', 'acc'): - metric_fn = metrics_module.binary_accuracy - elif metric in ('crossentropy', 'ce'): - metric_fn = metrics_module.binary_crossentropy - elif self.loss_functions[ - i] == losses.sparse_categorical_crossentropy: - # case: categorical accuracy/crossentropy with sparse targets - if metric in ('accuracy', 'acc'): - metric_fn = metrics_module.sparse_categorical_accuracy - elif metric in ('crossentropy', 'ce'): - metric_fn = metrics_module.sparse_categorical_crossentropy - else: - # case: categorical accuracy/crossentropy - if metric in ('accuracy', 'acc'): - metric_fn = metrics_module.categorical_accuracy - elif metric in ('crossentropy', 'ce'): - metric_fn = metrics_module.categorical_crossentropy - weighted_metric_fn = training_utils.weighted_masked_objective( - metric_fn) - else: - metric_fn = metrics_module.get(metric) - weighted_metric_fn = training_utils.weighted_masked_objective( - metric_fn) - metric_name = training_utils.get_base_metric_name( + metric_fn = training_utils.get_metric_function( + metric, output_shape=output_shape, loss_fn=loss_fn) + metric_name = training_utils.get_metric_name( metric, weighted=weights is not None) + with K.name_scope(metric_name): + weighted_metric_fn = training_utils.weighted_masked_objective( + metric_fn) metric_result = weighted_metric_fn( y_true, y_pred, weights=weights, mask=masks[i]) @@ -508,8 +485,9 @@ class Model(Network): self.stateful_metric_functions.append(metric_fn) self.metrics_updates += metric_fn.updates - handle_metrics(output_metrics) - handle_metrics(output_weighted_metrics, weights=weights) + handle_metrics(output_metrics, output_shape, loss_fn) + handle_metrics( + output_weighted_metrics, output_shape, loss_fn, weights=weights) # Prepare gradient updates and state updates. self.total_loss = total_loss diff --git a/tensorflow/python/keras/engine/training_eager.py b/tensorflow/python/keras/engine/training_eager.py index 397de42985..b1149ab5b5 100644 --- a/tensorflow/python/keras/engine/training_eager.py +++ b/tensorflow/python/keras/engine/training_eager.py @@ -30,35 +30,11 @@ from tensorflow.python.framework import ops from tensorflow.python.framework import tensor_util from tensorflow.python.keras import backend from tensorflow.python.keras import callbacks as cbks -from tensorflow.python.keras import losses -from tensorflow.python.keras import metrics as metrics_module from tensorflow.python.keras.engine import training_utils from tensorflow.python.keras.utils import generic_utils from tensorflow.python.platform import tf_logging as logging -def _get_metrics_info(metric, internal_output_shapes=None, loss_func=None): - if metric == 'accuracy' or metric == 'acc': - # custom handling of accuracy - # (because of class mode duality) - output_shape = internal_output_shapes - if output_shape[-1] == 1 or loss_func == losses.binary_crossentropy: - # case: binary accuracy - acc_fn = metrics_module.binary_accuracy - elif loss_func == losses.sparse_categorical_crossentropy: - # case: categorical accuracy with sparse targets - acc_fn = metrics_module.sparse_categorical_accuracy - else: - acc_fn = metrics_module.categorical_accuracy - - metric_name = 'acc' - return metric_name, acc_fn - else: - metric_fn = metrics_module.get(metric) - metric_name = metric_fn.__name__ - return metric_name, metric_fn - - def _eager_loss_fn(outputs, targets, loss_fn, output_name): with backend.name_scope(output_name + '_loss'): loss = loss_fn(targets, outputs) @@ -74,9 +50,8 @@ def _eager_metrics_fn(model, outputs, targets): targets: The predictions or targets of the given model. Returns: - Returns the metric names and metric results for each output of the model. + Returns the metric results for each output of the model. """ - metric_names = [] metric_results = [] if not isinstance(outputs, list): outputs = [outputs] @@ -87,18 +62,15 @@ def _eager_metrics_fn(model, outputs, targets): for i in range(len(model.outputs)): output_metrics = model.nested_metrics[i] for nested_output_metric in output_metrics: - metric_name, metric_fn = _get_metrics_info( + metric_fn = training_utils.get_metric_function( nested_output_metric, backend.int_shape(model.outputs[i]), model.loss_functions[i]) - - if len(model.output_names) > 1: - metric_name = model.output_names[i] + '_' + metric_name - if metric_name not in model.metrics_names: - model.metrics_names.append(metric_name) + # weighted metrics are not supported in eager mode + metric_name = training_utils.get_metric_name( + nested_output_metric, weighted=False) with backend.name_scope(metric_name): metric_result = metric_fn(targets[i], outputs[i]) - metric_names.append(metric_name) metric_results.append(backend.mean(metric_result)) return metric_results diff --git a/tensorflow/python/keras/engine/training_utils.py b/tensorflow/python/keras/engine/training_utils.py index 21495fd0bd..f2cd9c89da 100644 --- a/tensorflow/python/keras/engine/training_utils.py +++ b/tensorflow/python/keras/engine/training_utils.py @@ -702,17 +702,16 @@ def populate_metric_names(model): for i in range(len(model.outputs)): metrics = model.nested_metrics[i] for metric in metrics: - base_metric_name = get_base_metric_name(metric) + base_metric_name = get_metric_name(metric) add_metric_name(model, base_metric_name, i) -def get_base_metric_name(metric, weighted=False): - """Returns the metric name given the metric function. +def get_metric_name(metric, weighted=False): + """Returns the metric name corresponding to the given metric input. Arguments: metric: Metric function name or reference. - weighted: Boolean indicating if the metric for which we are adding - names is weighted. + weighted: Boolean indicating if the given metric is weighted. Returns: a metric name. @@ -736,6 +735,36 @@ def get_base_metric_name(metric, weighted=False): return metric_name +def get_metric_function(metric, output_shape=None, loss_fn=None): + """Returns the metric function corresponding to the given metric input. + + Arguments: + metric: Metric function name or reference. + output_shape: The shape of the output that this metric + will be calculated for. + loss_fn: The loss function used. + + Returns: + The metric function. + """ + if metric in ['accuracy', 'acc']: + if output_shape[-1] == 1 or loss_fn == losses.binary_crossentropy: + return metrics_module.binary_accuracy # case: binary accuracy + elif loss_fn == losses.sparse_categorical_crossentropy: + # case: categorical accuracy with sparse targets + return metrics_module.sparse_categorical_accuracy + return metrics_module.categorical_accuracy # case: categorical accuracy + elif metric in ['crossentropy', 'ce']: + if output_shape[-1] == 1 or loss_fn == losses.binary_crossentropy: + return metrics_module.binary_crossentropy # case: binary cross-entropy + elif loss_fn == losses.sparse_categorical_crossentropy: + # case: categorical cross-entropy with sparse targets + return metrics_module.sparse_categorical_crossentropy + # case: categorical cross-entropy + return metrics_module.categorical_crossentropy + return metrics_module.get(metric) + + def add_metric_name(model, metric_name, index): """Makes the metric name unique and adds it to the model's metric name list. -- GitLab From e3095dc262bfdda08d01fce105680515a3d1a7f4 Mon Sep 17 00:00:00 2001 From: "A. Unique TensorFlower" Date: Fri, 27 Jul 2018 21:17:43 -0700 Subject: [PATCH 517/519] Create a save_model and load_model util to support saving keras.Model to/from checkpoint. Currently, the model topology is still loaded from json (placed under saved_model/assets). Later, we will load from saved_model.pb. PiperOrigin-RevId: 206412614 --- tensorflow/contrib/saved_model/BUILD | 29 +++ tensorflow/contrib/saved_model/__init__.py | 3 +- .../python/saved_model/__init__.py | 1 + .../python/saved_model/keras_saved_model.py | 108 ++++++++++ .../saved_model/keras_saved_model_test.py | 201 ++++++++++++++++++ tensorflow/python/saved_model/constants.py | 6 +- 6 files changed, 345 insertions(+), 3 deletions(-) create mode 100644 tensorflow/contrib/saved_model/python/saved_model/keras_saved_model.py create mode 100644 tensorflow/contrib/saved_model/python/saved_model/keras_saved_model_test.py diff --git a/tensorflow/contrib/saved_model/BUILD b/tensorflow/contrib/saved_model/BUILD index 26fd4e2023..fbb50befdf 100644 --- a/tensorflow/contrib/saved_model/BUILD +++ b/tensorflow/contrib/saved_model/BUILD @@ -93,3 +93,32 @@ py_test( "//tensorflow/python/saved_model:utils", ], ) + +py_library( + name = "keras_saved_model", + srcs = ["python/saved_model/keras_saved_model.py"], + srcs_version = "PY2AND3", + tags = ["no_windows"], + visibility = ["//visibility:public"], + deps = [ + "//tensorflow/python:lib", + "//tensorflow/python:util", + "//tensorflow/python/keras:engine", + "//tensorflow/python/saved_model:constants", + ], +) + +py_test( + name = "keras_saved_model_test", + size = "small", + srcs = ["python/saved_model/keras_saved_model_test.py"], + srcs_version = "PY2AND3", + tags = ["no_windows"], + deps = [ + ":saved_model_py", + "//tensorflow/python:client_testlib", + "//tensorflow/python:training", + "//tensorflow/python/keras", + "//third_party/py/numpy", + ], +) diff --git a/tensorflow/contrib/saved_model/__init__.py b/tensorflow/contrib/saved_model/__init__.py index b4f27a055d..95e1a8967b 100644 --- a/tensorflow/contrib/saved_model/__init__.py +++ b/tensorflow/contrib/saved_model/__init__.py @@ -24,11 +24,12 @@ from __future__ import division from __future__ import print_function # pylint: disable=unused-import,wildcard-import,line-too-long +from tensorflow.contrib.saved_model.python.saved_model.keras_saved_model import * from tensorflow.contrib.saved_model.python.saved_model.signature_def_utils import * # pylint: enable=unused-import,widcard-import,line-too-long from tensorflow.python.util.all_util import remove_undocumented -_allowed_symbols = ["get_signature_def_by_key"] +_allowed_symbols = ["get_signature_def_by_key", "load_model", "save_model"] remove_undocumented(__name__, _allowed_symbols) diff --git a/tensorflow/contrib/saved_model/python/saved_model/__init__.py b/tensorflow/contrib/saved_model/python/saved_model/__init__.py index 7b91622b61..e3b76bb6f3 100644 --- a/tensorflow/contrib/saved_model/python/saved_model/__init__.py +++ b/tensorflow/contrib/saved_model/python/saved_model/__init__.py @@ -24,5 +24,6 @@ from __future__ import division from __future__ import print_function # pylint: disable=wildcard-import +from tensorflow.contrib.saved_model.python.saved_model import keras_saved_model from tensorflow.contrib.saved_model.python.saved_model import signature_def_utils # pylint: enable=wildcard-import diff --git a/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model.py b/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model.py new file mode 100644 index 0000000000..e2a969f053 --- /dev/null +++ b/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model.py @@ -0,0 +1,108 @@ +# 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 +"""Utility functions to save/load keras Model to/from SavedModel.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os + +from tensorflow.python.keras.models import model_from_json +from tensorflow.python.lib.io import file_io +from tensorflow.python.saved_model import constants +from tensorflow.python.util import compat + + +def save_model(model, saved_model_path): + """Save a `tf.keras.Model` into Tensorflow SavedModel format. + + `save_model` generates such files/folders under the `saved_model_path` folder: + 1) an asset folder containing the json string of the model's + configuration(topology). + 2) a checkpoint containing the model weights. + + Note that subclassed models can not be saved via this function, unless you + provide an implementation for get_config() and from_config(). + Also note that `tf.keras.optimizers.Optimizer` instances can not currently be + saved to checkpoints. Use optimizers from `tf.train`. + + Args: + model: A `tf.keras.Model` to be saved. + saved_model_path: a string specifying the path to the SavedModel directory. + + Raises: + NotImplementedError: If the passed in model is a subclassed model. + """ + if not model._is_graph_network: + raise NotImplementedError + + # save model configuration as a json string under assets folder. + model_json = model.to_json() + assets_destination_dir = os.path.join( + compat.as_bytes(saved_model_path), + compat.as_bytes(constants.ASSETS_DIRECTORY)) + + if not file_io.file_exists(assets_destination_dir): + file_io.recursive_create_dir(assets_destination_dir) + + model_json_filepath = os.path.join( + compat.as_bytes(assets_destination_dir), + compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON)) + file_io.write_string_to_file(model_json_filepath, model_json) + + # save model weights in checkpoint format. + checkpoint_destination_dir = os.path.join( + compat.as_bytes(saved_model_path), + compat.as_bytes(constants.VARIABLES_DIRECTORY)) + + if not file_io.file_exists(checkpoint_destination_dir): + file_io.recursive_create_dir(checkpoint_destination_dir) + + checkpoint_prefix = os.path.join( + compat.as_text(checkpoint_destination_dir), + compat.as_text(constants.VARIABLES_FILENAME)) + model.save_weights(checkpoint_prefix, save_format='tf', overwrite=True) + + +def load_model(saved_model_path): + """Load a keras.Model from SavedModel. + + load_model reinstantiates model state by: + 1) loading model topology from json (this will eventually come + from metagraph). + 2) loading model weights from checkpoint. + + Args: + saved_model_path: a string specifying the path to an existing SavedModel. + + Returns: + a keras.Model instance. + """ + # restore model topology from json string + model_json_filepath = os.path.join( + compat.as_bytes(saved_model_path), + compat.as_bytes(constants.ASSETS_DIRECTORY), + compat.as_bytes(constants.SAVED_MODEL_FILENAME_JSON)) + model_json = file_io.read_file_to_string(model_json_filepath) + model = model_from_json(model_json) + + # restore model weights + checkpoint_prefix = os.path.join( + compat.as_text(saved_model_path), + compat.as_text(constants.VARIABLES_DIRECTORY), + compat.as_text(constants.VARIABLES_FILENAME)) + model.load_weights(checkpoint_prefix) + return model diff --git a/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model_test.py b/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model_test.py new file mode 100644 index 0000000000..107ae1b07b --- /dev/null +++ b/tensorflow/contrib/saved_model/python/saved_model/keras_saved_model_test.py @@ -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. +# ============================================================================== +# pylint: disable=protected-access +"""Tests for saving/loading function for keras Model.""" +from __future__ import absolute_import +from __future__ import division +from __future__ import print_function + +import os +import shutil +import numpy as np + +from tensorflow.contrib.saved_model.python.saved_model import keras_saved_model +from tensorflow.python import keras +from tensorflow.python.framework import test_util +from tensorflow.python.keras.engine import training +from tensorflow.python.platform import test +from tensorflow.python.training import training as training_module + + +class TestModelSavingandLoading(test.TestCase): + + def test_saving_sequential_model(self): + 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) + + ref_y = model.predict(x) + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + temp_saved_model = os.path.join(temp_dir, 'saved_model') + keras_saved_model.save_model(model, temp_saved_model) + + loaded_model = keras_saved_model.load_model(temp_saved_model) + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + @test_util.run_in_graph_and_eager_modes + def test_saving_sequential_model_without_compile(self): + 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))) + + x = np.random.random((1, 3)) + ref_y = model.predict(x) + + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + temp_saved_model = os.path.join(temp_dir, 'saved_model') + keras_saved_model.save_model(model, temp_saved_model) + loaded_model = keras_saved_model.load_model(temp_saved_model) + + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + def test_saving_functional_model(self): + 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) + + ref_y = model.predict(x) + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + temp_saved_model = os.path.join(temp_dir, 'saved_model') + keras_saved_model.save_model(model, temp_saved_model) + loaded_model = keras_saved_model.load_model(temp_saved_model) + + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + @test_util.run_in_graph_and_eager_modes + def test_saving_functional_model_without_compile(self): + 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) + + x = np.random.random((1, 3)) + y = np.random.random((1, 3)) + + ref_y = model.predict(x) + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + temp_saved_model = os.path.join(temp_dir, 'saved_model') + keras_saved_model.save_model(model, temp_saved_model) + loaded_model = keras_saved_model.load_model(temp_saved_model) + + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + @test_util.run_in_graph_and_eager_modes + def test_saving_with_tf_optimizer(self): + 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.RMSPropOptimizer(0.1), + metrics=['acc']) + + x = np.random.random((1, 3)) + y = np.random.random((1, 3)) + model.train_on_batch(x, y) + + ref_y = model.predict(x) + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + + temp_saved_model = os.path.join(temp_dir, 'saved_model') + keras_saved_model.save_model(model, temp_saved_model) + loaded_model = keras_saved_model.load_model(temp_saved_model) + loaded_model.compile( + loss='mse', + optimizer=training_module.RMSPropOptimizer(0.1), + metrics=['acc']) + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, 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)) + + ref_loss = model.train_on_batch(x, y) + loss = loaded_model.train_on_batch(x, y) + self.assertAllClose(ref_loss, loss, atol=1e-05) + + ref_y = model.predict(x) + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + # test saving/loading again + keras_saved_model.save_model(loaded_model, temp_saved_model) + loaded_model = keras_saved_model.load_model(temp_saved_model) + y = loaded_model.predict(x) + self.assertAllClose(ref_y, y, atol=1e-05) + + def test_saving_subclassed_model_raise_error(self): + # For now, saving subclassed model should raise an error. It should be + # avoided later with loading from SavedModel.pb. + + class SubclassedModel(training.Model): + + def __init__(self): + super(SubclassedModel, self).__init__() + self.layer1 = keras.layers.Dense(3) + self.layer2 = keras.layers.Dense(1) + + def call(self, inp): + return self.layer2(self.layer1(inp)) + + model = SubclassedModel() + temp_dir = self.get_temp_dir() + self.addCleanup(shutil.rmtree, temp_dir) + temp_saved_model = os.path.join(temp_dir, 'saved_model') + with self.assertRaises(NotImplementedError): + keras_saved_model.save_model(model, temp_saved_model) + + +if __name__ == '__main__': + test.main() diff --git a/tensorflow/python/saved_model/constants.py b/tensorflow/python/saved_model/constants.py index 61c6ffbd0d..cb251f08bb 100644 --- a/tensorflow/python/saved_model/constants.py +++ b/tensorflow/python/saved_model/constants.py @@ -60,6 +60,10 @@ SAVED_MODEL_FILENAME_PBTXT = "saved_model.pbtxt" tf_export("saved_model.constants.SAVED_MODEL_FILENAME_PBTXT").export_constant( __name__, "SAVED_MODEL_FILENAME_PBTXT") +# File name for json format of SavedModel. +# Not exported while keras_saved_model is in contrib. +SAVED_MODEL_FILENAME_JSON = "saved_model.json" + # Subdirectory name containing the variables/checkpoint files. VARIABLES_DIRECTORY = "variables" tf_export("saved_model.constants.VARIABLES_DIRECTORY").export_constant( @@ -69,5 +73,3 @@ tf_export("saved_model.constants.VARIABLES_DIRECTORY").export_constant( VARIABLES_FILENAME = "variables" tf_export("saved_model.constants.VARIABLES_FILENAME").export_constant( __name__, "VARIABLES_FILENAME") - - -- GitLab From 2ee2fee11391952ee9e73c66ee99f265d924e15a Mon Sep 17 00:00:00 2001 From: Xuechen Li Date: Sat, 28 Jul 2018 17:22:03 -0700 Subject: [PATCH 518/519] Add batch norm updates for estimator. PiperOrigin-RevId: 206459313 --- .../contrib/eager/python/examples/revnet/main_estimator.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py b/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py index df25b5066f..a21b573c06 100644 --- a/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py +++ b/tensorflow/contrib/eager/python/examples/revnet/main_estimator.py @@ -55,8 +55,9 @@ def model_fn(features, labels, mode, params): learning_rate, momentum=config.momentum) logits, saved_hidden = model(inputs, training=True) grads, loss = model.compute_gradients(saved_hidden, labels, training=True) - train_op = optimizer.apply_gradients( - zip(grads, model.trainable_variables), global_step=global_step) + with tf.control_dependencies(model.get_updates_for(inputs)): + train_op = optimizer.apply_gradients( + zip(grads, model.trainable_variables), global_step=global_step) return tf.estimator.EstimatorSpec(mode=mode, loss=loss, train_op=train_op) else: -- GitLab From cde1c8e835f7fdfbb3ab8a4c8a01edb3a7e569aa Mon Sep 17 00:00:00 2001 From: Gunhan Gulsoy Date: Sun, 29 Jul 2018 00:11:42 -0700 Subject: [PATCH 519/519] Add a new kernels option to bazel rules to enable adding dynamic kernel dependencies. PiperOrigin-RevId: 206473913 --- tensorflow/BUILD | 8 +++ .../platform/default/build_config_root.bzl | 6 +++ tensorflow/tensorflow.bzl | 53 ++++++++++++++++--- 3 files changed, 60 insertions(+), 7 deletions(-) diff --git a/tensorflow/BUILD b/tensorflow/BUILD index c9d67021e1..60db234c9c 100644 --- a/tensorflow/BUILD +++ b/tensorflow/BUILD @@ -381,6 +381,14 @@ config_setting( }, ) +# Setting to use when loading kernels dynamically +config_setting( + name = "dynamic_loaded_kernels", + define_values = { + "dynamic_loaded_kernels": "true", + }, +) + config_setting( name = "using_cuda_nvcc", define_values = { diff --git a/tensorflow/core/platform/default/build_config_root.bzl b/tensorflow/core/platform/default/build_config_root.bzl index 09029a4b25..3a012c23fd 100644 --- a/tensorflow/core/platform/default/build_config_root.bzl +++ b/tensorflow/core/platform/default/build_config_root.bzl @@ -58,3 +58,9 @@ def if_static(extra_deps, otherwise=[]): str(Label("//tensorflow:framework_shared_object")): otherwise, "//conditions:default": extra_deps, }) + +def if_dynamic_kernels(extra_deps, otherwise=[]): + return select({ + str(Label("//tensorflow:dynamic_loaded_kernels")): extra_deps, + "//conditions:default": otherwise, + }) diff --git a/tensorflow/tensorflow.bzl b/tensorflow/tensorflow.bzl index f8e55abe79..58282ec1c7 100644 --- a/tensorflow/tensorflow.bzl +++ b/tensorflow/tensorflow.bzl @@ -9,6 +9,7 @@ load( "tf_additional_grpc_deps_py", "tf_additional_xla_deps_py", "if_static", + "if_dynamic_kernels", ) load( "@local_config_tensorrt//:build_defs.bzl", @@ -318,18 +319,36 @@ def tf_binary_additional_srcs(): clean_dep("//tensorflow:libtensorflow_framework.so"), ]) + +# Helper functions to add kernel dependencies to tf binaries when using dynamic +# kernel linking. +def tf_binary_dynamic_kernel_dsos(kernels): + return if_dynamic_kernels( + extra_deps=["libtfkernel_%s.so" % clean_dep(k) for k in kernels], + otherwise=[]) + +# Helper functions to add kernel dependencies to tf binaries when using static +# kernel linking. +def tf_binary_dynamic_kernel_deps(kernels): + return if_dynamic_kernels( + extra_deps=[], + otherwise=kernels) + def tf_cc_shared_object( name, srcs=[], deps=[], + data=[], linkopts=[], framework_so=tf_binary_additional_srcs(), + kernels=[], **kwargs): native.cc_binary( name=name, srcs=srcs + framework_so, - deps=deps, + deps=deps + tf_binary_dynamic_kernel_deps(kernels), linkshared = 1, + data = data + tf_binary_dynamic_kernel_dsos(kernels), linkopts=linkopts + _rpath_linkopts(name) + select({ clean_dep("//tensorflow:darwin"): [ "-Wl,-install_name,@rpath/" + name.split("/")[-1], @@ -353,18 +372,21 @@ register_extension_info( def tf_cc_binary(name, srcs=[], deps=[], + data=[], linkopts=[], copts=tf_copts(), + kernels=[], **kwargs): native.cc_binary( name=name, copts=copts, srcs=srcs + tf_binary_additional_srcs(), - deps=deps + if_mkl( + deps=deps + tf_binary_dynamic_kernel_deps(kernels) + if_mkl( [ "//third_party/mkl:intel_binary_blob", ], ), + data=data + tf_binary_dynamic_kernel_dsos(kernels), linkopts=linkopts + _rpath_linkopts(name), **kwargs) @@ -652,11 +674,13 @@ def tf_gen_op_wrapper_py(name, def tf_cc_test(name, srcs, deps, + data=[], linkstatic=0, extra_copts=[], suffix="", linkopts=[], nocopts=None, + kernels=[], **kwargs): native.cc_test( name="%s%s" % (name, suffix), @@ -676,11 +700,12 @@ def tf_cc_test(name, "-lm" ], }) + linkopts + _rpath_linkopts(name), - deps=deps + if_mkl( + deps=deps + tf_binary_dynamic_kernel_deps(kernels) + if_mkl( [ "//third_party/mkl:intel_binary_blob", ], ), + data=data + tf_binary_dynamic_kernel_dsos(kernels), # Nested select() statements seem not to be supported when passed to # linkstatic, and we already have a cuda select() passed in to this # function. @@ -781,6 +806,7 @@ def tf_cuda_only_cc_test(name, size="medium", linkstatic=0, args=[], + kernels=[], linkopts=[]): native.cc_test( name="%s%s" % (name, "_gpu"), @@ -788,8 +814,8 @@ def tf_cuda_only_cc_test(name, size=size, args=args, copts= _cuda_copts() + tf_copts(), - data=data, - deps=deps + if_cuda([ + data=data + tf_binary_dynamic_kernel_dsos(kernels), + deps=deps + tf_binary_dynamic_kernel_deps(kernels) + if_cuda([ clean_dep("//tensorflow/core:cuda"), clean_dep("//tensorflow/core:gpu_lib")]), linkopts=if_not_windows(["-lpthread", "-lm"]) + linkopts + _rpath_linkopts(name), @@ -832,9 +858,11 @@ def tf_cc_tests(srcs, def tf_cc_test_mkl(srcs, deps, name="", + data=[], linkstatic=0, tags=[], size="medium", + kernels=[], args=None): # -fno-exceptions in nocopts breaks compilation if header modules are enabled. disable_header_modules = ["-use_header_modules"] @@ -855,11 +883,12 @@ def tf_cc_test_mkl(srcs, "-lm" ], }) + _rpath_linkopts(src_to_test_name(src)), - deps=deps + if_mkl( + deps=deps + tf_binary_dynamic_kernel_deps(kernels) + if_mkl( [ "//third_party/mkl:intel_binary_blob", ], ), + data=data + tf_binary_dynamic_kernel_dsos(kernels), linkstatic=linkstatic, tags=tags, size=size, @@ -899,12 +928,13 @@ def tf_cuda_cc_tests(srcs, def tf_java_test(name, srcs=[], deps=[], + kernels=[], *args, **kwargs): native.java_test( name=name, srcs=srcs, - deps=deps + tf_binary_additional_srcs(), + deps=deps + tf_binary_additional_srcs() + tf_binary_dynamic_kernel_dsos(kernels) + tf_binary_dynamic_kernel_deps(kernels), *args, **kwargs) @@ -1078,6 +1108,15 @@ def tf_kernel_library( deps=deps, **kwargs) + # TODO(gunan): CUDA dependency not clear here. Fix it. + tf_cc_shared_object( + name="libtfkernel_%s.so" % name, + srcs=srcs + hdrs, + copts=copts, + deps=deps, + tags=["manual", "notap"]) + + register_extension_info( extension_name = "tf_kernel_library", label_regex_for_dep = "{extension_name}(_gpu)?", -- GitLab